Do welfare benefits deter migration?
Jordi Jofre-Monseny ∗
Universitat de Barcelona &
Institut d’Economia de Barcelona (IEB)
May 2012
Abstract: The aim of this paper is to estimate the effects of welfare
benefits on migration by using a national policy providing higher
unemployment protection in the lagging Spanish regions of Andalucía
and Extremadura. The regional implementation of the policy generates a
geographic discontinuity in the treatment status of municipalities.
Focusing on municipalities that are sufficiently close to (but at different
sides of) the border enables us to identify the effect of unemployment
benefits on population growth, out- and in-migration rates and on
unemployment. Our results indicate that higher unemployment benefits
reduced the rate at which rural municipalities lost population between
1981 and 1991, mainly through a reduction in out-migration. We also
find that in-migration and unemployment rates are higher on the welfare
‘generous’ side of the border. The effects found are large and mostly
driven by the behavior of the socioeconomic group with the lowest
educational level.
Key words: Welfare benefits, migration, unemployment.
JEL Codes: H73, J6, R23.
Contact Address: Dpt. d’Economia Pública, Economia Política i Economia
Espanyola, Diagonal 690, Torre 4, Planta 2, (08034)
Barcelona, e-mail: jordi.jofre@ub.edu
∗
I thank Gilles Duranton and Matthew Turner for useful comments as well as participants at the Urban
and Real State Day Workshop held in April 2012 at the Department of Economics of the University of
Toronto. Funding from grants ECO2010-16934 and 2009SGR102 is gratefully acknowledged.
1
1. Introduction
There is ample evidence that one unintended consequence of unemployment benefits is increased
unemployment. For instance, Katz and Meyer (1990) and Bover et al (2002) document a spike in
the probability to leave unemployment at the time unemployment benefits are about to expire for
the US and Spain, respectively. Card and Levine (2000) conclude that a temporary extension of
unemployment benefits in New Jersey caused longer spells of unemployment.
Unemployment benefits might distort migration decisions, too. First, they can limit the
net gains of migrating to areas with better labor market conditions. Second, geographical
variation in welfare generosity can attract welfare-prone individuals to areas offering higher
benefits. In this paper, we analyze the effects on migration of a national policy providing more
generous unemployment benefits in the Spanish regions of Andalucía and Extremadura. The
regional implementation of the policy, coupled with data on mobility at the municipality level,
enables us to credibly estimate the effects of welfare benefits on mobility.
A substantial literature, including Levine and Zimmerman (1999), Gelbach (2004) and
McKinnish (2005, 2007), has studied the mobility effects of US interstate differentials in welfare
generosity. ADFC (Aid to Families with Dependant Children) is the main program providing
variation in welfare generosity across US states. Since this program is aimed at single poor
mothers with children, this literature deals with female mobility. Levine and Zimmerman (1999)
use the National Longitudinal Survey of Youth to regress the individual probability to move-out
from a State as function of the State’s ADFC generosity. This paper adopts a ‘differences-indifferences’ approach by comparing poor single mothers with children to poor single mothers
without children. The results found are economically small and statistically insignificant. With
Census data from the 1980 and 1990, Gelbach (2004) also performs a ‘differences-in-differences’
analysis by comparing single mothers that are high-school dropouts to various suitable control
groups, noting that migration gains in terms of welfare payments are a function children’s age.
For 1980 data, the author finds that the probability to move out decreases with welfare generosity
more for the treated than for the control group. The results for 1990 are less conclusive.
McKinnish (2005) focuses on short-distance moves at US State borders. If geographic distance
reflects mobility costs, out-migration from ‘low’ ADFC States should be higher in border than in
interior counties. This pattern is confirmed by the data although the implied effects are small. In
a follow-up paper, McKinnish (2007) combines this ‘border’ approach with the ‘differences-indifferences’ analysis pursued in Gelbach (2004). The results of this ‘triple-difference’ model
confirm that ADFC causes a (modest) reduction in out-migration.
2
Non-US evidence on the effects of welfare benefits on migration includes Edmark
(2009), Fiva (2009) and De Giorgi and Pellizzari (2009). Edmark (2009) studies the introduction
of stricter rules to receive welfare benefits in Stockholm, finding no effects on out-migration
rates. Fiva (2009) tests if Norwegian municipalities offering more generous welfare benefits
attract more welfare recipients. He finds a large effect and that accounting for policy endogeneity
is important. Finally, De Giorgi and Pellizzari (2009) study the effect of welfare generosity as a
factor determining the location decisions of migrants across EU countries. Their results indicate
that, conditional on moving, migrants choose countries offering more generous benefits. To our
knowledge, Antolín and Bover (1997) is the only paper studying the role of unemployment
benefits on migration in Spain. Using micro data from the Labor Force Survey, they find that
registered unemployed are less likely to migrate than non-registered ones. Since registered
unemployed are more likely to receive benefits, this result suggests that unemployment benefits
might deter migration.
In 1984, a more generous welfare benefit was introduced by the national government in
the lagging and southern regions of Andalucía and Extremadura. This benefit is important both in
terms of recipients (8 percent of the labor force in these two regions) and in terms of the welfare
generosity change it generates at the border. This paper estimates the effects of this policy on
mobility by adopting a ‘border’ identification strategy similar to that developed in Holmes (1998)
and Black (1999). The regional implementation of the policy generates a geographic discontinuity
in the treatment status of municipalities. Focusing on municipalities that are sufficiently close to
(but at different sides of) the border enables us to identify the effect of unemployment benefits
on population growth, out- and in-migration rates and on unemployment.
Our main findings can be summarized as follows. The program increased population
growth by 5 percent between the census years of 1981 and 1991. This population growth effect is
explained by both a lower out-migration rate (4 percent) and a higher in-migration rate (1
percent). The effects of the policy on out- and in-migration are stronger for the demographic
group with the lowest attained educational level and, thus, the highest probability to be on
welfare. In 1991, unemployment was 15 percentage points higher on the welfare ‘generous’ side
of the border.
Methodologically, this paper is similar to McKinnish (2005, 2007) in that it uses the fact
that welfare generosity varies discontinuously at regional borders to (credibly) estimate the effects
of interest. However, due to the nature of the ADFC program, McKinnish (2005, 2007) - and the
rest of US papers- study the effects of welfare benefits on the mobility of poor single mothers.
3
Here, we can analyze the effects of a more general welfare program that, in contrast to ADFC,
can be accumulated over the entire lifecycle. Another attractive feature of this paper is that we
estimate the effects of welfare benefits on mobility in a rural environment experiencing intense
out-migration and high unemployment.
After this introduction, the rest of the paper is organized as follows. In section 2 we
describe the institutional details of the analyzed policy. Section 3 introduces the econometric
specification and section 4 the data used. The validity of the empirical design is addressed in
section 5 and the results are presented and discusses in section 6. Section 7 concludes.
2. Institutional background
2.1. The SIPTEA program
In Spain, there is an agrarian unemployment benefit (Subsidio agrario) that is only available to the
residents of Andalucía and Extremadura. This benefit is part of a broader national policy that aims
to protect temporary agricultural workers of these two southern regions. Its name is SIPTEA
(Sistema Integrado de Protección de los Trabajadores Eventuales Agrarios) and was implemented in 1984.
Besides the unemployment benefit, it has two other programs. The first one hires local
population in (minor) infrastructure projects (Plan de Empleo Rural). The second one offers short
training courses (between 60 and 90 days) with the objective of providing agricultural workers
with the necessary skills to become employable in other sectors (Formación Ocupacional Rural). Map
1 shows the location of Andalucía and Extremadura within Spain. The bordering regions are
Castilla-León, Castilla-la Mancha and Murcia.
[Insert Map 1 here]
The agrarian unemployment benefit (AUB) is only available to workers that, living in a
municipality of Andalucía and Extremadura, are in the Special Agrarian Regime of the Spanish
Social Security. Between 1984 and 1990, workers being employed a minimum of 60 days in the
agriculture in a given year were entitled to the subsidy the following year, provided their total
income was below the minimum wage1. The yearly subsidy was 4.5 months of the national
minimum wage. In 1985, a pay of 3.3 months of the minimum wage was introduced for workers
employed between 10 and 60 days. The AUB was reformed in 1990. From 1991 onwards, the
personal low income requirement (income below minimum wage) was replaced by a household
Days worked in the agriculture elsewhere in Spain or abroad are eligible to achieve this minimum of 60
days.
1
4
one (mean household income below minimum wage). At the same time, the pay became agedependant with more generous benefits for older individuals. For the oldest individuals in the
system, those aged 60 or more, the subsidy was as high as 7.5 months of the minimum wage.
The Plan de Empleo Rural (PER) program consists of ear-marked grants flowing from the
Spanish Unemployment Office to municipalities in rural areas of Andalucía and Extremadura with
high unemployment rates. In 1987, there were 237,700 jobs in this program. The length of these
jobs was typically below one month and there were 30 jobs in the average infrastructure project.
The worked days in these projects are eligible to achieve the minimum days of work to access the
AUB. In practice, the main objective of the PER projects has been to entitle its workers to the
AUB (González, 1990). In this context, the large autonomy of municipal governments in hiring
decisions in PER projects might have led to political clientelism behaviours (Cansino, 2000).
SIPTEA appeared in 1984 as a reform of the Empleo Comunitario (EC) program in place
since 1971. This program hired workers in infrastructure projects as a means to decrease
unemployment in the rural areas of Andalucía and Extremadura2. However, these projects were
often fictitious, implying that, de facto, the program was partly an unemployment benefit
(González, 1990). As a result, the 1984 reform creates an unemployment benefit (i.e. the AUB)
separately from a program hiring workers in infrastructure projects (PER). The solid line in
Graph 1 shows the evolution over time in (1) the number of ‘employees’ under the EC scheme
between 1979 and 1983 and (2) the number of recipients of the UAB between 1984 and 1991.
The number of ‘employees’ in the EC scheme increased steadily during the 70s to explode after
the Socialist party took office in 1982, going from 53 thousand in 1981 to 159 thousand in 1983.
In 1984, the first year of the AUB, there were 192,304 recipients and this number increased to
236,327 in 1991, representing 8 percent of the labor force in Andalucía and Extremadura.
[Insert Graph 1 here]
Given that (1) a reform extended the PER program to parts of other Spanish regions in
1996 and that (2) decennial census data prior to 1981 are not available at the municipal level, we
will use data from 1981 and 1991 to analyze the effects of the described program on mobility and
unemployment. Although we are aware that 1981 is not the ideal point in time to start the
analysis (EC was in place since 1971), notice that the quantitative importance of the policy
increased dramatically between 1981 and 1991 (from 53 thousand EC ‘employees’ to 236
2
A tiny fraction of the EC funds were directed to the region of Castilla-la Mancha.
5
thousand AUB recipients). Notice that the requirement to become an AUB recipient (i.e. to work
a given number of days either in the agriculture or in a PER project) are easier to meet in rural
than in urban areas. Hence, the SIPTEA policy might have increased the attractiveness of staying
in the rural areas of Andalucía and Extremadura vis-à-vis migrating to urban areas.
2.2. Unemployment benefits (elsewhere) in Spain
As in most European countries, in Spain there are two types of unemployment benefits:
Unemployment insurance and unemployment assistance3. Unemployment insurance is more
generous and is more directly linked to each worker past contributions. As for unemployment
assistance, there are two programs: The AUB described above (only available to the residents of
Andalucía and Extremadura) and the general unemployment benefit (GUB).
In 1991 a worker with tenure exceeding 6 months who was fired was entitled
unemployment insurance for half of the tenure period (with a maximum of 2 years). Pay was
proportional to past wages (with caps) and the proportion decreased over time from 80 to 60
percent. As for the GUB, there were typically two instances in which a fired worker was entitled
this less generous form of benefit. The first instance arose with a fired worker with tenure
between 3 and 6 months. In that case, the unemployed worker received 75 percent of the
minimum wage for the tenure period. This implies that a worker with tenure between 3 and 6
months perceived benefits amounting from 2.25 to 4.5 months of the minimum wage. The
second instance where a worker was entitled unemployment assistance arose with the exhaustion
of unemployment insurance. This was only the case for workers with 45 years of age or more
with family dependents. The length of these benefits ranged from 18 to 36 months and the pay
was 75/100/125 percent of the minimum wage for individuals with 1/2/3 dependents.
For the period we study, the AUB was more generous than the GUB. Whereas within the
AUB scheme a worker needed 60 days of work to be entitled to 4.5 months of the minimum
wage, a worker within the GUB needed 6 months of work to receive that same amount. Similarly,
whereas a worker within the GUB needed a minimum of 3 months of work to perceive some
benefit (2.25 months of the minimum wage), a worker within the AUB only needed 10 days of
work to perceive a slightly larger amount (2.5 months of the minimum wage). Besides, the days
worked in the numerous PER projects have been eligible to achieve the minimum days of work
that provide access to AUB.
3
For details on the reforms on unemployment benefits in Spain between 1987 and 1994, see Bover et al
(2002).
6
Table 1 shows the number of unemployment assistance recipients for Andalucía,
Extremadura and their neighboring regions in 2001. The first two columns report the number of
recipients of the GUB and the AUB schemes in each region. The second and third columns
show number of recipients as a fraction of the unemployed. Finally, column five shows the share
of unemployed that receive some form of unemployment assistance.
[Insert Table 1]
The fraction of unemployed that receive the GUB is relatively similar across the regions
(between 0.12 and 0.21). These figures are smaller than 0.28, which is the share of unemployed in
Andalucía and Extremadura that receive the AUB. As a result, the percentage of unemployed that
receive some form of unemployment assistance is 40 and 42 percent in Andalucía and Extremadura
versus 12, 21 and 15 percent in the regions of Castilla-León, Castilla-la Mancha and Murcia
respectively. Hence, relative to the rest of Spain, unemployment protection seems to have been
higher in Andalucía and Extremadura.
There are reasons to believe that a significant share of the recipients of the AUB were not
genuine agricultural workers. The dashed and dotted lines in Graph 1 show the evolution over
time in the number of recipients of the AUB between 1984 and 1991. While the number of male
recipients decreased by 32 percent the number of females almost quintupled. In fact, as of 1991,
there are more females than males in the system. This change in the gender profile of the
recipient does not coincide with a change in the profile of the agricultural worker in the regions
of Andalucía and Extremadura. The ratio of male to female workers in the agriculture in these two
regions was around 7 both in 1984 and in 1991. Furthermore, the number of recipients of the
AUB has systematically exceeded the Labor Force Survey figure for the number of unemployed
agricultural workers in Andalucía and Extremadura (García-Pérez, 2004). In 1991, the numbers of
unemployed agricultural workers was 159,850 versus 236,327 AUB recipients4.
3. Econometric specification
There are two main outcomes of interest in this paper. The first one is the population growth
rate (or related measures like out- and in-migration rates) for the 1981-91 period. The second one
is the municipal unemployment rate in 1991. The baseline econometric specifications are:
y i 81− 91 = β ⋅ SIPTEAi + x ′i 81δ + ε i 81− 91
4
(1)
This figure is the number of unemployed in the agriculture sector averaged across the four trimesters of
1991.
7
u i 91 = φ ⋅ SIPTEAi + x ′i 81λ + υi 91
(2)
where yi81-91 is the population growth rate between 1981 and 1991 in municipality i. The
explanatory variable of interest, SIPTEAi, is a variable that indicates if the policy aimed at
protecting temporary agriculture workers was in place in municipality i. Since this national policy
was only implemented in the regions of Andalucía and Extremadura, the variable SIPTEAi takes
the value of one if the municipality belongs to any of these two southern regions. The error term
ε i 81− 91 reflects population growth shocks over the decade spanning 1981-91. In its turn, ui91 is the
unemployment rate in municipality i in 1991 whereas υi 91 is an error term capturing shocks in this
variable. In some specifications, we will introduce a vector of control variables, x ′i 81 , containing
municipality characteristics in 1981 that could affect subsequent population and unemployment
rates.
The explicit aim of the SIPTEA program was to increase unemployment protection in
the regions where seasonal unemployment in the agriculture was more prevalent5. In 1981, the
overall and agricultural unemployment rates in Andalucía and Extremadura were the highest in
Spain: 19.2 and 15.7 in Andalucía and 16.6 and 9.4 in Extremadura6. The fact that SIPTEA was
introduced in the regions with more deteriorated labor market conditions suggests that the
estimates of β and φ will generally suffer from a policy endogeneity bias. We address this
identification problem by adopting a ‘border’ strategy similar to that developed in Holmes (1998)
and Black (1999). In particular, we will use municipality level data coupled with the regional
implementation of the policy to try to correctly identify the effects of interest. In other words,
the implementation of the policy at pre-existing regional borders creates a sharp geographic
discontinuity in the SIPTEA treatment status of municipalities. This identifying strategy is
implemented by estimating equations (1) and (2) with municipalities that are sufficiently close to
the SIPTEA regional border. That is:
y i 81− 91 = β ⋅ SIPTEAi + x ′i 81δ + ε i 81− 91 with db i ≤ db
(3)
u i 91 = φ ⋅ SIPTEAi + x ′i 81λ + υi 91
(4)
with dbi ≤ db
5 Card and Levine (2000) and Lalive and Zweimüller (2004) note that a deteriorated labor market is a
factor associated with increases in unemployment protection in US States and Austrian regions,
respectively.
6 These two unemployment figures were 13.4 and 5.4 for the Spanish economy, 9.7 and 0.8 for CastillaLeón, 13.5 and 6.9 for Castilla-la Mancha and 12.5 and 7.7 for Murcia.
8
where dbi is the distance from municipality i to the SIPTEA border and db is a threshold
distance that we set alternatively at 15, 20 and 25 Km. The distance from municipality i to the
SIPTEA border is computed as the air distance between the centre of municipality i and the
closest municipality centre at the other side of the border7. The empirical specifications that we
use can be interpreted within the regression discontinuity design framework8. The discontinuity is
a sharp one since the probability that a municipality is treated changes at the border with a
probability of one. Equations (3) and (4) can be seen as parametric specifications with a
bandwidth of 15, 20 or 25 Km and a zero-order polynomial in the distance to the SIPTEA
border (the forcing variable)9.
4. Data
We mostly use data from the 1991 Population and Housing Census. This census contains rich
information at the individual level including age, gender, municipality of residence in 1991 and
1981, attained educational level and labor market status (employed, unemployed, inactive…).
Unfortunately, the data at the individual level for the municipalities that are relevant to us are not
available to researchers10. However, we have data on relevant municipality characteristics such as
the number of people by age groups, by gender, by labor market status and by attained
educational level. We also know the number of individuals in 1991 that already lived in the
municipality in 1981 and the number of in-migrants arriving between 1981 and 1991 by region of
origin. Furthermore, we know the number of individuals in each of these migration categories by
labor market status and by attained educational level. We also have access (and use) some labor
market status data from the 1981 Population and Housing Census. We merge these data on
socio-economic characteristics of the population with non-agriculture employment data from the
7 An alternative to air distance is distance by road. This is a better proxy for transport or mobility costs
between two municipalities. However, in this application, we are interested in municipalities that are close
in terms of confounding unobservables that determine unemployment and out-migration in rural areas. In
this respect, plain geographic distance seems a more natural metric.
8 See Imbens and Lemieux (2008) and Lee and Lemieux (2010) for guides to practice in regression
discontinuity designs.
9 Alternatively one could set a broader bandwidth (e.g. 100 km) and a higher order polynomial in the
distance to the border. However, notice that there is a limit in how close two municipalities can be,
meaning that the density of municipalities goes to zero as the distance to the border approaches zero. In
this context, it seems more conservative to compare mean differences for observations that are reasonably
close to the border.
10
There are public-use microdata files containing 10% of the population in each municipality exceeding 20
thousand inhabitants and 10% of the population in each province that lives in municipalities below 20
thousand inhabitants. Unfortunately, the municipality of residence is not reported for the individuals living
in municipalities below 20 thousand inhabitants.
9
1980 Censuses of Establishments and with agriculture employment from the 1982 Agrarian
Census.
Setting a bandwidth of 15, 20 and 25 km in equations (3) and (4) leaves us with an
estimation sample of 184, 236 and 306 ‘border’ municipalities. In Table 2 we provide summary
statistics for the 236 municipalities that lie within 20 km from the relevant border. The SIPTEA
program was implemented in 40 percent of these 236 municipalities. These are relatively small
municipalities with a mean of 1,943 and a median of 754 inhabitants11. On average, municipalities
in our sample experience substantial population losses, with an average negative population
growth rate of 15.5 percent over the 1981-91 decade. This number is not too different from -12.7
percent, the average municipal population growth in the regions found along the SIPTEA
implementation border (i.e. Andalucía, Extremadura, Castilla-León, Castilla-la Mancha and Murcia).
This (negative) average population growth at the municipal level contrasts with a positive
aggregate growth (3.1 percent) over the same period. These two figures can be rationalized by
intense intraregional migration flows from rural to urban municipalities12. The aggregate
population growth for the municipalities in the top population quartile in 1981 was 5.1 percent
between 1981 and 1991. In contrast, the aggregate population growth for the municipalities in the
remaining three quartiles was a negative 9.2 percent. Besides loosing population over the 80s, the
average municipality in the sample is one where the 1991 unemployment rate is high (24.3
percent) and the level of education is low. In that same year, only 3.7 percent of the 16 to 64
year-olds had tertiary education whereas 34 percent had less than 5 years of education.
[Insert Table 2]
5. Identifying assumptions and validity of the empirical design
The (17) current Spanish regions (Comunidades Autónomas) were created between 1979 and 1981,
just after the Constitution was passed in 1978. These regions were constructed as aggregations of
provinces dating back to 1833. Therefore, regional borders, which in turn determine the
geographical implementation of SIPTEA, could not have been defined to reflect current
economic conditions. Nevertheless, borders in 1833 might have or might have not been placed at
random. The identifying assumption is that, controlling for observables, any confounding
For Spain, these figures were 5,013 and 600 in 1991. The difference between the mean and the median
is substantially larger for Spain than for the 199 municipalities in our sample. This is probably related to
the fact that there is no large city in the used sample of southern municipalities (the largest municipality
has 20,595 inhabitants).
12 See Bover and Arellano (2002) for a study on the personal and location determinants of the Spanish
intraregional migrations over the 80s and 90s.
11
10
unobservable evolves smoothly (and not discontinuously) at the border. An indirect way to
validate this empirical design is to examine if relevant confounding observables evolve
continuously or discontinuously at the border.
We will examine two types of variables. On the one hand, variables reflecting labor
market conditions with the potential of affecting subsequent population growth and
unemployment rates. On the other hand, features reflecting the attractiveness of a municipality in
terms of consumption opportunities in a rural environment. Ideally, we would like these two
groups of variables to be pre-determined and, thus, be measured before 1971 (the year Empleo
Comunitario was introduced). Unfortunately, for most of the variables that are relevant to us, the
first data available are drawn from different Censuses carried out around 1981. With this caveat
in mind and given that the program expanded dramatically between 1981 and 1991, we think it
still is an interesting exercise to check if potentially confounding observables evolved
continuously or not by 1981.
In order to check if physical conditions for the agriculture change discontinuously at the
border, the evolution of the ruggedness of terrain is shown in panel A of Graph 2. The
horizontal axis is the distance to the border where treated municipalities are assigned positive
values. The vertical axis is the (logged) ruggedness index constructed by Goerlich-Gisbert and
Cantarino-Martí (2010). Dots are averages for rank percentile bins (representing 39/40
observations) whereas solid lines are local linear regressions fits estimated separately at either side
of the border. For presentation purposes, only data for municipalities that belong to provinces
that are contiguous to the SIPTEA border are shown (see panel B in Map 1). Municipalities that
are close to the border have a more rugged geography than those located further away from it,
suggesting that provincial borders in 1833 were not set at random. Nevertheless, there is no
evidence that the physical conditions for the agriculture change discontinuously at the border.
[Insert Graph 2]
In panel B we directly examine the evolution of agriculture employment. To measure it,
use data from the 1982 Agrarian Census and divide the number of full-time equivalent jobs in the
agriculture (including owners and relatives worked day) over the 1981 population level. The
importance of agriculture increases as the border is approached but no clear sign of a
discontinuity is observed. In panels C and D we examine the behavior of industry and services
employment respectively. Both variables, constructed using the 1980 Census of Establishments,
11
are expressed relative to the 1981 population level. Although services employment seems to
increase at the border, no clear evidence of a large discontinuity in these variables is observed.
As described above, the unemployment rate in the regions of Andalucía and Extremadura
was the highest in the country in 1981. In Panel E we use unemployment data at the municipal
level from the 1981 Population and Housing Census. On average, unemployment is higher in the
treated municipalities but the difference shrinks as one move closer to the SIPTEA border. In
fact, the bin averages suggest a smooth evolution of the unemployment rate at the border.
Hence, Empleo Comunitario did not seem to have significantly increased unemployment by 1981.
Turning to municipality features capturing consumption opportunities, in Panel F we
examine the number of service establishments to reflect the number of bars, restaurants, hotels,
shops, hairdressers and bank branches in the municipality. In panels G and H, we show the
number of health and education centers, respectively. The data does not show a clear
discontinuity in any of these variables, either.
6. Results
A graphical preview of the main results is presented in Graph 3. The Population growth over the
1981-91 period is shown in panel A whereas panels B and C illustrate the out- and in-migration
results. Out-migration is proxied by the probability to stay, defined as the number of individuals
aged 16 to 64 in the municipality in 1991 that lived in the same municipality in 1981, divided by
the population in 1981. The in-migration rate is defined as the number of individuals aged 16 to
64 in the municipality in 1991 that lived in another municipality in 1981, divided by the
population in 1981. Finally, the unemployment rate in 1991 is plotted in panel D.
[Insert Graph 3 here]
The municipal population growth rate over the 1981-91 decade seems to evolve
discontinuously at the SIPTEA border. Although, on average, municipalities experienced a
negative population growth at both sides of the border, population losses were lower in the
treated side (around 15 percent) in comparison to the untreated side (around 20 percent). By
inspecting panels B and C, both a lower out-migration and a higher in-migration seem to explain
the smaller population losses on the treated side of the border. In turn, unemployment ‘jumps’ at
the SIPTEA border with the unemployment rate being around 35 and 20 percent on the treated
and untreated sides, respectively. Notice that the unemployment rate discontinuity at the SIPTEA
12
border in 1991 contrasts with the smooth evolution of this variable at the border in 1981 (see
panel E in Graph 2).
The corresponding (baseline) regression results on population growth, probability to stay,
in-migration and unemployment rates are reported in Table 3. Columns 1 to 2, 3 to 4 and 5 to 6
show the results with municipalities within 15, 20 and 25 km from the border. Columns 1, 3 and
5 only include region-pairs fixed effects as controls (i.e. Extremadura×Castilla-León,
Extremadura×Castilla-la Mancha, Andalucía×Castilla-La Mancha and Andalucía×Murcia dummies).
Columns 2, 4 and 6 further include the 1981 municipality characteristics examined in section 5
that might determine subsequent population growth and unemployment rates. Given that these
variables were shown to evolve smoothly at the border, their inclusion should leave the estimated
effects of interest unaltered. This is indeed the case.
[Insert Table 3 here]
Starting with the results on population growth (panel A), the estimates reported in
column 3 (municipalities within 20 km from the border and no control variables) imply that
SIPTEA increased population growth by 5 percent, which amounts to one third of the variable’s
mean (-15.5 percent). The graphical analysis suggests that both lower out-migration and higher
in-migration contributed to the observed discontinuity in population growth. The regression
results reported in panels B and C confirm this finding although assign a more important role to
the out-migration mechanism. If the SIPTEA policy brought about 1.3 more in-migrants per 100
inhabitants in 1981, it increased by 4 the number of 16 to 64 year-olds staying in the municipality
per 100 inhabitants in 1981. As for unemployment, the regression results reported in panel D
confirm that SIPTEA increased the unemployment rate by 15 percentage points. Overall, the
graphical and regression results are very much aligned. Besides, the regression estimates do not
change to a significant extent if control variables are included (column 4) and are quantitatively
similar if alternative bandwidths are specified (15 and 25 km in columns 1-2 and 5-6).
We now turn to the effects of SIPTEA on mobility for different levels of education. The
Census data contains five categories regarding the attained educational level: illiterates, less than 5
years of education, primary education (5 years of completed education), secondary education (8
years of completed education) and tertiary education (more than 12 years of education). In Spain,
a large expansion of education took place in the second half of the 20th century with a major
reform in 1970 that made education free and compulsory for children aged 6 to 14. As a result,
the proportion of the population with secondary education (8 years of schooling) increased very
13
rapidly. In the regions that are contiguous to the SIPTEA border (see panel A in Map 1), the
share of individuals with secondary education was 78, 68 and 43 percent for the cohorts aged 16,
26 and 36 in 1991. In this context, primary versus secondary education is likely to reflect age
rather than skill and, thus, we construct the following three educational categories: less than 5
years of schooling (includes illiterates), 5 to 12 years of schooling and tertiary education. For the
cohorts aged 26 and 36 in 1991, the proportions of individuals with less than 5 years of schooling
were 8 and 20, whereas the corresponding figures for the 5 to 12 years of schooling category
were 78 and 67 percent. Finally, 13 and 12 percent of the individuals aged 26 and 36 in 1991 had
tertiary education. The results on out-migration (panel A) and in-migration (panel B) behaviors
by the described educational groups are reported in Table 4, which shares the structure of Table
3 in terms of columns. The probability to stay for a given educational group is defined as the
count of individuals (aged 16 to 64) in 1991 in that group that were in the municipality in 1981,
divided by the population in 1981. Likewise, the in-migration rate for a given educational group is
the number of individuals (aged 16 to 64) in 1991 in that group that were in a different
municipality in 1981, divided by the population in 1981.
[Insert Table 4 here]
Starting with the probability to stay, the results in panel A indicate that the effect of
SIPTEA on out-migration is driven by the behavior of individuals within the lowest educational
group (less than 5 years of schooling). In fact, no statistically significant effects are found for the
remaining educational categories. If SIPTEA diminished the gains from migrating to an urban
area relative to staying in a rural municipality, these results suggest that the returns to migration
were lower for the less educated individuals. As for the in-migration rate, quantitatively similar
effects are found for the two categories with the lowest educational levels (about 0.5 additional
in-migrants per 100 inhabitants in 1981). However, if the effect is measured relative to the
variables’ mean, the effect is much larger for the category with less than 5 years of schooling
(0.46) than for the group with 5 to 12 years of education (0.15). Overall, the policy seems to
affect out- and in- migration rates, with the effects being larger for the less skilled individuals.
In Table 5 we analyze the effects of SIPTEA on the in-migration rate by geographic
origin of the inflow. In particular, we will consider in-migrants that come from (1) the untreated
regions that are contiguous to the SIPTEA border, (2) the core regions of Catalunya and Madrid
and (3) other municipalities within the same region. Focusing on the estimates of columns 3 and
4 (those using a bandwidth of 20 km), the largest inflows of in-migrants originated in the core
14
regions of Catalunya and Madrid. Given the massive waves of interregional migrants that received
these two leading regions in the 60s and early 70s, the results suggest that SIPTEA caused some
returned migration. The results also indicate the existence of modest welfare-induced flows of
(cross-border) migrants.
[Insert Table 5 here]
In Table 6 we explore the program effects on the unemployment rate of different
socioeconomic groups. To know if in-migration of welfare-prone individuals can partly explain
the unemployment rate discontinuity at the SIPTEA border, panels A and B in Table 6 report the
program estimates on the unemployment rate of stayers and in-migrants, respectively. Although
the unemployment rate increases discontinuously at the border for both groups, the effect is
larger for stayers than it is for in-migrants. Panels C and B report the unemployment results for
males and females. The effect is slightly larger for females, a result that can be rationalized by the
drastic increase in the proportion of women in the population of recipients of the agrarian
unemployment benefit shown in Graph 1.
[Insert Table 6 here]
Given that SIPTEA especially affected the migration behavior of the less-educated, the
discontinuous evolution of the unemployment rate at the SIPTEA border could potentially be
the result of sorting by skills. In the second column of Table 7, we estimate the effect on the
unemployment rate in 1991 holding fixed (1) the pre-treatment controls described in section 5
and (2) the shares of the municipal labor force in each of the educational groups. The results of a
similar exercise are reported in column 3 where we hold fixed the shares of the municipal labor
force in 18 different occupations. To ease comparability, the results of the baseline
unemployment specification with pre-treatment controls (panel D in Table 3) are reproduced in
the first column.
[Insert Table 7 here]
Controlling for the educational level of the labor force in the municipality leaves the
estimated effect of SIPTEA on unemployment virtually unaffected. In contrast, including the
share of the labor force in narrowly defined occupations reduces the estimated effect by half.
This result suggests that one channel through which unemployment benefits increase
15
unemployment is to reduce the mobility of workers from high to low unemployment
occupations.
7. Concluding remarks
In this paper we have analyzed the effects on mobility of a national policy providing higher
unemployment benefits to the residents of Andalucía and Extremadura, two lagging regions in the
south of Spain. By comparing municipalities that are sufficiently close to (but at different sides
of) the policy border, we identify the effect of unemployment benefits on population growth,
out- and in-migration rates and on unemployment. In a rural environment with high
unemployment and significant migration outflows, we find that unemployment benefits
significantly reduce out-migration rates and, thus, increase population growth. We also find
evidence that unemployment benefits increase unemployment and in-migration (from the ‘less
generous’ side of the border and from core regions that attracted migrants in the 60s and 70s for
labor market reasons).
This paper shows that unemployment benefits can significantly affect the location of
people and economic activities within a country by reducing the incentives to move from low to
high productivity areas. Other welfare state programs may have similar effects. By providing
uniform levels of basic services such as education or health care financed through ability-to-pay
national taxes, the public sector redistributes income from rich to poor regions. In Spain, for the
period 1991-1996, the annual difference between what the national government spent and
collected in the regions of Andalucía and Extremadura represented 10.5 and 19.5 of the region’s
value added13. This income redistribution at the regional level coexists with large and permanent
unemployment rate differentials. In 1991, the unemployment rate in Andalucía and Extremadura
was 25 and 23, well beyond 12 and 11 percent which were the unemployment rates in the leading
regions of Catalunya and Madrid, respectively. In fact, Jimeno and Bentolila (1998) find that, in
Spain, a reduction in a region’s unemployment rate does not cause any substantial inflow of
migrants. Hence, the overall effects of the welfare state on migration may well exceed the effects
identified in this paper.
These figures correspond to the average of the period 1991-1996. See Table 7.4 (b) in Castells et al
(2000) for details.
13
16
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17
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- (2007), “Welfare-induced migration at state borders: New evidence
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18
Table 1. Unemployment assistance in selected Spanish regions in 2001
Number of
Recipients
Recipients per
unemployed aged 16-64
AUB
AUB
GUB
+
GUB
0.12
0.28
0.40
0.14
0.28
0.42
0.12
-.0.12
0.21
-.0.21
0.15
-.0.15
Region
Andalucía
Extremadura
Castilla-León
Castilla-la Mancha
Murcia
AUB
GUB
85,021
14,806
17,301
17,670
9,752
206,248
30,079
-.-.-.-
Source: Boletín de Estadísticas Laborales. AUB and GUB stand for agrarian and general unemployment
benefits, respectively.
19
Table 2. Descriptive statistics (municipalities within 20km from border; N=236).
Variable
SIPTEA
Population in 1981
Population growth, 1981-91 (%)
Probability to stay, 1981-91
In-migration rate, 1981-91
Unemployment rate, 1991 (%)
Individuals with < 5 years of schooling, 1991 (%)
Individuals with 6 to 12 years of schooling, 1991 (%)
Individuals with tertiary education, 1991 (%)
Ruggedness (logged)
Agriculture employment per 100 inhabitants, 1982
Industry employment per 100 inhabitants, 1980
Services employment per 100 inhabitants, 1980
Unemployment rate, 1981 (%)
Number of service establishments, 1980
Number of health care centres, 1980
Number of education centres, 1980
Prob. to stay., <5 years of school., 1981-91
Prob. to stay., 5-12 years of school., 1981-91
Prob. To stay., tertiary education, 1981-91
In-migration rate, <5 years of school., 1981-91
In-migration rate, 5-12 years of school., 1981-91
In-migration rate, tertiary education, 1981-91
Mean
0.398
1,943
-15.496
48.082
5.781
24.256
33.824
62.504
3.672
3.482
13.591
1.851
6.104
13.591
58.114
1.826
3.216
17.120
29.524
1.438
1.214
4.019
0.547
S.D.
-.5,188
12.692
8.091
3.185
14.486
18.154
18.085
2.239
0.570
13.911
2.604
3.017
13.911
150.104
4.979
7.387
9.426
9.705
0.969
1.124
2.494
0.497
Min.
0
45
-57.778
21.875
0
0
0
18.033
0
1.579
0
0
0.978
0
0
0
0
0
7.657
0
0
0
0
Max.
1
60,627
20.368
64.622
15.868
81.429
80.562
100
13.333
4.591
91.270
22.500
22.813
91.270
1,666
55
81
42.057
55.709
5.941
8.721
12.079
3.425
Notes: Probability to stay defined as the number of 16 to 64 year-olds in 1991 that were in the municipality
in 1981, divided by the population in 1981. In-migration rate defined as the number of 16 to 64 year-olds
in 1991 that were not in the municipality in 1981, divided by the population in 1981. Data on attained
educational levels in 1991 are referred to individuals aged 16 to 64. Ruggedness of terrain from GoerlichGisbert and Cantarino-Martí (2010).
20
Table 3. The effects of SIPTEA on population growth, probability to stay, in-migration
rate and on unemployment.
<15 km
<20 km
<25 km
Panel A: Population growth 1981-91
SIPTEA
4.274**
(1.733)
4.233**
(1.679)
5.151***
(1.548)
4.925***
(1.449)
5.646***
(1.356)
5.418***
(1.289)
2.792**
(1.154)
4.048***
(1.031)
3.963***
(1.054)
4.273***
(0.950)
4.003***
(1.006)
1.303***
(0.433)
1.316***
(0.415)
1.195***
(0.406)
1.971***
(0.497)
2.108***
(0.527)
Panel B: Probability to stay 1981-91
SIPTEA
2.622**
(1.147)
Panel C: Move-in rate 1981-91
SIPTEA
1.493***
(0.454)
Panel D: Unemployment rate in 1991
SIPTEA
Control variables
N
14.051***
(2.032)
13.718***
(1.983)
14.790***
(1.878)
13.487***
(1.880)
16.378***
(1.562)
15.002***
(1.586)
N
Y
N
Y
N
Y
184
236
306
Notes: Robust standard errors in parenthesis. ***,** and * statisitically significant at 1, 5 and 10%. All
specifications include region-pairs fixed effects. Control variables are terrain ruggedness, agriculture
employment per 100 inhabitants in 1982, industry and services employment levels per 100 inhabitants in
1980, the unemployment rate in 1981 and the number of establishments in the services sector, healthcare centres and education centres in 1980.
21
Table 4. The effect of SIPTEA on the probability to stay and on the in-migration
rate, by educational level.
<15 km
<20 km
<25 km
Panel A: Probability to stay 1981-91
<5 years of schooling
SIPTEA
4.049***
(1.169)
3.829***
(1.294)
5.005***
(1.063)
4.389***
(1.189)
5.681***
(0.968)
4.841***
(1.055)
-0.624
(1.185)
-1.619
(1.039)
-1.073
(1.042)
0.198
(0.123)
0.211*
(0.111)
0.236**
(0.111)
0.789***
(0.176)
0.844***
(0.197)
0.541*
(0.300)
1.008***
(0.341)
1.106***
(0.348)
5 to 12 years of schooling
SIPTEA
-1.534
(1.311)
-1.185
(1.291)
-1.160
(1.171)
Tertiary education
SIPTEA
0.107
(0.139)
0.149
(0.139)
0.203*
(0.122)
Panel B: Probability to move-in 1981-91
< 5 years of schooling
SIPTEA
0.535***
(0.168)
0.524***
(0.180)
0.557***
(0.145)
0.558***
(0.160)
5 to 12 years of schooling
SIPTEA
0.826**
(0.340)
0.669**
(0.311)
0.621*
(0.318)
Tertiary education
SIPTEA
0.132*
(0.067)
Control variables N
N
0.110
(0.067)
0.138**
(0.064)
0.095
(0.065)
0.174***
(0.060)
0.158**
(0.064)
Y
N
Y
N
Y
184
236
306
Notes: Robust standard errors in parenthesis. ***,** and * statisitically significant at 1, 5 and 10%.
All specifications include region-pairs fixed effects. Control variables are terrain ruggedness,
agriculture employment per 100 inhabitants in 1982, industry and services employment levels per
100 inhabitants in 1980, the unemployment rate in 1981 and the number of establishments in the
services sector, health-care centres and education centres in 1980.
22
Table 5. The effect of SIPTEA on the in-migration rate, 1981-91, by geographic origin
of the inflow
<15 km
<20 km
<25 km
Panel A: Cross-border
SIPTEA
0.220*
(0.117)
0.147
(0.105)
0.251**
(0.103)
0.175*
(0.098)
0.267***
(0.082)
0.206**
(0.081)
0.405*
(0.220)
0.499***
(0.178)
0.401**
(0.197)
0.522***
(0.148)
0.482***
(0.165)
0.248
(0.207)
0.157
(0.199)
0.144
(0.197)
0.715**
(0.339)
0.868**
(0.379)
Y
N
Y
N
Y
Panel B: From the Core
SIPTEA
0.475**
(0.203)
Panel C: Within the region
SIPTEA
0.261
(0.208)
Control variables N
N
184
236
306
Notes: Robust standard errors in parenthesis. ***,** and * statisitically significant at 1, 5 and 10%.
All specifications include region-pairs fixed effects. Control variables are terrain ruggedness,
agriculture employment per 100 inhabitants in 1982, industry and services employment levels per
100 inhabitants in 1980, the unemployment rate in 1981 and the number of establishments in the
services sector, health-care centres and education centres in 1980.
23
Table 6. The effects of SIPTEA on different unemployment rates in 1991
<15 km
<20 km
<25 km
Panel A: Stayers
SIPTEA
14.335***
(2.102)
14.040***
(2.034)
15.089***
(1.936)
13.748***
(1.934)
16.834***
(1.605)
15.370***
(1.631)
9.991***
(2.538)
9.384***
(2.903)
12.023***
(2.235)
10.749***
(2.468)
12.318***
(2.086)
11.433***
(2.273)
12.872***
(2.103)
12.589***
(2.053)
13.848***
(1.930)
12.582***
(1.912)
15.260***
(1.593)
13.931***
(1.613)
14.371***
(2.477)
14.697***
(2.546)
14.481***
(2.418)
14.109***
(2.624)
16.130***
(2.063)
15.547***
(2.226)
N
Y
N
Y
N
Y
Panel B: In-migrants
SIPTEA
Panel C: Males
SIPTEA
Panel D: Females
SIPTEA
Control variables
N
184
236
306
Notes: Robust standard errors in parenthesis. ***,** and * statisitically significant at 1, 5 and 10%.
All specifications include region-pairs fixed effects. Control variables are terrain ruggedness,
agriculture employment per 100 inhabitants in 1982, industry and services employment levels per
100 inhabitants in 1980, the unemployment rate in 1981 and the number of establishments in the
services sector, health-care centres and education centres in 1980.
24
Table 7. The effect of SIPTEA on the unemployment rate in 1991: Further results.
Pre-determined
controls
Pre-determined
controls +
educational shares
Pre-determined
controls +
occupational shares
<15 km (N=184)
SIPTEA
13.718***
(1.983)
13.632***
(2.016)
7.281***
(2.247)
<20 km (N=236)
SIPTEA
13.487***
(1.880)
13.307***
(1.928)
6.823***
(1.953)
<25 km (N=306)
SIPTEA
15.002***
14.746***
7.601***
(1.586)
(1.609)
(1.692)
Notes: Robust standard errors in parenthesis. ***,** and * statistically significant at 1, 5 and 10%.
All specifications include region-pairs fixed effects as well as terrain ruggedness, agriculture
employment per 100 inhabitants in 1982, industry and services employment levels per 100
inhabitants in 1980, the unemployment rate in 1981 and the number of establishments in the
services sector, health-care centres and education centres in 1980. The results in column 2
correspond to a specification that further includes the shares of the population aged 16 to 64 in
different educational levels. The results in column 3 correspond to a specification that further
includes the shares of the labor force in 18 narrowly defined occupations.
25
300
100
200
Socialist first election win
0
Number of employees/recipients (in thousands)
Graph 1. ‘Employees’ in Empleo Comunitario (EC) and recipients of the Agrarian Unemployment
Benefit (AUB).
1979
1981
EC
1983
1985
AUB
1987
AUB, female
Source: INEM and González (1990).
26
1989
1991
AUB, male
Graph 2. ‘Pre-treatment’ municipality characteristics
B: Agriculture employment in 1982
2
Ruggedness index (logged)
2.5
3
3.5
4
Full-time equivalent jobs per 100 inhab.
5
10
15
20
A: Ruggedness of Terrain
-200
-100
0
100
200
-200
-100
0
100
200
D: Services employment in 1980
0
4
Employees per 100 inhab.
2
4
6
8
Employees per 100 inhab.
6
8
10
12
10
14
C: Industry employment in 1980
-200
-100
0
100
-200
200
0
100
200
F: Services establishments in 1980
0
5
Unemployment rate
10
15
20
25
Number of establishments
100
200
300
30
400
E: Unemployment in 1981
-100
-200
-100
0
100
200
-200
-100
100
200
H: Education centers in 1980
0
0
Number of centers
5
10
Number of centers
5
10
15
15
20
G: Health centers in 1980
0
-200
-100
0
100
200
-200
-100
0
100
200
Notes: The horizontal axis is the distance to the border where treated municipalities are
assigned positive values. Ruggedness index from Goerlich and Cantarino’s (2010) study.
27
Graph 3. Population growth, probability to stay and in-migration 1981-91, and
unemployment in 1991.
B: Probability to stay 1981-91
45
Probability to stay
50
55
Population growth rate
-20
-10
0
10
60
A: Population growth 1981-91
-200
-100
0
100
200
-200
-100
0
100
200
D: Unemployment in 1991
-200
-100
0
100
200
10
5
In-migration rate
10
Unemployment rate
20
30
15
40
C: In-migration 1981-91
-200
-100
0
100
200
Notes: The horizontal axis is the distance to the border where treated municipalities are assigned
positive values. Probability to stay defined as the count of individuals aged 16-64 in the municipality in
1991 that lived in the same municipality in 1981, divided by population in 1981. In-migration rate
defined as individuals aged 16-64 in the municipality in 1991 that lived in another municipality in
1981, divided by population in 1981. The unemployment rate is for individuals aged 16-64.
28
Map 1. SIPTEA regional implementation and border municipalities.
A: Regions in Spain
Castilla-León
B: Provinces at the border
Catalunya
Madrid
Extremadura
Castilla-la Mancha
Murcia
Andalucía
C: Municipalities within 20 km from the border
Notes: From left to right in panel B, the provinces that are contiguous to the SIPTEA implementation border
are Cáceres, Badajoz, Córdoba, Jaén, Granada and Almería (treated) and Salamanca, Ávila, Toledo, Ciudad
Real, Albacete and Murcia (untreated). There are 1,801 municipalities in these 12 provinces. Within these
provinces, there are 236 municipalities within 20 km from the border (Panel C).
29