Modelling the structure of the Polish manufacturing sector

Modelling the structure of the Polish manufacturing sector

Modelling the structure of the
Polish manufacturing sector:
1994-2002
Working paper
prepared by
Janusz Zaleski*/**
Pawel Tomaszewski*
Agnieszka Wojtasiak*/***
and
John Bradley****
*Wrocław Regional Development Agency (WARR)
**Wrocław Technical University
***Wrocław University of Economics
****The Economic and Social Research Institute (ESRI)
Wrocław, September 15, 2004
Contacts for communications
The Economic and Social Research Institute,
4 Burlington Road, Dublin 4, Ireland
Tel: (353-1) 667 1525 Fax: (353-1) 668 6231
e-mail: [email protected]
Wrocław Regional Development Agency,
Pl. Solny 16, 50-062 Wrocław, Poland
Tel: (48-71) 344 58 41 Fax: (48-71) 372 36 85
e-mail: [email protected]
Table of Contents
[1] INTRODUCTION .................................................................................................. 4
[2] THE STRUCTURE OF POLISH INDUSTRY ........................................................ 6
2.1 Industry as a sector of the national economy ..................................................................6
2.1.1 The role of industry in the national economy ...............................................................6
2.1.2 Main factors determining the development of industry................................................8
2.1.3 The industry breakdown .............................................................................................10
2.1.4 Evolution of industrial structures in Poland................................................................10
2.1.5 Structural transformations in Polish industry in the context of Poland’s integration
with the UE ..........................................................................................................................27
2.2 The scope and classification of data for the project......................................................30
2.2.1 The manufacturing sector, mining and quarrying in the HERMIN model .................30
2.2.2 Method of presentation of source data........................................................................31
2.2.3 The scope of data for the project.................................................................................32
2.2.4 Classification of manufacturing groups ......................................................................34
2.3 Aggregation of data into sub-sectors..............................................................................37
2.4 Manufacturing, mining and quarrying - analysis in the years 1995-2002..................40
2.4.1 Gross output ................................................................................................................40
2.4.2 Intermediate consumption...........................................................................................42
2.4.3 Gross value added .......................................................................................................45
2.4.4 Output sold..................................................................................................................47
2.4.5 The rate of growth in output sold................................................................................48
2.4.6 The price index of output sold ....................................................................................48
2.4.7 Revenue of enterprises................................................................................................49
2.4.8 Costs in enterprises .....................................................................................................51
2.4.9 Employment................................................................................................................52
2.4.10 Employment costs.....................................................................................................52
2.4.11 The rate of growth in labour productivity.................................................................53
2.4.12 Investment expenditures ...........................................................................................54
2.4.13 Gross fixed assets......................................................................................................56
2.4.14 Trade exchange .........................................................................................................57
2.4.15 Internal expenditures on research and development.................................................69
2.4.16 Numbers employed in R&D .....................................................................................69
[3] POLISH MANUFACTURING: A THEORETICAL FRAMEWORK...................... 71
3.1 Introduction......................................................................................................................71
3.2 The manufacturing side of the HERMIN model...........................................................74
3.2.1 Introduction................................................................................................................74
3.2.2 Output determination .................................................................................................74
3.2.3 Factor demands ..........................................................................................................75
2
3.2.4 Sectoral wage determination......................................................................................77
3.2.5 Sectoral output price determination ...........................................................................78
3.3 A simple modelling schema for disaggregating manufacturing ..................................78
[4] POLISH MANUFACTURING: CALIBRATION OF SUB-SECTORS .................. 81
4.1 Introduction......................................................................................................................81
4.2 The sub-sectoral output equations .................................................................................83
4.2.1 The AT sub-sector.......................................................................................................84
4.2.2 The FD sub-sector.......................................................................................................84
4.2.3 The MQ sub-sector .....................................................................................................85
4.2.4 The KG sub-sector ......................................................................................................86
4.2.5 The CG sub-sector ......................................................................................................86
4.3 The subsectoral factor demand and production functions ..........................................87
4.4 The subsectoral wage equations .....................................................................................88
4.5 The subsectoral price equations .....................................................................................88
4.6 Sub-sectoral trend productivity growth.........................................................................89
[5] SUMMARY AND CONCLUSIONS ..................................................................... 90
BIBLIOGRAPHY...................................................................................................... 92
APPENDIX 1: TSP REGRESSION LISTING........................................................... 95
APPENDIX 2: EXPLORATORY REGRESSION RESULTS .................................. 101
3
[1] Introduction
The process and mechanisms of cohesion (or catch-up) tend to pay particular attention to the
development of manufacturing as well as to market services. Cohesion can be studied in
many different ways. But since it is essentially a systemic process, and involves all aspects
of the economy, for its study there is a need for a systematic economy-wide analytic
framework. These systemic processes need to be analysed not just in isolation, but also
within frameworks that capture the feedbacks and interrelationships within the overall macro
economy. Hence, it is necessary to use macroeconomic models and draw on economic and
econometric research findings. The HERMIN-type macromodels of the EU periphery have
been used during the 1990s to explore these cohesion processes, including the structural
changes induced by trade liberalisation, increased flows of foreign direct investment, rapid
technological change and EU-financed CSF programmes of infrastructural and human-capital
development (Bradley et al., 1995a; ESRI, 1997).
The inspiration for the initial work on the model of Poland came from the earlier EU
cohesion country models, since the structural changes taking place in Poland had clear
similarities to those that occurred previously in EU cohesion countries and regions like
Ireland, Greece, Portugal and Spain (Bradley and Zaleski, 2003). By the late-1990s the
processes of change in countries like Poland had become somewhat more predictable as new
institutions and policies based on market economics gradually replaced the central-planning
of the earlier era, and as generally steadier growth resumed from a lower base level of
activity after initial severe downward adjustments. It is at this stage that it becomes
necessary to explore the structure of the new and evolving manufacturing sector in greater
detail than permitted by the single aggregate or manufacturing, mining and quarrying used in
the first version of the national Polish HERMIN model.
Transformation within manufacturing is at the heart of cohesion processes in the “old” EU, as
well as in the post-Communist economic liberalisation of the new EU members of Central
and Eastern Europe. HERMIN models had already been used within the EU, for example in
a study of the likely macroeconomic impact of the Single European Market (SEM) and the
Structural Funds (or Community Support Framework (CSF)) on the EU peripheral economies
(ESRI, 1997). A key finding of that study was that as trade liberalisation proceeds, major subsectors of the manufacturing sector as well as some aspects of services modernise and switch
from being essentially non-tradeable to being internationally tradeable. In the case of the SEM
and the CSF programmes, this change results from the dismantling of non-tariff barriers such as
restrictive public procurement policies or from, for example, a decline in transport costs arising
from improved access infrastructure. Similar considerations applied during the transition of the
CEE countries as they moved towards EU membership.
In this paper we describe our first attempt to disaggregate the manufacturing sector in the
Polish HERMIN model. The first version of the HERMIN model was described in Bradley
and Zaleski, 2003, and used annual time-series data from the year 1994 up to the year 2001.
But in the intervening period of almost two years, the Polish National Accounts have been
completely reformulated and the revisions have now been carried back to the year 1995. The
revised version of the original four-sector model was recently described in Zaleski et al,
2004(a).
In this paper we focus purely on the manufacturing sector, and the remainder of this paper is
structured as follows. In Section 2 we present a comprehensive account of the construction
4
of the new database of disaggregated manufacturing data, required to explore changes taking
place within manufacturing. We explain how we used the full level of sectoral
disaggregation available in the official statistics, and then how we defined five important subsectors of total manufacturing that have distinct characteristics. In all subsequent sectors of
the paper we will draw on this database.
Some background to the theoretical underpinnings and assumptions, used by the original four
sector HERMIN modelling framework, are presented in Section 3. But here we focus
attention purely on the manufacturing sector. We review the structure of the manufacturing
sector, and show how the treatment of aggregate manufacturing can be disaggregated into a
treatment of all the individual sub-sectors. The approach that we use is a fairly simple
application of the aggregate manufacturing model to the disaggregated sub-sector models.
This is partially because the quality of the disaggregated manufacturing data has not
previously been tried and tested by extensive econometric research. At a later stage, more
sophisticated approached to modelling the manufacturing sector can be examined.
In section 4 we describe how the disaggregated model of manufacturing was calibrated, and
describe the preliminary results obtained. The paper concludes with a summary of the main
results, assesses the usefulness of the analysis, and evaluates the likely impact when the
disaggregated manufacturing model is incorporated into the expanded Polish national
HERMIN model. The full re-commissioning of the original “basic” four sector HERMIN
model, incorporating the new disaggregated manufacturing sector, will be described in a later
report.
Section 5 concludes.
regression analysis.
There are two appendices, that present the complete exploratory
5
[2] The structure of Polish industry
An analysis of industry should start by highlighting essential characteristics of this
sector of the economy.
2.1 Industry as a sector of the national economy
2.1.1 The role of industry in the national economy
Poland’s national economy is divided into the following sectors: industry,
construction, agriculture, forestry, transport and communication, trade, utilities, housing,
research, education, culture, health and social work, public administration, justice system,
public security and defence. Industry plays the essential role among the sectors of the
economy.
Industry is related to economic activities conducted in buildings and facilities (factories)
which are specially designed for this purpose and furnished with adequate machinery and
equipment, where qualified work-force supplies (mines) and processes, by means of
manufacturing methods and production organisation, items of work into finished products
which meet human production and consumption needs1. Industry is the only sector of the
economy which manufactures means of work for all the other sectors of the economy.
Therefore, it decides the overall rate of economic growth.
The role of industry in the national economy results from the below-mentioned
reasons2.
1. The process of expanded reproduction proceeds most efficiently in industry, as the
following factors have an effect there:
a) highly developed and industry-specific distribution of work which promotes high
productivity,
b) possibilities to achieve quickly a high level of technology and proper work
organisation, as well as a relatively high level of independence of one’s economic
activity from forces of nature, what has a favourable effect on the performance of
manufacturing activities,
c) the use of rationalisation of work and the implementation of technological advances
are broader based, and they take place at a faster pace than in the other sectors,
d) methods of operation applied in industry make it possible not only to maintain what it
has, but also to self-finance its own further development.
1
M. Jaworska, A. Skowrońska, Zmiany strukturalne w przemyśle polskim w okresie transformacji systemowej /
Structural Changes in Polish Industry in the Period of the System Transformation, Wydawnictwo Akademii
Ekonomicznej im. Oskara Langego we Wrocławiu, Wrocław 2001, p. 11, See S. Chomątowski: Rozwój
przemysłu w świecie./ The Development of Industry in the World. Akademia Ekonomiczna, Kraków 1986, p. 17;
Podstawy ekonomiki przemysłu / Elements of Economics of Industry. Edited by W. Janasz. PWN, Warsaw 1997,
p. 42-44.
2
Ibid, p. 83-87
6
2. Through its activities, industry is also contributive to the process of expanded reproduction
in other sectors of the national economy, being a technological basis for this development,
because:
a) it manufactures means of production both for itself and for other sectors of the
national economy,
b) by implementing technological progress in the construction of machinery and
equipment for the needs of other sectors, it initiates progressive methods in the
operation of these environments,
c) it determines the level of technological progress in other sectors.
3. The activity of industry results in the creation of close links of cooperation with other
sectors of the national economy:
a) it cooperates with construction in construction investments for the needs of housing
production and construction; industry makes use of the functioning of construction in
order to expand its production capacities, on the other hand, it supplies construction
machinery and equipment,
b) the cooperation with agriculture develops in the area of supply of agricultural
machinery, equipment and fertilisers; industry also makes it possible to enhance the
level of mechanisation and electrification of agricultural production activities, which
are instrumental in increasing crops yields; on the other hand, industry utilises
agricultural raw materials for processing activities;
c) the cooperation with forestry is based on the same principles,
d) the cooperation with transport and communication involves the supply of means of
transport by industry, thereby developing operational capacities of transport, what
automatically streamlines the way in which transport needs of industry are met,
e) the cooperation with trade involves the supply of necessary raw materials to industry
and the distribution of manufactured goods by trade; in a way, trade is a link between
the country’s raw materials and users of finished products,
f) industry cooperates with utilities, which is comprised of water supply and sewage
disposal, municipal power supply, public city transport - individual and mass
transport, city roads, as well as sanitary and environmental protection facilities, by
supplying machinery, equipment and other means; on the other hand, utilities create
adequate living conditions, develop economic activity of persons employed in
industry,
g) other branches of the national economy do not form direct links of cooperation with
industry, but they contribute indirectly to the growth in work quality and work
organisation.
4. Given the above presented specific characteristics of industry as a sector of the national
economy and its correlation with other sectors of the economy, it should be stressed that
7
its development is associated with the socioeconomic development of the country, and it
forms a basis for its fast economic growth. Therefore, it has an effect on the country’s
defences and economic independence. It stems from the fact that industry:
a) produces new synthetic raw materials, thereby enriching raw material resources,
b) contributes to a change in the structure of exported goods, by shifting from materialintensive goods to labour-intensive goods, thereby enhancing operational efficiency,
c) boosts productivity in other sectors,
d) applies new methods of production technology in its own operations and seeks to
optimally use raw materials processed.
5. Industry’s activity also has an impact on changes in the structure of the society through:
a) the appropriate (rational) distribution of industry,
b) the enhancement of the society’s professional and cultural level.
2.1.2 Main factors determining the development of industry
The development of industry relates to all changes taking place in the first phase of
the social production process, i.e., the production which brings about consequences not only
in industry, but also in other sectors of the economy, the economy as a whole and in the life
of the society.
The most important factors determining the development of industry include the
following: the industrial growth, often described as the increase in the level of
industrialisation, as well as the structure of industry and changes in it.
The level of industrialisation, expressed by means of a set of quantitative measures, means
the degree and extent of the industry’s impacts on the national economy and the life of the
society. The knowledge of the level of industrialisation should enable a comprehensive
determination of the size of industry and its place in the national economy of a country, a
group of countries or larger economic blocs. In addition, data related to the level of
industrialisation allow us to assess the degree and extent of its dependence on the economic
development, and to determine effects of industrialisation in the form of manufactured goods
consumption3.
The term “industry structure” should be understood as quantitative relations between
separate, from the point of view of defined criteria, components of industry, relations taking
place between these components, as well as the share of each of these components in relation
to the whole. It follows from the definition that industry is treated as a certain whole which
changes under the influence of changes in the size of its components. The calculation of
changes in quantitative relations between these components is the essence of studies on
structural changes in industry. The type and number of disaggregated components of industry
3
M. Jaworska, A. Skowrońska, Structural Changes …p. 13
8
structure depend on the type and number of disaggregation criteria 4. The analysis of the
industry structure must enable the determination of not only its leading components, but also
its properties, such as: modernity, specialisation, diversification and others. It allows us to
identify on an initial basis industry development factors and to explain to what degree the
industry structure fits the structure of other sectors of the economy and social needs. The
above mentioned properties are also directly linked to the effectiveness of development
processes.
Structural changes are associated with the economic development. The deeper structural
changes are, which accompany this development, the faster is its rate. However, it does not
mean a reverse dependence, since economic stagnation or recession does not rule out
structural transformations. Only their character changes. In such case, structural changes are
directed, in the first place, towards processes of adaptation and adjustment of a given
economy to more difficult development conditions5.
The industrial policy plays the essential role in the shaping of optimal structures of
industry under the existing conditions. The industrial policy means all actions designed to
achieve a set of goals set for itself by the state. Two main currents of action undertaken by
the state can be distinguished. The first one is the use of an internally coherent system of
economic policy measures of the government aimed at the intensive development of these
branches of the economy which are able to face international competition. The other one is to
facilitate the movement of capital and labour from branches with a low and decreasing
efficiency to branches which ensure a high level of value added 6.
The industrial policy is closely linked to the industry development strategy and
industry restructuring.
The industry development strategy falls within the scope of the state activities related to the
preparation and implementation of defined programmes of industry functioning and
development. The setting of objectives and the undertaking of measures aimed to implement
these programmes are defined as the strategic policy. In the industry development strategy,
the primary goal is to stimulate the economic growth through the development of industry.
The industrial policy must be created in such a way that this strategic goal can be achieved. It
is not the short-term effectiveness of resource allocation which should be the aim, but the
long-term acceleration of the GDP growth rate.
Industry restructuring involves changes in the industrial production structure, the industry
management system and the legal and organisational status of particular sectors and industrial
enterprises. Undertakings designed to implement these structural changes are defined as the
restructuring policy7.
4
W. Janasz [ed.], Elements of Economics …, p.95
M. Jaworska, A. Skowrońska, Structural Changes … , p. 13, A. Karpiński: Restrukturyzacja gospodarki w
Polsce i na świecie / Restructuring of the Economy in Poland and in the World, PWE, Warsaw 1986, p. 29.
Structure and Change in European Industry, ONZ, Geneve-New York 1997, B. Klamut, Ewolucja struktury
gospodarczej w krajach wysoko rozwiniętych / Evolution of the Economic Structure in Highly Developed
Countries, AE, Wrocław 1996, p. 21
6
M. Jaworska, A. Skowrońska, Structural Changes… , p. 14
7
B. Pełka, Przemysł polski w perspektywie strategicznej / Polish Industry in the Strategic Prspective, Orgmasz,
Warsaw 1998, p. 11
5
9
2.1.3 The industry breakdown
In terms of the character of economic activity, industry is broken down into the following
sections: mining and quarrying which are engaged in the direct harvesting of natural
resources, manufacturing which processes raw materials and materials into consumer and
production goods, as well as the section of electricity, gas and water supply.
Mining and quarrying include the coal industry (mining of hard coal, mining of lignite,
auxiliary units for enterprises of this branch) and the following industries: oil, other fuels,
mining of metal ores, mining of non-ferrous metal ores, mining of chemicals, quarrying of
aggregate and mineral resources, as well as quarrying of construction stone.
The activity of manufacturing involves the refinement and processing of raw materials and
semi-finished products in order to adapt them to various human production and consumption
needs.
The last mentioned component of industry includes generation and supply of electricity, gas,
steam and hot water, as well as collection, purification and distribution of water.
Given the fact that the subject of this paper will be manufacturing, as well as mining
and quarrying, special attention will be paid to these sections further on in this paper.
The mining industry differs from manufacturing in several features. Firstly, items of work in
the mining industry generally cannot be moved around. Secondly, the mining industry
supplies products which are further processed, and only then they obtain adequate useful
properties, thus, this industry supplies input raw materials for manufacturing. Another
distinguishing feature is the fact that economic effects of the mining industry are strictly
dependent on the natural conditions existing in the production8.
2.1.4 Evolution of industrial structures in Poland
In order to understand the current condition of Polish industry, it is necessary to
analyse changes taking place in its structure since the end of World War II. It is also
necessary to analyse economic programmes which have had an impact on the development of
industry in the specific direction.
2.1.4.1 Evolution of industrial structures in the years 1947-1980
In post-war Poland, structural changes were one of the earliest efforts which were
undertaken and implemented. First of all, it was necessary to launch transformations of
ownership structures, as they closely corresponded with the overall political orientation of the
new authorities. The change of the social and economic system created new conditions for
the reconstruction and development of the national economy, for the elimination of structural
causes hindering this development and for the activation of multiple factors and stimulators
of economic growth. The processes taking place in industry, which was nationalised under
the decree of the Polish National Council dated 3 January 1946, should be especially stressed.
8
W. Janasz [ed.], Elementy ekonomiki przemysłu / Elements of Industry Economics, The University of Szczecin,
Szczecin 1994, p. 73
10
The industrialisation of the country was the essential assumption of the state’s economic
strategy and policy.
The years 1947-1949 were characterised by the growing role of the Centre, in
particular in the second half of this period. Until 1947 industrial enterprises enjoyed a
significant independence in the development of production plans and allocation of profit. In
1947, however, two normative acts appeared which substantially modified the economic and
financial system on which enterprises’ economic activities were based. A very important
factor shaping structures in industry - investment activities - was removed from the discretion
of enterprises. Generally speaking, we can say that the year 1949 started a period of the
significant acceleration of growth. The food and light industries were predominant in the
production structure by industry branch. In 1949 the share of the mining industry in output
was 10.8%, and it started to decline systematically, whereas the share of manufacturing was
at the level of 89.2%. The development processes in industry were intensive then, although
no intensive development strategy was assumed yet. The development was based on a rapid
growth in employment and a high rate of growth in investment expenditures. The
technological and organisational progress and labour productivity (that is, non-investment,
typically intensive factors), the growth of which markedly outpaced the growth rate of the
capital-labour ratio, had a significant impact in that period. The transformations which took
place in industry in 1949 had an effect on this branch of the national economy over the next
years. We should underline the fact that the centralised command and control system of
economy management, hence, a directive management system, was introduced in that year.
The beginning of the 50’s started the first stage of industrialisation, expansion of the
production apparatus and creation of a developed structure of industry following a model
based on the priority given to, first of all, the steel, electrical machinery, chemical and
building materials industries. It was also a continuation of the period of the significant
acceleration of growth, but a one-sided growth based on investment in the capital goods
industry. Already at that time quickly increasing disproportions emerged: between the growth
of industry and the growth of agriculture; between the developed front of production
investments and possibilities of their efficient implementation; between natural material and
consumption aspirations of the society and the degree to which they were satisfied. It should
also be added that, alongside these processes, the international situation, conditioned by the
“cold war” period, was then of essential significance in the shaping of industry structures.
The command and control system, developed at the turn of 1955/1956 to the maximum,
together with the strategy of extensive development, resulted in a very low efficiency of
incapacitated enterprises, as well as it heightened the economic and social barriers.
The years 1956-1970 were still a continuation of the first stage of the country’s
industrialisation associated, in the first place, with the expansion of the raw materials
industries, whereas since the middle of the sixties, also connected with a drive to modernise
the structure of industry through the development of manufacturing branches. The
assumptions of the first decade in question were dictated by the need to remove the
disproportions which had arisen, in particular, between the development of the mining
industry and manufacturing. A relatively high, although much lower than in the previous
years, rate of growth in industrial output was maintained in the conditions when extensive
factors were gradually exhausted. Nevertheless, it was still an extensive development, with
high and growing capital intensity and an insufficient rate of growth in social labour
productivity, based on the increase of the production apparatus as a result of investment.
Attempts to shift towards an intensive and selective development can be noticed as late as the
end of the sixties. Economic and socio-political conditions brought about the development
11
and modernisation of selected industries, and even assortment groups of manufactured goods,
but not including capital goods for agriculture, what contradicted the then proclaimed
consumption-oriented policy.
Economic developments and facts in the years 1956-1970, in the context of the
industry management system, can be divided into two periods. The first period, covering the
years 1960-1970, was associated with some attempts to decentralise the planning and
economy management system through the introduction of changes, among others, in the
organisational structure of the key industry, in the area of investment, as well as in the
financial system. The second period covers the years 1960-1970. The reforms were then
abandoned, that is, the authorities returned back to the directive system.
At the beginning of the seventies, a strategy of intensive, selective and balanced
development was formulated. In the new economic policy, which stressed the opening of the
country’s economy and its inclusion to a wide extent in the global economy, the quality
aspect, rationality, and above all, efficiency of use of factor resources and the modernisation
of the production structure, as well as its change, were to be predominant. The modified
economic and financial system and transformations in organisational structures of business
entities were to be the tool of implementation of reform undertakings. However, the
economic practice of the 70’s decade definitely contradicted these assumptions. The ever
increasing barriers in economy management processes resulted in a social and economic
collapse at the end of this period9.
In studies on development trends and proportions in industry, we cannot overlook the
development of its particular branches. In the years 1950-1980 we can see large differences
in the rate of growth in output in particular branches. Thus, the electrical machinery and
chemical industries had the highest growth rate, whereas the food, light and fuel and energy
industries had the lowest rates. It should be noted that the wood and paper, light and food
industries had the highest growth rate until 1960, after which this growth decreased below the
growth rate of the whole industry. A characteristic feature of the branch structure of Polish
industry was also an insignificant share in output of the so-called high technology industry,
which is the main platform for the implementation of the latest research and technology
advances into production practice10. In the period concerned, the rate of output growth in
manufacturing substantially exceeded the rate of output growth in the mining industry. Such
proportions are in line with general regularities of the development of industry in the world.
At the initial stages of industrialisation, there is a strong determining connection which makes
the development of manufacturing dependent on the development of the mining industry. The
share of industry in national income generation increased from 29.2% in 1949 up to 56.2% in
1980, what evidenced that Poland had transformed from an agricultural country into an
industrial and agricultural one. The intensity of those structural transformations was
diversified, however. The increase of the share of industry in national income was the most
9
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 44, Z. Bartosik, Strukturalne problemy przemysłu
polskiego / Structural Problems of Polish Industry, Ossolineum, Wrocław 1988, p. 125
10
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 48, A. Karpiński, S. Paradysz: Przemysły „wysokiej
techniki" w gospodarce polskiej / „High Technology” Industries in the Polish Economy. „Gospodarka
Planowa", 1984 no. 2, s. 51.
12
intensive in the years 1950-1955. It was then 23.0%, whereas in the next years it was in the
range of 9-14.0% 11.
In the structural evolution of manufacturing, one could however notice that the
development of manufacturing was strongly dependent on the periodically appearing barriers
in raw materials and materials, which were usually removed by the emergency-driven
reorientation of investment efforts. Investment was then concentrated in the mining industry.
It applied in particular to the period of expansion of the country’s raw materials base, thus,
1959-1970. In addition, one can point out the more stable annual rate of output growth in
mining than in manufacturing. Hence, the social and economic crisis was a consequence of
structural irregularities and development disproportions in industry. But it should be indicated
that it was mostly manufacturing which was affected by them.
The analysis of these issues also highlights trends in production factors. The first of
the factors discussed is employment, the next ones are fixed capital and investment
expenditures12. The employment policy was dominated by the principle of full employment,
therefore, a rapid growth in this factor can be noticed especially until 1997. The breakthrough
year 1997 started a very clear regression in the rate of employment growth. A growing
tendency in the share of industry employment in the total number of employed was reported.
The systematic increase of industry participation in total employment and compared to other
sectors of the national economy is treated in literature on this subject as the regularity of the
first stage of industrialisation. And thus, this factor was the basis for economic growth in the
period of implementation of the six-year plan. Nevertheless, it should be indicated that the
growth in employment was not then linked to investment. Hence, the dynamic increase in
employment became the essential impulse for economic development. One should remember
that in the entire period in question, thus, not only at the threshold of industrialisation,
mechanisms and instruments of the maximum employment policy dominated, and as a result
of such a policy, the extensive method with gradually diminishing economic effects became
the only way of economy management. The analysis of allocation of labour stock by branch
indicated a systematic increase of the share and the dominance of the steel, electrical
machinery, chemical and building materials industries. This process was associated with the
structure of gross fixed capital formation. The result of that was a faster growing capitallabour ratio in these branches. The changes in the branch-based fixed capital structure headed
in two directions: the first one involved the increase in the share of the electrical machinery
industry, the other one manifested itself in the decline in the share of the fuel and energy,
minerals and light industries.
The analysis of the structural evolution in industry is certainly complemented with the
transformations in the allocation of investment expenditures. It should also be said that the
quantitative growth in investment was the main driver of Poland’s economic development in
the years 1950-1980 and the key instrument of the country’s industrialisation. It is confirmed
by data which indicate both the quantum-leap increase in the rate of accumulation and
investment, as well as their very high level. It was likewise in the case of the participation of
industry in total investment expenditures. In the period of time in question, this share was
11
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 46, S. Felbur, W. Połeć: Rozwój i przemiany
strukturalne w przemyśle / Development and Structural Transformations in Industry. IGN, Warsaw 1986, p. 2728.
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 11-21
12
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 51, Ludność i zasoby pracy w liczbach /The
Population and Labour Stock in CSO Numbers. Zarządzanie 1985 no. 5, p. 40
13
40% on the whole (and even more). It was only in the period of the deep economic collapse
that this very high level declined.
The rate of growth in investment expenditures in industry was also very high. Investment
expenditures in industry grew much quicker than total investment. It can be seen very clearly
in the periods of the so-called acceleration of the development pace in which the growth in
industrial investment was particularly preferred. The growth rates of investment, in which a
conversion of the investment priorities took place, and as a consequence of that, a reduction
of investment expenditures, i.e., the discrimination of this most preferred sector, are the
opposite of these processes which were in a feedback dependence on each other. It can be
especially noticed during the period of the economic crisis in the eighties, but investment
expenditures in industry were reduced already in 1977.
The annual rate of growth in investment expenditures in industry was characterised by very
high fluctuation amplitude. The extreme points were in the years 1972 and 1981. It should be
underlined that the fluctuations in the rate of investment growth were cyclical in that sense
that after a period of a significant acceleration in the rate of investment growth there was a
period of slow-down, and as a result of that, even a decline in this rate. In the years 19501953 and 1971-1974 a very strong investment pressure can be especially noticed. The
analysis of the structure of investment expenditures by branch provides information on the
preferred directions of development and the structural policy in the period in question. The
intensity of investment in particular groups of branches was characterised by a significant
diversification. The fuel and energy industry and the electrical machinery industry were
particularly privileged. Their shares in total industry investment expenditures maintained on a
very high level all the time. It was only in the middle of the seventies that some other trends
emerged, namely, the share of the metallurgical industry in investment increased markedly.
On the other hand, the chemical industry participated in expenditure in a percentage too small
in relation to its needs, what was especially noticeable in the middle of the seventies. The
period of time 1966-1970, which was characterised by a relatively high level of its share in
investment, was an exception. Other branches of industry had a much lower rate than the
above mentioned groups (it reached the level of about 10%).
It should also be said that the electrical machinery, steel, chemical, building materials
industries were first of all preferred in the allocation of investment expenditures by branch.
Their shares in industrial investment oscillated around 70%. The periodic changes in the
investment priorities were only emergency and one-off interventions.
2.1.4.2 Evolution of industrial structures in the years 1981-1989
In the years 1981-1989 we observe the continuation of the previously discussed
development trends and consequences of the structural policy pursued in the years 19491980. The dominance of the electrical machinery industry can still be clearly seen. The share
of the fuel and energy industry declined. The rate of share of the food industry in the structure
also decreased substantially. On the other hand, the significance of the chemical industry
increased.
A predominant part of fixed assets was concentrated in the steel, electrical machinery,
chemical and building materials industries, what did not facilitate the transformations aimed
to increase the market-oriented production. In addition, the disadvantageous situation in the
fixed assets structure by type should be mentioned. A high level of wear and tear of
14
machinery and technical equipment, coupled with a low level of their modernity, was a
symptom of depreciation of a substantial part of fixed assets in industry. In all branches, this
rate exceeded 50%. An insufficient reproduction of fixed assets in industry was mainly a
consequence of the inefficient structure of investment in the seventies, when the estimated
share of modernisation and replacement expenditure was 20-25%, as well as the reduced
level of investment in the years 1981-1988 and the lengthening of investment cycles,
negligence in operation and maintenance of plant and machinery, etc. In the period of time in
question, there was no essential conversion in the allocation of investment funds, either.
Investment expenditures increased very dynamically in the iron industry and in the textile
industry, whereas investment in the coal industry continued to decline. The coal, energy,
food, chemical and electrical machinery industries had the highest shares in industrial
investment in the structure. Such a distribution of investment funds could not have a positive
impact on the pace of transformations in the industry structure. It should also be indicted that
continued investments, which had been started already in the second half of the seventies,
were an essential factor affecting the allocation of investment expenditures. As a result of
that, in the period 1981-1989 the already formed structure of fixed assets in industry
continued to be further strengthened.
The factors which had an effect on the restructuring processes in the period in
question can be divided into two groups: external factors and internal factors. In the group of
internal factors, the following are emphasized: the system of planning and industry
management, the state’s economic policy, and above all, the applied strategy of social and
economic development, the adopted model of industrialisation of the country, the investment
policy, the price system. Among external factors, the following are mentioned: demographic
factors, political factors, raw materials resources, and the economic cooperation with socialist
countries and with other countries13. It is not difficult to notice their mutual and cause-andeffect relation and invariability in time. It should also be underlined that the internal factors,
in the first place, determined the processes shaping the industry structures, and moreover,
they also had a pejorative impact on the structural evolution. The consequence of their
impacts was the ever increasing barriers in restructuring. The centralised command and
control system, as the basic element of the system solutions, was predominant among all the
abovementioned factors, subordinating and at the same time determining the scope of the real
economic policy of the country. The increasingly deepening anti-effective mechanisms were
a consequence of its functioning. The causes of the economic imbalance in the period in
question were the following:
a) an unfavourable structure of industry,
b) the maintenance of the unreal price system based on increasing subsidies on
production,
c) incapacitation of enterprises,
d) the instilled attitude of passivity and inertia,
e) the atrophy of initiative and ability to think and to act rationally,
f) the lengthening of time of response of the decision-making centre,
13
M. Jaworska, A. Skowrońska, Structural Changes .., p. 55, Z. Bartosik, Structural Problems..., s. 139
15
g) a reduced flow capacity of information channels due to an excessive amount of
information sent between the centre and the real sphere,
h) the loss of the centre’s ability to control, to process and to use data sent,
i) the formation of many decision-making subcentres as a result of specialisation and the
lack of coordination among them, etc.
The centralised command and control system could be effective only in the initial
period of creation of the material base for the economy, thus, in the years 1950-1955. But as a
result of the growing complexity of economic processes, this system took on features which
hindered, or even rendered impossible, any further progress. The directive character of
production tasks and the system of material incentives, tailored to it, also contributed to the
low efficiency of industry management. At the enterprise level, there was no freedom of
choice, since commands determined the assortment structure of production, marketing
directions, exports in accordance with relevant payment areas, the internal market, supplies
for investment and production. Employment and the payroll fund, average wages and means
of production were also subject to limitations. The direct consequence was that production
reserves were hidden in enterprises. The then existing motivation system was an antiincentive for the implementation of technological progress. Therefore, “tendering for a plan”
eliminated processes of innovative nature, what ruled out already in the assumptions, and
even more in practice, structural and qualitative changes in their functioning.
The management system, deprived of economic mechanisms, among others, costbenefit analysis, did not create conditions for a rational management of means which were
necessary for an effective and dynamic development from the microeconomic point of view.
It should be also added that the essential measure of production and the criterion of
evaluation of enterprises’ operations was the value of output, the essence of which favoured
high materials and energy intensity. Besides, pricing policies with regard to goods and
services provided by industries and the system of subsidies directly caused an increase in
costs, waste of production factors, as well as the choice of cost-intensive products assortment.
It should be stressed that this anti-efficiency mechanism which emerged at that time was one
of the main structure-forming factors in industry.
As the economic imbalance increased, the Centre continued to lose the ability to affect
economy management processes. Mechanisms and tools of the centralised command and
control system only led to the deepening of the economic disproportions which had arisen. In
the situation of the economic and social crisis, an attempt to change the existing system
solutions was undertaken again. But a comprehensive reform of the economy was the only
condition which enabled the collapse and economic recession of the eighties to be stopped.
However, one should indicate the existence of gaps already in theoretical studies, what of
course negatively affected the outcome of the real implementation of the reform14. The
reform, which was to lead to radical changes in the system of management, only boiled down
14
M. Jaworska, A. Skowrońska, Structural Changes..., p. 57, M. Mieszczankowski: Kryzys i reforma
gospodarcza (1980-1984) / Crisis and Economic Reform (1980-1984). „Życie Gospodarcze" 1984 no.30, B.
Rostor, P. Dockes: Cykle ekonomiczne. Kryzysy i przemiany społeczne -perspektywa historyczna./ Economic
Cycles. Social Crises and Transformations - Historical Perspective PWE, Warsaw 1987.
16
to three main slogans - self-dependence, self-government and self-financing15. It should be
noted that, from the point of view of the restructuring problem, the regulatory framework for
innovation activities of enterprises was essential. But principles of the reform and changes in
the economic and financial system, outlined in official documents which continued
progressive transformations of the functioning and development of the economy, did not treat
the innovation mechanism in a separate way16. The theoretical pressure on efficiency did not
bring about an effective stimulation of the innovative drive.
A characteristic feature of this period was also a definitely weak impact of the needs
to increase the production for the internal market and exports on the innovation processes.
Solutions in the area of financing innovation activities had also some other consequences,
namely, expenditures on the maintenance of research and development facilities which
directly burdened production costs, thereby reducing profit, contributed to the liquidation of a
large number of research and development facilities, what is a particularly unfavourable
development for the economy.
The proportion of enterprises which utilised resources from the technological and economic
progress fund was alarmingly low, what unequivocally evidenced the still unchanged tactics
of action - the avoidance of risky, economically profitable, effective innovations, and the
preference of only small improvements. The reform did not lead to the decentralisation of
management, did not make enterprises self-dependent or trigger efficiency mechanisms, what
meant a set-back for progressive economic processes leading to essential transformations in
the system of functioning and development of the economy. Restructuring processes which
were to spring from the fundamental system solutions were not even embedded in them, and
basic issues in that respect were still unsolved. The innovation mechanism, constructed
without a sense of reality, did not fulfil the intended function, and therefore it was not one of
the key instruments of the structural transformations of industry.
The role of the demographic factor and the raw materials resources factor came down
to the implementation of extensive development of industry. And their impacts on the
processes of forming industrial structures were defined in such terms, and at the same time
with such subjection. On the other hand, we may note that the cooperation within the CEMA
(the Council for Mutual Economic Assistance) was a form of dominance over the structural
trends17.
2.1.4.3 Evolution of the Polish industrial policy after 1989
The restructuring of Polish industry after 1989 became a necessity on account of the
symptoms of backwardness and regression in relation to Western Europe countries. As a
result of the policy pursued over the previous years, the hard coal, agricultural machines and
implements, shipbuilding, rolling stock, motorcycle and bicycle, cement, ceramic tiles and
sugar industries still played the dominant role in the structure of domestic industry. In the
world, these industries had been perceived as ballasts for years, and they showed the highest
declines in production and employment.
15
M. Kuraś: O reformowalności reformy (1)/ On the Reformability of the Reform. „Przegląd Techniczny" 1984
no. 5, s. 9-10.
16
Z. Borowska-Kwasik, W. Kasperkiewicz: Jaką droga do innowacji / What Way to Innovations. „Przegląd
Techniczny” 1984 no. 18 p. 14-15
17
M. Jaworska, A. Skowrońska, Structural Changes ..., p. 57
17
There was a regression in the most profitable industries which were considered in the
world as the major measure of a modern industrial structure and treated as the basis for
technological modernisation of industry. It was particularly noticeable in high technology and
export-oriented industries: aerospace, electronics, telecom technology, machine tools, plant
automation, IT, measuring and laboratory instruments, computation and office machinery,
electronic instruments, optical instruments, medical and veterinary equipment,
pharmaceuticals and chemical products. The negative condition of Polish industry was
largely attributable to the employment policy. In addition, the slump in exports to the markets
of the former Soviet Union after 1989 was the cause of the regression in high technology
industries and related research and development. In the years 1990 – 1995 Poland’s most
profitable high technology markets were captured by foreign products, and Polish own, less
competitive products were driven out of the market on a large scale18.
Three phases can be distinguished in the development of industry after 198919. Phase I
(1989-1991) was a period of the shock liberalisation of the economy and a rapid fall in the
activity of enterprises in industry. This was associated with the crisis-driven reduction in
demand and an essential change in its structure. This phase is often termed the “strategic
shock”, since the nature of the environment in which organisations operated underwent a
change, the form of signals flowing from this environment changed, bureaucratic structures,
on which enterprises often built their future, were broken up overnight - at least in part. In
spite of the radical changes which took place in the environment, the traditional
organisational culture, which manifested itself in the way of thinking and the attitude to
problem solving, as well as in management techniques and methods applied, still dominated.
This collision of the qualitatively different environment and the traditional organisational
culture brought about the phenomenon of negative adjustments at the enterprise level. In that
period, in spite of many difficulties, structural and ownership transformations were
initiated20. In the economic policy, however, the main focus was directed towards the
stabilisation programme and the fight with inflation. Restructuring problems were pushed
into the background. State subsidies for industry were significantly cut down21.
The state’s economic strategy in June 1989 was based on the conviction that it was necessary
to transform the economic system into a market-based system. The need to implement these
changes became apparent during the meetings of the Round Table. The concept of changes
developed by L. Balcerowicz, which involved the enactment of certain laws, played a very
important role in the process which was taking place at that time. Those laws aimed to
transform the economic system into a free market economy22.
The slump in exports to the markets of Russia, where 80% of total output were directed
before 1989, was the cause of the regression in high technology industries and related
research and development.
18
Z. Malara, Restrukturyzacja przemysłu Restructruing of Industry, Ekonomika i Organizacja Przedsiębiorstwa,
no. 5, 1997
19
A. Malewicz: Przemysł w 1996 roku / Industry in 1996, Ekonomika i Organizacja Przedsiębiorstwa, 1996 no.
2, s. 3-5; M. Jaworska, A . Skowrońska, Structural changes..., p. 75
20
M. Jaworska, A . Skowrońska, Structural changes..., p. 75
21
B. Pełka, Polish industry in the Strategic Perspective, Orgmasz, Warsaw 1998, p. 12
22
K. Moszkowicz, Innovation Processes in Polish Industry, Wydawnictwo Akademii Ekonomicznej im. Oskara
Langego we Wrocławiu, Wrocław 2001, p. 103
18
Phase II (1992-1995) which was a period of recovery of industry from the economic
collapse. In this period, positive effects of the market-oriented development of industry
appeared, there was an increase in output sold, the private sector was formed, and the
efficiency-oriented movement of production factors between industry branches started. In
addition, in the years 1990-1995 Poland’s most profitable high technology markets were
captured by foreign products and Polish own, less competitive products were driven out of
the market on a large scale 23.
In 1993 the Ministry of Industry and Trade (currently the Ministry of Economy and Labour)
developed its own assumptions of the industrial policy for the years 1993-1995, in which the
need to enhance competitiveness and innovative drive of Polish industry was indicated. This
document consisted of two parts: “Assumptions of the Industrial Policy” and “The
Programme of Implementation of the Industrial Policy in the Years 1993-1995”. The creation
of conditions for the development of enterprises and products characterised by high
competitiveness, efficiency and innovative character was set as a long-term objective in this
document. A medium-term objective was to change the industry structure with a view to
reducing the share of the energy-intensive and environmentally unfriendly industry and
creating economic growth centres. It was acknowledged in this document that it required the
following goals of the socioeconomic policy to be attained 24:
a) to check the recession,
b) to create stable conditions for economic activity in order to maintain growth trends in
the economy,
c) to create conditions for the growth in competitiveness and efficiency through the
promotion of innovation, transfer of technologies, the programme of adaptation of
Polish industry to European conditions, to support Polish technological thought, to
develop small and medium-sized enterprises (SMEs),
d) to activate regions and business self-regulatory organisations,
e) to actively create the labour market and the capital market (in that, to secure foreign
capital),
f) to develop primary, secondary and higher education in order to educate and train
industrial personnel.
The action areas were analysed in four types of sectors. The first type includes the strategic
sectors: the defence industry, the fuel and energy industry (mining of hard coal, oil, gas
extraction, liquid fuels, power and energy). The restructuring in these sectors was to be
supported by the government based on a special regulatory framework. Given the scope of
the subject of the presented paper, it is worth to note the necessity of changes in the hard coal
sector. In the 90’s a serious problem of the hard coal sector was its difficult financial
situation, and in the first place, its debt which had accumulated over the previous years. The
process of restructuring of the hard coal sector and leading the sector to economic efficiency
required some coal mines to be closed down, what entailed work-force reductions. Securing
23
24
M. Jaworska, A . Skowrońska, Structural changes..., p. 76, Z. Malewicz, Industry...p.3
K. Moszkowicz, Innovation processes ..., p. 105
19
jobs for miners made redundant was a major impediment in the restructuring process. Assets
and their structure were another important problem which had a significant impact on the
level of coal production costs. A large share of the so-called “unwanted assets” (residential
housing, leisure facilities, etc.) in total assets was a factor which increased coal production
costs. Similarly, a large share of obsolete and inefficient machinery and equipment in total
production assets contributed to cost increases. The replacement of that machinery for
modern and efficient machinery required huge capital expenditures, what was a significant
barrier in the restructuring process. In the first half of the 90’s the process of restructuring of
the hard coal sector was designed to achieve and to maintain an adequate level of profitability
of hard coal production and to maintain the competitiveness of Polish coal on global markets,
to write off debt of hard coal mining, to secure funds for the implementation of replacement
and modernisation investments in mines, to reduce, and even to eliminate, miners’
uncertainty with regard to possible job losses25.
The second type includes high energy and capital intensity sectors: the iron and steel, cement,
shipbuilding, pulp and paper, heavy chemistry industries. The restructuring in these sectors
(also ecology-oriented restructuring) was to be supported by the government.
The third type includes high need sectors: the petrochemicals, electronics, automotive,
packaging, pharmaceuticals, food and agriculture-related, light, environmental protection
equipment, rolling stock, building materials industries. The restructuring in these sectors was
to be supported indirectly by the government due to impacts of these sectors on other sectors.
The fourth type is high opportunity sectors. They were identified as competitive areas which
needed more the interest of banks rather than of the government26.
Based on analyses conducted in the years 1992-1993, the industry sectors were
selected which were characterised by higher-than-average productivity and which had a
chance to compete by employing efficiency improvement and development measures at a
relatively small level. These are the following industries: refinery, paints and varnishes,
energy, ceramics, mining of metal ores, cement, lignite, textiles. Among the selected eight
industries which are the main area of the Ministry’s interest, there is none from the group of
high technology industries27.
The conditions in which the Polish system transformation was to take place proved to
be very difficult, especially in the initial period. It is evidenced by, inter alia, research
conducted by the Polish Economic Society, the result of which is included in the study
“Identification of the Situation and Growth Opportunities of Particular Branches and Groups
of Industry in 1994” (A. Karpiński, S. Paradysz). The results of this research pointed out to a
very low technological and structural level of our industry as compared with the global level,
and the main deficiencies of the development of industry in the structural aspect turned out to
be the following:
25
Ministry of Industry and Trade, „Raport o stanie polskiego przemysłu w 1994” / “Report on the Condition of
Polish Industry in 1994”, Warsaw 1995, p. 10.
26
K. Moszkowicz, Innovation processes ..., p. 120-121, „Polityka przemysłowa – założenia. Program realizacji
w latach 1993-1995” / „The Industrial Policy - Assumptions. The Implementation Programme in 1993-1995”,
Warsaw, Ministry of Industry and Trade, September 2003.
27
B. Pełka, Polish Industry ..., p. 69
20
a) the dominance of heavy industries, the share of which in total industrial output has
been showing a declining tendency in the whole Europe for years,
b) a decrease in the share of export of processed goods in total exports,
c) the incompatibility of the structure of Polish industry with demand on foreign
markets,
d) underdevelopment of high technology and other knowledge processing industries,
e) distorted proportion between the raw materials and semi-finished products base and
processing and services branches to the disadvantage of the latter,
f) drastic underdevelopment of the small industry28.
G. Kołodko, as the then Deputy Prime Minister and Minister of Finance, also made an
attempt to develop an economic strategy in 1994. His Strategy for Poland29 included ten key
programmes:
1.
Partnership labour relations and wage bargaining mechanism
2.
A reform of the social security system
3.
Counteracting unemployment
4.
The development of rural areas
5.
Investment in human capital
6.
Management of state assets and processes of ownership transformations
7.
A medium-term financial strategy
8.
The development and reform of the financial sector
9.
Security of economic trading and absorption of the grey economy
10.
International competitiveness of the Polish economy
The implementation of these programmes was assumed to lead to the implementation of three
priorities: a fast economic growth, the system and macroeconomic stabilisation, the
improvement of living conditions. This document was prepared for the years 1994-199730.
Since 1994 the most urgent task for the Ministry of Industry and Trade became the
restructuring of hard coal mining, the iron and steel industry and the defence industry31.
28
A. Malewicz, Przemysł w 1996/ Industry in 1996, Ekonomika i Organizacja przedsiębiorstwa, no. 2, 1996
K. Moszkowicz, Innovation processes ... p. 105, G. Kołodko, Strategia dla Polski / Strategy for Poland,
„Życie Gospodarcze” 1994 no. 26
30
K. Moszkowicz, Innovation processes ..., p. 105-106
31
B. Pełka, Polish Industry ..., p. 69
29
21
Regional problems appeared in connection with the restructuring process and the
process of transformation of the economic system, as sectoral and regional problems tend to
overlap as a result of excessive specialisation. The crisis in mining can be an example. Hard
coal production in Poland is concentrated in the Katowice region. The acute crisis of this
sector also became a serious regional problem.
One of the measures undertaken to prevent this type of regional inequalities was the
identification of areas with a high risk of unemployment, which were then granted a number
of privileges. A list of such areas, kept since 1991, is primarily based on the criterion of
registered unemployment. The supporting criteria include the number of unemployed school
graduates, the scale of group redundancies, the number of unemployed who are not entitled to
unemployment benefits, and the degree of monopolisation of the market by a limited number
of employers. In such areas, the permissible period of receiving unemployment benefits is
lengthened, additional funds aimed to actively combat unemployment are channelled, and
lower tax rates or tax holiday are applied to new investors. The programme of special
economic zones functions, patterned particularly on Ireland’s experience. It is based on the
Act passed in October 1994 which grants to investors, e.g., tax privileges. Special economic
zones may be created only when they are deemed to be necessary for the restructuring of a
given region, and when they are aimed at attracting modern technologies32.
Phase III covers the period after 1995. Industry entered then a new phase which was a
phase of development changes of qualitative nature. This period is characterised by a growth
in competitiveness of products on international markets, the transition to manufacture of
highly processed goods, to modern technology. In the years 1995-1996 we can observe a
transition from a defensive type of the industrial policy, focused on the protection and
restructuring of certain traditional branches of industry, to an offensive type of policy in the
form of promotion of export- and future-oriented branches. Thus, this change-over involves
the transition from an emergency-driven policy to an ordered, long-term policy33. The
document “Ranking of Growth Opportunities of Industry Sectors”, adopted by the Economic
Committee of the Council of Minister on 6 December 1995, expresses this transition.
In January 1996 the Strategy for Poland programme was elaborated and expanded into
the so-called Package 2000. It precisely defined objectives, instruments and rules of the
economic policy in two key areas: the macroeconomic stabilisation and the functioning of the
tax system34. Package 2000 was a medium-term macroeconomic programme for the second
half of the 90’s, prepared with a view to Poland’s aspirations to join the European Union.
One of the essential goals set by this document was to maintain the average real GDP growth
at the level of 5.5%. Exports and imports would grow by 10.5% per year, though their growth
rate would weaken with time. The gross investment growth would be 11% per year, and
consumption growth - 4%. It was assumed that inflation measured at the end of the year
should decrease to a one-digit figure until 1998, and by the end of the century it was to drop
to 5-7%. The number of registered unemployed would decline to 10% of the work-force, and
the public debt/GDP ratio would fall to 42% in 2002. The programme does not devote too
much space to the discussion of the policy which was to lead to such favourable results;
however, it defines the main directions of the fiscal policy: in the area of taxes, the point of
gravity would continue to shift from direct taxes to indirect taxes, the share of government
32
OECD Economic Review, Poland 1996-1997, Ministry of Economy, 1997, p. 82.
Ibid., p. 12
34
Strategy fo Poland – Package 2000. Warsaw, January 1996, K. Moszkowicz, Innovation processes ..., p. 105106
33
22
spending in GDP would decrease by two percentage points, and the state budget deficit
would gradually decline, reaching 1.5% of GDP in 200035.
In the Industrial Policy Programme for the years 1995-1997 “International
Competitiveness of Polish Industry”, the Ministry of Industry and Trade defined the main
goal of the restructuring of industry sectors - to adjust the industry structure to the
requirements of the free market economy and to enhance competitiveness of Polish industry’s
product offer, especially in the sectors which determine the energy security of the country
and the defence sectors, as well as in “high opportunity” sectors which include branches
which determine the modernity of the economy.
Restructuring efforts in the sectors determining the energy security of the country related to:
a) restructuring of the hard coal mining sector,
b) restructuring of the power and energy sector,
c) restructuring of the oil and gas extraction sector,
d) restructuring of the oil sector.
The restructuring of the defence sector was to be implemented under the programme
submitted for approval to the Defence Affairs Committee of the Council of Minister. In the
Industrial Policy Programme for the years 1995-1997, restructuring efforts were presented
only for two out of ten “high opportunity” sectors: pharmaceuticals and non-ferrous metals.
The restructuring programme also covered the iron and steel industry and “heavy
chemistry”36.
In the Industrial Policy Programme for the years 1995-1997, the following industries were
included in the high opportunity sectors: aerospace, plant automation, medical equipment and
instruments, non-electrical measuring instruments, power generation machinery and
equipment, optical instruments, pharmaceuticals, electronics, telecom technology, nonferrous metals. In the Programme, the assumptions were orientated towards:
a) a change of the structure of domestic industry in order to increase the share of highefficiency manufacturing branches which could compete on international markets,
b) the streamlining of the operation of the sectors determining the state’s energy
security,
c) the limitation of operations of the heavy industry and its modernisation involving a
reduction in energy intensity, materials intensity, and in harmful impacts on the
environment,
d) rationalisation of production and employment.
35
36
OECD Economic Review, p. 117.
B. Pełka, Polish Industry ..., p. 70 and 75
23
The restructuring plans for 1995-1997 were to be implemented through the policy of
transformation and structural changes instrumental in boosting competitiveness of industry.
The policy of structural changes was shaped in different ways. The first way was the
commercialisation and privatisation of state-owned enterprises involving the transformation
of enterprises into commercial law companies and the implementation of modern forms of
management of state-owned enterprises - agreements for management (manager contracts).
The second way was to implement changes in industry structure which covered a wide
programme of changes involving the increase of the share of high-activity manufacturing
industries in the structure through the development of SMEs, the continuation of sectoral
restructuring (in particular industries), covering in the first place the sectors determining the
country’s energy and defence security, as well as “high opportunity” sectors. The third option
was to impact the regions with high industry concentration. This option involved measures
related to the support of SMEs, the synchronisation of efforts aimed at implementing
government sectoral restructuring programmes with regional economic programmes, the
establishment of special economic zones, the increase of efficiency of institutions coimplementing the industrial policy at the local and regional levels. Another way was the
internal restructuring of enterprises, supported by the below measures on the part of the
government: the creation of a regulatory framework for financial, organisational and
technological restructuring, organisational and financial assistance provided with the support
of non-government institutions (the Agency for the Development of Industry, the Agency for
Foreign Investments, the Regional Development Agency), the acceleration of the winding-up
of inefficient entities.
In the years 1995-1997, the following programmes were implemented in industry:
a) the programme of restructuring of the iron and steel industry,
b) programmes of restructuring of hard coal and lignite mining, gas extraction, the power
industry, the district heating industry and the liquid fuels industry,
c) the programme of restructuring of the pharmaceutical industry.
Changes in the structure aimed at boosting competitiveness of industry, as the second
restructuring goal, were designed to bring the sectors in line with standards of developed
countries’ economies (the so-called sectoral restructuring), thereby to enhance the
attractiveness of Polish industry’s offer in order to bring it closer to the level of proposals
offered by western competition37.
In May 2000 the Government Centre for Strategic Studies developed a strategy of
economic development for Poland until 202538. This document consists of 3 parts:
1. Objectives and conditions
2. Directions of action
3. Implementation and monitoring of the strategy
37
Z. Malara, Restructuring ....p. 4-5, M. Jaworska, A . Skowrońska, Structural changes..., p. 77,
K. Moszkowicz, Innovation processes ..., p. 111, Poland 2025 – a long-term development strategy. The
Government Centre for Strategic Studies. Warsaw, May 2000
38
24
In the first part, the objectives of the socioeconomic policy, visions for Poland until 2025,
development trends in the world and challenges to Poland were discussed, and barriers and
difficulties were presented. It was identified that the key objectives were to ensure an
increase of the prosperity of Polish families, to strengthen their financial self-dependence and
the sense of security. Reducing the distance to EU countries and the development of a
knowledge-based economy, as well as the environmental protection and cultural heritage
conservation, were identified as the related objectives. The vision for Poland until 2025 was
presented in three layers: society, economy, state. The society will dynamically adapt its
qualifications to the market needs in line with the requirements of a knowledge-based
economy. Poland’s economy until 2025 is to be competitive. The dissemination of leading
technologies and the provision of a high level of education, as well as the modernisation of
the economic structure and the construction of modern infrastructure, should serve the
purpose of competitiveness enhancement. The Polish state will remain a democratic and
sovereign state based on the principles of equality of citizens before the law. In the part
relating to the development of the world, it is stated that societies will move to an era of
information civilisation. The diagnosis of the Polish economy in the next part indicates many
difficulties. The ones related to industry are as follows:
a) a low level of innovation,
b) an obsolete structure of industry,
c) an unfavourable structure of GDP production,
d) an insufficient condition of technical infrastructure,
e) low competitiveness of the export offer,
f) a low level of the society’s education,
g) a low level of GDP per capita and efficiency indicators lower than the European
average.
In the chapter related to the directions of action, education and the development of science
and R&D were identified as the priorities.
An attempt to determine long-term objectives and priorities of the economic
development was also made in a study prepared by the Projections Committee “The 21st
Century Poland”, attached to the Polish Academy of Sciences, which is entitled “With the
Year 2010 in View”39. The Committee is of opinion that in order to face international
competition, human capital should be activated through an adequate education strategy,
health care and the environmental protection. Two directions of action are of essential
significance among the economic conditions to the implementation of the strategy in
question. The first direction is the shift to an export-oriented development model, in
particular, the specialisation of the economy and its entities from all ownership sectors
(private, public) where there are competitiveness opportunities on foreign markets. The other
direction consists in the implementation of deep restructuring of a very obsolete structure of
39
K. Moszkowicz, Innovation processes ..., p. 114, With the Year 2010 in View. The Projections Committee
“21st Century Poland” attached to the Presidium of the Polish Academy of Sciences, Elipsa, Warsaw 1995
25
the economy. Measures undertaken under the other direction of action should be aimed at a
radical increase of the share of high technology industries in total industrial output in Poland,
at giving special significance to the development of the so-called small entrepreneurship and
clearly boosting the share of export production in total industrial output. The assumptions of
the economic policy (including the industrial policy) presented in the study should underpin
the implementation of this strategy. At the same time, attention is drawn to the fact that the
new development policy requires the focus to be shifted to the development of new areas, and
not to the rescue of industry branches at risk of collapse. The latter should be saturated with
the new technology, if it allows them to compete on the market. The authors of the strategy
believe that its scope is dependent on a 1.5 increase of the share of investment in GDP, i.e.,
up to 30%, and on an increase of the share of foreign investment from the current level of 6%
of total investment up to 25-30% by 2010. Therefore, any moves in the fiscal policy which
would reduce the interest in investment growth, saving and export should be avoided. It is
said in the strategy that interventionism should be “market friendly”, however, in the
transition period it requires a larger role of the state in the economy. The state’s intervention
is aimed to carry out the following tasks:
1. To promote the development of entrepreneurship and innovation
2. To stimulate the process of deep restructuring of the economy
3. To enhance the export-orientation of the economy and to carry out measures aimed to
bring it line with the EU
4. To ensure that the development process is ecology-oriented
5. To create the education infrastructure40.
Three particularly highlighted branches of industry fall within the scope of
restructuring efforts resulting from the described programmes of transformations in the
industry sectors for the period from 1996 until 2010:
a) branches of industry whose main problem is to revise their production capacities in
view of competitive exports on world markets. This area includes the industries with
largest production capacities in relation to the market demand and exports, the highest
materials intensity and energy intensity of production, obsolete technologies and
production which do not meet technical and quality world standards,
b) branches of industry whose most urgent problem, given their significance to the
economy, is their further development and the creation of new business entities. This
area includes the branches of industry belonging to high value added sectors which
can boost the development of the whole economy and which have the greatest
opportunities to export to EU markets,
c) branches of industry which, on account of their significant role played in the Polish
economy, require special protection. This area includes the branches of industry
which have the strategic significance to the state’s defences, perform the function of
40
K. Moszkowicz, Innovation processes ..., p. 114
26
national industries, raw materials and energy industries, agriculture related industries
and industries which manufacture agricultural machines and implements41.
2.1.5 Structural transformations in Polish industry in the context of Poland’s integration with
the UE
2.1.5.1 Incompatibility of the structure of Polish industry with EU industry structure
The process of Poland’s integration with the European Union requires deep
transformations and adjustments. It gives rise to the need to pursue the adjustment policy.
This policy is aimed at removing structural differences still existing between the Polish
economy and the economies of EU countries, including the structural adjustment in industry.
The structure of Polish industry demonstrates features which do not allow it to compete
effectively on the single European market. The incompatibility of Polish industry is
manifested in:
a) a high share in total industrial output of the heavy industry, which is not today the
driving force of the development, and in addition, in EU countries there are
substantial excess capacities in this industry. As a result of that, the possibilities to
export Polish products manufactured by this industry to EU markets are very small,
b) underdevelopment of the industry manufacturing consumer goods which have a much
better chance to compete on EU markets,
c) a weak level of development of the so-called high technology industry, which is the
driving force of development today,
d) much higher, than in EU countries, employment in the material-intensive and labourintensive sectors of the economy.
The structure of Polish industry is for the time being extremely different than that of industry
in EU countries, what renders significant export expansion into EU markets impossible. In
addition, we should take account of the fact that the opening of the Polish economy for
imports in the conditions of such a structure puts it at risk of marginalisation, and the industry
itself is threatened with backwardness, but the highest risks relate to the industry where: there
is the largest technological gap in the structure and quality parameters of products (for
example, electronics, industrial automation, IT), exporting countries have access to
particularly cheap raw materials on the global market (for example, heavy chemistry), foreign
industry is largely subsidised (for example, the iron, textile and food industries), the domestic
market is more attractive due to higher cost-effectiveness of expenditure .
The European Commission has identified four main goals for the Community
industry, which are presented below.
1. To meet international industrial competition
2. To invest in equipment, know-how, training and qualifications more effectively
41
Z. Malara, Restructuring..., p. 4, M . Jaworska, A . Skowrońska, Structural changes..., p. 79
27
3. To control the diffusion of technological innovation in a comprehensive way.
4. To develop human resources.
2.1.5.2 Directions of the EU industrial policy
The implementation of the adjustment programme requires an active industrial policy,
but it should be underlined that the signing of the European Treaty restricts the freedom to
pursue such policy by Poland, as Community standards apply in this respect, as well. The
industrial policy is strongly linked to the competition policy, with mutual dependencies on
each other. The growth of importance of the industrial policy, the need to harmonise national
policies and to approach the industrial policy as the Community undertaking spring already
from the programme provided for in the Single European Act. We can speak here of two
trends. The first is manifested in the application of the principle of subsidiarity which has two
dimensions: the Community will not take action in areas where particular Member States can
take action more effectively, and any public (national or Community) actions are designed
only to support private actions (the Community should create a socioeconomic favourable
climate). The second direction in the development of European integration involves the
creation of the common industrial policy at the Community level. One of the more essential
elements of the industrial policy is the common R&D and technology policy aimed at
strengthening the research and technology base of European industry and underpinning the
growth in industrial competitiveness on international markets. Many programmed
undertakings are designed to serve this purpose.
In the Maastricht Treaty, objectives of the European industrial policy and principles of
coordination of actions undertaken by particular Member States are defined. The essential
objective of this policy is to create the conditions necessary for the competitiveness of the
Community’s industry in accordance with a system of open and competitive markets, without
disturbing competition. For that purpose, the following undertakings will be implemented:
a) speeding up the adjustment of industry to structural changes,
b) encouraging an environment favourable to private initiative and to the development of
undertakings throughout the Community, particularly small and medium-sized
undertakings,
c) encouraging an environment favourable to cooperation between undertakings,
d) fostering better exploitation of the industrial potential of policies of innovation,
research and technological development.
Sectoral aid is assessed by the European Commission based on the following criteria:
1. Sectoral aid should be restricted to necessary cases when the condition of industry requires
such aid
2. The time necessary for making adjustment changes must be taken into account, and the
conditions and circumstances of granting aid are strictly defined
28
3. The aid should restore long term viability of industry rather than being used for the
purpose of maintaining status quo and putting off unavoidable decisions
4. The aid must be gradually reduced and clearly related to the restructuring of a given
group of enterprises (sector)
5. The level of aid should be proportionate to the problem it is to solve
6. Industrial problems and unemployment should not be transferred from one Member State
to another.
The SMEs support programme and R&D programmes are tasks of essential
significance in the Community’s actions. The source of improvement in European industry’s
competitiveness is to be competition on the internal market and the related policy of
maintenance of an open and competitive socioeconomic environment. The idea of
competitiveness to a large extent determines the character of the industrial strategy. The
strategy is devised as a complex of measures supporting the permanent adaptation of business
entities to changes taking place in open and competitive markets, a part of which is, among
others, the improvement of the functioning of the internal market. The industrial policy
should play a special role in streamlining the goods markets and the factors market,
strengthening private initiative and entrepreneurship, supporting innovation and technological
progress, promoting the industrial mobility and the development of SMEs.
The strategy of structural adjustments incorporates three types of instruments. The
first instrument includes preconditions - competition, a stable economic environment, a high
level of education, supporting economic and social cohesion. The second instrument includes
catalysers - the development of the internal market, the trade policy. The third one includes
accelerators - research, technologies and innovations, occupational training, SMEs, services
for business.
In the industrial policy of the Community, the main role is assigned to horizontal
instruments as these which correspond to the concept of positive adjustments. The priority
given to horizontal objectives finds reflection in the competition policy with respect to the
state aid policy. The so-called problem (declining) sectors and high technology sectors are the
subject of the Community industrial strategy. With regard to the declining sectors, the
Community has assumed an obligation to coordinate national policies (it particularly applies
to such sectors as the shipbuilding, steel, coal, textile and apparel industries). Poland’s
membership in the EU means, on the one hand, the necessity to adapt itself to the
requirements of the industrial policy, and on the other hand, it ensures significant economic
benefits and underpins the further development of Polish export and import.
2.1.5.3 Measures under the Polish industrial policy
With a view to bring the Polish industrial policy in line with the EU industrial policy,
the following measures should be undertaken, among others: the harmonisation of technical
and quality norms and standards, the adjustment of law on intellectual and industrial property
protection, the formation and functioning of enterprises in accordance with the EU
legislation, preparation for the participation in international research work, in European
research programmes and regional programmes, the harmonisation of environmental
protection standards and laws.
29
Poland’s membership in the EU requires the creation of a comprehensive and active
industrial policy which should be directed towards the achievement of the following main
objectives:
1. To increase the competitiveness of Polish products on global markets
2. To better adjust the production structure to the structure of internal and foreign demand,
in particular, through the increased share of the consumer goods industry and export
production
3.
To overcome development barriers and limitations resulting from insufficient factor
supply
4. To invigorate technological progress and as a consequence to modernise the economy,
what is necessary in order to reduce the technological gap between the Polish economy
and highly developed countries.
A problem of essential significance in the process of transformations, that is, industry
restructuring, is the process of reallocation of factors from obsolete and unprofitable branches
to modern and efficient branches. Different solutions have been proposed in literature on this
subject. The first one is to release funds supporting investment processes and the
development of R&D work, such as preferential loans, state guarantees for loans, tax reliefs,
accelerated depreciation and the direct participation of the state in the implementation of
certain investments which are in line with the structural policy of the state. Another solution
is to take over by the state a part of costs and risk of research within the scope compliant with
the science and technology policy of the state, to support the development and accelerated
modernisation of the industrial potential, for instance, through tax reliefs. Another option is to
create favourable conditions for foreign capital inflows in the form of different types of
preferences for foreign companies, which are associated with permanent commitment of
foreign capital investments; it relates to the promotion of joint-ventures and the control of the
ownership transformations policy, in particular, mergers and demergers of enterprises. It is
also proposed that tariff and non-tariff customs protection measures be used in imports in
order to protect the country’s own R&D capabilities. Another solution is to facilitate access
to results and innovation processes of R&D facilities and cooperation between research and
industry. One of the proposed directions of action is also not to allow the liquidation of
entities which have a strategic importance to the economy and to create special-purpose
research and industry consortia for few key areas of restructuring and development of
strategic industries42.
2.2 The scope and classification of data for the project
2.2.1 The manufacturing sector, mining and quarrying in the HERMIN model
The HERMIN model is a macroeconomic model composed of four sector components:
manufacturing, agriculture, market services, public services. These sectors correspond to the
following sectors included in the National Accounts, which are presented in the table below.
42
M. Białasiewicz (et al), W. Janasz [ed.], Elementy rozwoju strategii przemysłu / Elements of Development of
an Industry Strategy, The University of Szczecin, Szczecin 2000 p. 478-486
30
Table 2.1. Sectors in the HERMIN model and sectors in the National Accounts
SECTORS - HERMIN
Manufacturing
Agriculture
Market services
Public services
SECTORS - NATIONAL ACCOUNTS
Manufacturing
Agriculture
Hunting
Fishing and operation of fish hatcheries and fish farms
Forestry
Mining and quarrying
Electricity, gas and water supply
Construction
Wholesale and retail trade; repair
Hotels and restaurants
Transport, storage, communication
Financial intermediation
Real estate, renting, research and development
Other business activities
Public administration
Education
Health and social work
As indicated in the first section of this paper, its scope covers the manufacturing sector,
mining and quarrying, which are included under the market services sector in the HERMIN
model. The fact is that Poland’s industry cannot be analysed excluding mining and quarrying.
Mining, in particular, mining of hard coal, remained for many years Poland’s strategic
industry on which the national energy security and independence were conditioned. Hard coal
was the major component of the fuel and energy balance, it met a considerable part of total
demand for primary fuels on the domestic market, and it had a significant position in exports.
The dominance of hard coal in the fuel and energy balance was attributable to the amount of
deposits of this raw material, its availability, as well as the production capacity of mines. In
terms of the volume, lignite was the second largest primary energy carrier in Poland.
2.2.2 Method of presentation of source data
Data presented in this paper relate to business entities which conduct economic activities in
Manufacturing and Mining and Quarrying in accordance with the Polish Classification of
Economic Activities (the so-called PKD classification). Data come from the Industry
Statistical Yearbook published by the Central Statistical Office. Sources of data for
manufacturing and mining and quarrying present these data in the following way:
1. Based on the Polish Classification of Economic Activities (the PKD classification)
prepared on the basis of the “Nomenclature des Activités de Communauté Européenne –
NACE rev.1” of the Statistical Office of the European Communities EUROSTAT. The
PKD classification came into effect on 1 January 1998 under the Council of Minister
Decree on the Polish Classification of Economic Activities dated 7 October 1997, as
amended, and replaced the European Classification of Economic Activities.
2.
By ownership sector:
a) the public sector – it is comprised of the state ownership (of the State Treasury and
state-run legal persons), the ownership of local self-government units, and the “mixed
ownership”, i.e., majority-owned by public sector entities,
31
b) the private sector – it is comprised of the domestic private ownership (of natural
persons and other private entities), foreign ownership (of foreign persons), and the
“mixed ownership”, i.e., majority-owned by private sector entities.
The “mixed ownership” mostly applies to companies, and it is determined based on the
capital structure declared in a company registration application.
Source data are presented by sections, groups, subgroups and classes of the PKD
classification and in accordance with the administrative division. They are primarily prepared
based on the so-called enterprise method. The enterprise (entity) method means that whole
entities of the national economy form the basis for the aggregation of data characterising their
economic activities according to particular classification levels and the administrative
divisions. Entities of the national economy are understood as legal entities, i.e., legal persons,
organisational units without legal personality and natural persons conducting economic
activity.
2.2.3 The scope of data for the project
Variables relating to manufacturing and mining and quarrying, shown in the table below, are
used in the project. Designations for variables relating to particular sections and groups are
the following:
Q - output
QT - manufacturing, mining and quarrying output
QTZZZ - output in particular sections/groups of manufacturing, mining and quarrying
QTZZZV - output sold in particular sections/groups of manufacturing, mining and quarrying
QTZZZI - rate of growth in output sold in particular sections/groups of manufacturing,
mining and quarrying
O - value added
YW - employment related costs
GTP - taxes on production
GSUP - subsidies
GOS - operating surplus
QST - output sold
PQS - output sold deflator
REVT - revenue
COST - costs
LT - numbers employed
NORT - productivity
IT - investment
KT - capital stock
XT - exports
MT – imports
32
Table 2.2. Variables describing manufacturing, mining and quarrying.
No.
Variable
1
2
3
4
5
6
7
Industry output by section and group (current prices)
Rate of growth in industry output by section and group (constant prices)
Industry intermediary consumption by section and group (current prices)
Rate of growth in industry intermediary consumption by section and group (constant prices)
Industry gross value added by section and group (current prices)
Rate of growth in industry value added by section and group (constant prices)
Components of industry gross value added by section and group (current prices)
General
Employment related costs
Other taxes on production
Subsidies on production
Gross operating surplus
Industry output sold by section and group (current prices)
Rate of growth in industry output sold by section and group (constant prices)
Price indexes of industry output sold by section and group
Revenue of industrial enterprises by section and group
Costs in industrial enterprises by ownership sector, section and group
Numbers employed in industry by section and group
Average employment in industry by section and group
Rate of growth in labour productivity in industry measured by gross value added per
employee by section and group (constant prices)
Rate of growth in investment expenditures in industry by section and group (constant
prices)
Investment expenditures in industry by ownership sector, section and group (current prices)
Gross value of fixed assets in industry by ownership sector, section and group
Exports by country groups and PKD classification groups (current prices)
EU
Other developed countries (developed minus EU)
CEE
Other developing countries (developing minus CEE)
Imports by country groups and PKD classification groups (current prices)
EU
Other developed countries (developed minus EU)
CEE
Other developing countries (developing minus CEE)
Internal expenditures on research and development (R&D) and R&D equipment in industry
by section and group
Numbers employed in research and development (R&D) in industry by section and group
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Designati
on
QTV
QTI
QMTV
QMTI
OTV
OTI
OTV
YWT
GTPT
GSUPT
GOST
QSTV
QSTI
PQS
REVT
COST
LT
WT
NORT
ITI
ITV
KT
XTEUV
XTODV
XTCEV
XTOTV
MTEUV
MTODV
MTCEV
MTOTV
RDTV
LRDTV
For each of these variables, numerical data are complied for particular groups of
manufacturing and mining and quarrying. These data show the values of variables in the
years 1994-2002. Thus, a database has been created, in which particular variables are
presented in matrixes. Lines of matrixes contain particular groups of manufacturing and
mining and quarrying, whereas columns contain values corresponding to them in the years
1994 -2002. The groups shown in Table 2.3 are included in the section of manufacturing
according to the PKD classification. Table 2.3 also incorporates mining and quarrying in
accordance with the subject of this analysis.
33
Table 2.3. Groups of manufacturing, mining and quarrying.
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Groups of manufacturing, mining and quarrying
Manufacture of food products and beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel and articles of fur
Manufacture of leather and leather products
Manufacture of wood and wood products, as well as articles of straw and plaiting
materials
Manufacture of pulp, paper and paper products
Publishing, printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of metals
Manufacture of metal fabricated products
Manufacture of machinery and equipment
Manufacture of office machinery and computers
Manufacture of electrical machinery and apparatus
Manufacture of radio, television and communication equipment
Manufacture of medical, precision and optical instruments, watches and clocks
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; manufacturing, not elsewhere classified
Recycling
Mining and quarrying
Designation
FDB
TOB
TEX
CLL
LET
WOD
PUP
PRM
PET
CHM
RUB
NMM
BMT
MET
OME
OMC
ELM
RTC
MPO
MOT
OTE
FUR
REC
MAQ
Each group is assigned a number. It is the same as the number of a given group in the
database.
2.2.4 Classification of manufacturing groups43
In order to analyse the manufacturing sector, including mining and quarrying, the groups
included in manufacturing must be classified. It will allow groups to be classified in keeping
with the adopted criteria.
The groups of manufacturing, including mining and quarrying, can be classified in
accordance with different criteria.
1. The criterion of the economic purpose of products
From the point of view of the economic purpose of products manufactured, we can
distinguish:
43
Based on: Z. Malara, Restructuring..., p. 5, M. Białasiewicz (i in), W. Janasz [ed.], Elements of Development
of an Industry Strategy , p. 478-486, W. Janasz [ed.], Elements of Economics ..., p. 83-87
34
a) groups which manufacture means of production (e.g.., manufacture of metals, pulp,
chemical products),
b) groups which manufacture means of consumption (e.g.., manufacture of food
products, tobacco products, wearing apparel, transport equipment).
This breakdown is based on different roles of means of production and means of consumption
in the reproduction process. As certain products can be used both as means of production and
means of consumption (for instance, sugar, flour), the classification is based on the principle
of the predominant use. The point is that the manufacture of a product which has a double use
is included in whole either in the first group, or in the second group, depending on what the
predominant use of such product is.
2. The criterion of properties of production objects (weight, size)
Based on this criterion, we divide industry into:
a) the heavy industry: e.g.., manufacture of machinery and equipment, motor vehicles,
trailers and semi-trailers, coal, coke and refined petroleum products, metals,
b) the light industry, inter alia, manufacture of food products and beverages, tobacco
products, wearing apparel and articles of fur, manufacture of leather and leather
products, pulp and paper.
3. The criterion of the type of product
In a sense, this criterion is a combination of the criterion of the economic purpose of product
and the criterion of properties of production objects. Based on this criterion, we divide
industry into:
a) manufacture of machinery, equipment and work accessories (e.g.., manufacture of
machinery and equipment, office machinery and computers, electrical machinery and
apparatus, motor vehicles, trailers and semi-trailers),
b) manufacture of consumer goods (e.g.., manufacture of tobacco products, leather and
articles of fur, textiles) and manufacture of semi-finished products (e.g.., manufacture
of pulp, manufacture of metals, manufacture of rubber and plastic products, mining
and quarrying).
4. The ownership criterion
The ownership criterion (the sector criterion) allows us to distinguish:
a) the public industry; the term “public industry” should be understood as state-owned
entities or mixed ownership entities, i.e., state majority-owned companies,
b) the private industry; the private industry includes domestic privately-owned entities
(cooperatives, domestic commercial law companies or companies with private
majority ownership) and foreign owned entities, i.e., companies with foreign capital
participation and foreign small production enterprises. In accordance with the adopted
35
principles of classification of the national economy, crafts are also included in the
private industry.
5. The criterion of territorial preference
The criterion of territorial preference justifies the existence of a given group of industry in a
particular area.
6. The criterion of opportunities of growth of competitive exports to EU markets
Taking into account the European Communities’ priorities, Poland’s industry can be divided
into three areas from the point of view of opportunities of growth of competitive exports to
EU markets:
a) Area I includes the industry which has the smallest chance of competitive exports to
the EU due to high materials intensity and energy intensity of production, as well as
significant excess production capacities in relation to the current demand and exports
capabilities. These branches should be entirely subjected to the free play of market
mechanisms. If they adapt themselves, they stand a chance of survival, and even
growth. The machinery industry, heavy chemistry, a part of the mineral industry and
the most material-intensive branches of raw materials and semi-finished products for
the consumer goods industry belong to this area. This area accounts for 65-67% of the
whole potential of Polish industry,
b) Area II includes branches which have a chance of entering EU markets mainly due to
economic and technological parameters and their position in the restructuring of
Polish industry. This industry should be considered to be strategic, as it may
contribute to a large extent to the improvement of the structure of the whole Polish
industry. Microelectronics, telecommunications, robotics and industrial automation,
aerospace, pharmaceuticals, medical and electronic measuring instruments, specialist
chemistry, as well as wearing apparel, knitting, footwear and furniture industries
should be included in this strategic industry. They account for 8-9 % of the total
industrial potential,
c) Area III includes industry (for example, mining and quarrying, iron and non-ferrous
metals, automotive, ship-building, defence and other industries) which should be
protected due to internal conditions. It is assumed that this is not growth industry, but
the process of reducing its role in the economy should not be a spontaneous one, but it
should continue gradually over a longer period of time and in a controlled way. About
25-26 % of the total industrial potential belongs to this area 44.
7. The criterion of employment intensity
Based on this criterion, the workforce employed in particular groups of industry can form the
basis for classification.
8. The criterion of output sold
44
M. Białasiewicz (et al). W. Janasz [ed.], Elements of Development..., p. 478-486
36
By adopting the criterion of output sold, groups of manufacturing can be classified according
to their shares in total revenues from output sold. Output sold is revenue from sales of
products, and it includes all receivables for sales of the following outside an enterprise:
finished products of own production, works and services performed, flat-rate fees of the agent
- in the case of a contract for services, full remuneration of the agent - in the case of an
agency contract, and the value of products manufactured which are not included in sales,
treated as sales equivalents, i.e., the value of own products transferred to an enterprise’s own
retail sales outlets, as well as its own catering outlets and wholesale warehouses, the value of
considerations rendered to the social benefits fund, products and services performed for
marketing and advertising purposes, products and services provided for personal needs of the
taxpayer, employees, as well as donations of goods and services provided free of charge45.
9. The criterion of output sold and employment intensity
This criterion allows groups to be distinguished in terms of labour intensity (the number
employed in a given sector as a share of employment in the whole economy) compared to
their performance in terms of output sold in these groups (the share of output sold in a given
group compared to output sold in the entire section of manufacturing and mining and
quarrying), i.e., profitability of sales. This is a combination of the criterion of employment
intensity and the criterion of output sold. The classification based on this criterion consists in
a percentage comparison of the share of employment in a given group in total employment in
manufacturing, mining and quarrying, with the percentage share of output sold in these
branches in relation to total output in manufacturing, mining and quarrying. This criterion
allows manufacturing to be divided into:
a) groups characterised by intensive employment - there is higher employment input in
relation to output sold,
b) groups characterised by intensive capital - in groups so defined there is higher output
sold in relation to employment, and
c) neutral groups – employment and output sold inputs are comparable.
2.3 Aggregation of data into sub-sectors
Taking into account the above criteria, manufacturing, including mining and quarrying, will
be analysed on the basis of the criterion of output sold and employment intensity.
Based on the 2001 Polish Yearbook and taking into account the 2000 data, groups of
manufacturing, as well as mining and quarrying, have been analysed for output sold and
employment intensity. Groups with intensive employment, intensive capital and neutral have
been disaggregated. The results are shown in the table below.
45
GUS, Nakłady i wyniki przemysłu w 2002 / Expenditure and Results of Industry in 2002, Warsaw 2003
37
Table 2.4. Classification of groups of manufacturing, mining and quarrying based on the
criterion of intensity of output sold and employment.
No.
I
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Manufacturing, mining and quarrying groups
Manufacture of food products and beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel and articles of fur
Manufacture of leather and leather products
Manufacture of wood and wood products, as well as
articles straw and plaiting materials
Manufacture of pulp, paper and paper products
Publishing, printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of metals
Manufacture of metal fabricated products
Manufacture of machinery and equipment
Manufacture of office machinery and computers
Manufacture of electrical machinery and apparatus
Manufacture of radio, television and communication
equipment
Manufacture of medical, precision and optical
instruments, watches and clocks
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture; manufacturing, not elsewhere
classified
Recycling
Mining and quarrying
Total
2000
Output sold
(in %)
21.4%
0.8%
1.9%
2.1%
0.8%
2000
Employment
(in %)
16.8%
0.5%
3.7%
8.4%
1.6%
3.7%
2.3%
3.7%
6.1%
6.1%
4.2%
5.2%
5.3%
5.2%
4.9%
0.3%
3.1%
5.2%
1.6%
3.1%
0.5%
3.7%
4.2%
5.8%
3.1%
7.3%
7.9%
0.0%
3.7%
2.1%
1.0%
1.0%
7.2%
2.4%
1.6%
3.1%
2.6%
4.1%
0.3%
5.9%
100.0%
6.3%
0.5%
7.9%
100.0%
LI/CI/
N*
CI
N
LI
LI
LI
LI
CI
CI
CI
CI
N
LI
CI
LI
LI
CI
LI
CI
LI
CI
LI
LI
LI
LI
*LI – intensive employment, CI – intensive output sold, N – neutrality of the group
Taken into account the selected criteria of aggregation, the character, the level of
development and the significance of manufacturing, mining and quarrying for the economy,
data have been aggregated into five subsectors. The groups of manufacturing have been
aggregated into four sub-sectors. Mining and quarrying is a separate sub-sector.
Given the above-described structure of Poland’s industry, the objectives which the European
Union has set for industry, and taking into account the fact that the automotive, electronic,
textile, defence, steel and environmental protection equipment industries are recognised as
priority industries in EU countries, we can state that the most desirable direction of the
development of Polish industry is the development of the high technology industry. For this
reason, the high technology sub-sector will be disaggregated within the criterion adopted. It
will be termed “Advanced technologies” (AT).
The group “Manufacture of food products and beverages” will also be distinguished as a
separate category due to its significant share both in output sold and in employment. As a
logical supplement, the group “Manufacture of tobacco products” will also be included in this
38
category. This sub-sector will be termed “Manufacture of food products, beverages and
tobacco products” (FD).
Taking into account the important role which mining and quarrying played in Poland in the
past, in particular, the strategic importance of hard coal mining, and enormous changes which
have taken place in this branch of industry since the beginning of the 90’s, “Mining and
quarrying” (MQ) will be distinguished as a separate sub-sector.
Other groups which are characterised by intensive output sold will be termed as the “Capital
Goods” (KG) subsector, whereas these which are characterised by intensive employment will
be termed “Consumer Goods” (CG).
The five tables below present the detailed disaggregation of manufacturing, including mining
and quarrying, into the five above described sub-sectors. The numbering of the groups in the
column “No..” is consistent with the numbers assigned to them in the previous tables.
Table 2.5. Aggregation of groups into the “Advanced Technologies” (AT) sub-sector.
No.
8
15
16
17
18
19
20
21
Groups classified to this sub-sector
Publishing, printing and reproduction of recorded media
Manufacture of machinery and equipment
Manufacture of office machinery and computers
Manufacture of electrical machinery and apparatus
Manufacture of radio, television and communication equipment
Manufacture of medical, precision and optical instruments, watches and clocks
Manufacture of motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment
Designation
PRM
OME
OMC
ELM
RTC
MPO
MOT
Table 2.6. Aggregation of groups into the “Manufacture of food products and beverages, and
tobacco products” (FD) sub-sector.
No.
1
2
Groups classified to this sub-sector
Manufacture of food products and beverages
Manufacture of tobacco products
Designation
FDB
TOB
Table 2.7. Aggregation of groups into the “Mining and quarrying”(MQ) sub-sector.
No.
24
Groups classified to this subsector
Mining and quarrying
Designation
MAQ
Table 2.8. Aggregation of groups into the “Capital Goods” (KG) sub-sector.
No.
7
9
10
13
Groups classified to this sub-sector
Manufacture of pulp, paper and paper products
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of metals
39
Designation
PUP
PET
CHM
BMT
Table 2.9. Aggregation of groups into the “Consumer Goods” (CG) sub-sector.
No.
3
4
5
6
11
12
14
22
23
Groups classified to this sub-sector
Manufacture of textiles
Manufacture of wearing apparel and articles of fur
Manufacture of leather and leather products
Manufacture of wood and wood products, as well as articles straw and plaiting materials
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of metal fabricated products
Manufacture of furniture; manufacturing, not elsewhere classified
Recycling
Designation
TEX
CLL
LET
WOD
RUB
NMM
MET
FUR
REC
In the further section of this paper, we shall analyse values of the selected variables shown in
Table 2.2 for particular sub-sectors.
2.4 Manufacturing, mining and quarrying - analysis in the years 1995-2002
Below, we shall analyse values of the selected variables shown in Table 2.2 for the five subsectors: AT, FD, MQ, KG, CG. The variables will be analysed for the years 1995-2002.
2.4.1 Gross output
in PLN mln
Output in a given sub-sector is the total of output of products and services of all
entities of the economy classified to a given sub-sector.
160000
140000
120000
100000
80000
60000
40000
20000
0
1995 1996 1997 1998 1999 2000 2001 2002
AT
FD
MQ
KG
CG
Chart 2.1. Industry output in the sub-sectors (current prices).
The level of gross output (current prices) in the advanced technologies (AT) and capital
goods (KG) sub-sectors increased in the years 1995-2000 (in current prices, output in the AT
sub-sector increased by about 150% between 1995 and 2000, in the KG sub-sector by about
90%), and then it declined. The decline in gross output in the advanced technologies subsector is consistent with world trends. In the years 2001-2002 there was a slump in world
markets of new technology companies, what can be noticed, e.g., in the analysis of stock
exchange indexes for the sub-sector concerned. In the food products, beverages and tobacco
products (FD) sub-sector, there was an increase in gross output in current prices in the years
1995-2001 (it was about 110% between 1995 and 2001), and a slight decline can be noticed
only in 2001-2002. There was a growth trend in output in current prices in mining and
quarrying (MQ) in the years 1995-1997 (a 34% increase), 1999-2000 (8.5%), whereas in the
40
years 1998, 2001, 2002 its value was slightly lower than in the previous year. In the
consumer goods (CG) sub-sector, which showed the highest value of output in current prices,
a growth trend can be observed in the whole period. Percentage changes in gross output (in
current prices) of particular sub-sectors are shown in Table 2.10.
Table 2.10. The rate of growth in gross output in the sub-sectors (current prices). Year-onyear percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
45.72%
43.21%
24.35%
45.22%
37.99%
1996
28.71%
27.47%
14.29%
9.07%
23.35%
1997
25.71%
21.55%
17.20%
21.87%
22.74%
1998
18.63%
12.23%
-6.51%
2.58%
15.56%
1999
11.15%
0.84%
1.58%
7.45%
9.15%
2000
16.16%
11.18%
8.50%
28.44%
11.80%
2001
-2.08%
8.24%
-0.27%
-5.54%
1.33%
2002
-1.91%
-1.72%
-0.23%
-2.83%
5.46%
The highest percentage increase in output was reported in all the sub-sectors at the beginning
of the period in question. There was also a significant increase, at the level of more than 28%,
in the capital goods sub-sector in 2000. But already in the next year, there was a significant
decline in gross output in this sub-sector. However, a percentage decline in output in current
prices took place in mining and quarrying in 1998, and it was 6.5%. Given the value of gross
output in all the sub-sectors altogether, it can be seen that output increased between 1995 and
2002. However, in the years 2001-2002 most of the sub-sectors showed a decline in output. If
gross output is expressed in 1995 constant prices, changes in its value in the years 1995-2002
are shown in Chart 2.2.
120000
80000
mln zł
in PLN mln
100000
60000
40000
20000
0
1995
1996 1997
AT
1998
FD
1999
MQ
2000 2001
KG
2002
CG
Chart 2.2. Gross output in the sub-sectors (constant prices).
Gross output in constant prices, with 1995 as the baseline year, is calculated as the product of
the 1995 output and the rate of growth in industry output in constant prices in particular
years. The table below shows year-on-year percentage changes in the sub-sectors in constant
prices.
41
Table 2.11. The rate of growth in gross output in the sub-sectors (constant prices). Year-onyear percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
18.06%
13.30%
1.20%
11.68%
9.14%
1996
14.90%
7.88%
2.50%
3.30%
11.72%
1997
17.49%
10.37%
0.90%
11.10%
14.56%
1998
12.40%
4.58%
-13.30%
-3.28%
8.64%
1999
7.92%
-2.52%
-3.20%
-1.27%
3.95%
2000
9.20%
3.60%
-1.80%
9.97%
10.16%
2001
-2.73%
4.51%
-5.50%
-2.56%
1.04%
2002
-0.69%
-1.50%
-3.00%
-1.39%
4.73%
The highest percentage increase in gross output in constant prices, among the presented subsectors, was in the advanced technologies sub-sector in the years 1995-1998 (the highest
increase, amounting to ca. 18%, was in 1995). There were also high output increases in
constant prices in the consumer goods sub-sector. At the same time, it is the only sub-sector
in which there was an increase in gross output in constant prices in the whole period. The
highest declines in gross output in constant prices were in mining and quarrying. This value
showed a declining trend since 1998, and it was in that year that the decline was the highest
at the level of 13.3%.
The table below shows the ratio of gross output in current prices to gross output in 1995
constant prices. We designate this ratio as PQT.
Table 2.12. The PQT ratio in the sub-sectors.
Sub-sector
AT
FD
MQ
KG
CG
1995
1.00
1.00
1.00
1.00
1.00
1996
1.12
1.18
1.12
1.06
1.10
1997
1.20
1.30
1.30
1.16
1.18
1998
1.27
1.40
1.40
1.23
1.26
1999
1.30
1.44
1.47
1.34
1.32
2000
1.39
1.55
1.62
1.56
1.34
2001
1.40
1.61
1.71
1.51
1.345
2002
1.38
1.60
1.76
1.49
1.354
The PQT measure rises between 1995 and 2001 in the AT, FD and KG sub-sectors, then its
value drops slightly. In the MQ and CG sub-sectors, it increases in the whole period. In 1996
the PQT measure had the highest value for the FD sub-sector. Over the next two years, the
value of this measure was equal for the FD and MQ sub-sectors, at the same time exceeding
the value of this measure for the other sub-sectors. Since 1999 the MQ sub-sector has the
highest PQT measure. In the MQ sub-sector, the value of the PQT measure is not only higher
than in the other sub-sectors since 1999, but every year this difference becomes higher and
higher. Thus, output in current prices in the MQ sub-sector is much higher than output in
constant prices.
2.4.2 Intermediate consumption
Intermediate consumption includes the value of consumed materials and fuels, raw materials,
energy, costs of external services and other similar costs. Intermediate consumption in current
prices in particular sub-sectors is shown in Chart 2.3.
42
100000
in PLN mln
80000
60000
40000
20000
0
1995
AT
1996
1997
FD
1998
1999
MQ
2000
KG
2001
2002
CG
Chart 2.3. Intermediate consumption in the sub-sectors (current prices).
The highest percentage increase in intermediate consumption was in all the sub-sectors in
1995. It can be seen in the chart that in the whole period in question intermediate
consumption in current prices increased only in the consumer goods (CG) sub-sector. The
highest increase was 39.7%, and it was in 1995. In the advanced technologies (AT) subsector, intermediate consumption in current prices increased until 2000, whereas there was a
decline in intermediate consumption in the next years 2001 an 2002. In the food products,
beverages and tobacco products (FD) sub-sector, there was a slight percentage decline in
intermediate consumption in the years 1999 and 2002. In the mining and quarrying (MQ)
sub-sector, a high percentage decline took place in 1998, and it was as much as 8.4%. In the
consumer goods (CG) sub-sector, in turn, there were significant increases in intermediate
consumption in the period between 1995 and 2000 (expect for 1998, when this increase was
relatively small), whereas in 2001 and 2002, 3% and 4% declines, respectively, were
reported. In the capital goods (KG) sub-sector, consumption in current prices showed a
constant increase in the years 1995-2000. In the next years, i.e., 2001 and 2002, however, its
value dropped. Percentage changes in intermediate consumption in current prices are shown
below in Table 2.13.
Table 2.13. The rate of growth in intermediate consumption in the sub-sectors (current
prices). Year-on-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
48.59%
37.81%
23.55%
41.40%
39.68%
1996
27.06%
31.29%
13.44%
12.03%
22.90%
1997
31.38%
19.20%
20.53%
20.96%
23.48%
1998
20.06%
12.90%
-8.39%
0.25%
16.32%
1999
11.71%
-1.47%
1.57%
10.00%
8.57%
2000
17.61%
12.27%
1.58%
32.87%
14.50%
2001
-1.96%
12.26%
0.74%
-3.15%
3.99%
2002
-1.81%
-1.22%
4.61%
-3.98%
6.08%
Intermediate consumption in constant prices has been also analysed, with 1995 as the
baseline year. Intermediate consumption in constant prices for the five (AT, FD, MQ, KG,
CG) sub-sectors analysed is shown in Chart 2.4 below.
43
70000
in PLN mln
mln zł
60000
50000
40000
30000
20000
10000
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
KG
2001
2002
CG
Chart 2.4. Intermediate consumption in the sub-sectors (constant prices).
The table below shows percentage changes in intermediate consumption in constant prices.
Table 2.14. The rate of growth in intermediate consumption in the sub-sectors (constant
prices). Year-on-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
18.23%
10.93%
0.90%
12.52%
9.71%
1996
16.28%
9.25%
-0.40%
1.77%
12.90%
1997
19.88%
11.65%
7.80%
8.64%
12.45%
1998
14.52%
1.52%
-16.40%
-4.87%
9.69%
1999
6.46%
-5.93%
-5.20%
1.98%
3.50%
2000
8.12%
5.28%
-6.50%
15.27%
7.31%
2001
-2.63%
7.51%
-3.30%
-5.39%
2.73%
2002
-0.50%
-4.15%
2.80%
-2.79%
6.12%
When analysing intermediate consumption in constant prices, it can be noticed that its
changes in the AT, FD and CG sub-sectors, as far as the direction of changes is concerned
(increase/decline), are analogous to changes in current prices. But the MQ sub-sector shows a
decline in intermediate consumption in constant prices in 1996, as well as in the period
between 1998 and 2001. In the KG sub-sector, intermediate consumption in constant prices
declines in the years 1998, 2001, 2002.
The analysis of the relation of intermediate consumption in current prices to intermediate
consumption in constant prices is shown in the table below. The ratio expressing the quotient
of these prices will be designated as PMT.
Table 2.15. The PMT ratio in the sub-sectors.
Sub-sector
AT
FD
MQ
KG
CG
1995
1.00
1.00
1.00
1.00
1.00
1996
1.09
1.20
1.14
1.10
1.09
1997
1.20
1.28
1.27
1.23
1.20
1998
1.26
1.43
1.40
1.29
1.27
1999
1.32
1.49
1.50
1.39
1.33
2000
1.43
1.59
1.62
1.61
1.42
2001
1.44
1.66
1.69
1.64
1.436
2002
1.42
1.71
1.72
1.62
1.435
The PMT ratio showed a growth trend in the whole period in question in the FD and MQ subsectors. In the other sub-sectors, it increased from 1995 until 2001, and in the next year its
value dropped slightly. Until 1998, the highest PQM measure is demonstrated by the FD subsector. Similarly as it was in the case of the PQT ratio, PMT in mining and quarrying is also
44
higher than in the other sub-sectors since 1999. The PMT ratio is also high for the FD subsector, and it is close to the value of this ratio for MQ.
2.4.3 Gross value added
in PLN mln
Value added is the growth in value of goods as a result of a given production process. By
deducting intermediate consumption from gross output, we arrive at gross value added. The
chart below shows gross value added in particular sub-sectors in current prices.
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
1995 1996 1997 1998 1999 2000 2001 2002
AT
FD
MQ
KG
CG
Chart 2.5 Gross value added in the sub-sectors (current prices).
The highest value added in current prices in the whole period is in the CG sub-sector. Value
added for the AT sub-sector is lower than for CG. The FD and KG sub-sectors have similar
values for gross value added. The MQ sub-sector shows the lowest gross value added in
current prices in the years 1995-2002. The rate of growth in gross value added in current
prices in particular sub-sectors is shown in the table below.
Table 2.16. The rate of growth in gross value added (current prices).
Sub-sector
AT
FD
MQ
KG
CG
1995
40.54%
64.26%
24.98%
57.09%
35.28%
1996
31.86%
14.97%
14.94%
0.77%
24.10%
1997
15.28%
30.34%
14.67%
24.71%
21.53%
1998
15.65%
9.92%
-5.02%
9.61%
14.30%
1999
9.95%
8.92%
1.59%
0.41%
10.15%
2000
12.95%
7.73%
13.81%
15.02%
7.28%
2001
-2.34%
-5.06%
-0.97%
-13.91%
-3.43%
2002
-2.13%
-3.68%
-3.60%
1.67%
4.26%
In the years 1995-2000 we can observe an increase in gross value added in current prices in
most of the sub-sectors. The only exception is the MQ sub-sector, in which there was a
decline in gross value added in current prices in 1998. The highest increases were in 1995. In
2001 value added declined in all the sub-sectors, whereas in 2002 it fell in the AT, FD, MQ
sub-sectors. Percentage shares in gross value added generation by particular sub-sectors in
the years 1995-2002 in current prices are shown in the table below.
45
Table 2.17. Shares of particular sub-sectors in gross value added in current prices.
Sub-sector
AT
FD
MQ
KG
CG
1995
22.27%
15.49%
14.92%
17.45%
29.88%
1996
24.68%
14.97%
14.41%
14.78%
31.16%
1997
23.56%
16.15%
13.68%
15.26%
31.35%
1998
24.64%
16.06%
11.75%
15.13%
32.41%
1999
25.22%
16.28%
11.12%
14.14%
33.24%
2000
25.76%
15.86%
11.44%
14.70%
32.24%
2001
26.39%
15.80%
11.88%
13.28%
32.66%
2002
25.81%
15.21%
11.45%
13.50%
34.03%
It can be seen in the table that the CG sub-sector has the highest share in value added
generation in the whole period. It generates about 30% of gross value added in current prices
each year. The AT sub-sector is the next sub-sector with a high share of gross value added.
Its share is ca. 25% each year. The FD, KG sub-sectors are the next in terms of the share in
gross value added. Mining and quarrying has the smallest share in value added generation.
Gross value added in constant prices is shown in the chart below, with 1995 as the baseline
year.
40000
35000
25000
mln zł
in PLN mln
30000
20000
15000
10000
5000
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
KG
2001
2002
CG
Chart 2.6 Gross value added in the sub-sectors (constant prices).
Table 2.18. The rate of growth in gross value added in the sub-sectors (constant prices). Yearon-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
17.72%
21.85%
1.43%
9.41%
8.20%
1996
12.27%
3.40%
4.72%
7.61%
9.75%
1997
12.76%
5.96%
-4.13%
17.64%
18.16%
1998
7.95%
15.75%
-10.76%
0.63%
6.93%
1999
11.19%
8.36%
-1.66%
-8.78%
4.70%
2000
11.50%
-1.05%
1.68%
-3.74%
14.86%
2001
-2.92%
-4.36%
-7.00%
6.20%
-1.56%
2002
-1.09%
7.29%
-7.11%
2.45%
2.50%
The direction of changes in gross value added in constant prices looks slightly different than
the rate of growth in current prices in some sub-sectors. In the AT and CG sub-sectors, the
situation relating to changes in gross value added in constant prices is similar to the
previously presented changes in gross value added in current prices. In the FD sub-sector,
there is a decline in gross value added in constant prices compared to the previous year
already since 2000. In the MQ sub-sector, gross value added in constant prices increases only
in the years 1995-1996 and 2000. In the KG sub-sector, gross value added in constant prices
falls in 1999 and 2000.
46
The table below shows a ratio calculated as the relation of gross value added in current prices
to its value in constant prices. This ratio will be designated as POT.
Table 2.19. The POT ratio in the sub-sectors.
Sub-sector
AT
FD
MQ
KG
CG
1995
1.00
1.00
1.00
1.00
1.00
1996
1.17
1.11
1.10
0.94
1.13
1997
1.20
1.37
1.31
0.99
1.16
1998
1.29
1.30
1.40
1.08
1.24
1999
1.27
1.31
1.44
1.19
1.31
2000
1.29
1.42
1.62
1.42
1.22
2001
1.30
1.41
1.72
1.15
1.20
2002
1.28
1.27
1.79
1.14
1.22
In the AT sub-sector, the POT ratio increased in the years 1995-1998 and 2000-2001. In the
FD sub-sector, this ratio grew in the years 1995-1997 and 1999-2000. In the MQ sub-sector,
this ratio increased in the whole period in question (it was the only sub-sector with a growth
trend in the whole period). In addition, since 1998 its value is higher than in the other subsectors, and every year this difference increases dynamically. In the KG sub-sector, the POT
ratio increased in the years 1996-2000, and then it declined. It was the only sub-sector where
the value of this ratio was less than 1 in the years 1996-1997.
2.4.4 Output sold
The chart below shows output sold for particular sub-sectors in current prices.
140000
120000
in PLN mln
100000
80000
60000
40000
20000
0
1995 1996 1997 1998 1999 2000 2001 2002
AT
FD
MQ
KG
CG
Chart 2.7. Output sold in the sub-sectors (current prices).
Industry output sold in current prices increased in the whole period in question in the AT and
CG sub-sectors. At the same time, the highest value of output sold was in the CG sub-sector
in the whole period. There is a growth trend in almost all the sub-sectors between 1995 and
2000 (the exception here is the rate of growth in output sold in the MQ sub-sector in 1998).
The highest increases in output sold, among the sub-sectors concerned, were in the years
1995-2002 in the AT sub-sector. It can be seen in the table below that output sold increased
the fastest in this sub-sector in 1995, and this increase in current prices was as much as
45.7%. Between 1995 and 1999, the growth trend was declining, then alternately growing
and declining. The CG sub-sector reported somewhat lower increases in output sold in
current prices than in the AT sub-sector in the successive years. In the FD sub-sector, output
47
sold grew until 2001, and in 2002 it declined by 1.6%. In the MQ sub-sector, output sold in
current prices slumped in 1998; its value declined by about 6% compared to 1997, and there
was also a slight decline in 2001. The KG sub-sector shows a decline in output sold since
2001.
Table 2.20. The rate of growth in output sold in the sub-sectors (current prices). Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
45.72%
22.47%
21.98%
34.19%
37.35%
1996
28.27%
27.37%
14.31%
9.23%
22.71%
1997
26.18%
20.23%
16.81%
22.03%
22.69%
1998
18.17%
10.86%
-5.83%
0.46%
15.14%
1999
10.58%
2.58%
2.22%
7.87%
8.22%
2000
11.29%
10.04%
7.66%
25.70%
14.11%
2001
0.50%
7.69%
-0.71%
-7.89%
1.31%
2002
1.77%
-1.56%
0.18%
-2.79%
5.93%
2.4.5 The rate of growth in output sold
Table 2.21 shows the average rate of growth in output sold in constant prices in particular
sub-sectors. The average rate of growth in sectoral output sold in a given year in constant
prices in each sub-sector has been determined as the arithmetic mean by adding up the rates
of growth in particular groups belonging to a given sub-sector and by dividing it by the
number of these groups.
Table 2.21. The average rate of growth in output sold in the sub-sectors (constant prices).
Year-on-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
13.16%
11.40%
4.60%
16.48%
13.07%
1996
20.95%
4.40%
-0.60%
13.03%
11.63%
1997
17.91%
2.10%
2.50%
4.38%
9.34%
1998
19.44%
6.85%
0.50%
11.40%
13.94%
1999
14.28%
4.60%
-12.80%
-4.25%
5.57%
2000
11.53%
-1.30%
-2.60%
1.73%
3.43%
2001
4.98%
-7.60%
-2.60%
8.50%
8.22%
2002
4.19%
-5.15%
-5.70%
-3.28%
0.24%
Similarly as it was in the case of the rate of growth in output sold in the AT and CG subsectors in current prices, the average rate of growth in output sold in constant prices in the AT
and CG sub-sectors also shows an increase in these prices. Output sold in constant prices in
the FD sub-sector declined already since 2000. The highest percentage decline in the FD subsector compared to the previous year was in 2001, and it was 7.6%. Output sold in constant
prices in the MQ sub-sector increased only in the years 1995, 1997-1998. In the KG subsector KG, output sold in constant prices declined in the years 1999 and 2002. In 1999, as
compared to 1998, output sold in constant prices significantly declined in mining and
quarrying. This decline was 12.8%, and the declining trend continued until the end of the
period in question. Output sold in constant prices rose in almost all the sub-sectors until 1996
(the only exception is output sold in the MQ sub-sector in 1996).
2.4.6 The price index of output sold
The price index of sectoral output sold for each year have been calculated for particular subsectors as the arithmetic means resulting from the values of the price indexes in particular
sub-sectors in a given year. The chart below shows the average price index of output sold in
particular sub-sectors, whereas Table 2.22. shows the rates of growth for this ratio.
48
%
140,00
135,00
130,00
125,00
120,00
115,00
110,00
105,00
100,00
95,00
90,00
1995
1996
AT
1997
1998
FD
1999
MQ
2000
2001
KG
2002
CG
Chart 2.8 The average price index of output sold. The previous year is 100%.
Table 2.22. The rate of growth in average price indexes of output sold in the sub-sectors.
Year-on-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
21.18%
37.60%
22.90%
32.75%
24.40%
1996
10.33%
23.70%
11.80%
4.13%
9.52%
1997
6.30%
20.60%
16.40%
9.68%
7.48%
1998
4.64%
12.30%
8.50%
6.40%
6.94%
1999
3.34%
7.15%
5.00%
9.42%
4.47%
2000
2.81%
8.05%
10.70%
16.75%
4.49%
2001
0.20%
-0.95%
5.10%
-2.90%
0.90%
2002
-0.03%
1.05%
3.00%
0.00%
1.12%
It can be seen in the table that the highest increase in output sold prices in the sub-sectors in
question was in 1995 in the FD sub-sector, and it was 37.6%. In the whole period in question
(1995-2002), it was the sub-sector in which output sold prices increased the most. In the same
period, output sold prices also increased significantly in the MQ sub-sector, by about 33%.
The lowest increase in output sold prices in the period in question was in the AT sub-sector.
This sub-sector shows a declining trend in the increase of the price index of industry output
sold in the whole period in question. Attention should be paid to a substantial increase in the
price index of output sold in the KG sub-sector in 2000. It was as much as 16.75%. In the
whole period in question, there are three cases of a decline in the price index of output sold in
the sub-sectors analysed as compared to the previous year: in 2001 in the FD sub-sector by
about 1%, in the KG sub-sector by about 3%, whereas in 2002 the decline in the AT subsector was 0.03%.
2.4.7 Revenue of enterprises
Chart 2.9 shows revenue of industrial enterprises in particular sub-sectors.
49
140000
120000
in PLN mln
100000
80000
60000
40000
20000
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
2001
KG
2002
CG
Chart 2.9 Revenue of industrial enterprises in the sub-sectors.
It can be seen in the chart that in the years 1995-2002 enterprises from the AT, FD, KG, CG
sub-sectors posted similar revenues. But revenue in the MQ sub-sector was much lower. The
table below shows exactly the shares of particular sub-sectors in total revenue of
manufacturing, mining and quarrying in particular years.
Table 2.23. Shares of particular sub-sectors in total revenue of manufacturing, mining and
quarrying.
Sub-sector
AT
FD
MQ
KG
CG
1995
21.09%
23.65%
9.78%
25.21%
20.26%
1996
22.69%
24.96%
8.94%
22.45%
20.95%
1997
23.76%
24.32%
8.32%
21.87%
21.73%
1998
25.25%
24.22%
7.48%
20.92%
22.14%
1999
26.09%
23.09%
7.12%
21.27%
22.42%
2000
25.00%
22.09%
6.91%
23.62%
22.38%
2001
23.77%
24.40%
6.83%
21.40%
23.60%
2002
23.41%
24.03%
6.67%
22.32%
23.56%
The AT sub-sector revenue showed a growing share in total revenue in the years 1995 –
1999, but in the years 2000- 2002 this share was declining. The FD sub-sector revenue
showed a growing share in total revenue in the years 1995 – 1996, then in the years 1997 –
2000 it declined every year. In 2001 it increased, and in 2002 it dropped slightly. It can be
seen in the table above that the MQ sub-sector’s share in total revenue declined year by year.
The share of the KG sub-sector revenue in total revenue declined between 1995 and 1998, it
increased in the years 1999-2000, declined in 2001, and then it increased again. In the CG
sub-sector, this share increased in the years 1995-1999, next it dropped slightly, and then
again it increased in 2001, and in 2002 it declined. Table 2.24 shows percentage changes in
revenue in particular sub-sectors.
Table 2.24. The rate of growth in revenue of enterprises in particular sub-sectors. Year-onyear percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
50.03%
46.28%
30.26%
42.17%
50.77%
1996
35.00%
32.43%
14.79%
11.74%
29.74%
1997
31.65%
22.52%
17.02%
22.46%
30.38%
1998
16.88%
9.54%
-1.13%
5.21%
12.10%
50
1999
14.53%
5.62%
5.52%
12.69%
12.23%
2000
7.67%
7.51%
8.97%
24.80%
12.15%
2001
0.17%
16.37%
4.16%
-4.58%
11.10%
2002
-6.82%
-6.79%
-7.62%
-1.30%
-5.52%
Total revenue of the sub-sectors increase until 2001 (in spite of a slight decline in 1998 in the
MQ sub-sector and in 2001 in the KG sub-sector), whereas in 2002 there was a year-on-year
percentage decline. This decline took place at the same time in all the sub-sectors. The
increase in total revenue showed a declining tendency as compared to the previous year in the
years 1995-1996, 1998 and in 2001. The AT sub-sector revenue showed a growth trend in the
years 1995 –2001, however, each year this growth was increasingly smaller. The FD subsector revenue showed a declining growth trend in the years 1995 – 1999, and then in the
years 2000-2001 the growth rate was faster. It can be seen in Table 2.20 that the rates of
growth in revenue of the MQ and KG sub-sectors in the years 1995- 2002 were different. In
the CG sub-sector, the increase in revenue showed a declining tendency year by year (only in
1997 and 1999 this increase was higher than in the previous year).
2.4.8 Costs in enterprises
The chart below show costs in enterprises aggregated into the sub-sectors.
140000
in PLN mln
120000
100000
80000
60000
40000
20000
0
1995
AT
1996
1997
1998
FD
MQ
1999
2000
KG
2001
2002
CG
Chart 2.10. Costs in industrial enterprises aggregated into the sub-sectors.
The distribution of costs between the sub-sectors in the period in question was analogous to
the distribution of revenue. The AT, FD, KG, CG sub-sectors had similar costs. The lowest
total costs are in the MQ sub-sector. The highest percentage increase in costs compared to the
previous year was in 1995 in all the sub-sectors. The highest increase in costs in the period
between 1995 and 2001 is in the CG sub-sector, the AT sub-sector demonstrates a high
increase in costs between 1995 and 2001, whereas in 2002 there was a decline in costs in all
the sub-sectors. The table below shows in detail percentage changes in costs in particular subsectors.
Table 2.25. The rate of growth in costs in enterprises in particular sub-sectors. Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
50.81%
46.79%
36.45%
41.90%
54.37%
1996
32.77%
32.29%
22.75%
17.59%
29.71%
1997
31.51%
22.75%
11.80%
21.39%
29.01%
1998
18.61%
9.55%
12.95%
6.77%
13.84%
51
1999
16.14%
7.10%
2.40%
13.88%
11.66%
2000
8.45%
7.15%
-1.61%
23.79%
13.22%
2001
0.84%
15.54%
2.74%
-2.52%
12.45%
2002
-7.08%
-7.48%
-6.78%
-2.75%
-6.52%
2.4.9 Employment
in '000
The chart below shows employment in particular sub-sectors.
1600
1400
1200
1000
800
600
400
200
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
2001
KG
2002
CG
Chart 2.11. Employment in the sub-sectors.
Among the five sub-sectors, the MQ sub-sector was characterised by the lowest employment
in the years 1995-2002, CG by the highest. In all the sub-sectors, there was a total decline in
employment of over 23% between 1995 and 2002. In the whole period in question, there is a
declining trend in employment in the MQ and KG sub-sectors, whereas there is a slight
increase in employment by 0.15% in the AT sub-sector, but it is only in 1997. In the FD subsector, a decline in employment takes place in the years 1998-2001, with the highest value in
2000. In the CG sub-sector, there is a decline in employment since 1998. Between 1995 and
2000, the highest percentage decline in employment was in the MQ sub-sector, and this
decline was 41.5%, next in the KG sub-sector, by as much as 38.5%. In the AT sub-sector,
this decline was 24%, in the CG sub-sector 19%, and in the FD sub-sector 11.5%. Changes in
employment in particular sub-sectors in the years 1995-2002 are shown in Table 2.26.
Table 2.26. The rate of growth in employment in the sub-sectors. Year-on-year percentage
changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
-1.35%
2.24%
-5.23%
-3.18%
3.17%
1996
-0.71%
4.26%
-5.04%
-1.47%
3.21%
1997
0.15%
1.61%
-3.89%
-2.63%
1.23%
1998
-1.13%
-0.75%
-8.87%
-9.69%
-1.99%
1999
-6.61%
-2.91%
-13.57%
-3.89%
-6.76%
2000
-8.17%
-9.69%
-13.05%
-14.66%
-6.72%
2001
-5.47%
-4.10%
-3.00%
-10.38%
-7.15%
2002
-4.68%
0.10%
-3.46%
-3.44%
-2.02%
2.4.10 Employment costs
Having data on employment related costs as a component of gross value added of industry in
the groups assigned to the respective sub-sectors and the number employed in particular subsectors, employment related costs per one employee can be determined. They are expressed
on an annual basis.
52
70
60
mln zł
in PLN mln
50
40
30
20
10
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
KG
2001
2002
CG
Chart 2.12. Employment costs per one employee in the sub-sectors (current prices).
It can be seen in the chart that the highest employment costs in current prices per one
employee are in the MQ sub-sector, then in the KG, AT, FD, CG sub-sectors. Table 2.27
below shows changes in employment costs in current prices per one employee.
Table 2.27. The rate of growth in employment costs (current prices). Year-on-year percentage
changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
38.62%
38.35%
33.64%
46.97%
34.04%
1996
33.99%
26.71%
26.51%
35.45%
22.92%
1997
20.79%
27.30%
19.65%
23.86%
20.14%
1998
18.95%
20.61%
17.57%
24.47%
24.74%
1999
19.70%
15.48%
7.67%
8.09%
15.66%
2000
18.09%
15.30%
11.80%
19.18%
21.64%
2001
10.53%
9.86%
5.61%
12.98%
8.14%
2002
-5.61%
-9.24%
8.67%
-3.24%
-3.90%
When considering the years 1995-2002, the fastest increase in employment costs per one
employee in current prices in all the sub-sectors was in 1995. In the years 1995-2001 the
fastest percentage increase in employment costs per one employee was in the KG sub-sector.
In 2002 there was a decline in costs in this sub-sector by 3.2%. In the AT sub-sector,
increases were increasingly smaller year by year (except for 1999), and finally in 2002 there
was a fall in costs by 5.6%. The situation was similar in the FD sub-sector (except for 1997,
when the increase was higher than in 1996). In the MQ sub-sector, the declining trend in the
increase of costs occurred in the years 1995-1999. In this sub-sector, as one of the sub-sectors
considered, there was an increase in employment costs in 2002. This increase was almost 9%
in that year.
2.4.11 The rate of growth in labour productivity
The chart below shows the average rate of growth in labour productivity in particular subsectors measured by gross value added per one employee in constant prices. It has been
determined as the arithmetic mean, and it is the average value in particular years for given
sub-sectors, taking into account the rate of growth in productivity in particular groups of a
given sub-sector and the number of groups.
53
%
180
170
160
150
140
130
120
110
100
90
80
1995
1996
AT
1997
1998
FD
1999
MQ
2000
2001
KG
2002
CG
Chart 2.13. The average rate of growth in labour productivity in the sub-sectors, measured by
gross value added per one employee (constant prices). The previous year =100.
In all the sub-sectors in the period in question, there are alternately increases and declines in
labour productivity. There is a significant increase in the productivity growth rate in the KG
sub-sector in 2001, and it is 137% compared to 2000. This increase in productivity is mainly
attributable to the increase in productivity in manufacture of coke and refined petroleum
products, which was as much as 267.8%. In the next year, there was a drastic fall in
productivity in this sub-sector, also mainly attributable to a fall in labour productivity in
manufacture of coke and refined petroleum products, which belongs to this sub-sector. A low
level of productivity in the KG sub-sector in 1999 and 2000 was also caused by a decline in
productivity in manufacture of coke and refined petroleum products. The level of labour
productivity markedly changed in the FD sub-sector in 2002. It resulted from, first of all, an
increase in labour productivity in manufacture of tobacco products, which was 226.5%
compared to 2001.
2.4.12 Investment expenditures
The chart below shows investment expenditures in the sub-sectors in current prices.
10000
in PLN mln
8000
6000
4000
2000
0
1995 1996 1997 1998 1999 2000 2001 2002
AT
FD
MQ
KG
CG
Chart 2.14. Investment expenditures in the sub-sectors (current prices).
54
In manufacturing, mining and quarrying, total investment expenditures in current prices
increased between 1995 and 1998, then they showed a declining tendency. In the whole
period in question, investment expenditures in current prices were the lowest in the MQ subsector. They increased in this sub-sector between 1995 and 1999 and in 2001. In 2001 the
MQ sub-sector was the only one to post an increase in investment expenditures in current
prices, and this increase was as much as 19.7% year-on-year. At the same time, it was the
highest increase in this sub-sector in the period 1995-2002. In the AT sub-sector, investment
expenditures in current prices rose in the years 1995-1999 and in 2002. The highest increase
in current prices took place in 1997; this increase was 66% compared to 1996. In the FD
subsector, the increase in investment expenditures in current prices also occurred in the years
1995-1999 and in 2002, and it was the highest in 1995. In the KG sub-sector, investment
expenditures increased in the years 1995-1998. Since 1999 their fall can be observed. Their
highest increase in current prices in the sub-sector in question took place in 1996, and it was
55.4%. When analysing investment expenditures in the years 1995-2002, we observe the
highest, among the sub-sectors in question, percentage decline in investment expenditures in
current prices in the KG sub-sector. This decline was 26%, and it occurred in 2001. In the CG
sub-sector, we can see an increase in investment expenditures in the years 1995-1998 and in
2000. The highest increase in the CG sub-sector took place in 1997, and it was 45.7%.
Investment expenditures in particular sub-sectors in constant prices are shown in Chart 2.15,
with 1995 as the baseline year.
7000
6000
mln zł
in PLN mln
5000
4000
3000
2000
1000
0
1995 1996 1997 1998 1999 2000 2001 2002
AT
FD
MQ
KG
CG
Chart 2.15. Investment expenditures in the sub-sectors (constant prices).
The rate of growth in investment expenditures in particular sub-sectors in constant prices
in the years 1995-2002 is shown in the table below.
Table 2.28. The rate of growth in investment expenditures in particular sub-sectors in
constant prices. Year-on-year percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
10.16%
30.04%
2.70%
15.70%
12.08%
1996
38.82%
25.23%
-4.00%
32.11%
18.61%
1997
55.68%
5.28%
0.20%
3.43%
29.29%
1998
25.77%
-3.99%
3.10%
16.77%
30.64%
55
1999
0.40%
12.83%
-2.10%
-20.17%
-14.35%
2000
-15.60%
-21.90%
-18.10%
-1.58%
-1.33%
2001
-8.40%
-1.19%
19.70%
-24.36%
-13.03%
2002
15.58%
-11.22%
-5.60%
-23.15%
-8.67%
Investment expenditures in constant prices in the advanced technologies (AT) sub-sector
increased between 1995 and 1999, and then their value decreased compared to the previous
year, however, in 2002 it rose again. Their highest year-on-year increase took place in 1997,
and it was 55.7%, whereas the highest decline in 2000, and it was 15.6%. In the food
products, beverages and tobacco products (FD) sub-sector, investment expenditures in
constant prices increased in the years 1995-1997 and in 1999 compared to the previous year
in the period in question. Their highest increase equal to 30% year-on-year was in 1995,
whereas the highest fall in 2000, and it amounted to as much as 21.9%. In the mining and
quarrying (MQ) sub-sector, investment expenditures in constant prices were much lower than
in the other sub-sectors in the whole period, and they increased in the years 1995, 1997, 1998,
2001. Their highest increase in constant prices occurred in 2001, and it was 19.7%, whereas
their highest decline took place in 2002, and it was 5.6%. MQ was the only sub-sector which
posted an increase in investment expenditures in constant prices in 2001. It should be noted
that for this sub-sector the increase in investment expenditures in constant prices in 2001 was
almost equal to the increase in expenditures in current prices. In the capital goods (KG) subsector, the increase in investment expenditures in constant prices took place until 1998, and in
the next years its value declined. The highest increase in this sub-sector was in 1996, and it
was 32.11%, whereas the highest decline in 2001, and it was 24.4%. In the consumer goods
(CG) sub-sector, the increase in investment expenditures continued until 1998, and in the
next years we observe their decline until the end of the period concerned, i.e., until the year
2002.
The table below shows the relation of investment expenditures in current prices to investment
expenditures in 1995 constant prices. We designate this ratio as PIT, and it is aggregated for
the period analysed, i.e., 1995-2002.
Table 2.29. The PIT ratio in the sub-sectors.
Sub-sector
AT
FD
MQ
KG
CG
1995
1.00
1.00
1.00
1.00
1.00
1996
1.09
1.15
1.15
1.18
1.15
1997
1.17
1.29
1.28
1.33
1.30
1998
1.22
1.39
1.38
1.41
1.39
1999
1.29
1.45
1.49
1.50
1.47
2000
1.35
1.51
1.5068
1.52
1.52
2001
1.33
1.52
1.5064
1.48
1.50
2002
1.36
1.72
1.53
1.55
1.61
The PIT measure increases between 1995 and 2000 in the AT, MQ, KG, CG sub-sectors. In
the next year, its value falls in all the sub-sectors, and in 2002 it increases again.
In the FD sub-sector, the PIT measure increases in the whole period analysed, i.e., in the
years 1995-2002. At the same time, in 2001 and 2002 it is the highest in this sub-sector. The
highest value for the PIT measure calculated for all the sub-sectors was recorded in 2002. The
advanced technologies (AT) sub-sector demonstrated the lowest PIT measure in all the years
analysed, with the values between 1.00 and 1.36 in the last (2002) year.
2.4.13 Gross fixed assets
The chart below shows gross fixed assets in particular sub-sectors.
56
in PLN mln
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
1995
1996
AT
1997
1998
FD
1999
MQ
2000
KG
2001
2002
CG
Chart 2.16. Gross fixed assets in the sub-sectors.
The table below show changes in gross fixed assets in particular sub-sectors.
Table 2.30. The rate of growth in gross fixed assets in the sub-sectors. Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1995
36.25%
53.76%
36.68%
39.09%
39.24%
1996
35.65%
27.12%
82.82%
64.81%
38.15%
1997
5.13%
10.39%
-1.35%
6.55%
9.97%
1998
6.52%
8.66%
-3.95%
6.18%
12.30%
1999
10.90%
15.62%
-1.23%
10.41%
13.56%
2000
6.12%
5.48%
-1.87%
4.04%
8.76%
2001
5.47%
7.05%
5.14%
2.51%
9.36%
2002
8.18%
10.55%
0.68%
2.80%
9.66%
In the period analysed, the highest value of gross fixed assets is in the KG sub-sector. The
value of gross fixed assets increased in the whole period in question in the AT sub-sector,
with the highest percentage increase in this sub-sector in 1995 (36.25%) and in 1996
(35.65%). In the FD sub-sector, the highest increase was in 1995, and it was 53.8%, and in
the KG sub-sector, the highest increase was observed in 1996, when it was 64.8%. In the CG
sub-sector, the highest increase was recorded in 1995, and it was 39.2%. The analysis of data
values shows that the KG sub-sector had the highest value of gross fixed assets in all the
years analysed. MQ was the only sub-sector where there was a decline in gross fixed assets.
The value of fixed assets showed a declining trend in this sub-sector in the next years in the
period 1997-2000. At the same time, in this sub-sector there was the highest annual increase
in fixed assets of all the sub-sectors. This increase was in 1996, and it was almost 83%.
2.4.14 Trade exchange
Exports in current prices for particular sub-sectors are shown in the chart below.
57
70000
in PLN mln
60000
50000
40000
30000
20000
10000
0
2000
AT
2001
FD
MQ
2002
KG
CG
Chart 2.17. Exports in the sub-sectors (current prices).
Available data on exports come from the years 2000-2002. It can be seen in the chart that the
AT sub-sector has the highest value of exports in the years 1995-2002, the next is the CG
sub-sector in terms of the value of exports. The KG, FD and MQ sub-sectors have much
lower values of exports, whereas exports in the MQ sub-sector in 2001 dropped by as much
as 18% year-on-year. Table 2.31 shows changes in exports for particular sub-sectors.
Table 2.31. The rate of growth in exports in the sub-sectors (current prices). Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
2001
12.74%
11.87%
-18.10%
4.73%
7.54%
2002
18.56%
13.62%
16.65%
11.73%
15.71%
In terms of Polish trade exchange, countries have been divided into EU countries, other
developed countries, Central and Eastern Europe (CEE) countries and other developing
countries. Total exports for all the sub-sectors to each of the abovementioned areas are shown
in the chart below.
120000
in PLN mln
100000
80000
60000
40000
20000
0
2000
2001
EU
CEE
2002
Other developed countries
Other developing countries
Chart 2.18. Exports to particular areas.
58
We can see that EU countries are the destination of a decided majority of exports, then
Central and Eastern Europe countries, other developed countries and other developing
countries. Exports to these areas increase in the whole period in question. Total exports in
2000 amounted to PLN 107,192.2 million, in 2001 it was PLN 116,461.1 million, whereas in
2002 – PLN 135,345.2 million. The table below shows in detail exports for particular subsectors to the aforementioned areas. Exports values are given in current prices.
Table 2.32. Exports to particular areas – sub-sectors [in PLN mln].
Sub-sector
AT
2000
EU
Other developed countries
CEE
Other developing countries
2001
2002
33671.4
3009.9
3906.0
2787.6
37605.3
3310.4
4498.8
3484.8
44647
4467.8
5568.8
3290.6
3222.7
448.9
2201.7
575.7
3567
492.2
2349.5
805.7
4156.7
683.9
2618
738.5
2593.5
124.0
237.9
498.6
2107.5
14.4
447.9
258.9
2435.2
75.1
449.4
339.9
FD
EU
Other developed countries
CEE
Other developing countries
MQ
EU
Other developed countries
CEE
Other developing countries
KG
EU
Other developed countries
CEE
Other developing countries
10302.9
927.8
3693.4
1449.2
10476.7 11220.3
751.7
925.8
4487.5 5532.5
1432.6
1482
30042.0
2095.1
4745.5
658.4
31047.6 34896.6
2267.6 2620.9
6174.5 7944.2
880.5
1252
CG
EU
Other developed countries
CEE
Other developing countries
It can be seen in the table that a substantial part of exports of the AT, KG, CG and MQ subsectors is destined for EU markets. In the FD sub-sector, exports to EU countries and CEE
countries are of special importance. The charts below show the shares of particular subsectors in total exports in successive years.
59
35%
41%
15%
AT
FD
3%
MQ
6%
KG
CG
Chart. 2.19. Shares of particular sub-sectors in total exports in 2000.
It can be seen in the chart that in 2000 the groups belonging to the AT sub-sector accounted
for the largest part of exports. This sub-sector accounted for as much as 41% of total exports
of all the sub-sectors. In the next years (2001 and 2002), the share of this sub-sector showed a
growth tendency. The CG sub-sector exported slightly less. The share of this sub-sector in
total exports was 35%. The MQ sub-sector accounted for the least part of exports, and its
share in exports in 2000 was 3%. In the next years, the share of this sub-sector further
decreased.
35%
42%
15%
AT
FD
2%
MQ
6%
KG
CG
Chart. 2.20. Shares of particular sub-sectors in total exports in 2001.
60
35%
43%
14%
AT
2%
FD
MQ
6%
KG
CG
Chart. 2.21. Shares of particular sub-sectors in total exports in 2002.
In 2001 and 2002 the general proportions of the shares in total exports between the subsectors were similar to those in 2000, and the rates of growth in the shares of particular subsectors in total exports were small. However, it should be stressed that the value of total
exports increased at the same time.
Charts 2.22.-2.24 below show the shares of particular sub-sectors to specified geographic
areas in successive years of the period analysed.
100%
80%
60%
40%
20%
0%
2000
AT
2001
FD
MQ
2002
KG
CG
Chart. 2.22. Shares in exports to the EU of particular sub-sectors.
The AT sub-sector has the highest share in exports to the EU. This share gradually increases.
The CG sub-sector ranks next, and the MQ sub-sector exports the least.
61
100%
80%
60%
40%
20%
0%
2000
AT
2001
FD
MQ
2002
KG
CG
Chart. 2.23. Shares in exports to other developed countries of particular sub-sectors.
The AT sub-sector exports the most to other developed countries, and the MQ sub-sector the
least, and its share decreases.
100%
80%
60%
40%
20%
0%
2000
AT
2001
FD
MQ
2002
KG
CG
Chart. 2.24. Shares in exports to CEE countries of particular sub-sectors.
The CG sub-sector exports the most to CEE countries, and its share in exports to the area
concerned increases. The AT and KG sub-sectors demonstrate comparable, although lower
than CG, shares of exports to CEE countries. The MQ sub-sector’s share is small. The FD
sub-sector shows a declining trend in exports to CEE countries.
62
100%
80%
60%
40%
20%
0%
2000
AT
2001
FD
MQ
2002
KG
CG
Chart. 2.25. Shares in exports to other developing countries of particular sub-sectors.
The AT sub-sector accounts for the decidedly largest exports to other developing countries.
Its largest share in exports to the area concerned was in 2001. The KG sub-sector is the next
sub-sector with a significant share. The CG sub-sector’s share in exports in question rises
every year. The FD sub-sector showed a similar share in 2000 and 2001, however in 2001 its
share in exports to other developing countries was slightly higher. Between 2000 and 2002,
the MQ sub-sector’s share in exports to other developing countries decreased.
The tables below show shares in total exports of particular sub-sectors by export destination.
At the same time, the tables are a summary of the previous observations regarding exports.
Table 2.33. The AT sub-sector’s share in total exports and in exports to particular areas.
2000
31.41%
2.81%
3.64%
2.60%
40.46%
Area/Year
EU
Other developed countries
CEE
Other developing countries
Total
2001
32.29%
2.84%
3.86%
2.99%
41.99%
2002
32.99%
3.30%
4.11%
2.43%
42.83%
Table 2.34. The FD sub-sector’s share in total exports and in exports to particular areas.
2000
3.01%
0.42%
2.05%
0.54%
6.02%
Area/Year
EU
Other developed countries
CEE
Other developing countries
Total
63
2001
3.06%
0.42%
2.02%
0.69%
6.19%
2002
3.07%
0.51%
1.93%
0.55%
6.06%
Table 2.35. The MQ sub-sector’s share in total exports and in exports to particular areas.
2000
2.42%
0.12%
0.22%
0.47%
3.22%
Area/Year
EU
Other developed countries
CEE
Other developing countries
Total
2001
1.81%
0.01%
0.38%
0.22%
2.43%
2002
1.80%
0.06%
0.33%
0.25%
2.44%
Table 2.36. The KG sub-sector’s share in total exports and in exports to particular areas.
2000
9.61%
0.87%
3.45%
1.35%
15.27%
Area/Year
EU
Other developed countries
CEE
Other developing countries
Total
2001
9.00%
0.65%
3.85%
1.23%
14.72%
2002
8.29%
0.68%
4.09%
1.09%
14.16%
Table 2.37. The CG sub-sector’s share in total exports and in exports to particular areas.
2000
28.03%
1.95%
4.43%
0.61%
35.02%
Area/Year
EU
Other developed countries
CEE
Other developing countries
Total
2001
26.66%
1.95%
5.30%
0.76%
34.66%
2002
25.78%
1.94%
5.87%
0.93%
34.51%
It can be seen in the tables and in the previous analyses that, of all the sub-sectors, the AT
sub-sector exported the most to EU countries in 2000, and its share in these exports, taking
into account all the sub-sectors and export areas, was 31.4%. The CG sub-sector was only
slightly behind it; in 2001 it exported 28% of output of all the sub-sectors destined for
exports. The value of the KG sub-sector output exported to EU countries was much smaller.
The percentage shares in exports to EU countries of the FD and MQ sub-sectors were small.
In 2000 exports to EU countries accounted for ca. 75% of exports to all export areas. As
indicated before, the EU was the largest recipient of our exports. In the next years, the
situation was similar, as far as the proportions of shares of particular sub-sectors are
concerned. However, the shares of exports to EU countries of the AT and FD sub-sectors
slightly increased, whereas it decreased slightly for MQ, KG, CG. Among all the sub-sectors,
in 2000 the AT sub-sector exported the most to other developed countries, and its share in
these exports, taking into account all the sub-sectors and export areas, was 2.8%. The CG
sub-sector exported about 1% less to other developed countries. The remaining sub-sectors
had shares in exports to other developed countries of less than 1%. In the next years the
situation was similar, as far as the proportions of shares of particular sub-sectors are
concerned. However, the shares of exports to other developed countries of the AT and FD
sub-sectors increased slightly, whereas it decreased slightly for MQ, CG. The share of the
KG sub-sector decreased in 2001, and it increased in 2002.
In 2000 exports to CEE countries, taking into account all the sub-sectors and export areas,
were the highest in the CG sub-sector (4.43%), lower in AT (3.64%) and KG (3,45%). In the
FD sub-sector, this share was ca. 2%, whereas for MQ it was 0.22%. In the next years, the
shares in exports to CEE countries of the CG, AT, KG sub-sectors increased, the share in
exports decreased for FD, whereas for MQ it increased in 2001 and decreased in 2002.
64
As regards exports to other developing countries, the AT sub-sector had the highest share in
these exports in 2000, taking into account all the sub-sectors and export areas, with the share
of 2.6%, and the next was the KG sub-sector (1.35%). The shares of the remaining subsectors were below 1%. In the next years the situation was similar, however, the share of the
CG sub-sector increased, KG’s share decreased, the shares of AT and FD increased in 2001
and decreased in 2002, MQ’s share decreased in 2001 and increased in 2002.
An interesting characteristic of exports is the share of sectoral exports to export areas in
output (in current prices) of a given sub-sector. The table below shows this relation.
Table. 2.38. The share of exports of a given sub-sector to particular areas in gross output of
such sub-sector.
Sub-sector
AT
2000
EU
Other developed countries
CEE
Other developing countries
FD
EU
Other developed countries
CEE
Other developing countries
MQ
EU
Other developed countries
CEE
Other developing countries
KG
EU
Other developed countries
CEE
Other developing countries
CG
EU
Other developed countries
CEE
Other developing countries
2001
2002
28.18%
2.52%
3.27%
2.33%
32.14%
2.83%
3.84%
2.98%
38.90%
3.89%
4.85%
2.87%
3.35%
0.47%
2.29%
0.60%
3.43%
0.47%
2.26%
0.77%
4.07%
0.67%
2.56%
0.72%
9.58%
0.46%
0.88%
1.84%
7.81%
0.05%
1.66%
0.96%
9.04%
0.28%
1.67%
1.26%
11.09%
1.00%
3.97%
1.56%
11.94%
0.86%
5.11%
1.63%
13.16%
1.09%
6.49%
1.74%
23.75%
1.66%
3.75%
0.52%
24.22%
1.77%
4.82%
0.69%
25.81%
1.94%
5.88%
0.93%
In the advanced technologies (AT) sub-sector, the value of output exported to EU countries
accounts for a significant part of this sub-sector’s output, and this value increases year by
year. The share of exports to EU in output of the AT sub-sector clearly exceeds this subsector’s share of exports to the other areas. At the same time, this sub-sector is characterised
by the highest share of exports to EU compared to the share of exports to EU of the other
sub-sectors in their output. In the AT sub-sector, the share of exports to other developed
countries and CEE countries also increases, whereas in 2002 the share of exports to other
developing countries compared to output slightly declines. The share of exports to EU of the
food products, beverages and tobacco products (FD) sub-sector in its output increases in the
period analysed, and it outstrips this share for other export areas. In the mining and quarrying
(MQ) sub-sector, the largest part of exported output is destined for EU markets. Similarly, in
the capital goods (KG) sub-sector, the share of exports to EU in output is the highest among
the export destinations analysed. The value of this share increases year by year. About one
fourth of output of the consumer goods (CG) sub-sector is destined for export. This share
65
demonstrates a growth trend. In the CG sub-sector, the share of exports to other export areas
in output also increases.
in PLN mln
Total imports in current prices for all the sub-sectors from all the abovementioned areas in
the years 2000-2002 are shown in the chart below.
50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
2000
AT
2001
FD
MQ
2002
KG
CG
Chart 2.26. Imports in the sub-sectors (current prices).
It can be seen in the chart that imports in the MQ sub-sector were much lower in the years
1995-2002 than in the other sub-sectors. The highest value of imports was in the AT subsector, then in CG, KG. The value of imports in the period in question in the FD sub-sector
was much lower. In 2001 there was a decline in imports in all the sub-sectors, except for the
FD sub-sector. In 2002 there was an increase in imports in all the sub-sectors. The highest
increase was in the FD sub-sector (12.2%), and the lowest in the KG sub-sector (5.5%).
Table 2.39. The rate of growth in imports in the sub-sectors (current prices). Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
2001
-0.28%
0.10%
-29.62%
-13.94%
-0.17%
2002
10.24%
12.24%
9.89%
5.53%
11.96%
The chart below shows total imports in all the sub-sectors in question from the
abovementioned areas.
66
80000
in PLN mln
70000
60000
50000
40000
30000
20000
10000
0
2000
UE
Other developed countries
2001
CEE
2002
Other developed countries
Chart 2.27. Imports from particular areas (current prices).
Taking into account the distribution of imports by area, it can be seen that the highest share in
total imports in the years 2000-2002 had imports from EU countries. These imports decreased
in 2001 and increased in 2002. Imports from CEE countries were much smaller. They
decreased in 2001, and then increased slightly in 2002. Imports from other developing
countries were lower than from the abovementioned areas, but they show a growth trend in
the period analysed. Imports from other developed countries declined in 2001 and increased
in 2002. The table below shows detailed values for imports in question.
Table 2.40. Imports for particular areas - sub-sectors [in PLN mln]
Sub-sector
AT
2000
EU
Other developed countries
CEE
Other developing countries
2001
2002
31580.2
4470.1
3327
3747
30972
4400.6
3583.9
4047.8
33912.3
5341.8
2953.2
5200.6
5502.7
696.8
1197.9
1347.5
5367.5
773.9
1207.4
1404.8
5978.5
741.8
1215.6
1888.8
158.2
30.2
19.2
7.8
99.5
15.8
13.5
22.8
112.1
17.4
15.6
21.5
9956.2
1623.7
17722.5
912.1
8978.4
1261
14638
1123.6
10009.8
1268.4
15173.8
986.2
23208.3
1597.7
2580.8
2099.3
22903.7
1468.3
2720
2344
25521.8
1499.4
2997.9
2938.8
FD
EU
Other developed countries
CEE
Other developing countries
MQ
EU
Other developed countries
CEE
Other developing countries
KG
EU
Other developed countries
CEE
Other developing countries
CG
EU
Other developed countries
CEE
Other developing countries
It can be seen in the table that in the AT sub-sector imports from EU countries had the
highest value in the period in question, whereas imports from CEE countries were the lowest.
67
In the FD sub-sector, a significant part of imports also came from EU countries, however,
their dominance over other areas was not as significant as in the case of the AT sub-sector.
The lowest value of imports in this sub-sector related to other developed countries. In the MQ
sub-sector, the highest imports came from EU countries. Imports from other developing
countries increased significantly. In the KG sub-sector, imports from CEE countries were
decidedly the highest, whereas the lowest imports were from other developing countries. In
the CG sub-sector, the highest imports came from EU countries, the lowest value of imports
was for other developing countries (however, this value shows a growth trend).
Similarly as for exports, the relation of imports of each sub-sector from particular areas to
gross output in current prices of a given sub-sector will also be analysed.
Table. 2.41. The share of imports of a given sub-sector from particular areas in gross output
of such sub-sector.
Sub-sector
AT
2000
EU
Other developed countries
CEE
Other developing countries
FD
EU
Other developed countries
CEE
Other developing countries
MQ
EU
Other developed countries
CEE
Other developing countries
KG
EU
Other developed countries
CEE
Other developing countries
CG
EU
Other developed countries
CEE
Other developing countries
2001
2002
26.43%
3.74%
2.78%
3.14%
26.47%
3.76%
3.06%
3.46%
29.54%
4.65%
2.57%
4.53%
5.73%
0.73%
1.25%
1.40%
5.16%
0.74%
1.16%
1.35%
5.85%
0.73%
1.19%
1.85%
0.58%
0.11%
0.07%
0.03%
0.37%
0.06%
0.05%
0.08%
0.42%
0.06%
0.06%
0.08%
10.71%
1.75%
19.07%
0.98%
10.23%
1.44%
16.68%
1.28%
11.74%
1.49%
17.79%
1.16%
18.34%
1.26%
2.04%
1.66%
17.87%
1.15%
2.12%
1.83%
18.88%
1.11%
2.22%
2.17%
In the AT sub-sector, the highest share in gross output has imports from EU countries. Its
value increases. In the period in question, the share of imports from other developed countries
and from other developing countries also increased. At the same time, imports to this subsector compared to its output are higher than in the other sub-sectors. In the FD sub-sector,
imports from EU countries also account for the largest part of imports compared to gross
output. Imports of the MQ sub-sector from the areas in question account for an insignificant
part of its output. In the KG sub-sector, imports from CEE countries account for the largest
part of its output, compared to the other areas. The share of imports from EU countries
accounts for more than 10% of KG sub-sector output. Imports from EU countries have the
largest share in output of the CG sub-sector. The share of imports in output of the CG subsector from CEE countries, other developed countries increases every year, the share of
imports from other developing countries decreases.
68
2.4.15 Internal expenditures on research and development
When discussing internal expenditures on research and development (R&D) and the value of
R&D equipment in particular sub-sectors, attention should be paid to the fact that available
data on these values in particular groups belonging to a given sub-sector come to a large
extent from the period 1996-2002. The exception is data on the following groups: publishing,
printing and reproduction of recorded media, manufacture of office machinery and
computers, manufacture of tobacco products, manufacture of furniture; other manufacturing
activities, recycling - they are available only for the years 2000-2002, therefore, the values
for these groups are included in the calculations only for the years 2000, 2001, 2002. In the
period in question, there are no data on the discussed variable for manufacture of wearing
apparel and articles of fur.
in PLN mln
1000
800
600
400
200
0
1996
1997
AT
1998
FD
1999
MQ
2000
2001
KG
2002
CG
Chart 2.28. Internal expenditures on research and development and the value of R&D
equipment in the sub-sectors.
Internal expenditures on R&D and the value of R&D equipment were the highest in the
period in question in the AT sub-sector. It increased dynamically until 1999, reaching the
peak year-on-year growth in 1998 equal to 56.2%, then it declined, in 2002 reaching a value
lower than in 1996. The decline in 2002 was 51% compared to 2001.
In the KG sub-sector, there are alternately declines and increases in the value discussed
in the years 1996-1999, with the highest decline in 1998 - it was almost 40%
year-on-year, but then after 2000 there are only increases, with the highest in 2000 at the
level of more than 36%. In the CG sub-sector, there is a stable increase in internal
expenditures on R&D and in the value of R&D equipment in the years 1996-1998, and then
there is a stable decline. In the FD sub-sector, there are alternately declines and increases in
the said value. However, these changes show high percentage values. The highest increase in
internal expenditures on R&D and the value of R&D equipment was in 1997, and it
amounted to 149% year-on-year, and in 2001 there was an equally significant increase of
138%. In 2002 this sub-sector records the highest percentage decline equal to almost 80%. In
the MQ sub-sector, internal expenditures on R&D and the value of R&D equipment increase
in the years 1996-2001, and then decline in 2002.
2.4.16 Numbers employed in R&D
69
The availability of data on this variable - the numbers employed in R&D in industry is
analogous to the above described availability of data on internal expenditures on R&D and
the value of R&D equipment. The chart below shows the numbers employed in R&D in
particular sub-sectors.
in '000
12000
10000
8000
6000
4000
2000
0
1996
1997
AT
1998
1999
FD
2000
MQ
2001
2002
KG
CG
Chart 2.29. The numbers employed in R&D in the sub-sectors.
The highest number employed in R&D in the whole period analysed is in the AT sub-sector,
in the KG sub-sector, the number employed was half this number. In the MQ and CG subsectors, employment was similar. In the years 1996-2002 a declining trend in employment
can be seen in all the sub-sectors. In the AT sub-sector, employment between 1996 and 2002
declined by 61%, and in the FD sub-sector by 23%, in the MQ sub-sector by 75%, in the KG
sub-sector by 51%, in CG by 71%. The highest fall in employment was recorded in all the
sub-sectors between 2001 and 2002. At the same time, 2002 was the only year in which
declines in employment in R&D were posted in all the sub-sectors. The table below shows
detailed changes in employment in particular years.
Table 2.42. The rate of growth in employment in R&D in the sub-sectors. Year-on-year
percentage changes.
Sub-sector
AT
FD
MQ
KG
CG
1997
-19.73%
34.75%
-2.53%
-22.59%
-9.63%
1998
-3.45%
2.29%
-13.67%
7.85%
-4.45%
1999
-10.59%
-2.80%
-1.90%
-11.79%
-2.14%
70
2000
9.34%
7.78%
-2.74%
15.07%
1.25%
2001
-6.34%
6.68%
-9.38%
-12.69%
-10.51%
2002
-45.97%
-50.13%
-66.76%
-34.01%
-62.75%
1997
-19.73%
34.75%
-2.53%
-22.59%
-9.63%
[3] Polish manufacturing: a theoretical framework
3.1 Introduction
It will be recalled that the basic HERMIN framework has four sectors: manufacturing (a
mainly internationally traded sector), market services (a mainly non-internationally traded
sector), agriculture, and government (or non-market) services. Given the data restrictions
that often face modellers in cohesion and transition economies, this is as close to an initial
empirical representation of the traded/non-traded disaggregation as one is likely to be able to
implement in practice. Although agriculture also has important traded elements, its
underlying characteristics demand special treatment. Similarly, the government (or nonmarket) sector is non-traded, but is best formulated in a way that recognises that it is mainly
driven by policy instruments that are available – to some extent, at least – to policy makers.46
Thus, the two crucial sectors that are usually singled out for modelling attention are
manufacturing and market services.
Before we address the question of further disaggregation of manufacturing, we recall that the
structure of the overall HERMIN model framework can be thought of as being composed of
three main blocks: a supply block, an absorption block and an income distribution block.
Obviously, the model functions as integrated systems of equations, with interrelationships
between all their sub-components. However, for expositional purposes we describe the
HERMIN modelling framework in terms of the above three sub-components, which are
schematically illustrated in Figures 3.1 and 3.2.
Conventional Keynesian mechanisms are at the core of any HERMIN model. Expenditure
and income distribution sub-components generate the standard income-expenditure
mechanisms. But the model also has neoclassical features. Thus, output in aggregate
manufacturing is not simply driven by external and internal demand. It is also potentially
influenced by price and cost competitiveness, where firms seek out minimum cost locations
for production (Bradley and Fitz Gerald, 1988).
In addition, factor demands in
manufacturing and market services are derived using a CES production function constraint,
where the capital/labour ratio is sensitive to relative factor prices. The incorporation of a
structural Phillips curve mechanism in the manufacturing wage bargaining mechanism
introduces further relative price effects.
From Figure 2.2 we see that the model handles the national accounts’ use of three
complementary ways of measuring GDP: the output, the expenditure and the income basis.
On the output basis, the original “basic” HERMIN model disaggregates four sectoral outputs,
in both nominal and real terms: manufacturing (OT, OTV), market services (ON, ONV),
agriculture (OA, OAV) and the public (or non-market) services sector (OG, OGV). On the
expenditure side, HERMIN disaggregates GDP into the usual five components, once again, in
nominal and real terms: private consumption (CONS, CONSV), public consumption (G,
GV), investment (I, IV), stock changes (DS, DSV), and the net trade balance (NTS, NTSV).47
National income is determined on the output side, in nominal terms only, and disaggregated
into private and public sector elements.
46
Elements of public policy are endogenous, but we handle these in terms of policy feed-back rules rather than
behaviourally.
47
The traded/non-traded disaggregation implies that only a net trade surplus is logically consistent. Separate
equations for exports and imports could be appended to the model, but would function merely as conveniently
calculated “memo” items that were not an essential part of the model’s behavioural logic.
71
Figure 3.1: The HERMIN Model Schema
Supply aspects
Manufacturing Sector (mainly tradable goods)
Output = f1( World Demand, Domestic Demand, Competitiveness, t)
Employment = f2( Output, Relative Factor Price Ratio, t)
Investment = f3( Output, Relative Factor Price Ratio, t)
Capital Stock = Investment + (1-δ) Capital Stockt-1
Output Price = f4(World Price * Exchange Rate, Unit Labour Costs)
Wage Rate = f5( Output Price, Tax Wedge, Unemployment, Productivity )
Competitiveness = National/World Output Prices
Market Service Sector (mainly non-tradable)
Output = f6( Domestic Demand, World Demand)
Employment = f7( Output, Relative Factor Price Ratio, t)
Investment = f8( Output, Relative Factor Price Ratio, t)
Capital Stock = Investment + (1-δ)Capital Stockt-1
Output Price = Mark-Up On Unit Labour Costs
Wage Inflation = Manufacturing Sector Wage Inflation
Agriculture and Non-Market Services: mainly exogenous and/or instrumental
Demographics and Labour Supply
Population Growth = f9( Natural Growth, Migration)
Labour Force = f10( Population, Labour Force Participation Rate)
Unemployment = Labour Force – Total Employment
Migration = f11( Relative expected wage)
Demand (absorption) aspects
Consumption = f12( Personal Disposable Income)
Domestic Demand = Private and Public Consumption + Investment + Stock changes
Net Trade Surplus = Total Output - Domestic Demand
Income distribution aspects
Expenditure prices = f13(Output prices, Import prices, Indirect tax rates))
Income = Total Output
Personal Disposable Income = Income + Transfers - Direct Taxes
Current Account = Net Trade Surplus + Net Factor Income From Abroad
Public Sector Borrowing = Public Expenditure - Tax Rate * Tax Base
Public Sector Debt = ( 1 + Interest Rate ) Debtt-1 + Public Sector Borrowing
Key Exogenous Variables
External: World output and prices; exchange rates; interest rates;
Domestic: Public expenditure; tax rates.
72
Figure 3.2: Schematic outline of the HERMIN modelling approach
Since all three measures of GDP are modelled, the system is over-determined. In
order to force equality between all three measures, the following assumptions are
made:48
i. the output-expenditure identity is used to determine the net trade surplus/deficit
residually (in both nominal and real terms);
ii. the output-income identity is used to determine corporate profits residually, in
nominal terms.
Finally, the equations in the model can be classified as behavioural or identity. In the
case of the former, economic theory and calibration to the data are used to define the
relationships. In the case of identities, these follow from the logic of the national
accounts, but have important consequences for the behaviour of the model as well.
3.2 The manufacturing side of the HERMIN model
3.2.1 Introduction
The manufacturing sector is probably the most important sector in a cohesion country,
since it functions as the main “engine” of growth. We repeat below the schematic
diagram of the structure of structure of the sector, emphasising the main behavioural
and identity equations. In this diagram, we have indicated exogenous variables, or
variables that are determined outside the manufacturing sector, by underlining them.
Thus “world demand”, the “world price”, and the “exchange rate” are truly
exogenous. However, “domestic demand”, the “tax wedge”, “unemployment”, and
the “cost of capital” are determined elsewhere in the model. For simplicity of
exposition, we suppress these mechanisms from Figure 3.3.
3.2.2 Output determination
The theory underlying the macroeconomic modelling of a small open economy
requires that the equation for output in a mainly traded sector should reflect both
purely supply side factors (such as the real unit labour costs and international price
competitiveness), as well as the extent of dependence of output on a general level of
world demand, e.g. through operations of multinational enterprises, as described by
Bradley and Fitz Gerald (1988). By contrast, domestic demand should play only a
limited role in a mainly traded sector, mostly in terms of its impact on the rate of
capacity utilisation.
48
If all three measures of GDP were derived by the Statistics Office in a truly independent fashion,
then they would only agree up to a statistical discrepancy. However, we impose the identities, and
“bury” any statistical discrepancy in the residually determined variable (the net trade surplus or
corporate profits).
74
Figure 3.3; A Schematic Diagram of the Manufacturing Sector in HERMIN
Output = f1( World Demand, Domestic Demand, Competitiveness, t)
Employment = f2( Output, Relative Factor Price Ratio, t)
Investment = f3( Output, Relative Factor Price Ratio, t)
Productivity = Output / Employment
Output Price = f4(World Price * Exchange Rate, Unit Labour Costs)
Wage Rate = f5( Output Price, Tax Wedge, Unemployment, Productivity )
Relative Factor Price Ratio = Wage Rate / Cost of Capital
Competitiveness = Output Price/World Output Price
Unit Labour Cost = Wage Bill / Employment
Wage Bill = Employment * Wage Rate
However, manufacturing activities in any but extreme cases often includes a large
number of partially sheltered subsectors producing items that are non-traded, at least
to some extent. Hence, we would expect domestic demand to play a more substantial
role in this sector, possibly also influencing capacity output decisions of firms.
In the standard four-sector HERMIN, a hybrid supply-demand equation of the form
below is assumed::
(3.1)
log( OT ) = a 1 + a 2 log( OW ) + a 3 log(ULCT / POT )
+ a 4 log( FDOT ) + a 5 log( POT / PWORLD) + a 6 t
where OW represents the crucial external (or world) demand, and FDOT represents
the influence of domestic absorption. We further expect OT to be negatively
influenced by real unit labour costs (ULCT/POT) and the relative price of domestic
versus world goods (POT/PWORLD).
3.2.3 Factor demands
Macro-sectoral models like HERMIN usually feature production functions of the
general form:
(3.2)
Q = f ( K , L)
(where Q represents output, K capital stock and L employment), without output being
actually determined by this relationship. We have seen above that manufacturing
output is determined in HERMIN by a mixture of world and domestic demand,
75
together with price and cost competitiveness terms. Having determined output in this
way, the role of the production function is to constrain the determination of factor
demands in the process of cost minimisation that is assumed. Hence, given Q
(determined as above in a hybrid supply-demand relationship), and given (exogenous)
relative factor prices, the “cost minimizing” levels of factor inputs, L and K, are
determined by the production function constraint. Hence, the production function
operates in the model as a technology constraint and is only indirectly involved in the
determination of output. It is partially through these interrelated factor demands that
the longer run efficiency enhancing effects of policy and other shocks like the EU
Single Market and the Structural Funds operate.
Ideally, a macro policy model should allow for a production function with a fairly
flexible functional form that permits a variable elasticity of substitution. As the recent
experience of several peripheral countries - especially Ireland - suggests (Bradley et
al., 1995), this issue is important. When an economy opens and becomes
progressively more influenced by activities of foreign-owned multinational
companies, the traditional substitution of capital for labour following an increase in
the relative price of labour need no longer happen to the same extent. The
internationally mobile capital may choose to move to a different location than seek to
replace costly domestic labour. In the terminology of the neoclassical theory of firm,
the “isoquants” become more curved as the technology moves away from a CobbDouglas towards a Leontief type.
Since the Cobb-Douglas production function is too restrictive, we use the CES form
of the added value production function and impose it on both manufacturing (T) and
market service (N) sectors. Thus, in the case of manufacturing;
(3.3)
OT
= A exp
(λ t )[δ {LT }− ρ
+ (1 − δ
){KT }− ρ
]
−
1
ρ
,
In this equation, OT, LT and KT are added value, employment and the capital stock,
respectively, A is a scale parameter, ρ is related to the constant elasticity of
substitution, δ is a factor intensity parameter, and λ is the rate of Hicks neutral
technical progress.
In both the manufacturing and market service sectors, factor demands are derived on
the basis of cost minimisation subject to given output and given relative factor prices,
yielding a joint factor demand equation system of the schematic form:
(3.4a)
(3.4b)
 r
K = g 1  Q, 
 w
 r
L = g 2  Q, 
 w
where w and r are the cost of labour and capital, respectively. 49
49
The above treatment of the capital input to production in HERMIN is influenced by the earlier work
of d’Alcantara and Italianer, 1982 on the vintage production functions in the HERMES model. The
implementation of a full vintage model was impossible, even for the four EU cohesion countries. A
hybrid putty-clay model is adopted in HERMIN (Bradley, Modesto and Sosvilla-Rivero, 1995), that
76
Although the central factor demand systems in the manufacturing (T) and market
services (N) sectors in HERMIN are functionally identical, they will usually have
very different estimated parameter values and two further crucial differences that are
due to specific sectoral characteristics:
(a) First, output in the aggregate manufacturing (mainly traded) sector (OT) is driven
by world demand (OW) and domestic demand (FDOT), and is influenced by
international price competitiveness (PCOMPT) and real unit labour costs
(RULCT). On the other hand, in the aggregate market services (mainly nontraded) sector, output (ON) is driven mainly by final demand (FDON), with
possibly a limited role for world demand (OW). This captures the essential
difference between the neoclassical-like tradable manufacturing sector and the
sheltered Keynesian non-traded market services sector.
(b) Second, the output price in the aggregate manufacturing (T) sector is partially
externally determined by the world price. In the aggregate market services (N)
sector, the producer price is mainly a mark-up on costs. This puts another
difference between the partially price taking tradable sector and the price making
non-tradable sector.
3.2.4 Sectoral wage determination
Modelling of the determination of wages and prices in the four-sector HERMIN
model is influenced by the so-called Scandinavian model (Lindbeck, 1979). Thus, the
behaviour of the manufacturing (T) sector is assumed to be dominant in relation to
wage determination. The wage inflation determined in the manufacturing sector is
assumed to be passed through to the down-stream “sheltered sectors, i.e., market
services, agriculture and non-market services. Hence, for market services (WN):
(3.5)
WNDOT = WTDOT + stochastic error
where WTDOT and WNDOT are the wage inflation rates in manufacturing and
market services.
In the important case of manufacturing, wage rates are modelled as the outcome of a
bargaining process that mainly takes place between organised trades unions and
employers, with the possible intervention of the government. Formalised theory of
wage bargaining points to four paramount explanatory variables (Layard, Nickell and
Jackman (LNJ), 1990):
a) Output prices: The price that the producer can obtain for output clearly
influences the price at which factor inputs, particularly labour, can be
purchased profitably.
b) The tax wedge: This wedge is driven by total taxation between the wage
denominated in output prices and the take home consumption wage actually
enjoyed by workers.
permits the specification of the factor demand system in terms of employment and investment.
77
c) The rate of unemployment: The unemployment or Phillips curve effect in the
Layard, Nickell and Jackman (1990) model is a proxy for bargaining power.
For example, unemployment is usually inversely related to the bargaining
power of trades unions. The converse applies to employers.
d) Labour productivity: The productivity effect comes from workers’ efforts to
maintain their share of added value, i.e. to enjoy some of the gains from higher
output per worker.
A simple log-linear formulation of the LNJ-type wage equation might take the
following form:
(3.6)
Log(WT) = a1 +a2 log(POT) + a3 log(WEDGE) + a4 log(LPRT) + a5 UR
where WT represents the wage rate, POT the price of manufactured goods, WEDGE
the tax “wedge”, LPRT labour productivity and UR the rate of unemployment.
3.2.5 Sectoral output price determination
Finally, we model the pricing behaviour of the manufacturing sector as a mixture of
both price taking and price setting behaviour. For the latter, one can assume a markup on the unit labour cost which is also consistent with constant labour shares of
added value according to the neoclassical theory of firm. What is more important,
though, is that this sectoral price behaviour be constrained in relation to the nontradeables by direct international competition. Therefore, a full pass through of
labour cost increases into prices in a way that does not lead to any loss of
competitiveness is only possible if foreign producers (i.e., PWORLD) face the same
shock.
The following linearly homogenous equation is specified and calibrated:
log( POT ) = a1 + a 2 log( PWORLD ) + (1 − a 2 ) log(ULCT ) ,
where PWORLD stands for a weighted measure of price indices external to Poland.
At present, this is taken as a trade-weighted average of the prices in Poland’s main
trading partners.
3.3 A simple modelling schema for disaggregating manufacturing
In the previous section we described how the aggregate manufacturing sector is
handled in the standard HERMIN model. We now describe how the same simple
approach can be adapted to handle manufacturing when it is disaggregated into a
series of sub-sectors. Due to the untested and uncertain quality of the data, this first
attempt makes a series of simplifying assumptions, as follows:
78
i.
We remain within an essentially two-factor model (with labour and capital
factor inputs), and do not generalise to more than two factor inputs (e.g., KLE
or KLEM);50
ii.
We impose a common CES technology constraint, and only permit parametric
differences between manufacturing subsectors;51
The basic measure of output is QTs, i.e., gross output of the “s” subsector of
manufacturing, measured in constant 1995 year prices. Gross added-value (OTs) is
defined as:
(3.7)
OTs
=
QTs - MTs
where MTs represents material inputs (i.e., intermediate consumption). Rather than
regard MTs as a factor input into a nested three-factor CES production function, we
assume for simplicity a fixed (or at most a time-dependent) MTs /QTs ratio, and use
this to derive QTs residually. In other words, the material input decisions made by the
firm are completely separable from the capital-labour input decisions.
The simple schema for modelling the individual manufacturing sub-sectors is set out
below:
Sectoral output (added value, OTs) is determined as follows:
(3.8)
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs)
+ a4 log(FDOTs) + a5 log(POTs/PWORLDs) + a6 t
where the subscript denotes that the determining variables may take different forms
depending on the particular sub-sector being modelled. For example, the “world”
output (OW) and price (PWORLD) variables may have different weightings. The
domestic demand variable (FDOT) will have different weights assigned to the
components of demand (e.g., consumption and investment).
The sectoral CES production function uses the same form as for the aggregate
manufacturing production function used in the “basic” HERMIN model, and the
prameters have the same definitions (see equation (3.3) above):
(3.9)
OT
s
[
≈ A s exp( λ s t ) δ
−ρ
s (LT s )
s
+ (1 − δ
−ρ
s )(KT s )
s
]
1
ρs
Sub-sectoral factor demand equations for capital (Ks) and labour (Ls) are derived by
assuming cost minimization, and lead to identical factor demand equations:
50
A shift away from a two-factor value-added approach to three or more factor inputs raises very
complex issues concerning whether factor are substitutes or complements (i.e., whether increasing one
factor input will save another, or require more of another. To resolve these questions requires fairly
sophisticated econometric techniques.
51
Only when one moves beyond two factor inputs does the question of the functional form of the
production function become serious. For two factors, the CES serves as an adequate approximation.
79
(3.10a)
(3.10b)

r 
K s = g1  OTs , s 
ws 


r 
Ls = g 2  OTs , s 
ws 

We specify sub-sectoral wage equations, similar to the aggregate manufacturing wage
equation used in the “basic” HERMIN model:
(3.11)
log(WTs) = a1 +a2 log(POTs)+ a3 log(WEDGE)
+ a4 log(LPRTs) + a5 UR
However, depending on the regression analysis, it may not be necessary to specify
separate and independent wage equations for every sub-sector.
The final behavioural equation concerns the determination f the sub-sectoral output
price deflators, and is the usual hybrid of price-taking and cost mark-up that is used in
the “basic” HERMIN model specification:
(3.12)
log( POTs ) = a1 + a 2 log( PWORLD ) + (1 − a 2 ) log(ULCTs ) ,
The imposition of such a simple modelling schema is dictated by a few factors. For
example, there is we only a very short period for which we have data (1994-2002).
With only nine annual observations, it is not possible to carry out hypothesis testing
with even the simplest bivariate regression model. At best we can use the available
data is a simple “curve-fitting” exercise, and bring to bear any prior judgement that
will assist in selecting appropriate parameter values. In addition, the examination of
the disaggregated data in Section 2 suggested that there may be a “quality” problem.
Certain of the wage and price inflation rates are anomalous, and need to be treated
with caution.
In Section 2 above we also examined the export orientation of the sub-sectoral
disaggregation of the manufacturing sector that we finally selected. In a very open
(export-oriented) sector we would expect the coefficient on OWs in equation (3.8) to
be larger than in a relatively closed sector. We would expect the elasticity of
substitution (σs) to be lower in a sector that is dominated by multinational investment,
than in a purely locally owned sector (Bradley and Fitz Gerald, 1988). Concerning
wage determination (equation 3.11),we might expect to use information about subsectors that are particularly influential in wage bargaining, as well as sectors that are
likely to be weak. Finally, if a sector is very open, we would expect the “world” price
(PWORLDs) to play a greater role in price determination (i.e., price-taking behaviour)
than domestic unit labour costs (i.e., price mark-up). If we can use such insights, then
we can mitigate the problems caused by lack of sufficiently long data series.
80
[4] Polish manufacturing: calibration of sub-sectors
4.1 Introduction
Having selected the level of sub-sectoral disaggregation that we wish to use, there are
four stages to the augmentation of the original aggregate manufacturing sector used in
the “basic” Polish HERMIN model (as most recently described in Zaleski et al,
2004a):
i. First, we select sets of functional forms for the behavioural equations describing
the disaggregated sectors. The simplest example of this choice was described in
the previous section, where we selected a common set of functional forms that
generalised the forms used in the “basic” version of the HERMIN model.
ii. Second, we have to assign appropriate numerical values for the parameters in
these behavioural equations, using the small set of available data with some
form of regression analysis or curve fitting.
iii. Third, we have to construct the additional “identities” that are needed within the
HERMIN model (e.g., the additional definitional equations that make the use of
the model work as an integrated system).
iv. Finally, when the new “augmented” model is constructed, we have to test its
properties, and compare these with the known properties of the “basic” model.
Having taken the simplest decision on functional forms (as discussed in the previous
section), in this section we concentrate on item (ii) above, i.e., the calibration of the
parameters of the disaggregated behavioural equations. The remaining two tasks (i.e.,
construction of new identities, and bringing all the work together in a new
“augmented” HERMIN model), are quite straightforward, and will be treated in a
future paper.
In carrying out the calibration, we make use of the data on the five sub-sectors that
were described in Section 2 above. These are as follows:
AT: Manufacturing activities characterised as “advanced technology”
FD: Manufacturing activities in the areas of food processing and beverages
MQ: Mining and quarrying activities
KG: manufacturing activities in the area of capital goods (i.e., “heavy” industry)
CG: The manufacture of a broad range of mainly consumer goods (i.e., “light”
industry)
We have already described the characteristics of these sectors in Section 2. Here, we
repeat the data on export orientation, since this will play an important role in the
model calibration exercise (Table 4.1).
The most open sub-sector is Advanced Technologies (AT), where in the year 2002
over 50 per cent of gross output was exported, with almost 40 per cent going to
destinations within the EU. The next most open sub-sector was Consumer Goods
(CG), with almost 35 per cent being exported, once again mostly to destinations
81
within the EU. The other sub-sectors are less open, with export shares of 23 per cent
(Capital Goods (KG)); 12 per cent (Mining and Quarrying (MQ); and 8 per cent
(Food and Beverages (FD)).
In the case of Advanced Technologies (AT), the export share is increasing rapidly
(from 36 to 50 per cent between 2000 and 2002). None of the other four sub-sectors
displays such a rapid increase, and in the case of Mining and Food Processing, the
export share is fairly static, with a tendency to decline.
Table 4.1: Export shares of sectoral gross output
Destination
EU
Developed less EU
CEE
Developing less CEE
Total export share
EU
Developed less EU
CEE
Developing less CEE
Total export share
EU
Developed less EU
CEE
Developing less CEE
Total export share
Fraction of Gross Output Exported
Advanced Technologies
Capital Goods
2000
2001
2002
2000
2001
2002
0.282
0.321
0.389
0.111 0.119 0.132
0.025
0.028
0.039
0.010 0.009 0.011
0.033
0.038
0.049
0.040 0.051 0.065
0.023
0.030
0.029
0.016 0.016 0.017
0.363
0.418
0.505
0.176 0.195 0.225
Food Processing & Beverages
Consumer Goods
2000
2001
2002
2000
2001
2002
0.034
0.034
0.041
0.237 0.242 0.258
0.005
0.005
0.007
0.017 0.018 0.019
0.023
0.023
0.026
0.038 0.048 0.059
0.006
0.008
0.007
0.005 0.007 0.009
0.067
0.069
0.080
0.297 0.315 0.346
Mining & Quarrying
2000
2001
2002
0.096
0.078
0.090
0.005
0.001
0.003
0.009
0.017
0.017
0.018
0.010
0.013
0.128
0.105
0.122
In the remainder of this section, we take each major behavioural equation in turn, and
search for appropriate parameter values using the available data. It must be stressed
that we are unable to carry out any formal econometric testing of these functional
forms, since we simply do not have data time-series of sufficient length.52 In effect,
we are engaging in a rather crude form of “data mining”, and selecting results that
appear to be most in keeping with our prior views about how the subsectors might
perform. However, these “priors” are informed by extensive work on other EU
economies that are at a more advanced stage of development than is Poland. The
resulting parameter choices can be looked on as a way of quantifying our views about
how we think the sectors should behave. In situations where more data were available
(e.g., over 30 years annual data at least), we could actually test the various economic
hypotheses and reject those that are inconsistent with the assumed model.
52
Our time series of annual data runs from 1994 to 2002, but we usually lose one year to defining lags.
Hence, we have only eight observations.
82
The results of running a large number of systematic regressions are presented in
Appendix 1 and Appendix 2 at the end of the paper. Appendix 1 lists the TSP
computer file that was used to generate the results. Appendix 2 lists all the results,
including ones that make no economic sense. But the totality of results can be
“filtered” and usually serve to point to some appropriate subsets of the general
functional form. These economically reasonable results can them be incorporated into
the revised HERMIN model.
4.2 The sub-sectoral output equations
The first equation to be examined is the one that determines output (or value-added)
for each of the five sub-sectors. The general functional form is repeated below
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs)
+ a4 log(FDOTs) + a5 log(POTs/PWORLDs) + a6 t
For each of the five sub-sectors we initially run a common set of regressions, that start
with the full model, and gradually reduce its complexity down to a much reduced subset.
The following standard set of regressions were run in every case:
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs)
+ a4 log(FDOTs) + a5 log(POTs/PWORLDs) + a6 t
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs)
+ a4 log(FDOTs) + a5 log(POTs/PWORLDs)
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs) + a4 log(FDOTs) + a6 t
log(OTs) = a1 + a2 log(OWs) + a3 log(ULCTs/POTs) + a4 log(FDOTs)
log(OTs) = a1 + a2 log(OWs) + a4 log(FDOTs) + a5 log(POTs/PWORLDs) + a6 t
log(OTs) = a1 + a2 log(OWs) + a4 log(FDOTs) + a5 log(POTs/PWORLDs)
log(OTs) = a1 + a2 log(OWs) + a4 log(FDOTs) + a6 t
log(OTs) = a1 + a2 log(OWs) + a4 log(FDOTs)
With six parameters and only eight observations, in the first version, clearly little can
be retrieved. But as we progressively simplify the versions, it becomes possible to
select a subset of the general model.
Having run the standard set of regressions, we then select special regressions that
depart from the standard. In each case we comment on the special selection.
The final regression in each case is a constrained version of the most general version,
but with imposed values for the coefficients a2 to a5. In every case, we force the
elasticity with respect to OW (i.e., a2) to be equal to the export share, as shown in
83
Table 4.1 above. We also impose homogeneity, and force the elasticity with respect
to FDOT (i.e., a4) to be equal to one minus the elasticity with respect to OW. Our
selection for the two competitiveness elasticities (a3 and a5) is judgement-based.
Given the difficulty of selecting on the basis of unconstrained versions, except for the
most simple cases, the constrained regression would be the one that we would propose
for insertion in the first revised version of the HERMIN model.
4.2.1 The AT sub-sector
We know that the AT sub-sector is highly export oriented, so we can reasonably
expect the OW variable to play an important role in output determination. Since the
export orientation is growing over time, we also believe that this sector will tend to
grow autonomously, as the more traditional sectors decline.
In the standard set of regressions (equations 1-13 in Appendix 2), we see that the
elasticities with respect to OW and FDOT are similar in size, and that there tend to be
negative elasticities with respect to the two competitiveness measures (real unit labour
costs and relative prices). But there is a suspicion that the scale variables OW and
FDOT are multi-collinear with t, and the coefficient on t becomes insignificant.
In the constrained regression, we impose the following elasticities on a2 – a5, and
estimate a6 using ordinary least squares (OLS):53
AT sub-sector
Coefficient value
a2
0.50
a3
-0.50
a4
(1-0.50)
a5
-0.50
a6
0.0391
This implies that we believe that output will be equally responsive to changes in OW
and FDOT, and that any loss of competitiveness will be severely penalised. Output
will also tend to grow autonomously at 3.9 per cent per year. The plot of actual
versus predicted values indicates that there is a serious over-prediction error (8.8 per
cent) in the year 2002, but modest errors for other years.
4.2.2 The FD sub-sector
We know that the FD sub-sector is the least export oriented of all five sub-sectors, so
we can reasonably expect the FDOT variable to play an important role in output
determination, and the OW variable to be much less important. Since the export
orientation is hardly growing at all over time, we also believe that this sector will have
little tendency to grow autonomously. Although the FD sun-sector is almost
completely oriented towards the local market, competitiveness issues could still be
quite important, due to the existence of a wide range of import substitutes.
These assumptions tend to be borne out in the standard set of regressions (equations
14-26 in Appendix 2), we see that the elasticities with respect to OW tend to be small,
and those with respect to FDOT tend to be larger, and that there tend to be negative
elasticities with respect to the two competitiveness measures (real unit labour costs
53
We will always ignore the estimated intercept (a1), since it has no economic significance in the
equation.
84
and relative prices). Once again, there is a suspicion that the scale variables OW and
FDOT are multi-collinear with t, and the coefficient on t becomes insignificant.
In the constrained regression, we impose the following elasticities on a2 – a5, and
estimate a6 using ordinary least squares (OLS):
FD sub-sector
Coefficient value
a2
0.08
a3
-0.20
a4
(1-0.08)
a5
-0.20
a6
0.0103
This implies that we believe that output will be quite unresponsive to changes in OW
and will react strongly to changes in FDOT, and that any loss of competitiveness will
be moderately penalised. Output will also tend to grow autonomously at 1.03 per cent
per year. The equation provides a reasonably good with-sample fit, with no outliers.
4.2.3 The MQ sub-sector
We know that the MQ sub-sector is also oriented towards serving the local market, so
we can reasonably expect that the OW variable will not play an important role in
output determination. But we also suspect that the output of this sector may be fairly
autonomous, and unresponsive to local demand.
In the standard set of regressions (equations 27-43 in Appendix 2), we see that the
elasticities with respect to OW are actually negative! Indeed, the only fairly robust
regressor is time, and this is also negative. Even in a special regression, where we
replace the local demand variable by total Polish manufacturing GDP. The scale
variable is meaningless.
In the constrained regression, we impose the following elasticities on a2 – a5, and
estimate a6 using ordinary least squares (OLS):
MQ sub-sector
Coefficient value
a2
0.12
a3
-0.10
a4
(1-0.12)
a5
-0.10
a6
-0.081
This implies that we believe that output will be equally unresponsive to changes in
OW and fairly responsive to changes in FDOT, and that the competitiveness effects
are very small. Output will also tend to shrink autonomously at 8.1 per cent per year.
The within-sample tracking record is poor, with a series of quite large errors.
It seems a bit too drastic to model output of this sector as a negative time trend, and as
being unresponsive to any other driving variable. But that may, indeed, be the case.
However, further investigation will be necessary.
85
4.2.4 The KG sub-sector
The KG sub-sector is the third most highly export oriented, so we can also reasonably
expect the OW variable to play at least some role in output determination. But the
export orientation is only growing very slowly over time, so it is unclear if this subsector will tend to grow autonomously or not. Closer examination of the subcomponents of KG may be required in order to understand this.
In the standard set of regressions (equations 44-52 in Appendix 2), we see that the
elasticities with respect to OW are erratic, an tend to be negative. But the elasticities
with respect to FDOT tend to be unity, and there tend to be negative elasticities with
respect to the two competitiveness measures (real unit labour costs and relative
prices).
In the constrained regression, we impose the following elasticities on a2 – a5, and
estimate a6 using ordinary least squares (OLS):
KG sub-sector
Coefficient value
a2
0.23
a3
-0.25
a4
(1-0.23)
a5
-0.25
a6
-0.020
This implies that we believe that output will be equally generally unresponsive to
changes in OW and more responsive to changes in FDOT, and that any loss of
competitiveness will only be modestly penalised. Output will also tend to shrink
autonomously at 2.0 per cent per year.
4.2.5 The CG sub-sector
The CG sub-sector is the second most export oriented of the five, so we can
reasonably expect the OW variable to play quite an important role in output
determination. Since the export orientation is growing gradually over time, we also
believe that this sector will tend to grow autonomously, as the more traditional sectors
decline.
In the standard set of regressions (equations 53-61 in Appendix 2), we see that the
elasticities with respect to OW and FDOT are similar in size, and that there tend to be
moderately large negative elasticities with respect to the competitiveness measures
(real unit labour costs and relative prices).
In the constrained regression, we impose the following elasticities on a2 – a5, and
estimate a6 using ordinary least squares (OLS):
Coefficient value
a2
0.35
a3
-0.4
a4
(1-0.35)
a5
-0.4
a6
0.038
This implies that we believe that output will be slightly more responsive to changes in
FDOT than to changes in OW, and that any loss of competitiveness will be fairly
severely penalised, due mainly to the presence of many potential competing import
substitutes. Output will also tend to grow autonomously at 3.8 per cent per year.
86
4.3 The subsectoral factor demand and production functions
The coefficients of the sub-sectoral production functions are calibrated by using the
highly non-linear factor demand equations, as explained in Bradley and Fanning,
1984.
Exactly the same approach was used for calibrating the aggregate
manufacturing production function in Bradley and Zaleski, 2003, and Zaleski et al,
2004a.
The sub-sectoral CES production functions take the following form:
OT
s
[
≈ A s exp( λ s t ) δ
−ρ
s (LT s )
s
+ (1 − δ
−ρ
s )(KT s )
s
]
1
ρs
In very few cases can one actually recover meaningful values for the important
elasticity of substitution (σs), so it is usually imposed.54 We take the view that the
more open a sub-sector is (in terms of export share), the more the elasticity of
substitution will be nearer zero (the Leontief case) than unity (the Cobb-Douglas
case). The remaining parameters are calibrated from the data. The calibrated
parameters are shown in the table.
AT sub-sector
FD sub-sector
MQ sub-sector
KG sub-sector
CG sub-sector
σs (imposed)
0.20
0.80
0.80
0.50
0.50
δs
0.016537
0.73247
0.82314
0.74222
0.29841
As
4.59347
8.71809
19.33630
15.49380
5.32683
λs
0.098556
0.073224
0.041235
0.093660
0.081894
Besides the elasticity of substitution, another very important parameter is λs , the rate
of Hicks-neutral technical progress. This is essentially the rate of growth of output
due to influences over and above the factor inputs, capital and labour. Technical
progress is usually the result of advances in science, or investment in research and
development But it can also appear as a result of sectoral restructuring, i.e., in a
situation where inefficient firms drop our of a given sub-sector, and when they are
replaced by more efficient firms.
On the basis of our small sample of data series, we would characterise the subextors
in terms of high elasticity of substitution (FD and MQ); average elasticity of
substitution (KG and CG), and low elasticity of substitution (AT). It should be noted
that the two sub-sectors that have high elasticities of substitution (FD and MQ), also
have somewhat lower rates of technical progress. The AT subsector has the highest
rate of technical progress. These findings are in keeping with the stylised facts of the
sub-sectors, as discussed in Section 2.
54
It should be noted that, in the CES production function, σ = 1/(1-ρ).
87
4.4 The subsectoral wage equations
The basis of the wage equation was explained in Section 3, and results for the
aggregate manufacturing sector have already been presented in the revised basic
HERMIN model (Zaleski et al, 2004a). In the results presented below, we eliminate
the tax wedge, since it was nowhere meaningful or significant. We also impose full
indexation to consumer prices. This assumption will have to be checked, but it
appears to be in keeping with the stylised facts of wage settlements, at least over the
medium term.
By estimating separate sub-sectoral wage equations, we are making the implicit
assumption that workers and employers bargain in sub-sectorally specific ways, and
that the resulting labour markets are separated from each other. It will be recalled that
in the basic HERMIN model, we invoked the so-called Scandinavian model of
Lindbeck, 1979, where all labour markets were assumed to be homogeneous. After
examining the specific sub-sectoral wages equations, it would always be open to us to
invole the Scandinavian model, and use an aggregate manufacturing wage equation to
drive wage determination in all other sectors.
log(WTs/POTs) = a1 +a2 log(LPRTs) + a3 UR
a2
0.638
0.780
0.594
0.609
0.768
0.579
AT sub-sector
FD sub-sector
MQ sub-sector (1995-2001)
KG sub-sector
CG sub-sector
Aggregate Manufacturing
a3
-0.00665
-0.00734
-0.0148
-0.00372
-0.0147
-0.0110
Note that we have dropped the 2002 observation for calibrating WTMQ, since there
was a wage and severance deal agreed in this sector in the year 2002, that had the
effect of distorting average annual earnings.
What calibration shows is that there is, in fact, a high degree of homogeneity between
the five sub-sectors of manufacturing. There is some variation in the semi-elasticity
with respect to unemployment (the so-called Philips curve), but this is probably not
significant. This suggests that one might adopt the Scandinavian model, and suppress
the individual sub-sectoral wage equations, replacing them by an aggregate wage
equation for all manufacturing (plus mining and quarrying)
4.5 The subsectoral price equations
The fin behavioural equation concerns the determination of the deflator of output in
the five sub-sectors. The same standard model is adopted as was used in the basic
HERMIN model.
log( POTs ) = a1 + a 2 log( PWORLD ) + (1 − a 2 ) log(ULCTs ) ,
88
The estimation results are tabulated below.
AT sub-sector
FD sub-sector
MQ sub-sector
KG sub-sector
CG sub-sector
Aggregate Manufacturing
a2 (PWORLD)
0.4054
0.6872
0.4246
0.7724
0.5532
0.7332
(1-a2) (ULCT)
0.5946
0.3128
0.5754
0.2276
0.4468
0.2668
The results here are slightly puzzling. We would have expected the value of a2 to be
highest for the AT sub-sector, since it is the most open to international trade. We
would also expect the value for the FD sub-sector to be lowest, since this is the least
open to trade. In the aggregate manufacturing sector equation, the value of a2 is 0.73.
This suggests that one might suppress the individual subsector price equations, and
adopt the aggregate equation. The individual sub-sectoral equations could them be
linked to the aggregate, by forcing the inflation rates t be equal, up to a stochastic
error.
4.6 Sub-sectoral trend productivity growth
The final set of regressions explores the trend growth rates of productivity. A simple
equation of the following form is estimated for each subsector:
Log(LPRT**) = a1 + a2 t
Where LPRT** represents sub-sectoral productivity (for AT, FD, MQ, KG and CG).
The results are tabulated below:
a2
AT sub-sector
FD sub-sector
MQ sub-sector
KG sub-sector
CG sub-sector
Aggregate Manufacturing
0.1219
0.0790
0.0494
0.0980
0.0833
0.0843
Trend productivity growth is lowest in MQ (at 4.9 per cent per year), and highest in
the AT sector (12.2 per cent per year). The other three sub-sectors have trend
productivity growth rates clustering around 9 per cent per year.
89
[5] Summary and conclusions
We have described in this paper our first attempt to disaggregate the manufacturing
sector in the Polish HERMIN model.
This required the construction and
computerisation of a new database of disaggregated manufacturing time series, and in
Section 2 we presented a comprehensive account of the construction of this database.
We also selected five important sub-sectors of total manufacturing that had distinct
characteristics, and this was the sub-sectoral disaggregation that we wish to model.
Some background to the theoretical underpinnings and assumptions, used by the
original four sector HERMIN modelling framework, were presented in Section 3.
The approach that we used for the disaggregated sub-sectors was a simple application
of the aggregate manufacturing model to the disaggregated sub-sector models. This
was partially because the quality of the disaggregated manufacturing data had not
previously been tried and tested by extensive econometric research.
In section 4 we described how the disaggregated model of manufacturing was
calibrated, and described the preliminary results obtained. The most difficult
equations to calibrate were those that determined added value, since we required too
many parameters in order to specify the model adequately. We explored a nested
sequence of regressions for each sub-sector, but these results were merely suggestive
of the underlying values of the parameters. In each case, we tried to distil the main
insights into a standard equation with parameters imposed, having selected the
parameters in light of the sub-sectoral stylised facts.
The calibration of the sub-sectoral CES production functions also required the
imposition of parameter values for the important “elasticity of substitution. But we
recovered values for the rate of technical progress from the data, and noted the
important sub-sectoral differences.
The wage and output price equations were easier to calibrate, and suggested a high
degree of homogeneity between subsectors. In each case, it would appear to be
feasible to use an aggregate equation, and to link the sub-sectoral variables to that
aggregate.
We noted in Section 4 that there are four stages to the augmentation of the original
aggregate manufacturing sector used in the “basic” Polish HERMIN model (as most
recently described in Zaleski et al, 2004a):
v. First, we needed to select sets of functional forms for the behavioural equations
describing the disaggregated sectors. The simplest example of this choice was
described above, where we selected a common set of functional forms that
generalised the forms used in the “basic” version of the HERMIN model.
vi. Second, we had to assign appropriate numerical values for the parameters in
these behavioural equations, using the small set of available data with some
form of regression analysis or curve fitting. We showed that this is more a
matter for judgement than for econometrics, since the data time series are so
short (eight observations).
90
vii. Third, it remains to construct the additional “identities” that are needed within
the HERMIN model (e.g., the additional definitional equations that make the use
of the model work as an integrated system).
viii. Finally, when the new “augmented” model is constructed, we will have to test
its properties, and compare these with the known properties of the “basic”
model.
The remaining two tasks (i.e., construction of new identities, and bringing all the
work together in a new “augmented” HERMIN model), are quite straightforward, and
will be treated in a future paper.
91
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Appendix 1: TSP REGRESSION LISTING
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COMMAND ***************************************************************
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1 IN HPO4MANDB, HPO4DB;
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2
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2 ? Store the "actual" and "predicted" vaues in MANPLTDB.TLB
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2 ? These are for use in preparing graphs for HPO4WP03.DOC
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2
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2 OUT MANPLTDB;
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3
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3 OPTIONS LIMERR=10 LIMWARN=1 LIMWNUMC=1;
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4
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4 ? ------------------------------------------------|
4 ? Calibration of disaggregated equations used in
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4 ? manufacturing sector in revised version of
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4 ? Polish HERMIN model
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4 ?
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4 ? Always update after data or model change
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4 ?
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4 ?
Last modified: September 12, 2004
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4 ? ------------------------------------------------|
4
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4 SMPL 1994 2002;
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5
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5 ? ------------------------------------|
5 ? Aggregate national export share
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5 ? ------------------------------------|
5
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5 print X M GDPM;
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6 smpl 1994 2002;
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7 XSHR=X/(GDPFC+M);
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8 print XSHR;
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9 MSD XSHR;
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10
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10 ? ---------------------------------------------|
10 ? OTAT: GDP arising in AT manufacturing sector |
10 ? ---------------------------------------------|
10
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10 ? The AT sub-sector is the most traded (export share - 50%)
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10 ? We expect OW to be important. Very little deviation of
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10 ? POT from PWORLD, so competitiveness elasticities difficult
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10 ? to estimate. But they are probably large. We expect a
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10 ? positive trend growth factor over time.
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10
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10 y1=log(OTAT);
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x1=log(OW);
x2=log(ULCTAT/POTAT);
x3=log(FDOT);
x4=log(POTAT/PWORLD);
Title "Advanced Technology Sub-sector (AT)";
ols y1 c x1 x2 x3 x4 t;
ols y1 c x1 x2 x3 x4;
ols y1 c x1 x2 x3 t;
ols y1 c x1 x2 x3;
ols y1 c x1 x3 x4 t;
ols y1 c x1 x3 x4;
ols y1 c x1 x3 t;
ols y1 c x1 x3;
? Try world demand alone
ols
ols
ols
ols
y1
y1
y1
y1
c
c
c
c
x1 x2 t;
x1 x2;
x1 t;
x1;
? Constrain coeffs using diaggregated export share and
? impose price elasticities of -0.5
y2=y1-0.50*x1-(1-0.50)*x3+0.5*x2+0.5*x4;
ols y2 c t;
temp=@fit;
temp=temp+0.50*x1+(1-0.50)*x3-0.5*x2-0.5*x4;
OTATP=exp(temp);
OTAT=OTAT;
PEROTAT=100*(OTATP-OTAT)/OTAT;
print OTAT OTATP PEROTAT;
? ---------------------------------------------? OTFD: GDP arising in FD manufacturing sector ? ---------------------------------------------? FD is the least traded sub-sector. We expect FDOT
? to dominate, and price effects to be important, due
? to the presence of potential import substitutes.
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y1=log(OTFD);
x1=log(OW);
x2=log(ULCTFD/POTFD);
x3=log(FDOT);
x4=log(POTFD/PWORLD);
Title "Food
ols y1 c x1
ols y1 c x1
ols y1 c x1
ols y1 c x1
ols y1 c x1
ols y1 c x1
ols y1 c x1
ols y1 c x1
and Beverage Sub-sector (FD)";
x2 x3 x4 t;
x2 x3 x4;
x2 x3 t;
x2 x3;
x3 x4 t;
x3 x4;
x3 t;
x3;
? Try local demand alone
ols
ols
ols
ols
y1
y1
y1
y1
c
c
c
c
x3 x4 t;
x3 x4;
x3 t;
x3;
? Constrain coeffs using sub-setoral export share (8%)
? and impose price elasticities
y2=y1-0.08*x1-(1-0.08)*x3+0.2*x2+0.2*x4;
ols y2 c t;
temp=@fit;
temp=temp+0.08*x1+(1-0.08)*x3-0.2*x2-0.2*x4;
OTFDP=exp(temp);
OTFD=OTFD;
PEROTFD=100*(OTFDP-OTFD)/OTFD;
print OTFD OTFDP PEROTFD;
? ---------------------------------------------? OTMQ: GDP arising in MQ manufacturing sector ? ---------------------------------------------?
?
?
?
Is this really a market-driven sector?
It has a low export orientation (12%)
Energy: Special characteristics of coal.
Probably insensitive to price/profitability.
y1=log(OTMQ);
x1=log(OW);
x2=log(ULCTMQ/POTMQ);
x3=log(FDOT);
x4=log(POTMQ/PWORLD);
Title "Mining & Quarrying Sub-sector (MQ)";
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ols
ols
ols
ols
ols
ols
ols
ols
y1
y1
y1
y1
y1
y1
y1
y1
c
c
c
c
c
c
c
c
x1
x1
x1
x1
x1
x1
x1
x1
x2 x3 x4 t;
x2 x3 x4;
x2 x3 t;
x2 x3;
x3 x4 t;
x3 x4;
x3 t;
x3;
? Try local demand alone
ols
ols
ols
ols
y1
y1
y1
y1
c
c
c
c
x3 x4 t;
x3 x4;
x3 t;
x3;
? Try industrial demand (OT)
x3T = log(OT);
ols
ols
ols
ols
y1
y1
y1
y1
c
c
c
c
x3T x4 t;
x3T x4;
x3T t;
x3T;
? Constrain coeffs using export share (12%) and
? impose very low price elasticities
y2=y1-0.12*x1-(1-0.12)*x3+0.1*x2+0.1*x4;
ols y2 c t;
temp=@fit;
temp=temp+0.12*x1+(1-0.12)*x3-0.1*x2-0.1*x4;
OTMQP=exp(temp);
OTMQ=OTMQ;
PEROTMQ=100*(OTMQP-OTMQ)/OTMQ;
print OTMQ OTMQP PEROTMQ;
? ---------------------------------------------? OTKG: GDP arising in KG manufacturing sector ? ---------------------------------------------?
?
?
?
?
This has some characteristics of a modern
sub-sector and some of a declining sector.
If declining, then time trend important.
Its export orientation is modest (23%)
Is it internationally competitive?
y1=log(OTKG);
x1=log(OW);
x2=log(ULCTKG/POTKG);
x3=log(FDOT);
x4=log(POTKG/PWORLD);
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Title "Capital Goods Sub-sector (KG)";
ols y1 c x1 x2 x3 x4 t;
ols y1 c x1 x2 x3 x4;
ols y1 c x1 x2 x3 t;
ols y1 c x1 x2 x3;
ols y1 c x1 x3 x4 t;
ols y1 c x1 x3 x4;
ols y1 c x1 x3 t;
ols y1 c x1 x3;
? Constrain coeffs using export share and impose
? price elasticities
y2=y1-0.23*x1-(1-0.23)*x3+0.25*x2+0.25*x4;
ols y2 c t;
temp=@fit;
temp=temp+0.23*x1+(1-0.23)*x3-0.25*x2-0.25*x4;
OTKGP=exp(temp);
OTKG=OTKG;
PEROTKG=100*(OTKGP-OTKG)/OTKG;
print OTKG OTKGP PEROTKG;
? ---------------------------------------------? OTCG: GDP arising in CG manufacturing sector ? ---------------------------------------------?
?
?
?
This is probably a rather "traditional" sector,
but has a relatively high oriention towards the
world market (35%). We expect OW to be important,
as well as price competitiveness.
y1=log(OTCG);
x1=log(OW);
x2=log(ULCTCG/POTCG);
x3=log(FDOT);
x4=log(POTCG/PWORLD);
Title "Consumer Goods Sub-sector (CG)";
ols y1 c x1 x2 x3 x4 t;
ols y1 c x1 x2 x3 x4;
ols y1 c x1 x2 x3 t;
ols y1 c x1 x2 x3;
ols y1 c x1 x3 x4 t;
ols y1 c x1 x3 x4;
ols y1 c x1 x3 t;
ols y1 c x1 x3;
? Constrain coeffs using export share and impose
? price elasticities
y2=y1-0.35*x1-(1-0.35)*x3+0.4*x2+0.4*x4;
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ols y2 c t;
temp=@fit;
temp=temp+0.35*x1+(1-0.35)*x3-0.4*x2-0.4*x4;
OTCGP=exp(temp);
OTCG=OTCG;
PEROTCG=100*(OTCGP-OTCG)/OTCG;
print OTCG OTCGP PEROTCG;
? Sectoral output prices (POT**)
?----------------------------------------------------? POTAT: Deflator of GDP in AT manufacturing --------?----------------------------------------------------? All prices are modelled as a hybrid of price taking
? and mark-up over unit labour costs.
y1=log(POTAT/ULCTAT);
x1=log(PWORLD/ULCTAT);
Title "POTAT: Advanced Technology Sub-sector (AT)";
Title " POTAT on PWORLD and ULCTAT:homogeneity imposed ";
ar1 y1 c x1;
temp=exp(@fit);
POTATP=temp*ULCTAT;
POTATPDT=100*(POTATP/POTATP(-1)-1);
POTATDT=POTATDOT;
PERPOTAT=POTATPDT-POTATDT;
print POTATDT POTATPDT PERPOTAT;
?----------------------------------------------------? POTFD: Deflator of GDP in FD manufacturing --------?----------------------------------------------------y1=log(POTFD/ULCTFD);
x1=log(PWORLD/ULCTFD);
Title "POTFD: Food & Beverages Sub-sector (FD)";
Title " POTFD on PWORLD and ULCTFD:homogeneity imposed ";
ar1 y1 c x1;
temp=exp(@fit);
POTFDP=temp*ULCTFD;
POTFDPDT=100*(POTFDP/POTFDP(-1)-1);
POTFDDT=POTFDDOT;
PERPOTFD=POTFDPDT-POTFDDT;
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print POTFDDT POTFDPDT PERPOTFD;
?----------------------------------------------------? POTMQ: Deflator of GDP in MQ manufacturing --------?----------------------------------------------------y1=log(POTMQ/ULCTMQ);
x1=log(PWORLD/ULCTMQ);
Title "POTMQ: Mining & Quarrying Sub-sector (MQ)";
Title " POTMQ on PWORLD and ULCTMQ:homogeneity imposed ";
ar1 y1 c x1;
temp=exp(@fit);
POTMQP=temp*ULCTMQ;
POTMQPDT=100*(POTMQP/POTMQP(-1)-1);
POTMQDT=POTMQDOT;
PERPOTMQ=POTMQPDT-POTMQDT;
print POTMQDT POTMQPDT PERPOTMQ;
?---------------------------------------------------------? POTKG: Deflator of GDP in KG manufacturing -------------?---------------------------------------------------------y1=log(POTKG/ULCTKG);
x1=log(PWORLD/ULCTKG);
Title "POTKG: Capital Goods Sub-sector (KG)";
Title " POTKG on PWORLD and ULCTKG:homogeneity imposed ";
ar1 y1 c x1;
temp=exp(@fit);
POTKGP=temp*ULCTKG;
POTKGPDT=100*(POTKGP/POTKGP(-1)-1);
POTKGDT=POTKGDOT;
PERPOTKG=POTKGPDT-POTKGDT;
print POTKGDT POTKGPDT PERPOTKG;
?----------------------------------------------------? POTCG: Deflator of GDP in CG manufacturing --------?----------------------------------------------------y1=log(POTCG/ULCTCG);
x1=log(PWORLD/ULCTCG);
Title "POTCG: Consumer Goods Sub-sector (CG)";
Title " POTCG on PWORLD and ULCTCG: homogeneity imposed ";
ar1 y1 c x1;
98
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temp=exp(@fit);
POTCGP=temp*ULCTCG;
POTCGPDT=100*(POTCGP/POTCGP(-1)-1);
POTCGDT=POTCGDOT;
PERPOTCG=POTCGPDT-POTCGDT;
print POTCGDT POTCGPDT PERPOTCG;
? Sectoral wage equations (WT**)
? ---------------------------------? WTAT: Wage rate in AT manufacturing
? ---------------------------------? We examine segmented wage bargaining by sub-sector.
? However, we will probably keep the original aggregate
? wage equation (WT), used in HPO4.
y1=log(WTAT/PCONS);
x1=log(WEDGE);
x2=log(LPRTAT);
Title "WTAT: Advanced Technology Sub-sector (AT)";
Title "WTAT/PCONS on WEDGE (0.0), LPRTAT, URBAR";
ar1 y1 c x2 urbar;
temp=exp(@fit);
WTATP=temp*PCONS;
WTATPDOT=100*(WTATP/WTATP(-1)-1);
WTATDOT=WTATDOT;
PERWTAT=WTATPDOT-WTATDOT;
print WTATDOT WTATPDOT PERWTAT;
? ---------------------------------? WTFD: Wage rate in FD manufacturing
? ---------------------------------y1=log(WTFD/PCONS);
x1=log(WEDGE);
x2=log(LPRTFD);
Title "WTFD: Food & Beverages Sub-sector (FD)";
Title "WTFD/PCONS on WEDGE (0.0), LPRTFD, URBAR";
ar1 y1 c x2 urbar;
temp=exp(@fit);
WTFDP=temp*PCONS;
WTFDPDOT=100*(WTFDP/WTFDP(-1)-1);
WTFDDOT=WTFDDOT;
PERWTFD=WTFDPDOT-WTFDDOT;
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print WTFDDOT WTFDPDOT PERWTFD;
? ---------------------------------? WTMQ: Wage rate in MQ manufacturing
? ---------------------------------y1=log(WTMQ/PCONS);
x1=log(WEDGE);
x2=log(LPRTMQ);
Title "WTMQ: Mining & Quarrying Sub-sector (MQ)";
Title "WTMQ/PCONS on WEDGE (0.0), LPRTMQ, URBAR";
ar1 y1 c x2 urbar;
? Drop the final year (2002)
smpl 1994 2001;
Title "WTMQ/PCONS on WEDGE (0.0), LPRTMQ, URBAR";
ar1 y1 c x2 urbar;
temp=exp(@fit);
WTMQP=temp*PCONS;
WTMQPDOT=100*(WTMQP/WTMQP(-1)-1);
WTMQDOT=WTMQDOT;
PERWTMQ=WTMQPDOT-WTMQDOT;
print WTMQDOT WTMQPDOT PERWTMQ;
smpl 1994 2002;
? ---------------------------------? WTKG: Wage rate in KG manufacturing
? ---------------------------------y1=log(WTKG/PCONS);
x1=log(WEDGE);
x2=log(LPRTKG);
Title "WTKG:Capital Goods Sub-sector (KG)";
Title "WTKG/PCONS on WEDGE (0.0), LPRTKG, URBAR";
ar1 y1 c x2 urbar;
temp=exp(@fit);
WTKGP=temp*PCONS;
WTKGPDOT=100*(WTKGP/WTKGP(-1)-1);
WTKGDOT=WTKGDOT;
PERWTKG=WTKGPDOT-WTKGDOT;
print WTKGDOT WTKGPDOT PERWTKG;
? ----------------------------------
99
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? WTCG: Wage rate in CG manufacturing
? ---------------------------------y1=log(WTCG/PCONS);
x1=log(WEDGE);
x2=log(LPRTCG);
Title "WTCG: Consumer Goods Sub-sector (CG)";
Title "WTCG/PCONS on WEDGE (0.0), LPRTCG, URBAR";
ar1 y1 c x2 urbar;
temp=exp(@fit);
WTCGP=temp*PCONS;
WTCGPDOT=100*(WTCGP/WTCGP(-1)-1);
WTCGDOT=WTCGDOT;
PERWTCG=WTCGPDOT-WTCGDOT;
print WTCGDOT WTCGPDOT PERWTCG;
? Sectoral trend productivity (ELPRT**)
?-----------------------------------------------------? ELPRTAT: Trend productivity in AT manufacturing ----?-----------------------------------------------------y1=log(LPRTAT);
title " LPRTAT on T: log-lin";
ar1 y1 c t;
LPRTATP=exp(@fit);
LPRTAT=LPRTAT;
PERLPRAT=100*(LPRTATP-LPRTAT)/LPRTAT;
print LPRTAT LPRTATP PERLPRAT;
?-----------------------------------------------------? ELPRTFD: Trend productivity in FD manufacturing ----?-----------------------------------------------------y1=log(LPRTFD);
title " LPRTFD on T: log-lin";
ar1 y1 c t;
LPRTFDP=exp(@fit);
LPRTFD=LPRTFD;
PERLPRFD=100*(LPRTFDP-LPRTFD)/LPRTFD;
print LPRTFD LPRTFDP PERLPRFD;
?-----------------------------------------------------? ELPRTMQ: Trend productivity in MQ manufacturing ----?------------------------------------------------------
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y1=log(LPRTMQ);
title " LPRTMQ on T: log-lin";
ar1 y1 c t;
LPRTMQP=exp(@fit);
LPRTMQ=LPRTMQ;
PERLPRMQ=100*(LPRTMQP-LPRTMQ)/LPRTMQ;
print LPRTMQ LPRTMQP PERLPRMQ;
?-----------------------------------------------------? ELPRTKG: Trend productivity in KG manufacturing ----?-----------------------------------------------------y1=log(LPRTKG);
title " LPRTKG on T: log-lin";
ar1 y1 c t;
LPRTKGP=exp(@fit);
LPRTKG=LPRTKG;
PERLPRKG=100*(LPRTKGP-LPRTKG)/LPRTKG;
print LPRTKG LPRTKGP PERLPRKG;
?-----------------------------------------------------? ELPRTCG: Trend productivity in CG manufacturing ----?-----------------------------------------------------y1=log(LPRTCG);
title " LPRTCG on T: log-lin";
ar1 y1 c t;
LPRTCGP=exp(@fit);
LPRTCG=LPRTCG;
PERLPRCG=100*(LPRTCGP-LPRTCG)/LPRTCG;
print LPRTCG LPRTCGP PERLPRCG;
? ---------------------------------------
100
291
291
291
291
292
292
293
293
294
295
296
297
298
298
299
300
301
302
303
303
304
305
306
307
308
308
309
310
311
312
313
313
313
313
? Summary printout of actual vs predicted
? --------------------------------------TITLE "Summary printout of actual vs predicted";
smpl 1994 2002;
print
print
print
print
print
OTAT
OTFD
OTMQ
OTKG
OTCG
OTATP
OTFDP
OTMQP
OTKGP
OTCGP
PEROTAT;
PEROTFD;
PEROTMQ;
PEROTKG;
PEROTCG;
print
print
print
print
print
POTATDT
POTFDDT
POTMQDT
POTKGDT
POTCGDT
POTATPDT
POTFDPDT
POTMQPDT
POTKGPDT
POTCGPDT
PERPOTAT;
PERPOTFD;
PERPOTMQ;
PERPOTKG;
PERPOTCG;
print
print
print
print
print
WTATDOT
WTFDDOT
WTMQDOT
WTKGDOT
WTCGDOT
WTATPDOT
WTFDPDOT
WTMQPDOT
WTKGPDOT
WTCGPDOT
PERWTAT;
PERWTFD;
PERWTMQ;
PERWTKG;
PERWTCG;
print
print
print
print
print
LPRTAT
LPRTFD
LPRTMQ
LPRTKG
LPRTCG
LPRTATP
LPRTFDP
LPRTMQP
LPRTKGP
LPRTCGP
PERLPRAT;
PERLPRFD;
PERLPRMQ;
PERLPRKG;
PERLPRCG;
? --------------------------------------END
Appendix 2: Exploratory regression results
Advanced Technology Sub-sector (AT)
===================================
EXECUTION
********************************************************************
1994
1995
1996
1997
1998
1999
2000
2001
2002
X
M
GDPM
.
78171.70313
87525.00000
98216.53906
112307.38281
109435.09375
134854.48438
139097.87500
145827.76563
.
70935.00000
90805.89844
110239.75781
130682.72656
131969.48438
152511.93750
144352.59375
148096.67188
.
330340.62500
349180.90625
394059.71875
414370.06250
429158.96875
446226.56250
453121.68750
457785.56250
Current sample:
1994
1995
1996
1997
1998
1999
2000
2001
2002
Equation
1
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
1994 to 2002
XSHR
.
0.21802
0.22287
0.21698
0.22902
0.21759
0.25112
0.25978
0.26880
Variable
C
X1
X2
X3
X4
T
Estimated
Coefficient
-10.3198
1.91297
-.179064
1.03391
.039473
-.022051
= 10.0399
= .193886
=
=
=
=
=
LM het. test = .123977 [.725]
Durbin-Watson = 2.49174
.108644E-02
.543219E-03
.023307
.995871
.985550
Standard
Error
3.91440
.752674
.367010
.224663
.250713
.020571
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.63637
2.54157
-.487900
4.60204
.157444
-1.07195
=
=
=
=
=
1.13047 [.568]
.535594 [.598]
96.4827 [.010]
-18.0273
24.2657
P-value
[.119]
[.126]
[.674]
[.044]
[.889]
[.396]
Equation
2
============
Univariate statistics
=====================
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Number of Observations: 8
XSHR
Mean
0.23552
Std Dev
0.021089
Minimum
0.21698
Maximum
0.26880
XSHR
Sum
1.88417
Variance
0.00044475
Skewness
0.71307
Kurtosis
-1.47753
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
X4
101
Estimated
Coefficient
-6.37183
1.19780
-.011941
.920488
-.080536
= 10.0399
= .193886
=
=
=
=
=
LM het. test = 1.18801 [.276]
Durbin-Watson = 1.82755
.171064E-02
.570212E-03
.023879
.993499
.984831
Standard
Error
1.35852
.356973
.340404
.203052
.229834
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-4.69028
3.35543
-.035079
4.53327
-.350407
=
=
=
=
=
.357552 [.836]
.590613 [.523]
114.621 [.001]
-17.2512
22.4498
P-value
[.018]
[.044]
[.974]
[.020]
[.749]
Equation
5
============
Equation
3
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
T
Estimated
Coefficient
-10.1232
1.85986
-.156821
1.02216
-.020605
= 10.0399
= .193886
=
=
=
=
=
.109990E-02
.366635E-03
.019148
.995820
.990247
Standard
Error
3.04790
.552759
.278280
.174097
.015121
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .338199 [.561]
Durbin-Watson = 2.40521
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-3.32138
3.36468
-.563537
5.87122
-1.36262
=
=
=
=
=
.634571 [.728]
.817008 [.461]
178.681 [.001]
-19.0178
24.2164
Variable
C
X1
X3
X4
T
P-value
[.045]
[.044]
[.612]
[.010]
[.266]
Estimated
Coefficient
-9.18553
1.79989
.964809
-.761314E-02
-.017787
= 10.0399
= .193886
=
=
=
=
=
.121575E-02
.405250E-03
.020131
.995380
.989220
Standard
Error
2.72010
.618510
.150634
.199860
.016085
Variable
C
X1
X2
X3
Estimated
Coefficient
-6.21287
1.21362
-.040692
.931463
= 10.0399
= .193886
Mean of dep. var.
Std. dev. of dep. var.
[.000,.980]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 2.00043 [.157]
Durbin-Watson = 1.75829
.178065E-02
.445163E-03
.021099
.993233
.988158
Standard
Error
1.13144
.312876
.291903
.177263
t-statistic
-3.37691
2.91004
6.40499
-.038092
-1.10586
=
=
=
=
=
.268491 [.874]
1.55777 [.338]
161.584 [.001]
-18.6172
23.8158
P-value
[.043]
[.062]
[.008]
[.972]
[.350]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation
6
============
Equation
4
============
Mean of dep. var.
Std. dev. of dep. var.
[.000,.962]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .439060 [.508]
Durbin-Watson = 2.30827
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-5.49110
3.87893
-.139402
5.25469
=
=
=
=
=
.196058 [.907]
.777895 [.443]
195.705 [.000]
-18.1305
22.2894
Variable
C
X1
X3
X4
P-value
[.005]
[.018]
[.896]
[.006]
102
Estimated
Coefficient
-6.34165
1.19985
.916650
-.082479
= 10.0399
= .193886
=
=
=
=
=
LM het. test = 1.06720 [.302]
Durbin-Watson = 1.84052
.171134E-02
.427835E-03
.020684
.993497
.988619
Standard
Error
.910665
.305039
.148165
.193213
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-6.96375
3.93342
6.18668
-.426880
=
=
=
=
=
.356518 [.837]
.683802 [.469]
203.686 [.000]
-18.2893
22.4482
P-value
[.002]
[.017]
[.003]
[.691]
Equation
9
============
Equation
7
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.059,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
T
Estimated
Coefficient
-9.19813
1.80873
.965525
-.017995
= 10.0399
= .193886
=
=
=
=
=
.121634E-02
.304085E-03
.017438
.995378
.991911
Standard
Error
2.33875
.496578
.129465
.013109
Mean of dep. var.
Std. dev. of dep. var.
[.000,.974]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .374184 [.541]
Durbin-Watson = 2.31826
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-3.93293
3.64240
7.45783
-1.37267
=
=
=
=
=
.318024 [.853]
1.83668 [.268]
287.120 [.000]
-19.6550
23.8139
Variable
C
X1
X2
T
P-value
[.017]
[.022]
[.002]
[.242]
Estimated
Coefficient
.267650
2.13241
.786373
.013339
= 10.0399
= .193886
=
=
=
=
=
.013738
.343456E-02
.058605
.947792
.908635
Standard
Error
7.59490
1.68584
.695473
.042765
Variable
C
X1
X3
Estimated
Coefficient
-6.08979
1.22242
.918535
= 10.0399
= .193886
Mean of dep. var.
Std. dev. of dep. var.
[.067,.827]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 1.68505 [.194]
Durbin-Watson = 1.80246
.178930E-02
.357860E-03
.018917
.993200
.990480
Standard
Error
.634440
.274756
.135448
t-statistic
.035241
1.26489
1.13070
.311907
=
=
=
=
=
.798576 [.671]
4.18720 [.133]
24.2054 [.005]
-9.95766
14.1165
P-value
[.974]
[.275]
[.321]
[.771]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 10
============
Equation
8
============
Mean of dep. var.
Std. dev. of dep. var.
[.055,.802]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .084769 [.771]
Durbin-Watson = 1.81283
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-9.59870
4.44911
6.78148
=
=
=
=
=
.163443 [.922]
.961552 [.382]
365.162 [.000]
-19.1508
22.2700
Variable
C
X1
X2
P-value
[.000]
[.007]
[.001]
103
Estimated
Coefficient
-1.99812
2.64072
.761779
= 10.0399
= .193886
=
=
=
=
=
LM het. test = .060191 [.806]
Durbin-Watson = 1.85247
.014072
.281448E-02
.053052
.946522
.925131
Standard
Error
2.00653
.390610
.625510
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-.995811
6.76051
1.21785
=
=
=
=
=
1.12225 [.571]
4.02691 [.115]
44.2481 [.001]
-10.9013
14.0204
P-value
[.365]
[.001]
[.278]
Equation 13
============
Equation 11
============
Dependent variable: Y2
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,.244]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
T
Estimated
Coefficient
-2.35972
2.63047
.785657E-02
= 10.0399
= .193886
=
=
=
=
=
.018129
.362586E-02
.060215
.931105
.903547
Standard
Error
7.42934
1.67198
.043657
Mean of dep. var.
Std. dev. of dep. var.
[.076,.354]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .768936 [.381]
Durbin-Watson = .961028
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-.317621
1.57326
.179962
=
=
=
=
=
.737223 [.692]
7.59965 [.051]
33.7870 [.001]
-9.88800
13.0072
Variable
C
T
P-value
[.764]
[.176]
[.864]
1994
1995
1996
1997
1998
1999
2000
2001
2002
Equation 12
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.011,.133]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
Estimated
Coefficient
-3.66261
2.92447
= 10.0399
= .193886
=
=
=
=
=
LM het. test = 1.18489 [.276]
Durbin-Watson = 1.02303
.018247
.304112E-02
.055146
.930658
.919102
Standard
Error
1.52708
.325892
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.39844
8.97375
=
=
=
=
=
.687404 [.709]
7.40096 [.042]
80.5283 [.000]
-10.9019
12.9813
P-value
[.053]
[.000]
104
Estimated
Coefficient
-.853804
.039089
OTAT
14079.65039
16575.00000
18608.58984
20982.77734
22651.75000
25186.64453
28083.32227
27264.45898
26966.45898
= -.482461
= .106018
=
=
=
=
=
LM het. test = 1.03877 [.308]
Durbin-Watson = 1.44545
.014506
.241771E-02
.049170
.815628
.784899
Standard
Error
.074145
.758713E-02
OTATP
.
17573.42773
18395.25586
20633.07031
22089.65820
24743.11328
27230.61523
26175.69141
29339.04492
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-11.5154
5.15198
PEROTAT
.
6.02370
-1.14643
-1.66664
-2.48145
-1.76098
-3.03635
-3.99336
8.79829
=
=
=
=
=
1.66602 [.435]
11.5915 [.019]
26.5429 [.002]
-11.8195
13.8990
P-value
[.000]
[.002]
Food and Beverage Sub-sector (FD)
=================================
Equation 16
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Equation 14
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
X4
T
Estimated
Coefficient
-8.15639
1.01785
.036029
.929695
-.579731
-.022586
= 9.55049
= .135236
=
=
=
=
=
LM het. test = .670245 [.413]
Durbin-Watson = 2.40925
.113862E-02
.569312E-03
.023860
.991106
.968871
Standard
Error
3.68020
.764134
.273385
.233221
.156345
.021618
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.21629
1.33204
.131789
3.98633
-3.70802
-1.04477
=
=
=
=
=
.623689 [.732]
.357253 [.657]
44.5740 [.022]
-17.8397
24.0780
Variable
C
X1
X2
X3
T
P-value
[.157]
[.314]
[.907]
[.058]
[.066]
[.406]
Variable
C
X1
X2
X3
X4
Estimated
Coefficient
-5.06809
.307570
-.102238
.952273
-.512755
=
=
=
=
=
Standard
Error
2.22548
.354145
.242839
.235734
.144757
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.27730
.868485
-.421012
4.03962
-3.54217
Standard
Error
7.53988
1.65823
.597668
.534086
.045178
Mean of dep. var.
[.959]
Std. dev. of dep. var.
[.004,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .947916 [.330]
Durbin-Watson = 2.13284
.176005E-02
.586683E-03
.024222
.986252
.967921
.896632E-02
.298877E-02
.054670
.929962
.836578
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-.271444
.065510
-.447458
1.79318
.227605
=
=
=
=
=
.313659 [.855]
3.05683 [.223]
9.95850 [.044]
-10.6248
15.8234
P-value
[.804]
[.952]
[.685]
[.171]
[.835]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
= 9.55049
= .135236
=
=
=
=
=
LM het. test = .065077 [.799]
Durbin-Watson = 1.99500
Equation 17
============
Equation 15
============
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Estimated
Coefficient
-2.04666
.108630
-.267431
.957711
.010283
= 9.55049
= .135236
=
=
=
=
=
.620422 [.733]
1.12448 [.400]
53.8029 [.004]
-17.1373
22.3359
Variable
C
X1
X2
X3
P-value
[.107]
[.449]
[.702]
[.027]
[.038]
105
Estimated
Coefficient
-3.35050
.439863
-.210948
.947126
= 9.55049
LM het. test = .267637E-02
= .135236
=
=
=
=
=
Durbin-Watson = 2.05401
.912116E-02
.228029E-02
.047752
.928753
.875317
Standard
Error
4.28210
.694298
.474915
.464736
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-.782445
.633536
-.444180
2.03799
=
=
=
=
=
.408384 [.815]
2.95736 [.184]
17.3808 [.009]
-11.5960
15.7549
P-value
[.478]
[.561]
[.680]
[.111]
Equation 20
============
Equation 18
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
X4
T
Estimated
Coefficient
-8.18784
.976843
.949044
-.573563
-.021207
= 9.55049
= .135236
=
=
=
=
=
.114851E-02
.382837E-03
.019566
.991029
.979067
Standard
Error
3.01154
.572301
.148595
.122329
.015512
Mean of dep. var.
Std. dev. of dep. var.
[.000,.996]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .371234 [.542]
Durbin-Watson = 2.43354
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.71882
1.70687
6.38680
-4.68869
-1.36712
=
=
=
=
=
.546067 [.761]
.499216 [.553]
82.8502 [.002]
-18.8448
24.0434
Variable
C
X1
X3
T
P-value
[.073]
[.186]
[.008]
[.018]
[.265]
Estimated
Coefficient
-1.26023
.364131
.802380
.188892E-02
= 9.55049
= .135236
=
=
=
=
=
.956473E-02
.239118E-02
.048900
.925288
.869254
Standard
Error
6.55833
1.39250
.363045
.036761
Variable
C
X1
X3
X4
Estimated
Coefficient
-4.25261
.298817
.885671
-.520457
= 9.55049
= .135236
Mean of dep. var.
Std. dev. of dep. var.
[.097,.874]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 1.54716 [.214]
Durbin-Watson = 2.02031
.186404E-02
.466010E-03
.021587
.985440
.974519
Standard
Error
.976704
.315085
.155762
.127979
t-statistic
-.192158
.261494
2.21014
.051383
=
=
=
=
=
.605372 [.739]
10.2928 [.049]
16.5129 [.010]
-11.4061
15.5649
P-value
[.857]
[.807]
[.092]
[.961]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 21
============
Equation 19
============
Mean of dep. var.
Std. dev. of dep. var.
[.001,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .038443 [.845]
Durbin-Watson = 1.94366
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-4.35404
.948368
5.68606
-4.06673
=
=
=
=
=
.470072 [.791]
.028291 [.877]
90.2392 [.000]
-17.9474
22.1063
Variable
C
X1
X3
P-value
[.012]
[.397]
[.005]
[.015]
106
Estimated
Coefficient
-1.58652
.425676
.807313
= 9.55049
= .135236
=
=
=
=
=
LM het. test = .018601 [.892]
Durbin-Watson = 1.95927
.957105E-02
.191421E-02
.043752
.925239
.895334
Standard
Error
1.46733
.635456
.313263
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.08123
.669874
2.57711
=
=
=
=
=
.613388 [.736]
8.77502 [.041]
30.9397 [.002]
-12.4431
15.5623
P-value
[.329]
[.533]
[.050]
Equation 24
============
Equation 22
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.034,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X3
X4
T
Estimated
Coefficient
-4.07998
.990416
-.525886
.173804E-02
= 9.55049
= .135236
=
=
=
=
=
.226387E-02
.565967E-03
.023790
.982316
.969054
Standard
Error
2.20115
.178253
.144807
.941182E-02
Mean of dep. var.
Std. dev. of dep. var.
[.070,.832]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 3.99627 [.046]
Durbin-Watson = 2.22384
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.85357
5.55625
-3.63162
.184666
=
=
=
=
=
.492229 [.782]
.752318 [.450]
74.0663 [.001]
-17.1701
21.3290
Variable
C
X3
T
P-value
[.137]
[.005]
[.022]
[.862]
Estimated
Coefficient
.128787
.823445
.010157
= 9.55049
= .135236
=
=
=
=
=
.972824E-02
.194565E-02
.044109
.924011
.893615
Standard
Error
3.46970
.319317
.016913
Variable
C
X3
X4
Estimated
Coefficient
-4.43674
1.02072
-.532473
= 9.55049
= .135236
Mean of dep. var.
Std. dev. of dep. var.
[.214,.591]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 4.43652 [.035]
Durbin-Watson = 2.25602
.228317E-02
.456634E-03
.021369
.982166
.975032
Standard
Error
.947534
.062466
.126063
t-statistic
.037118
2.57876
.600574
=
=
=
=
=
.616912 [.735]
4.24209 [.108]
30.3994 [.002]
-12.3780
15.4971
P-value
[.972]
[.050]
[.574]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 25
============
Equation 23
============
Mean of dep. var.
Std. dev. of dep. var.
[.229,.956]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .452967 [.501]
Durbin-Watson = 1.86487
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-4.68241
16.3406
-4.22387
=
=
=
=
=
.409280 [.815]
.904946 [.395]
137.679 [.000]
-18.1759
21.2950
Variable
C
X3
P-value
[.005]
[.000]
[.008]
107
Estimated
Coefficient
-1.76286
.999004
= 9.55049
= .135236
=
=
=
=
=
LM het. test = 1.01087 [.315]
Durbin-Watson = 1.82485
.010430
.173834E-02
.041693
.918529
.904950
Standard
Error
1.37561
.121464
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.28151
8.22471
=
=
=
=
=
.596746 [.742]
5.00650 [.075]
67.6458 [.000]
-13.1391
15.2185
P-value
[.247]
[.000]
Mining & Quarrying Sub-sector (MQ)
==================================
Equation 26
============
Dependent variable: Y2
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.215,.592]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
T
1994
1995
1996
1997
1998
1999
2000
2001
2002
Estimated
Coefficient
-2.36149
.010347
OTFD
9460.76855
11528.09961
11920.40625
12630.64746
14619.87695
15842.33691
15676.25391
14992.69824
16086.05566
Equation 27
============
= -2.26320
= .036268
=
=
=
=
=
.471141E-02
.785235E-03
.028022
.488320
.403040
Standard
Error
.042255
.432390E-02
OTFDP
.
11297.78711
12160.53906
13176.77148
14249.83789
15281.13965
15753.70996
15156.04785
16148.01367
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
LM het. test = 1.25892 [.262]
Durbin-Watson = 1.82616
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-55.8868
2.39292
=
=
=
=
=
.322090 [.851]
.044080 [.842]
5.72609 [.054]
-16.3179
18.3973
Mean of dep. var.
Std. dev. of dep. var.
[.558,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
P-value
[.000]
[.054]
Variable
C
X1
X2
X3
X4
T
PEROTFD
.
-1.99784
2.01447
4.32380
-2.53107
-3.54239
0.49410
1.08953
0.38517
Estimated
Coefficient
13.0988
-2.63085
-.710373
.855505
.247117
-.030437
= 9.22286
= .102654
=
=
=
=
=
LM het. test = 3.03506 [.081]
Durbin-Watson = 3.67003
.661400E-03
.330700E-03
.018185
.991034
.968618
Standard
Error
2.78736
.784250
.152332
.200099
.180380
.018980
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
4.69936
-3.35461
-4.66331
4.27542
1.36998
-1.60363
=
=
=
=
=
.721776 [.697]
.091883 [.813]
44.2118 [.022]
-20.0125
26.2509
P-value
[.042]
[.079]
[.043]
[.051]
[.304]
[.250]
Equation 28
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.264,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
X4
108
Estimated
Coefficient
16.4238
-3.45031
-.805644
.811269
.088934
= 9.22286
= .102654
=
=
=
=
=
LM het. test = 1.54886 [.213]
Durbin-Watson = 3.14707
.151184E-02
.503945E-03
.022449
.979505
.952178
Standard
Error
2.29961
.734395
.173156
.244654
.186426
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
7.14199
-4.69817
-4.65269
3.31599
.477048
=
=
=
=
=
.313763 [.855]
.733403 [.482]
35.8440 [.007]
-17.7454
22.9440
P-value
[.006]
[.018]
[.019]
[.045]
[.666]
Equation 29
============
Equation 31
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
T
Estimated
Coefficient
12.6353
-2.48754
-.696680
.728127
-.016218
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
= 9.22286
= .102654
=
=
=
=
=
LM het. test = .033853 [.854]
Durbin-Watson = 2.76155
.128208E-02
.427359E-03
.020673
.982620
.959446
Standard
Error
3.14521
.883559
.172796
.201422
.018065
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
4.01732
-2.81537
-4.03180
3.61494
-.897767
=
=
=
=
=
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
1.36652 [.505]
1.54303 [.340]
42.4020 [.006]
-18.4048
23.6034
P-value
[.028]
[.067]
[.027]
[.036]
[.435]
Variable
C
X1
X3
X4
T
Estimated
Coefficient
6.91673
.098167
.293117
.191926
-.064957
= 9.22286
= .102654
=
=
=
=
=
.785295E-02
.261765E-02
.051163
.893542
.751597
Standard
Error
6.89836
1.46886
.449231
.506395
.049172
Equation 30
============
Variable
C
X1
X2
X3
Estimated
Coefficient
15.3883
-3.18018
-.775762
.756481
LM het. test = .787286 [.375]
Durbin-Watson = 3.05862
.162652E-02
.406630E-03
.020165
.977950
.961413
Standard
Error
.682086
.420065
.145008
.194046
t-statistic
1.00266
.066832
.652486
.379004
-1.32102
=
=
=
=
=
.334301 [.846]
.033888 [.871]
6.29501 [.081]
-11.1551
16.3537
P-value
[.390]
[.951]
[.561]
[.730]
[.278]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
= 9.22286
= .102654
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 32
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.527,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 2.10478 [.147]
Durbin-Watson = 2.17373
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
22.5607
-7.57068
-5.34980
3.89846
=
=
=
=
=
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
.230727 [.891]
.154236 [.721]
59.1356 [.001]
-18.4926
22.6515
P-value
[.000]
[.002]
[.006]
[.018]
Variable
C
X1
X3
X4
109
Estimated
Coefficient
13.1988
-1.04316
-.806147E-02
-.224843
= 9.22286
= .102654
=
=
=
=
=
LM het. test = 2.21918 [.136]
Durbin-Watson = 2.01057
.012421
.310526E-02
.055725
.831615
.705326
Standard
Error
5.44287
1.29382
.421592
.431428
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.42497
-.806259
-.019121
-.521160
=
=
=
=
=
.943665 [.624]
.041452 [.852]
6.58502 [.050]
-10.3608
14.5197
P-value
[.072]
[.465]
[.986]
[.630]
Equation 35
============
Equation 33
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.005,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
T
Estimated
Coefficient
6.64816
.168920
.202216
-.053346
= 9.22286
= .102654
=
=
=
=
=
.822896E-02
.205724E-02
.045357
.888444
.804778
Standard
Error
6.08316
1.29161
.336741
.034098
Mean of dep. var.
Std. dev. of dep. var.
[.021,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 3.80028 [.051]
Durbin-Watson = 2.06206
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
1.09288
.130782
.600508
-1.56451
=
=
=
=
=
.721339 [.697]
.208345 [.679]
10.6189 [.022]
-12.0078
16.1666
Variable
C
X3
X4
T
P-value
[.336]
[.902]
[.581]
[.193]
Estimated
Coefficient
7.29117
.300741
.196227
-.063024
= 9.22286
= .102654
=
=
=
=
=
.786464E-02
.196616E-02
.044341
.893383
.813421
Standard
Error
3.48795
.376572
.435319
.034464
Variable
C
X1
X3
Estimated
Coefficient
15.8630
-1.56922
.062912
= 9.22286
= .102654
Mean of dep. var.
Std. dev. of dep. var.
[.078,.847]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 2.62832 [.105]
Durbin-Watson = 1.92484
.013264
.265289E-02
.051506
.820181
.748254
Standard
Error
1.72740
.748084
.368786
t-statistic
2.09039
.798629
.450766
-1.82868
=
=
=
=
=
.340733 [.843]
.017496 [.903]
11.1725 [.021]
-12.1889
16.3478
P-value
[.105]
[.469]
[.676]
[.141]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 36
============
Equation 34
============
Mean of dep. var.
Std. dev. of dep. var.
[.086,.860]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 1.75937 [.185]
Durbin-Watson = 2.16264
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
9.18314
-2.09766
.170591
=
=
=
=
=
.354357 [.838]
.614270 [.477]
11.4029 [.014]
-11.1378
14.2569
Variable
C
X3
X4
P-value
[.000]
[.090]
[.871]
110
Estimated
Coefficient
10.1523
-.277549
-.496224
= 9.22286
= .102654
=
=
=
=
=
LM het. test = .434280 [.510]
Durbin-Watson = 1.89583
.014440
.288792E-02
.053739
.804250
.725950
Standard
Error
3.77804
.247791
.260266
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.68720
-1.12009
-1.90660
=
=
=
=
=
.839092 [.657]
2.54199 [.186]
10.2714 [.017]
-10.7982
13.9174
P-value
[.043]
[.314]
[.115]
Equation 39
============
Equation 37
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.132,.908]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X3
T
Estimated
Coefficient
7.29253
.211988
-.049511
= 9.22286
= .102654
=
=
=
=
=
.826415E-02
.165283E-02
.040655
.887967
.843154
Standard
Error
3.19797
.294310
.015588
Mean of dep. var.
Std. dev. of dep. var.
[.022,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 2.91583 [.088]
Durbin-Watson = 2.05547
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.28036
.720288
-3.17613
=
=
=
=
=
.777616 [.678]
.044518 [.843]
19.8149 [.004]
-13.0304
16.1496
Variable
C
X3T
X4
T
P-value
[.072]
[.504]
[.025]
Estimated
Coefficient
7.38605
.259879
.119789
-.060154
= 9.22286
= .102654
=
=
=
=
=
.813157E-02
.203289E-02
.045088
.889765
.807088
Standard
Error
3.76062
.372947
.406474
.034728
Equation 38
============
Variable
C
X3
Estimated
Coefficient
16.5130
-.643746
LM het. test = .017285 [.895]
Durbin-Watson = 1.24245
.024938
.415627E-02
.064469
.661934
.605590
Standard
Error
2.12706
.187816
t-statistic
1.96405
.696826
.294703
-1.73215
=
=
=
=
=
.393750 [.821]
.250285 [.651]
10.7620 [.022]
-12.0554
16.2143
P-value
[.121]
[.524]
[.783]
[.158]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
= 9.22286
= .102654
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 40
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.034,.236]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 2.55780 [.110]
Durbin-Watson = 2.16957
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
7.76330
-3.42754
=
=
=
=
=
Mean of dep. var.
Std. dev. of dep. var.
[.084,.857]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
.668960 [.716]
3.73352 [.111]
11.7480 [.014]
-9.65233
11.7318
P-value
[.000]
[.014]
Variable
C
X3T
X4
111
Estimated
Coefficient
10.4698
-.280534
-.432546
= 9.22286
= .102654
=
=
=
=
=
LM het. test = .148131 [.700]
Durbin-Watson = 1.91876
.014231
.284618E-02
.053350
.807079
.729911
Standard
Error
3.91951
.241773
.298264
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.67120
-1.16032
-1.45021
=
=
=
=
=
.814623 [.665]
1.98564 [.232]
10.4587 [.016]
-10.8564
13.9756
P-value
[.044]
[.298]
[.207]
Equation 43
============
Equation 41
============
Dependent variable: Y2
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.144,.916]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X3T
T
Estimated
Coefficient
7.23824
.219003
-.052125
= 9.22286
= .102654
=
=
=
=
=
.830813E-02
.166163E-02
.040763
.887371
.842320
Standard
Error
3.36955
.312990
.019471
Mean of dep. var.
Std. dev. of dep. var.
[.011,.129]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 3.22638 [.072]
Durbin-Watson = 2.08228
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.14814
.699713
-2.67710
=
=
=
=
=
.650457 [.722]
.193958 [.682]
19.6968 [.004]
-13.0092
16.1283
Variable
C
T
P-value
[.084]
[.515]
[.044]
1994
1995
1996
1997
1998
1999
2000
2001
2002
Equation 42
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.069,.338]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X3T
Estimated
Coefficient
15.7221
-.573978
= 9.22286
= .102654
=
=
=
=
=
LM het. test = .013609 [.907]
Durbin-Watson = 1.41918
.020217
.336946E-02
.058047
.725932
.680254
Standard
Error
1.63043
.143979
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
9.64290
-3.98653
=
=
=
=
=
.472933 [.789]
3.05166 [.141]
15.8924 [.007]
-10.4918
12.5712
P-value
[.000]
[.007]
112
Estimated
Coefficient
-1.00330
-.081007
OTMQ
10943.85254
11100.50000
11624.98535
11144.33594
9945.59863
9780.22461
9944.77832
9248.74902
8591.26367
= -1.77287
= .203922
=
=
=
=
=
LM het. test = .015275 [.902]
Durbin-Watson = 1.01440
.015478
.257972E-02
.050791
.946826
.937964
Standard
Error
.076588
.783721E-02
OTMQP
.
10954.81836
11019.53125
11008.79590
10676.24902
10485.66016
10015.13770
8957.35449
8282.19238
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-13.0998
-10.3363
PEROTMQ
.
-1.31239
-5.20821
-1.21622
7.34647
7.21288
0.70750
-3.15064
-3.59751
=
=
=
=
=
.929974 [.628]
4.68531 [.083]
106.838 [.000]
-11.5601
13.6395
P-value
[.000]
[.000]
Capital Goods Sub-sector (KG)
=============================
Equation 46
============
Equation 44
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
X4
T
Estimated
Coefficient
-27.4020
5.18012
-.501394
.451666
-1.80827
-.122376
= 9.61931
= .083032
=
=
=
=
=
LM het. test = .563428 [.453]
Durbin-Watson = 3.28199
.269403E-02
.134702E-02
.036702
.944177
.804618
Standard
Error
12.9654
2.78528
.286562
.399274
.561422
.059456
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-2.11347
1.85982
-1.74969
1.13122
-3.22087
-2.05827
=
=
=
=
=
.820348 [.664]
1.73597 [.413]
6.76545 [.134]
-14.3948
20.6331
Variable
C
X1
X2
X3
T
P-value
[.169]
[.204]
[.222]
[.375]
[.084]
[.176]
Variable
C
X1
X2
X3
X4
Estimated
Coefficient
-1.56636
-.382036
-.366600
.777988
-.839076
=
=
=
=
=
Standard
Error
4.68232
.972690
.402234
.528350
.440804
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-.334527
-.392763
-.911409
1.47249
-1.90351
Standard
Error
10.2636
2.83579
.484096
.757623
.065756
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 3.99958 [.046]
Durbin-Watson = 2.51993
.840062E-02
.280021E-02
.052917
.825929
.593835
.016668
.555599E-02
.074538
.654621
.194115
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
1.07711
-.910578
.022572
1.20126
.581520
=
=
=
=
=
.543838 [.762]
.037579 [.864]
1.42153 [.402]
-8.14472
13.3433
P-value
[.360]
[.430]
[.983]
[.316]
[.602]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
= 9.61931
= .083032
=
=
=
=
=
LM het. test = 1.83263 [.176]
Durbin-Watson = 2.30595
Equation 47
============
Equation 45
============
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Estimated
Coefficient
11.0551
-2.58220
.010927
.910104
.038239
= 9.61931
= .083032
=
=
=
=
=
.616865 [.735]
.766327 [.474]
3.55860 [.163]
-10.8855
16.0841
Variable
C
X1
X2
X3
P-value
[.760]
[.721]
[.429]
[.237]
[.153]
113
Estimated
Coefficient
5.55110
-1.10523
.158230
.825166
= 9.61931
= .083032
=
=
=
=
=
LM het. test = 2.57108 [.109]
Durbin-Watson = 1.99174
.018547
.463670E-02
.068093
.615689
.327455
Standard
Error
3.62665
1.15223
.376863
.679130
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
1.53064
-.959214
.419861
1.21504
=
=
=
=
=
.717350 [.699]
.060739 [.821]
2.13608 [.238]
-8.75721
12.9161
P-value
[.201]
[.392]
[.696]
[.291]
Equation 50
============
Equation 48
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.093,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
X4
T
Estimated
Coefficient
-17.4715
4.18708
.219093
-1.26301
-.098602
= 9.61931
= .083032
=
=
=
=
=
.681781E-02
.227260E-02
.047672
.858727
.670364
Standard
Error
15.1415
3.54190
.489034
.606574
.075184
Mean of dep. var.
Std. dev. of dep. var.
[.054,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 3.53835 [.060]
Durbin-Watson = 2.91440
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.15388
1.18216
.448012
-2.08220
-1.31148
=
=
=
=
=
.901127 [.637]
.154072 [.733]
4.55888 [.121]
-11.7205
16.9191
Variable
C
X1
X3
T
P-value
[.332]
[.322]
[.685]
[.129]
[.281]
Estimated
Coefficient
11.1075
-2.62465
.921784
.039015
= 9.61931
= .083032
=
=
=
=
=
.016671
.416770E-02
.064558
.654562
.395484
Standard
Error
8.65834
1.83839
.479294
.048533
Variable
C
X1
X3
X4
Estimated
Coefficient
2.22250
-.314511
.549663
-.563690
= 9.61931
= .083032
Mean of dep. var.
Std. dev. of dep. var.
[.054,.799]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 1.36013 [.244]
Durbin-Watson = 2.67334
.010727
.268167E-02
.051785
.777731
.611030
Standard
Error
2.10848
.949113
.455229
.314086
t-statistic
1.28287
-1.42769
1.92321
.803899
=
=
=
=
=
.553928 [.758]
.041154 [.852]
2.52650 [.196]
-9.18376
13.3426
P-value
[.269]
[.227]
[.127]
[.467]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 51
============
Equation 49
============
Mean of dep. var.
Std. dev. of dep. var.
[.212,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = 1.86403 [.172]
Durbin-Watson = 2.30243
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
1.05408
-.331374
1.20744
-1.79470
=
=
=
=
=
.426239 [.808]
.091195 [.782]
4.66541 [.085]
-10.9475
15.1063
Variable
C
X1
X3
P-value
[.351]
[.757]
[.294]
[.147]
114
Estimated
Coefficient
4.36822
-1.35345
1.02366
= 9.61931
= .083032
=
=
=
=
=
LM het. test = 3.84129 [.050]
Durbin-Watson = 1.79743
.019364
.387284E-02
.062232
.598752
.438253
Standard
Error
2.08712
.903869
.445584
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
2.09294
-1.49739
2.29736
=
=
=
=
=
.767433 [.681]
.411466 [.556]
3.73056 [.102]
-9.62442
12.7436
P-value
[.091]
[.195]
[.070]
Consumer Goods Sub-sector (CG)
==============================
Equation 52
============
Equation 53
============
Dependent variable: Y2
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
[.076,.355]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
T
1994
1995
1996
1997
1998
1999
2000
2001
2002
Estimated
Coefficient
-1.31566
-.020090
OTKG
11868.42188
12984.90039
13973.05566
16437.33984
16541.18945
15088.75586
14525.18164
15425.78809
15803.85742
= -1.50651
= .074454
=
=
=
=
=
LM het. test = .262774 [.608]
Durbin-Watson = 1.44683
.021853
.364215E-02
.060350
.436842
.342982
Standard
Error
.091003
.931224E-02
OTKGP
.
13659.67676
14028.16504
15176.51367
15535.13965
15741.09473
15749.54883
15165.12695
15518.03711
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-14.4573
-2.15736
=
=
=
=
=
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
.460976 [.794]
.136652 [.727]
4.65421 [.074]
-10.1805
12.2599
P-value
[.000]
[.074]
Variable
C
X1
X2
X3
X4
T
PEROTKG
.
5.19662
0.39440
-7.67050
-6.08209
4.32334
8.42927
-1.68978
-1.80855
Estimated
Coefficient
17.9942
-6.04941
1.88818
.713129
-2.66614
.128066
= 10.3319
= .195648
=
=
=
=
=
LM het. test = 1.38049 [.240]
Durbin-Watson = 2.42627
.870140E-04
.435070E-04
.659598E-02
.999675
.998863
Standard
Error
3.78191
1.11665
.307204
.054622
.374443
.019512
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
4.75797
-5.41746
6.14635
13.0556
-7.12028
6.56345
=
=
=
=
=
.561511 [.755]
1.72642 [.414]
1231.34 [.001]
-28.1257
34.3640
P-value
[.041]
[.032]
[.025]
[.006]
[.019]
[.022]
Equation 54
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
[.980]
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
X4
115
Estimated
Coefficient
-6.67759
1.20426
-.043088
.865808
-.332890
= 10.3319
LM het. test = .611657E-03
= .195648
=
=
=
=
=
Durbin-Watson = 1.79098
.196124E-02
.653748E-03
.025568
.992680
.982921
Standard
Error
1.61221
.619333
.342205
.191576
.455918
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-4.14189
1.94444
-.125911
4.51940
-.730152
=
=
=
=
=
.649209 [.723]
1.08327 [.407]
101.716 [.002]
-16.7044
21.9030
P-value
[.026]
[.147]
[.908]
[.020]
[.518]
Equation 57
============
Equation 55
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
[.998]
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X2
X3
T
Estimated
Coefficient
-7.13935
1.68626
-.180321
.840312
-.383345E-02
= 10.3319
= .195648
=
=
=
=
=
Durbin-Watson = 2.23962
.229275E-02
.764250E-03
.027645
.991443
.980034
Standard
Error
5.68980
1.08148
.418679
.216347
.025687
Mean of dep. var.
Std. dev. of dep. var.
[.000,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .433312E-05
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.25476
1.55921
-.430692
3.88410
-.149236
=
=
=
=
=
.277051 [.871]
1.36624 [.363]
86.9004 [.002]
-16.0797
21.2783
Variable
C
X1
X3
X4
T
P-value
[.298]
[.217]
[.696]
[.030]
[.891]
Estimated
Coefficient
-4.58053
.618866
.849574
-.489757
.013197
= 10.3319
= .195648
=
=
=
=
=
.173060E-02
.576868E-03
.024018
.993541
.984930
Standard
Error
3.28299
.962474
.181731
.443364
.020417
Mean of dep. var. = 10.3319
Variable
C
X1
X2
X3
Estimated
Coefficient
-6.32736
1.53947
-.143600
.827759
Mean of dep. var.
[.970]
Std. dev. of dep. var.
[.000,.951]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .230059E-03
= .195648
Durbin-Watson = 2.36964
.230977E-02
.577443E-03
.024030
.991380
.984915
Standard
Error
1.44659
.390686
.294445
.173260
t-statistic
-1.39523
.642995
4.67491
-1.10464
.646358
=
=
=
=
=
.906645 [.636]
1.62033 [.331]
115.372 [.001]
-17.2048
22.4034
P-value
[.257]
[.566]
[.018]
[.350]
[.564]
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
=
=
=
=
=
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
Equation 58
============
Equation 56
============
[.988]
Std. dev. of dep. var.
[.075,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .018691 [.891]
Durbin-Watson = 2.09190
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-4.37400
3.94041
-.487698
4.77756
=
=
=
=
=
.132942 [.936]
2.03881 [.249]
153.341 [.000]
-17.0898
21.2487
Variable
C
X1
X3
X4
P-value
[.012]
[.017]
[.651]
[.009]
116
Estimated
Coefficient
-6.55454
1.15947
.865947
-.355982
= 10.3319
LM het. test = .145714E-02
= .195648
=
=
=
=
=
Durbin-Watson = 1.71522
.197161E-02
.492902E-03
.022201
.992642
.987123
Standard
Error
1.11339
.440232
.166345
.362435
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-5.88701
2.63377
5.20573
-.982196
=
=
=
=
=
.487446 [.784]
1.44881 [.315]
179.871 [.000]
-17.7230
21.8819
P-value
[.004]
[.058]
[.006]
[.382]
Equation 61
============
Equation 59
============
Dependent variable: Y2
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
[.981]
Std. dev. of dep. var.
[.118,1.00]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
T
Estimated
Coefficient
-5.28054
1.36689
.810846
.266856E-02
= 10.3319
= .195648
=
=
=
=
=
Durbin-Watson = 2.48359
.243452E-02
.608629E-03
.024670
.990914
.984100
Standard
Error
3.30874
.702532
.183160
.018546
Mean of dep. var.
Std. dev. of dep. var.
[.286,.677]
Sum of squared residuals
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
LM het. test = .591032E-03
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-1.59594
1.94567
4.42699
.143885
=
=
=
=
=
.012703 [.994]
1.19945 [.353]
145.416 [.000]
-16.8794
21.0383
Variable
C
T
P-value
[.186]
[.124]
[.011]
[.893]
1994
1995
1996
1997
1998
1999
2000
2001
2002
Equation 60
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
[.983]
Std. dev. of dep. var.
[.303,.977]
Sum of squared residuals
[.995]
Variance of residuals
Std. error of regression
R-squared
Adjusted R-squared
Variable
C
X1
X3
Estimated
Coefficient
-5.74149
1.45384
.817814
= 10.3319
LM het. test = .475775E-03
= .195648
Durbin-Watson = 2.38486
= .244712E-02
=
=
=
=
.489423E-03
.022123
.990867
.987214
Standard
Error
.741952
.321317
.158401
Jarque-Bera test = .999181E-02
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-7.73836
4.52464
5.16295
=
=
=
=
1.54641 [.282]
271.238 [.000]
-17.8985
21.0176
P-value
[.001]
[.006]
[.004]
117
Estimated
Coefficient
-1.07961
.037777
OTCG
20549.55664
22234.19922
24401.17188
28832.26953
30830.67188
32281.02148
37078.33594
36498.08203
37410.84766
= -.720721
= .097364
=
=
=
=
=
LM het. test = 4.01795 [.045]
Durbin-Watson = 1.96698
.641900E-02
.106983E-02
.032708
.903267
.887145
Standard
Error
.049321
.504700E-02
OTCGP
.
22772.10938
24649.91602
28279.98047
29951.67773
33094.22266
35887.00781
35593.33594
39215.00000
Jarque-Bera test
Ramsey's RESET2
F (zero slopes)
Schwarz B.I.C.
Log likelihood
t-statistic
-21.8892
7.48510
PEROTCG
.
2.41929
1.01939
-1.91552
-2.85104
2.51913
-3.21300
-2.47889
4.82254
=
=
=
=
=
.805898 [.668]
3.35912 [.126]
56.0267 [.000]
-15.0808
17.1602
P-value
[.000]
[.000]
POTAT: Advanced Technology Sub-sector (AT)
==========================================
POTFD: Food & Beverages Sub-sector (FD)
=======================================
Equation 62
============
Equation 63
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X1
RHO
Standard
Error
.751769
.148997
.314445
Estimate
-1.59936
.405449
.404109
.439736
.039940
.590214E-02
.118043E-02
.034357
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTATDT
.
19.38078
17.45436
2.23497
7.12537
-1.12003
1.30032
0.58809
-1.04941
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
-2.12746
2.72120
1.28515
=
=
=
=
=
.486388
.280943
1.84760
-14.3611
17.4802
P-value
[.033]
[.007]
[.199]
=
=
=
=
=
Parameter
C
X1
RHO
Standard
Error
.917252
.180132
.498786
Estimate
-2.96413
.687250
-.043323
.541273
.097636
.017842
.356834E-02
.059736
Standard Errors computed from
(Newton)
analytic second derivatives
POTATPDT
.
.
15.21649
9.48549
4.73945
3.48850
-3.16623
2.36040
-4.00416
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
PERPOTAT
.
.
-2.23787
7.25052
-2.38592
4.60853
-4.46655
1.77231
-2.95475
1994
1995
1996
1997
1998
1999
2000
2001
2002
118
POTFDDT
.
34.80526
11.18665
23.01481
-5.03483
0.51441
8.86877
-0.73635
-10.23104
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
-3.23153
3.81526
-.086857
=
=
=
=
=
P-value
[.001]
[.000]
[.931]
analytic second derivatives
POTFDPDT
.
.
13.52987
13.40299
3.86194
6.99106
2.35424
-2.49471
-2.17460
PERPOTFD
.
.
2.34322
-9.61182
8.89678
6.47665
-6.51453
-1.75837
8.05644
.732768
.625875
1.70870
-9.95135
13.0705
POTKG: Capital Goods Sub-sector (KG)
====================================
POTMQ: Mining & Quarrying Sub-sector (MQ)
=========================================
Equation 65
============
Equation 64
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X1
RHO
Standard
Error
1.16741
.249905
.339762
Estimate
-1.76560
.424556
.600455
.216940
.075573
.026555
.531096E-02
.072876
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTMQDT
.
23.21245
9.75756
19.61574
6.43081
3.30660
11.92983
6.48037
3.77218
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
-1.51240
1.69887
1.76728
=
=
=
=
=
.338314
.073640
1.32070
-8.25585
11.3750
P-value
[.130]
[.089]
[.077]
=
=
=
=
=
Parameter
C
X1
RHO
Standard
Error
2.20173
.415260
.573756
Estimate
-3.48544
.772353
-.135973
.619240
.106706
.041833
.836670E-02
.091470
Standard Errors computed from
(Newton)
analytic second derivatives
POTMQPDT
.
.
8.65354
14.18289
15.70304
-3.56549
-0.39494
11.01062
11.94456
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
PERPOTMQ
.
.
-1.10402
-5.43285
9.27223
-6.87209
-12.32476
4.53025
8.17238
1994
1995
1996
1997
1998
1999
2000
2001
2002
119
POTKGDT
.
43.58293
-6.35620
6.01168
8.92214
10.07398
19.48639
-18.93607
-0.75887
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
-1.58305
1.85993
-.236988
=
=
=
=
=
P-value
[.113]
[.063]
[.813]
analytic second derivatives
POTKGPDT
.
.
9.42640
10.44079
6.43347
8.93340
1.71031
-8.78080
3.30461
PERPOTKG
.
.
15.78260
4.42911
-2.48867
-1.14058
-17.77608
10.15528
4.06348
.476110
.266554
1.62700
-6.56107
9.68024
WTAT: Advanced Technology Sub-sector (AT)
=========================================
POTCG: Consumer Goods Sub-sector (CG)
=====================================
WTAT/PCONS on WEDGE (0.0), LPRTAT, URBAR
========================================
Equation 66
============
Equation 67
============
Dependent variable: Y1
Current sample: 1995 to 2002
Number of observations: 8
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X1
RHO
Standard
Error
.844483
.163956
.242753
Estimate
-2.33182
.553217
.672403
.501884
.055790
.761679E-02
.152336E-02
.039030
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTCGDT
.
25.02772
13.07818
2.84849
6.88779
5.19751
-6.60083
-1.89954
1.71385
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
-2.76124
3.37419
2.76991
=
=
=
=
=
Dependent variable: Y1
Current sample: 1996 to 2002
Number of observations: 7
.656036
.518450
1.34769
-13.4413
16.5605
P-value
[.006]
[.001]
[.006]
analytic second derivatives
POTCGPDT
.
.
14.18451
7.72397
6.30803
4.44935
-0.49538
-7.54609
-0.35118
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.059945
.023206
.196763E-02
.104931
Estimate
.708312
.638048
-.665154E-02
-.927536
2.85001
.142927
.137635E-02
.458784E-03
.021419
Standard Errors computed from
(Newton)
PERPOTCG
.
.
1.10633
4.87548
-0.57977
-0.74816
6.10545
-5.64655
-2.06503
1994
1995
1996
1997
1998
1999
2000
2001
2002
120
WTATDOT
.
38.61598
33.98854
20.79409
18.94632
19.70423
18.09256
10.53369
-5.60816
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
11.8161
27.4950
-3.38048
-8.83951
=
=
=
=
=
P-value
[.000]
[.000]
[.001]
[.000]
analytic second derivatives
WTATPDOT
.
.
.
22.17244
21.60753
17.42998
19.51695
7.41069
-2.16448
PERWTAT
.
.
.
1.37835
2.66120
-2.27425
1.42439
-3.12300
3.44368
.989931
.979862
2.35073
-15.9756
19.8674
WTFD: Food & Beverages Sub-sector (FD)
======================================
WTMQ: Mining & Quarrying Sub-sector (MQ)
========================================
WTFD/PCONS on WEDGE (0.0), LPRTFD, URBAR
========================================
WTMQ/PCONS on WEDGE (0.0), LPRTMQ, URBAR
========================================
Equation 69
============
Equation 68
============
Dependent variable: Y1
Current sample: 1996 to 2002
Number of observations: 7
Dependent variable: Y1
Current sample: 1996 to 2002
Number of observations: 7
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.182807
.068086
.416939E-02
.199544
Estimate
.140083
.779582
-.733681E-02
-.859262
2.60157
.129545
.674786E-02
.224929E-02
.047427
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTFDDOT
.
38.34828
26.70912
27.29847
20.60873
15.48214
15.30428
9.86348
-9.23973
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
.766287
11.4499
-1.75969
-4.30612
=
=
=
=
=
.937957
.875913
2.49257
-10.2910
14.1828
P-value
[.444]
[.000]
[.078]
[.000]
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.892800
.275570
.957098E-02
.624303
Estimate
2.51024
.184213
.011695
.189467
3.35220
.069287
.010740
.358008E-02
.059834
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
2.81165
.668482
1.22195
.303486
=
=
=
=
=
P-value
[.005]
[.504]
[.222]
[.762]
analytic second derivatives
WTFDPDOT
.
.
.
22.78130
19.33169
19.46942
14.20609
2.30642
-0.16683
Standard Errors computed from
(Newton)
PERWTFD
.
.
.
-4.51717
-1.27703
3.98728
-1.09819
-7.55706
9.07290
Current sample:
121
1994 to 2001
analytic second derivatives
.628369
.256739
1.30910
-8.89664
12.7885
WTKG:Capital Goods Sub-sector (KG)
==================================
WTMQ/PCONS on WEDGE (0.0), LPRTMQ, URBAR
========================================
WTKG/PCONS on WEDGE (0.0), LPRTKG, URBAR
========================================
Equation 70
============
Dependent variable: Y1
Current sample: 1996 to 2001
Number of observations: 6
Equation 71
============
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.670536
.229389
.013326
.595308
Estimate
1.38239
.593958
-.014786
-.301868
3.33468
.056411
.558947E-02
.279473E-02
.052865
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
WTMQDOT
.
33.64137
26.50998
19.64846
17.57261
7.66970
11.79685
5.60565
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
2.06163
2.58930
-1.10954
-.507079
=
=
=
=
=
.650291
.125727
1.83512
-8.87030
12.4538
Dependent variable: Y1
Current sample: 1996 to 2002
Number of observations: 7
P-value
[.039]
[.010]
[.267]
[.612]
analytic second derivatives
WTMQPDOT
.
.
.
17.57058
10.39189
10.90578
16.44714
-0.41110
PERWTMQ
.
.
.
-2.07789
-7.18072
3.23608
4.65029
-6.01676
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.509191
.148468
.897875E-02
.424787
Estimate
.665261
.609614
-.371881E-02
.398802
3.02537
.134200
.010127
.337574E-02
.058101
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
122
WTKGDOT
.
46.96895
35.45493
23.86005
24.47327
8.09135
19.17618
12.98411
-3.23807
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
1.30651
4.10603
-.414179
.938830
=
=
=
=
=
P-value
[.191]
[.000]
[.679]
[.348]
analytic second derivatives
WTKGPDOT
.
.
.
28.16204
17.30307
4.41014
18.56363
16.30100
3.83240
PERWTKG
.
.
.
4.30199
-7.17019
-3.68121
-0.61255
3.31689
7.07047
.906482
.812964
1.15766
-8.98742
12.8792
LPRTAT on T: log-lin
=====================
WTCG: Consumer Goods Sub-sector (CG)
====================================
Equation 73
============
WTCG/PCONS on WEDGE (0.0), LPRTCG, URBAR
========================================
Dependent variable: Y1
Current sample: 1994 to 2002
Number of observations: 9
Equation 72
============
Dependent variable: Y1
Current sample: 1996 to 2002
Number of observations: 7
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
X2
URBAR
RHO
Standard
Error
.111030
.047707
.376132E-02
.269763
Estimate
.199389
.768385
-.014749
-.809934
2.45357
.153292
.473253E-02
.157751E-02
.039718
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTCGDOT
.
34.04007
22.91506
20.13837
24.74331
15.65877
21.64417
8.14247
-3.89602
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
=
=
=
=
=
.974043
.948085
1.53351
-12.6874
16.5792
=
=
=
=
=
Parameter
C
T
RHO
Standard
Error
.062399
.681439E-02
.443906
Estimate
2.28556
.121873
.122001
3.38433
.338682
.019879
.331320E-02
.057560
Standard Errors computed from
(Newton)
t-statistic
1.79581
16.1064
-3.92114
-3.00239
P-value
[.073]
[.000]
[.000]
[.003]
1994
1995
1996
1997
1998
1999
2000
2001
2002
analytic second derivatives
WTCGPDOT
.
.
.
25.79134
29.34105
11.05790
21.50567
6.75952
0.52010
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
PERWTCG
.
.
.
5.65297
4.59774
-4.60087
-0.13850
-1.38295
4.41612
123
LPRTAT
17.31388
20.66193
23.36295
26.30410
28.72036
34.19311
41.51881
42.64069
44.24358
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
36.6283
17.8847
.274836
=
=
=
=
=
P-value
[.000]
[.000]
[.783]
analytic second derivatives
LPRTATP
18.08223
20.31796
23.10566
26.10354
29.47496
33.15744
37.69588
42.95866
47.96615
PERLPRAT
4.43777
-1.66474
-1.10127
-0.76246
2.62740
-3.02888
-9.20771
0.74569
8.41381
.978354
.971139
1.43610
-11.4515
14.7473
LPRTMQ on T: log-lin
=====================
LPRTFD on T: log-lin
=====================
Equation 75
============
Equation 74
============
Dependent variable: Y1
Current sample: 1994 to 2002
Number of observations: 9
Dependent variable: Y1
Current sample: 1994 to 2002
Number of observations: 9
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
T
RHO
Standard
Error
.051066
.545122E-02
.343430
Estimate
2.51941
.078979
-.026098
3.23004
.221039
.016610
.276839E-02
.052615
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTFD
17.82696
21.24604
21.07196
21.97399
25.62643
28.60144
31.33997
31.25432
33.49866
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
49.3363
14.4884
-.075992
=
=
=
=
=
.957505
.943340
1.88579
-12.2609
15.5567
P-value
[.000]
[.000]
[.939]
=
=
=
=
=
Parameter
C
T
RHO
Standard
Error
.067348
.727659E-02
.365264
Estimate
3.14278
.049408
.043597
3.58791
.147182
.025693
.428218E-02
.065438
Standard Errors computed from
(Newton)
analytic second derivatives
LPRTFDP
18.43610
19.96873
21.55545
23.38008
25.32599
27.35389
29.57806
31.99844
34.70207
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
PERLPRFD
3.41697
-6.01201
2.29451
6.39889
-1.17238
-4.36184
-5.62192
2.38086
3.59243
1994
1995
1996
1997
1998
1999
2000
2001
2002
124
LPRTMQ
29.04420
31.08513
34.28188
34.19557
33.48686
38.09982
44.55546
42.71939
41.10653
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
46.6649
6.79002
.119358
=
=
=
=
=
P-value
[.000]
[.000]
[.905]
analytic second derivatives
LPRTMQP
29.66063
31.13439
32.73772
34.46864
36.13255
37.84636
39.90157
42.11878
44.07590
PERLPRMQ
2.12240
0.15847
-4.50429
0.79855
7.90066
-0.66525
-10.44516
-1.40595
7.22362
.851744
.802326
1.71552
-10.2973
13.5932
LPRTCG on T: log-lin
=====================
LPRTKG on T: log-lin
=====================
Equation 77
============
Equation 76
============
Dependent variable: Y1
Current sample: 1994 to 2002
Number of observations: 9
Dependent variable: Y1
Current sample: 1994 to 2002
Number of observations: 9
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
=
=
=
=
=
Parameter
C
T
RHO
Standard
Error
.060796
.647043E-02
.327180
Estimate
2.96638
.098026
.047774
3.84886
.273079
.021005
.350083E-02
.059168
Standard Errors computed from
(Newton)
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTKG
30.69949
34.69116
37.88790
45.77371
51.00583
48.40794
54.60595
64.70549
68.65273
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
48.7921
15.1498
.146018
=
=
=
=
=
.964793
.953057
1.88434
-11.2041
14.4999
P-value
[.000]
[.000]
[.884]
=
=
=
=
=
Parameter
C
T
RHO
Standard
Error
.061331
.655934E-02
.414213
Estimate
2.14019
.107771
.083280
3.10995
.300129
.015303
.255051E-02
.050503
Standard Errors computed from
(Newton)
analytic second derivatives
LPRTKGP
31.70616
34.91775
38.55854
42.50966
47.09217
51.96759
56.90967
62.83822
69.54774
Mean of dep. var.
Std. dev. of dep. var.
Sum of squared residuals
Variance of residuals
Std. error of regression
PERLPRKG
3.27912
0.65316
1.77007
-7.13085
-7.67296
7.35344
4.21882
-2.88580
1.30369
1994
1995
1996
1997
1998
1999
2000
2001
2002
125
LPRTCG
15.32520
16.07214
17.09005
19.94761
21.76385
24.44051
30.09361
31.90392
33.37572
R-squared
Adjusted R-squared
Durbin-Watson
Schwarz B.I.C.
Log likelihood
t-statistic
34.8959
16.4301
.201056
=
=
=
=
=
P-value
[.000]
[.000]
[.841]
analytic second derivatives
LPRTCGP
14.57110
16.29752
18.06130
20.03902
22.40654
24.91340
27.76735
31.18648
34.59278
PERLPRCG
-4.92062
1.40231
5.68316
0.45828
2.95303
1.93487
-7.73008
-2.24876
3.64655
.978778
.971703
1.68654
-12.6316
15.9274
Summary printout of actual vs predicted
=======================================
1994
1995
1996
1997
1998
1999
2000
2001
2002
Output equations
-----------------
1994
1995
1996
1997
1998
1999
2000
2001
2002
1994
1995
1996
1997
1998
1999
2000
2001
2002
1994
1995
1996
1997
1998
1999
2000
2001
2002
1994
1995
1996
1997
1998
1999
2000
2001
2002
OTAT
14079.65039
16575.00000
18608.58984
20982.77734
22651.75000
25186.64453
28083.32227
27264.45898
26966.45898
OTFD
9460.76855
11528.09961
11920.40625
12630.64746
14619.87695
15842.33691
15676.25391
14992.69824
16086.05566
OTMQ
10943.85254
11100.50000
11624.98535
11144.33594
9945.59863
9780.22461
9944.77832
9248.74902
8591.26367
OTKG
11868.42188
12984.90039
13973.05566
16437.33984
16541.18945
15088.75586
14525.18164
15425.78809
15803.85742
OTATP
.
17573.42773
18395.25586
20633.07031
22089.65820
24743.11328
27230.61523
26175.69141
29339.04492
OTFDP
.
11297.78711
12160.53906
13176.77148
14249.83789
15281.13965
15753.70996
15156.04785
16148.01367
OTMQP
.
10954.81836
11019.53125
11008.79590
10676.24902
10485.66016
10015.13770
8957.35449
8282.19238
OTKGP
.
13659.67676
14028.16504
15176.51367
15535.13965
15741.09473
15749.54883
15165.12695
15518.03711
PEROTAT
.
6.02370
-1.14643
-1.66664
-2.48145
-1.76098
-3.03635
-3.99336
8.79829
OTCG
20549.55664
22234.19922
24401.17188
28832.26953
30830.67188
32281.02148
37078.33594
36498.08203
37410.84766
OTCGP
.
22772.10938
24649.91602
28279.98047
29951.67773
33094.22266
35887.00781
35593.33594
39215.00000
PEROTCG
.
2.41929
1.01939
-1.91552
-2.85104
2.51913
-3.21300
-2.47889
4.82254
Output deflators equations
--------------------------
PEROTFD
.
-1.99784
2.01447
4.32380
-2.53107
-3.54239
0.49410
1.08953
0.38517
PEROTMQ
.
-1.31239
-5.20821
-1.21622
7.34647
7.21288
0.70750
-3.15064
-3.59751
PEROTKG
.
5.19662
0.39440
-7.67050
-6.08209
4.32334
8.42927
-1.68978
-1.80855
126
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTATDT
.
19.38078
17.45436
2.23497
7.12537
-1.12003
1.30032
0.58809
-1.04941
POTATPDT
.
.
15.21649
9.48549
4.73945
3.48850
-3.16623
2.36040
-4.00416
PERPOTAT
.
.
-2.23787
7.25052
-2.38592
4.60853
-4.46655
1.77231
-2.95475
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTFDDT
.
34.80526
11.18665
23.01481
-5.03483
0.51441
8.86877
-0.73635
-10.23104
POTFDPDT
.
.
13.52987
13.40299
3.86194
6.99106
2.35424
-2.49471
-2.17460
PERPOTFD
.
.
2.34322
-9.61182
8.89678
6.47665
-6.51453
-1.75837
8.05644
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTMQDT
.
23.21245
9.75756
19.61574
6.43081
3.30660
11.92983
6.48037
3.77218
POTMQPDT
.
.
8.65354
14.18289
15.70304
-3.56549
-0.39494
11.01062
11.94456
PERPOTMQ
.
.
-1.10402
-5.43285
9.27223
-6.87209
-12.32476
4.53025
8.17238
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTKGDT
.
43.58293
-6.35620
6.01168
8.92214
10.07398
19.48639
-18.93607
-0.75887
POTKGPDT
.
.
9.42640
10.44079
6.43347
8.93340
1.71031
-8.78080
3.30461
PERPOTKG
.
.
15.78260
4.42911
-2.48867
-1.14058
-17.77608
10.15528
4.06348
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTMQDOT
.
33.64137
26.50998
19.64846
17.57261
7.66970
11.79685
5.60565
8.67076
WTMQPDOT
.
.
.
17.57058
10.39189
10.90578
16.44714
-0.41110
.
PERWTMQ
.
.
.
-2.07789
-7.18072
3.23608
4.65029
-6.01676
.
1994
1995
1996
1997
1998
1999
2000
2001
2002
POTCGDT
.
25.02772
13.07818
2.84849
6.88779
5.19751
-6.60083
-1.89954
1.71385
POTCGPDT
.
.
14.18451
7.72397
6.30803
4.44935
-0.49538
-7.54609
-0.35118
PERPOTCG
.
.
1.10633
4.87548
-0.57977
-0.74816
6.10545
-5.64655
-2.06503
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTKGDOT
.
46.96895
35.45493
23.86005
24.47327
8.09135
19.17618
12.98411
-3.23807
WTKGPDOT
.
.
.
28.16204
17.30307
4.41014
18.56363
16.30100
3.83240
PERWTKG
.
.
.
4.30199
-7.17019
-3.68121
-0.61255
3.31689
7.07047
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTCGDOT
.
34.04007
22.91506
20.13837
24.74331
15.65877
21.64417
8.14247
-3.89602
WTCGPDOT
.
.
.
25.79134
29.34105
11.05790
21.50567
6.75952
0.52010
PERWTCG
.
.
.
5.65297
4.59774
-4.60087
-0.13850
-1.38295
4.41612
Wage equations
--------------
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTATDOT
.
38.61598
33.98854
20.79409
18.94632
19.70423
18.09256
10.53369
-5.60816
WTATPDOT
.
.
.
22.17244
21.60753
17.42998
19.51695
7.41069
-2.16448
PERWTAT
.
.
.
1.37835
2.66120
-2.27425
1.42439
-3.12300
3.44368
1994
1995
1996
1997
1998
1999
2000
2001
2002
WTFDDOT
.
38.34828
26.70912
27.29847
20.60873
15.48214
15.30428
9.86348
-9.23973
WTFDPDOT
.
.
.
22.78130
19.33169
19.46942
14.20609
2.30642
-0.16683
PERWTFD
.
.
.
-4.51717
-1.27703
3.98728
-1.09819
-7.55706
9.07290
127
Sectoral productivity trends
----------------------------
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTAT
17.31388
20.66193
23.36295
26.30410
28.72036
34.19311
41.51881
42.64069
44.24358
LPRTATP
18.08223
20.31796
23.10566
26.10354
29.47496
33.15744
37.69588
42.95866
47.96615
PERLPRAT
4.43777
-1.66474
-1.10127
-0.76246
2.62740
-3.02888
-9.20771
0.74569
8.41381
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTFD
17.82696
21.24604
21.07196
21.97399
25.62643
28.60144
31.33997
31.25432
33.49866
LPRTFDP
18.43610
19.96873
21.55545
23.38008
25.32599
27.35389
29.57806
31.99844
34.70207
PERLPRFD
3.41697
-6.01201
2.29451
6.39889
-1.17238
-4.36184
-5.62192
2.38086
3.59243
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTMQ
29.04420
31.08513
34.28188
34.19557
33.48686
38.09982
44.55546
42.71939
41.10653
LPRTMQP
29.66063
31.13439
32.73772
34.46864
36.13255
37.84636
39.90157
42.11878
44.07590
PERLPRMQ
2.12240
0.15847
-4.50429
0.79855
7.90066
-0.66525
-10.44516
-1.40595
7.22362
1994
1995
1996
1997
1998
1999
2000
2001
2002
LPRTKG
30.69949
34.69116
37.88790
45.77371
51.00583
48.40794
54.60595
64.70549
68.65273
LPRTKGP
31.70616
34.91775
38.55854
42.50966
47.09217
51.96759
56.90967
62.83822
69.54774
PERLPRKG
3.27912
0.65316
1.77007
-7.13085
-7.67296
7.35344
4.21882
-2.88580
1.30369
1994
1995
1996
1997
1998
1999
2000
2001
2002
128
LPRTCG
15.32520
16.07214
17.09005
19.94761
21.76385
24.44051
30.09361
31.90392
33.37572
LPRTCGP
14.57110
16.29752
18.06130
20.03902
22.40654
24.91340
27.76735
31.18648
34.59278
PERLPRCG
-4.92062
1.40231
5.68316
0.45828
2.95303
1.93487
-7.73008
-2.24876
3.64655