Annual WIEM at University of Warsaw is a wonderful opportunity for young scholars to exchange their PhD ideas. I presented my recent results on evaluating the effects of introducing the program Rodzina 500 Plus - a nearly universal child benefit program - on female labor supply. Large scale government interventions affect economic outcomes through different channels of various magnitude and direction of the effects. In order to account for this feature, I develop a model in which a woman decides whether to participate in the labor market in a given period. I show how to use the resulting decision rules to explain flows in aggregate labor supply and simulate counterfactual paths of labor force participation. My framework combines flexibility of reduced form approaches with an appealing structure of dynamic discrete choice models. The model is estimated nonparametrically using recent advances in machine learning methods. The results indicate a 2-4 percentage points drop in labor force among the eligible females, mainly driven by changes in women's perceived trade-offs and beliefs that discouraged inflows.

1. Large Scale Child Benefit Program and Female Labor Supply
Large Scale Child Benefit Program and
Female Labor Supply
Filip Premik
PhD candidate at University of Minnesota, FAME|GRAPE
25rd
June 2021
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2. Large Scale Child Benefit Program and Female Labor Supply
Motivation
Motivation
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3. Large Scale Child Benefit Program and Female Labor Supply
Motivation
I Program Rodzina 500 Plus – large scale child benefit program introduced
in Poland in 2016.
,→ monthly non-equivalent payment of ∼ 20% of the 2016 net average wage.
,→ eligibility – all second and further child in a household, income threshold for
the first child until 2019.
,→ participation – over 90% of the eligible children.
I How the benefits affected female labor supply?
,→ Standard frameworks: reduced form approach (Schirle, 2015; Koebel and
Schirle, 2016; Magda et al., 2018), structural approach (Blundell et al.,
2000; Myck, 2016; Myck and Trzciński, 2019).
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4. Large Scale Child Benefit Program and Female Labor Supply
Motivation
Labor Force Participation
P[yt = 1]
2013 2014 2015 2016 2017 2018 2019
65
70
75
>= 2 children
childless
95% CI
95% CI
women of age 20-60. shaded areas: quarters before the program
introduction. source: Labor Force Survey.
,→ women who works or
wants, can, actively
searches for a job
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5. Large Scale Child Benefit Program and Female Labor Supply
Motivation
Labor Force Flows
inflows rate: outflows rate:
P[yt = 1|yt0 = 0] P[yt = 0|yt0 = 1]
2013 2014 2015 2016 2017 2018 2019
2
4
6
8
10
>= 2 children
childless
95% CI
95% CI
2013 2014 2015 2016 2017 2018 2019
2
4
6
8
10
>= 2 children
childless
95% CI
95% CI
women of age 20-60. shaded areas: quarters before the program introduction. source: Labor Force Survey.
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6. Large Scale Child Benefit Program and Female Labor Supply
Motivation
Today
I A micro-founded model for explaining labor market flows dynamics.
,→ decompose into channels driven by changes in woman’s decision rule and her
observed characteristics
I Point out variation resulting from introduction of the program.
,→ compare the decomposition elements’ time series of the eligible and ineligible
females
I Eliminate this variation to simulate counterfactual paths as if the program
has not been introduced.
,→ key element: law of motion for labor force participation
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7. Large Scale Child Benefit Program and Female Labor Supply
Motivation
I Summary of the results:
,→ 2 (end of 2017) to 4 (end of 2019) percentage points drop in female labor
supply as a result of program introduction – consistent with earlier findings
(Magda et al., 2018)
,→ mainly driven by changes in women’s perceived trade-offs and beliefs that
discouraged inflows
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8. Large Scale Child Benefit Program and Female Labor Supply
Model
Model
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9. Large Scale Child Benefit Program and Female Labor Supply
Model
I Conditional probability of choosing (CCP) being in the labor force (yt = 1):
%t(s) =
Z
%t(s, ε)dFt(ε|s)
,→ see the Behavioral Model Behind for derivation.
I Observed conditional participation rates of individuals with sit:
yt(sit) =
X
i
yit(sit)
I Distinguish labor force status at the time the decision is made:
sit = (s̃it, yit0 )
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10. Large Scale Child Benefit Program and Female Labor Supply
Model
I What drives the dynamics in individual CCP?
%it(sit) − %it−1(sit−1)
,→ changes in the decision rule functional form %t(·) (treatment parameter).
β(siu) = %t(siu) − %t−1(siu), u ∈ {t, t − 1}
evolution of per-period payoff, beliefs regarding future outcomes.
,→ changes in the observed characteristics st (selection parameter):
γu(sit, sit−1) = %u(sit) − %u(sit−1), u ∈ {t, t − 1}
shift in individual characteristics, self-selection mechanisms.
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11. Large Scale Child Benefit Program and Female Labor Supply
Model
I The true CCP is not observed.
,→ in finite samples may not able to fully integrate out ε|s, giving rise to the
idiosyncratic parameter:
ˆ
ξ(sit, sit−1) ≡ yt(sit) − yit−1(sit−1)
− %̂r (sit) − %̂it−1(sit−1)
,→ a specification test:
H0 : ξ(sit, sit−1) = 0
I Decomposition of the observed participation rates:
yt(st) − yt−1(st−1) = β̂(st−1)
| {z }
treatment
+ γ̂t(st, st−1)
| {z }
selection
+ ˆ
ξ(st, st−1)
| {z }
idiosyncratic
(1)
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12. Large Scale Child Benefit Program and Female Labor Supply
Model
I Decomposition of observed changes in labor force flows:
,→ Integrate (1) over the distribution Γ of observed state variables in the popu-
lation conditionally on the labor force status at the decision time (y0
t):
y1(·, y) − y0(·, y)
| {z }
change in flow rate
=
Z
β̂(st−1)
| {z }
treatment
+ γ̂t(st, st−1)
| {z }
selection
+ ˆ
ξ(st, st−1)
| {z }
idiosyncratic
dΓ (s̃t, y), (s̃t−1, y)
,→ set y = 0 for inflows, y = 1 for (negative) outflows.
I Identify channels affected by the program among the eligible and the ineligible.
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13. Large Scale Child Benefit Program and Female Labor Supply
Model
I Program Evaluation Exercise:
,→ law of motion for aggregate labor force participation:
P[yt = 1] = P[yt = 1|yt−1 = 0]
| {z }
inflow rate
·P[yt−1 = 0]+P[yt = 1|yt−1 = 1]
| {z }
(negative) outflow rate
·P[yt−1 = 1]
,→ flow rates (for y ∈ {0, 1}):
P[yt = 1|yt−1 = y]
| {z }
flow rate
= P[yt−1 = 1|yt−2 = y]
| {z }
lagged flow rate
+ P[yt = 1|yt−1 = y] − P[yt−1 = 1|yt−2 = y]
| {z }
change in flow rate - decomposition (1)
I Simulate counterfactual changes in flow rates to obtain flow rates and
labor force participation paths as if the program had not been introduced.
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14. Large Scale Child Benefit Program and Female Labor Supply
Data and Estimation
Data and Estimation
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15. Large Scale Child Benefit Program and Female Labor Supply
Data and Estimation
I Labor Force Survey (BAEL) – rotating panel of labor market outcomes by
Central Statistical Office in Poland (GUS).
,→ all women with no children (ineligible) or 2 (and more) children below 18
(eligible), ages 20-60, all quarters 2013-2019.
I ∼ 400 observed state variables sit – avoid arbitrary variable selection.
,→ current and past labor force situation, education, demographics
,→ characteristics of woman, her spouse and parents (if present in the household).
,→ measured a quarter before the labor force participation decision
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16. Large Scale Child Benefit Program and Female Labor Supply
Data and Estimation
I The decision rule %t(·) is identified through a simple local moment condition:
E
yit − %t|sit
= 0
,→ use Generalized Random Forest estimator (Athey et al., 2019; Tibshirani et al.,
2020) to obtain consistent asy. normal estimates of %t(·).
,→ separate estimations in subsamples defined by initial labor force status and
program eligibility.
,→ rolling estimation window to improve efficiency: decision rule for quarter t is
estimated using data for quarters t − 3 to t.
,→ bootstrap standard errors for elements of decomposition (1).
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17. Large Scale Child Benefit Program and Female Labor Supply
Results
Results
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18. Large Scale Child Benefit Program and Female Labor Supply
Results
Specification Test
idiosyncratic parameters: ˆ
ξt(st, st−1) ≡ yt − yt−1
− %̂t(st) − %̂t−1(st−1)
inflows outflows
2014 2015 2016 2017 2018 2019
−4
−2
0
2
= 2 children
childless
95% CI
95% CI
2014 2015 2016 2017 2018 2019
−1.0
0.0
1.0
= 2 children
childless
95% CI
95% CI
idiosyncratic variation successfully integrated out.
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19. Large Scale Child Benefit Program and Female Labor Supply
Results
Inflows
treatment parameters: selection parameters:
β̂(st−1) ≡ %̂t(st−1) − %̂t−1(st−1) γ̂t(st, st−1) ≡ %̂t(st) − %̂t(st−1)
2014 2015 2016 2017 2018 2019
−4
−2
0
2
= 2 children
childless
95% CI
95% CI
2014 2015 2016 2017 2018 2019
−4
−2
0
2
= 2 children
childless
95% CI
95% CI
shaded areas: quarters likely impacted by the program – input for the simulation exercise.
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20. Large Scale Child Benefit Program and Female Labor Supply
Results
Outflows
treatment parameters: selection parameters:
β̂(st−1) ≡ %̂t(st−1) − %̂t−1(st−1) γ̂t(st, st−1) ≡ %̂t(st) − %̂t(st−1)
2014 2015 2016 2017 2018 2019
−1.0
0.0
1.0
= 2 children
childless
95% CI
95% CI
2014 2015 2016 2017 2018 2019
−1.0
0.0
1.0
= 2 children
childless
95% CI
95% CI
shaded areas: quarters likely impacted by the program – input for the simulation exercise.
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21. Large Scale Child Benefit Program and Female Labor Supply
Results
Program Evaluation
I Differences between realized and simulated paths of labor force participation
among the eligible females More Details
counterfactual changes in parameters: 2017Q4 2019Q4
(1) treatment at inflows −1.868
(−3.109)∗∗
−3.583
(−3.034)∗∗
(2) treatment at inflows, selection at outflows −2.557
(−3.421)∗∗∗
−4.306
(−2.824)∗∗
(3) treatment at inflows and outflows, selection at out-
flows
−2.557
(−3.421)∗∗∗
−2.051
(−1.182)
t-statistics in the brackets.
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22. Large Scale Child Benefit Program and Female Labor Supply
Conclusion
I A flexible method to analyze how a large scale government intervention affects
economic outcomes through individuals’ decisions proposed.
I The program Rodzina 500 Plus can be associated with 2-4 percentage points
decrease in female labor supply among the eligible woman.
I This drop has been mainly driven by changes in women’s perceived trade-offs
and beliefs that discouraged inflows into the labor force.
I Thank you for your attention.
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23. Large Scale Child Benefit Program and Female Labor Supply
References I
Athey, S., J. Tibshirani, S. Wager, et al. (2019): “Generalized random forests,” The Annals of Statistics, 47, 1148–1178.
Blundell, R., A. Duncan, J. McCrae, and C. Meghir (2000): “The labour market impact of the working families’ tax credit,” Fiscal studies,
21, 75–104.
Koebel, K. and T. Schirle (2016): “The differential impact of universal child benefits on the labour supply of married and single mothers,”
Canadian Public Policy, 42, 49–64.
Magda, I., A. Kiełczewska, and N. Brandt (2018): “The Effects of Large Universal Child Benefits on Female Labour Supply,” IZA Discussion
Paper No. 11652.
Myck, M. (2016): “Estimating Labour Supply Response to the Introduction of the Family 500+ Programme,” Centre For Economic Analysis
(CenEA) Working Paper Series WP01/16.
Myck, M. and K. Trzciński (2019): “From Partial to Full Universality: The Family 500+ Programme in Poland and its Labor Supply
Implications,” ifo DICE Report, 17, 36–44.
Schirle, T. (2015): “The effect of universal child benefits on labour supply,” Canadian Journal of Economics/Revue canadienne d’économique, 48,
437–463.
Tibshirani, J., S. Athey, and S. Wager (2020): grf: Generalized Random Forests, r package version 1.2.0.
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24. Large Scale Child Benefit Program and Female Labor Supply
Choice Model Back to the CCP
I Dynamic choice model in discrete time:
,→ a woman chooses labor force participation yt ∈ {0, 1} in each t given
observed state variables s and unobserved state variables ε ∼ Ft(ε|s) with
finite first moment.
,→ woman’s beliefs regarding future evolution of state variables: Gt(ε0, s0|ε, s),
also with finite first moment.
,→ measurable payoff function: vt(y, s, ε; Gt) ≡ vt(y, s, ε).
,→ The value of the decision problem at time t:
Vt(s, ε) = max
y∈{0,1}
n
vt(y, s, ε)
o
(2)
,→ Key object: conditional probability of choice (CCP):
%t(s) ≡ P[y = 1|s, t] =
Z
ε:vt (1,s,ε)−vt (0,s,ε)≥0
dFt(ε|s) (3)
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25. Large Scale Child Benefit Program and Female Labor Supply
Simulation Details Back to Simulation Results
I The three potential channels of how the program affected flows have been
identified. This is how I shut them down in the counterfactual scenarios:
,→ treatment parameter at inflows for the eligible takes values of treatment pa-
rameter at inflows for the ineligible in 2016Q2-2017Q2.
,→ selection parameter at outflows for the eligible takes values of selection pa-
rameter at outflows for the ineligible in 2016Q2 and 2017Q3.
,→ treatment parameters at outflows for both the eligible and ineligible for 2018Q2-
2019Q2 take their respective average values for periods 2016Q2-2017Q4.
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