The Impact of Private and Public Childcare Provision on the Distribution of Children s Incomes in Germany

Transcription

1 The Impact of Private and Public Childcare Provision on the Distribution of Children s Incomes in Germany Work in progress Do not quote nor circulate! Maximilian Stockhausen June 13, 2016 Abstract This paper investigates the impact of extending disposable cash income of children by the monetary value of private and public childcare provision on economic inequality in Germany between 2009 and It takes account of the multidimensionality of children s well-being and access to economic resources. Combining survey data from the Socio-Economic Panel (SOEP) and Familien in Deutschland (FiD) with administrative data from the German Federal Statistical Office, extended income inequality is found to be significantly lower than cash income inequality across all years. In addition, extended income inequality decreased at a much lower rate over time than cash income inequality. This difference is strongly influenced by the expansion of public childcare provision, which profits children living with single parents most, and gives additional evidence for its equalizing potential as a policy instrument. JEL Classification: D13 D31 H52 I24 Keywords: In-kind Benefits, Opportunity Costs, Non-cash Incomes, Extended Income, Economic Inequality Maximilian Stockhausen (m.stockhausen@fu-berlin.de) is affiliated to Economics Department of Freie Universität Berlin. I thank Giacomo Corneo, Charlotte Bartels, Luna Bellani, Katharina Wrohlich, Johannes König, Guido Neidhöfer, the participants of the workshop Public Economics and Inequality at Freie Universität Berlin, and the participants of the SOEP Brown Bag Seminar for helpful comments and discussions.

2 1 Introduction Improving the work-life-balance of parents with dependent children has been one of the major goals in German social policy during the past decade. One cornerstone of this policy has been the massive expansion of public childcare provision, including both early childhood care as well as all-day schools, to enhance female labour market participation (Schober and Stahl, 2014). Children need care, especially the very young, and parents often have to chose between staying at home or working in the labour market falling back to public childcare. This decision is largely driven by the composition and structure of families and their feasible economic resources. One of the most disadvantaged groups in terms of disposable cash income are single parents and their respective children who are most likely to belong to the bottom in the distribution of disposable cash income (OECD, 2011). Furthermore, Bartels and Stockhausen (2016) show that the share of children living with single parents has increased from 12.6 percent in 1991 to 19.9 percent in In addition, these children also tend to be disadvantaged in terms of parental education and parental time investments. Even though public childcare provision can partly compensate lower parental time investments, children from single parent families have command over relatively less parental resources which constitute their economic and social environment and determine the development of their cognitive and non-cognitive skills necessary to be successful in later life (Heckman and Mosso, 2014). Thus, using disposable cash income as a single indicator of material wellbeing of children seems to be incomplete, even though disposable cash income is a widely accepted and resilient measure for material well-being. This paper argues that disposable cash income should be extended by the monetized value of public in-kind benefits and home production to receive a more complete measure of children s access to economic resources and material well-being, respectively (Aaberge et al., 2010). Furthermore, expressing all dimensions in monetary units bears two additional advantages: (i) every dimension of well-being can still be intuitively interpreted from an economic perspective, and (ii) a classic pecuniary incidence analysis can be performed. 1 Therefore, an extended version of Becker s full-income concept is constructed which is the basis for quantifying the impact of extending the income definition by the imputed value of parental and non-parental childcare on the distribution of disposable cash income of children (Becker, 1965). 1 However, this approach implicitly assumes that the rate of substitution between each pair of dimensions is equal to one. A comprehensive overview on alternative multidimensional measures of inequality is given in (Aaberge and Brandolini, 2015) 1

3 An early and prominent paper that applied an extended income definition is Jenkins and O Leary (1996) for the United Kingdom. 2 They investigate the impact of extending the cash income of households by the imputed value of household production time to consider the overall amount of economic resources. Estimating the distribution of extended income amongst non-elderly, one-family households in 1986, they find a substantially lower level of inequality in the distribution of extended income compared to disposable cash income, while overall inequality trends are similar. Furthermore, changes in the income distribution due to the extension of the income concept shift singles down the distribution relative to married couple families. Frick et al. (2012) investigate the impact of home production on economic inequality in Germany. Their main finding is that extending cash income by the monetary value of home production has an inequality reducing effect independent of the evaluation technique and inequality measure used. Hence, their findings for Germany show the same patterns as the results of Jenkins and O Leary (1996) for the United Kingdom. Recent U.S. studies have also found substantial inequality reducing effects if the monetary value of home production is taken into account (see, e.g., Gottschalk and Mayer, 2002; Zick et al., 2008; Frazis and Stewart, 2011). 3 However, Frick et al. (2012) neither investigate the differences between family types nor do they consider the effects of both home production and in-kind benefits. Nevertheless, they show that childcare activities constitute a major part of home production whenever children are living in a household. Therefore, the expected transfers from parental childcare time are likely to be large among families with dependent children. In addition, the fictitious transfer added from public childcare is expected to be large as well, since the probability of using public childcare has increased across all social groups. Without considering the benefits from non-cash income and their impact on the distribution of economic resources comparisons of subgroups are incomplete and overall inequality estimations are incorrect (Aaberge et al., 2010). 4 The rest of the paper is organized as follows: In Section 2, a theoretical concept is introduced to define each component of extended income. An extended version of Becker s (1965) full-income concept is used for this purpose. In Section 3, the 2 Other early empirical works are Bryant and Zick (1985) or Bonke (1992), among others. See Frick et al. (2012) for a comprehensive overview. 3 Earlier studies also found a negative impact of extending the income definition. 4 This and further conceptual issues have been raised and widely discussed in several other works, too (see Smeeding, 1977; Ruggles and O Higgins, 1981; Gemmell, 1985; Smeeding et al., 1993; Evandrou et al., 1993; Ruggeri et al., 1994; Slesnick, 1996; Antoninis and Tsakloglou, 2001; Garfinkel et al., 2006). 2

4 data sources are described and it is shown how each extended income component is measured. In Section 4, the distributional effects of extending the income definition are discussed. Finally, in Section 5, the results are summarized and conclusions discussed. 2 Definition and Measurement of Extended income To define extended income I draw on the full-income concept introduced by Becker (1965). It provides the basis for a multidimensional approach to measure a child s access to economic resources and considers the different needs of heterogeneous individuals. This approach is, thus, able to take account of non-monetary resources that are similarly important for children s well-being and the development of their later capabilities. But before the level of children s well-being in terms of disposable income can be derived, the household level has to be considered first. This is a necessary intermediate step because children do not generate income by themselves and, thus, are dependent on their parents and other household members which are able to generate income. In general, full-income, F, of an adult household member is defined as F = wt + v, (1) where w is the gross hourly wage rate, T is the time endowment, and v is unearned income including, for instance, capital income. 5 A main property of the full-income concept is that income opportunities are measured independently of preferences as long as two assumptions are fulfilled: first, time spent at home and at work can be freely substituted for one another. Second, the budget constraint is linear such that the opportunity cost of each hour of a person s time is constant at all levels of paid work (Jenkins and O Leary, 1996). This full-income concept is further extended by a factor, B, covering the value of in-kind benefits, and a factor, OP, which is out-of-pocket payments for non-parental educational goods and services including fees for schooling, cribs, kindergarten, after school care clubs, and private childminders each child, j = 1..., C, receives. Furthermore, it is assumed that the time endowment, T, of all adult household members, i = 1,..., N, can be perfectly 5 Garfinkel and Haveman (1977), for instance, apply a different approach using a fixed number of work hours instead of the time endowment of a person. 3

5 split into market working time, M, home production time, H, and leisure time, L. 6 Furthermore, opportunity cost of time are assumed to differ between time allocation categories and individuals. The modified full-income of a household can therefore be written as the sum of the value of market income and unearned income, the value of non-market income from home production, and the net value of publicly provided in-kind benefits: F = N P C w i M i + ω i L i + v i + λ i H i + B j i=1 p=1 j=1 j=1 C OP j. (2) The coefficients w, ω, and λ are shadow prices of M, L, and H, respectively. Due to the difficulties in measuring leisure and identifying different kinds of leisure activities, leisure will be excluded from the analysis such that extended income of a household, E, is finally defined as E = N w i M i + v i + i=1 } {{ } Y P λ i H i + p=1 } {{ } D C B j j=1 C OP j. j=1 } {{ } K Accordingly, disposable extended income, E, of a family is the sum of disposable cash income from labour, Y, fictitious non-cash income from parental childcare activities, D, and fictitious non-cash income from non-parental childcare net of out-of-pocket payments for education and childcare, K. In addition, it is assumed that incomes from each source are equally distributed between family members (see Aaberge et al., 2010, p. 334). To account for different family sizes and composition, the extended income is, then, equivalized using the modified OECD scale (see, for instance, Frick et al., 2012; Jenkins and O Leary, 1996). 7 3 Data The main analysis is based on data from the Socio Economic Panel (SOEP), which is an annually repeated survey among German households. It includes a broad range 6 Only biological and non-biological parents, p = 1,..., P are considered in determining the value of home production time. Grandparents and other relatives living in the same household are not considered in this case. 7 An alternative equivalence scale was used to check for the robustness of the results. In particular, a scale is used which is derived from a survey estimating the direct costs of having children (Koulovatianos et al., 2009). The overall finding is that absolute mean income levels as well as absolute inequality coefficient slightly change, but not trends and relative associations. Results are available upon request from the author. 4

6 of demographic and socio-economic characteristics for all years since East German households are included in the panel since By 2012, 12,322 households participated in the SOEP which corresponds to 18,577 individuals (Wagner et al., 2007; Schupp and Rahmann, 2013). The sample includes East and West German children and information from their respective parents. 8 Children are defined as individuals that are aged 13 or below and still live with their parents. Since the SOEP does not provide detailed information on expenditures for schooling, private and public childcare, and other related educational expenditures encompassing, for instance, private tutoring, information from the supplement panel survey Familien in Deutschland (FiD) is used to extend the information content of the SOEP by means of imputation. The FiD was first launched in 2010 and covers more than households every year and puts a special focus on single parents, families with more than two children, low-income families, and families with very young children in the German population (Schröder et al., 2013). Furthermore, the panel survey data is extended by information from official statistics from the German Federal Statistical Office on the number of and total expenditures on children enrolled in publicly provided or subsidized childcare institutions and schools. Due to the limited availability of the FiD, the analysis covers the years between 2010 and 2013 (income years 2009 to 2012). 3.1 Cash Income Cash income, Y, is measured as real net equivalent household income including imputed rents. Net household income is the sum of households labour earnings, asset flows, private retirement income, private transfers, public transfers, and social security pensions minus total household taxes. 3.2 Net Monetary Value of Non-Parental Childcare Time The monetary value of non-parental childcare time, K, is derived by a standard production cost approach. This approach is associated with the assumption that the value of public childcare provision is as high as the expenditures of providing 8 Immigrants from the first wave of the new IAB-SOEP migration sample (Sample M) are not included since the likelihood of non-response is higher among those who are interviewed for the first time. 5

7 it (Aaberge et al., 2010). 9 In contrast to many previous studies, out-of-pocket payments are considered here, too. Since families of different composition and gross income levels have to bear different educational costs due to degressive tariffs, they are very likely to influence the distribution of disposable income. Thus, they are deducted from the monetary value of publicly provided childcare to receive its net value. Alternatively, it could be argued that out-of-pocket payments are a deliberate consumption decision of a household that need no further consideration. In addition, private educational expenditures still have a relatively low importance in Germany since the share of private educational institutions is just about five percent. However, accounting for out-of-pocket payments tends to slightly increase the Gini coefficient of real equivalized disposable cash income by two percent each year. Due to this distributional impact they are considered in the analysis. All public expenditures on pre-school and primary school childcare are considered as public spendings (per capita) including expenditures on cribs, kindergarten, primary school, after school day care, and subsidies for private childminders. 10 Statistics on yearly public expenditures per child on schooling and childcare are provided by the German Federal Statistical Office for each federal state (Statistisches Bundesamt, 2014). Yearly public expenditures per child on schooling are defined as the sum of public expenditures on general primary and secondary schools and vocational schools divided by the total number of pupils enrolled in these institutions. 11 Yearly public expenditures per child on childcare are measured as the sum of public expenditures on cribs, kindergartens, after-school care clubs, and other forms of publicly subsidized day care divided by the total number of children consuming these services. 12 One major drawback of this evaluation approach is that existing regional differences in the quality and efficiency of childcare provision cannot be considered at its best. To slightly reduce this problem and to allow for some degree of regional heterogeneity, public expenditures are, at least, differentiated on the federal state level. However, it is assumed that children living in the same federal state and 9 Another approach is to measure individual s willingness to pay for similar services that are provided in the market. This approach, however, is very data demanding and thus rarely used. 10 Primary school expenses include staff and administrative expenditures as well as investments in infrastructure. 11 Unfortunately, there is no disaggregated data on public expenditures on general primary and secondary schools alone. 12 If a child receives half day care in the respective year of observation, yearly public expenditures per child on childcare are divided by two (see Frick et al., 2011). All expenditures are measured in 2010 Euros. 6

8 attending the same educational level receive a similar amount of non-cash income from childcare and education. 13 Table 1 depicts the evolution of mean yearly real public expenditures per child on childcare including spendings on cribs, kindergarten, after school care clubs and publicly subsidized child minders. In 2009, Berlin spent most with an average of 7,367 Euro per child followed by Hamburg with 7,189 Euro. In contrast, Mecklenburg Western Pomerania and Saxony Anhalt spent least. Their mean expenditures amounted to 3,416 Euro and 3,950 Euro per child, respectively. In 2012, Berlin was still in the leading position spending an average of 8,594 Euro per child followed by Northrhine-Westphalia (7,885 Euro) and Bremen (7,257 Euro). Saxony Anhalt (3,783 Euro), Mecklenburg Western Pomerania (3,837 Euro), and Saxony (3,995 Euro) spent least on childcare per child in However, almost all German federal states increased their per capita spendings on childcare over the past years except of Saxony, Saxony Anhalt, and Hamburg. The latter might be the consequence of less demand of childcare provision due to a decreasing number of children in these federal states. At the same time, most of the West German states increased their per capita expenditures by more than the East German states did (see Figure A.2 in the Appendix). Taking all West German federal states together, they spent on average 5,587 Euro in 2009 per child on childcare and increased their spendings to an average of 6,520 Euro in 2012 (+17 percent). In contrast, mean per capita expenditures across all East German federal states were 4,718 Euro in 2009 and 5,087 Euro in 2012 (+8 percent). Table 2 shows the trend in average yearly real public expenditures per child on schooling between 2009 and In 2009, the highest per capita spendings on schooling are observed in Thuringia and Saxony Anhalt: on average they spent 6,125 Euro and 6,035 Euro per child, respectively. In contrast, Schleswig Holstein and Northrhine-Westphalia spent least with 4,253 Euro and 4,254 Euro, respectively. In 2012, Saxony Anhalt and Thuringia changed position in the ranking: mean per capita spendings on schooling amounted to 6,474 Euro in Saxony Anhalt compared to 6,365 Euro in Thuringia. The lowest mean spendings were undertaken by Schleswig-Holstein with 4,413 Euro, and Saarland and Lower Saxony both with 4,550 Euro. However, all federal states managed to increase their real per capita expenditures on schooling over the past years. At the same time, taking all East German federal states together they spent more on schooling on average than their 13 Undoubtedly, there are further differences in the quality of schools and educational qualities of childcare within federal states also, which cannot be considered due to the limitation of available data. 7

9 Table 1: Mean Yearly Real Public Expenditures Per Child on Childcare by Region (in Euro) Region Baden-Württemberg 4,703 5,406 5,342 6,354 Bavaria 4,759 5,152 5,411 5,452 Berlin 7,367 7,944 8,342 8,594 Brandenburg 4,234 4,343 4,567 4,480 Bremen 6,265 6,638 6,718 7,257 Hamburg 7,189 6,991 6,713 7,062 Hesse 5,666 6,198 6,293 6,360 Mecklenburg Western Pomerania 3,416 3,486 3,614 3,837 Lower Saxony 4,880 5,156 5,404 5,471 Northrhine-Westphalia 5,835 6,546 7,016 7,885 Rhineland Palatinate 6,082 6,733 6,970 6,990 Saarland 5,564 7,137 6,622 6,981 Saxony 4,334 4,359 4,041 3,995 Saxony Anhalt 3,950 4,053 3,842 3,783 Schleswig-Holstein 4,926 5,840 5,357 5,392 Thuringia 5,007 5,600 5,960 5,833 Note: All expenditures are in 2010 Euros. Source: German Federal Statistical Office, Bildungsfinanzbericht (2014), own calculations. West German counterparts. In addition, the East German federal states increased their mean spendings by eight percent, while the average increase across all West German federal states was seven percent (see Figure A.3 in the Appendix). Since access to public childcare and schooling is not free of charge in Germany, the scope of out-of-pocket payments for childcare and schooling is investigated in the next section. Unfortunately, information on yearly out-of-pocket payments for schooling, private and public childcare, and related educational expenditures, P, are not directly provided by the SOEP such that they are imputed by means of the FiD. In a first step, a simple OLS regression model is applied to regress the log of monthly spendings on childcare and schooling, S j, for each child, j = 1,..., C, on a broad set of covariates including child s sex, child s age and age squared, real monthly net family income, household type, parental education in years of schooling, federal state of residence at the time of the interview, migrational background, and each the number of family members aged 0-2 years, 3-5 years, and 6-13 years. The OLS regression equation, thus, has the form: ln(s j ) = α + βx j + ɛ j, (3) 8

10 Table 2: Mean Yearly Real Public Expenditures Per Child on Schooling by Region (in Euro) Region Baden-Württemberg 4,708 4,804 4,849 4,935 Bavaria 4,899 5,204 5,240 5,345 Berlin 5,438 5,723 5,833 6,018 Brandenburg 4,786 5,171 5,419 5,370 Bremen 4,594 5,184 5,102 5,093 Hamburg 5,604 5,651 6,003 5,942 Hesse 5,031 5,364 5,219 5,367 Mecklenburg Western Pomerania 5,253 5,755 5,610 5,930 Lower Saxony 4,308 4,479 4,500 4,550 Northrhine-Westphalia 4,254 4,497 4,550 4,641 Rhineland Palatinate 4,691 4,978 5,208 5,192 Saarland 4,361 4,587 4,503 4,550 Saxony 5,709 6,242 6,052 5,982 Saxony Anhalt 6,035 6,553 6,664 6,474 Schleswig-Holstein 4,253 4,444 4,369 4,413 Thuringia 6,125 6,572 6,371 6,365 Note: Expenditures on employees and administrational staff including fictitious social contributions for civil servants, aid expenditure (Beihilfeaufwendungen), current operating expenses and capital expenditures. All expenditures are in 2010 Euros. Source: German Federal Statistical Office, Bildungsfinanzbericht (2014), own calculations. with α being an intercept, β being a row vector of regression coefficients, X j being a column vector of the described covariates, and ɛ j being an i.i.d. white noise error term. The regression is computed for each year separately and restricted to children aged between zero and fourteen years living in their parents private household only. The estimated β coefficients from the FiD sample are, then, used for an out-of-sample prediction using the SOEP data set to impute the monthly spendings on childcare and schooling for children aged below fourteen years. Since all socio-economic control variables are also available in the SOEP and, furthermore, identically measured, this approach is quite straightforward and returns ample approximations at the mean with less variation in the tails. 14 According to the number of children in a family, the child related spendings are summed up across all children in a family and are transformed, again, to an annual basis. Table 3 depicts the trends in average real spendings on childcare and schooling by family type between 2009 and Both total and equivalized spendings have increased over time: families with children aged under fourteen years spent on average 1,704 Euro in 2009 and 1,753 Euro in This is an increase of See Tables A.3 to A.4 in Appendix for descriptive statistics of the original and fitted values. 9

11 percent. In contrast, single parents increased their spendings by 5.6 percent while married couples raised their spendings by only 4.1 percent. However, in absolute terms married couples still have spent most on their children s education in every single year. In 2009, their mean expenditures amounted to 1,801 Euros compared to 1,139 Euros of single parents. Average spendings of cohabiting couples are only slightly smaller than what married couples payed for schooling, private, and public childcare but they are considerably higher than those of single parents. Actually, cohabiting couples reduced their total real spendings from Euro to Euro which is a decrease of almost 20 percent. Nevertheless, this might be due to the relatively small sample size and, thus, has to be treated with caution. However, differences in total household spendings might just be driven by different family sizes and needs for childcare as well as education; for instance, married couple families tend to have more children than singles or cohabiting couples and, therefore, they tend to have higher absolute spendings. To account for these differences, expenditures are equivalized for comparison using the modified OECD scale. Although absolute equivalized expenditures are, of course, smaller than total spendings, overall patterns do not change: overall spendings increased from an average of 792 Euro in 2009 to 809 Euro in 2012 which is an increase of 2.2 percent and, thus, slightly smaller than before. As expected, the relative differences in spendings between married couple and single parents decrease if the number of children is considered through equilization: single parents increased their equivalized spendings by 2.7 percent, while married couple parents raised their equivalized spendings by 3.6 percent. Cohabiting couple parents decreased their spendings by around 17.3 percent which is also slightly smaller than before. Furthermore, differentiating by children s age at the time of the survey reveals that the level of both total and real equivalized spendings are decreasing with children s age. 3.3 Monetary Value of Parental Childcare Time The monetary value of parental childcare time, D, constitutes a major part of home production that is not reflected in the household s cash income flow, although it is associated with positive affects on household s welfare (Frick et al., 2012). Therefore, the objective of this chapter is to discuss possible ways to quantify the value of nonmarket working time in general and of parental childcare time in particular. The main challenge is the absence of market prices for private childcare as part of home production. There are two widely used approaches to derive fictitious (gross) hourly 10

12 Table 3: Yearly Average Real Total and Equivalized Spendings on Childcare and Schooling by Family Type (in Euro, weighted) Total Household Spendings Equivalized Spendings Year Family Type Mean SD Min Max Mean SD Min Max All Children Single 1, , ,303 Cohabiting 1,791 1, , ,367 Married 1,801 1, , ,531 Total 1,704 1, , ,531 Single 1, , ,102 Cohabiting 1, , ,162 Married 1, , ,681 Total 1, , ,681 Single 1, , ,927 Cohabiting 1, , ,867 Married 1,927 1, , ,639 Total 1, , ,867 Single 1, , ,887 Cohabiting 1, , ,806 Married 1, , ,975 Total 1, , ,975 Children 0-5 Single 1, , ,826 Cohabiting 2,017 1, , ,367 Married 2,245 1, ,428 1, ,512 Total 2,138 1, , ,512 Single 1, , ,102 Cohabiting 1, , ,162 Married 2, , ,341 Total 1, , ,341 Single 1, , ,927 Cohabiting 1, , ,867 Married 2, ,657 1, ,639 Total 2, ,021 1, ,867 Single 1, , ,887 Cohabiting 1, , ,806 Married 2, ,139 1, ,975 Total 2, ,139 1, ,975 Children 6-13 Single 1, , ,303 Cohabiting 1, , ,795 Married 1, , ,531 Total 1, , ,531 Single 1, , ,102 Cohabiting 1, , ,004 Married 1, , ,681 Total 1, , ,681 Single 1, , ,776 Cohabiting 1, , ,652 Married 1, , ,624 Total 1, , ,624 Single 1, , ,887 Cohabiting 1, , ,650 Married 1, , ,975 Total 1, , ,975 Note: All expenditures are in 2010 Euros. Expenditures have been equivalized using the modified OECD scale. Source: SOEP (v30) and FiD (v4), own calculations. 11

13 wage rates for non-market workers: (1) the housekeeper wage approach, and (2) the opportunity cost approach. Both approaches mainly differ in their assumption on the underlying productivity of individuals: the housekeeper wage approach assumes that all individuals are similarly productive, whereas the opportunity cost approach accounts for the heterogeneity in the productivity of individuals Housekeeper Wage Approach The major feature of the housekeeper wage approach is that a uniform hourly wage rate is assigned to all parents participating in childcare activities on an average week day. Each individual is assumed to be similarly productive and differences in the productivity of unskilled home workers and skilled labour force are also neglected. One way to derive the gross hourly wage rate is to use the average wage rates of employees working in sectors that produce comparable goods and services as in home production. Therefore, the housekeeper approach is close to a market value approach, where the hourly wage rate is comparable to the market price. In particular, the shadow price of parental childcare time is directly derived from the SOEP using information on monthly gross earnings of childcare workers, w m, which are identified by ISCO-88 code These are, then, transformed into gross hourly wage rates by: w h = w m/4.3 h w, where h w denotes the actual weekly working hours of a childcare worker. This is done for each year separately. Next, the estimated gross hourly wage rates, w h, are used to derive the monetary value of parental childcare time by multiplying them with the hours of parental childcare time on an average week day, ct h. The estimated gross wage rates can be found in Tables A.13 and A.14 in the Appendix. 15 To receive an annual value of parental childcare time, the following transformation is done D h = w h ct h ( ), (4) where D h is the annual fictitious gross income from parental childcare time according to the housekeeper wage approach. 16 The fictitious yearly gross income 15 Another possibility is to use agreed wages of childcare workers working in the public sector only. This would result in much higher gross wage rates than those derived from the SOEP. Therefore, the results presented in Section 4 provide a lower bound for the distributional impact of the housekeeper wage approach. Using the lower wages could also be justified as an adjustment regarding the different productivity of trained childcare workers and untrained parents. 16 It is assumed that an average year consists of 258 working days (258days = 5days 4.3weeks 12

14 is, then, multiplied by the household s average tax rate, which is approximated by the ratio of taxes and social security contributions to market income at the household level, and deflated to the base year (Frick et al., 2012). 17 Finally, the monetary value of parental childcare time is summed up with the household s disposable cash income and the net monetary value of publicly provided childcare which are all equivalized as described in the end of Section Opportunity Costs Approach In contrast to the housekeeper wage approach, the opportunity cost approach allows for heterogeneity in the productivity of individuals and measures the foregone earnings that an individual with specific skills could have received in the labour market instead of doing childcare at home by himself. Furthermore, it is assumed that people can deliberately choose between working in the labour market or at home to satisfy a given set of needs for childcare. Thus, the decision whether to work at home or in the labour market depends on the individual s earnings capacity and its productivity in childcare. If a parent is more productive in childcare, he would need to work more hours in the labour market than in home production to afford the same amount of childcare on the market. Therefore, he will choose to do it by himself instead of working in the labour market. Furthermore, this is accompanied by the very strong assumption that individuals can freely choose the amount of working hours in the labour market. This is challenged by the presence of labour market rigidities, for instance, the existence of fix working hours that are part of contracts of employment (see Frick et al. (2012)). There are two widely used approaches to predict the shadow wage rates of home workers from the observable gross hourly wage rates of working age individuals: (i) the standard OLS regression model as well as (ii) the Heckman selection correction model. Selection correction might be useful because it controls for a possible relation between gross hourly wage rates and unobserved characteristics that influence the decision to work or to take care of the child at home. In both cases, the value of parental childcare time is estimated using a different sample from the SOEP. For this purpose, the sample is restricted to the working age population (20-60 years) excluding all individuals who are still in education, in military or community service, 12months). National holidays and private vacation (the minimum statutory holidays could be subtracted) or the fact that Saturdays are also working days are not considered for simplicity. 17 Alternatively, Jenkins and O Leary (1996) propose to regress ln(hh taxes) on a quadratic of household taxes to estimate the marginal tax rates of households. 13

15 in apprenticeship including trainee- and internships, who work as civil servants, who are pensioners (e.g. early retirement), and who help in family business. 18 OLS Regression First, a Mincer-like OLS wage regression is applied to predict the shadow price of parental childcare time in home production. This is done separately for each year and sex (the subscripts are left out for simplicity) estimating the following equation: ln(w i ) = α + βx i + ɛ i, (5) where w i is the gross hourly wage rate of individual i. According to Frick et al. (2012), amongst others, the column vector X i contains age and age squared, full-time and part-time working experience as well as their squared terms, schooling (lower secondary (reference), intermediate, college), vocational education (none (reference), basic vocational, higher vocational, tertiary), federal state (Schleswig-Holstein (reference), etc.), migrational background (no migrational background (reference), 1st generation migrant, 2nd generation, information not available), self-rated health (very good (reference), good, satisfactory, bad, very bad), marital status (married (reference), single, divorced, widowed), the number of children younger than 6 years (none(reference), one child, two and more children), and the location in 1989 (GDR (reference), FRG, abroad) as covariates. The predicted coefficients are then used to estimate the log of gross hourly wage rates for all men/women in the respective year. Note that predicted wage rates are only used if information on gross hourly wages is missing. After antilogging the gross hourly wage rates, they are multiplied with the hours of childcare activities on an average week day. These are then transformed to an annual basis for each parent and summed up across all biological and non-biological parents living in the same household. The fictitious yearly gross income is, again, multiplied by the household s average tax rate to receive a net value and it is also deflated to the base year Heckman Selection Correction Model In order to reduce potential estimation bias due to self-selection into paid and unpaid work, a two-step Heckman selection correction model is computed for comparison. 19 It controls for possible relations between gross hourly wages and 18 Again, immigrants from the first wave of the new IAB-SOEP migration sample (Sample M) are dropped out since the likelihood of non-response is higher among who are interviewed for the first time. 19 See Cameron and Trivedi (2009) for a detailed description of the two-step Heckman selection model. 14

16 unobserved characteristics that might influence the decision to work or to take care of the child at home. Again, the regressions are done separately for each year and sex. In a first step, an unrestricted binary outcome model (probit model) is estimated to predict the probability of observing positive earnings: p i = α + β 1 X 1 + ɛ 1, ɛ 1 N(0, 1) (6) where p i is a binary response variable that is one if an individual is working and zero if it is not working. The column vector X 1 contains a wide set of control variables which are age and age squared, full-time and part-time working experience as well as their squared terms, schooling (lower secondary (reference), intermediate, college), vocational education (none (reference), basic vocational, higher vocational), federal state of residence (Schleswig-Holstein (reference), etc.), migrational background (no migrational background (reference), 1st generation migrant, 2nd generation, information not available), and the location in 1989 (GDR (reference), FRG, abroad). In addition, self-rated health (very good (reference), good, satisfactory, bad, very bad), marital status (married (reference), single, divorced, widowed), and the number of children younger than 6 years (none (reference), one child, two and more children) are used as exclusion restrictions such that they are assumed to only influence the decision to work but not the level of earnings. This choice might be debatable but it is widely accepted that the number of dependent children and marital status are important determinants for the choice to work, especially for women. Being mentally or physically ill is also very likely to influence the ability to work more than the level of earnings due to anti-discrimination legacy. In a second step, the following restricted linear regression model (OLS) is estimated: with ln(w) = α + β 2 X 2 + ɛ 2, ɛ 2 N(0, σ) (7) E[ln(w) X 2, p > 0] = β 2 X 2 + σ 12 λ(β 1 X 1), (8) where the inverse Mills ratio λ = φ(.)/ψ(.) such that E[ɛ 2 p > 0] = σ 12 λ(β 1 X 1). Furthermore, it is assumed that the correlated error terms, ɛ 1 and ɛ 2, are jointly normally distributed and homoskedastic. These two assumptions are vital to receive unbiased estimators of the variance and covariance. Mostly, the assumption 15

17 of homoskedasticity is violated due to insufficient model specifications. However, this will not automatically lead to biased coefficient estimators but the estimated standard errors might be biased such that test statistics tend to be imprecise and misleading. Being aware of this, gross hourly wages and the corresponding fictitious income from parental childcare are derived as described before using the predicted coefficients from the Heckman model Results Extending the income definition by fictitious income from parental and non-parental childcare time has both a remarkably large effect on the level of children s disposable income and its distribution. Accordingly, I will, first, discuss how average real disposable income levels change when fictitious income components are added to the disposable cash income of children, and, second, I will describe the distributional impact of extending the income definition. Level Effects Table 4 depicts the trends in yearly average real equivalized disposable cash and extended incomes of children by family type between 2009 and It includes the fictitious cash transfers added from parental and non-parental childcare time as well as educational expenditures. The third column entails the commonly used average real equivalized disposable cash income which is the basis for each extended income concept. Column four states the average real equivalized educational expenditures. Column five encompasses the average real equivalized monetary value of non-parental childcare time (in-kind benefits). Columns six to eight depict the average real equivalized monetary value of parental childcare time according to the three different estimation approaches introduced in Section 3. Finally, columns nine to eleven show mean real equivalized extended incomes. 21 Note that real equivalized disposable incomes will be denoted just as incomes in the remainder of the results section for simplicity. In general, mean extended income is remarkably higher than cash income. Nevertheless, both have decreased between 2009 and In 2009, mean cash 20 See Table A.13 and Table A.14 in the Appendix for an overview of estimated gross wage rates according to the different approaches. 21 As in Frick et al. (2012), OLS and Heckman estimations yield very similar results. 16

18 income was 20,668 Euro, while mean extended income amounted to 30,911 Euro (HK), 33,032 Euro (OLS), or 32,930 Euro (HM). In 2012, cash income decreased to an average of 20,115 Euro and extended income to an average of 29,886 Euro (HK), 32,177 Euro (OLS), or 32,340 Euro (HM). This reduction is not only due to the decrease in cash income; also fictitious average income from parental childcare time has fallen over time. Only transfers added from in-kind benefits have increased between 2009 and Although income from in-kind benefits adds a significant amount to cash income, it is still smaller in levels than income added from parental childcare time. However, the importance of in-kind benefits has increased over time such that its share on total extended income has risen from around 13 percent in 2009 to 15 percent in 2012, if the housekeeper wage approach is used to evaluate non-parental childcare time. The same share is 12 percent in 2009 and 14 percent in 2012, if the OLS or Heckman approaches are used, respectively. Mean fictitious income from in-kind benefits amounted to 3,925 Euro in 2009 compared to an average fictitious income of 7,042 Euro from parental childcare time according to the housekeeper wage approach. While mean income from in-kind benefits has continuously increased to 4,400 Euro in 2012, income from parental childcare time has decreased to an average of 6,180 Euro in 2012, again, regarding the housekeeper wage approach. Results only slightly differ if the two opportunity cost approaches are applied to evaluate parental childcare time. In this case, the average value of fictitious income from parental childcare time exceeds the value of parental childcare time derived by using the housekeeper wage approach. The former decreased from an average of around 9,459 Euro (OLS) and 9,354 Euro (HM) in 2009 to around 8,903 Euro (OLS) and 9,074 Euro (HM) in The increasing importance of in-kind benefits is very likely to be explained by the substantial expansion of publicly provided childcare in Germany and a greater willingness of parents to send their children to public childcare institutions. The motives for the latter might originate from a rising economic pressure on families with children or a vanishing dominance of the single breadwinner role model in Western industrialized countries. Differentiating between family types reveals that children living with single parents profit most from in-kind benefits as well as parental childcare time. In 2009, mean income from in-kind benefits summed up to 5,073 Euro which is 39 percent of cash income. Regarding children from cohabiting and married couple families the same share was only 19 percent and 18 percent, respectively. In 2012, the shares have even increased to 42 percent for children 17

19 living with singles, 22 percent for children living with cohabiting couples, and 21 percent for children living with married couples. Furthermore, the monetary value of parental childcare time is also largest for children from single parents and almost twice as high than the income from in-kind benefits. In 2009, the mean transfer added from parental childcare time was 9,855 Euro for children from single parent families compared to an average of 6,553 Euro for children from married couple parents applying the housekeeper wage approach. Thee monetary value is even larger if the opportunity cost approaches are applied: it adds another 2000 to 3000 Euro on top of the value of parental childcare time compared to the housekeeper wage aprroach. Trends over time are ambigous. Income from parental childcare time decreased for children living with single parents if the housekeeper wage rate is applied, while it has slightly increased if the opoortunity cost approaches are used. In contrast, children living with married parents experienced a loss in income from parental childcare time across all three evaluation approaches. Furthermore, the income differential between children from single and married couple parents decreases if the the income definition is extended. In 2009 and applying the housekeeper wage approach, the cash income differential amounted to 8,135 Euro on average, whereas the extended income differential was only 3,467 Euro. If the OLS and Heckman approaches are used instead, the extended income differentials are 5,407 Euro and 5,586 Euro, respectively. In any case, they are still smaller than the initial cash income differential such that the transfers added from parental and non-parental time tend to equalize the income distribution. This holds for every year. Distributional Effects Since children from single parent families are more likely to be found at the lower part of the income distribution, a closer look at the different regions of the cash and extended income distribution is also of great interest (OECD, 2011). Figure 1 provides insights into this question by showing the relative change in mean incomes by the initial cash income quintile and year. In general, all children benefit from adding transfers from parental and non-parental childcare time, but the relative increase in extended income is the largest for children from the lowest quintile and diminishes with higher quintiles. Applying the housekeeper wage approach, extended income is by around 150 percent larger than cash income in the first quintile in In the fifth quintile mean extended incomes are 25 percent larger than the respective cash 18

20 Table 4: Trends in Yearly Average Real Equivalized Disposable Income by Component and Family Type (in Euro) Family Cash Educ. In-Kind Opport. Costs Total Extended Income Year Type Income Exp. Benefits HK OLS HM HK OLS HM Single 13, ,073 9,855 11,437 11,161 27,993 28,617 28,364 Cohab. 21, ,825 6,641 7,449 7,357 30,940 31,597 31,508 Married 21, ,806 6,553 9,327 9,250 31,460 34,024 33,950 Total 20, ,993 7,042 9,459 9,354 30,911 33,032 32,930 Single 13, ,777 9,223 9,762 9,717 27,702 26,838 26,799 Cohab. 21, ,889 7,489 9,562 9,583 31,726 33,625 33,646 Married 21, ,006 6,734 9,021 9,054 31,896 33,973 34,009 Total 20, ,263 7,165 9,163 9,184 31,254 32,876 32,900 Single 14, ,640 8,373 9,032 8,809 27,534 27,121 26,924 Cohab. 19, ,796 6,971 7,578 7,507 29,138 29,469 29,401 Married 21, ,088 6,552 8,389 8,270 31,343 32,933 32,822 Total 20, ,283 6,853 8,396 8,268 30,585 31,767 31,648 Single 13, ,578 8,254 11,809 12,082 27,059 28,972 29,207 Cohab. 18, ,932 6,912 8,784 8,878 28,963 30,340 30,429 Married 21, ,262 5,773 8,504 8,667 30,433 32,881 33,040 Total 20, ,400 6,180 8,903 9,074 29,886 32,177 32,340 Note: All incomes and expenditures are in 2010 Euros and have been equivalized using the modified OECD scale. Abbreviations: HK = Housekeeper wage approach, OLS = Ordinary least squares model, HM = Heckman selection correction model. Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. 19

21 incomes. For the third to the fifth quintile the relative increase of mean incomes is larger using the opportunity cost approaches. This can be easily explained by the differences between observed and estimated wages: for the first quintile the flat housekeeper wage rate is just slightly higher than the actually observed wage rate of this group. The opposite argumentation holds for the third to the fifth quintile. Figure 1: Relative Change in Mean Incomes Across Cash Income Quintiles by Year Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. Next, I turn to the question of how cash income is generally related to income advantages from each fictitious income components and whether the latter tend to generally increase or decrease inequality. Table 5 depicts the correlation coefficients between cash income and income from parental and non-parental childcare, respectively, accounting for the different approaches to determine the value of parental childcare time. On the one hand, there is no distinct correlation between cash income and income from in-kind benefits. In 2009 and 2011 it is positive and close to zero while it is negative and also close to zero in 2010 and In contrast, the correlation between income from parental childcare based on the housekeeper wage approach and cash income is unambiguously negative and, therefore, tends to equalize the income distribution. On the other hand, cash income is positively correlated with income from parental childcare time for both opportunity cost approaches. Therefore, the opportunity cost approaches tend to reproduce existing 20

22 cash income inequalities, because it reproduces inequalities from existing differences in the productivity of children s parents that are highly correlated with their market cash income, whereas fictitious income according to the housekeeper wage approach tends to slightly decrease inequality. This is due to the simple mechanism: the housekeeper wage rate is flat and the same for all parents and, thus, narrows the income distribution more. Furthermore, the transfers added from in-kind benefits only differ by the age of children and the region they live in and tend to have no clear effect on the distribution of incomes. Table 5: Correlations: Real Equivalized Disposable Household Income and Income From Parental and Non-Parental Childcare Year In-Kind Benefits Housekeeper OLS Heckman Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. Next, Figures 2 to 4 show the impact of extending the income definition for Germany between 2009 and 2012 using inequality coefficients that differ in their sensitivity to changes along the income distribution. A major finding is that extended income inequality is found to be significantly lower than cash income inequality across all years and inequality measures. In addition, extended income inequality increased at a much lower rate over time than cash income inequality did. Furthermore, extended income inequality is the lowest, if the monetary value of childcare is measured in terms of the housekeeper wage approach. This is as expected, because applying a flat wage rate to differently productive individuals will automatically narrow the income distribution by more than any approach allowing for heterogeneity in estimated wage rates. Therefore, measured inequality in extended income using the opportunity cost approach is larger compared to the housekeeper wage approach but it is still significantly smaller than cash income inequality. According to Figure 5, adding transfers from parental and non-parental childcare reduces inequality more if measures are used which are more sensitive for changes at the tails, namely the Mean Logarithmic Deviation (MLD) coefficient and half the square root coefficient of variation (HSQCV). Extending the income definition reduces the Gini coefficient by ten to thirty percent while the MLD coefficient decreases by 20 to 50 percent. The equalizing effect is largest if HSQCV is 21

23 Figure 2: Trend in Extendend Income Inequality, Note: Significance at the five percent level is calculated using bootstrap standard errors with 100 replications. Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. Figure 3: Trend in Extendend Income Inequality, Note: Significance at the five percent level is calculated using bootstrap standard errors with 100 replications. Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. 22

24 Figure 4: Trend in Extendend Income Inequality, Note: Significance at the five percent level is calculated using bootstrap standard errors with 100 replications. Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. considered: it is more sensitive for changes at the top of the distribution and income inequality decreases by 30 to 55 percent. Note that the differences between the OLS and the Heckman selection correction model are, again, only small and negligible. However, comparing inequality coefficients is not sufficient to make reliable social welfare comparisons. Therefore, Figure 6 depicts generalized Lorenz curves for each year to evaluate and rank the different income distributions on welfare grounds. Since all three extended income distributions are lying strictly above the cash income distribution - showing no points of intersection and having larger mean income values - they are dominating the cash income distribution according to Shorrocks Theorem and, thus, they are welfare superior. Even considering the cash distribution including the value of in-kind benefits only, leads to a higher welfare level. No clear ranking is possible in case of comparing the extended income distributions with each other, since there is an intersection in the upper right part of the figure between the extended income curve from the housekeeper wage approach and the extended income curves from the two opportunity cost approaches. 23

25 Figure 5: Relative Change of Inequality (in Percent) Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. Figure 6: Generalized Lorenz Curves Source: SOEP (v30), FiD (v4), and Federal Statistical Office, own calculations. 24

26 5 Conclusion This paper is the very first to assess the impact of extending children s disposable cash income to encompass the monetary value of parental and non-parental childcare time on economic inequality in Germany. Combining survey data from the German Socio-Economic Panel (SOEP) and Familien in Deutschland (FiD) with administrative data from the German Federal Statistical Office covering the years 2009 to 2012, it is shown that extended income inequality is significantly lower than cash income inequality across all years. This major finding is largely in line with previous research by, for instance, Jenkins and O Leary (1996) for the UK and Frick et al. (2012) for Germany, who investigate the distributional effect of adding the value of home production to disposable cash income. Furthermore, while cash income inequality of children has increased between 2009 and 2012, the percentage change in extended income inequality is considerably smaller across all evaluatin approaches regarding parental childcare time. To a large extent this is due to the expansion of public childcare, since transfers from non-parental childcare add a considerable amount of fictitious income to the cash income of children in absolute and relative terms that has even increased over time. Nevertheless, fictitious income from parental childcare still adds more to cash income although its share has decreased between 2009 and This paper also shows that differences in family structures are a pressing issue: children living together with a single parent profit most by adding transfers from parental and non-parental childcare time to disposable cash income. In particular, children living with single parents profit a lot from in-kind benefits in relative terms. Nevertheless, whether a child profits from non-parental childcare - but also from parental childcare - also depends on its position in the initial cash income distribution. Children from the lowest quintile gain, by far, more than children from the fourth or fifth quintile. Nevertheless, even children from high quintiles profit from extending the income definition in absolute terms. These findings provide further evidence on the hypothesis that the provision of public childcare is an appropriate measure for social policy makers to reduce inequalities among children in Germany. At least it shows that the provision of public goods lowers the importance of cash income regarding the present welfare of children. 25

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30 A Appendix Table A.1: Number of Observed Children (Aged 0-14) by Family Type (Unweighted) Year Single Parent Cohabiting Parents Married Parents Total ,269 2, ,451 3, ,396 3, ,136 2,863 Total 1,700 1,304 9,252 12,256 Source: SOEP (v30), own calculations. Table A.2: Average Hours of Parental and Non-parental Childcare Time on an Average Week Day by Family Type (Weighted) Parental Time Parental Time Per Child Non-parental Time Year Family Type Mean SD Min Max Mean SD Min Max Mean SD Min Max Single Cohabiting Married Total Single Cohabiting Married Total Single Cohabiting Married Total Single Cohabiting Married Total Source: Own calculations, SOEP (v30). 29

31 Figure A.1: Time Spent on Childcare Activities on an Average Week Day by Age, Gender, and Family Type Source: SOEP (v30), own calculations. 30