XV April International Academic Conference on Economic and Social Development
Differences in Spatial Effects Between West and East: The Case of Regional
Unemployment in Germany
by Elena Semerikova1
Introduction
As one of the main indicators of economic well-being, unemployment has always been a
subject of surveys. There exist numerous macroeconomic approaches, which explain the
severity of unemployment at the national level. However, disparities in unemployment are not
only observed among the countries, but also among the regions within the same country as
well. Treating unemployment on a more detailed level might lead to more reliable results.
Moreover, the reduction of unemployment diversities between regions leads to desirable
outcomes such as higher national product and lower inflation (Taylor, 1996). The country
benefits from more equal regional unemployment rates also because the reduction of
disparities "lessens the adverse effect related to geographical concentrations of high
unemployment and counteracts the downward spiral effect of economically depressed
regions" (Elhorst, 2003).
Due to the magnitude of the "adverse effect related to geographical concentrations of
high unemployment", most literature considers regional unemployment. Decisions of labor
market participants are not restricted by regional boards. Those in search of jobs also consider
possibilities to move to other regions. On the other hand, firms’ decisions on the location are
dependent on situations in local labor markets. Bronars and Jansen (1987) established that a
one-period shock in the local labor market has an impact on the neighbouring regions, which
are located up to 200 kilometers away. Spatial distance costs might be the reason for the slow
equilibrating mechanisms of labor markets and thus, cause higher unemployment. In addition,
observed spatial dependence might serve as a proxy for unobserved omitted variables
(Fingleton, 1999). Therefore, spatial autocorrelation of unemployment has to be taken into
account in the regression analysis. Ignoring spatially dependent variables in the regression
model leads to biased and inefficient estimates, whereas ignoring spatial dependence in errors
leads to unbiased but inefficient estimates. The existence of the upward bias in the
coefficients was theoretically determined by Franzese and Hays (2007) and empirically
shown by Lottmann (2012) for the estimation of unemployment.
Using spatial panel data modeling, this paper assesses factors in regional
unemployment and spatial spillover effects in Germany. Due to historical reasons there exist
1
National Research University Higher School of Economics, Subdepartment of Mathematical Economics and
Econometrics
XV April International Academic Conference on Economic and Social Development
differences between eastern and western regions of Germany. We explore differences in
spatial effects by applying special model specification. We use panel data for 407 out of 413
German regions (using the NUTS III regional structure) for 2001 through 2009.
Data & Variables
As a starting point for my analysis, we calculate Moran’s and Geary’s indexes for
each year as most studies devoted to spatial analysis do. The calculated indices show
significant positive spatial correlation (see Table 1). A weak downward tendency is observed,
which might be a sign of the developing integration process in the country. Spatial correlation
in Western regions is substantially higher than in Eastern regions. Thus, significant spatial
autocorrelation indices provide evidence of the spatial interactions between the regions, which
have to be taken into account when analyzing the unemployment rates in the regression
analysis. In order to visualize spatial dependence between the unemployment rates of
different regions, one can plot Moran’s scatter plot (see Figure 2). The plot illustrates the
dependence between the unemployment rate (Y) and the spatially weighted sum of
unemployment rates of other regions (WY). The plot also illustrates that this dependence
might be better de- scribed by quadratic than the linear function.
Years
2001
2009
2002
2003
2004
2005
2006
2007
2008
Moran’s spatial correlation index for unemployment rate
Germany
0.249***
0.243***
0.237***
0.233***
0.235***
0.233***
0.240***
0.245***
0.231***
West
0.233***
0.211***
0.192***
0.193***
0.222***
0.236***
0.253***
0.275***
0.251***
East
0.038***
0.025**
0.033***
0.039***
0.026**
0.030**
0.052***
0.057***
0.028**
Geary’s spatial correlation index for unemployment rate
Germany
0.707***
0.713***
0.717***
0.719***
0.724***
0.726***
0.717***
0.714***
0.733***
West
0.757***
0.781***
0.800***
0.801***
0.772***
0.762***
0.744***
0.723***
0.752***
East
0.957**
0.970
0.960**
0.951***
0.966*
0.967*
0.942***
0.937***
0.972
Note:***, **, * indicate the values that are significant at 1%,5% and 10% respectively.
Table 1: Spatial correlation indices for unemployment rates
XV April International Academic Conference on Economic and Social Development
Figure 2: Moran’s plot
In order to account for possible spatial interactions between regions, we use a spatial
weights matrix of inverse distances in the regressions. In the current study we use the air (as a
crow) distances between the regional centers. The spatial weights matrix is row-standardized
for easier interpretation (e.g. Niebuhr, 2003; Lottmann, 2012; Aragon et al., 2003).
Empirical Estimation
We estimate the Spatial Autocorrelation Model (SAC) model, following Lottmann
(2012) in the choice of the specification. In order to find the appropriate specification, she
uses the LM statistics and tests five different hypothesis, using the approach of Debarsy and
Ertur (2010):
where Yt is a (N × 1) vector of dependent variables, Xt is a (N × k) matrix of explanatory
variables, μ is a (N × 1) vector of individual specific effects, and 1N is a (N × 1) vector of
ones. γt represents time effects, εit ∼ (0, σ2).
W is assumed to be a nonnegative exogenous spatial matrix, which describes the
intencity and the structure of the spatial dependence between regions. The diagonal elements
of the matrix are zero by construction. Vector WYt denotes endogenous spatial effect among
the dependent variables. Vector WVt represents the spatial dependence in the disturbances.
The spatial coefficients of interest are the spatial autoregressive coefficient 𝜌, the spatial
autocorrelation coefficient 𝜆 and the vector of fixed unknown parameters 𝜃.
XV April International Academic Conference on Economic and Social Development
As it was determined earlier (e.g. Lottmann, 2012; Niebuhr, 2003), a dynamic
approach is more appropriate for investigating the labour market. Hence, we estimate the
spatial autoregression (SAR) model with a lagged dependent variable:
where Yt is a (N × 1) vector of dependent variables, Xt is a (N × k) matrix of explanatory
variables, μ is a (N × 1) vector of individual specific effects, and 1N is a (N × 1) vector of
ones. γt represents time effects, εit ∼ (0, σ2). The static model is known as Spatial
Autocorrelation Model (SAC), whereas the specification of our dynamic model is known as
the Spatial Autoregression Model (SAR).
According to the theory, unemployment can be driven by equilibrium and
disequilibrium effects. In order to account for both effects, we base our analysis on a
combined set of factors according to equilibrium2 (e.g. Marston, 1985) and disequilibrium3
(e.g. Aragon et al., 2003) views of regional unemployment variance (Partridge and Rickman,
1995). In order to account for the disequilibrium view, we include the employment growth
rate, the share of young people in the population, the share of old people in the population,
characteristics of the educational level (share of employees without any professional training,
education with university degree), GDP per capita and population density. With regard to the
equilibrium approach, we consider the effect of the sectoral structure of the local labour
market, utilizing employment shares of agricultural, manufacturing, construction, and
financial sectors and public/private sector provision. We include the average hourly regional
wage as an additional factor, which is essential within the equilibrium approach.
The equation can be estimated by the maximum likelihood estimating procedure for
spatial lag model (Anselin, 1988), developed for the panel data case. However, this does not
solve all the estimation problems. In the case of a small T and large N one gets inconsistent
estimates of the variance parameter when the model includes fixed individual specific effects
and excludes fixed time effects. One also gets inconsistent estimates of the other parameters if
the model includes both fixed individual and time effects. Even when both N and T are large,
the distributions of the estimators of the parameters are not centered (Lee and Yu, 2010a).
Therefore, we use the maximum likelihood approach corrected for this bias by Lee and Yu
(2010). In order to avoid the inconsistency of the parameters, they propose a simple
transformation. Instead of applying the within transformation, they suggest two orthogonal
The equilibrium view assumes that external shocks or economic disturbances in the labor market affect the unemployment rate for
a short period of time, allowing it to converge back to its mean value. According to this approach, each region has its own underlying
mean unemployment rate in the stable equilibrium.
3 The disequilibrium view assumes that unemployment rate reaches its underlying mean only in the long run period since the adjustment can be sluggish. Hence, differences in unemployment rates will not vanish during a long period of time.
2
XV April International Academic Conference on Economic and Social Development
transformations in order to eliminate fixed effects and time effects. In order to get proper
interpretation, we compute the direct and indirect effects, as proposed by LeSage and Pace
(2009).
Similarly to Lottmann (2012), we find that the dynamic spatial model is more
appropriate using the information criteria (BIC), presuming that BIC is a better criteria for
picking the best model (Haughton et al., 1997). The direct effects are similar to the
coefficients but are not equivalent. They lead to the same conclusions as the main coefficients
estimates. However, for the exact interpretations about the explanatory variables, one has to
employ direct effects. The estimates of the direct effects primarily have the expected sign.
The influence of employment growth on the unemployment rate is negative, as we expected.
The influence is stronger when we consider the dynamic model. The costs of labor, measured
as the average regional hourly wage, affects the unemployment positively. This result
confirms the theory (Harris and Todaro, 1970). The shares of the persons employed in the
agricultural sector affects the unemployment positively. The indirect effects are significant
only for the dynamic model. This shows that the impact of the change in the unemployment
rate of one region on another can be captured only by the dynamic model. This confirms the
fact that changes in unemployment in the neighbouring regions influence the labor market of
the explored region with a time lag. The positive and significant spatial coefficients confirm
the hypothesis of the spatial influence of the neighbour districts on the regional
unemployment.
We repeat the estimation of the static and dynamic models for the Eastern and
Western parts of Germany separately, following Lottmann (2012), assuming that the
coefficients of the explanatory variables differ between the West and East. Most of the
coefficients appear to be substantially different for West and East Germany. The negative
impact of employment growth is slightly higher for East Germany. Among the industry
variables, only the share of people employed in the agricultural sector and in manufacturing
are significant for the West, and the share of people employed in agricultural sector is
significant for the East. The share of young people becomes insignificant for the East. The
spatial coefficients are significant for both parts of Germany. The spatial autocorrelation
coefficient and the spatial autoregressive are higher for West Germany in the static model.
The spatial autoregressive coefficient in the dynamic model is also higher for West Germany.
Thus, the spatial dependence is stronger for West Germany.
We also implement the system generalized method of moments for the estimation of
the dynamic model, which is still not widely used for the estimation of spatial models,
although it allows us to handle possible endogeneity. While implementing this approach, we
XV April International Academic Conference on Economic and Social Development
find a quadratic relationship between the unemployment rate and its spatial lag. While
including a second dynamic lag, which appears to be significant, we also conclude that
unemployment has a cyclical behaviour. The revised specification is the following:
where Yt-1 is the lag of the dependent variable.
The negative dependence of the second dynamic lag might be explained by the businesscycle-frequency fluctuations in unemployment. The unemployment is quadratically dependent
on its spatial lags. Before the unemployment rate reaches the value of 18,25%, it is positively
dependent on the spatial lag, although it becomes negative after reaching this value.
We analyze spillover effects in unemployment within western and eastern regions of
Germany by estimating the spatial models for the East and West separately. We investigate
spillover effects in unemployment not only within, but also between western and eastern
regions of Germany by using the special specification, developed by Demidova et al. (2013):
where the spatial weights matrix is decomposed into four parts:
Here, the coefficients ρwe and ρew reflect the influence of eastern counties on western ones and
vice versa. Coefficients ρww and ρee represent the spatial interaction effects within western and
eastern regions of Germany.
Estimation Results
The contribution of the study to the spatial panel data analysis of regional
unemployment in Germany is the following. Firstly, we use new potential proxies for regional
amenities. Secondly, we compute the direct and indirect effects for spatial models, as required
for proper interpretation. Thirdly, we consider a rarely used in spatial analysis system GMM
approach for the estimation of spatial panel data models. We show that the relationship
between the dependent variable and the spatially lagged dependent variable might be
nonlinear. Finally, we explore differences in the determinants of unemployment in western
and eastern regions of Germany by extending the specification of the model. Within this
specification we investigate the spillover effects not only within, but also between eastern and
XV April International Academic Conference on Economic and Social Development
western regions of Germany.
We find that the unemployment in eastern regions of Germany affects both the
unemployment in western and eastern regions, whereas the unemployment in western regions
of Germany has an impact only on other western regions. Thus, if the unemployment rate
reduces in one eastern region, the decrease in the unemployment rates occurs also in other
eastern and western regions. When the unemployment rate changes in one western region, it
leads to the similar changes in other western regions, but not in eastern ones. This onedirection spatial effect of the eastern unemployment on the western regions is intuitively
clear. Eastern regions suffer from more severe unemployment rates in general, which results
in the bigger migration flows from eastern regions to western regions of Germany.
Unemployment in western regions of Germany is of a more disequilibrium nature, whereas
unemployment in eastern regions of Germany is closer to an equilibrium type. Hence, in order
to reduce the unemployment, policy makers should manipulate the factors of the
disequilibrium view (such as share of young population, education) in western regions and
pay more attention to the factors of the equilibrium view (such as population density) in
eastern regions. The spatial relationship is stronger for western regions of Germany.
We also find that the unemployment in whole Germany is of both an equilibrium and
disequilibrium nature, revealing new appropriate proxies for regional amenities (regional
gross product per capita and the density of the population). Spatial dependence is significant
for both the static and the dynamic spatial panel data models. In line with the results obtained
by Lottmann (2012), we find the dynamic spatial model more appropriate for investigating
regional unemployment rates since changes in unemployment in the neighboring regions
influence the labor market of the explored region with a time lag. Furthermore, our analysis
reveals a quadratic relationship between the unemployment rate and its spatial lag. In
addition, we conclude that unemployment has a cyclical nature.
Conclusion
The current study investigates the determinants of unemployment in Germany with the
help of spatial panel data models. We base analysis on a combined set of factors according to
the equilibrium and the disequilibrium theory of regional unemployment diversities. In order
to account for possible spatial interactions between regions, in the regressions we use spatial
weights matrix of the inverse distancesWe analyse the spillover effects in unemployment
within West and East Germany by estimating the spatial models for the East and West
separately, based on the ML approach. We investigate the spillover effects in unemployment
not only within, but also between the West and East parts of Germany by implementing the
special specification.
XV April International Academic Conference on Economic and Social Development
The contribution of the study to the spatial panel data analysis of regional
unemployment in Germany is the following. Firstly, we use new potential proxies for regional
amenities. Secondly, we compute the direct and indirect effects for spatial models, as required
for proper interpretation. Thirdly, we consider a rarely used in spatial analysis system GMM
approach for the estimation of spatial panel data models. We show that the relationship
between the dependent variable and the spatially lagged dependent variable might be
nonlinear. Finally, we explore differences in the determinants of unemployment in western
and eastern regions of Germany by extending the specification of the model. Within this
specification we investigate the spillover effects not only within, but also between eastern and
western regions of Germany.
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