Population deconcentration in Hungary during the post-socialist transformation

Journal of Rural Studies 18 (2002) 233–244

Population deconcentration in Hungary during the post-socialist transformation$ David L. Brown*, Kai A. Schafft Department of Rural Sociology, Cornell University, 118 Warren Hall, Ithaca, NY 14853-7801, USA

Abstract This paper investigates the patterns of population redistribution in contemporary Hungary to better understand the demographic articulations of post-socialist restructuring. We analyze published data for 1980–1997, and machine-readable municipal-level data for 1990–1997 to find that post-socialist restructuring coincides with pronounced population deconcentration after several decades of steady urbanization. While suburbanization accounts for much of this deconcentration, many of the more remote villages in the nation’s rural periphery also experienced net in-migration. Our findings call into question the ways in which population deconcentration may be thought of as a social indicator, raise important empirical questions about the attractiveness of remote places as resettlement locations for economically marginal populations, and assert the relevance of demographic analysis for coming to more complete understandings of the effects and implications of post-socialist restructuring. r 2002 Elsevier Science Ltd. All rights reserved.

1. Introduction The spatial redistribution of a nation’s population is inextricably linked to changes in its economic and political organization. However, relatively little attention has been paid to the distributional consequences of the transformation from state socialism that has restructured societies and economies in East Central Europe. This study uses Hungary as its case, focusing on what appears to be a pronounced process of internal population deconcentration during the 1990s, a trend coincident with the industrial and economic restructuring in which redundant labor was shed as industry and agriculture experienced rapid downsizing. While the 1970s and early 1980s were characterized by steady urbanization and concentration of Hungary’s population in larger cities (Bies and Tekse, 1980; Vining et al., 1982), coupled with an emptying out of many villages (Danta, 1987a), published census data for urban and rural areas provide evidence that the 1990s have been $ This research was supported by the US Department of Agriculture Hatch Grant 159442 awarded by the Cornell University Agricultural Experiment Station. It contributes to multi-state coordinating committee WCC-84, ‘‘Community, Institutional Change and Migration’’. *Corresponding author. Fax: +1-607-254-2896. E-mail addresses: [email protected] (D.L. Brown), [email protected] (K.A. Schafft).

marked by a distinct deconcentration with particularly pronounced internal migration streams flowing into villages from Budapest, other cities and larger towns (Hungarian Central Statistical Office, 1998). Pre-1990 urbanization in large part was a consequence of socialist economic development strategies emphasizing the establishment of an hierarchical urban network (Enyedi, 1996) and rapid industrial expansion (Lada! nyi and Szele! nyi, 1998). But what explains deconcentration in Hungary since 1990? We analyze published data for 1980–1997, and machine-readable municipal-level data for 1990–1997, to address two central questions. First, to what extent has deconcentration actually occurred in Hungary’s settlement system during the last two decades? Secondly, to the extent that deconcentration has occurred, how much of this trend can be characterized as suburbanization in which those rural areas experiencing the most growth are located on the urban fringe, and how much is accounted for by growth in more peripheral rural areas? This work focuses on the demographic articulations of post-socialist restructuring in Hungary. It extends population redistribution scholarship by exploring circumstances under which deconcentration, which heretofore has been focused mostly on Western Europe, the United States and other postindustrial nations, may occur in the context of postsocialist restructuring.

0743-0167/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 7 4 3 - 0 1 6 7 ( 0 1 ) 0 0 0 4 6 - 8

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2. Documenting deconcentration in post-industrial nations ‘‘Deconcentration’’ generally refers to the redistribution of populations within urban systems towards medium-sized or smaller cities (United Nations, 1993) or the growth of non-metropolitan areas at rates exceeding those of metropolitan areas after an historical period of urbanization (Champion and Vandermotten, 1997). Deconcentration trends have been identified in places as diverse as the United States (Brown and Wardwell, 1980), Canada (Bourne, 1982), Japan (Kawashima, 1982), Australia (Hugo, 1989; Hugo and Bell, 1998), the United Kingdom (Champion, 1989b), and Denmark (Court, 1989). In the United States, for example, metropolitan–nonmetropolitan deconcentration has characterized two of the last three decades (the 1970s and 1990s), and many scholars believe that these trends confirm a fundamental alteration in the nation’s settlement system (Rayer and Brown, 2001; Johnson, 1999). Deconcentration has typically been identified as part of a larger process of post-industrial restructuring characterized by shifts from reliance on heavy industry to more decentralized service-oriented economies (Champion, 1989a) and with the increasing incidence of amenity-based migration (Zelinsky, 1971). Vining et al. (1982) have classified nations into four types according to the extent to which they had experienced declines in net in-migration to core regions or, conversely, continued to experience urban growth and population concentration as a consequence of internal migration. After completing a comparative study of 20 nations, they concluded that ‘‘the concentration paradigm no longer provides an accurate model of the population geography of the developed countries’’ (172) because of the increasingly prohibitive costs associated with continued concentration of population and capital in already high density core areas. Accordingly, the most industrially and economically developed countries were also those most likely to experience population deconcentration. It is interesting to note that within this study (using time series data up to 1977) Hungary, Czechoslovakia, East Germany (the GDR), and Poland, were all classified as belonging to the category of nations which continued to experience net migration toward their major cities and regional metropolitan areas. Explanations of deconcentration have typically emphasized the interaction of increased economic opportunities in less dense areas, a convergence of inter-area differences in standards of living, and long held preferences for living in more rural environments (Brown et al., 1997). When jobs become available in peripheral areas, households are thought to respond by acting on their preferences for suburban, small city and rural living. Residential deconcentration is facilitated by

the outward diffusion of transportation and communication infrastructure, which makes longer distance travel to work possible and provides the basis for telecommuting. The counter-urbanization phenomenon is considered to be part of a process of societal development in which quality of life and amenities play increasing roles in residential relocation of both working age and retired persons towards less densely populated areas (Zelinsky, 1971). In Hungary, and other parts of East-Central Europe, in contrast, high amenity values or employment opportunities do not characterize most rural areas and hence the preference-oriented explanation does not seem adequate for explaining post-1990 deconcentration. Metropolitan expansion into lower density rural and semi-urban peripheral areas is another dimension of population redistribution that contributes to overall deconcentration. Suburbanization has characterized the United States and other highly developed nations since at least the 1920s (Schnore, 1959). Suburban expansion has also been observed in Hungary since as early as 1960. Between 1960 and 1970, the share of industrial employment in Budapest remained constant, but grew from 3.3 to 5.5 percent in its suburban municipalities (Friedrichs, 1988). Hence the post-1990 deconcentration may simply be a continuation of this trend. Clearly deconcentration must be viewed as a multi-causal phenomenon. From the standpoint of our analysis of population redistribution in Hungary since 1990, it is important to emphasize that all major explanations involve economic restructuring, on the one hand, and the location of population and economic activities, on the other. Hence, these explanations are all consistent with our overall contention that the transformation of socialist economies, and especially restructuring that features significant downsizing of urban industries, will result in population deconcentration.

3. Economic restructuring in Hungary To understand the social and demographic circumstances of Hungary in the 1990s, it is essential to place it within a regional–historical perspective, with particular attention paid to the changes and transitions occurring in Hungary at the time of the comprehensive reforms of 1990. At the time of the first comprehensive political restructuring in 1990, the economic transition both in East Central Europe generally and within Hungary had a profound impact on all sectors of the nation’s economy in both urban and rural areas, but especially the large urban sites of production. Industrial output, in large part affected by the collapse of the East trade bloc, the Council for Mutual Economic Assistance (CMEA), in 1990 fell by 9.2 percent. In 1991 output fell by a further 21.5 percent and in 1992 by another 10 percent

D.L. Brown, K.A. Schafft / Journal of Rural Studies 18 (2002) 233–244

(Economist Intelligence Unit, 1995). The shock was a combined effect of the absence of import demand by the USSR, lack of competitiveness within Western markets, and the effects of European Community protectionism (Ernst et al., 1996). Hungary’s industries were also negatively affected by the sudden unavailability of cheap energy and raw materials from the East, especially after the Soviet Union began demanding world prices for its oil. The cumulative effect was a dramatic increase in inflation which peaked in 1991 at about 35 percent, and unemployment which peaked in 1993 at about 14 percent (Ernst et al., 1996). Fiscal and monetary policies were tightened to help bring the inflation under control and to ease Hungary’s growing foreign debt which by 1994 had increased to 28 billion dollars, making it the highest foreign debt per capita in Eastern Europe (Economist Intelligence Unit, 1995). Unemployment and economic dislocation took place in Hungary’s larger cities and in its rural areas. Urbanbased heavy industry was especially hard hit and many establishments shed redundant labor to enhance their competitive position in the new capitalist system. Many firms went bankrupt and simply shut down, while others downsized substantially. Between 1989 and 1993 industrial output dropped by one-third, releasing an equivalent share of factory workers into the ranks of the unemployed (Bossa! nyi, 1995). Rural economies were also disadvantaged as a consequence of economic restructuring. The economies of smaller places were both less diversified and more dependent upon agriculture and industry, the sectors hardest hit. Moreover, large numbers of rural residents commuted into cities for their employment as part of the urban industrial labor force. By 1996, about 38 percent of Hungarians lived in villages, but they accounted for 50 percent of the unemployed (Centre for Co-Operation with Economies in Transition, 1996). Agricultural employment shrank from 18.5 to 9.9 percent of the Hungarian workforce between 1988 and 1993 (Kova! ch, 1994). In the years since 1990, most development and foreign investment has occurred in Hungary’s metropolitan areas. Its more rural areas, on the other hand, have tended to face continued economic stagnation and underdevelopment. The scaling back of the state’s redistributive role has increased economic and social insecurity in all rural and urban settlement types. In the wake of the shocks caused by the initial restructuring process, Hungary has continued to experience significant social dislocation. These changes have had a pronounced effect on the average Hungarian. Between 1989 and 1995 the percentage of the population living below the poverty level rose from 8 percent to an estimated one-third of the population. Additionally, the number of Hungarians collecting pensions in 1994 was 2.9 million, or somewhat less than one-third of the

235

population, while the active work force (including the unemployed) consisted of only 4.2 million persons (Bossa! nyi, 1995). Hungary has also seen an increase in income and wealth inequality. Although some Hungarians have been able to take advantage of the new economic opportunities, for many others the 1990s has been a time of privation and economic uncertainty with little in the way of personal economic benefit. Becoming more competitive in international commerce has had a considerable social cost. The transformation from state socialism has contributed to increased social inequality, and this inequality has had a spatial dimension with larger cities and rural areas experiencing a disproportionate amount of dislocation. Trends and changes in both urban and rural areas since 1990 have resulted in a set of social and economic conditions that have altered the attractiveness of rural and urban areas as places to live and work, and consequently the prospects for rural–urban migration and population redistribution. In Fig. 1, we identify a variety of push and pull factors potentially associated with internal migration for both urban and rural areas. Industrial downsizing and the high cost of living may reduce the possibilities for urban population retention, while whole new service industries, the development of a managerial upper class, and new suburban housing may retain or attract population, especially in the urban fringe. Agricultural restructuring and reduced availability of social services are rural push factors reducing the possibilities for population retention, but the low cost of living, possibilities for self-provisioning, available housing, and social network ties may attract dislocated urban workers and retain longer-term rural residents. In the following section, we first briefly describe Hungary’s settlement system and discuss previous research on population redistribution within Hungary. We then present our own analysis of patterns of population redistribution within Hungary between 1980 and 1997.

4. Population redistribution in post-socialist Hungary Research on rural–urban population redistribution in Hungary during the past two decades has yielded contradictory findings. By evaluating the rate of change in population of municipalities associated with a rate of change in rank-size function, Danta (1987b)1 has argued that a deglomerative trend can be observed in Hungary as early as 1976. However, aggregate census data from 1 Danta uses Hungarian Census data consisting of population totals for 96 cities for the years 1870, 1900, 1910, 1920, 1930, 1941, 1949, 1960, 1970 and 1980. Municipalities with less than 10,000 population were excluded from the analysis because they ‘‘generally do not demonstrate demographic dynamism in the contemporary Hungarian setting’’ (5).

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D.L. Brown, K.A. Schafft / Journal of Rural Studies 18 (2002) 233–244 →PULL←

←PUSH→ From URBAN

From RURAL

To URBAN

To RURAL

Industrial downsizing

Agricultural restructuring

Opportunities in new service industries

Informal economic opportunities

High cost of living, including housing costs

Limited formal employment opportunities

Opportunities for managerial upper class

Lower cost of living, and opportunities for selfprovisioning

Housing shortages

Reduced access to services

New housing in the suburbs

Greater housing availability Pre-existing kinship and community social network ties in rural areas

Fig. 1. ‘‘Push’’ and ‘‘pull’’ factors associated with internal migration in Hungary since 1990.

the 1970s show movement into urban areas, and as late as the early 1980s declining village populations were identified as a potentially significant social problem (Major, 1984). Whether a deglomerative trend in Hungary existed as far back as the mid 1970s is debatable. Lacko! (1982), for instance, characterized Hungarian resettlement patterns during this time as relatively static but with continued population growth in large urban settlements and medium sized towns. Bies and Tekse (1980) also argued that internal migration streams in Hungary from 1955 through the 1970s experienced sizable declines especially between villages, in urban to rural migration streams, and in in-migration to Budapest. Increasing urbanization was continuous from the 1950s through the 1980s, largely as a result of accelerated urban industrialization strategies (Enyedi, 1996; Lada! nyi and Szele! nyi, 1998) and likely would have been even more pronounced had investments in urban infrastructure matched industrial development resulting in conditions of underurbanization in which urban population growth lagged behind industrial job creation (Konrad and Szele! nyi, 1977; Kennedy and Smith, 1989). Industrial suburbs began to develop in the far peripheries of Budapest and other manufacturing centers during the late 1960s and 1970s as an alternative to rural–urban migration or long distance commuting (Enyedi, 1996). Blue-collar industrial workers lived intermixed with rural persons and were typically involved in both industrial work and gardening for the market and for self-provisioning. Contemporary suburban development along the metro fringe in Hungary, in contrast, appears to be more similar to the United States and Western European models which are primarily residential locations for both production and professional workers who commute to the urban center

(Kok and Kova! cs, 2000). Moreover, there is evidence that some of the new enterprises that have been established in Hungary since 1990 have located in towns and villages in the metropolitan periphery. This would further enhance the attractiveness of these areas for new residential development. 4.1. Examining trends of population concentration and deconcentration We use two sources of data in this paper. First, we describe overall patterns of population redistribution since 1980 by reanalyzing printed tabulations of Hungarian census data published by the state statistical office. Next, to investigate the dynamics of redistribution we use a municipal-level machine-readable data file acquired from the Hungarian state statistical office which permits us to develop our own classification of places for 1990 and 1994–1997. Our analysis of printed census tables for urban and rural areas (data not shown here) shows slow but steady population concentration in Hungary between 1900 and 1950, accelerating urbanization between 1950 and 1980, and continued but slower urbanization between 1980 and 1990 when 62 percent of Hungary’s population was classified as ‘‘urban’’ (Hungarian Central Statistical Office, 1993). These official tabulations group Hungarian municipalities into villages and towns, which along with Budapest, completely exhaust the nation’s land area. That is, the Hungarian system of statistical geography does not include an ‘‘open space’’ category as is typically used in the United States and in other national statistical systems. Prior to 1983, a municipality gained town status when its population exceeded 8,000, while smaller municipalities were designated as villages. However, in 1983 there was a deregulation of criteria, and larger villages with

D.L. Brown, K.A. Schafft / Journal of Rural Studies 18 (2002) 233–244

less than 8000 inhabitants could attain town status if they had a certain density of urban infrastructure (Hajdu, 1994). As of 1997, 205 places (not including Budapest) were classified as towns and 2921 were classified as villages. Town populations range from several thousand to Hungary’s larger ‘‘satellite cities’’ and county capitals of which Miskolc is the largest with about 200,000 inhabitants (Hungarian Central Statistical Office, 1998). Budapest has almost 2 million inhabitantsFnearly one-fifth of the nation’s population. Official Hungarian statistical practice frequently identifies municipalities with 2000 or more inhabitants as ‘‘urban’’ areas and municipalities with fewer than 2000 inhabitants as ‘‘rural’’. The data in Table 1 show annualized rates of population change between 1960 and 1990 for municipalities aggregated by size. During 1960–1970, the largest places in Hungary, those with over 50,000 inhabitants, grew the fastest, followed closely by urban areas with 15,000–49,999 inhabitants. Smaller areas showed slight decreases in size while places with less than 2000 inhabitants experienced clear declines. During the 1970s all places with over 2000 inhabitants grew, while the smallest places continued to experience losses. Between 1980 and 1990, in the context of overall national population decline, larger places continued to increase their share of population because even though urban population growth had ceased, places with less than 2000 inhabitants continued to lose population at over one percent per year. Hence, prior to 1990 Hungary experienced continuous urbanization. Urban redistribution had slowed but was still apparent in the 1980s, the decade prior to the transition from state socialism and the dramatic restructuring and downsizing of Hungarian industry and agriculture. While urbanization slowed in the 1980s, the census data show no evidence of population deconcentration. In fact, the nation’s overall decline was concentrated in smaller municipalities. Fig. 2 shows the internal migration balance of those classified as permanent migrants2 between Budapest, towns and villages. In 1980–1981 the data show a clear movement out of villages and into towns and the capital. This trend continues during 1985–1986, but at a much decreased level. Migration out of villages decreased about 40 percent, migration into Budapest grew slightly, while migration into towns decreased by about twothirds. Nonetheless, in both 1980 and 1985 the directions of internal migration within Hungary remained constant. That is, migration flows into Budapest and 2 ‘‘Permanent migrants’’ are defined by the Hungarian Statistical Office as those migrants ‘‘having given up his permanent residence (having) indicated another residence in another settlement as a permanent one’’ (Hungarian Central Statistical Office, 1998: p. 612). Annual data are unfortunately unavailable for the years 1981–1984 and 1986–1989. These data exist but were not included in the published aggregate-level figures.

237

Table 1 Annualized rate of population change by settlement population size, 1960–1990 Municipalities, by population size

1960–1970

1970–1980

1980–1990

50,000+ 15,000–49,999 2000–14,999 o 2000

1.60 1.24 0.29 1.17

0.98 1.28 0.07 1.06

0.00 0.12 0.38 1.09

All municipalities

0.36

0.37

0.32

Source: 1990 Population Census Summary Data (Hungarian Central Statistical Office, 1993). Figures based on resident population figures.

towns were positive while villages were net internal migration losers. This all changed by 1990 when villages experienced the first net gain in internal migrants and towns experienced a net migration loss. Net migration into Budapest, while still positive at this point was barely one-third of what it was only five years earlier. From 1991 on, the deconcentration pattern intensified with villages gaining increasing numbers of internal migrants, and towns and Budapest sustaining consistent losses. These data, then, show a clear reversal from concentration to deconcentration before and after 1990. 4.2. Rural growth and/or suburbanization? To what extent is the deconcentration of the 1990s shown in Fig. 2 reflective of suburbanization or of a more diffuse pattern in which more peripheral rural areas account for the deconcentration? To investigate this question we move from our analysis of printed census tables to municipal-level data available on machine-readable files. The advantage of the machinereadable files is that they enable us to produce a settlement typology customized to our particular analytical question. However, machine-readable data are not available for municipalities for each year between 1990 and 1997. This ‘‘T-Star’’ file we obtained from the Hungarian Central Statistical Office includes the births, deaths and total population for 1990, and 1994–1997. We calculated migration as a residual.3 Thirty-eight municipalities were missing relevant demographic data for one or more of the study years post-1990 and so we were forced to exclude these places from our study. Because of this, our analysis slightly under-represents aggregate population figures for 1990 3 Annual numbers of births and deaths are reported to the Central Statistical Office by municipalities. Municipalities have a strong incentive to report annually because state funds for some purposes are allocated to localities on a per capita basis and political representation is determined by the spatial location of the nation’s population. However, we are not aware of any systematic evaluation of this reporting system by the Central Statistical Office.

D.L. Brown, K.A. Schafft / Journal of Rural Studies 18 (2002) 233–244

238

Budapest

Towns

Villages

40000 30000 20000 10000 0 -10000 -20000

1997-98

1996-97

1995-96

1994-95

1993-94

1992-93

1991-92

1990-91

1985-86

-40000

1980-81

-30000

Fig. 2. Net permanent migration in Hungary, 1980–1998. Source: 1990 Population Census Summary Data (Hungarian Central Statistical Office, 1998).

and 1994–1997. In order to determine whether excluding these places resulted in systematic bias based on size or location in the settlement system, we used the 1990 data to compare the group of municipalities we excluded with those which remained in the analysis. We were unable to discover any appreciable differences between these two groups based on these criteria, and therefore we are confident that this methodological decision has not biased our analysis.4 4.3. Residential classification scheme We developed a residential classification scheme that preserves the official distinction between Budapest, towns and villages presented Fig. 2, but disaggregates the latter two categories with respect to population size and location within the settlement structure. The ‘‘town’’ category includes places ranging from a few thousand inhabitants to places with over 50,000 residents, and both towns and villages are located in remote reaches of the country as well as close to large urban centers. Because of this, these categories are not ideally suited to the analysis of suburbanization and peripheral growth. Our solution was to establish a fixed municipal designation based on beginning-of-period data to classify places by size and location within 4 Additionally, during the period of 1990–1997, 57 ‘‘new’’ municipalities were formed out of existing, larger municipalities. In order to keep the data set consistent we have simply aggregated demographic figures where necessary, maintaining a de facto analysis of places as they existed in 1990. The basic strategy of maintaining a fixed beginning-of-period municipal classification follows Fuguitt et al. (1988).

Hungary’s settlement system. We did this by grouping cities of at least 50,000 inhabitants in 1990 into an ‘‘urban center’’ category in order to compare them with less urbanized places. Smaller municipalities, including both towns and villages, were then subdivided into ‘‘near’’ and ‘‘far’’ categories depending on whether or not they were located within 30 km of the center of the nearest urban place with at least 50,000 inhabitants. Our rationale for the 30 km cutoff is that 30 km, in most cases, provides a reasonable outside commuting distance in a country in which most people rely upon public transportation. Finally, to account for Budapest’s urban primacy within Hungary’s settlement system, we identified the subset of municipalities (both towns and villages) within 30 km of the Budapest municipal boundary as ‘‘Budapest suburbs’’5 (see Fig. 3). While all urban classification schemes are subjective to some extent or another, we believe our scheme permits a clear comparison of the demographic dynamics of more and less urbanized areas, and within the less urbanized areas category between places within or beyond the labor market of nearby urban employment centers. This scheme should permit us to determine whether post-1990 population deconcentration in Hungary is attributable to suburban spread within 30 km of the largest centers and/or to growth in peripheral areas. While most municipalities have been consistently classified as villages, towns or county capitals since 1980, the total number of towns and villages has changed slightly. As stated earlier, in order to maintain 5

Our classification scheme is shaped by similar logic as used by Boyle (1995) in his study of population redistribution in England and Wales.

D.L. Brown, K.A. Schafft / Journal of Rural Studies 18 (2002) 233–244 Hungarian Census Residential Categories

239

Reconfigured Residental Categories

Budapest

Budapest Budapest Urban Centers (50,000 inhabitants or more)

Towns NEAR Towns (<30 km to an Urban Center) NEAR Villages (<30 km to an Urban Center)

FAR Towns (>30 km to an Urban Center) Villages FAR Villages (>30 km to an Urban Center)

Fig. 3. Reconfiguring residential categories to account for rural–suburban differentiation. Source: T-Star machine-readable database, Hungarian Central Statistical Office.

comparability of units, we use a constant 1990 classification in this analysis.

4.4. Suburbanization and rural growth since 1990 The data in Table 2 show that between 1990 and 1997, in a period of overall population decline at the national level, villages near to urban centers and the municipalities within Budapest’s suburban fringe experienced population gains, while Budapest, other urban centers and places further than 30 km from an urban place had marked rates of decline. However, if the 1990–1997 period is split into two parts, 1990–1994, and 1994– 1997, we see that Budapest’s suburbs grew by more than one percent per year in both periods while the bulk of the village population growth occurred during 1990– 1994. Between 1994 and 1997 all places experienced population declines with the exception of the Budapest’s suburbs. Note though, that the rate of population decline for ‘‘far’’ villages in 1990–1994 as well as for the entire 1990–1997 period is somewhat less than the national-level aggregate rate of decline, suggesting higher rates of population retention despite overall declines in size. As indicated in Table 2, all residence categories except Budapest’s suburbs experienced population losses between 1994 and 1997. This seems inconsistent with the picture of deconcentration presented earlier in the paper in Fig. 2. For example, the population losses shown for villages in Table 2 does not seem to fit with the reversal from out to in-migration shown earlier. This seeming inconsistency can be resolved by examining the natural and migration components of population change. Since

all residential categories experienced natural decrease during 1994–1997, e.g. had more deaths than births, differences in net migration were extremely important in reducing the variability among categories in overall rates of population change. As indicated earlier, Budapest’s suburbs were the only places to experience population growth during this period and they did so solely on the basis of net in-migration. However, as Table 3 indicates, towns and villages near to urban centers and villages located beyond the urban commuting zone while losing population gained in-migrants which partially offset their natural decrease.6 Hence, their rates of population loss were less than would have been true in the absence of migration. In contrast, Budapest, urban centers, and towns located more than 30 km from a city of 50,000 experienced both net out-migration and natural decrease. One should be cautious in assuming that all suburbs have experienced net in-migration and, conversely, that most peripheral places have experienced net migration losses. In fact, while the ‘‘near’’ category as a whole registered net in-migration of over 25,000 people, only 60 percent of municipalities within the category gained more migrants than they lost. The far category also experienced a slight net migration gain (of 1,650), but only about one-third of places located outside an urban center commuting zone had net in-migration. This suggests that while the suburban category as a whole is more attractive to migrants than other parts of 6 The population figures in Table 3 are the sum for all municipalities within particular categories. Natural increase is determined by subtracting the sum of all deaths within a category from the sum of all live births, with net migration appearing as the residual.

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Table 2 Annualized rates of population change by residential category, 1990– 1997 1990–1994

1994–1997

1990–1997

Budapest Budapest suburbsa Urban centers Total urban

1.10 1.04 0.57 0.57

1.21 1.41 0.52 –0.52

1.14 1.20 0.55 0.55

NEARb Towns Villages Total near

0.20 0.18 0.04

–0.23 –0.03 –0.11

0.21 0.09 0.02

FAR Towns Villages Total far

0.32 0.17 0.24

0.61 0.39 0.50

0.44 0.27 0.35

All municipalities

0.31

0.39

0.34

Source: T-Star machine-readable database, Hungarian Central Statistical Office. Figures are based on end of year population totals. a Budapest suburbs include all municipalities within 30 km of the Budapest municipal boundary. b ’’Near’’ indicates a location less than or equal to 30 km from an urban center. ‘‘Far’’ indicates a location greater than 30 km from an urban center.

Hungary’s settlement system, some suburban places are more attractive to in-migrants than others, and similarly, some distant places are viewed less negatively than others. In the analysis that follows, we investigate two possible explanations for variability in net migration within residential categories. First, we ask whether in-migration in the far category is simply further suburbanization at the edge of our 30 km commuting zone, and then we examine whether in-migration is more likely in places with relatively better employment possibilities. We examine the further suburban spread question by mapping in and out-migration. These data are displayed at the municipal-level with the 30 km urban center buffers indicated in a map of Hungary presented in Fig. 4. The positive relationship between access to large urban areas and net migration shown in Table 3 is also clearly evident in this map, but what is also evident is that many of the far villages that received net inmigration are not simply located directly beyond the 30 km commuting range. That is, the in-migration to far villages as indicated in Table 4 cannot be interpreted simply as further suburbanization. We investigated the association between migration and employment by disaggregating the near and far settlement categories by their level of unemployment in 1994, the beginning of the migration interval analyzed in Table 3. Excluding Budapest and the 20 ‘‘Urban Center’’ municipalities, we ranked all other Hungarian

towns and villages by their unemployment rate7 and then compared those in the highest and lowest quintiles. Not surprisingly, the data presented in Table 4 indicate that in-migration is highly selective of areas with relatively low unemployment. In other words, these data show that the relationship between migration and position in the urban hierarchy as shown in both Table 3 and Fig. 4 is contingent on a place’s economic condition. In every residential category, regardless of the degree of urban development and/or suburban location, migration is more strongly positive where unemployment is the lowest. In two of four categories, near towns and far villages, places with low unemployment experienced net in-migration, while places with high unemployment experienced net out-migration. Towns near to a large city had net in-migration, but the rate was much higher where unemployment is the lowest. In contrast, towns far from large cities experienced net out-migration, but the rate was twice as high where unemployment is the highest. None of Budapest’s suburbs fell into the highest unemployment quintile so we could not determine whether migrants are selectively drawn to better-off areas in the capital region. However, a number of Budapest’s suburbs did fall into the second highest quintile, and similar to the relationship described above, these places experienced net out-migration. We are aware of the fact that living in an area of low unemployment does not guarantee one a job, and that residents of high unemployment areas can commute to jobs in nearby areas. However, the data in Table 4 provide a solid basis for concluding that post-1990 inmigration to remote rural villages has focused on areas with relatively low official unemployment.

5. Conclusion In this research, we have described population deconcentration trends occurring in Hungary during a period of time coincident with post-socialist economic restructuring. We showed that Hungary experienced slow but consistent urbanization and population concentration throughout most of the 20th century. While the rate of population concentration slowed during the 1980s, reflecting the under-urbanization characteristic of socialist East-Central Europe, it was nonetheless positive during this time. The early 1990s, in contrast, marked a demographic break in which there has been a clear trend towards population deconcentration. Deconcentration was more pronounced between 1990 and 1994, with migrant streams directed primarily toward 7 Official unemployment indicates only persons who have registered at local labor offices. This admittedly underestimates local economic distress, but there is no reason to believe the underestimate varies across settlement types.

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241

Table 3 Components of population change by residential category, 1994–1997 Number 1994 1997 Population change, 1994–1997 of places population population Total change Rate of changea Budapest Budapest suburbs Urban centers Total urban

Natural increase, 1994–1997

Net migration, 1994–1997

Natural increase

Net migration Rate of net migration

Rate of increase

1 118 20 139

1,930,014 1,861,383 668,884 697,873 1,948,288 1,918,000 4,547,186 4,477,256

68,631 28,989 30,288 69,930

1.21 1.41 0.52 0.52

48,511 2,829 13,203 64,543

0.85 0.14 0.23 0.48

20,120 31,818 17,085 5,387

0.35 1.55 0.29 0.04

Near Towns Villages Total near

86 1,515 1,601

1,104,437 1,096,791 1,819,807 1,818,114 2,924,244 2,914,905

7,646 1,693 9,339

0.23 0.03 0.11

9,606 25,490 35,096

0.29 0.47 0.40

1,960 23,797 25,757

0.06 0.44 0.29

Far Towns Villages Total far

91 1,201 1,292

1,245,900 1,223,261 1,380,372 1,364,125 2,626,272 2,587,386

22,639 16,247 38,886

0.61 0.39 0.50

14,847 25,689 40,536

0.40 0.62 0.52

7,792 9,442 1,650

0.21 0.23 0.02

All municipalities 3,032

10,097,702 9,979,547

118,155

0.39

140,175

0.47

22,020

0.07

Source: T-Star machine-readable database, Hungarian Central Statistical Office. Figures are based on end of year population totals. a All rates are annualized.

Table 4 Population change in non-urban places disaggregated by residential category and unemployment level, 1994–1997

Budapest suburbsc Near towns Near villages Far towns Far villages All non-urban

Unemployment levela

No.

1994 population

1997 population

Lowest Highest Lowest Highest Lowest Highest Lowest Highest Lowest Highest Lowest

70 2 17 219 343 7 13 372 157 600 600

534,300 15,282 222,956 180,979 362,784 50,213 215,352 334,104 206,170 580,578 1,541,562

562,505 15,122 223,149 181,475 363,388 49,145 213,120 329,359 205,385 575,101 1,567,547

Natural increase 1,668 13 2,347 160 5,495 467 975 2,877 3,551 3,491 14,036

Rate of natural increaseb 0.10 0.03 0.35 0.03 0.50 0.31 0.15 0.29 0.58 0.20 0.30

Net migration 29,873 173 2,540 656 6,099 601 1,257 1,868 2,766 1,986 40,021

Rate of net migration 1.82 0.38 0.38 0.12 0.56 0.40 0.20 0.19 0.45 0.11 0.86

Source: T-Star machine-readable database, Hungarian Central Statistical Office. Figures are based on end of year population totals. a Municipalities within this table are classified according to whether their 1994 unemployment rates were in the highest quintile or the lowest quintile. Nine municipalities had missing values for 1994 unemployment figures. The 20 ‘‘urban center’’ municipalities and Budapest are not included in this table’s analysis. b All rates are annualized. c There were no municipalities in the suburbs of Budapest that fell into the highest unemployment quintile in 1994.

villages in the urban fringe, and secondarily toward peripheral towns and villages. While deconcentration has diminished since 1994, and only Budapest’s suburbs and villages in urban commuting zones gained population, net positive migration took place in towns and villages near to large cities and in villages far from city influence. This demonstrates that post-1990 population deconcentration in Hungary involves two distinct migration streams: a pronounced movement toward metropolitan suburbs and a smaller, yet significant

movement to remote villages in the nation’s rural periphery. One might be inclined to interpret these divergent migration streams as representing two distinct social processes: movement toward opportunity in metropolitan suburbs and the flight of dislocated urban workers to remote towns and villages, but our analysis in Table 4 suggests a simpler process. Regardless of suburban or rural destination, net in-migration appears to be contingent on the availability of economic

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Fig. 4. Map of Hungary showing urban centers and patterns of net migration, 1994–1997. Source: T-Star machine-readable database, Hungarian Central Statistical Office. Figures are based on end of year population totals.

opportunities. Migration is strongly positive where unemployment is lowest. The movement of population into Hungary’s most rural areas, villages far from urban centers, may indicate that the social inequality generated by capitalism is concentrating in rural areas. The rural migration trend is apparent even during 1994–1997 when movement into smaller areas became less pronounced. However, as shown in Table 4, this migration is highly selective of rural destinations with relatively low unemployment rates. Thus, rather than concentrating in rural areas with few employment opportunities, the data suggest that dislocated urban workers avoid moving to rural areas with excessively tight employment possibilities. Lada! nyi and Szele! nyi (1998) observed that in the 1990s rural villages have become resettlement destinations for economically marginal populations. They argued that this migration has the potential for creating chronic pockets of rural disadvantage and increasing economic stratification along the urban–rural continuum. While this observation is consistent with Hugo and Bell’s (1998) research on ‘‘welfare-led migration’’ in Australia, and Fitchen’s (1995) study of the migration of poor people to depressed rural areas in the United States, our finding that rural places with the highest unemployment have not experienced net in-migration may tend to moderate this concern in Hungary. However, our use of net migration data obscures both the gross volume of migration and the selectivity of in and out-migration flows. In other words, consistent with Lada! nyi and Szele! nyi, poverty could become increasingly concentrated in rural areas regardless of their net

migration rate if higher status persons are leaving and are being replaced by an equal or greater number of inmigrants with lower human capital and other types of economic and social resources. Because of this, we are still not certain what this migration to remote rural areas means for the destination communities. They may, as Lada! nyi and Szele! nyi (1998) argue, be becoming poorer because of the inmigration of these populations who then effectively become ‘‘trapped’’ because of limited opportunities elsewhere. In other words, this dimension of Hungarian population deconcentration may not reflect positive movement toward opportunities or amenities as is true of ‘‘counter-urbanization’’ in much of the west, but rather result from economically coerced moves by persons with no other viable options. Moreover, many in-migrants are undoubtedly steered to rural villages by the social network resources of kin and community (see e.g. Schafft, 2000; Sik and Wellman, 1999), resources which were likely overburdened to begin with. This calls into question the ways population deconcentration might be considered as an indicator of social and economic change. While deconcentration in the United States, Western Europe and other developed countries is frequently thought of as evidence of a ‘‘rural revitalization’’ or reflecting ‘‘lifestyle decisions’’ of movers, Hungarian deconcentration, at least with respect to towns and villages located outside of the suburban periphery, may be the result of in-migration that is occurring precisely because their underdevelopment makes them attractive resettlement locations. While migration destinations in far villages are not the most

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economically distressed in rural Hungary, they still possess very limited opportunities for anything exceeding a subsistence level of household livelihood. In contrast, the suburban aspect of Hungarian deconcentration seems to be associated with the ‘‘pull’’ of jobs, housing, or amenities in a similar way as in the United States and Western Europe. Similar to the western model, suburban growth in Hungary is occurring in areas with the lowest unemployment, a fact that seems to support the notion of suburban living as having increased opportunities and amenities. The Hungarian case suggests the need to examine demographic shifts which may (or may not) have occurred in other countries within East Central Europe since their transitions from state socialism. This is perhaps among the most compelling unknowns: to the extent that Hungary has experienced population deconcentration in coincidence with the transformation from state socialism, is this a phenomenon which is unique to Hungary, or which can be generalized as part of the regional process of social and economic change? Research by Ioffe and Nefedova (1998) shows that suburbanization and counter-urbanization, along the western model has not occurred in post-socialist Russia. While an acute urban crisis resulted in rural growth during 1992–1995, urban areas have reasserted their dominance since then. But what is the situation in Poland, the Czech Republic, Slovakia, and Hungary’s other post-socialist neighbors? Because population redistribution since 1990 appears to involve movement into both suburban and more remote areas, this research can provide important insights into the ways in which inequality is produced and reproduced along spatial dimensions. Although the data used in this research do not permit us to analyze the selectivity of migration among particular categories of urban, suburban and rural places, or the socioeconomic effects of migration on either origin or destination areas, we can speculate about these issues. First, it seems clear that the migration streams are diverse with respect to socioeconomic status and motivations for moving. We feel confident in speculating that suburbanward migration has been largely positively selected, while migrants to rural areas are largely industrial refugees. We lack direct evidence to support this view, but it seems certain that many suburban in-migrants are professionals, managers and administrators in the country’s emerging capitalist middle class. For these persons, movement to the suburbs is quite likely a positively selected search for amenities, better housing, and for residential proximity to jobs in new service industries. In contrast, given the wide-scale economic dislocation that has occurred in Hungary since 1990 as a result of its fundamental economic restructuring, it seems likely that much post1990 internal migration involves out-migrants from Budapest and other large cities who were displaced

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from industrial jobs, and who are seeking enhanced economic opportunities in suburban areas or lower costs of living and opportunities for self-provisioning in rural locales. As noted, these observations are speculative. A definitive examination of the impact of post-socialist internal migration would require further demographic analysis of data that include information on the socioeconomic characteristics of in and out-migrants, and case studies conducted in destination communities which determine motivations for moving and migrant adaptation in their new communities. In conclusion, we have provided clear evidence of population deconcentration in Hungary during postsocialism, but further research needs to clarify what these demographic trends and changes mean for the region’s social and economic development and well being. Additional research focused on the selectivity of migration and on migrant adaptation will clarify the ways in which migration benefits destination communities, exacerbates existing problems, and/or creates new difficulties. Answers to these questions will strengthen the empirical basis for the region’s urban and rural development policies. Such policies can help postsocialist countries in East-Central Europe moderate the trend toward spatially concentrated poverty which many observers fear is a ‘‘natural’’ outcome of the transformation from state socialism to market-driven economies.

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