MIGRATION AND COMMUTING:
TWO POTENTIAL FORCES REDUCING
REGIONAL INEQUALITIES
IN ECONOMIC OPPORTUNITIES?
by Gábor Kertesi
SOCO Project Paper No. 77b
Vienna 2000
MIGRATION AND COMMUTING: TWO POTENTIAL FORCES REDUCING
REGIONAL INEQUALITIES IN ECONOMIC OPPORTUNITIES?
Gábor KERTESI*
2000
Abstract
The extent to which regional disparities in employment opportunities in Hungary were alleviated
since the onset of transition by the two specific market mechanisms on the labor supply side:
migration and commuting, are herewith examined. First, based on aggregate in and out-migration
data by settlement we show that the magnitude of migration is, contrary to common belief, far from
negligible in Hungary and that migratory behavior also reacts to economic incentives. Regions with
high levels of unemployment suffer migration losses, whereas those with low levels of unemployment
make migration gains. However, the magnitude of this effect is quite modest. Using the results of
some settlement level computations and putting them into the framework of a simple simulation model
we show that even a migration of considerable size will not lead to a sufficient narrowing of the
regional unemployment rate differentials over a long time interval. The other potential equalizing
mechanism, the problem of daily commuting of the rural population to surrounding towns is also
examined. The impact of transport costs on the openness or closure of local labor markets is the key
issue here. Using aggregate unemployment data and estimated rural-urban travel costs by settlement,
we show that the equalization of regional unemployment rate differentials is strongly limited by the
high costs of commuting and the resulting segregation of the local labor markets. High commuting
costs result in large local unemployment rates, as disadvantages of the rural labor markets are less
eased by the existence of nearby towns in this case. Aggregate predictions are confronted with actual
commuting data in the last section. A participation model with commuting costs is set up and tested
on a large representative database from 1996. How does the geographical position of villages – the
costs of availability of better urban labor markets – affect the probability of work for those people
who could not find jobs or did not have job offers rewarding enough in their own places of residence?
This is the question posed in this section. We found strong effects, particularly for male labor force
and a particularly strong impact by schooling. Education raises the chances of employment by
commuting enormously: travel cost induced job finding differentials are very large for unskilled
workers, whereas similar travel costs have only trivial consequences to job finding chances of people
with higher education. Economic backwardness of the larger region as an additional factor may
aggravate further the bad employment prospect of low educated rural job searchers. Regional and
transport disadvantages may create a 35-50 percent differential in job finding probabilities for
people with low education. Regional inequalities of this size are intolerable.
Research for this paper was made possible by the “Social Consequences of Economic Transformation
in East-Central Europe” (SOCO) program (Institute for Human Sciences, Vienna), which is financed
by the Austrian Federal Chancellery’s “Fund for Co-operation with Central and Eastern Europe,”
and by the Ford Foundation, New York.
Edited and revised by Dr. Clare Tame. Editorial Manager: Dr. Marianne Obi (SOCO).
The views expressed in this paper are entirely the authors’ own, and do not necessarily represent those
of SOCO/IWM.
*
Senior Researcher, Institute of Economics, Hungarian Academy of Science
2
Gabor Kertesi
1.
Migration
An analysis of the role of geographical mobility, or migration, may well help alleviate the
pressure of unemployment in crisis-ridden areas by indicating how and where to allocate the labor
force more efficiently. To what extent have individual migration decisions in Hungary been affected
by purely economic variables (i.e. earnings and employment prospects) during the transition period?
Research in this, as in many other related areas, in Hungary is seriously constrained by the
availability of reliable and representative data. Large-scale individual and longitudinal geographical
mobility micro-data is non-existent. What we do have are data files of the 1990-1994 community
database called TSTAR, on the in-migration and out-migration flows for specific years by settlement
provided by the Central Statistical Office which consists of a large number of potential explanatory
variables, and allows us to analyze the determinants, and some consequences, of internal migration.
We set up migration models for the community and district levels for the periods 1980–1990
and 1990–1994, and for net and gross migration flows. District level data are created from the
community database by aggregation. Units of observation are districts of the local labor centers
responsible for registering the unemployed and issuing unemployment benefits. We have divided the
country into 170 districts by local labor centers. Each center is located in a town, although some cover
more than one town. Given the explanatory value of urban vs. rural residence migration patterns, we
distinguished between towns and villages in the original labor center districts, the latter being
nominated village-districts. The total number of towns and village-districts is 353. Gross migration is
defined as the total in-migration flow into a settlement, or total out-migration flow from a settlement,
and net migration is the difference between the two. Since the data only provide information on
marginal flows, as opposed to transition flows, gross migration flows cannot be aggregated at the
district level. Thus, in what follows we present a model for net migration by district and a model for
gross migration by settlement for the period 1990–1994.
1.1.
Migration patterns: 1980–1990 and 1990–1994
In Hungary, migration affected about 3.5–4.6% of the population in the early 1990s (see
Table 1). The trend is slightly decreasing: the number of migrants dropped from 474,000 in 1990 to
360,000 in 1994.1 The internal composition of the migrant population is fairly stable by type of
settlement with the exception of Budapest, which had a migration gain of 12,000 in 1990 that turned
into a 10,000 migration loss by 1994, mainly as a reason of increased sub-urbanization. In order to
determine whether geographical mobility patterns have changed after the transition to a market
economy relative to the 1980s we need comparable data. Thus, net migration balances (the differences
of in-migration and out migration flows) were re-calculated into mean annual net migration flows
measured as percentages of the mean population stock. Figure 1 plots annual migration percentages
for the period 1990–1994 against the same data for the period 1980–1990. The units of observation
are towns (1) and village-districts (0). The figure is broken down into six graphs, each representing a
Hungarian area or region (Budapest, Center, North-West, South-West, North-East, South-East).2 The
same tendencies are characteristic of both periods. Trends for the 1990s are simply a continuation of
the trends for the 1980s in each region, the Center and North-East representing the two extremes.
Districts in the Center region report clear migration gains in both periods, whereas most districts in
the North-East (the most underdeveloped part of the country) suffered migration losses in both the
1980s and the 1990s. District level correlation between migration percentages of the 1980s and 1990s
was 0.5321 (p<0.00001) for the country as a whole.
1
2
The long-term trend is also decreasing; the number of migratory moves has been decreasing continuously over
the period 1955–1993 (see Szauter 1974; Daróczi 1981; and Illés 1995).
The regions are Budapest, Central (Fejér, Komárom, Pest), North-West (Győr-Sopron, Vas, Veszprém, Zala),
South-West (Baranya, Somogy, Tolna), North-East (Borsod-Abaúj-Zemplén, Hajdú-Bihar, Heves, Nógrád,
Szabolcs-Szatmár), and South-East (Bács-Kiskun, Békés, Csongrád, Jász-Nagykun-Szolnok).
3
Migration and Commuting
Table 1
Size and composition of internal migration, 1990–1994
Settlement,
population
Out-migrants
1991
1992
1990
1993
1994
Budapest
N
69,249
%
14
N
56,403
%
14
N
57,139
%
14
N
57,640
%
15
N
52,186
%
15
County capital
Town 20,000 +
85,586
60,272
18
13
72,090
50,379
18
13
72,199
50,208
18
12
70,223
47,860
18
12
61,309
44,084
17
12
Town 10,000–
20,000
Town -10,000
40,817
9
35,212
9
35,690
9
34,456
9
31,703
9
23,995
5
20,521
5
20,012
5
20,118
5
18,261
5
Village 5,000–
10,000
Village 2,000–
5,000
Village 1,000–
2,000
Village -1.000
Total
32,609
7
27,600
7
27,654
7
27,230
7
24,756
7
67,539
14
57,437
14
59,527
14
57,351
14
53,761
15
48,638
10
41,264
10
42,985
11
41,146
10
38,396
10
39,104 10 37,373 10 35,105
404,518 100 393,397 100 359,561
In-migrants
1991
1992
1993
10
100
45,919 10
474,624 100
Settlement,
population
Budapest
County capital
Town 20,000 +
Town 10,000–
20,000
Town –10,000
Village 5,000–
10,000
Village 2,000–
5,000
Village 1,000–
2,000
Village -1,000
Total
% population
38,084 10
398,990 100
1990
1994
N
81,000
86,240
56,845
39,789
%
17
18
12
8
N
63,470
77,150
49,877
33,504
%
16
19
12
8
N
60,441
76,902
50,495
34,006
%
15
19
12
9
N
56,521
73,292
48,699
33,547
%
14
19
12
9
N
43,606
64,385
46,225
31,252
%
12
18
13
9
22,463
33,269
5
7
18,753
28,160
5
7
19,347
29,653
5
7
18,740
30,484
5
8
17,386
30,234
5
8
66,607
14
55,451
14
57,419
14
57,272
14
54,695
15
46,814
10
38,203
10
39,733
10
39,797
10
38,265
11
41,722
9
474,749 100
4.58%
–
34,867
399,435
3.86%
9
100
–
36,823
404,819
3.91%
9 35,297
100 393,649
–
3.81%
9 33,823
9
100 359,871 100
– 3.50%
–
4
Figure 1
Gabor Kertesi
Changes in regional annual net migration balances (differences of in and out migration flows), 1980–1990 and 1990–1994
(1= towns, 0 = village districts)
5
Migration and Commuting
What impact have the emergence of private enterprise and unemployment in the early 1990s
following the onset of transition had on patterns of geographical mobility in Hungary? Taking the
unemployment rate and the density of private enterprise as two compact indicators of economic
development, we relate their values to the migration balances of both the current and precedent
period. A comparison of predicted3 migration balances between the pre- and post-transitional periods
shows the amplification of mobility impact of regional development differentials (see Figure 2). In
those with very low unemployment rates and high density of private business in the 1990s, migration
gains increased rapidly relative to the 1980s. However, this did not change the position of districts
with high unemployment rates and low business density a great deal.
Figure 2
Predicted annual mean migration balances (1980–1990, 1990–1994), in
dependent variables: first panel: unemployment rate (%), September 1993
second panel: number of economic organizations per 1,000 inhabitants, 1993
1.2
Migration and economic incentives: net migration
In examining the impact of unemployment and the spread of business firms on net migration
differentials we find that low unemployment rates combined with high level of business activity
create a ‘pull force’, and high unemployment rates and a low level of business activity create a ‘push
force’ with respect to migration. In the upper left panel of Figure 3 net migration balances are plotted
against regional (district level) unemployment rates for the period 1990–1994, distinguishing towns
(1) and village districts (0). The fitted linear regression curve indicate a less than strong inverse
relationship: districts with a low of unemployment have positive migration balances, whereas regions
with high unemployment suffer migration losses. Predicted net migration balances are in the 6, +3%
interval. Regions with the lowest unemployment rates face net migration gains of 3%. In the case of a
typical medium-sized town with 50,000 inhabitants this means a population gain of 1,500 inhabitants.
In regions with the highest unemployment rates (predicted) net migration losses amount to the 5–6%
of the base year population during the period 1990–1994. These are mainly village districts with a
mean population of 18,000 inhabitants. As a reaction to the push of unemployment, average
population dropped by about 1,000 over five years. The relationship of business density and migration
balance is of similar strength but moves in a different direction (see Figure 3, upper right panel).
3
Independent variables are the district level unemployment rate (September 1993 when local unemployment
rates reached their peak), or density of private enterprise (number of private entrepreneurs per 1,000
population).
6
Gabor Kertesi
Figure 4 shows the relationship of unemployment and migration by region. The most
developed parts of Hungary are Budapest, the Center and the North-West (low unemployment rates,
positive and high net migration rates), and the most backward areas are in the North-East and most
village districts in the South-East (high unemployment rates and large migratory losses).
The two bottom panels of Figure 3 clearly indicate that the relation between marked
differences in regional human capital stocks with unemployment rate differentials and differences in
the density of business activity. Thus, we need to take the average level of educational attainment by
region into account as an exogenous variable when attempting to explain the regional spread of
migration balances.
The model consists of three equations. Exogenous variables are the human capital stock
measured by the mean number of school years completed by the population (SCHOOLING), and the
proportion of Gypsies in the population4 (GYPSY). Endogenous variables are the density of business
activity measured by the number of economic organizations per 1,000 population (ECORG),
unemployment rate (URATE), and net migration balance (MIGBAL9094).
We hypothesize four kinds of impacts of regional human capital stock on migration balance.
1. In regions with high level of schooling, business density and wages are high, and both the formal
and informal economy are prosperous. Irrespective of the level of unemployment, these types of
districts are attractive targets of in-migration mainly because of the high demand for labor in the black
economy (construction, the retail trade, catering, etc.). As this sort of labor demand does not reduce
unemployment (and paradoxically tends to increase it by stimulating black employment), the impact
on migration balance is independent of the unemployment rate.
2. Educational level also has migration consequences with regard to the unemployment rate. Firstly,
because the employment opportunities for those with a good level of schooling are better,
unemployment rates will be lower in districts where the mean level of schooling is high (a simple
consequence of a property of the arithmetic mean). Secondly, because individual employment
opportunities for those with a low level of schooling are significantly worse in regions with low
average schooling and high unemployment rates. Thirdly, because there is a strong inverse
relationship between regional unemployment rates and earnings, known as the wage curve (see
Blanchflower and Oswald 1994). Thus, high unemployment rates result in net migration losses not
only by reducing employment opportunities, but also by pushing down mean pay levels.
3. Human capital stock can also have an indirect effect on migration. Since business activity is high in
those districts where the mean level of schooling is high, demand for labor is high not only for illegal
employment but also for legal employment. As a consequence, unemployment rates are low where the
density of economic organizations is high, but our model does not confirm this assumption. After
controlling for the effect of schooling (and Gypsy percentage), the less than strong inverse relationship5
between the density of economic organizations and the unemployment rate proved to be a spurious
correlation.
4. Finally, based on former research results (Ábrahám and Kertesi 1998) we assumed that low average
schooling co-moves with a high proportion of Gypsy population, which in turn produces a high rate of
unemployment. As a final link in the causal explanation, low unemployment attracts in-migrants whereas
high unemployment generates out-migrants.
4
Gypsies are the largest ethnic minority in Hungary, and are characterized by a low level of formal education and
a high level of unemployment, and are therefore subject to prejudice and discrimination.
5
The correlation coefficient was –0.5681 (p<0.00001).
7
Figure 3
Migration and Commuting
Upper two graphs: Net migration balance, 1990–1994 (% population of 1990) in the function of unemployment
rate and the density of business activity; bottom two graphs: unemployment rate and density of business
activity in the function of mean number of completed classes (1 = towns, 0 = village districts)
8
Figure 4
Gabor Kertesi
The relationship of net migration balances and unemployment rates by region (xline and yline are unweighted region level
averages, 1 = towns, 0 = village districts)
9
Figure 5
Migration and Commuting
Model I, a causal model of net migration balance, 1990–1994 (N =353 districts,
normalized regression coefficients)
Turning to Figure 5 which gives the results of our Model I, the expected mechanisms 1, 2, and 4
are confirmed statistically by the model. Raw correlation between schooling and migration (+0,4201) can
be decomposed as follows.
1. Schooling creates migration gains via business density and by stimulating informal economy
(SCHOOLING => ECORG => MIGBAL9094: 0.7841*0,3386 = 0.2655).
2. Low schooling raises unemployment rates which makes individual employment and earning
prospects worse thus inducing people to move out (SCHOOLING => URATE => MIGBAL9094: –
0.3772* (–0.3487) = 0.1315.
4. In regions with low average schooling the Gypsy percentage is usually high. Both components
contribute to the high unemployment rates and unemployment results in migration losses (SCHOOLING
=> GYPSY => URATE => MIGBAL9094: –0.5251*0.4523*(–0.3487) = 0.0828.
Summing up, 0.2655 + 0.1315 + 0.0828 = 0.4798. Model I reveals a slightly stronger relationship
between the human capital stock and the migration balance than the raw correlation of the two variables.
The reason being that there are obviously some omitted explanatory variables which would weaken the
predicted strength of this relationship. However, we reached an important conclusion: regions with a
well-qualified labor force produce migration gains primarily because they create favorable conditions for
the development of the informal economy which in turn provides employment opportunities for the most
mobile segments of society.
1.3.
Migration and economic incentives: gross migration
Switching to settlement level, the analysis can be expanded with the separate dimensions of inmigration and out-migration. We should first reiterate one of the most uniform observations made in
migration literature, namely the high correlation between in-migration and out-migration flows. As
Figures 6 and 7 demonstrate, this observation holds true for Hungary. The figures for in-migration flows
(as a % of the population for 1990) are plotted against out-migration flows at settlement level. Figure 6
gives all settlements in Hungary, and in Figure 7 Hungary is broken down by settlement types by
distinguishing towns and villages as well as settlement size. Each graph reveals the close relationship
between in-migration and out-migration. The connection is particularly strong in the case of towns (R2
move between 0.68–0.78, and parameters move between 0.91 and 1.13).
10
Gabor Kertesi
Figure 6
The relationship between in-migration and out-migration, all settlements
(thin straight line is the 45 degree line)
In attempting to explain this phenomenon some argue that since migration selects out the most
mobile segments of society, areas with high levels of in-migration have relatively large numbers of
people who are ‘migration prone’, and thus likely to move again (Greenwood 1975:413). Moreover, a
sizeable number of in-migrants may be disappointed with their new residence and move on further.
Another explanation stresses the mechanism of vacancy chains in the housing market (Hegedűs and
Tosics 1988). If there are no marked demographic differences in terms of birth and death rates or
composition by gender, and the housing market is stable,6 the demand for vacant apartments left by
out-migrants is met finally, sometimes by the intermediation of multiple transactions, by those
families who plan to move into the given settlement.7 Given the lack of micro data we cannot make a
proper test of these propositions, but can, using settlement data, distinguish between the forces that
induce moving-ins and moving-outs (see Model II).
We set up a system of two simultaneously estimated equations, one for in-migration, the other
for out-migration. Since both are highly correlated the model is very sensitive to the choice of
exogenous variables. After several attempts the following ’push and pull’ model was set up.
The in-migration equation forces which pull people into a new settlement are represented by:
• the density of business activity as a measure of economic prosperity;
• pre-tax income per taxpayer (another measure of economic prosperity);
• the percentage of out-migrants per year in the period 1990–1994.
6
7
There are no settlements that experienced sudden increases or drops in population.
For the smooth functioning of the mechanism of vacancy chains, it is not required that in-migrants move into
the same apartment left vacant by an out-migrant family (see Hegedűs and Tosics 1988).
11
Figure 7
Migration and Commuting
The relationship between in-migration and out-migration, towns (continued on next page)
12
Gabor Kertesi
Figure 7 (cont’d)
The relationship between in-migration and out-migration, villages
(thin straight line is the 45 degree line)
13
Migration and Commuting
The out-migration equation forces which push people out of their former residence are
represented by:
• the local unemployment rate;
• pre-transitional agricultural employment share (1990);
• percentage of Gypsy population;
• percentage of in-migrants per year in the period 1990–1994;
• type of the settlement in terms of commuting costs.8
Our original intention was to include the variables unemployment rate and business density as
independent variables in both equations, proved not to be feasible. Trying both alternative
specifications, we found a better fit when unemployment was put in the out-migration equation and
the density of business activity was put in the in-migration equation than the other way about.9
Table 2
The determinants of in-migration and out-migration (Model II)*
Independent
Variables
Out-migrants %/year
Econ. organization/1,000 pop.
Income/taxpayer
Constant
Independent
Variables
In-migrants %/year
Unemployment rate
Agric. employment % (1990)
Commuting type 2
Commuting type 3
Gypsy %
Constant
Dependent variable: Mean % of in-migrants per year, 1990-1994
Coefficient
Standard error t
P>|t|
beta**
0.662
0.033
20.127
0.000
0.225
0.225
0.039
5.772
0.000
1.459
0.005
0.001
5.586
0.000
0.002
0.238
0.295
0.807
0.042
–
Dependent variable: Mean % of out-migrants per year, 19901994
Coefficient
standard error T
P>|t|
beta**
0.361
0.095
3.799
0.000
0.709
0.039
0.005
7.506
0.000
0.108
0.016
0.001
11.642
0.000
0.003
0.364
0.086
4.252
0.000
0.052
0.379
0.097
3.921
0.000
0.528
0.020
0.003
6.448
0.000
0.009
1.829
0.380
4.809
0.000
–
* Simultaneous estimation, 2SLS
Number of observations = 3,044 settlements
In-migration equation: F(3, 3040) = 155.64, Adj. R2 = 0.5396, Root MSE = 1.2268
Out-migration equation: F(6, 3037) = 237.48, Adj. R2 = 0.5423, Root MSE = 1.1961
Exogenous variables: Economic organization/1,000 pop., income/taxpayer, unemployment rate, agricultural
employment % (1990),
Commuting type dummies: type 2, type 3, Gypsy %
** Normalized regression coefficient
The fit of the model is good, and all parameters are significant. The effects are comparable in
terms of strength by normalized regression parameters. With regard to the in-migration equation, the
strongest role is played by the density of business activity measured by the number of economic
organizations per thousand inhabitants. The parameter of pre-tax income is significant but its effect is
8
Settlements are classified by the unemployment rate of the local labor market, and the average unemployment
rate of more distant labor markets accessible at a traveling cost of half mean real earnings for 1993. Type 1
settlements are those whose local unemployment rates are not higher than the (unweighted) mean; type 2 are
settlements with above average local unemployment rates but where some districts with low unemployment
rates are accessible at a given (high) cost; settlements of type 3 have high local unemployment rates and people
cannot find better employment prospects (lower unemployment rates) even at high commuting costs. The
transport situation of these settlements is critical. Local labor markets are defined as accessible at commuting
costs of Ft. 2,000, more distant labor markets are defined by the accessibility at commuting costs of Ft. 6,000
(equivalent to half mean real earnings in 1993).
9
In the reverse case, the R2 would drop (in the case of the in-migration equation from 0.54 to 0.44, in the case of
out-migration equation from 0.54 to 0.16), and in the latter case the parameter of unemployment rate would not
be statistically significant.
14
Gabor Kertesi
very weak. It should be noted that in-migration is induced not by low unemployment rates,10 but by
the high density of business organizations. This is again interpreted as an indication of the importance
of the informal or black economy as an incentive to change residence. The impact of out-migration on
in-migration is the second strongest effect and is interpreted by the ‘vacancy chain’ model. Taken as a
whole, in-migration decisions are controlled by two forces: a labor-market force and a housing-market
force, the importance of the labor-market force in the form of the pull effect of a flourishing informal
economy, being the more important of the two.
In the out-migration equation three effects can be considered as important: in order of
importance they are, in-migration, settlements with extremely bad transport opportunities or
excessively high transport costs (commuting type 3), and the unemployment rate. In-migrations can
induce (future) out-migrations if some of the in-migrants become antagonistic with their new
residence and decide to move again. This indicates the risk involved in migration decisions insofar as
the higher the physical and human costs of migration investment, the higher the probability that this
will have to be corrected by a further move. The relatively strong effect of commuting type 3,
settlements with extremely bad transport opportunities and the much weaker impact of the local
unemployment rate indicate that high unemployment per se is not sufficient to drive people out of a
locality. Out-migration occurs where high local unemployment is accompanied by extremely bad
transport opportunities or extremely high commuting costs which leave most people with no choice
other than to move. It is unclear how successfully these persons settle in their new places of residence.
They may be precisely those in-migrants who move again because their former decisions proved
wrong. In order to test this proposition individual and longitudinal data are needed.
1.4
Migration as a mechanism reducing regional unemployment differentials
We have shown that the magnitude of migration is—contrary to common belief—far from
negligible in Hungary, and that migratory behavior also reacts to economic incentives. That is, people
move out of towns and villages severely hit by unemployment and tend to remain in those that are not.
In other words, regions with high levels of unemployment suffer migration losses, whereas those with
low levels of unemployment make positive migration gains. To what extent then can migration help to
level out the regional differentials in unemployment rates? Here we try to show that even a migration
of considerable size will not lead to a sufficient narrowing of the regional unemployment rate
differentials over a long time interval.
Using the results of the preceding sections and a simple simulation model we take, for
example, a village with 2,000 inhabitants and a high rate of unemployment (say 25%), and a mediumsized town (20,000 thousand inhabitants) with a low rate of unemployment (5%). We want to know
the extent to which the migration between the village and the town would cause, based on the real
trends in migration, a convergence of unemployment rates. To make computation easier we assume
that the ratio of the active population (employed plus unemployed) to total population is 50% in both
settlements, and constant over time. The basis of our calculations are the predicted annual migration
balance, 1990–1994 ( ∆ ) determined by the unemployment rate (urate) in March 1993 according to
the following equation (where the observations are Hungarian towns and village districts).11
∆ = 0.796 − 0.063 urate , N = 353 districts, R2 = 0,29, p< 0,0001.
Using this equation we can predict a 3.9% decrease in the population of the village with a
25% unemployment rate in five years and a 2.4% growth in the population of the town with a 5%
unemployment rate in the same period. To guarantee the comparability of village districts we imagine
the migration process as if ten villages of the same size and unemployment rate and a town with a
population ten times this size were concerned. In this case the processes of migration would be as
follows:
• the ten villages with a high unemployment rate (25%) would lose 3.9% of their population,
altogether 3,900 active (employed or unemployed) persons;
• the town would experience a 2.4% gain of active population, i.e. 240 persons;
10
11
The variable unemployment would not even be statistically significant in the in-migration equation.
Equation taken from the first panel of Figure 2.
15
Migration and Commuting
• the other active persons migrating from the ten villages (150 people) would spread across
settlements with 5–25% unemployment rate and thus do not interest us.
In our example of settlements with a high unemployment rate emitting, and those with a low
unemployment rate receiving the migratory population, to what extent would the migration with the
above-mentioned characteristics narrow the original 20 percentage point unemployment rate
differential?
The effect of migration on the equalization of unemployment rates depends on the
composition of the population (employed or unemployed) leaving their former residence, and on the
future labor market-status of this population in their place of residence (will they be employed or
unemployed?). Given the absence of empirical information on the probability of this transition we
experimented with alternative probabilities and report the different resulting scenarios (see Table 3).
Table 3
Assumed transition probabilities
Transition probabilities
udif1
udif2
udif3
Udif4
Pr(r = E | e = E) = pEE
Pr(r = U | e = E) = pEU
2/3
1/3
½
½
1/3
2/3
½
½
Pr(r = E | e = U) = pUE
Pr(r = U | e = U) = pUU
1/3
2/3
½
½
2/3
1/3
2/3
1/3
r = labor market status in the receiving settlement
e = labor market status in the emitting settlement
E = employed; U = unemployed
In the first scenario, any person changing their place of residence, independent of their
original labor-market status, has twice the probability of maintaining, as opposed to changing, their
former labor-market status in the new place of residence. In the second scenario, we assume that these
two events have the same probability. The third scenario is the opposite of the first: in this case the
change of residence results in a twofold probability of changing, as opposed to maintaining, labormarket status. The fourth scenario is “every politician’s dream comes true” - the migrating
unemployed have a probability twice as high of becoming employed in their recipient settlement than
of remaining unemployed, while the migrating employed persons’ expected chance of employment
are the same as their expected chance of unemployment.
Independent of the set of assumptions on which we base our calculations, the expected
difference in the unemployment rate of the two settlements also depends on the ratio of unemployed
persons (u) in the migrating population. The above difference (udif) as a function of u is the
following:
 250 − u39 500 + u 240 pUU + (1 − u )240 p EU
−
10240
 961
udif = 100 

.

The value of the udif function depends on the choice of assumed scenario or transition
probability (see Figure 8). It is evident that the results depend less on the chosen transition probability
than on our expectations, based on realistic considerations, about the ratio of unemployed persons in
the migrant population of villages hit by high unemployment.
Using the only large database available, that of the Community Development Research, which
contains individual level information on migration for the adult population and former employment
status, we can estimate the number of people who were unemployed at some time during the ten years
before the date of the survey. This in turn allows us to estimate the maximum ratio of unemployed in
the population that left their former place of residence for different migratory directions (see Table 4).
Unfortunately, the survey only contains uncoded information on the former settlements of
migrants so that our estimation of the exact settlement only contains information about settlement
type (village, town or county capital) of the former residence and whether the emitting and receiving
16
Gabor Kertesi
communities were in the same region. This left us with no choice than to examine the most depressed
regions where we can reasonably assume that village-to-town migrations are motivated by high intraregion unemployment rate differentials. As a control group we selected the most prosperous regions
where unemployment rate differentials between villages and towns are small, and do not play a major
role in migratory decisions.
Figure 8
The change in the difference between the unemployment rates of the emitting
settlement with high unemployment (25% and the receiving settlement with low
unemployment (5% in five years as a function of the ratio of unemployed
persons (u) in the migrant population
udif1
udif3
udif2
udif4
21
20
19
18
17
16
15
0
10
20
30
40
50
60
70
unemployed/out-migrating pop.,%
80
90
100
Note: udif1-udif4: the difference in the unemployment rate between the emitting and receiving settlements as a
function of the alternative transition probabilities of change in the labor-market status of the transmigrating
persons (see Table 3).
Contrary to expectations, there are no significant differences in the ratio of unemployed in the
migrating population between the depressed regions with high unemployment differentials and
prosperous regions with low unemployment differentials (see Table 4). In both types of region, the
ratio of those hit by unemployment in their former place of residence amongst those migrating from
villages to towns is about the same (30–40%). This ratio appears to be determined by personal
characteristics rather than the characteristics of the settlement, while the regional differences in
unemployment rates affect the total volume of migration.
Fixing the ratio of unemployed persons at around 30–40% of the migrant population the
lesson to be drawn from our example, based on the hypothetical functions in Figure 8, is that
migration from villages with a high rate of unemployment to towns with a low rate of unemployment
would only reduce the original 20 percentage point difference in the unemployment rates by about
1.1–1.8 percentage points in five years. Assuming the existence of a steady state, twenty years of
geographical mobility of an unchanged volume, direction and composition, would only suffice to
narrow the original gap of 20 percentage points in the unemployment rates to a difference of about
12.8–15.8 points. Indeed, using a more elaborate model and a British database, Pissarides and
McMaster (1990) conclude that it would take fifty years for geographical mobility to level out the
differences in regional unemployment rates.
17
Table 4
Migration and Commuting
Percentage of persons moving in the given direction who have been unemployed
at some time during the ten years before 1997
Direction of mobility
(from settlement to
settlement)c
Village to county capital
Village to other town
Any to county capital
Any to other towns
Prosperous countiesa
Depressed counties b
Migrant populationd
Average
Migrant populationd
difference in
unemployment
rates
Average
difference in
unemployment
rates
Ratio
unemployed
(%)
N
(%)e
Ratio
unemployed
(%)
N
(%)e
–
14
0.1
30.4
43
9.7
39.0
29.1
35.0
54
58
185
0.5
–
–
30.1
35.7
35.9
76
104
188
7.1
–
–
a
Fejér, Győr-Sopron, Komárom, Pest.
Borsod-Abaúj-Zemplén, Hajdú-Bihar, Nógrád, Szabolcs-Szatmár.
c
The first two rows: all the migrations within the county. The centers of the counties, towns (county centers and
other towns) and villages are those within the counties indicated at the top of the column.
d
The ratio of unemployed in the migrating population (%) = the ratio of those persons in the geographically
mobile population who have been unemployed at least once between 1988 and 1997.
Source: the 3rd wave of the settlement development research of Szonda Ipsos (1997 Fall); N = 26.800 (18 years
old and above) persons, not representative of Hungary, weighted data. The number of cases in columns two and
five are the number of migrating persons (unweighted).
e
The difference of settlement level (weighted) unemployment rates (1993. March).
Source: National Labor Centre’s unemployment register. The means (standard deviations) of settlement level
(unweighted) unemployment rates, in percentage: Prosperous counties: villages = 11,8 (4.7); county capitals =
11.6 (2.0); other towns = 11.2 (3.3). Depressed counties: villages = 24.1 (7.4); county capitals = 14.1 (3.2); other
towns = 17.6 (4.9).
b
2.
Commuting
In this section we estimate the cost of daily commuting from 2,936 Hungarian villages to
surrounding towns, compare these costs to the earnings of the population with a high risk of
unemployment, and draw conclusions for regional unemployment.
The research was motivated by the high variance of unemployment rates in Hungary within
counties and even within labor office districts. We hypothesize that the bottlenecks of the public
transport system, and the binding cost of using a car for daily commuting are partly responsible.
Given that the cost of using a car is about ten times higher in Hungary than in Western Europe, in
terms of wage units, the labor market is heavily exposed to the density and price level of public
transport, much more so than in developed OECD countries. To date, this problem has received
relatively little attention but may well account for more of the unemployment problem than many
sophisticated theories.
Transport costs are basically a function of gasoline prices. A number of reasons favor keeping
fuel prices at their world market level which arguably limits the scope for government action. If fuel
prices in terms of wage units are high this places an inevitable and, apparently, unavoidable burden on
the employment relationship. We believe that this is an over-simplification. Given the particularly
high tax component of fuel prices in Hungary there is much scope for redistributing the implied tax
burden in fiscally neutral schemes involving transport subsidies. Most workers use vehicles to reach
their place of work and now pay a ‘transport tax’ that increases progressively with the distance
travelled. Reducing the cost of commuting may have the desired effect for at least two reasons. First,
transport subsidies can generate labor market mobility and thus shift the Beveridge curve inwards.
Second, transport subsidies may increase the level of aggregate employment by decreasing the
18
Gabor Kertesi
average cost of labor. In recent years transport costs have increased to about half the average wage for
the population with a high risk of unemployment for a distance of 30 km. It is reasonable to assume
that employers were unable to shift this burden to take-home pay, so the typical consequence was that
workers commuting from a distance of 30 km lost their jobs. (We could indeed observe this at a largescale early on in the transition period.) By reducing the tax burden here, and increasing it where the
probability of shifting tax to employees is better, the government could increase the aggregate level of
employment without fiscal upheaval. For an analogy see the arguments for low-wage subsidies in
Nickell and Bell (1995) and Pencavel (1995). Thus, we believe, that the scope for government action
is not as limited as it initially appears, and that the study of the consequences of recent practices is
justified from a policy perspective.
By increasing transport expenditure, the job-seeker is able to expand and improve his labor
market subject to decreasing marginal returns, and constraints imposed by the unemployment rate of
the broader geographical unit in their local area.
In analyzing the scope for such an improvement we estimate the cost of reaching the four
closest towns taking into consideration train and bus connections, patterns of car ownership, and the
cost of driving to work for each Hungarian settlement (town or village). Using the settlement-level
data we estimate travel costs as a function of distance for the whole country. We estimate how the
number of towns ‘available’ at different cost levels explains the variance of unemployment rates in
villages. We draw the boundaries of the labor market for each Hungarian village at different levels of
commuting expenditure and estimate the rate of unemployment for these hypothetical geographical
units. In order to measure how an individual may improve his or her labor market position by
spending more on transport we consider the average unemployment rate of villages and towns at
different expenditure levels.
Since transport costs constitute just one of many variables affecting the local rate of
unemployment our calculations only show the locus of potential improvement due to better transport.
In the last part of this section we present a detailed micro-level investigation based on actual
commuting data. The calculations presented here are limited to the cost of traveling from villages to
towns. We have placed the emphasis on villages on the grounds that an improvement in transport
could have a disproportianately positive effect. From the data on villages and towns we can verify that
the number of available towns, or the cost of travelling, have a statistically significant and strong
impact on unemployment in villages but not in towns. The area covered is nevertheless large, and
almost half the unemployed (49%) lived in villages in the year of our calculations (1993); the
unemployment rate was 17.5% in the median Hungarian village (as opposed to 9.9% in the median
town), almost half the villages (45%) had an unemployment rate over 20%, and 234 villages (8 %)
had a rate over 40%. However, whilst the omission of commuting from towns to villages is not a
major shortcoming, commuting between villages, which has always been intense in Hungary, does
indeed constitute a major limitation in the analysis.
2.1
Transport costs
For the purpose of this research, towns are defined as places of residence with a labor office.
In locating labor offices availability is a key consideration so the labor office districts roughly
correspond to travel-to-work areas. Furthermore, most towns thus defined used to be centers of a
public administration unit. Other settlements are designated as villages.
A maximum of four towns were attached to each village. (Those in a 40 km range by road,
although an exception was made for the labor office center attached to the village irrespective of
distance). It is important to mention that a fifth center within a reasonable distance, from a commuting
perspective, occurs only very rarely.
A town is regarded as available from a village by means of public transport where there is at
least one train or bus arriving at the town from the village between 5.30 a.m. and 7.30 a.m. daily. The
data are taken from the timetables of the state railways and the Volán bus companies. The definition
of availability is clearly on the generous side as it ignores the fact that some jobs are not de facto
available if the arrival time is close to 7.30 a.m. and it also neglects the waiting time implied by an
early arrival.
19
Migration and Commuting
In principle, we could register arrival times using shorter time intervals but the benefits would
have been minimal given the lack of information on the distribution of jobs by the beginning of work
hours. Therefore we preferred an optimistic assumption by which the Type II error could be
minimized. The towns classified as ‘unavailable’ here are probably genuinely unavailable (and
inversely some towns classified as available are in fact not accessible for daily commuters). One
shortcoming of this rather simplistic definition is that it makes no distinction between a single bus or
train and intense connections. This problem is alleviated by the fact that there is strong correlation
between the intensity of the connections and the number of accessible towns and we rely heavily on
this latter measure.
The monthly cost of commuting was measured by the price of a railway season ticket for train
connections, and by the price of a bus season ticket for bus connections. Obviously, we took the total
price (part paid by the employee plus part paid by the employer) into consideration (instead of the
‘part paid by the employee’) since the employment relationship bears the burden of the total price.
Where public transport connections were not available we estimated the monthly cost of traveling by
car. This cost depends on distance, type of car, whether the worker already owns a car or has to buy
one, and on the number of passengers. Thus, the cost of commuting from village i to town k is finally
defined as:
cik = pui (cik1 / n1 ) + (1 − pui )(cik2 / n2 )
where pui is the probability that a randomly selected unemployed job-seeker in village i owns a car;
cik1 is the expected cost of using a car given the distance; cik2 is the cost if a car has to be bought in
order to start commuting; and (n1 , n2 ) is the assumed number of passengers in the two cases. We will
discuss each component of the formula briefly in the following text.
Availability of a car ( pui ): We know the number of cars per capita in each village from the
TSTAR database (1990-1994) ( p Ni ) and the country-wide mean ( p N ) . We also know from the
Household Panel Survey (1993), the probability of an unemployed worker’s household owning a car
(32.7% as opposed to a 55.4% grand mean) ( qu ). The probability that the household of an
unemployed worker owns a car in village i was approximated by pui = qu ( p Ni / p N ).
Cost of driving ( cik1 and cik2 ): The monthly cost of using a car was estimated
as: cik1 = 245 ∗ 2 ∗ d ik [ s1 (v1 + a1 ) + s2 (v2 + a2 ) + s3 (v3 + a3 )] / 12 , where, 245 ∗ 2 ∗ d ik is the
cumulative annual distance traveled;(s1, s2, s3) refer to the proportion of Trabants, other Eastern
models, and Western models in the car stock of the county where village i is located; (v1, v2, v3) stand
for the per-kilometer variable cost of using Trabant, Lada 2104, or Golf II for an annual distance of
245⋅2⋅dik. (in each case the car was assumed to be nine years old with a mileage of 60,000 km); and
(a1, a2, a3) denote depreciation/km attributable to extra mileage associated with daily commuting,
estimated on the basis of mileage-specific differences in the price of used cars.
In cases where an unemployed worker does not own a car the cost includes components above
those associated with extra mileage. (Part of the purchase price, insurance, car weight tax, cost of
compulsory revision, battery replacement, etc.). We assumed that the worker buys the cheapest
Trabant (Forints (Ft.) 40,000) from savings or an interest-free five-year loan and added 1/60 part of
the sales price to cik1 alongside the components mentioned above to obtain cik2 .
Number of passengers. In cases where the household owns a car it can be shared between
members of the family. We choose an optimistic assumption of two passengers so the per-passenger
cost in this case was calculated as cik1 / 2 . 12 In cases where the household has no car the probability
that a new car will be shared by family members is very low. If there are no train or bus connections
12
The average number of passengers is estimated at 1.5 in towns, and 2.4 on highways. However the latter figure
includes tourists, families on vacation, traveling groups of servicemen and other business-related ‘car pools,’ so
an assumption of 2.0 passengers in inter-settlement daily commuting is clearly optimistic.
20
Gabor Kertesi
between village i and town k, and the family has no car, we can rule out the possibility of any member
of the household obtaining a job in k before the selected date in our estimation. To share use of a new
car at least two members of the family must obtain employment in k, at or soon after the selected date.
Taking this case as exceptional, we assume one passenger in the hypothetical case of commuting with
a new car.
Finally, on the basis of these considerations the cost of commuting from i to k with a car is
calculated as:
cik = pui (cik1 / 2) + (1 + pui )cik2 .
It is important to note that the results are not sensitive to the assumptions made about car
usage because the cost of commuting is principally a function of the availability of public transport, as
will be shown later. It should also be pointed out that the shadow price of travel time has not been
taken into account. Obviously, this leads to a major underestimation of total commuting costs (and
strengthens the optimistic character of the estimations).
2.2
Local unemployment
The local unemployment rate is calculated as the ratio of registered unemployed to the active
population in 1990. Due to outward-migration from high-unemployment regions, however, this
underestimates the ‘true’ rate in some areas (see section 1). With regard to multiple registration and
other technical problems we were unable to use vacancy data for a more comprehensive analysis.
We used the survey carried out by Köllö and Nagy (1996) on post-unemployment wages to
obtain wage figures relevant for unemployed job-seekers. Their observations were divided by average
wage increases observed in unemployment inflow samples in January 1993 and March 1994 in order
to obtain comparable cost and wage data for the former date. We used the benchmark values of the
minimum wage (Ft. 8,000) and the mean unemployment insurance benefit (UI) (Ft. 8,920) as proxies
for the reservation wage.
Commuting can improve the market for individual job-seekers if towns with low levels of
unemployment become available for them as a result of spending more on travel. We shall measure
the scope for improvement by comparing local unemployment and the unemployment rate of the
market available at a given cost by the following formula where U and N refer respectively to the
number of the unemployed and the active population:
u i (c ) =
U i + ∑ k ijU k
j
N i + ∑ k ij N k
, where: k ij = 1 if : cij ≤ c,
kij = 0 otherwise .
j
The formula captures the combined unemployment rate of a village and the towns available at
a cost of Forints. c. is the unemployment rate for the inhabitants of a fairly isolated village is for
example such that it does not differ from that of a broader environment which is available at a—not
negligible—cost, e.g. Ft. 6,000. In this particular case: u i (6000) = u i (0).
The calculations refer to January 1993 as do the unemployment figures and the cost data, but
the railway and bus timetables and the wage data are from 1994. The wage data have been adjusted as
was mentioned above and we assume that the timetables did not change considerably between 1993
and 1994.
2.3
Commuting as a mechanism reducing regional unemployment differentials?
The equalization of regional unemployment rate differentials is strongly limited by the high
commuting costs and the consequent segregation of labor markets. In Hungary, traveling to work is
especially costly for those living in villages and crossing districts. Rural residents living in region A
are able to reach the labor market of central town of region A, but cannot normally commute to the
center of neighboring region B, which is only available to the population of the central town A. This
circle (the accessibility of central town B for the rural residents of area A via central town A),
regarding the narrowing of unemployment and wage differences, is not equivalent to the direct effect
21
Migration and Commuting
(the case where low commuting costs enable villagers in district A to appear in the labor market of the
central town of region B). It is easy to show that the constraints on commuting are higher for the
inhabitants of the most backward regions, in this case staggering the equalization of unemployment
rates.
Let us begin with a short review of commuting costs. The size of these costs depends on
whether a job-seeker can travel from their place of residence to the workplace by bus or train.13 The
public transport system generally links towns and villages with the former district centers (roughly
equivalent to the centers of the labor office districts), which makes commuting from villages to towns
in other regions relatively expensive.
Table 5 gives data on the existence of public transport between towns and villages, and
between towns, together with estimated commuting costs. We can see that the Hungarian urban
population can reach two or three other towns at a relatively low cost, while for villagers it is, or
would be, only possible to reach the second-nearest towns by car, therefore at a very high cost.
Table 5
Destination
The ratio of the active population able to reach their workplace by public
transport (by bus or train), and estimated commuting costs to gross monthly
income per taxpayer (%)
What percentage of the active
Commuting costs to gross monthly
population is able to reach their
income per taxpayer (%)?
destination by bus or train between
5.30 a.m. and 7:30 a.m.?
to nearest town
to 2nd nearest town
to 3rd nearest town
to 4th nearest town
From towns
From villages
From towns
From villages
92
86
75
46
91
40
29
20
10
12
14
13
12
28
31
34
Travel costs raise barriers between towns and villages: the higher the costs, the weaker the
equalizing forces. Figure 9 gives the squared cost-distance14 of villages from the two nearest towns by
the average difference in unemployment rate between the village and the two towns. We expect that
the higher commuting costs will generate larger unemployment differentials, as the disadvantages of
rural labor markets are eased less by the existence of a nearby town in this case (see Figure 9). The
estimated linear relationship between the two variables is relatively strong—the coefficient of D in
the fitted equation is 19,8 (t = 20,6)
13
In 1993 a monthly pass for traveling 15 km cost Ft. 2,100 by bus, and Ft. 2,375 by train and the estimated cost
of commuting by car was Ft. 5,126.
14
Denoting the relative cost-distance of the given village from the two nearest towns by k1 and k2 (calculated
from Table 5, columns 3 and 4) the squared cost-distance are given by the following formula: D = (k12 + k 22 ) .
22
Gabor Kertesi
Figure 9
Rural-urban unemployment rate differential as a function of squared villagetown cost-distance
mean village-town u.rate diff.,%
30
20
10
0
-10
-20
0
.2
.4
.6
.8
sq. village-town cost-distance
1
1.2
y = −0.531 + 19.77 D, N = 2671 villages, R 2 = 0.136, p < 0.0001
The equalization of the district level unemployment rate differentials is hindered by the
transport barriers between villages and towns in other regions because the indirect equalizing forces
which function across towns, are relatively weak. Even if it is true that the rural labor markets in
region A adjust to that of the center of region A, which is in turn in alignment with the conditions of
the central town of region B, this adjustment process would be much stronger if the commuting costs
did not inhibit the appearance of villagers on the labor market of towns in the neighboring region.
This hypothesis is tested by a simple regression model. Measuring the impact of the
unemployment rate of the closest and second closest urban centres on the neighboring rural
unemployment rates, we attempt to distinguish between villages in terms of easily accessible urban
centers. If commuting costs are low we expect the second nearest town to have a strong impact on the
rural unemployment rate, and if commuting costs are high this impact is expected to be weak.
Namely, in the latter case the probability of rural job-seekers entering the labor market of the
neighboring urban centers is rather low.
The results indicate that when travel costs to the second nearest town are low, the
unemployment rate of that town has a stronger impact on the village rate; while in the case of costly
commuting the conditions of the rural labor market are mainly affected by the central town of the
same region (see Table 6).
In depressed regions high travel costs constitute a particularly imposing obstacle (see Table
7). Although there appear to be only minor differences in the commuting costs to the center of the
region, travel to the second-nearest town costs more than 1.5 times as much as in the most prosperous
regions.
Although these results cannot be accepted on their face value without qualification without
testing them on direct commuting data, it is probable that the high transportation costs which generate
geographical segregation contribute to the existence of intra-regional, as well as inter-regional
unemployment rate differentials. This test is performed in the next sections where we compare our
predictions with actual commuting data.
23
Migration and Commuting
Table 6
The relationship between rural unemployment rates (%) and the unemployment
rate in the center of the regional capital or the second nearest towna
Regression coefficients
(t-values)
The cost of commuting to the second
nearest townb
below mean
above mean
Rate in the center of the regional capital (u1) 0.958 (22.7)
Rate in 2nd nearest town (u2)
0.231 (6.1)
Constant
1.963 (2.9)
1.215 (35.4)
0.149 (3.7)
2.766 (3.7)
R2
Number of villages
Mean of u1 (st. deviation)
Mean of u2 (st. deviation)
0.3888
1,445
14.7 (5.5)
16.0 (6.6)
a
b
Table 7
0.3735
1,252
13.8 (5.6)
14.2 (6.1)
The villages were weighted by the number of active population.
Commuting costs are as defined in Table 5.
The ratio of commuting costs to the two nearest towns relative to the gross
monthly income per taxpayer (%), means by regions defined based on the
district unemployment ratesa
Unemployment rate in the The ratio of commuting costs to the two nearest
district
towns to the gross monthly income per taxpayer
(%), where the destination is
<10%
10–15%
15–20%
>20%
a
2.4.1
nearest town
2nd nearest town
10
11
14
13
23
29
30
35
The villages were weighted by the number of active population.
A rural-to-urban labor market participation model
The following model is a variant (Cogan 1981; and Heckman and McCurdy 1981) of the
simplest static labor supply model where individual well-being (U) depends on tastes ( ϕ ), and the
amount of consumer goods (c), and leisure hours (l ) consumed in a given period. In maximizing
utility the individual faces a budget constraint set by his initial non-labor income (A), and potential
market revenues (depending on the market wage offers (w), and the amount of time devoted to earning
(h = 1 – l )). The solution to this problem is the number of working hours where market wage offers
available to an individual and his reservation wage (the marginal rate of substitution of consumer
goods for leisure) are equal.
A special case is where we have to model the situation of an individual unable to find a wage
offer rewarding enough in his own place of residence. This is a case when all the wage offers which
prevail in the local community are below his particular reservation wage. In this case we have a
corner solution with maximum leisure and zero working hours ( l ∗ = 1 , h ∗ = 0 ). However, the labor
market of the individual can be enlarged by incurring extra costs: at certain regular travel costs (by
commuting), better labor markets with higher wage offers can be made accessible. Denote these fixed
cost of (daily) commuting by the F symbol. How is the probability of work affected by the extent of
these costs? and How can we measure the strength of this relation, given the problem of commuting
from rural areas to urban labor markets in Hungary?
24
Gabor Kertesi
In Figure 10 the utility function is concave, with ϕ representing the heterogeneity of
individual preferences: U (c,1, ϕ ) . The consumer’s no work (h = 0 → l = 1; F = 0) indifference curve
is denoted by the U 0 symbol:
U 0 = U (c,1, ϕ )
Figure 10
A model of labor market participation with fixed commuting costs
c
V ( A – F, w, ϕ )
w
A
A–F’
U ( A, 1, ϕ ) = U 0
w’R
A–F
wR
0
l R’
lR
1
l
The maximum utility of work is defined by the indirect utility function V:
V = {max U (c, l , ϕ ) s.t. c = A − F + w(1 − l ) }
= U (c ∗ ( A − F , w, ϕ ), l ∗ ( A − F , w, ϕ ), ϕ )
= V ( A − F , w, ϕ ) .
where c* and l* mean optimal choices. The reservation wage is defined by equating maximum utility
by working and not working:
V ( A − F , wR , ϕ ) = U 0
Solving this equation for wR we obtain the reservation wage function:
wR = wR ( A − F , U 0 , ϕ )
The value of wR is such that given the fixed costs of work (F), the consumer is indifferent as to
working for wage wR and not working at all. The lower these fixed costs the lower the reservation
wage: ∂ wR / ∂ F > 0.
The participation decision can be described by the following index function:
25
Migration and Commuting
1
I=
0
working by commuting
if : w > wR
not working
if : w ≤ wR
The market wage offers and reservation wages are not directly observable. The following simple
functional form is used for the market wage offers available for an individual:
(1)
ln w = a 0 + a1i x i + ε .
∑
i
where the elements of the vector x represent wage predictors such as gender, age and schooling. The
disturbance term is denoted by ε . A similar linear functional form is chosen for the reservation wage.
(2)
ln wR = b0 + b1 F + ∑ b2i xi + ∑ b3j z j + κ
i
j
F represents commuting costs, x and z vectors stand for the measurable and κ for the unmeasurable
heterogeneity of preferences. x represents those predictors which are included in the wage offer
equation, and z represents those which are not (such as family status and number of dependent
children). As we have no data on non-wage income (A), the variability due to it is represented only by
the disturbance term. Commuting costs (F) are not directly observable, and are estimated by the
following equation:
(3)
F = d 0 + d1k Tk + d 2l Rl +λ
∑
k
∑
l
Rl dummies represent fixed regional effects. Commuting costs (F) are estimated by a series of dummy
variables (Tk) which measure the gains of commuting in terms of improving employment prospects by
a fixed travel expenditure of Ft. 4,000. Unemployment rates for the place of residence and of a larger
district available at a travel cost of Ft. 4,000 are broken down into the following four categories:
1=less than 10%, 2=10–15%, 3=15–20%, 4=over 20%. Tk dummies represent transitions from one
category to another category in the larger district available at a travel cost of Ft. 4,000. For example,
the village with an unemployment rate of over 20% (initial type “4”) from which a larger district with
less than 10% unemployment rate (type “1”) available at a travel cost of Ft. 4,000 is classified as a
member of the T41 group. The worse the travel opportunities of a village, the higher the relative
commuting costs. The interpretation of the (d144 − d 141 ) cost differential is straightforward. The
inhabitants of T44 need by (d144 − d 141 ) more expenditure if they want to enter a low unemployment
urban labor market (where the unemployment rate is less than 10%) in the same proportion as the
inhabitants of T41. Thus:
d144 > d143 > d142 > d141
d133 > d132 > d131
d122 > d121
The meaning of the parameters of regional dummies are similar. If, for example, the employment
situation in the North-East region is worse than in the Central one, we can expect to obtain d 2NE > d 2C
which means that the labor force of villages in the North-East requires ( d 2NE − d 2C ) more expenditure
on commuting if it is to have the same access to the low unemployment labor markets as those living
in the Central region. Substituting equation (3) with (2) we obtain the following equation:
(4)
ln wR = e0 + ∑ e1i xi + ∑ e2j z j + ∑ e3k Tk + ∑ e4l Rl + u .
i
j
k
l
Assuming normality of all disturbance terms (ε, κ, λ), and denoting the standard deviation of (u – ε )
by σ , and the cumulative density of the standard normal distribution by Φ (.), the probability of
working (by commuting) will be:
26
(5)
Gabor Kertesi
Pr ( I = 1) = Pr(ln w > ln wR )
= Pr (a 0 + ∑ a xi + ε > e0 + ∑ e1i xi + ∑ e2j z j + ∑ e3k Tk + ∑ e4l Rl + u )
i
1
i
i
j
k
l
 u − ε a 0 − e0

−e
−e
− el
a −e
= Pr 
≤
+∑
xi + ∑
zj +∑
Tk + ∑ 4 Rl 
σ
σ
σ
σ
σ
k
l
i
j
 σ

u −ε

= Pr 
≤ α + β ′x + γ ′z + δ′T + η′R 
 σ

= Φ( y) .
i
1
i
1
j
2
k
3
The probability of not working is then:
Pr ( I = 0) = Pr (ln w ≤ ln wR )
(6)
= 1 − Φ( y) ,
where: y = α + β ′x + γ ′z + δ ′T + η′R
As to the measurement strategy, a probit model is estimated with the following log likelihood function
(M is the sample size):
(7)
log L = I m Φ( y m ) + (1 − I m )(1 − Φ( y m )) ,
(m = 1, 2, … , M )
∑
m
Parameters of interests are: α , β, γ, δ, η . Since δ = −e3 / σ = −(b1 / σ )d1 and (as a consequence of
∂wR / ∂F > 0 ) b1 > 0 , predictions concerning the effects of the gains acquired by commuting on the
probability of commuting can be reformulated as follows:
δ 44 < δ 43 < δ 42 < δ 41
δ 33 < δ 32 < δ 31
δ 22 < δ 21
2.5
Measuring the rural-urban commuting model
The model outlined in the previous section is tested by the following specification (see Table
8). The problem is limited to the labor market participation decisions of those who meet the following
three criteria simultanously: (i) expressed a preference to work (either by actually working or by
actively searching for jobs); (ii) were unable to find jobs in their own place of residence (two types of
people belong to this set: those working-by-commuting and those searching for jobs); and (iii) live in
villages and consider working in a neighboring town.15 Repeating our question: How does the
geographical position of villages – the costs of availability of better urban labor markets – affect the
probability of work for those unable to find jobs or unable to find sufficiently attractive job offers in
their own places of residence?
We used the only database containing a sufficiently large number of individuals (n=circa
200,000), the Microcensus conducted by the Central Statistical Office in 1996 on a 2% representative
sample of the Hungarian population. Focussing on the special problem of village-to-town commuting
means concentrating on the most severe part of the issue: rural unemployment. Less than 30 % of the
total cases of working-by-commuting represent the town-to-town cases. Slightly over 70% are
commuting cases with rural places of residence. The majority of these cases (56% of the total) are the
village-to-town commuting cases. In what follows we take a closer look at the problem.
Table 9 reports the estimates for both of men and women, whereas Table 10 presents separate
estimates for male labor market participation and female workers.
Figures 11 and 12 present the evidence of the impact of cost-availability of better urban labor
markets, represented in the model by village type dummies, on the probability of working-by15
A town is considered ‘neighboring’ if it is within a 40km distance.
27
Migration and Commuting
commuting. The graphs that show pure marginal effects (probit coefficients from Tables 9 and 10)
strongly support predictions drawn from the theoretical model. Irrespective of whether the overall or
separate male or female equations are concerned, in all but one case we find parameters increasing in
the function of the improvement of the broader district available at given fixed commuting costs.
Moreover, the effects are strong enough, particularly in the most depressed category (where the initial
unemployment rate is over 20%).
Effects are particularly strong for men and much weaker for women. This, together with the
result that married family status inversely affects the probability of working-by-commuting for men
and women, indicates the much stronger importance of commuting in male labor market participation
than in female participation.
The inverse relationship of married family status on commuting decisions for men and women
is consistent with the predictions of human capital models of the household division of labor. If
women are, even slightly, more productive in the household than their husbands, and men are, even
slightly, more productive in the market sector than their wives, both will specialize according to their
comparative advantages. Specialized investments in these two different kinds of human capital
reinforce initial differences which tend to lead to a strong sexual division of labor within the family
leading to a type of complementarity of male and female participation. As wives specialize in
household activities, husbands should earn more and have a higher probability of participating
successfully on the labor market; and as husbands specialize more in labor market work, wives can
allocate more time to household work and are less likely to participate in the labor market (see Becker
1985). This is of key importance considering that over 75% of the rural population of working age is
married or not single. In what follows we shall focus on male labor market participation decisions
where commuting is a key issue.
Using our estimation function (5) we can compute predicted probabilities of commuting for
different groups, and compare them to obtain an idea of the relative strengths of the forces at work.
These predictions are based on the parameters of the male equation (Table 10, upper part). The three
factors of age, family status and number of dependent children are fixed. All our predictions are
formulated for the case of a 30 year old married man, with two dependent children. Using the notation
of formula (5) and Table 8, predictions are made using following the equation where ( Φ(.) denotes
cumulative normal density):
(8)
pˆ ijk = Φ (αˆ + 30 βˆ 2 x2 + βˆi xi + γˆ1 z1 + 2γˆ2 z 2 + δˆ jT j + ηˆk Rk ) .
For example, p̂4, 44 , 4 predicts the probability of commuting for a 30-year old married man, with two
children who have completed primary education, and is living in a type 44 village in the North-East of
Hungary. In the following sections these predicted probabilities will be compared across schooling
categories, village types and larger geographical regions.
28
Gabor Kertesi
Table 8
Variables used in testing the participation model with fixed commuting costs
Variable
Content of the variable
Comments
Commute
Working in a town by commuting?
1=yes; 0=no (unemployed: actively searching for a job)
Dependent variable
Men
1=male; 0=female
Age
If male: 15 ≤ age ≤ 60, if female: 15 ≤ age ≤ 55
Schooling
Five schooling dummies: incomplete primary education;
completed primary education: BASE category;
vocational training school; secondary school; higher
education
Parameter β 1 in formula
(5)
Parameter β 2 in formula
(5)
Parameters β 3 - β 7 in
formula (5)
Married
1=married/not single; 0=not married/single
Number of
children
Number of dependent children in family
Village type
Village type with respect to its own unemployment rate
(u) and the unemployment rate of a broader district
which is available at a monthly commuting expenditure
of Ft. 4,000 (u4). Types are defined as cells in the
transition matrix of the initial (u) and the broader
district’s unemployment rate (u4). Transitions from one
category of u to another category of u4:
1 → 1; 2 → 2; 3 → 3; 4 → 4: BASE category
2 → 1; 3 → 1; 3 → 2; 4 → 1; 4 → 2; 4 → 3
Parameter γ 1 in formula
(5)
Parameter γ 2 in formula
(5)
Both u and u4 are broken
down into four categories:
1 = u, u4 < 10%; 2 = 10%
≤ u, u4 < 15%; 3 = 15%
≤ u, u4 < 20%; 4 = 20%
≤ u, u4
Parameters in formula (5),
with predicted relative
strengths:
δ 44 < δ 43 < δ 42 < δ 41
δ 33 < δ 32 < δ 31
δ 22 < δ 21
δ 11
Region
Larger geographical region the place of residence (the Parameters η1 - η 5 in
village) in (group of counties):
formula (5)
Central: Fejér, Komárom, Pest: BASE category
North-West: Győr, Vas, Veszprém, Zala
South-West: Baranya, Somogy, Tolna
North-Eeat: Borsod, Hajdú, Heves, Nógrád, Szabolcs
South-East: Bács, Békés, Csongrád, Szolnok
29
Table 9
Migration and Commuting
Predicting the probability of working-by-commuting (1 = yes) or remaining
unemployed (0 = no). Working age population living in villages and considering
commuting to neighboring towns (probit estimates)
Variable
Coefficient
Standard Error
t
P>|t|
Male
Age
Incomplete
primary education
Vocational
Secondary
Higher education
Married
Number of
children
Village type
1→1
2→2
3→3
2→1
3→1
3→2
4→1
4→2
4→3
North-West
South-West
North-East
South-East
Constant
–0,009
0,013
–0,723
0,027
0,002
0,087
–0,339
8,375
–8,355
0,735
0,000
0,000
0,394
0,617
1,225
0,185
–0,057
0,031
0,037
0,093
0,035
0,014
12,561
16,594
13,238
5,330
–4,072
0,000
0,000
0,000
0,000
0,000
0,925
0,723
0,527
0,832
0,694
0,602
0,540
0,333
0,287
–0,100
–0,367
–0,154
–0,190
–0,637
0,065
0,057
0,063
0,065
0,088
0,053
0,121
0,054
0,048
0,044
0,051
0,044
0,048
0,074
14,138
12,782
8,356
12,721
7,880
11,399
4,472
6,180
5,957
–2,298
–7,211
–3,459
–3,957
–8,627
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,022
0,000
0,001
0,000
0,000
Log likelihood
LR chi2 (21)
Number
= –6145,78
= 1650,23
= 11,541
Prob > chi2
Pseudo R2
= 0,0000
= 0,1184
30
Gabor Kertesi
Table 10
Predicting the probability of working-by-commuting or remaining unemployed
by gender. Working age population living in villages and considering commuting
to neighboring towns (probit estimates)
Variable
Coefficient
Men
Standard Error
t
P>|t|
Age
Incomplete primary
education
Vocational
Secondary
Higher education
Married
Number of children
Village Type
1→1
2→2
3→3
2→1
3→1
3→2
4→1
4→2
4→3
North-West
South-West
North-East
South-East
Constant
0,009
–0,646
0,002
0,110
4,594
–5,868
0,000
0,000
0,490
0,853
1,325
0,459
–0,046
0,039
0,054
0,122
0,048
0,018
12,451
15,671
10,901
9,568
–2,551
0,000
0,000
0,000
0,000
0,011
0,942
0,764
0,562
0,839
0,855
0,675
0,657
0,332
0,310
–0,139
–0,424
–0,204
–0,204
–0,799
0,085
0,073
0,082
0,085
0,116
0,068
0,156
0,068
0,061
0,057
0,066
0,059
0,063
0,094
11,113
10,437
6,869
9,909
7,346
9,859
4,205
4,848
5,079
–2,445
–6,406
–3,469
–3,235
–8,460
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,014
0,000
0,001
0,001
0,000
Log likelihood
LR chi2 (21)
Number
= –3642,38
= 1297,58
= 7,067
Prob > chi2
Pseudo R2
= 0,0000
= 0,1512
Variable
Coefficient
Women
Standard Error
t
P>|t|
Age
Incomplete primary
education
Vocational
Secondary
Higher education
Married
Number of children
Village type
1→1
2→2
3→3
2→1
3→1
3→2
4→1
4→2
4→3
North-West
South-West
North-East
South-East
Constant
0,014
–0,817
0,002
0,142
5,864
–5,741
0,000
0,000
0,233
0,369
1,138
–0,192
–0,137
0,054
0,053
0,146
0,052
0,023
4,321
7,018
7,776
–3,670
–6,006
0,000
0,000
0,000
0,000
0,000
0,921
0,682
0,476
0,822
0,454
0,530
0,362
0,351
0,273
–0,005
–0,287
–0,077
–0,166
–0,270
0,105
0,091
0,101
0,105
0,139
0,085
0,195
0,089
0,080
0,070
0,081
0,069
0,075
0,118
8,748
7,494
4,696
7,841
3,262
6,233
1,853
3,948
3,420
–0,077
–3,527
–1,111
–2,205
–2,283
0,000
0,000
0,000
0,000
0,001
0,000
0,064
0,000
0,001
0,939
0,000
0,266
0,027
0,022
Log likelihood
LR chi2 (21)
Number
= – 2407,53
=
543,23
=
4,474
Prob > chi2
Pseudo R2
= 0,0000
= 0,1014
31
Migration and Commuting
Figure 11
Marginal effects of the types of village on the probability of commuting (probit
coefficients from Table 2)
villages of initial type 4
villages of initial type 2
villages of initial type 3
villages of initial type 1
1
.8
.6
.4
.2
0
4
3
2
1
Unemployment rate of a broader district available at a travel cost of Ft. 4,000
Note: from 4 to 4, 3, 2, 1: δ 44 , δ 43 , δ 42 , δ 41 , ….. , from 2 to 2, 1: δ 21 , δ 11 , from 1 to 1:
δ 11
Unemployment rate: 1=–10%; 2=10–15%; 3=15–20%; 4=20%+
32
Gabor Kertesi
Figure 12
Marginal effects of village types on the probability of commuting (probit coefficients from Table 2)
villages of initial type 4
villages of initial type 2
villages of initial type 3
villages of initial type 1
villages of initial type 4
villages of initial type 2
1
1
.8
.8
.6
.6
.4
.4
.2
.2
villages of initial type 3
villages of initial type 1
0
0
4
3
2
1
Unemployment rate of a broader district available at a travel cost of Ft. 4,000
Men
4
3
2
1
Unemployment rate of a broader district available at a travel cost of Ft. 4,000
Women
Note: from 4 to 4, 3, 2, 1: δ 44 , δ 43 , δ 42 , δ 41 , ….. , from 2 to 2, 1: δ 21 , δ 11 , from 1 to 1: δ 11
Unemployment rate: 1=–10%; 2=10–15%; 3=15–20%; 4=20%+
33
Migration and Commuting
2.5.1
The impact of schooling on the likelihood of working by commuting
Figures 13 and 14 show the results by combined schooling and village type categories. For
simplicity, regional effects are fixed (at the value of the North-Eastern region), and Figure 13 is
broken down by schooling and shows differences by village type for given schooling categories.
Figure 14 is broken down by initial village type16 and depicts the differences by level of education for
village type.
The results are consistent with human capital theory, which predicts that education increases
the probability of employment, and thus the likelihood of commuting, within each type of village,
enormously. Comparing any of the graphs of Figure 14 vertically clearly proves this. However, the
relative strength of schooling and commuting costs (measured by village type) is asymmetric
depending on the level of schooling. For those with a low level of schooling, as with the unskilled,
(not higher than completed primary education) commuting probabilities vary widely. Differentials
generated by travel costs are particularly high for unskilled workers in the most depressed category of
villages with over 20% unemployment (initial type 4). The range between the two extremes (type 44
and 41) is 20–25% (see Figure 13, panels (a) and (b) and Figure 14, panel (a)). We find exactly the
opposite relationship in the case of workers with a medium to high level of education: the impact of
schooling on the probability of commuting is so strong as to dominate the effect of commuting cost
differentials across all initial village types. At the highest educational level (university) the costinduced differentials vanish almost entirely. Those with a high level of education can find jobs easily,
irrespective of where they live insofar as the wages offered are so much higher than for people with a
low level of education that even high commuting expenditures can be covered.
The probability of finding work-by-commuting for those with a low level of education is
gradually improving by moving upwards in the hierarchy of villages: in the initial type 3 the costinduced differential range between 8–10%, and in type 2 they are almost zero (see Figure 14, panels
(c), (d)).
16
Initial types mean types by community level unemployment rates at zero commuting costs.
34
Gabor Kertesi
Figure 13
Predicted probabilities of working by commuting of men in the North-East by
village types and level of schooling (%)
villages of initial type 4
villages of initial type 2
villages of initial type 3
villages of initial type 1
villages of initial type 4
villages of initial type 2
100
100
90
90
80
80
70
70
60
60
50
50
40
villages of initial type 3
villages of initial type 1
40
4
3
2
u.rate at travel costs of 4000Ft
1
4
(a) incompleted primary school
villages of initial type 4
villages of initial type 2
3
2
u.rate at travel costs of 4000Ft
1
(b) completed primary school
villages of initial type 4
villages of initial type 2
villages of initial type 3
villages of initial type 1
100
100
90
90
80
80
70
70
60
60
50
50
villages of initial type 3
villages of initial type 1
40
40
4
3
2
u.rate at travel costs of 4000Ft
1
(c) Vocational training school
villages of initial
villages of initial
90
80
70
60
50
40
3
2
u.rate at travel costs of
(e) Higher education
3
2
u.rate at travel costs of 4000Ft
(d) Secondary school
villages of initial
villages of initial
100
4
4
1
1
35
Migration and Commuting
Figure 14
Predicted probabilities of working-by-commuting of men in the North-East by village types and level of schooling (%)
100
100
90
90
80
80
70
70
60
60
50
50
40
40
44
43
42
41
(a) From villages of initial type 4 (20% ≤ u.rate )
33
32
31
(b) From villages of initial type 3 (15% ≤ u.rate < 20%)
(d) From villages of initial type 1 (u.rate < 10%)
100
90
99.4 :
98.7 :
96.7 :
91.6 :
76.8 :
80
70
60
50
40
22
21
(c) From villages of initial type 2 (10% ≤ u.rate < 15%)
Higher education
Secondary school
Vocational training school
8 classes
0–7 classes
36
2.5.2
Gabor Kertesi
The problem of selectivity
People living in rural areas who appear potentially in the same type of urban labor markets at
the same commuting cost (Ft. 4,000) have different probabilities of actually finding a job by
commuting depending on the type of village of origin, even if their personal (gender, age, education,
family status, number of children), and contextual (regional) characteristics are controlled. The three
panels of Figure 15 show this relationship by the unemployment rate of the targeted urban labor
market. In panel (a) for example, the best urban markets are targeted: an unemployment rate of below
10%. The commuting probability for villagers from type 11 areas is taken as a base. This base
probability is always higher than the probabilities for those targeting the same urban labor market as a
commuting objective but who come from a village where the local unemployment rate is higher. This
lag is particularly great for the unskilled and those living in the most depressed type of village where
the local unemployment rate is highest (over 20%): the raw probability differentials range between
10–16% for the unskilled living in villages of initial type 4. This lag is even higher in relative terms.
In the case of villages with an unemployment rate of over 20%, differentials expressed in the
percentage of the respective base probabilities range between 12–24% for those with less than
completed primary education, and 5–12% for those who completed primary school, irrespective of the
type of urban labor market (1, 2, or 3) taken as their commuting objective (see Figure 16).
There are a range of possible explanations for why this is the case. One possible explanation
may be unobserved heterogeneity, that is, where villages with extremely bad transport opportunities
have a low quality labor force in terms of unobserved human capital characteristics even if their
observable characteristics are controlled. This is why they are offered systematically lower wages
(than potential commuters from more fortunate villages) which reduces their propensity and ability to
commute. Moreover, the functioning of the housing market may operate as a social mechanism
responsible for the “crowding” of workers with low skills in peripheral villages: low housing costs
attract this sort of people, whilst low employment prospects (low quality of life) deter the opposite
kind of people.
Another different explanation may rely on a sort of “wage curve” mechanism where
employers in urban labor markets exploit the difficult situation of those workers who live in villages
with extremely high levels of unemployment rate, by offering them sub-standard wages which
systematically reduce their ability to cover commuting costs.
37
Migration and Commuting
Figure 15
The probability of working-by-commuting for men relative to those from villages of the respective base category. Probability
differentials are presented by level of schooling and regional effects are fixed at the level of the North-East) (%)
16
16
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
0
41
31
21
(a) ∆ i1 = Pr ( I = 1 | T11 = 1,...) − Pr ( I = 1 | Ti1 = 1,...)
16
14
12
10
8
6
4
2
0
43
33
(c) ∆ i3 = Pr ( I = 1 | T33 = 1,...) − Pr ( I = 1 | Ti 3 = 1,...)
11
42
32
22
(b) ∆ i2 = Pr ( I = 1 | T22 = 1,...) − Pr ( I = 1 | Ti 2 = 1,...)
38
Gabor Kertesi
A third explanation is the possible impact of discrimination against potential commuters.
Irrespective of whether they have the same observed and unobserved human capital characteristics as
those from villages with better transport connections or lower transport costs they are offered lower
wages or are refused more often if they belong to a discriminated minority, i.e. a very high proportion
of gypsies live in this particular type of village. Obviously any mix of these three mechanisms may be
at work here. Further research is required to ascertain whether such mechanisms really do operate,
and if so, to what extent.
Figure 16
Relative lags in commuting probabilities by village types 43, 42 ,41 and village
types 32, 31, 21 from their base types in the percentage of absolute probabilities
of the respective base types (maximum and minimum values),%
p ij = Pr ( I = 1 | Tij = 1,...) and ∆ ij = ( p ij − p jj ) → max | min relative lags = max | min ( ∆ ij / p jj ).
upper bound
lower bound
upper bound
25
25
20
20
15
15
10
10
5
5
0
0-7class
lower bound
0
8class
vocation
schooling
second.
high.ed.
0-7class
8class
vocation
schooling
second.
high.ed.
Village types 43 (base: 33), 42 (base: 22), 41 (b) Village types 32 (base: 22), 31 (base: 11), 21
(base: 11)
(base: 11)
2.5.3
Estimating effective commuting costs
Although full identification of the parameters of the structural model (see equations (1) to (3))
cannot be obtained, an estimation of the effective commuting costs (F), or more accurately, an
estimation of some relative costs can be derived from the received parameters of the reduced
equation. Consider the auxiliary equation (3) which determines the unknown effective commuting
costs (F) using community (Tk) and regional (Rl) proxies. Let us set the effect of the larger region and
the village type at d 2R and d1ij level, respectively. Then the effective transport costs of a village in
region R and village type ij can be expressed as follows:
(7)
FRij = d 0 + d1ij + d 2R .
By setting a base, we can lose the fixed and the regional effect. This base is given by those villages
which are in the most favorable situation in terms of commuting costs (type 11):
(8)
F 11 − F ij = d111 − d1ij .
In order to express the unknown structural parameters using the estimated parameters
d 1ij = δ ij /(−b1 / σ ) we need to make a ratio to avoid the biasing factor in the denominator. This ratio
is called „relative cost ratio”, and is defined as follows:
(9)
rij =
F 11 − F ij
δ 11 − δ ij
.
=
F 11 − F 44 δ 11 − δ 44
The relative cost ratio measures the distance in travel costs of a given (ij) type of village from the type
of village with the lowest commuting costs in the percentage of the maximum cost-distance defined
by the cost-distance of villages with the best and worst transport opportunities. By definition:
0 ≤ rij ≤ 1. This cost distance is effective in the strict sense: it reflects the effective travel costs of a
39
Migration and Commuting
representative commuter traveling from his or her place of residence to a nearby urban labor market.
The relative cost ratio is the best means to classify villages on an interval scale. Figure 17 gives
village types by their values of rij, by the exact values of their relative cost ratios (%).
Figure 17
Relative cost ratios (rij ) by type of village (%), regional effect fixed
rij =
F11 − Fij
F11 − F44
=
δ 11 − δ ij
,
δ 11 − δ 44
where: ij = village type
100
90
80
70
60
50
40
30
20
10
0
11
31
21
22
32
41
33
42
43
44
Village type
Figure 17 shows a sharp dividing line in the Hungarian community structure in terms of
effective commuting costs. Some villages are in a good position insofar as their neighboring urban
labor markets can be reached at a relatively low cost, within the 30% range of the maximum costdistance. At the other extreme, we find villages whose relative commuting costs, in terms of rij ratios,
are at least twice as high. This dual structure will be even more apparent if we present the distribution
of villages and those with no employment in their places of residence by the relative cost ratio of their
village type (Figure 18).
Figure 18
The distribution of villages and people unable to find jobs in their places of
residence by the relative cost ratio of their village type
40
800
700
30
600
500
20
400
300
10
200
100
0-10%
20-30%
40%
65-67%
relative cost ratio
(a) number of villages (N = 2,859)
100%
0
0-10%
20-30%
40%
65-67%
100%
relative cost ratio
% unable to find employment in own place
of residence (N = 740,000)
40
Gabor Kertesi
About 1,200 villages (mostly small ones) and 250,000 villagers (33% of those of working age
unable to find a job in their own villages) are affected by extremely high commuting costs.
2.5.4 The impact of the larger region
The size of the region has an important independent role in shaping chances of employment
by commuting. This impact reflects regional disparities in economic prosperity which in turn effect
the overall human capital stock of a larger region and the transport connections of that region with the
national capital and Western European markets (Fazekas 1996; Ábrahám & Kertesi 1998). In this
section we try to measure the consequences of this kind of regional disparity. Figure 19 shows pure
regional effects in the case of potential male commuters with vocational training school. Vocational
training school has been taken as the base because it is the modal schooling category for each region
and type of village. Employment opportunities are presented by village type. Large regional effects
can only be depicted from graphs of the most depressed types where local unemployment rates exceed
20%. Only modest (3–5%) differentials can be found between the most prosperous and the most
depressed regions (Central vs. South-West) in the case of most village types (type 11 to type 41).
However, cross-regional differences do matter in the case of the most backward types of villages: the
Central vs. South-West differential being about 8% for village types 42, 43, and 12% for village type
44.
Figure 20 measures regions by the extent of their contribution to commuting probabilities on
the horizontal axis, whereas the vertical axis measures these probabilities by village type. The same
graph is repeated by five schooling categories and allows us to make the following observations:
The lower the level of education the larger the regional differentials (which is consistent with
the experiences of the impact of village type on employment opportunities). The Central/South-West
differential representing the maximum inter-regional range is 15–16% for those with a low level of
education (0-8 completed primary classes) compared to the 5% difference for those with higher levels
of education (secondary or higher education).
41
Migration and Commuting
Figure 19
Predicted probabilities of working-by-commuting for men with vocational training school by types of village and region (%)
Central
North/South-East
Central
North/South-East
North-West
South-West
100
100
95
95
90
90
85
85
80
80
75
75
North-West
South-West
70
70
44
43
42
41
(a) From villages of initial type 4 (20% ≤ u.rate )
Central
North/South-East
33
32
31
(b) From villages of initial type 3 (15 % ≤ u.rate < 20%)
North-West
South-West
100
(d) From villages of initial type 1 (u.rate < 10%)
95
98.1 :
97.3 :
97.0 :
97.0 :
95.0 :
90
85
80
75
70
22
21
(c) From villages of initial type 2 (10% ≤ u.rate < 15%)
Central
North-West
North-East
South-East
South-West
42
Gabor Kertesi
100
100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
0
CE
NW
NE
region
SE
SW
CE
(a) Incompleted primary school
NW
NE
region
SE
SW
(b) Completed primary school
100
100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
0
CE
NW
NE
region
SE
SW
(c) Vocational training school
CE
NW
NE
region
SE
SW
(d) Secondary school
100
Figure 20 Predicted probabilities of working
by commuting for men by regions (CE=Central,
NW=North-West, NE=North-East, SE=SouthEast, SW=South-West), schooling and village
type (from top to bottom: type 11, 21, 22, 31, 32,
33, 41, 42, 43, 44)
90
80
70
60
50
40
30
20
10
0
CE
NW
(e) Higher education
NE
region
SE
SW
43
Migration and Commuting
• Place of residence (village type) is about twice as important in shaping regional inequalities by
chances of commuting than the larger region. Among those with a low level of education (0-8
completed primary classes), where regional backwardness really matters, the within-regional range17
(Central vs. South-West) is 32–37% for those with less than completed primary education, and 20–
29% for those who completed primary school (the lower number always representing the Central, the
higher number the South-Western region). The between-regional range18 for the same schooling
groups is significantly lower: 13–16% for those with less than complete primary education, and about
16% for those with completed primary education (the lower number always representing the most
prosperous, the higher number the most depressed village type).
• Regional backwardness and high commuting costs are stiff obstacles to employment for those
living in rural areas with a low or medium level of education. This is seen in Figure 21 which reports
commuting differentials between the best villages with the lowest commuting costs in the most
prosperous region (Central) (type 11), and the most disadvantaged villages with highest commuting
costs in the most depressed region (South-West) (type 44) by level of schooling.
Figure 21
Commuting differentials between the villages with lowest commuting costs in
the most prosperous region (Central) (type 11) and the villages with the highest
commuting costs in the most depressed region (South-West) (type 44) by level of
schooling
50
per cent
40
30
20
10
0
0-7class
8class
vocation
schooling
second.
high ed.
As a consequence of the cumulation of regional and transport disadvantages those with a low
level of education may have a 35–50% lower chance of finding employment depending on where they
live. Those with incomplete primary schooling, but living, for example, in the Pest area of the Central
region in a village with very good commuting facilities (type 11) will have a very high (82%)
probability of finding employment by commuting if they are unable to find employment in their own
village, whereas this figure drops sharply to 33% for those living at the other end of the “community
ladder” in remote villages with bad transport facilities in the South-West The same absolute numbers
for individuals with completed primary schooling are 94% and 58%. Regional inequalities of this size
are clearly intolerable.
17
18
The type 11 minus type 44 differential for given region.
The Central minus South-West differential for given village type.
44
Gabor Kertesi
3.
3.1
Conclusions and policy recommendations
Migration: a policy dilemma
Unlike the US (where inter-regional migration has a decisive role in the elimination of
regional crises as shown by Blanchard and Katz (1992), Hungary is characterized by relatively lower
migration rates and a weak impact of migration on regional unemployment. Migration can hardly ever
become as intense as in the US under the institutional conditions and traditions characteristic of the
region: the rental housing sector is small, secondary school students generally go to school in the
family’s region of residence, kinship relations are strong, people adhere to their place of living more
than Americans do, and so on. The impact of migration on unemployment is reduced by the
composition of out-migrants and in-migrants and vacancy chain effects: high out-migration rates
imply high in-migration rates, and the people who move out from the depressed areas tend to have
better endowments than those who move in.
Choosing adequate policies presupposes a deeper knowledge of the actual patterns of
migration as well as an open discussion of preferences. On the one hand, with a lack of data it is
difficult to assess how the composition of the population is affected by migration and how the labor
market status and earnings of the migrants are affected by their change of residence. On the other
hand, politicians should decide how they evaluate the (probably positive) direct impact of moving on
the migrants’ welfare as opposed to the (presumably negative) indirect effect of migration on the
human capital endowments of depressed regions.
Provided there is a priority for supporting migration the most important policy action is the
expansion of the rental housing sector accompanied by the development of mortgage financing. Given
that substantial regional differences exist in real estate prices we cannot expect that people who own a
house in a depressed region will buy property in a prospering district, on a massive scale. The creation
of a rental housing sector presupposes changes in the enforcement of law because the recent practices
favor the tenants versus the owners. The expansion of the social housing sector seems to be an
important prerequisite for the enforcement of owners’ rights.
Equally important is the promotion of mobility among secondary school and university
students. The related policies might include the subsidization of the accommodation and traveling of
non-resident students, the offering of temporary assistant positions and training for young workers
coming from depressed areas.
3.2
Improving the accessibility of urban centers
The benefit from job search and employment at given wages is substantially reduced by the
cost of travel to work in Hungary. Since travel costs are strongly affected by fuel prices (while fuel
prices are adjusted to the EU levels for a number of reasons) the per-kilometer cost of traveling to
work is high compared to wages. This can hardly be changed by government action but there is a
point where policies matter.
Short-distance geographical mobility is heavily exposed to the availability of public transport
in Hungary. At the end of 1997 the average Austrian industrial worker had to work 35 minutes to earn
the price of 10 liters of regular gasoline, and 39 minutes to purchase a train ticket for 100 km. These
numbers indicate, roughly as they do, that the costs of traveling by car versus public transport were
not substantially different for a single driver in this country, unlike in Hungary where fuel prices are
extremely high compared to public transport fares.
The relative costs imply that – in case the cutting of train or coach services necessitates it –
the shift from public to private transport implies extremely high costs. It should also be taken into
account that many people would have to buy a car in order to change their mode of transport.
According to our estimation based on Household Panel Survey data only 28% of the rural households
with unemployed members owned a car in Hungary in 1993. Public transport policies should therefore
play a distinguished role in maintaining the availability of regional centers for village dwellers.
The subsidization of fuel prices seems to be an infeasible option given the enormous deadweight loss implicit in such a policy. The subsidization of public transport companies raises similar
concerns, and therefore generally opposed in the EU, but should be considered in Hungary in our
opinion. Given the external economic effect of public transport there seems to be a strong case for
mixed finance. Projects aimed at maintaining or starting particular coach services, with the financial
45
Migration and Commuting
contributions of the affected local governments and firms, should be considered for state support.
Bringing the middle class back to trains and buses by improving travel conditions, building park-andride facilities, and adjusting the time-tables to office hours could slow down the squeeze of demand
for public transport and help to maintain the accessibility of urban workplaces.
46
Gabor Kertesi
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