The theoretical models of the NEG - which have been chosen as the theoretical framework for this research - make general predictions about the location of industry and regional specialization in the course of economic integration. Proceeding economic integration is modelled through falling trade costs. While these are falling between countries due to the measures implied by a customs union and technological progress in the transport industry, they are also falling between regions within a country, not only due to the latter, but also due to infrastructure improvements such as those implied for beneficiaries of the European regional policy and the respective pre-accession instruments54 . At

53 This period was later accelerated for most sectors, except for certain ones considered sensitive.

54 In the case of Hungary and the other CEECs, this was called !SPA.

initially relatively high trade costs, dispersion of industry will prevail (symmetry in two region models), firms will locate close to consumers; at medium-level trade costs, centripetal forces will reinforce agglomeration, centre-periphery structures will prevail, regional specialization will be highest; at relatively low trade costs, firms and workers will disperse again, a lower degree of regional specialization will be entailed.

The models by *Ludema & Wooton (1997)* - assuming inter-regional but not full migration of workers - and the model by *Puga (1999)* assuming no inter-regional migration could be used as theoretical framework. Which of these seems appropriate depends on the actual levels of inter-regional migration in Hungary (high or low).55 The models by *Krugman & Venables (1996)* and by *Livas-Elizondo & Krugman (1996)* are also suitable for the international context involving regional implications in a country with more than one region.

### **Table 4: Formulation of the hypotheses**

Source: Own formulation.

Questions which are posed by this empirical research are: Which influences did European integration have on regional development in Hungary? The location of industry and changes in the degree of industry agglomerations, were they shaped by the influence of European integration, or by domestic economic policies, or by other factors such as local labour markets, other locational advantages, or the role of history? Which development did agglomeration of manufacturing industries undergo during increasing integration with the EU? Did integration and trade reinforce industry agglomeration in the manufacturing sector? Did agglomeration

55 The inter-regional migration in Hungary is analysed in detail in chapter 3, sections 3.5.1, 3.5.2, and 3.5.3.

decrease? Were there perhaps phases of both? How did this affect the specialization of regions in Hungary? And where in space did the concentration of manufacturing industries take place? Was there perhaps a direct influence of the trade provisions of the Europe agreement on manufacturing concentration or regional specialization in Hungary? In light of these questions, the hypotheses shown in **Table 4** are formulated.

Most previous empirical research on industry agglomeration in CEECs only remained at the country level, such as *Hildebrandt & Worz (2004),* or - where it went to the regional level - such as that by *Traistaru et al. (2003)* - used data series which were too short or even contradictory. Hence, there is a need for in depth empirical research on the industry and regional level for individual countries in Central and Eastern Europe such as Hungary. The relatively long time period from 1992 to 2008 will be one of the advantages of the research results of this research.

A unique contribution of this research in measuring industry agglomeration in Hungary is that it applies six different concentration indices to the same set of data, thus allowing for a comparison of them. All of these indices were applied to Hungarian employment data by manufacturing subsector by region. The relatively long time series is the longest available and reliable at the time of writing using the Hungarian regional sectoral employment data series of the HCSO, namely 1992 to 2008. Regional specialization is measured also by means of calculating appropriate index figures.

The hypothesis of this research will be approached as follows by the remaining chapters of this research: Chapter 3 will evaluate the hypothesis by means of empirical descriptive analysis of main economic and regional indicators for Hungary. Chapter 4 will analyse industry concentration in Hungary by means of empirical calculations of 6 different concentration indices. Chapter 5 will present empirical results on the development of regional specialization in Hungary over the period. Chapter 6 will proceed with regression analysis on industry concentration and regional specialization. Finally, chapter 7 concludes this research with a view to policy recommendations regarding European regional policy and a perspective on future developments. Before proceeding with the remainder of this research, an overview of the data used as a basis shall be given in section 2.5.

## **2.5 Data used for this research**

For the purpose of analysing industry agglomeration and regional development in Hungary in the context of European integration, I have chosen to use *Hungarian*

*data* provided by the Hungarian Central Statistical Office (HCSO). This was decided in order to obtain data on the longest l,ossible time period for my research, and also the most recent ones at the end. 5 This time period was 1992 to 2008 for the most important data series. 57 According to the opinion of Hungarian researchers which I interviewed on this subject, data from 1992 onwards are largely trustworthy by international standards. Therefore, that year has been chosen as a starting point. The main series used were the "Regional Statistical Yearbook of Hungary", the "Statistical Yearbook of Hungary", and the "Yearbook of External Trade".58 The data for the years 1992 until 1998 had to be typed into the computer manually from the printed format, as electronic versions started to be available only from 1999 onwards. For 2006 until 2008, the data were available from a special database of the HCSO. As the methodology had changed slightly, the data for the latter three years were adapted for consistency.59

As this research deals with Hungary at the regional and industry level on the one hand and the EU on the other hand, there was no need to use data from Eurostat which would make several countries internationally comparable. More importantly, data on foreign trade provided by the HCSO also include exports from the socalled ,,customs-free zones" while Eurostat data do not. ,,Customs-free zones" were an important economic factor for Hungarian industry during the research period, as they contributed a share of 43% of Hungarian exports in 1999.60

With regards to the industry level, the manufacturing sectors have been chosen as the level of detail, as the Europe agreement, the institutional framework of preaccession integration and trade, covered mostly manufacturing products. For the entire period 1992 to 2008, the HCSO provides data for 8 sectors (more sectors would have been available only from 1999 on). These are shown in **Table 5.**

56 Harmonisation and verification of national data by Eurostat typically takes around two years at least before Eurostat publishes them.

57 The data for 2008 for that series have been released on the HCSO website on 20th of February 2009.

58 Where different sources are used in this research, such as Eurostat, the European Commission, or some by the Hungarian government, this is explicitly mentioned.

59 The difference was that from the electronic platform, only data for the manufacturing subsectors per region for enterprises with more than 4 employees and also institutions of central and local government, social security and non-profit institutions were included. This makes a difference mainly for the capital of Budapest. Hungary is a rather centralised country by its administrative tradition. To abstract from the latter three categories for the data of Budapest, the difference of the employment for the overlapping years of the two data series (2003, 2004, 2005) was taken as a %, and then this divergence was averaged and used as a corrector on the raw figures for Budapest for the years 2006 to 2008.

60 For more details on these customs-free zones, see the summary of the article by *Vadasz (2000)* in section 3.2.4.

## Table 5: The 8 manufacturing sectors used in this research

Notes: \*DA-DN: Hungarian TEAOR-classification \*\* 15-37: Corresponding NACE 2-digit classification.

Sources of data: HCSO: Statistical Yearbook of Hungary, Regional Statistical Yearbook of Hungary, Statistical Yearbook of External Trade, Statistical Yearbook of Industry and Construction, subsequent years from 1992 to 2005 for each; database economy on HCSO website for 2006 to 2008, Budapest.

Where data on external trade are used, these were published according to the SITC classification. These also had to be entered manually into the computer for the years 1992 to 1998. In order to make these categories compatible, which by far exceeded the 8 sector classification in **Table 5,** and also suitable for common empirical and regression analysis, I have converted the SITC data into the 8 manufacturing industries used in my research according to the official product category descriptions of both classifications.

As one of the main new elements of this research is that it looks at the regional level when analysing industry concentration and specialization, the regional level of detail shall be explained here. It was chosen to use the 20 Nuts-3 regions in Hungary as the relevant level. Hungary consists only of 7 Nuts-2 regions; using those would have made the data base too small. Further, the Nuts-2 regions in Hungary are merely "planning-statistical regions" with no political structure behind, whereas the Nuts-3 regions correspond broadly to municipal district administrations. These 20 regions will be presented in their geography and main economic characteristics in chapter 3, section 3.3.

Furthermore, for the purpose of deepening the analysis with respect to border regions and the importance of closeness to EU markets, the 20 Nuts-3 regions were categorized into four groups of regions according to the actual length of their dominating border: 61 7 internal regions (INT), and among the 13 border regions 2 regions bordering the EU-15 (BEU), 6 regions bordering CEECs (BCE), and 5 regions bordering external countries (BEX).

For the purpose of analysing industry concentration and regional specialization, I have used data on manufacturing employment per region in the 8 sectors. To give an illustration, this produced a data sheet for each region, say Budapest, with the 8 industries as rows and the 17 years 1992 to 2008 in the columns, with 20 such data sheets for the 20 Nuts-2 regions as a data base for the empirical calculations. My idea to use output/production data in addition to these employment data proved to be unrealisable, as production data are not published at the regional level for the 8 subsectors by the HCSO. 62

At this point, it shall be pointed out that one of the specialities of my research is that it is based on regional sectoral manufacturing employment data as a basis for calculating industry concentration in Hungary, and not at sectoral country-wide data for a group of countries such as EU-15 or a set of CEECs, as most previous empirical studies in the field.

Data on the actual level of tariffs and quotas which prevailed during the period, for which the Europe agreement between Hungary and the EU was in force, are not publicly available. Even authors working within the European Commission, but in DGs other than the Customs DG or DG Trade, could not get access to such data (not even aggregates from the detailed customs code level). The data which I have found have been summarized in section 2.3. Those, however, are not suitable in any way for a chronological analysis over the entire period covered by this research.

I would have liked to also include into my analysis data on NTBs, as in my view this multitude of potential obstacles to trade in the norms, standards, packaging and safety rules areas are at least as important in the economic reality of internationally trading firms as tariffs and quotas. In a series of reports on the effects of the Single market done for the *European Commission (1998),* the importance ofNTBs and other regulations on different manufacturing sectors such as the pharmaceuticals industry, the motor vehicles sector, food, drink and tobacco, and telecommunications equipment are examined in a descriptive way and data based on firm level inquiries for selected years. Whilst some researchers

61 The km length of the 20 regional borders with neighbouring countries was provided by the Hungarian expert for Schengen implementation in Hungary, Prof. Dr. habil Janos Sallai, Colonel, Budapest. His information is gratefully acknowledged.

62 This was verified by using the series "Yearbook oflndustry and Construction", and by a correspondence with a competent data official from HCSO.

also make reference to NTBs63, there exists no set of consistent data on NTBs, however, for each year of the time period of this research. NTBs could not be used, therefore, as a variable in the regression analysis of chapter 6. In the theoretical framework of NEG models, they are assumed to be included in "transport costs" which comprise transport and other trade costs.

This concludes the overview of the relevant theoretical framework given in chapter 2, as well as of previous empirical work in the field, of the provisions of the Europe agreement, the formulation of the hypotheses, and of the data used for this research. Chapter 3 will now approach the subject "industry agglomerations and regional development in Hungary" by means of descriptive empirical analysis of economic data for the manufacturing industries and the regions in Hungary.

63 For example *European Commission (1994), Briilhart* & *Torstensson (1998),* as summarized in section 2.2.3., *Buigues* & *Ilzkovitz (1988),* and *Sapir (1996).*

Cordula Wandel - 978-3-631-75112-1 Downloaded from PubFactory at 01/11/2019 08:11:44AM via free access

## **3. Main Characteristics of Hungary's Regions and Industries**

This chapter approaches the hypothesis with respect to industry agglomerations and regional development in Hungary during European integration by means of descriptive empirical analysis. Economic developments in Hungary's regions and manufacturing sectors shall be taken into consideration as well as indicators of growing regional integration with the EU such as the development of foreign trade and foreign direct investment (FDI).

Chapter 3 is organised as follows: section 3.1 introduces Hungary and its geographic location, section 3.2 points out current macroeconomic developments and main government policies during the research period 1992 to 2008 which could have had an influence on the research subject. Section 3.3 introduces the 20 Hungarian Nuts-3 regions which shall be the general level of detail in this research. Section 3.4 presents main economic indicators of Hungary's regions, including GDP and employment. Section 3.5 analyses internal net migration at regional level over 1992 to 2008 and puts it into context with the development of manufacturing wages as well as with the housing market. Section 3.6 analyses the manufacturing industry sectors which will be the subject of further analysis of agglomeration in chapters 4 and 6. The final sections 3.7 and 3.8 analyse the integration indicators foreign trade with the EU as well as FDI. Section 3.9 draws first conclusions with respect to regional development in Hungary during the preand post-accession phase and summarises indications with respect to the hypotheses regarding agglomeration and regional specialization.

## **3.1 Introduction to Hungary and its geographic location**

Hungary had a population of 10.1 million in 200564. The average population density is 108.6 persons per km2 in 2004, population growth was at -0.2 (average annual percentage change from 1995 to 2004). GDP per capita in PPS was at 64.0% of the EU-27 average in 2004. Annual GDP growth was 4.9% on average from 1995 to 2004. As to the structure of the economy, overall employment in industry accounted for 32.5% of total employment, agriculture for 4.9% of total employment in 2005, and the services sector for 62.7%. R&D expenditure was 0.4% as a percentage of GDP in 2004 *(European Commission 2007).*

64 This was the most recent census date by the time of writing, May 2009.

Hungary is located in the Central and Eastern part of Europe and is as such included in the abbreviation of CEECs - Central and East European economies. The map in **Figure 4** presents the geographic location of Hungary.

**Figure 4: Geographic location of Hungary among neighbouring countries**

Source: Own map based on regiograph as tool.

Hungary has 7 neighbouring countries. Starting in the North-West and going counter clock-wise, these are Austria, Slovenia, Croatia, Serbia & Montenegro, Romania, Ukraine and Slovakia. The map also shows the Danube river, a main economic and transport axis passing from the North through the capital of Budapest to the South where it flows - after passing further countries - into the Black Sea. It also shows Lake Balaton, a recreation and tourism area, as well as the second main river, Tisza.

The Hungarian territory was settled around 900 A.D. King St. Stephen (1000- 1038) converted himself to Christianity and was the first Hungarian king crowned by the Pope. Since that time, Hungary is divided into more or less the same regional structure prevailing up to this date (today, these are called Nuts-3 regions). Later on, Hungary became part of the Austro-Hungarian empire reigned by the Habsburgs from 1437 until the end of World War I. The Trianon peace treaty of 1920 placed one third of all Hungarians outside the new borders, causing a legacy of Hungarian minorities abroad up to today. The one-party system of the Stalinist type prevailed since 1949, which was challenged by the Hungarian people's insurrection led by Imre Nagy in 1956.

Economic reforms started in 1968, combining elements of a market economy with the existing planned economy. This was followed up in the 1970s and 1980s - including then reforms in the housing market leaning to private ownership - and developed into a special form called "goulash communism". The Hungarian people's voice became louder and louder in demanding a pluralist democracy in 1989 such that the break-down of the communist party became unavoidable. The opening of the Hungarian borders to Austria in the summer became instrumental to the fall of the Berlin Wall in November 1989 and to the reshaping of regimes over the whole Eastern part of the continent *(Hantke 199565).*

Hungary was declared a parliamentary Republic on 23rd of October 1989. At the same time, the transition from a socialist-planned economy to a functioning market economy got a new impetus. A successful transformation process from a state-owned to a functioning market economy based on private enterprises, which the CEECs were undergoing each in a different way, had to include 3 common elements: (i) the privatisation of the state-owned enterprise sector; (ii) the liberalisation of prices, foreign trade, and of market entry for new companies; and (iii) the stabilisation of prices. The latter alludes to the upward correction in prices upon their liberalisation, which in Hungary was of the order of +35%, diminishing the large monetary savings which in former socialist countries were much larger than the supply of goods offered. The size of the correction was much less in Hungary than in Poland (+585%) for example, which was due to the fact that in Hungary, prices had been liberalised stepwise since 1975 and a large part was free by 1989 already *(Eger 2000).*

The political events in 1989, both in the EU and at the national level, opened the way for Hungary's increasing integration with the EU. The institution of the Europe agreement entered into force in February 199266, opening up trade for the bulk of manufacturing goods, and Hungary applied for full membership in 1994 and became an EU member state in 2004.

65 Hantke is the maiden name of the author Cordula Wandel; Hantke (1995) pp. 48-51.

66 The trade provisions entered into force at this date, which are the essential for this study' s economic analysis; the institutional provisions required ratification in all EU member states and followed two years later.

Hungary has a three year system of professional schooling and training on the job for blue collar workers67 and professional and university education for white collar workers. The previous is perhaps one of the strengths of the Hungarian manufacturing industry- which is in the centre of this study's analysis of agglomeration developments - which gave Hungary a competitive advantage over most other CEECs and which resulted in the massive FDI-inflows in the later 1990s and early years of this decade, resulting in the largest FDI stock among CEECs.

## **3.2 Macroeconomic developments in Hungary**

In order to be able to put the analysis of agglomeration in the manufacturing sector in Hungary - which shall be analysed in detail in Chapter 4 - into the right context, this section seeks to present Hungary as a country from the macroeconomic sight. Section 3.2.l gives an overview of selected macroeconomic data of Hungary in recent years, while section 3.2.2 provides an international comparison to enable an evaluation in context with other Central and East European economies.

## **3.2.1 Overview of macroeconomic characteristics**

An overview of selected macroeconomic data of Hungary is given in this section for the years 2006 to 2009. The figures result from the spring economic forecast of the *European Commission (2009a),* values for 2009 and 2010 are forecasts. Real GDP growth in Hungary had declined from 3.9% in 2006 - when this was above the EU-25 average - to very low levels by 2008, with a severe contraction of -6.3% expected due to the world economic crisis for 2009.

Inflation in terms of the harmonised consumer price index was relatively high in Hungary. Between 2004 and 2008, it had been around 6% on average. The unemployment rate was 6.0% in 2008, slightly lower than the average for the EU-27. The activity rate in Hungary is around 60.8%, rather high in the EU comparison. This signals a high participation rate of women in the labour force.

Continuing with the main indicators of **Table 6,** the general government deficit had been an extraordinary -9.2% in 2006. Government gross debt amounted to 65.8% of GDP in 2007, but rising rather fast to an alarming 80.8% forecast for 2009, due to expansionary fiscal policies and declining tax revenues during the severe economic recession. With respect to Hungary's relations to the rest of the

67 System of "duale Berufsausbildung" stemming from the times of the Austro-Hungarian empire.

world, Hungary showed a current account deficit of -8.4% in terms of GDP in 2008. Exports to the world grew by 15.9% in 2008, yet for 2009 a decline of -11.9% is forecast compared to previous year levels. With respect to the attractiveness of Hungary for international economic investments, a look at real unit

**Table 6: Overview of selected macroeconomic data for Hungary 2007 to 2010**

\* forecast

Source: *European Commission (2009a):* Economic forecast spring 2009, **4th** of May 2009.

labour costs shows a decline or moderate growth from 2007 to 2009. Long-term interest rates in Hungary are higher than in the Euro area, with 8.2% p.a. in 2008. Hungary's real effective exchange rate, of the Hungarian Forint (HUF), has suffered a severe depreciation in international terms by half its value up to 2008 (year 2000 value set= 100), which presents a disadvantage for Hungary's economy, affecting firms and private persons severely.

The so-called *Balassa-Samuelson effect68* taken from neo-classical theory may explain part of what happened during European integration of Hungary and other

68 The Balassa-Samuelson effect model was developed in 1964 by both Bela Balssa and Paul Samuelson, working independently, and submitting their work simultaneously to

CEECs *(KjW 2004).* The adjustment process taking place in the CEECs during economic catch-up and trade integration with the EU-15 brought about this effect due to different productivity development in the sector of tradable and nontradable goods in the accession countries69. The European Central Bank estimates the order of magnitude of that effect in the 1 to 2 percentage points range even for countries undergoing very rapid traded-goods productivity growth *(Deutsche Bundesbank 2001).* The effects of rising price levels coupled with a slowdown in productivity growth and a loss of competitiveness of exports to EU markets have indeed been observed in Hungary and other CEECs in the early years of this millennium.70

## **3.2.2 GDP growth in Hungary and other CEECs**

This section puts Hungary's GDP growth performance into an international perspective. For Hungary, annual GDP growth rates ranged from 4.1 % in 2005 in real terms to only 0.5% in 2008, with a severe decline of -6.3% forecast for 2009, worse than its neighbours. Table 7 shall put these developments in context with other CEECs.

**Table 7** presents real GDP growth rates for the CEECs which became member of the EU in 2004 for the years 2005 up to 2009. As the comparison shows, Hungary is a country only ranging in the middle to low rates field with respect to real GDP growth during this period.

different journals. It is sometimes referred to as the Balassa-Samuelson "hypothesis" instead of the "effect".

Table 7: GDP Growth rates in large CEECs of the EU-27 for 2005 to 2009 (percentage change over previous year).

\* values for 2008 provisional, for 2009: forecast.

Source of data: European Commission (2009a), Economic forecast spring 2009, **4th** of May 2009.

From 2005 to 2008, real GDP growth rates in Bulgaria, the Czech Republic and Slovakia reached the levels in the catch-up scenarios of the early 1990s such as that by *Hamilton & Winters (1992).* These scenarios for a future membership of CEECs in the EU, born out of their political desire for stability and economic wealth, set out as target real GDP growth rates of between 6 to 7% annually. In this case, it would take the CEECs between 15 and 20 years to catch up with welfare levels in the EU of those times (then of only 12 member states). In the case of lower real average growth rates in CEECs, between 3 and 4% annually, it would take these countries 30 to 35 years to achieve the same. 71 According to the current economic data presented in **Table** 7, Hungary and to a certain extent Poland were rather in the latter range with respect to real GDP growth rates for the period 2005 to 2008.

A more recent study on cohesion with the EU average by *Hallet (2002)* was based on country-level GDP growth for the period 1995 to 2001. This study found that the overall speed of convergence was rather slow. A differentiation was made between the poorer CEECs which tended to grow faster and the richer CEECs which tended to grow slower. Among possible explanations for the speed, factors such as the rate of productivity growth, macroeconomic stabilisation, the design of the national transition process and the geographic location in Europe played a role.

71 The other side of Eastern enlargement concerns the economic effects on the current EU member states. Various studies have been made on these effects, the figures given by the European Commission seemed rather optimistic with hindsight. Straubhaar (2004) also warned about too optimistic expectations, while pointing out the opportunities to be ceased.

### **3.2.3 Main government policies with economic or regional implications**

In this section, main or special government policies of Hungary shall be taken into consideration in order to identify any influences which could have worked on agglomeration and regional specialization in the sense of the hypotheses. Hungary was the first Central and East European economy to start economic reforms for a transition towards a market economy. Hungary introduced a two-tier banking system already in January 1987 and has recapitalised banks during reform of the banking system in the 1990s, ending a period of credit-crunch and high real interest rates for the economy. Hungary introduced a value added tax in 1989 to replace the old system of cumulative tum-over taxes, and further simplified the system in 1994. The personal income tax is progressive with 6 zones. The corporate income tax system foresees various exemptions and tax benefits for foreign investors. The law describing all forms for economic enterprises was initiated in 1988 and sparked a wave of new enterprise start-ups from 1989 onwards *(Hantke 199572).* The legislation protecting foreign investors (FDI) is at international standards, including tax benefits, free capital transfer in foreign currency and repatriation of profits and investments. The Hungarian policy towards privatisation is dealt with in section 3.7 in context with foreign direct investment.

Hungary is regarded as a functioning market economy since the 1997 Regular Report of the European Commission. In 2001, the privatisation process was viewed as almost completed. An extraordinary export performance in the late 1990s provided the proof for a high level of international competitiveness. In order to widen the economy's growth base and to address social problems, the Szenchenyi plan focused on structural reforms for the period 2001 to 2006. The territorial focus was to open up the economically less developed Eastern part of the country, including motorway development, home construction, tourism, **R&D**  and employment policies *(European Commission 2001).* More precisely, the Szechenyi plan - which was elaborated mainly during the year 2000 - started a ten-year industrial parks programme. The number of industrial parks grew to 146 after the end of the first year in 2001, on track towards the goal of 250 industrial parks by the end of the decade *(The Hungarian Economy 1/2002).* The aims of this programme are also supported by the EU's structural funds in the pre- and post-accession phase.

A specific Hungarian policy which had great success is that of the customs-free zones. Industrial customs-free zones contributed to a rising share of Hungarian

72 *Hantke (1995),* pp. 57-58.

exports, from 18.1% in 1996 to 43% by the end of 1999. For manufacturing exports, those from customs-free zones even exceeded those from the rest of the country in that year. Hungary's top ten exporters operated in eight industrial customs-free zones and produced 35% of Hungary's total exports in 1999; the 43% of total exports quoted above were produced by 101 companies operating in customs-free zones. According to a contemporary map, the majority of these industrial customs-free zones is located in the Western part of the country, some in the central part surrounding the capital Budapest, and some are located in Northern Hungary and the East near the border to Ukraine, almost none are in the Great Plains area *(Vadasz 2000).*

The policies of customs-free zones as well as the industrial parks programme under the Szechenyi plan are both liable to have had an influence on the location of manufacturing industry in Hungary. As such, they might have influenced the degree of agglomeration or dispersion of individual sectors, especially those with high economies of scale and those with a strong export orientation (see Figure 19 in section 3.6, and section 3.7). The two policies might also have contributed to the observed development regarding regional specialization (see Chapter 5). Finally, the importance of exports from customs-free zones played a role in the choice of Hungarian data over other data sources as a basis of this research (see section 2.5).

## **3.3 Hungary's regional set-up**

This section shall introduce the territorial set-up of Hungary. A description of the regional set-up will be given, and particularities of the regions will be described. These shall be examined with a view to finding any influences which could have worked on agglomeration or regional specialization as in the hypotheses. Main economic indicators of the regions will be presented in the following section 3.4.

Hungary used to be divided into three large regions: Transdanubia in the West, the lands dominated by the presence of the river Danube, Central and Northern Hungary in the middle, and the agricultural Great Plain region to the South. Since the territorial reforms during the process of increasing integration with the EU, Hungary is organised in 7 Nuts-2 regions and 20 Nuts-3 regions. It shall be pointed out that these are merely planning-statistical regions, i.e. no regional government structure is behind them (unlike the German Lander and Landkreise, for example). **Figure 5** shows a map of the Hungarian Nuts-2 and Nuts-3 regions.

In this research, the focus will be on the 20 Nuts-3 regions in order to take account of a sufficient level of detail and broadness of data base for the calculations later on in chapters 4, 5 and 6.

**Figure 5: Hungary's 20 Nuts-3 regions and 7 Nuts-2 regions.**

Source: Own map based on regiograph tool.

In the following paragraphs, selected characteristics judged of economic importance will be pointed out for each of the 20 Nuts-3 regions (the information is drawn from *Eurostat 200873* unless indicated otherwise):

**Budapest** is the capital of Hungary, occupying 0.6% of the country's territory yet holding a population of 1.7 million in 2005 or 16.8% of the country's total. The international airport, several railway stations and the crossing of main motorways make it also an important hub for national and international transport. The central government and administration as well as many company head quarters are located here. **Pest** region is surrounding Budapest, forming together with the capital **CENTRAL HUNGARY.** Pest has a relatively low unemployment rate due to

73 Eurostat (2008): Portrait of the Regions, drawn from the Eurostat Website on 09.09.2008. While the text in that portrait was finalised in March 2004, most economic data such as employment and wages dated apparently from 2001 or 2002.

the proximity of Budapest. The region is characterised by an economy capable of development, but with inadequate basic services (according to *Eurostat 2008).*  This perhaps can explain the much lower GDP at PPS than in Budapest (see also Figure 8 in section 3.4).

Passing towards the Western part of the country, to **CENTRAL TRANSDANUBIA, Fejer** was settled in Roman times and became the coronation and burial site of the Hungarian kings in the Middle Ages. Three important factors played the most important role in placing Fejer among the fastest economically developing regions: its favourable geographical location, the qualified labour force, and an infrastructure which helped to attract investments. **Komarom-Esztergom** was the birthplace of King St. Stephen, the first king of the Hungarians (see section 3.1 ). Almost half of Hungary's exploitable brown coal deposits are found in this region, yet the mining sector is in decline due to geological conditions of the reserves. The presence of the Danube encouraged the establishment of industrial activities requiring large quantities of water. The region's economic structure is decidedly industrial. Its metallurgy, food processing, machine and chemical industries are particularly advanced. The influx of international capital has been remarkable, with 550 companies with foreign interests operating there, including Japanese Suzuki automobile. **Veszprem's** rich mineral deposits make it Hungary's most important supplier of many basic raw materials, most importantly bauxite. Unemployment was lower than nationally, and industry is diversified and export-oriented (in 2002, 84% of sales of the machinery and equipment industry went abroad).

Passing to **WESTERN TRANSDANUBIA,** the region **Gyor-Moson-Sopron** shows a good economic performance thanks to its considerable foreign economic connections and a highly qualified labour force. The region's border situation with Austria and Slovakia is more of an advantage than a hindrance. The principal branches are engineering, food-processing and light industries. The country's largest exporter, AUDI Hungaria, is also located here. **Vas** is a region with economic variety and world class engineering. Wages and salaries are relatively low compared to the national average (4,289 Euro per month in 2001 as compared to 4,832 for Hungary as a whole). **Zala** borders on Croatia and Slovenia and is in a good geographical situation, including that it holds a part of Lake Balaton, yet with infrastructural problems, namely a dense road network but no motor ways. R&D requires more funds in this region, as only 0.3% of the total Hungarian personnel of the 19 regions (except for Budapest) were working there.

In **SOUTHERN TRANSDANUBIA,** the region **Baranya** is under a submediterranean climatic influence which is unique in Hungary. The settlement structure is characterised by small villages. The region's capital Pees is a university city with

160,000 inhabitants and is set to become European Capital of Culture in 2010. The unfavourable transportation links to Baranya region were obstacles to economic development in the past; the government has promised, however, to complete the M6 motorway to the city by 2008 *(The Hungarian Economy IV/ 2005).* **Somogy** is a forested region between Lake Balaton and the river Drava. Significant oil fields have recently been discovered and there are plans to commence production. The services sector is the biggest employer there. Job losses during the transition to a market economy affected the region's most important economic branches, with the highest number of dismissals in agriculture with the closing of the large agricultural and food processing enterprises, but engineering was also hard hit. **Tolna** is characterised by a low population density, the presence of Hungary's only nuclear power plant, and a good agricultural base. More than 40% of manufacturing employment is concentrated in the production of textiles, leather and apparel products.74 Tolna is lacking both foreign and domestic capital, making economic development extremely difficult. Waste water treatment and nuclear waste storage are environmental problems of the region.

**NORTHERN HUNGARY** is the mountainous Northern part of the country. Northern Hungary was the centre of Hungary's heavy industry under the former CMEA system. The caves of the Aggtelek karst have been declared a World Heritage Site by UNESCO. Since the start of restructuring in 1989, unemployment is consistently high. There are about 25 industrial parks located there (BOSCH is one of the large employers). Infrastructure is a key issue, with the M3 and M30 motorway connections needing further construction *(The Hungarian Economy /V/2004).*  **Borsod-Abauj-Zemplen** is the second largest region by its territory. It was mining, the concentration of iron and steel, and the machine industry that determined the direction of economic development in the past. An economic crisis in this region began in the 1980s and peaked in 1993. The region's Northern strip adjoining Slovakia is a backward agricultural region with very little industry, undeveloped infrastructure, and a small entrepreneurial sector. South Borsod - which includes the volcanic Zemplen hills with the Tokaj wine region - is a more advantaged agro-industrial area. The level of FDI is less than would be expected given the region's area and population size. In **Heves,** heavy industry was established in the 1950s and 1960s. Today, the region has a qualified workforce but not enough jobs such that high unemployment affects the middle-aged the most. Among the largest companies many are foreign owned. **Nograd** is a mountainous region with industrial traditions, but a shortage of both domestic and foreign capital. Metallurgy and metal processing are the most prominent branches today.

74 The 40% figure was taken from Mafioli (2003), all the rest is from Eurostat (2008) again.

Passing to the South-Eastern part of Hungary, the **NORTHERN GREAT PLAIN, and Szabolcs-Szatmar-Bereg** is situated in Hungary's East, bordering Ukraine, Slovakia and Romania. It is a region with economic problems, no motorway connection, a low standard of living and high unemployment. The population is relatively young, with 35% below the age of 25 in 2003; the activity rate was 9.4 percentage points below the national average, and youth unemployment with 15.2% is one of the highest in Hungary. Radical reforms in agriculture began in 1990 with a drop in livestock, yields and labour force. **Hajdu-Bihar** is characterised by a flat landscape and a low population density. Unemployment is high as labour supply exceeds demand particularly in agriculture and in construction and steel production as well as in administrative-type jobs. The average wheat yield has decreased in the past decade due to natural and social reasons. **Jasz-Nagykun-Szolnok** is located in the heart of the Great Plain and has the most continental climate in Hungary, which means the hottest summers, coldest winters and highest temperature fluctuations, low precipitation and common droughts. The settlement structure is typical of the Great Plain, with settlements sprawling, far from each other, and tending to be quite populous. The region is full of contradictions, bearing at the same time the marks of economic crisis and of prosperity, and it is still searching for the way towards development *(Eurostat 2008).*

In the **SOUTHERN GREAT PLAIN,** the region **Bekes** is predominantly agricultural with 70% of the area being arable land. The region lost its advantageous position held previously during the Austro-Hungarian empire due to the failure to switch to intensive agriculture caused by a lack of capital two or three decades ago. Throughout history, the region has been destroyed and depopulated several times. Industries present today are those which require large quantities of cheap, unskilled labour (textiles and shoe making, building materials). The region has a low purchasing power, wages are 78% of the national average, and 30% of the unemployed are under the age of 25. **Bacs-Kiskun** is the region with the largest territory in Hungary. The flexible structure of its economy has been able to cope with the challenges of the transition period and has attracted considerable foreign capital. The region has an important place in Hungary's food industry exports. One of the biggest problems, however, is that of the water supply on the sand tableland where desertification threatens the region's traditional agriculture. Finally, the region **Csongrad** can most accurately be described as an industrialagrarian region. While the soils are predominantly agricultural, the region provides three quarters of the national crude oil and two-thirds of the national natural gas production. Manufacturing is by far the most important industry branch of the region. The region's **R** & D outlays represented 9% of the national total and 75% of the 19 regions (without Budapest). Although well disposed by its infrastructure

to serve as a multi-function logistic centre, a disadvantage for Csongrad has been the wage level which is comparatively higher than in neighbouring Romania and Serbia.

## **3.4 Main economic indicators of Hungary's regions**

The intention of this section presenting the main economic indicators of Hungary's 20 regions is to identify influences which could have worked on the development of industrial agglomeration or regional specialization in the course of European integration.

**Table 8** gives an overview over the area, population and population density of the 20 regions at Nuts-3 level and for Hungary as a whole. While the Great Plain regions are large in territory, Central Hungary and especially Budapest host the largest population. The capital region of Budapest, although the most populated with 16.85% of Hungary's total population in 2005, was the smallest in territory and the most populated, with 3,233 inhabitants per km2 - a figure almost 30 times the national average. Most other regions except for Pest, Komarom-Esztergom and Gyor-Moson-Sopron have a lower population density than the national average. Particularly low in this respect are Southern Transdanubia and the Great Plain regions in general, while Somogy is the region with the lowest population density in Hungary overall.

With respect to the area of the 20 Nuts-3 regions, there have been several territorial rearrangements in the period 1992 to 2005.75 The regions Pest, Vas and Tolna were recut in 1995 by the third decimal of a km2; in 1996, Baranya and Bacs-Kiskun were resized by 0.047 km2 • In 1999, the territory of Vas and of Hungary as a whole declined by one third decimal of a km2• In 2001, a large set of regions was resized, perhaps due to political considerations in expectation of benefits from the European regional policy. The regions concerned were Pest, again, Fejer, Komarom-Esztergom, Veszprem, Gyor-Moson-Sopron, Jasz-Nagykun-Solnok, Szabolcs-Szatmar-Bereg, and Bacs-Kiskun, again. In 2003, the regions Pest and N6grad were resized by 0.002 km2• And finally, Baranya was resized in 2005 by one third decimal point of a km2• For the purpose of simplicity, the territory recorded in 2005 will be the basis of further calculations in this research.

75 Source: Eurostat: http://epp.eurostat.ec.europa.eu.

#### **Table 8: Area and population by region at Nuts-3 level**

Source of data: *HCSO (2006):* Regional Statistical Yearbook of Hungary 2005, Budapest.

## **3.4.1 GDP**

With a view to regional development in Hungary, the next series of figures and tables will focus on income of the regions compared to the national GDP or EU average, whichever is appropriate.

Source of data: *Eurostat* Website, regional database, 7th of October 2008.

**Figure 6** shows the contribution of the seven Nuts-2 regions to national GDP (Nuts-3 level is not available). This provides a first indication as to the economic strength of the regions and to centres of industrial agglomeration. The lion share, namely 46%, is contributed to national GDP by Central Hungary, consisting of the capital Budapest and Pest region. Western Transdanubia, Central Transdanubia and Southern Transdanubia, the regions in the Western part near the EU-15, contribute together more than a quarter of national GDP. Northern Hungary, the former centre of heavy industry, contributed a mere 8% in 2005.

**Table 9** shows real GDP growth rates for Hungary and for the 7 Nuts-2 regions for the years 2000 to 2005 as well as the average over the period.76 The national

76 The figures are based on Eurostat's experts, as the HCSO does not publish these. 2005 were the most recent data available at the time of writing.

growth rates were well above the 3% soil required for net job creation according to the EU economic experts. 77 There are great variations in real growth rates at the Nuts-2 level from year to year. Therefore, the average growth rates will be evaluated here for that period. This can give an indication towards growth poles or overall declining regions in Hungary.

**Table 9: Real GDP growth per Nuts-2 region 2000 -2005\***

\* based on GDP at current prices, change in% of previous year

Notes: All figures are estimates by Eurostat; data at Nuts-3 level are not published;

data for 2006 and 2007 were not available.

Source of average rate: Own calculations.

Source of data: *Eurostar* website, regional database, 8th of Oct. 2008.

Central Hungary was well above the national average, an important contribution given the economic size of the region (see Figure 6). Central Transdanubia's real growth was also higher than the national average over the period. Western Transdanubia - contrary to what would be expected - contributed below average, which was due to two years of economic decline or stagnation, while the years 2000, 2003 and 2005 showed much higher growth rates than the national rate. The Southern Great Plain can be called a declining region, as real growth there was below the national rate in all but one year.

77 See for example *European Commission (1995):* White Paper on Competitiveness and Growth, Brussels.

## **3.4.2 Regional disparities in Hungary and other CEECs**

A further analysis and evaluation of regional disparities at the level of the 20 Nuts-3 regions is presented in this section in order to obtain a true picture of economic disparities in Hungary, including an international comparison with other CEECs.

**Figure 7: GDP per capita at Nuts-3 level at current prices (2005, in Euro)**

Source of data: *Eurostat* website, regional database, 7th of Oct. 2008.

**Figure 7** shows a map of the regional per capita GDP in Euro in 2005.78 While the capital Budapest has an exceptionally high income, the richest regions are located in the Centre and the Western part of the country. The poorest regions are located in the Northern part and in the Great Plain to the South-East of the country. The difference between Budapest with 18,807 Euro per capita and the poorest region N6grad with 4,451 Euro per capita is more than 4-fold; when comparing with the next non-capital region, Komarom-Esztergom with 10,069 Euro per capita, this is more than the double.

78 The data for 2005 were the most recent available at the time of writing.

The next part will focus on GDP of the regions measured in Purchasing Power Standards (PPS) for groups ofregions, according to their location in the country.

#### **Figure 8: Comparison of GDP per capita in PPS for the 11 Western and Central regions, 1995 and 2005, as** % **of national average**

#### **Source: Own calculations.**

Source of data: *Eurostat* website, regional database, 7th Oct. 2008.

The graph in **Figure 8** shows the relative position of 11 Western and Central Nuts-3 regions with respect to per capita income in PPS measured relative to the national average (=100) in the years 1995 and 2005. This graph includes the regions with above average income, mainly Budapest with 213% in 2005, Komarom-Esztergom with 114% in 2005 and Gyor-Moson-Sopron with 111 % in 2005. Comparing 1995 and 2005, only four regions (even out of the 20 total regions) improved their position with respect to the national average, namely Budapest, Pest, Komarom-Esztergom and Gyor-Moson-Sopron. **Figure 9** shows the development for the remaining regions located in Northern Hungary and the Great Plain regions.

#### **Figure 9: Comparison of GDP per capita in PPS for 9 Northern, Eastern and Southern regions, 1995 and 2005, as% of national average**

#### **Source: Own calculations.**

Source of data: *Eurostat* website, regional database, 7th Oct. 2008.

**Figure 9** shows the development of per capita income in PPS relative to the national average for the remaining 9 Nuts-3 regions in 1995 and 200579• All of these Northern and Great Plain regions suffered deteriorations with respect to their per-capita income relative to the national average over the period. The poorest regions in Hungary are also among this group. The average of this group declined from 75.1 % of the national average in 1995 to 64.8% in 2005, whereas the average of the 11 Central and Western regions in Figure 8 remained almost constant at 98.1 and 98. 7% respectively.

This gives a first indication as to the development shed in Hungary between relatively prosperous Central and Western regions and the relatively poorer Northern and Great Plain regions. It will be interesting to follow this up in chapters 4 and 5 when examining whether the agglomeration of industry and regional specialization patterns have contributed to this trend.

79 Please note the different scales in Figures 7 and 8 due to technical reasons.

#### **Table 10: Regional disparities at Nuts-3 level based on GDP in PPS per inhabitant, as** % **of EU average, large CEECs (selected years)**

\* Data for 1999 (2000 not available for Hungary).

\*\* Data for 1996 (1995 not available for Bulgaria).

Note: Data for Poland at Nuts-3 level were not published at the time of writing. Source of data: *Eurostat* website, regional database, 8th Oct. 2008.

**Table 10** shows the development of regional disparities in Hungary and the large CEECs over the period 1995 to 2005, as measured for GDP per inhabitant in PPS as % of the EU average. The large CEECs consisting of many Nuts-3 regions were selected for this comparison (no data were available for Poland). Hungary had the greatest regional disparities among these CEECs, namely 40.0% in 2005. Regional disparities increased in all these CEECs over the period. The largest increase was observed in Romania with a rise of +19.l percentage points, while regional disparities in Hungary increased by +8.6 percentage points over the period.

The figures in Table 10 clearly indicate that processes of divergence were going on in all of the CEECs, to a different extent. The general widening of regional disparities during the process of economic growth - which happened during the transition to a market economy and the early phases of the catching-up processes points to new challenges for European regional and cohesion policy after Eastern enlargement of the EU. These will be discussed more in detail in chapter 7, section 7.2.

## **3.4.3 Employment and active enterprises at regional level**

With respect to employment, the major difference after Hungary's transition to a market economy is that full employment has ceased to exist, and unemployment has become a mass phenomenon. The economically inactive share of the society has increased. Nearly 1.3 million people lost their employment between 1989 and 1993. Employment levels for Hungary reached its lowest point in 1996 with a workforce of 3.9 million, a number 30% lower than that registered at the end of the nineteen eighties. 80 In the following graphs, unemployment per region as well as the workforce and industrial employment per region shall be analysed more in detail.

**Figure 10: Regional unemployment rates at the Nuts-3 level in%, 2005**

Source of data: *HCSO (2006),* Regional Statistical Yearbook 2005, Budapest.

The map in **Figure 10** shows the regional unemployment rates for Nuts-3 regions in the year 2005.81 The highest unemployment was found in Szabolcs-Szatmar-Bereg, the Eastern-most region bordering Ukraine, followed by the three regions of Northern Hungary, Borsod-Abauj-Zemplen, N6grad and Heves. The Western part of Hungary and the Centre near Budapest are characterised by low unemployment. Unemployment in the Eastern part tended to be higher than in the Western part of the country. That points to a general decline and weaker economic base in regions bordering to the former Eastern bloc countries (CMEA), and a better economic base in regions bordering or situated near the large EU market.

80 HCSO (2005): "Change of course - Hungary 1990 - 2004", Budapest, 2005, pp. 60-61.

81 The data for 2005 were also the most recent available at the Eurostat Website at the time of writing (8.5.2009).

**Table 11** shows the distribution of the labour force over the 20 regions and 7 Nuts-2 regions. Column 3 indicates the activity rates of the population. They are highest in Budapest and the Western regions and tend to be very low in Northern Hungary - where high unemployment prevails - and the Great Plain area.

**Table 11: Economic activity of population aged 15--74 per region at Nuts-2 level, 2004**

Source of data: *HCSO (2005),* Regional Statistical Yearbook of Hungary 2004, Budapest.

When looking at the number of registered and unincorporated enterprises per 1000 inhabitants at the level of the 168 subregional entities, a map at the Nuts-4 subregional level would show that entrepreneurial activity is sparse in the North-Eastern part of the country. The Western and Central part, on the contrary, has multiple patches with higher entrepreneurial activity. These centres often span across county borders, and also across Nuts-2 regional planning borders. Two things shall be retained here: Firstly, this indicates clusters of economic activity, thus in the sense of agglomeration. Secondly, these areas of intense economic activity are not identical with the administrative entities and borders.

The final statistic in this section is the development of exports in industry for selected regions and for Hungary as a whole over the period 1992 to 2005. The data show the percentage of production which is exported, i.e. between 12 and 40% in 1993, and between 57% and 82% in 2005. **Figure 11** shows the dynamic

growth of the share of production exported in five Western regions of Hungary. All of them were also regions with a large share of employment and output in industry, except for Somogy. Thus, it can be concluded that their industry is not only concentrated and efficient, but it is also internationally competitive, as its strong export orientation proves.

#### **Figure 11: Development of exports per region: export share (to world) in industry production for five Western regions and Hungary as a whole, 1992-2005**

Source of data: *HCSO,* Regional Statistical Yearbook, subsequent years, Budapest.

## **3.5 Influences on internal net migration per region**

In the theories of the NEG, workers migrate between regions due to regional differences in wage levels and living conditions. For this reason, section 3.5.1 examines internal net migration between the 20 Hungarian regions in the period 1992 to 2008. Section 3.5.2 looks at manufacturing wages per region and analyses whether there is a correlation between high wages and positive internal migration, or low wages and negative internal migration. With respect to living conditions, section 3.5.3 examines the housing market which can act either as an incentive for improving living conditions or as a restraint to attracting migrants where housing is scarce.

## **3.5.1 Internal net migration per region**

This section analyses internal net migration between the 20 Hungarian regions during the period 1992 to 2008. This is done with the research question of this study in mind, namely whether there are any indications for influences which could be linked to processes of industry agglomeration or regional specialization during this period. It is taken as granted that the data shown for internal net migration in this section comprise only the voluntary changes in residence in the sense of the definition of migration by *Fischer & Straubhaar (1994). 82*

#### **Figure 12: Internal net migration in the 20 Hungarian regions, balance over 1992 to 2008, number of persons**

Notes: 2007 and 2008: provisional figures. Source of figures for balance 1992 to 2008: own calculations. Source of data: *HCSO,* Regional statistical yearbook, subsequent years, Budapest.

82 Migration is "voluntary" in the sense that political refugees would be excluded *Fischer & Straubhaar (1994).* Political refugees fortunately do not exist at the inter-regional level within a country in the EU.

The map in **Figure 12** shows the overall picture for the 20 Nuts-3 regions with respect to internal net mi§ration between Nuts-3 regions, number of persons, for the period 1992 to 2008.8 As the map shows, the regions with positive balances are located either in the Centre of the country or in the Western part. The regions with clear negative balances, apart from the capital, are located in Northern Hungary, in the Eastern part of the country, and in the Great Plain area.

These migratory balances of the Hungarian Nuts-3 regions can be evaluated as follows: centres of agglomeration, such as those in the Western part of Hungary, show a higher economic attractiveness for industry and workers. On the other

**Table 12: Nuts-3 regions with positive internal net migration balances from 1992 to 2008, number of persons**

#### **Source: Own calculations.**

Notes: 2005 was the most recent figure for population per region available at the time of writing, HCSO Website, 8th May 2009; migration figures 2007, 2008 based on provisional data.

Source of data: HCSO, Regional Statistical Yearbook of Hungary, subsequent years, Budapest, and HCSO database on website.

83 The data by the HCSO for the internal net migration at Nuts-3 level are apparently a sum of permanent internal migration and temporary internal migration, as could be deducted in some years. No data were available, however, for the balances of just the permanent internal net migration at Nuts-3 level for all years from 1992 to 2008, although this would have been preferable and also in line with the above definition by Fischer & Straubhaar (1994).

hand, positive net migration contributes to creating a favourable environment for the location of further industry. Whether internal net migration between Nuts-3 regions and the degree of industry concentration were correlated in Hungary in · this period will be analysed in Chapter 6 (regression 1 about agglomeration).

The data on internal net migration show that 8 of the 20 Nuts-3 regions had a positive balance during the period 1992 to 2008. These are shown in **Table 12.**  By far the highest positive internal net migration was registered in Pest, with a total near 248,000 persons, or 84% of the total positive net migration of all regions. Second by numbers was Gyor-Moson-Sopron, a region bordering EU-15, with 6.74% of the total. Third place was Fejer, a region bordering Pest in the West, with 4.5% of the total. The first 5 regions listed in the table are located in the Western part of Hungary and show a clear positive migratory trend. The 3 other regions located in the North and the Southern Great Plain not only had a small positive balance, but also varied between negative and positive data for net migration from year to year. These data illustrate the agglomeration processes going on among the population in Hungary. Industry agglomeration is analysed more in detail in chapter 4.

**Figure 13** shows the overall development of positive internal net migration. The year with the highest balance in these 8 population attracting regions was the year 2000. In this year, 23,831 persons migrated to these 8 regions from within Hungary. This amounts to 0.23% of the total population of Hungary in that year. This is rather interesting. The year 2000 was the year with the largest FDI-inflow in Hungary over the period. Further, the year 1999 was found to be a turning point for the development of industry agglomeration (see Chapter 4) and for the degree ofregional specialization (see Chapter 5).

The total positive internal net migration of these 8 regions amounts to 295,277 when summed up over the period. This amounts to 2.93% in relation to the total population of Hungary in 2005. These internal net migration figures seem to point to the *Ludema & Wooton (1997)* model - which is based on inter-regional partial migration of workers - as the appropriate framework for analysing agglomeration and regional specialization in Hungary.

The results of Table 12 and Figure 13 are interesting with respect to the questions posed by this research. Wes tern regions have been attracting positive internal net migration flows over the period 1992 to 2008. They have also had the highest ratio of employees in industry over population as well as the highest industry output per inhabitant. The activity rates of the population were among the highest in the country (see Table 11), and the export share in industry output was higher than for Hungary as a whole.

#### **Source: Own calculations.**

Notes: Internal net migration for 2007 and 2008 based on provisional data.

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, and database economy on website ofHCSO.

These facts taken together can be seen as an indication for a higher degree of industry agglomeration in these regions. Section 3.5.2 will look at whether somewhat higher wage levels prevailed there.

The data for the 12 regions with negative balances of internal net migration are shown in **Table 13.** The highest loss of migrants was registered in Budapest, -125,000 or less than half of the total of these 12 regions. The number was less, however, than the inflow of positive internal net migration in the neighbouring region Pest (which had +248,000), 84 such that an urban sprawl belt phenomenon, which is typically going on around European capitals due to problems of congestion and high real estate prices, was - where it may have occurred - at least limited in extent here.

84 It is acknowledged that these data do not allow drawing direct conclusions on the destination of the internal migrants.

#### Table 13: Nuts-3 regions with a negative internal net migration balance from 1992 to 2008, number of persons

#### **Source: Own calculations.**

Notes: 2005 was the most recent figure for population per region available at the time of writing, HCSO Website, 8.5.2009; migration figures for 2007 and 2008 based on provisional data.

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest, and HCSO database on website.

The second largest negative balance of internal net migration was registered in Borsod-Abauj-Zemplen, a region of Northern Hungary, with -53,000 persons or 18.6% of the total. In third place was Szabolcs-Szatmar-Bereg, the Eastern-most region bordering Ukraine and the region with the second lowest per capita income in Hungary, with -35,000 or 12.1 % of the total.

## **3.5.2 Internal net migration and manufacturing wages**

In NEG theory, internal net migration between regions is linked to the development of real wages in the manufacturing sector. NEG theories state that workers migrate from regions with lower real wages to regions with higher real wages, causing an intensification of industrial agglomeration, as in *Krugman (199 la)* and *Ludema & Wooton (1997).* In their theoretical overview of factors causing migration, *Fischer & Straubhaar (1994)* point out that differences in wage levels have a strong positive influence on the migration decision taken by individuals. 85 In this section, data on manufacturing wages per region shall be analysed in context with internal net migration in Hungary.

#### **Figure 14: Monthly gross wages in manufacturing per region as% of national average for 2005 and 1992**

**Source: Own calculations; own graphical illustration.**  Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, Budapest.

85 *Fischer & Straubhaar (1994),* p. 100.

In an analysis ofreal earnings during the 1990s, the *HCSO (2005)* stated that real income per inhabitant reached its level before 1989 by the year 2000. In fact, real income decline from year to year by an average of -5% between 1990 and 1996, in the worst year, 1995, even by -12%, in comparison to 1989. Real income started to recover from 1997 onwards. From 2000 until 2003, real earnings rose by 7%, 14% and 9%, in 2004 they declined slightly by half a percentage point.86 The severe decline in real earnings points at the constraints in overall living standards which the Hungarian population had to endure during the transition to a market economy and during increasing integration with the EU aiming at full membership from 2004 onwards. As this research analyses industry agglomeration and regional developments, the following analysis points out regional differences in manufacturing wages for 2005 and 1992 in comparison.

The graph in Figure 14 shows the wage level for the manufacturing industry for the 20 regions in 2005 and 1992.87 For the year 2005, apart from Budapest with the highest wage level, regions with above average wages in manufacturing were Pest, Fejer, Komarom-Esztergom, and Gyor-Moson-Sopron. These 4 regions also had positive balances of internal net migration during the period 1992 to 2005 (see Table 12). Comparing wage levels in 2005 with those in 1992, the 5 regions with above-average wages in 2005 had an increase over the period, while 13 other regions suffered a decline in relative wages over the period. The most dramatic decline in manufacturing wages was registered in Szabolcs-Szatmar-Bereg, the Eastern-most region. This region also registered the third strongest negative migration balance over the period (Table 13) and had the second lowest per-capita income nationally. The data of Figure 14 seem to confirm that higher wage levels acted as a pull-factor in Hungary, attracting migrants to these regions or agglomeration centres, and lower or declining wage levels were an incentive for individuals to rather leave these regions. Figure 15 analyses the relationship of the two in more detail.

The bar diagrams in **Figure 15** and **Figure 16** show the net change in gross manufacturing wages per region and the balance of internal net migration wages over the period 1992 to 2005.88 Figure 15 shows 10 Hungarian regions for which

86 HCSO (2005): Change of course - Hungary 1990 - 2004, pp. 64-65.

87 A disadvantage is that the data of Figure 14 is that they abstract from the sectoral composition, regional specialization and industry-specific characteristics, i.e. not taking into account that some sectors like the manufacturing of chemicals and chemical products tend to be rather high-wage while other sectors like textile and clothing tend to be rather low wage.

88 More in detail, the bar diagrams of Figure 15 and Figure 16 show net migration per region (persons) which was summed up over the period 1992 to 2005, then expressed as % of a region's population in 2005 (in %), on the one hand; and the change in monthly gross

#### **Figure 15: Cumulative changes in internal net migration and gross manufacturing wages for 1992 to 2005** (%), **10 regions with both values exceeding 1** %

#### **Source: Own calculations; own graphical illustration.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, Budapest.

both values exceed 1 %, while Figure 16 shows the remaining 10 regions for which either value or both are smaller 1 %. For the first group of regions in Figure 15, the two bars for the wage development and internal net migration point in the same direction for all regions except for just two. For the second group of regions in Figure 16, the bars for internal net migration and the development of gross wages point in the same direction for *5* regions. Of those where this is not the case, for one region even both values are smaller than 1 %. Taken together, the two are correlated for 13 out of 20 values.

This is striking evidence for a positive correlation between the two factors, the manufacturing wage level and internal net migration, although this analysis does not indicate the direction of causation, i.e. falling wage levels and living standards

wages per region, (in HUF) as% of national average, for the year 2005 and the year 1992 (in%).

#### **Figure 16: Cumulative changes in internal net migration and gross manufacturing wages for 1992 to 2005** (%); **remaining 10 regions where either value is less than 1** %

#### **Source: Own calculations; own graphical illustration.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest.

are entailing negative net migration. It also confirms the theories of NEG at the basis of this research, namely by *Krugman (1991a, Ludema & Wooton (1997), Krugman & Venables (1995) and Livas-Elizondo & Krugman (1996).* This is also more or less in line with empirical findings of *Cseres-Gergely (2003)* for the 1990s only based on mobility and migration on a settlements - not inter-regional level. He found that the flow of people followed wage and unemployment differences during 1990 to 1999. He also pointed out that mobility in Hungary was among the least frequent in Europe, while migration was more comparable to that of other European countries.

#### **3.5.3 Internal net migration and the housing market**

With respect to living conditions, the section will also look at the housing market which can act either as an incentive for improving living conditions or as a restraint to attracting migrants where housing is scarce. Housing privatisation in Hungary started already in the late 1980s, even prior to the fall of the Berlin Wall. Hungarians were first introduced to the concept of private ownership by the housing privatisation scheme. Under this scheme, 622,000 tenement dwellings were sold by the year 2004. The share of tenement dwellings owned by local governments diminished below 5% of the total dwelling stock by the end of 2004 *(HCSO 2005).* 89 The regional development of the housing market was quite differentiated, however.

#### **Figure 17: Internal net migration and change in dwelling stock, Nuts-3 regions, 1992 to 2005**

#### **Source: Own calculations; own illustration.**

Notes: the data for dwelling stock were not available for 1992 to 2005, so the closest date which was 1990 was chosen.

Source of data: *HCSO,* Regional Statistical Yearbook, Budapest.

The analysis in **Figure 17** shows the development of internal net migration in the 20 Hungarian regions and of the regional housing market in context. The first bar for each region shows the change of the dwelling stock from 1990 to 2004 in %,

89 *HCSO (2005):* Change of course - Hungary 1990 - 2004, pp. 6-7.

relative to the national average. This gives an indication as to whether livin~ conditions in the region developed better or worse than the national standard.9 The second bar shows the internal net migration in % of the population in 1992 for the period 1992 to 2005.91 Calculations for net migration have also been tried in% of the population in 2005. As the differences with the shown values are only marginal, it has been decided to stick with the start year of the period for consistency with the dwelling stock.

The graph in **Figure 17** shows that for the majority of regions (13 out of 20), internal net migration and the net change in dwelling stock developed in the same direction. Among these regions, for 9 regions the development of the dwelling stock was better than the development of net internal migration. This seems to indicate that better housing conditions acted as an incentive for or did at least not hinder internal net migration. For 4 regions, the development of the housing stock lagged behind internal net migration. The latter can be seen best for Pest, the region with the strongest positive internal net migration. Pest region also fell back by 12 positions in the national ranking of regions with respect to per capita income during the period 1995 to 2005.

This analysis of the regional housing market in context with internal net migration in Hungary seems to confinn the aspect of the NEG theories that workers migrate between regions in order to improve their living conditions with respect to housing. Stronger positive net migration than housing improvement also seem to indicate that the situation on the housing market did not act as a constraint on internal net migration in Hungary during the period 1990 to 2005.

## **3.6 The sectors of the manufacturing industry**

This section analyses developments in the different manufacturing sectors in Hungary during the period 1992 to 2008 with a view to getting useful indications with respect to the hypotheses. As other CEECs, Hungary also suffered a severe decline in industry output at the start of the transfonnation period to a market economy. In 1992, the level of industrial output (IPAR92) was equal to merely two

90 For example, the stock of dwellings in Pest improved by 21.21 % over 1990; this was better by 13.93% than the national improvement of7.28%.

91 The periods differ slightly due to the limited availability of data.

92 The Hungarian IPAR (industry) comprises industry sectors C+D+E, C=Mining and Quarrying, D=Manufacturing, E=Electricity, gas and water supply. !PAR is used in this research where no data for manufacturing alone are available. It is a good approximation, however, as the manufacturing sector amounts to over 80% of Industry output or value added.

thirds of the output of 1989. It took almost 10 years for the level of output to reach and exceed that of 1989, which happened in 1998. In 2004, it was 65% higher than 15 years before. Value added of industry rose more moderately, by 35% in 2004 compared to 1989.

#### **Figure 18: Sectors' shares in total manufacturing output, percent, 1992 to 2008, based on the value of production in million HUF**

#### **Source: Own graphical illustration.**

Source of Data: *HCSO,* Statistical Yearbook of Hungary, subsequent years, Budapest.

The bar diagram in **Figure 18** shows the sectoral composition of manufacturing output in percent of each year's total output for the period 1992 to 2008. The largest increase was achieved by the machinery and equipment sector, from 17 .8% in 1992 to 51.5% in 2008. The share of food, beverages and tobacco declined from 27.4% to 10.8% over the period. The textiles, leather and clothing sector also realised a decline from 7.2% in 1992 to 1.7% in 2008. The shares of the other sectors remained more or less unchanged. The above figures exhibit a strong specialization pattern in Hungary at the aggregate level which emerged over the period.

#### **Figure 19: Export ratio (to world) as** % **of sales per manufacturing sector in 2004, based on figures in million HUF.**

#### **Source: Own graphical illustration.**

Source of data: *HCSO,* Statistical Yearbook of Hungary, subsequent years, Budapest.

The bar diagram in **Figure 19** shows the degree of export orientation of each manufacturing sector based on the share of exports (to world) in sales for the year 2004. The export ratio for manufacturing as a whole was 63.3% in that year. This is confirmation for Hungary as a country being a "small open economy", i.e. a country with a relatively small domestic market due to its population ( 10 million in Hungary) and a large dependence on export markets, manifested in a large share of sales or production being exported.93

The machinery and equipment sector achieved the highest export ratio among the sectors with 85.4%. Textiles and clothing was also above average with 71.3%. The lowest export orientation prevailed in the food, beverages and tobacco sector where only 22.3% of sales were destined for export.

93 The contrary example, i.e. not a "small open economy", would be the U.S.A. with its population of about 296 million, which exports only about 10% of its production internationally.

The extraordinarily high export ratio of the machinery and equipment sector points to the fact that production of this sector is internationally competitive. The rising share in manufacturing resulting from Figure 18 shows the rising importance for the manufacturing industry in Hungary. The two facts taken together will be kept in mind for further analysis of manufacturing agglomerations in Hungary in the sense of the hypotheses.

## **3.6.1 Regional set-up of manufacturing in Hungary**

This section analyses the regional set-up of manufacturing in Hungary. The aim is to find indications for larger manufacturing industry agglomerations in the regions. Further, the location of regions with a higher proportion of industry shall be determined which could have influenced their regional development in tum. This touches on the second aspect of the research question of this research, namely industry agglomerations and regional development in Hungary seen in context with European integration.

**Table 14** shows industrial production as a share of total industrial production in Hungary in the first column, and industry output per inhabitant as a percentage of national average in the second column.94 Budapest and the Western regions Pest, Fejer, Komarom-Esztergom as well as Gyor-Moson-Sopron each contribute a share of around 10% to national industry output, followed by Borsod-Abauj-Zemplen in Northern Hungary with 6.4%. Industrial production per inhabitant was highest in Komarom-Esztergom with almost 4-fold the national average. Four other Western regions, namely Fejer, Gyor-Moson-Sopron, Fejer, Zala and Vas, were also well above the national average with between 210% to 141 % respectively. These data indicate the importance of industry for these regions. It could also mean that industrial production in these regions is particularly efficient, perhaps due to above average presence of foreign direct investment (see section 3.8). Finally, the high per-capita production in industry is a hint towards the specialization of these regions in this field, a topic which shall be analysed further in chapter 5.

94 Industry based on the Hungarian !PAR= C+D+E, where C is Mining and quarrying, Dis manufacturing, and E is electricity, gas and water supply; Data only for manufacturing production per region are not published. For Hungary as a whole, D has the lion share in IP AR with around 80% of output.

#### **Table 14: Production of industry per region in 2004, based on million HUF**

\* **!PAR=** C+D+E, where C is Mining and quarrying, Dis manufacturing, and Eis electricity, gas and water supply.

Source of data: *HCSO (2005),* Regional Statistical Yearbook of Hungary 2004, Budapest.

#### Table 15: Development of manufacturing employment per Nuts-3 region, 1992 to 2008

#### **Source: Own calculations.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, and HCSO database on website.

Overall manufacturing employment in Hungary declined from 857,000 in 1992 to 648,000 in the year 2008, or by -24.2% over the period. **Table 15** shows manufacturing employment per region as a share of national manufacturing employment for the years 1992 and 2008. The last column shows the change in 2008 over 1992. The greatest decline was suffered by Budapest, followed by Borsod-Abauj-Zemplen, a Northern region with heavy industry. The regions of the Great Plain area also suffered a decline in their manufacturing employment shares. A rise in manufacturing employment share was strongest in Komarom-Esztergom, followed by Pest, Fejer and Gyor-Moson-Sopron.

These figures provide indications as to certain developments in the manufacturing sector in Hungary during the observation period 1992 to 2008. In particular, they hint at agglomeration tendencies towards the Western part of the country, and a relative decline of manufacturing in the Eastern part and the North of Hungary. Looking at individual Nuts-3 regions, however, the relative importance of a region as a location for industry has changed only moderately in the national context over the period. These results will be kept in mind as a first reply contrib. uting to analyse the hypotheses, namely of actual manufacturing agglomerations in Hungary in the pre- and post-accession period.

## **3. 7 Foreign trade developments**

This section analyses Hungary's foreign trade with the EU in order to find indications for factors which could have had a direct influence on manufacturing industry agglomerations and regional development in Hungary in the research period. For the purpose of this study, foreign trade data published by Eurostat are not suitable.95 Foremost, the EU data for exports from Hungary into the EU-15 do not include the goods from the so-called customs-free zones, which are an important economic factor for Hungary. A majority of enterprises with FDI and export orientation are located in such customs-free zones. The exports from these made up over 40 % of of exports from Hungary to the EU in 1999. Therefore, these exports should not be ignored (see section 3.2.3). For these reasons, I have decided to base my analysis for this section and for the later analysis in Chapter 6 on the Hungarian foreign trade data.

Historically, the geographic distribution of Hungary's exports - especially those to the EU countries - underwent significant changes *(Hantke 199596).* **Main**

95 On the Eurostat Website, data for the EU-15 (SITC nomenclature) with the CEEC group as a whole are published since 1989, thus not for Hungary bilaterally with EU-15; date of Website consultation 08.10.2008.

96 *Hantke (1995),* p. 69.

influences were certainly the overall political conditions as well as the trade regimes prevailing, notable the Council of Mutual Economic Assistance (CMEA) and its internationally planned division of labour, as well as economic conditions after the break-down of communism and of the former Soviet Union in 1989.

After the break-down of the CMEA, three effects prevailed: (1) a terms of trade effect; (ii) a market loss effect; and (iii) a removal of exchange rate subsidies effect97• The share of exports directed to the EC was 49.7% in 1938, and 10.3% to European CMEA countries (including the former Soviet Union). In 1978, the respective shares were 27.8% to the EC and 45.9% to the European CMEA countries (30.5 percentage points to the former Soviet Union). In 1988, just before

97 Trade effects according to *Rodrik (1992).*

the breaking-down of the Iron Curtain, the EC had a share of 22.6% and the CMEA of 39.3%, while in 1992, the EC had risen to a share of 49.8%, whereas the former CMEA had declined to a mere 19.3% *(Gacs 1993).* Trade liberalisation with the EU under the Europe agreement - whose trade provisions entered into force in February 1992 - has certainly contributed to the speed of redirection of Hungary's external trade flows towards the EU.

It should be kept in mind that the trade provisions of the Europe agreement covered about 80% of Hungary's prior trade with the EU countries, according to the *European Commission (2006a).* **Figure 20** shows the rapid increase in the share of exports to the EU in total Hungarian exports from 46.5% in 1993 to over 80% in 2008. The development in imports was similar, from a share of 40.1 % in 1993 to 68.0% in 2008.

**Figure 21: Composition of Hungary's manufacturing exports to the EU-27, in** % **for 2008, based on data in HUF**

**Source: Own conversion** from SITC into the Hungarian TEAOR industry classification which is generally used in this research.

Source of data: *HCSO,* database economy on website, 27th of April 2009.

**Figure 21** shows the composition of Hungary's exports to the EU for the year 2008 in tenns of the 8 manufacturing sectors which are the focus of this study. The overwhelming majority, 65%, was contributed by the machinery and equipment sector (machinery, electrical and optical equipment, transport equipment, machinery n.e.c.). The second largest sector in tenns of export share were chemicals and chemical products with 12%, followed by basic metals with 7% and food, beverages and tobacco with 7%. This is quite different from the times prior to the Europe agreement. In 1988, food and agricultural products had a share of28.5% in Hungary's exports to the EU and of23.8% still in 1990, while textiles and clothing made up for 15.8% in 1988 and 15.5% in 1990 respectively *(Hantke 199598).* Therefore, as the data show, a profound transfonnation of the export oriented production structures of the Hungarian economy has taken place in these two decades, namely from rather basic towards higher valued and more sophisticated products.

Looking at this in perspective, the share of Hungarian exports of manufactured goods, machinery and transport equipment made up 74.6% of total exports to EU countries in 1992, rising up to 87.4% in 2008 (EU-27). The equivalent share of manufactured goods imports from the EU remained more or less at the same level over the period, with 87.7% both in 1992 and 2008.

**Figure 22** shows the development of Hungary's manufacturing exports per sector to the EU (in million HUF). In order to correct for inflationary effects of the base currency, the exports were converted to constant prices of 1992 by means of the producer price index.99 In these tenns, overall manufacturing exports to the EU had increased 5.5-fold over the period. The machinery and equipment sector contributed most to this development, as it had the largest share in every single year since 1994. The contribution of the other sectors remained more or less the same over the period. The graphic clearly illustrates the fast pace of integration of the goods markets in Hungary into the international division of labour in Europe.

The expansion of the machinery and equipment sector could be explained by two reasons: on the one hand, light electrical machinery and household equipment was identified by the *European Commission (1994)* as an area of potential growth due to a comparative advantage of Hungary in this field. On the other hand, this sector was liberalised early on under the Europe agreement's trade provision, opening up

98 *Hantke (1995),* p. 71.

99 The export statistic of the HCSO is published at current prices; it was corrected by a producer price index for each sector in prices of 1992. I constructed this index based on the HCSO producer price index per sector, which is published annually as an index with previous year =100. To give an example, the producer price index for manufacturing as a whole had increased from 1992= 100 to a value of 352. 7 in 2008.

the access to the EU market for these products. A slightly later analysis of revealed comparative advantage (RCA) by *Ba/done & Sdogati (1997)* found that Hungary showed a traditional RCA in relatively advanced sectors such as chemicals, machinery and mechanical appliances, iron and steel products, and motor vehicles. 100

**Source: Own calculations; own conversion from SITC to the 8 TEAOR sectors.**  Source of data: *HCSO,* Statistical Yearbook of External Trade; subsequent years, and Statistical Yearbook oflndustry and Construction (for the producer price index), subsequent years.

These figures underline the importance of the trade liberalisation under the Europe agreement which encompassed almost exclusively these sectors, notably leaving out unprocessed agricultural goods of which Hungary was a potential supplier to the EU market. This also justifies the focus of my analysis on trade of the manufacturing goods. Further, the extent to which regional development was influenced by the change in agglomeration of the manufacturing industry in Hungary will be analysed in context with economic integration with the EU from 1992 to 2008.

JOO Own calculations for RCA based on the formula by Balassa (1985) were not feasible with the data base used in this study; and the procurement of data from other sources just for this purpose was judged too expensive in comparison to the expected result, as the present analysis already speak a clear language with respect to Hungary's strength in international trade.

#### Table 16: Trade coverage ratios per manufacturing sector for Hungary's trade with the EU 1992 to 2008, selected years.

**Source: Own calculations of trade coverage ratios, and own conversion from SITC to the 8 TEAOR sectors.**

Sources of data: *HCSO,* Statistical Yearbook of External Trade, 1992, 1999, 2004; data for 2008: *HCSO* website, database economy.

Hungary's imports from the EU in general grew less and not as consistently as exports to the EU did. Especially imports in the machinery and equipment sector declined between 2001 and 2004 in absolute terms and continued to increase only thereafter. Instead of showing the detailed development for Hungary's imports from the EU, the concept of "trade coverage" will be used here. For a given sector, the trade coverage index shows the ratio of Hungary's exports to the EU over Hungary's imports from the EU. This gives an indication about Hungary's relative strength in certain exports. It is also a notion for intra-industry trade between the two. The concept cannot give a picture, however, of whether Hungary perhaps imported relatively more in the sector from other parts of the world (as would be the case for textiles in recent years, for example). In fact, at the start of the period in 1992, Hungary imported 38.6% of chemical products from the former Soviet Union countries and another 19.7% from other CMEA countries, while the imports of this product from the EC amounted to only 16. l % *(Hantke 1995).* The data presented in Table 16 should be interpreted in this light.

**Table 16** shows the development of trade coverage ratios for the 8 manufacturing sectors. 101 In the food, beverages and tobacco sector, the initially high ratio of 5.2 still shows the previous orientation of Hungary; the ratio declined to 1.0 as a result of the Europe agreement and the price competitiveness effects. For the more traditional textiles sector, there was also a decline from 1.7 in 1992 to 0.9 in 2005,

101 Trade coverage ratio is the ratio of exports (X) to imports (M) of the sector, TCRi=Xi/Mi. The concept is applied here to Hungary's trade with the European Union only.

as for the wood, paper and printing sector from 1.1 in 1992 to 0.6 in 2005, and for mineral products from 1.5 to 0.6. A marked increase could be noted for the machinery and equipment sector from 0. 7 in 1992 and to 1.4 in 2008.

This analysis confirms Hungary's comparative advantage in the machinery and equipment sector. Moreover, it stresses the importance for Hungarian exports overall as for the development of the manufacturing sector and the linked employment and agglomeration effects in the national and regional context.

## **3.8 Foreign direct investment in Hungary**

The final section of this chapter analyses foreign direct investment (FDI) in the manufacturing industry in Hungary during the research period. This is done with the following question in mind: Could the development of FDI have contributed to increase agglomeration of certain sectors? Or could FDI have enhanced the development of certain Hungarian regions over others?

The total stock of FDI in manufacturing amounted to 4,850 billion HUF in Hungary in the year 2008. The pie diagram in **Figure 23** shows how this stock of FDI was distributed over the 8 manufacturing sectors. More than half was invested in the machinery and equipment industry, followed by chemicals and chemical products with 21 % of the total. Third place ranged the food, beverages and tobacco sector with a 10% share. The share of manufacturing FDI invested in the machinery and equipment industry corresponds almost exactly to the share of this sector in total manufacturing output in Hungary, 51% for FDI and 51.5% for output respectively (see Figure 18).

Privatisation has influenced FDI inflows in the CEECs to a large extent. 102 Hungary pursued a policy of early privatisation via the capital market. This attracted comparatively large FDI inflows into all sectors, which proved to be an advantage for the competitiveness of Hungarian industry, as compared to other countries such as Romania and Bulgaria which opted for a voucher privatisation scheme.

During the privatisation period 1990 - 2004, the Hungarian privatisation and state holding company received HUF 1.9 trillion (at current prices). 103 The greatest

I 02 *Ka/otay & Hunya (2000)* have analysed the relation between FD! inflows and privatisation policies in CEECs to this effect.

I 03 The so-called Act on Pre-Privatisation, adopted in 1990, was the start phase encompasssing the sale of shops of state-owned enterprises providing commercial, catering and other

**Figure 23: FDI stock in manufacturing industry in Hungary 2008, shares of the 8 sectors (billion HUF)**

Source of data: *HCSO* website, database economy.

sum, one quarter of the receipts, was deposited in 1995 as the counter-value of a determined proportion of state-owned shares of strategic companies; the second largest sum was deposited in 1997. The sum deposited by foreigners (FDI) during these 15 years exceeded HUF 1.2 trillion. In a table indicating the origin of FDI for the period 1990 to 2004, the share of Germany is the largest single country share with 23.4%, second place ranges the USA with 13.3%, followed by the EU member states France and Austria. The total of EU member states came to a share as large as 49%. 104

In an international comparison, the stock of inward FDI as a percentage of GDP reached 39.9% in Hungary in 1999. This was higher than in the Czech Republic with 33.0% and Poland with 17.2%. In the countries of the EU, Ireland stood out with the exceptionally high rate of 50. 7%. The share of FDI in gross fixed capital formation was around 20% on average in Hungary over the period 1992 to 1999 *(Csengodi et al. 2003).*

consumer services. The purchase was targeted exclusively to resident private persons. This scheme was completed in 1994. In the remaining fields of the national economy, the transformation of state-owned enterprises into private economically operating firms started in 1990.

104 HCSO (2005): Change of course - Hungary 1990 - 2004, pp. 6-7. In that table on the foreign revenues in the Hungarian privatisation, the shares of individual countries add up to 66.6%, the remainder of33.4% is shown to be the international placement of shares.

#### **Table 17: FDI stock in the Hungarian manufacturing industry (in million Euro), selected years**

Notes:\* provisional data.

#### **Source: Own conversion from HUF into Euro.**

Sources of data: HCSO Statistical Yearbook of Hungary and Website Database Economy, Budapest; for annual average exchange rates HUF-EUR: HCSO for 1992-1998, ECB-Website for 1999-2008.

**Table 17** shows the development of the FDI stock in the Hungarian manufacturing industry for selected years from 1992 to 2008 (in million Euro). 105 Hungary had been able to attract a stock of FDI of over 21 billion Euro over the period. Compared to 1992, the stock ofFDI increased more than 9-fold by the year 2008.

The bar diagram in **Figure 24** shows the FDI intensities for the Hungarian manufacturing industry as a whole and for the 8 sectors in the year 2005. 106 The manufacturing industry had an average FDI intensity of 32%. FDI intensities of individual sectors varied. They were lowest for the textiles sector with 22% and for other manufacturing industries and recycling with 15%, and highest for other non-metallic mineral products with 46%. For the machinery and equipment sector, the FDI intensity was equal to the average of the manufacturing sector. A comparison with the FDI intensities calculated for the year 1992 resulted in the following: The average FDI intensity was 11 % for manufacturing industry in 1992, i.e. much lower than the 32% average for the year 2005.

105 The FD! data were originally published in HUF. They have been converted into Euro, as they originated to about half from countries of the European Union. This was also done in order to filter out the inflationary effects of the HUF. Finally, the conversion to Euro makes sense as machines and equipment for new production sites in Hungary were probably purchased abroad to a large extent.

106 FD! intensity was calculated as the ratio of the FD! Stock of the sector (in million HUF) over output of the sector (in million HUF).

#### **Figure 24: FDI intensities in the manufacturing industry sectors in Hungary in** % **for the year 2005, based on FDI and output in million HUF**

#### **Source: Own calculations.**

Source of data: *HCSO,* Statistical Yearbook of Hungary, Budapest

An interesting question is whether higher FDI intensities in certain sectors promoted stronger agglomeration of these industries than in other sectors. This shall be analysed further with a view to the hypothesis in the regression analysis of Chapter 6.

The final analysis of this section focuses on the spread of FDI over the country. This is based on data for the share of FDI in enterprises with FDI per region (in billion HUF). 107 **Table 18** shows the results for two groups ofregions, those in the Western part and those in the Eastern part of Hungary (division according to *Laszlo Farago (1999).* While FDI for Hungary as a whole has increased over the period, the relative shares of the Western and Eastern part of the country have changed only slightly. The 13 Western regions accounted for 91.3% of the total in 1992 and 87.3% of the total in 2008, while the 7 Eastern regions had only 8.7% in 1992 and 12.7% in 2008. Thus, the industrial parks policy of the Szechenyi plan seems to have yielded certain positive effects in this respect.

The facts regarding the high FDI stock can be taken as a hint at the extent to which the Europe agreement induced foreign direct investors from EU member states to locate in Hungary. The still unequal distribution of FDI over the country seems to be one of the factors which could have contributed to the strong economic development shed between a prospering Western part and a declining

I 07 This statistic is not available just for the manufacturing sector and per region.

#### **Table 18: FDI stock in Western versus Eastern regions 1992 to 2008 (based on billion HUF), selected years**

#### **Source: Own calculations.**

Notes: Division in Western and Eastern part according to *Laszlo Farago (1999).*  Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest.

Eastern part. The strong overall increase of the level of FDI in the Hungarian manufacturing industry can be taken as a strong indication for the proceeding degree of integration of Hungary's economy with that of the EU.

## **3.9 First conclusions with respect to the hypotheses**

This chapter has analysed the economic situation in Hungary by means of descriptive empirical analysis with respect to regional development and the condition of the manufacturing industry during the period 1992 to 2008. Integration variables relevant for the development in the pre- and post-accession period, such as external trade and foreign direct investment, have been analysed at the country and regional level. 108 First influences have been identified with respect to the hypothesis regarding industry agglomeration and regional development over the almost two decades.

About one fifth of the total population of Hungary of around l 0 million is concentrated in the capital Budapest, while the rest is spread over the 19 remaining Nuts-3 regions. The economic weight of Central Hungary in terms of its share in the national GDP is even higher than the concentration of population. 109 This was a first indication towards increased agglomeration in a central location. The spread in GDP-per-capita among the 20 Hungarian regions is great, ranging between 50% of the national average for N6grad and 213% for Budapest in 2005.

I 08 Exports per region were available and have been presented in Figure 11.

109 46% share in GDP in 2005, whereas the population share was 28.3% in that year (see Figure 6 and Table 8).

GDP-per-capita increased for most Western regions from 1995 to 2005, while that of the regions of Northern Hungary and the Great Plain decreased to levels well below the national average. 110 Hungary had the highest level of regional disparities at the Nuts-3 level among the large CEECs 111 relative to the EU average; they increased from 31.4% to 40.0% over the period 1995 to 2005. This pointed to profound restructuring processes at the regional level.

Regional activity rates as well as unemployment rates tend to reflect regional differences in welfare. High unemployment coupled with low activity rates prevailed in the Eastern-most region and in Northern Hungary, lower unemployment and higher activity rates in Central Hungary and the Western regions in 2005 .112 More enterprises were concentrated in central locations and in the Wes tern part of Hungary than in the Eastern part. Regions with a higher employment in industry tended to have a larger output of industry per inhabitant. The Western regions had a higher export share in industry production than the other regions. These data point to a specialization of Western regions in industry and increased agglomeration of export-oriented sectors in this part of the country. They also confirm a certain pull-factor of the EU market, attracting the location of industry agglomerations in the Wes tern part of the country closest to the EU market.

The analysis of internal net migration among the 20 regions showed that 8 mostly Western and Central regions had a positive balance over the period 1992 to 2008 while the 12 other regions registered negative migration flows. The years of the strongest internal migration were 1999 and 2000, which is interesting when put in relation with industry agglomeration and regional specialization trends. 11 The regions which attracted positive migration flows also registered higher and rising wage levels in the manufacturing sector over the period 1992 to 2008. 114 The analysis of the regional housing market seemed to confirm the idea that people migrated between regions not only to earn higher wages, but also to improve their living conditions. Overall, the agglomeration pull-factor seemed to have been exerted by the Western regions and Pest in Central Hungary.

In the analysis of the manufacturing industry, the rising importance of the machinery and equipment sector in terms of output, employment and export orientation

110 See Figure 8 and Figure 9.

111 No data were available for Poland at the Nuts-3 level.

112 See Figure 10 and Table 11.

113 These years were a turning or peak point in the development of agglomeration and of regional specialization, as the results of Chapters 4 and 5 will show.

I 14 See sections 3.5.1, 3.5.2 and 3.5.3.

stood out. 115 The very high export ratio of the sector of 85% indicates international competitiveness. It also points to an enhanced role that the machinery and equipment sector may have played during increasing integration of Hungary with the EU. This seems to confirm that the focus of this research on manufacturing is a well-chosen basis for analysing developments in the pre- and post-accession period. The regional data on the development of manufacturing employment and output point to an increased concentration of this industry in certain Wes tern regions, relatively little importance in the Great Plain, and a declining role in the regions of Northern Hungary. The results give another hint towards centres of regional agglomeration of manufacturing activities in the direction of the hypotheses.

The analysis of Hungary's foreign trade showed the rising importance of the EU as an export market, with the share in total exports rising from 46% in 1993 to 80% in 2008, and a similar development on the import side. The Europe agreement - whose trade liberalisation benefited about 80% of Hungary's prior trade with the EU countries - certainly contributed to this development. Among manufacturing exports to the EU, which rose 5.5-fold measured in constant prices of 1992,116 the outstanding sector also was machinery and equipment, with a share of 65% in 2008. This sector also showed a high degree of intra-industry trade. The analysis confirmed Hungary's comparative advantage in the machinery and equipment, electrical appliances and transport equipment sector. It further stressed the importance to locate employment and agglomeration effects in the national and regional context.

The final analysis of the stock of foreign direct investment (FDI) in Hungary showed a 9-fold increase in manufacturing FDI during the period 1992 to 2008. The sector with the largest FDI stock was the machinery and equipment sector, followed by the chemicals and chemical products. The FDI intensity was 32% for the manufacturing industry as a whole. The regional distribution of the FDI stock was very uneven, with the Western part of the country holding 87.3% and the Eastern part a mere 12.7%. This seems to point to agglomeration trends and towards an enhanced influence of FDI on regional development in the Western part of Hungary.

In the next chapter, the development of agglomeration in the Hungarian manufacturing industry shall be analysed in detail with the help of empirical tools. This shall be based on the series of HCSO data of the 8 manufacturing sectors on the Nuts-3 regional level for the period 1992 to 2008.

115 See sections 3 .6 and 3 .6.1.

116 The producer price index with base year 1992. See footnote 99 for the methodology.

Cordula Wandel - 978-3-631-75112-1 Downloaded from PubFactory at 01/11/2019 08:11:44AM via free access

## **4. Manufacturing Industry Concentration in Hungary 1992 to 2008**

This research analyses various questions regarding industry agglomeration. Which development did agglomeration in the manufacturing sectors in Hungary undergo during increasing integration with the EU, first under the Europe agreement and then in the post-accession phase: Did integration reinforce industry agglomeration in the manufacturing sector? Did agglomeration decrease? Were there perhaps phases of both? Was there a turning point, and if so, when was it reached?

Chapter 4 shall approach this part of the research topic by presenting results of empirical calculations. The data shall be treated in a way such that information shall be provided about the actual development of agglomeration in Hungary during the period 1992 to 2008. The results will be commented and put into context of the predictions made by NEG theories ( chapter 2.1.2), by previous empirical studies, and, where applicable, by the descriptive empirical analysis of chapter 3.

## **4.1 Measuring industry agglomeration**

Before embarking into the analytic part, the issue of concentration versus specialization shall be set out here clearly.

**Concentration** looks at the industry, a sector of manufacturing, at the degree of its agglomeration or dispersion in space.

**Specialization** looks at the region, at the structure of sectoral employment, whether only a few sectors are dominant, or whether a region is highly diversified.

While industry concentration is analysed in this chapter, the topic of regional specialization is reserved for the following Chapter 5.

With respect to concentration, this research looks at the research questions whether manufacturing industry concentration in Hungary has increased or declined in the course of European integration, or whether there were both trends and at which stages of the integration process. It also verifies whether there may have been a turning point in the development up to which there was an increase and after which there was a decline. Such a development would correspond to the predictions made by the selected NEG models *(Puga 1999, Ludema & Wooton*

1997, *Livas-Elizondo & Krugman 1996).* The analysis shall also find out whether such a peak point was already reached prior to full EU-membership of Hungary.

Here again, the speciality of my research is that it is based on regional sectoral manufacturing employment data as a basis for calculating industry concentration for the country of Hungary, and not at sectoral country-wide data for a group of countries such as the EU-15 or a set ofCEECs, as most previous empirical studies in the field.

The subject of industry agglomeration can best be measured using the tool of concentration indices. The unique contribution of this research is that it applies six different concentration measures to the same set of data, thus allowing for a comparison of them. The period under study is 1992 to 2008, which is the longest available and with reliable data at the time of writing using the Hungarian regional sectoral employment data series of the HCSO.

## **4.2 Overview of indices**

**Table 19** gives an overview of the six concentration measures used in this research. They were all calculated based on Hungarian regional employment data by manufacturing sectors. The detailed formula of each index will be given in the

#### **Table 19 Concentration measures used for measuring agglomeration.**

Source: Own selection and formulation.

respective subsections. Some of these indices are absolute measures of concentration, some are relative measures, i.e. in relation to some other benchmark value.

All of these indices were applied to employment data by manufacturing sector and for the 20 Hungarian regions. The idea to calculate values for these indices also based on output, production or value added data was not feasible due to the lack of such data at the regional level; they are not available from HCSO.

Some of the 6 indices were used in previous studies of agglomeration, some of them rather rarely though. These 6 are not the only existing concentration indices, however. Two other indices could not be applied here due to the unfitting set-up of the data. These are the Ellison-Glaeser index *(Ellison & Glaeser I 997* and *1999)* the calculation of which would require firm-level data; such were not publicly available for Hungary. Another index not applicable here is the so-called spatial separation index *(Midelfart-Knarvik et al. 2000);* calculations for that index would require regional output data by subsector which are not available for Hungary either; from its conceptual definition, that index cannot be transformed to using employment data, so that this possibility had to be ruled out. Another recent paper by *Giacinta & Pagnini (2008)* proposed two modified indices measuring industry agglomeration in Italy. These include properties of the economic environment such as regional adjacency. As they also require plant level data, they are not feasible for my research.

The remainder of this chapter is structured as follows: first, industry agglomeration is analyzed by means of the 6 different concentration indices or measures. Each section first presents the index, then the results obtained by its calculation; where applicable, they are put into relation with the results of other empirical studies. A comparative section analyses all 6 concentration indices based on their results for the data set. The final section of this Chapter draws first conclusions with respect to industry concentration in Hungary under the Europe agreement.

## **4.3 The Krugman concentration index**

As first index, the Krugman concentration index shall be presented here. As *Krugman* is one of the founding fathers of the theories of the New Economy Geography regarding the development of industry agglomeration in space *(Krugman 1991a),* this index is presented first. The Krugman concentration index is a relative measure of the degree of an industry's agglomeration or dispersion in space. In this form, it was derived from the index proposed by *Krugman (1991b).*  The index shall be applied here to the 8 manufacturing subsectors and 20 Nuts-3 regions in Hungary based on HCSO employment data, i.e. regional employment by manufacturing subsector for the 20 Hungarian regions for the years 1992 to 2008.

## **4.3.1 Formula of the Krugman concentration index**

The formula of the index is as follows: 117

$$K\_i = \sum\_{j=1}^{N} \left( s\_{ij} - x\_{ij} \right)$$

Ki is the numerical value of the Krugman concentration index for industry sector i. In the term on the right hand side, Sij is the share of industry i's employees working in region j and xii is the share of total manufacturing employment in region j; the Krugman index for industry i takes the absolute value of this term and sums it over all regions j= l to N (N=20) to give the value of the index.

A specific industry i is considered to be concentrated, if a large part of production is carried out in a small number of regions. The higher is the value of the index, the larger is the degree of concentration of the industry.

## **4.3.2 Results of the Krugman concentration index**

In this section, the results of the calculations of the Krugman concentration index for the 20 Hungarian Nuts-3 regions for the years 1993-2008 shall be presented. 118

**Figure 25** shows the values of the Krugman index for the four manufacturing sectors food, beverages, tobacco, chemicals and chemical products, other nonmetallic mineral products, and basic metals and fabricated metal products. At the first glace, the results show strong differences between the degree of concentration of the industries in this sample. While the mineral products industry had values of 0.7416 at the start of the period, the food, beverages and tobacco industry industry showed much lower concentration levels of around 0.2940 only.

117 Formula according to *Traistaru et al. (2002).* They applied this index to a (private) database collected under a Phare ACE project, on five Central and East European countries on manufacturing industry and total employment for the years 1990-1999 ( even though the initial years 1990, 1991 and to some extent 1992 can be regarded as distorted by the end of communist regimes data collection). In my research, it will be targeted more precisely on manufacturing employment.

118 The data for 1992 were also calculated, but were left out here for clarity, as they seemed to be still under some influences from the strong restructuring recession.

**Figure 25: Krugman concentration index for industry agglomeration based on manufacturing employment data per region, 1993-2008, 4 sectors**

#### **Source: Own calculations; own illustration.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest.

The larger concentration of the former can be attributed to the fact that natural resources rich in minerals are located in only a small number of regions of the country as a location factor, whereas the food, beverages and tobacco industry has a broader resource base which is also easier transportable.

As to the development over the period, the graph shows a clear rise in overall industry concentration levels from 1993 up to a peak in the year 1999; from then onwards, concentration levels were falling; by the year 2004, the date of EU membership of Hungary, they were generally below the start level. For the remainder of the period up to 2008, they were rising again, for mineral products and chemicals a decline set in towards the end of the period.

**Figure 26** illustrates industry concentration for the remaining 4 manufacturing industries for the same period, 1993-2008119• At the first glace, the results show noticeable differences between the degree of concentration of the industries in the second sample. The highest concentration at the start was found in the wood, paper and printing, and publishing industry, 0.5290. This is due to the resources

119 See footnote 121.

**Source: Own calculations; own illustration.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest.

for wood and paper manufacturing and to the concentration of the publishing activity in the Budapest agglomeration. The lowest concentration was found in the machinery and equipment sector at the start and at the end, with a decline over the period, namely 0.3117 in 1993 and 0.2176 in 2008.

As to the development, interestingly, these four industries all show a pronounced hub after 2004, i.e. since the EU membership of Hungary, with the decline setting in from 2007 onwards. The prior period, up to 2004, shows a rather shallow development for these four industries. The only clear line is the textiles industry with a rising concentration up to 2007.

#### **4.3.3 Comparison of the empirical results with NEG**

The model by *Livas-Elizondo & Krugman (1996)* for regional integration modelled in the context of international trade described that an (irreversible) agglomeration would form within the country consisting of more than one region. The model by *Puga (] 999)* predicted an 0-shaped relationship between agglomeration and trade costs with proceeding integration. The model by *Ludema & Wooton (1997)* predicted (less than complete) agglomeration to form at some intermediate level of trade costs, followed by dispersion as trade costs decline even further in the course of proceeding integration.

The empirical results based on calculations of the Krugman concentration index for the Hungarian manufacturing industry in the period 1992 to 2008 show the following: for a majority of manufacturing industries, concentration has generally increased up to a point around 1999; afterwards, decreasing concentration set in to lower levels than at the start. The highest value for concentration, i.e. the degree of agglomeration, was reached in 1999, thus prior to EU accession of Hungary. Since 2004 a second rise but to lesser levels than in 1999 set in, which was followed by a new decline by 2007.

With regards to the hypothesis, the empirical results provide an overall confirmation in the sense that agglomeration in the manufacturing industry in Hungary under the Europe agreements, i.e. up to 2004, first increased, before dispersion set in to lower degrees of agglomeration than at the start of the period. The highest concentration was indeed reached prior to EU membership of Hungary, namely in 1999.

It would be interesting to find out whether there were other influences at work than the mere decline in trade costs, especially which might have triggered the turning point in 1999. Trade costs - as important as they may have been - are one factor among various factors which influence an industry's concentration in space. The Hungarian privatisation regime underwent different phases throughout the period, new sectors and enterprises were opened up to foreign investors gradually, larger scale enterprises which were formerly state owned were put for sale in specific years, and there was also a tendency towards more green field investments rather than the take-over of existing plants or companies in the latter part of the period *(Csengodi et al. 2003).* These developments could have contributed to the reaching of the turning point and the reversal of the concentration trend in 1999.

Moreover, there was a phase of privatisation and recapitalisation of the national banking system as part of the transition to a market economy, a policy which was also supported by the PHARE programme. Further, the availability of a more stable banking system all over the country rather than only in Budapest and other economic centres around towns could also have been a precondition enabling the spreading out of economic activity from 1999 onwards. Furthermore, the internal policy of the Szechenyi plan with its industrial parks scheme spread out on purpose in the Eastern and Western part of the country and in new sites could have contributed to the turning of the tendency of concentration (see chapter 3, section 3.2.4).

Finally, an external factor in 1999 which took place in Hungary's main export market, the EU, was that 1999 was the start year of the Euro in the majority of EU

member states, which were Hungary's main trading partner already at that point. As the Euro brought with it more stability and better planning for foreign investors, these could have been incited to locate their investments more spread out over the country. Indeed, the following year 2000 saw the largest single FDI inflow over the almost two decades analysed in this study. Whether FDI had a significant influence on manufacturing industry concentration and on regional specialization will be analysed by econometric analysis in Chapter 6. To sum up, apart from falling trade costs and the predictions of the NEG models regarding a turning point of industry concentration during proceeding integration, various other factors were also potentially influencing the development of manufacturing concentration in Hungary around the tum of the millennium.

#### **4.3.4 Comparison of concentration ranks**

In this subsection, the values of the Krugman concentration index for the 8 industries shall be ranked according to their degree of agglomeration and be compared. **Table 20** compares ranks for the start and end years 1993 and 2008 respectively.

**Table 20: Ranks of manufacturing industry concentration in Hungary, 1993 and 2008 in comparison; Krugman concentration index based on employment data**

#### **Source: Own calculations; own presentation.**

Source of data: *HCSO,* Regional Statistical Yearbook of Hungary, subsequent years, Budapest.

As **Table 20** with the comparison of concentration levels in 1993 and 2008 demonstrates, the highest concentration level prevailed in the mineral products industry. This high absolute concentration level of the industry is due to the natural resources in minerals being found in only a few regions over the country, and due to the difficulty to transport them, creating a strong reason for firms of the industry to locate near them. The chemicals industry was still highly agglomerated in 2008, ranking third instead of second. This industry is one in which economies of scale in production play an important role, according to the *OECD (1994).*  Industries with low concentration were machinery and equipment, as well as food, beverages and tobacco, with ranks 8 and 6 respectively. The most marked change in concentration ranks was undergone by the textiles industry, from rank 6 to second rank, a sign for the ongoing consolidationofthat industry.

**Figure 27: Employment in the textiles sector in Hungary, number of employees, 1993-2008**

#### **Source: Own illustration.**

Source of data: *HCSO,* Statistical Yearbook of Hungary, subsequent years, Budapest.

For the various manufacturing sectors, very different conditions prevailed and played a role in shaping the developments of the industry and the degree of concentration. In the following, a closer look at the Hungarian textiles industry shall be taken, one of the specialities of Hungary during the CMEA division of production, which also played a specific role during the early phases of integration with the EU under the Europe agreement. The industry was in strong decline during the period 1992 to 2008 - as in most European countries in the 1990s and around the turn of the millennium - due to increased competition in a

context of global trade liberalisation, producers in the Far Eastern countries, mainly India, Bangladesh, and China took increasing shares of the market. Thus, globalisation had its effects on the textiles industry in Hungary, too.