Chapter

IV Understanding Persistent Regional Disparities in Unemployment

Author(s):
Philippe Egoume Bossogo, Jerald Schiff, Miho Ihara, Tetsuya Konuki, and Kornelia Krajnyak
Published Date:
July 2006
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Labor market performance not only varies across transition countries, but also across regions within a country, and such regional differences are substantial.

Differences in Labor Market Performance: An Overview

For most transition countries, the unemployment rate in the highest-unemployment NUTS2 region exceeds that in the lowest by 10 to 20 percentage points.19 The picture is similar for participation rates as well as the incidence of long-term unemployment. Although such differences are not out of line with the experience of EU–15 countries, they appear to be at the high end, comparable to Belgium or Spain (Figure 4.1). In addition, the differences are persistent—for example, the ranking of regions with respect to unemployment rates tended to remain unchanged in 1995–2001 within each country, with increasing or stable dispersion.

Figure 4.1.Unemployment and Its Regional Dispersion1

Source: Eurostat and IMF staff calculations.

1 Regional dispersion is the difference between maximum and minimum unemployment rates in regions.

These persistent disparities are a cause for concern. First, the ossification of pockets of high-unemployment may contribute to higher aggregate unemployment. Unemployed workers in areas with depressed labor markets are at high risk of losing their labor market attachment. Persistently poor job prospects may weaken motivation to update skills. As a result, a portion of the labor force may become unemployable. Second, job search intensity and labor market participation rates may decline. And third, regional fragmentation of the labor market may weaken social cohesion, making needed structural reforms more difficult to implement.

What causes these regional disparities? A full explanation is unlikely to be provided by institutional factors discussed in previous chapters. Arguably, regions of the same country are at roughly the same stage of economic transition, share the same institutional and legal infrastructure,20 and are exposed to largely the same policy shocks. At least part of the answer should, therefore, lie elsewhere. This Section explores—by building on the existing empirical literature that mostly covers Hungary and the Czech Republic, as well as on the insights from two simple, stylized models—what other factors may contribute to the emergence and persistence of the regional variation in labor market performance.

The origins of regional differences in unemployment are likely to be found in transition-related shocks. Many of these shocks were related to sudden relative price changes and therefore had significant industry specific components. This in turn translated into initial regional differences in unemployment. In addition, ongoing structural change—for example, due to second and third rounds of restructuring in heavy industry and mining—may have contributed to widening regional unemployment differentials even at later stages of transition. To illustrate this, Figure 4.2 plots the level and change in unemployment against a measure of structural change in 1998–99 for transition countries and transition country regions.21 Changes in industry structure and unemployment indeed appear to be positively correlated. However, transition-related shocks would need to be very particular—recurring in certain regions but not in others—in order to explain fully the observed pattern of persistent regional differences in unemployment.

Figure 4.2.Unemployment and Structural Change

(In percent)

Sources: Eurosatc; and IMF staff calculations.

A fuller explanation of persistent regional disparities could rely on a long list of factors. Among the possibilities that have been put forth are the following:

  • Inadequate wage flexibility. If wages are not sufficiently flexible downward—because the minimum wage is binding, or because centrally negotiated wage demands are excessive for high-unemployment regions—the brunt of labor market adjustment in response to a negative regional shock will occur through reduced employment. Inadequate wage flexibility also prolongs the period necessary for increased unemployment to dissipate.

  • High benefits. If the difference between labor and benefit income is small or negative, workers will voluntarily become unemployed, will have minimal incentive for job search and improving skills, and may drop out of the labor market altogether. For practical reasons, benefit payments usually do not take into account regional variation in wage and price levels;22 hence, these effects may be especially strong in distressed regions, and differences in unemployment and participation rates may persist.

  • Limited factor mobility. Movement of workers and jobs can help even out differences in regional labor markets. However, the equalizing process may slow or halt if factor mobility is impeded. Examples of impediments include the limited transferability of certain assets—such as housing—labor market segmentation, high transportation costs, regional differences in physical or business infrastructure endowments, and the emergence of agglomeration externalities.

There is little evidence to support the notion that wages across regions are particularly inflexible in transition economies (Table 4.1). In contrast to a number of EU–15 countries—for example, Belgium or Italy—with national wage bargaining systems, wage setting is typically local,23 and regional wage differentials can be substantial. For example, Kertesi and Köllő (1999) report a 30 percent gap between the wages of comparable workers in Hungary’s highest and lowest unemployment regions; similar differences are observed in other countries. However, with large regional productivity differentials, a nationally set minimum wage may contribute to inadequate wage flexibility at the low end of the wage distribution, particularly in low-productivity, high-unemployment regions.

Table 4.1Basic Indicators, Hungarian and Czech Regions
Hungary–1997 and 2000CentralWestern TransdanubiaCentral TransdanubiaSouthern PlainSouthern TransdanubiaNorthern PlainNorth
1997
Employment rate61.665.658.558.95648.950.5
Unemployment rate (LFS)7.76.88.99.99.912.815.8
Educational level11071019996989497
Personal income per capita 1124949484878489
Wages per worker100737869716972
Adjusted for personal characteristics100748168716973
Adjusted for personal characteristics and productivity100969992929090
2000
Unemployment rate (LFS)4.04.15.06.37.97.98.9
Educational level11081009997979496
Personal income per capita113410810578837582
Czech Republic, 2002PragueJihočeskýPlzeňskýStředočeskýKrálovéhradeckýVysočinaPardubickýLibereckýKarlovarskýZlinskýJihomoravskýOlomouckýMoravskoslezskýÚsteckyý
Unemployment rate (registered)3.76.77.17.27.38.38.78.710.110.211.212.215.917.1
Investment per capita147115841107491857321475801178678
Wages per worker10071757869686871687071677572
Sources: Czech and Hungarian statistical offices; and Köllö (2001).

In percent of country average.

Sources: Czech and Hungarian statistical offices; and Köllö (2001).

In percent of country average.

Of course, some of this difference reflects the regional labor market situation. A frequently used measure of wage flexibility is the responsiveness of individual wages to regional unemployment, or the slope of the wage curve (Blanchflower and Oswald, 1994). In mature economies, typical estimates of the elasticity of wages with respect to regional unemployment are around −0.1, implying that a doubling of the regional unemployment rate is associated with a 10 percent fall in wages. Estimates for transition countries by Blanchflower (2001) and others24 indicate that the responsiveness of wages to regional unemployment is at this level or even higher. In addition, nonparticipation tends to be very high in high-unemployment, low-wage regions—including because subsistence activity may generate income levels comparable to employment, or because outdated or insufficient skills and the scarcity of jobs owing to large regional capital destruction discourage workers.25 If these factors are relevant, further falls in wages alone are unlikely to markedly improve the labor market situation in high-unemployment regions.

Can high benefits be blamed? The empirical evidence here is mixed. As noted above, unemployment benefits are generally not high in transition economies. Further, for Hungary, Köllő (2001) finds no difference in the job-finding probability of unemployed with and without benefits. In the same vein, workers leaving unemployment appear to take larger wage cuts compared with their previous wages in depressed regions—that is, the reservation wage appears to decline with regional unemployment.26 If relatively high benefits kept the reservation wage high, we would observe the opposite: job-finding unemployed would accept smaller wage cuts in high-benefit regions. On the other hand, social or family benefits may give rise to welfare traps for specific demographic groups—for example, low-wage families with several children.27 For these workers, benefits may exceed their likely wages, and unemployment or nonparticipation may be an appealing option.

While migration of workers could reduce regional unemployment differentials, it appears that this mechanism is not particularly strong or speedy. Workers in transition countries appear even less willing to migrate than their Western European counterparts. Cseres-Gergely (2002) reports that annual gross migration between Hungarian regions is, at about 2 percent of the population, comparable to Swedish or Dutch internal migration, but migration flows are only half this large or smaller for the Czech Republic, the Slovak Republic, and Poland. Fidrmuc (2002) estimates that, despite large regional differentials in unemployment rates and wages, migration flows induced by such variations in regional labor market conditions are less important in the Czech Republic, Slovak Republic, Hungary, and Poland than they are in Italy or Portugal. Migration appears to respond to economic incentives—people tend to move from high-unemployment, low-wage regions to more prosperous ones (Fidrmuc, 2002; Cseres-Gergely, 2002; Fidrmuc and Huber, 2003). But so few people move that migration is unlikely to eliminate regional differences in unemployment rates.

Commuting to work is a possible substitute to migration. With commuting, a change of residence is not necessary to participate in another labor market. In fact, Eurostat data indicate that the frequency of commuting is correlated with regional unemployment (Figure 4.3), with people who reside in depressed regions commuting more. This is consistent with Köllő (2002a), who reports, for Hungary, that about half of those exiting unemployment to employment find work in a settlement other than their primary residence. It appears that commuting may alleviate regional differences in unemployment but is not a strong enough mechanism to narrow them substantially. There appear to be two main obstacles to commuting on a larger scale. First, as (Table 4.2). shows, commuting is expensive—both in money and time reflecting in part poor public transportation28 and transport infrastructure. Second, if the size of the depressed region is large, there may be nowhere to commute to, especially if car ownership is limited (Figure 4.4). For example, the highest-unemployment regions in Hungary appear to be rural regions that are either isolated or are in the neighborhood of a high-unemployment urban center.

Figure 4.3.Commuting Frequency and Unemployment

(In percent)

Source: Eurostat.

1 Excluding Strředni Čhechy region.

Figure 4.4.Unemployment and Car Ownership, Hungarian Regions, 2002

Source: Hungarian Statistical Office.

Table 4.2.Indicators of Commuting Costs in Selected Countries1
Czech RepublicHungaryPolandSlovak RepublicSlovenia
Cost of gasoline per liter (in U.S. dollars)0.810.940.830.740.80
In percent of monthly average wage0.150.170.140.170.07
In percent of monthly minimum wage0.400.420.420.430.17
Cost of rail ticket, 30 km return (in U.S. dollars)1.772.122.03
In percent of monthly average wage0.320.390.45
In percent of monthly minimum wage0.880.951.17
Cost of monthly rail ticket (in U.S. dollars)2,326.846.829.3
In percent of monthly average wage4.88.65.0
In percent of monthly minimum wage13.321.015.0
Source: IMF staff calculations.

Data for 2003 (rail prices) and 2002 (gasoline prices-minimum and average wage).

For distance of about 30 km.

For Hungary, includes employee and employer share.

Source: IMF staff calculations.

Data for 2003 (rail prices) and 2002 (gasoline prices-minimum and average wage).

For distance of about 30 km.

For Hungary, includes employee and employer share.

Capital mobility could also potentially help eliminate regional disparities, but capital appears to favor advantaged regions despite their tighter labor markets. For instance, Fazekas (2003) reports that during 1993–2000, foreign firms increased their employment in the quartile of lowest-unemployment regions in Hungary by 8 percent. In the highest-unemployment quartile, they expanded employment by less than 1 percent. Other indicators also show that high-unemployment regions are not attractive for capital (Table 4.3), and instead of disappearing, the differences between advantaged and disadvantaged regions appear to widen over time.29

Table 4.3.Indicators of Job Creation by Regional Unemployment Quintiles in Hungary1
Q1Q2Q3Q4Q5
Firm creation219936.33.32.82.11.5
19962.32.41.71.51.0
Start-ups319939.18.58.26.55.4
19952.71.30.50.80.6
Employment by foreign firms4199313.18.56.55.74.8
199622.316.814.314.911.7
Source: Kertesi and Köllő (1999).

From lowest to highest unemployment.

Annual change in the number of firms per thousand population (excluding sole-proprietorships).

Annual change in the number of sole-proprietorships per thousand population.

Percent of workers employed in majority foreign-owned firms.

Source: Kertesi and Köllő (1999).

From lowest to highest unemployment.

Annual change in the number of firms per thousand population (excluding sole-proprietorships).

Annual change in the number of sole-proprietorships per thousand population.

Percent of workers employed in majority foreign-owned firms.

The chapter focuses on limited factor mobility, with a view to identifying impediments to such mobility and policies that could help mitigate them. Because of data limitations—regional information on a comparable basis across countries is available only for a few variables—we center the analysis around two illustrative models. First, we examine a stylized model of individual labor market behavior, holding capital constant. This provides an opportunity to take a closer look at the individual’s incentives to participate in the local labor market or to move to another region. Next, we look at another simple model to describe how jobs might be allocated across regions and draw attention to factors that might make regions attractive or unattractive for capital. The number and skills of available workers may influence the attractiveness of regions, so that migration and participation decisions may have an effect on job creation.

Labor Mobility: The Individual Worker’s Decision

This section considers the determinants of regional labor supply—workers’ incentives to participate in the local labor market and to move to another region. To simplify the exposition, we focus on the individual’s decisions when facing a fixed labor demand—that is, we abstract from job creation or destruction stemming from capital accumulation or capital decumulation. While labor supply decisions depend broadly on the same factors in transition and mature market economies, some structural features of the economy are likely to have particular importance in transition countries.

  • Regional differences in unemployment rates have been accompanied by a large differentiation in participation rates, but migration flows remained even weaker than in other European countries. To the extent that migration and changes in labor market participation are substitute mechanisms in adjusting to labor market shocks, they should be jointly examined. If “too low” migration rates shift the burden of adjustment to participation rates in transition countries, employment and output will be lower during an adjustment to a negative shock. In transition, tilting the balance toward migration may result in a brighter employment outcome.

  • An inefficient housing market is a possible impediment to mobility. Housing markets are probably more distorted in transition economies than in more developed economies. In many countries, the state-owned housing stock was privatized by selling the apartments to the sitting tenant at low prices, generating very high home ownership rates but keeping the housing market thin (Table 4.4). There is some evidence suggesting that countries with high rates of home ownership tend to have higher unemployment rates (Figure 4.5). The problem is compounded by credit market imperfections—mortgage financing is unlikely to be available without significant collateral, something that migrants from depressed regions are unlikely to offer. Further, the rental market is small, and rent controls and a bias toward tenant rights may be an obstacle for its future development. As a result, moving to a new address is relatively infrequent. For example, while in Sweden, the Netherlands, and the United Kingdom 10–12 percent of the population moves house each year, in Hungary only about 4 percent does so.30

  • Labor market segmentation along dimensions other than regional may be stronger in transition economies than elsewhere in Europe because of more skill heterogeneity. At one extreme is the part of the labor force with skills rendered obsolete by transition. These workers have joined the ranks of the unskilled, who in many cases comprise a larger share of the labor force than in industrial countries (Table 4.5). At the other extreme are young and newly trained individuals with highly marketable skills. Because the skills and work experience of older workers with similar training—obtained before transition—may have been devalued, they may represent another, medium-skill category and compete with the young workers only to a limited degree. Kertesi and Köllő (2003) find evidence that, with the appearance of new technologies, this skill obsolescence for the educated workers took place in Hungary, as “skill appreciation” (an increase in the returns to education) became restricted to younger workers. Jurajda (2004) finds similar evidence of skill obsolescence for the Czech Republic.

  • Subsistence activity is more important in transition economies, and may be a viable substitute to labor mobility or labor market participation. The reasons for this are many. First, a larger gray economy provides more opportunities for the unemployed or those outside the labor force to earn income (monetary or in kind). Second, land or plot ownership is more widespread as a result of land privatization and agricultural reform and the popularity of “dachas.” More people are able to supplement their income with their own agricultural produce. Third, average income levels are much closer to subsistence in transition countries than in industrial countries. For example, calculations by the Romanian Statistical Office indicate that per capita income was significantly lower in households involved in private farming than in employed or unemployed households (about 40 percent and 90 percent, respectively). However, the differences disappear if “in kind” income is also considered.31

Table 4.4.House Ownership in Selected Countries1
Czech RepublicEstoniaHungary2LatviaPolandSloveniaSlovak Republic3GermanyUnited Kingdom
Share of owner-occupied housing519290557284753865
Size of housing loans relative to GDP3.85.21.52.51.833.35654
Sources: OECD data; and IMF staff calculations.

For transition countries-data refer to 1999 (share of owner-occupied housing) and 2000 (mortgages); for EU countries-data refer to 1995 and 1998, respectively.

Share of owner-occupied housing in 1997.

“Owner-occupied” includes private rented. In percent of country average.

Sources: OECD data; and IMF staff calculations.

For transition countries-data refer to 1999 (share of owner-occupied housing) and 2000 (mortgages); for EU countries-data refer to 1995 and 1998, respectively.

Share of owner-occupied housing in 1997.

“Owner-occupied” includes private rented. In percent of country average.

Table 4.5.Skill Endowments
High Skill1Low Skill2
OECD high-income countries average0.160.78
Austria0.070.91
Finland0.120.78
France0.240.76
Germany0.150.76
Italy0.100.86
Netherlands0.260.74
Sweden0.130.72
United Kingdom0.150.75
CEE-average30.120.83
Czech Republic0.110.90
Hungary0.160.84
Poland0.110.85
Slovak Republic0.110.57
Slovenia0.070.86
Baltic states0.200.59
Sources: Marin (2004-Table 12); and IMF staff calculations.

Tertiary education. Ratio of people with tertiary education.

Up to second level. Ratio of people up to secondary education.

Weighted by 2003 population.

Sources: Marin (2004-Table 12); and IMF staff calculations.

Tertiary education. Ratio of people with tertiary education.

Up to second level. Ratio of people up to secondary education.

Weighted by 2003 population.

Figure 4.5.Unemployment and Home Ownership: Selected European Nations in the 1990s

(In percent)

Source: Oswald (1999).

1 Data refer to western Germany.

A stylized model that incorporates these features (Appendix III) is applied to examine the costs and benefits of moving. Workers decide simultaneously whether to move and whether to participate based not only on their expected wages in the two regions, but also on available benefits and subsistence income. An additional consideration for the moving decision is transaction costs (including capital gains and losses) related to selling a house in the home region and buying another one in the target region. The model yields the following conclusions:

  • Migration follows economic incentives: relatively higher wages and lower probability of unemployment in the target region tend to induce individuals to move.

  • However, deteriorating labor market conditions—declining wages or increasing unemployment—may trigger nonparticipation rather than out migration. This can be particularly the case for older workers and suggests that high unemployment regions may end up with a large number of nonparticipating workers who do not have the necessary incentives to migrate.

  • The level of benefits and subsistence income tends to influence participation but has less impact on mobility. Participation may be more strongly influenced by changes in subsistence income than changes in benefits.

  • Imperfections in the housing market—large differences in house prices across regions and/or large transaction costs—bias workers toward staying in their region of origin. This effect is likely to be stronger for older workers, who are more likely to own a house or rent at favorable rates.

  • Migration and participation decisions differ across worker groups with different labor market status. If workers are split between outsiders (those with a weaker labor market position, such as long-term unemployed with few marketable skills) and insiders (those with a strong labor market position—for instance, employed highskill workers), the labor-supply behavior of the two groups will differ. Because of their worse labor market prospects, outsiders are more prone to dropping out of the labor force—they decide not to participate at lower levels of benefits and subsistence income than insiders do. In addition, assuming that insider status is transferred with the worker when he moves to another region, insiders will have a higher propensity to move. Outsiders will tend to drop out of the labor force rather than move to another region.

These conclusions suggest that labor mobility should not necessarily be relied on to address persistent high regional unemployment. Rather, they point to the possibility that high-unemployment regions could move toward low participation, with older and lower-skilled potential workers remaining at home, and others moving to more favored regions.

Job Mobility: The Firm’s Decision

Having considered short-term determinants of regional labor supply, this section examines factors that are likely to influence regional labor demand. When thinking over the factors influencing the firm’s location decision—incentives for or deterrents to regional investment and job creation—four stylized facts that can be gleaned from the literature need to be considered.

  • Although cheap labor can help attract capital, direct cost factors appear to be a relatively unimportant motive, at least for foreign direct investment. A survey of Austrian investors (Table 4.6), for example, shows labor costs as distant third to market access and other considerations. Because foreign direct investment inflows are strongly correlated with other indicators of investment and job creation activity, the framework should allow market size to play a role as an incentive for capital flows.

  • Empirical evidence suggests that agglomeration economies influence regional productivity and therefore attractiveness for capital. Wage differentials have tended to widen between the central agglomerations and less densely populated regions in transition economies, owing in part to the tightness of the regional labor markets. However, Kertesi and Köllő (1999) find for Hungary that the main agglomeration’s labor costs declined relative to other regions as its relative productivity increased. Fazekas (2003) also finds quicker productivity growth for foreign direct investment firms that settled in the advantaged low-unemployment regions.

  • Third, a region’s location appears to matter both for its ability to attract capital and its labor market performance. (Table 4.7) illustrates this for the case of Hungary. The capital and the regions close to the EU–15 border (those listed in italics in Table 4.7) tend to do much better along both dimensions. Similar location effects are observable in the Czech Republic and Slovak Republic, suggesting that proximity to large markets and low transportation costs are a factor in capital’s location decision.

  • Last, regional unemployment rates and investment (particularly foreign direct investment) activity are negatively correlated (Tables 4.3 and 4.7). This confirms the idea that the performance of regional labor markets is strongly related to the region’s ability to attract businesses and foster job creation.

Table 4.6.Motivation of Austrian Outward Direct Investors-End-1999
Labor CostsTaxationMarket AccessSupply SecurityOtherLabor CostsTaxationMarket AccessSupply SecurityOtherTotal Investments
Share in the number of investments (in percent)Share in the value investments (in percent)NumberIn millions of euros
Total327232013505412,1729,261
EU–111127022502460515652,846
EU–15127022503405516643,841
Eastern Europe607331741712231,0983,314
Hungary80684197069222404841
Czech Republic60741193061135257993
Poland207412111079416105236
Source: Oesterreichische National bank, “Austrian Outward and Inward Direct Investment at the End of 1999” (Focus on Austria 2/2001), Table 10.1.

EU–11 refers to: Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain.

Source: Oesterreichische National bank, “Austrian Outward and Inward Direct Investment at the End of 1999” (Focus on Austria 2/2001), Table 10.1.

EU–11 refers to: Austria, Belgium, Denmark, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain.

Table 4.7.Unemployment Rate and the FDI Sector’s Share in the Corporate Sector’s Employment in Hungarian Regions1(In percent)
FDI Share in Employment, 1998Unemployment, 1998
Budapest240.53.4
Baranya2.910.9
Bács-Kiskun2.58.7
Békés1.713.5
Borsod-Abaúj-Zemplén4.418.6
Csongrád3.27.9
Fejér4.57.7
Gyór-Sopron-Moson37.34.5
Hajdú-Bihar3.214.4
Heves2.310.9
Komérom-Esztergom2.89.3
Nógréd0.914
Pest27.15.5
Somogy1.810.7
Szabolcs-Szatmér-Bereg1.618.3
Jész-Nagykun-Szolnok2.113.4
Tolna1.311.7
Vas34.75.2
Veszprém3.07.4
Zala2.37.1
Sources: Eurostat; and Fazekas (2000-Table 2).

FDI: foreign direct investment.

Central agglomeration.

Western border.

Sources: Eurostat; and Fazekas (2000-Table 2).

FDI: foreign direct investment.

Central agglomeration.

Western border.

A “new economic geography” model provides an analytical framework for examining firm location decisions. Puga (1999) explains the location of manufacturing as a function of the strength of agglomeration and dispersion forces. Similarly to Krugman and Venables (1995), manufacturing firms generate demand for other firms’ output, which makes “bunching” of firms advantageous. At the same time, however, transportation costs and the limited availability of labor in regions tend to induce firms to spread across regions. More precisely, agglomeration of manufacturing is promoted by two effects. The first is demand linkages. In a region where many firms are located, demand for manufactured goods is higher, since the manufacturing sector itself generates demand for its own product as an intermediate input. The second is cost linkages. If many firms are located in the same region, intermediate inputs for a new firm are cheaper, since the prices of locally supplied varieties are not augmented by transport costs. If nothing else were at work, these forces—demand and cost linkages—would cause manufacturing to concentrate in one region. However, a larger number of firms means stronger competition. If a larger share of the demand for manufactures is satisfied by local production, the average price of manufactures is lower, forcing individual firms to lower their prices or reduce the scale of production. Second, the presence of many firms generates larger labor demand and pushes up wage costs.

This framework suggests that two types of equilibria can be distinguished: those in which manufacturing spreads across regions, and those with agglomerates in one region. In the first type of equilibrium, agglomeration and dispersion forces balance each other, and firms earn zero profits in both regions. In the second type, all firms concentrate in one region without any incentive to relocate. As transport costs influence the strength of some of the agglomeration and dispersion forces, they also have an effect on the long-run equilibrium, with the relationship between transport costs and agglomeration being Ω–shaped. With very high transport costs, the advantages of producing close to market dominate, most sales occur locally, and manufacturing is dispersed across regions. As transport costs fall, export markets gain in importance, local product market competition weakens as a dispersion force, and manufacturing tends to agglomerate. If transport costs become lower yet, manufacturing starts to spread again, attracted by lower wages and low competition in the deindustrialized regions. In the limit, manufacturing is again fully dispersed, and wages equalize across regions. Under some conditions, multiple equilibria may also arise—regions with a small initial manufacturing base may lose their manufacturing sector altogether, while regions with a critical mass of manufacturing will attract additional firms.

This model suggests that different initial positions may cause regions to diverge over both the short and the long run. We assume that the model economies are characterized by low-to-medium transport costs, and that multiple equilibria are possible.32 In this case, in a region hard-hit by the transition shock, the manufacturing sector and thus the home market may be too small to make production profitable, and firms exit. Manufacturing shrinks, and the region gets caught in a vicious cycle. By contrast, in the lucky region a virtuous cycle operates: profits are positive, new firms enter, and the home market expands. Even if the manufacturing base is large enough in both regions to eventually attract new firms, the lucky region may initially be more successful. Because the manufacturing sector is larger there, agglomeration effects may lead to higher profits. The faster entry of firms to the lucky region may reinforce the differences temporarily, since it strengthens the benefits from agglomeration. But with the entry of more and more firms, keener competition on the product market and factor cost increases start eroding profits. Eventually, the cost advantage of the unlucky region will induce firms to locate there, and the regions start to converge.

The analysis also points to transport costs as a significant factor in a region’s attractiveness. There is a large variation in the density and quality of road and rail networks across transition country regions, and anecdotal evidence suggests that the poor state of transport infrastructure acts as a deterrent to investment. In addition, it may also hinder commuting and further contribute to persistent high unemployment. Having a disadvantage in the area of transport infrastructure may increase the likelihood that a region will see more firm exit or less enthusiastic firm entry compared with a better-provided region, especially if other elements of business infrastructure also compare unfavorably (Figure 4.6). Therefore, offsetting such disadvantages—that is-reducing transport costs where they are particularly high—may increase the chances of eventual regional convergence.

Figure 4.6.Unemployment and Main Phone Lines, Hungarian Regions, 2002

Source: Hungarian Statistical Office.

The size of the effective labor force may be another important factor in attracting firms. The effective supply of labor not only depends on the number of workers available, but also on their skill. The labor force may shrink more in unlucky regions than in lucky ones, for several reasons. Skill mismatch—that is, a large number of workers with skills appropriate for the outdated industry but not fit for modern manufacturing—is likely to reduce the size of the effective labor force more in the unlucky region. In addition, if the economy shrinks, non, participation may become more attractive, again reducing the size of the labor force and the potential to exploit agglomeration economies. Further, local knowledge spillovers may increase the productivity edge of the region with a large manufacturing base, increasing the likelihood of regional divergence in the size of manufacturing. This points to the potential importance of reducing regional differences in productivity—possibly by ensuring an even level of general education and the preconditions for an adequate skill mix across regions—to help even out differences in regions’ attractiveness to capital and possibly labor market outcomes.

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