Figures and data in Dynamic Microsimulations of Regional Income Inequalities in Germany

Abstract

Income is one of the key indicators to measure individual opportunities, regional differences, and societal inequalities. Previous research has shed considerable light on gender- and migration-related disparities at the national level. However, the regional distribution of these inequalities, especially over time, has received less attention. Moreover, how these regional inequalities might develop in the future or under different conditions remains unclear. Regionalized dynamic microsimulations provide a powerful tool to analyze individual, household, as well as local long-term income developments under ‘what-if’ scenarios. This contribution examines the potential regional dynamics in gender and migrant income gaps across the districts in Germany. We extend the data, model, and infrastructure of the MikroSim project with an income module that accounts for migrant groups. By simulating four independent scenarios, we can isolate and evaluate their unique effects on inequality dynamics within different regions. Specifically, we adjust the full-time employment rates and tertiary education levels for a) women to align with those of men, and b) immigrants to align with those of the majority population, separately for each district. The results show that the scenarios have regionally differing effects on inequality dynamics, highlighting the considerable potential of dynamic microsimulations for regional evidence-based policies.

This article contains 16 figures and 6 tables.

Figures

**Figure 1.** Modules in MikroSim 2.1.4. Source: own illustration.

**Figure 3.** Gender income gaps by region and scenario.. Note: The figure shows the simulated percentage changes in the gender income gap from 2020 to 2040. The colors indicate regions: East Germany (blue) and West Germany (green), while the line types differentiate between the scenarios: base (solid), university endowments of women adapted men (dotted), and full-time rates women adapted to men (dashed). Source: own illustration.

**Figure 4.** Gender income gaps in East and West by district type and scenario.. Note: The figure shows the simulated percentage changes in the gender income gap from 2020 to 2040 for rural (left side) and urban areas (right side). The colors indicate regions: East Germany (blue) and West Germany (green), while the line types differentiate between the scenarios: base (solid), university endowments of women adapted men (dotted), and full-time rates women adapted to men (dashed). Source: own illustration.

**Figure 5.** Regional differences in gender income gaps.. Note: The figures illustrate the simulated gender income gaps in 2040 across the 401 German districts under different scenarios: base (left), full-time rates of women adapted to men (middle), and university endowments of women adapted to men (right). Lighter col-ors represent smaller gaps, whereas darker colors correspond to larger wage gaps. Source: own illustration.

**Figure 6.** Migrant income gaps by region and scenario.. Note: The figure shows the simulated percentage changes in the migrant income gap from 2020 to 2040. The colors indicate regions: East Germany (blue) and West Germany (green), while the line types differentiate between the scenarios: base (solid), university endowments of migrants adapted to the majority population (dotted), and full-time rates of migrants adapted to the majority population (dashed). Source: own illustration.

**Figure 7.** Migrant income gaps in East and West by district type and scenario.. Note: The figure shows the simulated percentage changes in the migrant income gap from 2020 to 2040 for rural (left side) and urban areas (right side). The colors indicate regions: East Germany (blue) and West Germany (green), while the line types differentiate between the scenarios: base (solid), university endowments of migrants adapted to the majority population (dotted), and full-time rates of migrants adapted to the majority population (dashed). Source: own illustration.

**Figure 8.** Regional differences in migrant income gaps.. Note: The figures illustrate the simulated migrant income gaps in 2040 across the 401 German districts under different scenarios: base (left), full-time rates of migrants adapted to the majority population (middle), and university endowments of migrants adapted to the majority population (right). Lighter colors represent smaller gaps, whereas darker colors correspond to larger wage gaps. Extreme outliers result from a limited number of observations in some districts. Source: own illustration.

**Figure A1.** Income gap decomposition by gender¹⁵.. Note: The figure shows how far the coefficients (left-hand side) and endowments (right-hand side) widen or narrow the gender income gap. The decomposition results show differences in the magnitude of the coefficients of all independent variables according to gender. While the left-hand side illustrates the effects that narrow the gender income gap, the right-hand side shows the driving forces behind the widening of the gap. The endowments show compo-sitional differences between the sexes. The reducing factors of the income gap are shown on the left-hand side, whereas the drivers of the income gap are illustrated on the right-hand side.For detailed descriptions of the variables and reference categories, see Table A2. Source: own illustration.

**Figure A2.** Income gap decomposition by migration status.. Note: The figure shows how far the coefficients (left-hand side) and endowments (right-hand side) widen or narrow the income gap between the migrant and majority population. The decomposition results show differences in the magnitude of the coefficients of all independent variables according to migration. While the left-hand side illustrates the effects that narrow the migrant income gap, the right-hand side shows the driving forces behind the widening of the gap. The endowments show compositional differences between the majority and the migrant population. The reducing factors of the income gap are shown on the left-hand side, whereas the drivers of the migrant income gap are illustrated on the right-hand side. For detailed descriptions of the variables and reference categories, see Table A2. Source: own illustration.

**Figure A3.** Validation of relative individual income by sex and districts, 2014.. Note: Since the absolute values are on different scales due to the conceptual mismatches and different frames of the underlying datasets, only the comparison of the relative spatial income distributions is meaningful between the three datasets. Darker colors indicate larger individual income medians. Source: own illustration based on Tax Data 2014, INKAR database, and MikroSim.

**Figure A4.** Working population by region in the base scenario.. Note: The figure shows the simulated working population in millions from 2011 to 2040. The colors indicate regions: East Germany (blue) and West Germany (green). Source: own illustration.

**Figure A5.** Population by region in the base scenario.. Note: The figure shows the simulated population in millions from 2011 to 2040. The colors indicate regions: East Germany (blue) and West Germany (green). Source: own illustration.

**Figure A6.** First generation migrants by region in the base scenario.. Note: The figure shows the simulated migrant population in millions from 2011 to 2040. The colors indicate regions: East Germany (blue) and West Germany (green). Source: own illustration.

**Figure A7.** Rate of tertiary education by region in the base scenario.. Note: The figure shows the simulated rate of tertiary education of men (green) and women (blue) in the labor force from 2011 to 2040. Source: own illustration.

**Figure A8.** Migrants by region in the base scenario.. Note: The figure shows the simulated rate of tertiary education of the majority population (green) and migrants (blue) in the labor force from 2011 to 2040. Source: own illustration.

Tables

Table A1. Income Estimation in Selected Microsimulation Models*

Author & Date	Model	Country	Data	Measure	Estimation	Covariates	Alignment	Regional variables
Andreassen et al. (2020)	MOSART	Norway	Administrative data registers from Norway	Logarithm of labor income	RE models by different cohorts	Age, gender, partnership status, children, (ongoing) education, seniority, pension (type of pension), stable career path, time outside the labor force, year of observation	Demographic modules are aligned to results from Statistics Norway’s official population projections; for years with actual observations aligned to these	No
Bonin et al. (2015)	ZEWDMM	Germany	German Socio-Economic Panel (GSOEP) 2009 data of 25 to 29 aged cohort	Logarithm of gross hourly wage	OLS by gender	Education, working experience, firm tenure	No	No
Bönke et al. (2020)		Germany	German Socio-Economic Panel (GSOEP) 1964 to 1985 cohorts until age 60	Lifetime income (logarithm of labor income)	OLS-LDV by gender	Full- and part-time, employment experience, income from last two years, education, marriage, working hours	No	No
Conti et al. (2023)	T-DYMM	Italy	AD-SILC; administrative and longitudinal survey data	Monthly labor income	RE models by occupational group	Differentiated by groups, complete list: gender, born in EU, education, number and ages of children, working experience, working experience^†, permanent contract, part-time, employment status of partner	Demographic modules; income aligned to labor productivity growth and consumer price index	No
Dekkers et al. (2009)	MIDAS	Belgium, Italy	Belgium: Panel Households (PSBH); Germany: German Socio-Economic Panel (SOEP); Italy: European Community Household Panel (ECHP)	Logarithm of hourly	Belgium: OLS by germany and Italy: RE by gender	Belgium: Age, age^†, education; Germany: Potential experience (= age - years in education), potential experience^†, education, marital status, firm size, number of children, chronically ill, tenure, public sector; Italy: Potential experience, potential experience^†, education, public sector, permanent contract, duration in work	Corrected growth individual hourly wage rates, additional macroeconomic productivity assumptions	No^†
Emmerson et al. (2004)	Pensim2	UK	British Household Panel Survey (BHPS)	Earnings	RE models	Work history (including occupation, industry and sector), year of education	No	No
Favreault and Smith (2004); Favreault et al. (2015)	DYNASIM III & MINT8	US	Survey of Income and Program Participation (SIPP), Panel Study of Income Dynamics (PSID), National Longitudinal Survey of Youth (NLSY)	Logarithm of hourly wages for individuals with positive income for the year	RE models by age, gender and race	Marital status, education level, additional age splines, region of residence, disability status, in school, birth cohort, job tenure, education level*age splines, number and ages of children, health status, disability beneficiary status	Wage growth assumptions are used for future periods	Yes
Flood (2008)	SESIM III	Sweden	LINDA administrative panel database; SCB income distribution survey and other	Logarithm of full-time earnings	RE models by occupational sector and gender	Working experience, education, marital status, nationality	Only demographics	No
Holm et al. (2007)	SVERIGE	Sweden	Base dataset population of Sweden, ASTRID panel database	Relative earnings (lagged monthly earnings divided by the average earnings)	OLS for full- and part-time workers	Age, gender, education, education the year before, regional unemployment rate	Demographic modules are aligned to aggregate indicators from observed data and projections from Statistics Sweden	Yes
				Income change (yearly change of earnings)	OLS-LDV for six different groups (got unemployed, emigrated, got employed, immigrated, migrated, ordinary group)	Gender, age, education, civil status, former salary, born in Sweden
Schwabish and Topoleski (2013)	CBOLT	US	Data from the Social Security Administration (SSA), Survey of Income and Program Participation (SIPP), Health and Retirement Survey, Current Population Survey (CPS)	Logarithm of annual earnings	RE (Carroll et al., 1992)	Age, gender, lifetime educational attainment, marital status, number of children u. 6, in school, birth cohort, social security benefit status, permanent and transitory shocks	Modules are aligned to demographic and economic projections from Congressional Budget Office (CBO)	NO
Zaidi et al. (2009)	SAGE	UK	British Household Panel Survey (BHPS)	Logarithm of monthly earnings	RE models with first-order disturbance terms by gender and qualifications	Recent employment experience, age, age^†, education, occupation, industry, partnership status, employment status of partner, restriction of work by health, public or private sector, children living at home, age of youngest child; full- and part-time status	It is possible to add alignment parameters in future versions of the SAGE model.	No

This table is not a complete list of all microsimulation models with income modules worldwide, but an attempt to shed light on the "black boxes" of relevant and ongoing microsimulation models. We have only included microsimulation models that can be cited and provide all information for the table shown

The updated version of MIDAS-BE 2.0 includes regional information on Flanders, the Walloon area and Brussels-Capital region (Dekkers et al., 2023).

Table A2. Definition of Covariates

Individual Characteristics
occupational group	blue-collar (reference), other worker, self-employed,
	state worker, white collar,
age	age in years
education	ISCED levels 1 and 2 (middle school), ISCED level 3 (high school),
	ISCED level 4 (reference), ISCED levels 5 and 6 (= university degree)
family type	divorced, married, widow, single (reference)
part time	1 if individual works part-time, 0 otherwise (full-time)
migrant group	born in Germany (reference), late resettlers,
	Turkey, South-EU, EU before 2004 (without South-EU)
	EU after 2004 (including Poland and Croatia),
	Former yug. Countries (without Croatia), Africa,
	Near and Middle East, South and South-East Asia,
	Rest of the world (incl. Russia, America, Rest of Europe)
female	1 if individual is female, 0 otherwise
Household Characteristics
number of kids	0 (reference), 1, 2, 3 or more
Regional Characteristics
share of unemployed	Unemployed persons per 1000 working-age population (Unemployed_time/Employed(15− < 65 years)_time * 1000)
population density	Inhabitants per squared kilometer inhabitants_time/area_timecontinuous
5 regions of federal states	North, East, South, West, City-States (reference)

Note: *employees are only employees subject to social insurance at the workplace.

Table A3. Initial income estimation results for men and women

	Dependent variable:
	Log Monthly Income
	Male	Female
Intercept	6.125*** (0.011)	5.853*** (0.013)
Other Worker	0.397*** (0.003)	0.276*** (0.004)
Self Employed	0.094*** (0.002)	0.096*** (0.004)
State-Worker	0.280*** (0.003)	0.554*** (0.005)
White Collar	0.098*** (0.001)	0.185*** (0.002)
Age	0.058*** (0.0005)	0.065*** (0.001)
Age Squared	0.001*** (0.00001)	0.001*** (0.00001)
ISCED Levels 1 and 2	0.326*** (0.003)	0.331*** (0.003)
ISCED Level 3	0.116*** (0.002)	0.154*** (0.002)
ISCED Level 5	0.142*** (0.003)	0.088*** (0.003)
Late resettlers	0.095*** (0.004)	0.073*** (0.004)
Turkey	0.069*** (0.005)	0.101*** (0.008)
South-EU	0.094*** (0.007)	0.084*** (0.010)
EU before 2004	0.054*** (0.009)	0.028** (0.011)
EU after 2004	0.147*** (0.005)	0.112*** (0.006)
Former Yugoslavia	0.113*** (0.008)	0.081*** (0.010)
Africa	0.220*** (0.012)	0.105***(0.020)
Middle East	0.123*** (0.008)	0.089***(0.010)
South-East-Asia	0.169*** (0.010)	0.157*** (0.011)
Rest of the World	0.127*** (0.007)	0.126*** (0.007)
Married	0.157*** (0.002)	0.160*** (0.003)
Divorced	0.013*** (0.003)	0.055*** (0.003)
Widow	0.119*** (0.009)	0.223*** (0.003)
Part-time	0.542*** (0.002)	0.457*** (0.002)
1 Child in Household	0.036*** (0.002)	0.037*** (0.002)
2 Children in Household	0.104*** (0.002)	0.037*** (0.003)
3 or more Children	0.159*** (0.004)	0.032*** (0.005)
Unemployment Rate	$-$ 0.002*** (0.0001)	0.002*** (0.0001)
Population Density	0.00003*** (0.00000)	0.0001*** (0.00000)
East	$-$ 0.120*** (0.004)	0.008(0.005)
North	0.066*** (0.004)	0.019*** (0.005)
South	0.088*** (0.004)	0.006 (0.005)
West	0.089*** (0.004)	0.011** (0.004)
Number of Individuals	344,463	318,102
var(Ind)	0.157	0.193
var(Residuals)	0.076	0.105
Observations	635,492	573,659
Log Likelihood	$-$ 340,397.200	389,648.800
Akaike Inf. Crit.	680,864.300	779,367.700
Bayesian Inf. Crit.	681,262.000	779,761.800

Notes: Standard errors given in parenthesis. ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001.

Table A4. Forecast income estimation results for men and women

	Dependent variable:
	Log Monthly Income
	Male	Female
Intercept	3.134*** (0.013)	2.929***(0.015)
Previous Log Income	0.571*** (0.001)	0.570***(0.001)
Other Worker	0.271*** (0.004)	0.198***(0.004)
Self Employed	0.054*** (0.002)	0.046***(0.004)
State-Worker	0.121*** (0.003)	0.256***(0.004)
White Collar	0.055*** (0.002)	0.109***(0.003)
Age	0.004*** (0.0004)	0.010***(0.001)
Age Squared	0.00003*** (0.00001)	0.0001***(0.00001)
ISCED Levels 1 and 2	0.147*** (0.003)	0.130***(0.003)
ISCED Level 3	0.048*** (0.003)	0.063***(0.003)
ISCED Level 5	0.097*** (0.003)	0.074***(0.003)
University Graduation	0.014*** (0.003)	0.029***(0.004)
Late resettlers	0.039*** (0.003)	0.031***(0.004)
Turkey	0.005 (0.005)	0.032***(0.007)
South-EU	0.015** (0.006)	0.024***(0.009)
EU before 2004	0.023*** (0.008)	0.006 (0.010)
EU after 2004	0.062*** (0.005)	0.035***(0.005)
Former Yugoslavia	0.045*** (0.007)	0.032***(0.009)
Africa	0.065*** (0.011)	0.008 (0.020)
Middle East	0.032*** (0.008)	0.064***(0.011)
South-East-Asia	0.070*** (0.010)	0.069***(0.010)
Rest of the World	0.062*** (0.007)	0.050***(0.007)
Married	0.060*** (0.002)	0.058***(0.002)
Divorced	0.010*** (0.003)	0.032***(0.003)
Widow	0.048*** (0.008)	0.128***(0.005)
Part-time	0.286*** (0.002)	0.265***(0.002)
1 Child in Household	0.031*** (0.002)	0.028***(0.002)
2 Children in Household	0.062*** (0.002)	0.054***(0.003)
3 or more Children	0.097*** (0.004)	0.068***(0.005)
Unemployment Rate	0.001*** (0.00005)	0.0005***(0.0001)
Population Density	0.00001*** (0.00000)	0.00002***(0.00000)
East	0.060*** (0.004)	0.006 (0.004)
North	0.031*** (0.004)	0.015***(0.004)
South	0.042*** (0.004)	0.018***(0.004)
West	0.042*** (0.003)	0.017***(0.004)
Number of Individuals	194,132	174,839
var(Ind)	0.003	0.003
var(Residuals)	0.119	0.154
Observations	307,605	272,851
Log Likelihood	113,292.900	134,186.900
Akaike Inf. Crit.	226,659.800	268,447.800
Bayesian Inf. Crit.	227,053.400	268,836.900

Note: Standard errors given in parenthesis. ∗p<0.05; ∗∗p<0.01; ∗∗∗p<0.001.

Table A5. Descriptive Statistics of Individual Net Income

	20%-Q	40%-Q	60%-Q	80%-Q	Mean	SD
Female	700	1,000	1,400	1,900	1,400	1,400
Male	1,200	1,700	2,100	3,000	2,300	2,600
Migrants	800	1,200	1,600	2,200	1,600	1,300
Majority population	900	1,400	1,800	2,500	1,900	2,200

Note: Q means quantile. SD means standard deviation. Values are provided in Euro and refer to monthly net income (rounded to hundreds) based on generalized Pareto interpolation.

Source: Microcensus 2014.

Table A6. Descriptive Statistics of the Covariates

Covariates	2011	2040
Blue Collar	7,268,341	9,035,568
Other Worker	6,851,373	2,329,589
Self Employed	3,491,204	3,513,550
State-Worker	1,795,529	2,039,539
White Collar	20,442,203	26,469,607
ISCED Levels 1 and 2	32,941,690	29,983,438
ISCED Level 3	16,727,562	18,786,855
ISCED Level 4 (post-secondary)	14,408,798	16,856,388
ISCED Level 5	4,723,944	5,343,423
Single	32,186,460	35,449,969
Divorced	5,099,718	5,954,598
Married	30,730,796	27,663,157
Widow	1,949,041	2,352,947
Part-time	9,047,698	11,373,085
1 Child in Household	5,452,759	5,461,470
2 Children in Household	3,649,681	3,598,167
3 or more Children	1,770,310	2,780,059
City	5,703,399	6,207,812
East	13,082,995	13,012,529
North	10,290,519	10,628,254
South	22,997,750	24,295,421
West	28,781,700	29,496,169
Germans (working age)	46,644,839	45,461,984
Late resettlers	2,492,969	2,195,640
Turkey	897,308	1,444,008
South-EU	695,417	1,175,148
EU before 2004	407,257	623,370
EU after 2004	650,194	1,273,984
Former Yugoslavia	295,555	502,382
Africa	127,658	244,669
Middle East	173,182	331,790
South-East-Asia	180,834	365,227
Rest of the World	535,904	917,020

Note: The table includes only first-generation working-age migrants.