To investigate the causal impact of the media’s treatment of the 2011 revelations on targeted group’s worries and social assimilation outcomes, this study implemented the matching difference-in-differences estimator (MDiD) first suggested by Heckman et al. (1997). The basic idea of the estimator is to estimate the causal effect of interest—the average treatment effect on the treatment group (ATT)—by applying a two-step procedure. In the first step, the propensity score matching technique is implemented to find the control group
55 This restriction is not crucial for the main message of the paper. Appendix D presents the main results when conditioned for pre-treatment worries and pre-treatment assimilation outcomes together. This substantially reduces the sample size, but the main results are qualitatively unchanged.
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observations that are similar to the treatment group observations in all relevant pre-treatment characteristics.56 The identification strategy requires the model to include all the variables that simultaneously influence the treatment assignment (Turkish immigrants or not) as well as the outcomes of interest (social worries and assimilation outcomes). Table 3.3 presents the list of conditional variables used in this step. These include respondents’ demographic, economic, and migration-related characteristics.
Another requirement is that these variables should not be affected by the treatment or by the respondent’s anticipation of the treatment. To ensure this, a matching step is performed on the sample restricted to the pre-treatment years (Caliendo and Kopeinig, 2008, p. 38). To prevent comparison between treatment and control observations that are not comparable, the sample is restricted to the common support region.
In the second step, the difference-in-differences regressions are applied. The following regression equation is estimated:
yit=α0+α1Post2011t+λ Post2011t∗Turksi+β′Xit+γi+γs+γt+ uit, (3.1)
where yit is the outcome variable of the respondent i in year t. The dummy variable Post2011t is 1 if the observation is recorded after the 2011 revelations in Germany and 0 otherwise. The dummy variable Turksi
is 1 if the respondent belongs to the treatment group (Turkish immigrants) and 0 otherwise. The treatment effect of interest is captured by the coefficient λ on the interaction term (Post2011t∗Turksi). Xit is a vector of individual-level characteristics and includes all the variables used for conditioning. Additionally, Xit includes variables that are relevant for outcomes of interest but do not directly affect respondents’ treatment status. These variables mainly include two annually collected state-level variables relevant to the study of worries and assimilation outcomes: immigrant share of the population and the number of right-wing violent crimes.57γi is an individual-specific fixed-effect.58γs and γt are state and year dummy variables, and uit
is the error term.
Initially, the treatment effect is assumed to be homogeneous across respondent’s immigration statuses and education levels. Section 4.3 investigates whether the treatment effect is heterogeneous across respondent’s immigration statuses (FGI vs. SGI), education (high education vs. low education), and religiousness (attends
56 The 1:1 nearest-neighbor caliper matching was implemented without replacement with the caliper set at 0.005. The program used is psmatch2, which was developed by Leuven and Sianesi (2003) on Stata 14.2. The results also hold when matching with replacement is implemented.
57 These variables provide useful controls for changing socio-economic factors in contemporary Germany, as discussed in section 2.
58 The baseline regressions are estimated using the fixed-effect estimator. For robustness, the appendix B presents the estimates with the random effects model and the OLS. The results are qualitatively similar.
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religious services or not). Furthermore, treatment intensity checks were performed based on the respondents’ state of residence and newspaper readership.
The crucial identifying assumption in estimating the treatment effect is that the outcome variables of both the treatment and control groups would follow similar trends in the absence of the 2011 revelations. This assumption is referred to as the common trend assumption (CTA). The analysis begins with an investigation of the plausibility of this assumption. Even if the CTA holds, the estimated ATT might be biased. One source of bias is that the control group observations were also affected by the 2011 revelations. That is, although the NSU group primarily targeted Turkish immigrants, its more general opposition to foreigners (particularly non-white immigrants) makes it likely that non-Turkish immigrants were also affected by the news treatment. In this case, the estimates presented are likely to understate the true effect of the 2011 treatment (i.e., downward bias).
An important threat to identification comes from the coarse sources of variation as potential explanations of the estimated effects, such as the 2011 Turkish general election and the mass migration of Syrians into Turkey as a result of the escalating Syrian Civil war between 2011 and 2012. Around 2014-15, the European migration crisis escalated. In response, xenophobic crimes steadily increased in Germany and coincided with the 2011 treatment, which might have played a confounding role in respondents’ worsened social outcomes. These coarse sources are particularly concerning as there is no variation in the timing of the treatment, with all Turkish immigrants being treated at the same time. To address these concerns, the time dimension of the data is used to perform a number of validation tests.
Although the matching was performed on a number of observable characteristics, a crucial distinction between the groups is their ethnicity. This is particularly problematic if there are other shocks that vary by ethnicity or by unobserved characteristics that correlate with ethnicity. To address this issue, the main results were re-estimated after controlling for ethnicity-specific linear time trends.
3.4.2 Conditioning variables and the matching quality
Table 3.3 presents a list of the 34 conditioning variables used for matching. Other conditioning variables not shown in Table 3.3 include dummy variables representing the respondents’ state of residence and the pre-treatment survey years. Baseline outcome variables were also used as conditioning variables (pre-treatment worries about xenophobic hostility, worries about crime development, health satisfaction, and life satisfaction). The matching quality is generally assessed by comparing the means of the conditioning variables for the treatment and control observations after the matching process. Table 3.3 shows that the matching process improves the comparability of the sample means of the conditioning variables for the treatment and control groups.
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To show statistically that the post-matching difference between the means is not too large, following Rosenbaum and Rubin (1985), the table includes the measure of standardized percentage bias (%SB). %SB is calculated twice (before and after the matching procedure) to show the improvement in the comparability between sample means achieved by matching. Caliendo and Kopeinig (2008) report that an after-matching
%SB of less than 3% or 5% is often considered a sufficient indicator of good matching quality. For most of the conditioning variables, Table 3.3 shows that the achieved post-matching %SB is significantly lower than 5%.59 Another indicator of matching quality is the post-matching reduction in mean and median %SB. The mean %SB for the selected variables is 2.6, which is a substantial reduction of 86% from the unmatched sample. The median %SB of 1.7 is also well within the acceptable level of 5%.
The matching quality of the sample consisting of assimilation outcomes (#3-5) is briefly discussed. Variable balance is achieved without conditioning for state dummy variables, survey year dummy variables, and work experience. The means of the conditioning variables for the treatment and the control groups are shown in the appendix E. The matching quality is greatly affected due to the low sample size, as denoted by the substantial increases in %SB.