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Close to Home: A Simultaneous Equations Model of the Relationship Between Child Care Accessibility and Female Labor Force Participation

Abstract

Rising rates of maternal employment among current and former welfare recipients have increased the use of non-parental child care. Little empirical work examines the relationship between women’s labor supply and the geographic supply of child care. We combine census data with child care provider information for the state of Maryland to explore the relationship between female labor supply and the geographic supply of child care. OLS and 3-SLS equations are estimated, and the findings are consistent across each estimator: Women’s labor supply is sensitive to the geographic supply of child care and vice versa. These results are important because states now spend significant money on quality improvement initiatives, many of which increase child care supply in low-income neighborhoods.

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Notes

  1. The term “substitute” child care is used in this paper to denote sources of formal and informal child care that are not provided by the parent. Formal modes of child care include center-based or family-based providers, for example, while informal modes are typically defined as relatives, neighbors, and babysitters.

  2. A study conducted in Wales using a reduced form specification finds that an increase of 10 child care providers within a community leads to a 3% increase in the female labor supply (Webster and White 1997b). Another analysis from the Netherlands shows that female labor supply increases significantly when the supply of accessible subsidized child care is raised (Van Dijk and Siegers 1996).

  3. This study models the social and economic correlates of “feeling” constrained, rather than a quantitative measure of service accessibility.

  4. To make the framework tractable, we allow a few simplifying assumptions: (a) the decisions of other household members, including the spouse, are exogenous to the work decision of the mother; (b) that all of the mother’s non-work time is spent caring for her children, which removes the complication of having to distinguish between leisure and home production of child care; (c) that one type of non-mother child care is used; and (d) that the price of child care reflects its quality.

  5. Highly trained workers who possess advanced education in child development are more skilled and provide higher-quality care, but they also cost more to employ. Child care services provided to smaller groups or special populations are deemed higher quality, but cost more to provide.

  6. The dependent variable in our equations, however, only uses slots from center-based and family-based providers. Head Start is used as a control variable in the child care supply equation.

  7. The extent to which our data capture the total paid child care market in Maryland is unknown. However, estimates from recent national studies suggest that the two modes of child care observed in this study capture approximately 50% of the total (paid and unpaid) child care market (Capizzano et al. 2000; Ehrle et al. 2001).

  8. The final analysis sample contains full information on 1,128 census tracts. Deletions were made for the following purposes: five census tracts were deleted for having zero population or zero working age females; three census tracts were deleted for having zero for the female labor force participation rate; 43 and 35 mutually exclusive census tracts, respectively, were deleted because they were not able to be geocoded; and an additional four observations were deleted due to missing data.

  9. The census tract, rather than zip codes or counties, is used as the unit of analysis for theoretical and empirical reasons. First, recall that the overarching hypothesis of this research is that there is a reciprocal relationship between the characteristics of families and the social, economic, and institutional environment in which they reside. This provides a framework for examining community-level factors related to the level of formal child care availability, as well as the extent to which service accessibility has an effect on the labor supply of working-age women. It is expected that the precision with which this relationship can be observed and modeled increases as the geographic area becomes smaller and thus more homogenously defined. Although there is no consensus on the size of local child care markets, most studies opt to define larger rather than smaller geographic boundaries. Gordon and Chase-Lansdale (2001), for example, argue that a 30-mile radius around a particular zip code might closely approximate center-based markets. However, Queralt and Witte (1998) state that their decision to focus on census tracts was made in consultation with workers at the Child Care Search Resource and Referral Agency. This agency apparently also provided evidence that census tracts best proxy what is commonly known as the boundaries of child care markets.

  10. Note that this participation measure is an aggregate for an entire census tract. As a result, we are unable to estimate models for part-time and full-time work status across individuals, nor can we examine participation at the extensive (decision to work) and intensive (hours) margins.

  11. Variables in the labor supply equation are common in the literature. To guide the selection of our variables, we carefully reviewed the models in Blau and Hagy (1998), Anderson and Levine (2000), and Meyers et al. (2002). These studies used individual-level data, and so our task was to create a set of control variables using aggregate data that mirror as closely as possible the controls in the above studies. Specifically, we create variables that reflect women’s underlying preferences for work (age, disability rate and race), their human capita (educational attainment), characteristics of the local child care market (average price and subsidy-acceptance rate), and potential barriers to employment (non-English speaking families and those that do not own a car).

  12. One such unobserved factor is child care quality. Quality likely influences child care prices, which in turn influences the employment decision. Fixed effects control for many of these unobserved factors, at least as they exist at the county-level. Ideally, we would like to use fixed effects at a lower level of aggregation. Census-tract-level fixed effects are not possible give that this is the unit of analysis. However, we did estimate models that control for census tract that reside in specific cities or major towns, as well as others that code census tracts along an urban-rural dimension. None of these alterations substantially changed our results.

  13. As in the labor supply model, we consult recent work on child care supply to select and justify the inclusion of control variables in (10). Specifically, we pay particular attention to Webster and White (1997a; b) and Queralt and Witte (1998).

  14. Ideally, we would prefer to use income net of the woman’s earnings, but that is not possible for our data source because family income depends in part on whether the woman works. This problem would likely be more severe if we were using individual data rather than aggregate data.

  15. It is important to note that these figures reflect the weekly price of child care, averaged across all family- and center-based providers in a given census tract. The variable also reflects differential pricing across different age groups. In other words, the price variable reflects the average aggregate cost of child care services across all providers and age groups served in a given neighborhood.

  16. This perhaps explains why the estimate of child care price becomes statistically significant when accounting for such unmeasured factors.

  17. This is most likely due to the presence of husbands in certain neighborhoods who earn enough to allow the female to stay home and specialize in child care production.

  18. However, neighborhoods at the mean of family income ($54,726) actually have a greater supply of child care when a Head Start provider is present.

  19. The procedure for conducting the endogeneity test is as follows: First, we estimate a separate reduced form equation for child care supply and female labor force participation, controlling for all exogenous variables and the additional instrumental variables. Second, we calculate the reduced form residuals for each equation, or τ. The final step is to include τ as a regressor in the structural participation and child care supply equations, which include the endogenous independent variables. Evidence of endogeneity is found when τ is statistically significant, as shown in Table 3.

  20. High-income neighborhoods are define as census tracts with median family incomes of at least 400% the FPL, and low-income neighborhoods are those tracts with median family incomes of less than 200% the FPL.

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Acknowledgments

The authors would like to thank Mark Lopez, Jeffrey Smith, and two anonymous referees for their helpful comments on previous drafts of this paper. Participants at the 2006 Head Start National Research Conference and the 2002 Association for Public Policy Analysis and Management Fall Research Conference provided helpful advice. Special thanks go to the Maryland Committee for Children for providing data and technical assistance. All remaining errors belong to the authors. The views expressed here are those exclusively of the authors.

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Correspondence to Chris M. Herbst.

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Herbst, C.M., Barnow, B.S. Close to Home: A Simultaneous Equations Model of the Relationship Between Child Care Accessibility and Female Labor Force Participation. J Fam Econ Iss 29, 128–151 (2008). https://doi.org/10.1007/s10834-007-9092-5

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Keywords

  • Child care
  • Female labor supply
  • Geography
  • Simultaneous equations