In many studies on the health and nutrition impacts of the SBP, researchers evaluate the effects of the program either by comparing participants with non-participants in the same school, or comparing students in schools that offer breakfast with students in schools that do not provide breakfast. However, this method may lead to selection bias because children are not randomly
22
assigned into treatment. Some unobserved characteristics, such as mothers’ preference about how much time is devoted to household production, may influence the SBP participation decision and the maternal employment decision simultaneously. Similarly, a school’s decision to offer the SBP is not random. School-level (or district-level) unobservables like local residents’ attitudes toward child nutrition and health could be associated with both maternal employment rate and the SBP participation rate. Additionally, there would be a reverse causality problem if a full-time employed mother were more likely to send her children to a school that offered the SBP.
Without an appropriate instrument for program participation, this endogeneity issue cannot be addressed. More recently, a reduced-form model has become the most common way to estimate the effects of programs, with a reliance on using variation in policy changes. A central challenge for applying this method to evaluate the SBP is that this program exhibits no variation across states or within each state over time in income eligibility rules or level of reimbursement for meals. However, as discussed in the previous section, each state implemented the SBP mandates at different times. Thus, I use the changes in the SBP mandates within each state over time as a source of variation to identify the effect of the SBP on maternal labor supply in this study.
Figure 3 illustrates the substantial growth in the percentage of mothers living in a state with the SBP mandate over the study period 1989 to 2012. As shown, the percentage of mothers living in a state with a certain type of mandate was only about 14 percent in 1989, but it went up rapidly to 50 percent in 1996, and finally reached 65 percent in 2012. Additionally, there was a moderate increase in the percentage of mothers living in a state with a full coverage mandate during this period. Here, the full coverage mandates are defined as the type of mandates that require all school districts to participate in the SBP. The ratio rose from less than one percent in 1989 to 14 percent in 1995, and grew slowly to 16 percent in 2012.
23
5.1 The Effects of Mandates on SBP Participation
Using within-state variation in the SBP mandate policies, I first examine the direct impact of mandates on SBP participation between 1995 and 2012.15 During this period, eight states began
to implement certain types of mandates. Among them, Vermont, Washington D.C. and Rhode Island implemented full coverage mandates that require all school districts to provide the SBP.16 I
employ the following linear probability model to test the effect of the mandate policies on program participation:
78' = 9%+ :%0 ;< =' + 8' >%+ ?' @%+ A'+ B + C' + D8' (6) where P is a binary variable indicating SBP participation; it equals to one if mother i living in state
s in year t has at least one child receiving free/reduced-price breakfast and zero otherwise.
0 ;< =' is an indicator variable equal to one if there is a mandate in state s in year t and zero otherwise; X are maternal demographic characteristics; Z stands for a vector of state-level controls; A' and B are state and year fixed effects; C' is a series of state-specific linear time trends, and D8' is the error term.
The maternal demographic controls include age, race/ethnicity, citizenship, educational attainment, marital status, family size, an indicator of residence in a metropolitan area, number of children, and an indicator of having children younger than five. The state-level controls are annual unemployment rate, per capita personal income, poverty rate and population. Several other state/time varying policies might also affect maternal employment. Therefore, I control for the
15 The SBP participation data was first available in the CPS Food Security Supplement in 1995.
16 As shown in Figure 3, there is little variation in the percentage of mothers potentially affected by full coverage mandates.
24
maximum monthly AFDC/ TANF benefits for a three-person family, an indicator for an AFDC waiver, the maximum annual state EITC for a family with two children, and a measure of the generosity of Medicaid or State Children’s Health Insurance Program (SCHIP), which is a simulated eligible measure constructed similarly to the one in Currie and Gruber (1996). Using a 1990 national sample, I calculate for each state and each year the percent of infants and children who would be eligible for Medicaid or State Child Health Insurance Programs (SCHIP). This variable varies only by legislative generosity within each state and over time, which does not capture the demographic characteristics of an actual state population that might affect infant health outcomes.
In addition to these control variables, state and year fixed effects are included because unobservable national time trends or cross-sectional state characteristics could bias the results if these factors are correlated with both SBP participation and the mandate policies. For example, national trends in people’s attitudes toward child nutrition and each state’s generosity in subsidizing the child care could affect SBP participation rate and also influence the passage of mandate policies. The time trends of some factors, such as food prices, may even differ across states. As a result, state-specific linear time trends are also included to account for differences in time trends across states. All estimates are weighted using the CPS supplement weights, and robust standard errors are clustered at the state level.
25
Next, making use of the same source of policy variation and data from 1989 to 2012, this paper estimates the effect of the SBP mandates on maternal labor supply.17 During this period, 21 states initiated SBP mandates and seven states changed the thresholds in their mandates. Six states (Florida, New York, Rhode Island, South Carolina, D.C., and Vermont) adopted full coverage mandates (the mandate threshold of zero) that require all school districts to provide the SBP. A number of mandate implementations and changes during the study period ensure that there is enough power to detect any impact of the SBP mandates on maternal labor supply. I estimate the following model:
8' = 9E + :E0 ;< =' + 8' >E+ ?' @E+ A'+ B + C' + F8' (7) where Y denotes the labor supply outcomes of mother i living in state s at time t, and 0 ;< =' refers to the mandate policy in state s at time t, which varies by state and year. The estimate of primary interest is :E. The only difference between Equation (6) and Equation (7) is that the outcome is maternal labor supply rather than program participation; the control variables X, Z, A' and B are exactly the same.
Three labor supply outcomes are examined: (1) the probability of being employed, (2) the probability of working full time, and (3) weekly hours of work. Outcomes (1) and (2) are estimated using linear probability models for ease of interpretation of estimated marginal effects. For the outcome of weekly hours of work, I use the Heckman (1993) two-step procedure, which generates a consistent estimator when there is non-random selection into maternal employment.18
17 Note that this is a reduced-form analysis. Complete data on employment (used in this analysis) and SBP participation (used in the previous section) are not available during the same CPS month.
18 The selection equation estimates the probability of being employed on mothers’ age, race, citizenship, marital status, number of children, and an indicator for having a child less than five.
26