' export_on_save: html: true print_background: true bibliography: "../../../FEM.bib" titleDelim: . figureTemplate: __$$figureTitle$$ $$i$$$$titleDelim$$__ $$t$$ subfigureTemplate: __$$figureTitle$$ $$i$$$$titleDelim$$__ $$t$$ subfigureChildTemplate: __($$i$$)__ $$t$$ tableTemplate: __$$tableTitle$$ $$i$$$$titleDelim$$__ $$t$$ ccsTemplate: "" figPrefix: - "figure" - "figures" tblPrefix: - "table" - "tables" subfigureRefIndexTemplate: $$i$$$$suf$$$$s$$ --- --- # Strategy --- ## Setting This paper draws on data from the Microcensus Scientific Use Files (DOI: [10.21242/12211.1976.00.00.3.1.0](https://doi.org/10.21242/12211.1976.00.00.3.1.0) to [10.21242/12211.2015.00.00.3.1.0](https://doi.org/10.21242/12211.2015.00.00.3.1.0)) to provide a long-term overview of the labor market performance of different arrival cohorts of female and male migrants to Germany. Whereas there is a large body of research on the labor market outcomes of migrants to Germany, a more descriptive long-term and gender-specific overview is missing. We provide descriptive analyses for the employment rates, working hours, and occupational status levels of different arrival cohorts by gender, calendar year, and duration of stay. The data cover the time period 1976-2015. To model labor market outcomes over time for first generation immigrants in Germany, we distinguish between __arrival year cohorts__. These correspond to the following periods: | 1964-1973 | 1974-1983 | 1984-1993 | 1994-2003 | 2004-2010 | | --------- | --------- | --------- | --------- | --------- | : Arrival cohort periods {#tbl:def_cohorts} ## Indicators and Variables We provide descriptive statistics (means) for the following __labor market outcomes__ by arrival cohort, gender, and calendar year: - Employment rates - Weekly working hours (actual); augmented by some figures on employment types - ISEI-88 scores | Variable | Definition | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Employment | Dummy, 1 for individuals who state to be self-employed, working family members, employees and workers in public or private sector, in vocational training; 0 for un- and non-employed | | Working hours | Actual weekly working hours of individual

Capped at 80h/week (values 80-95h/week are recoded as 80h/week, rest to missing) | | ISEI-88 | ISEI-88 score for occupation of individual in employment | | Duration of stay | Years of residence in Germany | | Education | Categorical, highest schooling or vocational degree:

ISCED 1-2 / ISCED 3-4 / ISCED 5-6 | : Variable defintion. {#tbl:def_vars} ## Sample | Characteristics | Values | | ------------------ | ----------------------------------------------------------------- | | Residence | Main | | Age | 25-54 | | Citizenship | Foreign (age at immigration otherwise not available) | | Years of residence | Capped at 30y. | | Age at immigration | 18+ | | Sample Region | West Germany | | Employed | DV Employment: Yes/No

DV Working hours: Yes

DV ISEI-88: Yes | : Sample restrictions. {#tbl:def_sample} - The sample is not further restricted by current main activity (e.g. if in education or not) - We have some missing data on the ISCED values (~2% for the analysis sample, similar for migrants and Germans). We apply listwise deletion, so that all the estimates apply for the same sample (only distinct by availability of dependent variable information) ## KldB to ISEI-88 conversion Originally, occupations have been recorded according to the _Klassifikation der Berufe (KldB)_ [@bundesagenturfuerarbeit2020klassifikation] in the Microcensus. The KlbB classification is not directly translatable to _ISEI-88_ [@ganzeboom1992standard], but involves the intermediate step of converting to _ISCO-88 COM_ [@internationallabororganization2020isco]. Moreover, the KldB classification underwent several revisions over our analysis period that have to be translated to our base classification KldB92 (which offers the best overall feasability in the present context). In sum, we follow the conversion logic: __KldB92 (3-digit)__ $\rightarrow$ __ISCO-88 COM__ $\rightarrow$ __ISEI-88__ This approach involves the following steps: - __Kldb70/75/88__ $\rightarrow$ __Kldb92:__ no problem, nearly 1:1 correspondence - __Kldb2010 (4-digit)__ $\rightarrow$ __Kldb92 (3-digit):__ KldB2010 codes have multiple corresponding KldB92 codes, because the official corresondence tables refer to the 5-digit classification. Consequently, within the 4-digit KldB2010 codes provided in the Microcensus might be various 5-digit codes corresponding to different KldB92 codes. Approximation: - In the Microcensus 2013, both KldB92 as well as KldB2010 have been coded. We use the actual incidences to derive _relative correspondence probabilities_ of single KldB92 codes within one KldB2010 code. Individuals with a particular KldB2010 code are randomly assigned to a KldB92 code based on these probabilities. - KldB2010 codes not present in the Microcensus 2013 (but possibly in later waves) are assigned according to a _list based probability_. For example, let a single Kldb2010 5-digit code have 4 corresponding KldB2010 4-digit codes. Of these 4 KldB2010 4-digit codes, the first 2 have a common KldB92 correspondence and the other 2 do not. Then the relative probability for each individual to receive one of the possible KldB92 codes would be $P=0.5$ for the first KldB92 code and $P=0.25$ for the other two. - __KldB92__ $\rightarrow$ __ISCO-88 COM:__ Similar (but less pronounced) problem that some KldB92 codes have multiple corresponding ISCO-88 COM values. Translation is done using the list-based correspondence approached decribed above. - __ISCO-88 COM__ $\rightarrow$ __ISEI-88:__ Direct correspondence based on the do-files provided by [GESIS](https://www.gesis.org/missy/materials/MZ/tools/isei). --- # Descriptives ---

Source is the Microcensus 1976-2015, except for the cohort composition in [@Fig:cohortcomp] which is based on municipality register data.

## Cohort composition
![Immigrant inflows to Germany by year and gender (lower panel). Citizenship composition of arrival cohorts by gender (upper panel).](graphs/sum_cohortcomp.svg){#fig:cohortcomp}

## Selection
### Cohort size by period
Numbers include inflows to eastern Germany since 1991. Citizenship shares plotted for countries of origin with consistently available information for the full arrival period. For the Soviet Union, the Czech Republic, and Yugoslavia, both aggregated and disaggregated data are provided by the municipalities in some years. In these cases, we distributed the aggregated numbers among the constituting countries corresponding to their respective disaggregated shares.

Source: German municipality registers [@destatis2020bevoelkerung-1].

![Women, age 25-54](graphs/sum_cohortsize_period_f.svg){#fig:sum_cohortsize_period_f}
![Men, age 25-54](graphs/sum_cohortsize_period_m.svg){#fig:sum_cohortsize_period_m}
![Women, age 25+](graphs/sum_cohortsize_period_f_unres.svg){#fig:sum_cohortsize_period_f_unres}
![Men, age 25+](graphs/sum_cohortsize_period_m_unres.svg){#fig:sum_cohortsize_period_m_unres}
Cohort size development by period. Different age restrictions.

Sample is restricted to western Germany, including Berlin.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

Why do we see an increase in cohort size for all arrival cohorts from 2004-2006 and for the first cohort from 1976-1985? The reason is non-response on the arrival year variable, which substantially varies over years:

![Non-response on arrival year variable by year and gender.](graphs/sum_yimmi_nonres.svg){#fig:yimmi_nonres}

Since 2005, the immigration year question is part of the mandatory questionnaire. The lower non-response rates mean overall higher observation numbers. [@Tbl:sum_cohort_sel_nonres_2004-2006] shows that the relative size of cohorts in percent increased to a similar extent at this cut-off. So, probably no selection on the cohort (selection on other characteristics still possible, of course). The changes for cohorts 1964-73 and 1994-2003 are due the age range of 25-54, shares are even closer across years without this restriction.
@import "tables/sum_cohort_sel_nonres_2004-2006.md"
### Cohort size by duration of stay
Corresponds to analysis sample except for arrival year restrictions. Shares for persons with non-German citizenship who were born abroad.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

Generally, these plots are not really interpretable because many processes are at play that determine the population numbers. One issue is that persons who immigrated at age 18-23 show up in our analysis sample with a timelag due to the age restriction 25-54 (not a problem for the plots by period above). So, some initial gains in cohort size are due to this lag. Other gains certainly follow from the large variation in non-response rates on the arrival year (see above).

![Women, age 25-54](graphs/sum_cohortsize_timeres_f.svg){#fig:sum_cohortsize_timeres_f}
![Men, age 25-54](graphs/sum_cohortsize_timeres_m.svg){#fig:sum_cohortsize_timeres_m}
![Women, age 18-54](graphs/sum_cohortsize_timeres_f_18_54.svg){#fig:sum_cohortsize_timeres_f_18_54}
![Men, age 18-54](graphs/sum_cohortsize_timeres_m_18_54.svg){#fig:sum_cohortsize_timeres_m_18_54}
![Women, age 25+](graphs/sum_cohortsize_timeres_f_unres.svg){#fig:sum_cohortsize_timeres_f_unres}
![Men, age 25+](graphs/sum_cohortsize_timeres_m_unres.svg){#fig:sum_cohortsize_timeres_m_unres}
Cohort size development by duration of stay. Different age restrictions.

Sample is restricted to western Germany, including Berlin.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, by period](graphs/nat_empl_dummy_period_f.svg){#fig:nat_empl_dummy_period_f}
![Men, by period](graphs/nat_empl_dummy_period_m.svg){#fig:nat_empl_dummy_period_m}
Employment rates shown for non-German immigrants (our standard definition, solid lines) and all immigrants including those who naturalized (dashed lines).

Sample is restricted to western Germany, including Berlin.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, by period](graphs/nat_ahours_period_f.svg){#fig:nat_ahours_period_f}
![Men, by period](graphs/nat_ahours_period_m.svg){#fig:nat_ahours_period_m}
Weekly working hours shown for non-German immigrants (our standard definition, solid lines) and all immigrants including those who naturalized (dashed lines).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, by period](graphs/nat_isei88_res_period_f.svg){#fig:nat_isei88_res_period_f}
![Men, by period](graphs/nat_isei88_res_period_m.svg){#fig:nat_isei88_res_period_m}
Employment rates shown for non-German immigrants (our standard definition, solid lines) and all immigrants including those who naturalized (dashed lines).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

Indicator means. Plotted if cell count >= 100.

## Employment
![Women, by duration of stay](graphs/empl_dummy_timeres_f.svg){#fig:empl_dummy_timeres_f}
![Men, by duration of stay](graphs/empl_dummy_timeres_m.svg){#fig:empl_dummy_timeres_m}
![Women, by period](graphs/empl_dummy_period_f.svg){#fig:empl_dummy_period_f}
![Men, by period](graphs/empl_dummy_period_m.svg){#fig:empl_dummy_period_m}
![Women, by period](graphs/empl_dummy_period_f_east.svg){#fig:empl_dummy_period_f_east}
![Men, by period](graphs/empl_dummy_period_m_east.svg){#fig:empl_dummy_period_m_east}
Employment rates (as share of sample population) by arrival cohort, gender, duration of stay and period.

Sample is restricted to western Germany, including Berlin (except for the plots differentiating between East and West Germany).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, by duration of stay](graphs/ahours_timeres_f.svg){#fig:ahours_timeres_f}
![Men, by duration of stay](graphs/ahours_timeres_m.svg){#fig:ahours_timeres_m}
![Women, by period](graphs/ahours_period_f.svg){#fig:ahours_period_f}
![Men, by period](graphs/ahours_period_m.svg){#fig:ahours_period_m}
![Women, by period](graphs/ahours_period_f_east.svg){#fig:ahours_period_f_east}
![Men, by period](graphs/ahours_period_m_east.svg){#fig:ahours_period_m_east}
Weekly working hours by arrival cohort, gender, duration of stay and period.

Sample is restricted to western Germany, including Berlin (except for the plots differentiating between East and West Germany).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, full-time employment](graphs/emplst1_period_f.svg){#fig:emplst1_period_f}
![Men, full-time employment](graphs/emplst1_period_m.svg){#fig:emplst1_period_m.svg}
![Women, part-time employment](graphs/emplst2_period_f.svg){#fig:emplst2_period_f}
![Men, part-time employment](graphs/emplst2_period_m.svg){#fig:emplst2_period_m.svg}
![Women, marginal employment](graphs/emplst3_period_f.svg){#fig:emplst3_period_f}
![Men, marginal employment](graphs/emplst3_period_m.svg){#fig:emplst3_period_m.svg}
Full-time, part-time and marginal employment (as share of employed sample population) by arrival cohort, gender, and period.

Full-time employment means working at least 35 hours per week, part-time employment 15-35 hours, and marginal employment 1-15 hours. Sample is restricted to western Germany, including Berlin (except for the plots differentiating between East and West Germany).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, by duration of stay](graphs/isei88_res_timeres_f.svg){#fig:isei88_res_timeres_f}
![Men, by duration of stay](graphs/isei88_res_timeres_m.svg){#fig:isei88_res_timeres_m}
![Women, by period](graphs/isei88_res_period_f.svg){#fig:isei88_res_period_f}
![Men, by period](graphs/isei88_res_period_m.svg){#fig:isei88_res_period_m}
![Women, by period](graphs/isei88_res_period_f_east.svg){#fig:isei88_res_period_f_east}
![Men, by period](graphs/isei88_res_period_m_east.svg){#fig:isei88_res_period_m_east}
ISEI-88 scores by arrival cohort, gender, duration of stay and period.

Sample is restricted to western Germany, including Berlin (except for the plots differentiating between East and West Germany).

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women, age 25-54](graphs/isei88_res_timeres_f.svg){#fig:isei88_res_timeres_f}
![Men, age 25-54](graphs/isei88_res_timeres_m.svg){#fig:isei88_res_timeres_m}
![Women, age 18-54](graphs/isei88_res_timeres_f_18_54.svg){#fig:isei88_res_timeres_f_18_54}
![Men, age 18-54](graphs/isei88_res_timeres_m_18_54.svg){#fig:isei88_res_timeres_m_18_54}
ISEI-88 scores by arrival cohort, gender, and duration of stay and period. Different age restrictions.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.

![Women](graphs/empl_dummy_timeres_edu_n_f.svg){#fig:empl_dummy_timeres_edu_n_f}
![Men](graphs/empl_dummy_timeres_edu_n_m.svg){#fig:empl_dummy_timeres_edu_n_m}
Number of persons in employment (population projection) by arrival cohort, gender, and duration of stay.

Source: Microcensus Scientific Use Files, DOI: 10.21242/12211.1976.00.00.3.1.0 to 10.21242/12211.2015.00.00.3.1.0, own calculations.