centre for market and public organisation using difference-in-difference methods to evaluate the...

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)

0

5

10

15

20

25

30

35

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

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

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10

Decile of income, households with children

Cha

nge

as a

% o

f in

com

e

Child_benefit FC_WFTC Income_support

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


• 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)

∆ childbearingT – ∆childbearingC = effect of the reform


• 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

Control Treatment

Proportion entitled to FC/WFTC or IS

Before .098 .237

After .141 .401

Mean weekly entitlementFC/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 haveif 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% levelControls include age, education, numbers and ages of children, region, housing tenure,ethnicity and wages

Women aged 20-45Data from 1995 – 2003


Treated (Low*post) .0143**(.0069)

. 0220(.0127)

Trend .0005

(.0025)

Differential trend -.0014(.0029)

itititititit uXLowPostPostLowBirth 321

itititititit uXTLowTLowPostPostLowBirth 54321

Regression analysis

Dependent variable = birth in last 12 months

** indicates statistically significant at 5% level, * at 10% levelControls include age, education, numbers and ages of children, region, housing tenure,ethnicity and wages



Treated_no children .0253**

(.0102)

.0333*(.0189)

Treated_ children .0113

(.0072)

.0192

(.0173)

Controlling for differential trends

No Yes

Some robustness checks

• Use longer time period (1990-2004) 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?

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