Sample of Integrated Labour Market Biographies

The Sample of Integrated Labour Market Biographies (SIAB) is an administrative data set provided by the Research Data Center (FDZ) of the Institute for Employment Re-search (IAB) at the German Federal Employment Agency. We use these data to test a

118The MC also samples individuals who are in the military and live in barracks.

119Extrapolated from information on 8,426,756 surveyed individuals using sample weights. The average number of observations per wave is 443,513. The population increases somewhat after reunification and reaches a maximum of almost 55 million people in 2007. Afterwards it starts declining.

120The participation-age profiles are hump-shaped. In the 2006 MC wave, participation for men is highest in the age bracket 35-39 (88%) and the maximum for women (77%) is reached for ages 40-44.

central assumption of our empirical analysis: interethnic marriages were disproportion-ately affected by the labor market reform. The idea is that non-German workers face a higher risk of becoming unemployed. They and their partners are thus more affected by the tightening of means-testing thresholds in the unemployment insurance system.

The SIAB data cover a 2% random sample of the German social security registers. One observation in the data corresponds to a time period (spell) with at least one of the fol-lowing characteristics: (i) employment subject to social security (in the data since 1975), (ii) marginal part-time employment (in the data since 1999), (iii) benefit receipt¹²¹, (iv) officially registered job-seekers at the German Federal Employment Agency or (planned) participation in programs of active labor market policies (in the data since 2000). We observe these (un)employment spells with daily precision.

(Un)employment Spells end either by a change of employment status, employer or always at the end of calender year. The duration variable (tenure) is the accumulated time from the beginning of employment. We are interested in estimating conditional rates of job loss (firings/quits) and job finding (hirings). To identify the rate of job loss, we count transitions from employment into unemployment and from employment into inactivity. Transitions from unemployment into employment, both full and part time, identify the job finding rate. Changes from full to part-time employment (and vice versa) and transitions between employers are treated as continuous employment.

The SIAB data also include information about, among other things, gender, nationality (German, non-German), regional identifiers, and education.¹²²

To estimate the effect of the labor market reform on marital surplus below, we use a differences-in differences strategy and divide individuals into treatment and control groups according to their nationality, based on the idea that unemployment risk is higher for workers with an immigrant background and, thus, the treatment intensity of the reform is higher for interethnic marriages. The SIAB data allows us to formally test this identifying assumption. To this end, we estimate labor market transition probabilities

121According to the German Social Code: SGB III since 1975 and SGB II since 2005, The introduction of SGB II was part of the implementation of the Hartz IV reform.

122The education variable in German social security data suffers from missing values and inconsis-tencies, essentially because misreporting has no negative consequences. We impute missing and incon-sistent observations using the methodology proposed by Fitzenberger et al. (2006). We use five levels of education: Lower secondary education without/with vocational training, higher secondary education without/with vocational training and tertiary education (University, University of Applied Sciences).

Table 4.3: Labor Market Hazard Rates

Transitions into Unemployment Transitions into Employment

(1) (2) (3) (4) (5) (6) (7) (8)

German -0.130^∗∗∗ -0.144^∗∗∗ -0.184^∗∗∗ -0.199^∗∗∗ 0.025^∗∗ -0.025^∗∗∗ -0.005 -0.052^∗∗∗

(0.012) (0.011) (0.010) (0.010) (0.010) (0.009) (0.008) (0.008)

[0.878] [0.866] [0.832] [0.820] [1.025] [0.975] [0.995] [0.949]

Female -0.191^∗∗∗ -0.125^∗∗∗ -0.188^∗∗∗ -0.116^∗∗∗ -0.161^∗∗∗ -0.143^∗∗∗ -0.160^∗∗∗ -0.143^∗∗∗

(0.009) (0.008) (0.010) (0.008) (0.011) (0.011) (0.010) (0.010)

[0.826] [0.882] [0.829] [0.890] [0.851] [0.867] [0.852] [0.867]

N 1,857,659 1,857,659 1,857,659 1,857,659 1,232,908 1,232,908 1,232,908 1,232,908

Stratified by Education X X X X X X X X

Year FE X X X X

Region FE X X X X

Notes: robust standard errors (clustered by region) in parentheses. Hazard rates reported in square brackets.

∗p <0.1,^∗∗p <0.05,^∗∗∗p <0.01

conditional on gender and nationality (German, non-German). We do not observe marital status in the SIAB data. Additionally, we stratify by education. To test whether non-natives face a higher unemployment risk in Germany, both in terms of transitions into unemployment and duration, we estimate Cox proportional hazard models (Cox, 1972).

This Cox model assumes a baseline hazard that is common to both employed and un-employed individuals along with a log-linear function of covariates.¹²³ With stratification, the five different education groups are allowed to have different baseline hazards.¹²⁴

We calculate hazard rates for transitions into unemployment and into new employ-ment out of unemployemploy-ment. Table 4.3 presents the results. The covariates of interest are nationality and gender of the employed/unemployed individuals. Columns (1)–(4) of ta-ble 4.3 present the hazard rates for job loss (transitions into unemployment) and columns (5)–(8) present the hazard rates for job findings (transition into employment). We find that the hazard of transitioning from employment into unemployment is significantly higher for non-natives as compared to Germans. Thus, as we conjectured, non-natives

123The hazard rate for transitions out of and into unemployment after a number of days, d, with the vector of covariates Z is denoted h(d, Z). γ indicates the vector of coefficients to be estimated

and λ(d) the baseline hazard and v is an error term. Thus, the hazard model can be specified as

h(d, Z) =λ(d)·exp(γ⁰Z)·v.

124Separate Cox models for each education group are estimated with stratification under the assump-tion that coefficients are the same across strata and that each educaassump-tion group has its own baseline hazard for job loss and job findings, respectively. As education is unobserved in the marriage and di-vorce data, we also estimate hazard rates without stratification, see Table C.2 in the Appendix. The conclusions are unchanged.

face a higher risk of becoming unemployed. Our preferred specification includes both year and labor market region fixed effects, see columns (4) and (8). German nationals have a job loss hazard rate that is 20% lower than the respective hazard rate for workers without German citizenship, who are thus on average more likely to lose their jobs. For transitions into employment, the hazard rate of Germans is 5% lower than the hazard rate for foreigners. Thus, workers without German citizenship find new jobs out of unem-ployment quicker than Germans, their unemunem-ployment duration is on average shorter. An explanation for this finding are lower reservation wages, for example due to lower unem-ployment benefit entitlements or because for some nationalities continued emunem-ployment is a necessary requirement for residence status.¹²⁵

Note that the estimated hazard rates reflect differences between Germans and a di-verse group of individuals without German citizenship. One would expect that labor market transition probabilities vary greatly across individuals of different foreign nation-alities. For example, citizens of other EU15 countries face no legal barriers to employment in Germany and unemployment benefit entitlements can be transferred across countries.

Thus, they might be more comparable to German workers in terms of labor market at-tachment than workers from non-EU countries are. The hazard rate differences we find can therefore be interpreted as a lower bound for the differential exposure to labor market risk for workers without German or EU15 citizenship.

Finally, women are 12% less likely to become unemployed and about 14% less likely to move into employment according to our preferred specification. That is, women are on average employed longer, but it takes them longer to find a new job out of unemployment.

Thus, the need for additional insurance after the reform might be higher for women.