Centre for Market and Public Organisation Using difference-in-difference methods to evaluate the effect of policy reform on fertility: The Working Families Tax Credit Mike Brewer (Institute for Fiscal Studies) Anita Ratcliffe (CMPO, University of Bristol) Sarah Smith (CMPO and IFS)

The impact of welfare reform on fertility Government spending per child rose by 50% (in real terms), 1999 – 2003 –Working Families Tax Credit (WFTC) –Means-tested benefits –Child benefit Biggest increases for low-income families – equivalent to 10% income

Average spending per child (£ per week, 2003 prices)

Change in child-contingent benefits, 1998 – 2002 Couples, one child

Impact on fertility The policies were aimed at incentivising work and tackling child poverty, but might they also have affected childbearing? Effect of reforms: Economic model of childbearing Higher incomes will increase demand for quantity of children OR quality Lower income volatility will increase demand for quantity of children Higher benefits reduce the “price” of children (increase fertility) Employment effect – if gain to work rises (falls) then opportunity cost rises (falls) and fertility falls (rises) Employment effect positive for lone mothers, but mainly negative for women in couples

Empirical strategy: Differences-in-differences How can we tell whether childbearing has been affected by the reform (and by how much)? “Before” versus “after” may be misleading because of other changes over time Missing data problem – what would childbearing have been in the absence of the reform? Solution – use a control group (not affected by the reform) to proxy for the change that would otherwise have taken place

Empirical strategy: Differences-in-differences Differences-in-differences, also known as “natural experiment” Compare change in childbearing among a treatment group (affected by the reform) with change in childbearing among a control group (not affected by the reform) ∆ childbearing T – ∆childbearing C = effect of the reform

Empirical strategy: Differences-in-differences Strengths Clear, simple, intuitive Potential weaknesses Plausibility of control Black box – estimate combined effect of a bundle of changes; little insight into mechanism

Choosing the treatment and control groups The treatment group must be affected by the reform and the control group must be unaffected (including spillover effects) The composition of the groups must be the same over time. Otherwise changes that are driven by selection effects will be wrongly attributed to the reform –Cannot split by income, instead split by education Both groups must be affected by time-varying factors in the same way (or differential changes must be controlled for)

Treatment and control groups Education –Treatment: Both male and female partner left school at/before compulsory school leaving age –Control: Both male and female partner left school at 18+

Entitlement to child-contingent benefits, couples with children ControlTreatment Proportion entitled to FC/WFTC or IS Before After Mean weekly entitlement FC/WFTC, IS + child benefit Before£29.71£39.00 After£37.27£56.76 Difference£7.56 (25.4%)£17.76 (45.5%)

Data Family Resources Survey 1995 – 2003 Large sample, extensive information on education, income and other socio-demographic characteristics Derive the probability that a woman had a birth in the previous 12 months –Step 1: Allocate children in household to natural mothers –Step 2: Assign randomly-generated date of birth to children (based on their age) if none available. –Step 3: Infer probability that a woman had a birth in previous 12 months based on date of interview and date of birth of child Use information on number and ages of children to derive (approximate) fertility histories

Comparison of estimated TFR with official measure Annual total fertility rate = number of children a woman would have if she had the age-specific birth rates in that year

Identifying the effect of the reform relies on successfully controlling for everything else that might affect fertility in the treatment group Rich set of demographic controls –Age, education, kids in household and age of kids in household, and interactions; region, housing tenure, ethnicity –Average wages for treatment and control groups Control group intended to capture other (unobservable) time-varying characteristics, but control group has different fertility, and possibly different fertility trends –Control explicitly for differential trends Identification

Regression analysis Dependent variable = birth in last 12 months ** indicates statistically significant at 5% level Controls include age, education, numbers and ages of children, region, housing tenure, ethnicity and wages Women aged Data from 1995 – 2003 Women aged Data from 1995 – 2003 Treated (Low*post).0143** (.0069) (.0127) Trend.0005 (.0025) Differential trend (.0029)

Regression analysis Dependent variable = birth in last 12 months ** indicates statistically significant at 5% level, * at 10% level Controls include age, education, numbers and ages of children, region, housing tenure, ethnicity and wages Women aged Data from 1995 – 2003 Women aged Data from 1995 – 2003 Treated_no children.0253** (.0102).0333* (.0189) Treated_ children.0113 (.0072).0192 (.0173) Controlling for differential trends NoYes

Some robustness checks Use longer time period ( ) to control for quadratic trends Estimate effects of spurious reforms in 1995 and 1996 Allow for reform to take effect from announcement as well as implementation

Conclusions The evidence suggests a significant increase in births (particularly first births) among women in couples affected by the reforms 1.4 percentage point increase in probability of a birth very roughly translates into 20,000 extra births (total births 670,000) Implied elasticity around 0.25; within range estimated by previous studies Is it plausible that such a large increase in child-contingent benefits would not affect fertility?