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Appendix out of Labor and Into the Labor Force? the Role of Abortion Access, Social Stigma, and Financial Constraints

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Appendix out of Labor and Into the Labor Force? the Role of Abortion Access, Social Stigma, and Financial Constraints Appendix Out of Labor and Into the Labor Force? The Role of Abortion Access, Social Stigma, and Financial Constraints Nina Brooks Tom Zohar July 28, 2021 Contents A Additional Figures and Tables3 B Context 13 B.1 Committee Approval Process and Motivation.............................. 13 B.2 Illegal Abortion Market.......................................... 13 B.3 Abortion Norms and Rates Among Populations of Interest....................... 14 B.4 Child Rearing Cost............................................ 16 C Data 18 C.1 Data Structure.............................................. 18 C.2 Variable Definition............................................ 18 D Robustness of Difference-in-differences Strategy 20 D.1 Parallel Trends.............................................. 20 D.2 Comparing Linear Time Trends Specifications............................. 21 E Toy Model 26 E.1 Canonical Model - ‘Abortion as Insurance’ (Levine & Staiger 2002).................. 26 E.2 Updating the Model........................................... 26 E.2.1 Decision I: Contraception Intensity............................... 27 E.2.2 Decision II: Abortion Decision.................................. 27 E.3 Statistical Model............................................. 27 E.3.1 Mapping Parameters to Reduced-Form Moments........................ 27 E.3.2 Adding Credit Constraints.................................... 28 E.3.3 MLE Estimation (Potential Extension).............................. 29 F Selection Bias in Abortion Decision: OLS vs. IV 31 F.1 Estimation................................................. 31 F.2 Results................................................... 31 G Event-study Relative To (Potential) Birth 36 G.1 Identification Assumptions........................................ 36 G.2 Baseline Estimates for Young, Unmarried Women........................... 37 G.3 Abortion vs. Birth............................................. 38 H Effect on Human Capital and Labor Market Outcomes (18-21, Unmarried) 41 H.1 Human Capital Investment........................................ 41 H.2 Labor-Market Outcomes......................................... 42 1 I Policy Counterfactual Model 46 I.1 Model’s Setup............................................... 46 I.2 Tagging Errors.............................................. 46 I.3 Woman’s Decision - Applying for Funding............................... 46 I.4 Government’s Problem - Who to Fund?................................. 47 I.5 Simplifications of Government’s Decision................................ 47 J Miscellaneous 48 J.1 Hassle Cost................................................ 48 J.2 Age Heterogeneity............................................ 49 J.3 What Did The Counterfactual Woman Do?............................... 49 J.4 Estimating Wage Premiums (AKM)................................... 50 2 A Additional Figures and Tables Figure A1: ‘Can You Afford an Unexpected Bill of $2k Within a Month?’ 60 40 20 ‘Will require family support (%) ‘Will require family / can't afford‘ 0 18−24 25−34 35−44 Age Group (a) All Unmarried Women 60 40 20 ‘Will require family support (%) ‘Will require family / can't afford‘ 0 18−24 25−34 35−44 Age Group Below Median Above Median (b) Split by SES Notes: estimates for unmarried women. Source: Calcalist, 2014 3 Figure A2: Effect of the 2014 Policy on Abortion Rates ● DiD ● ● ● LTT no year FE ● ● ● LTT + year FE ● ● ● Diff age LTT ● ● 0 10 20 30 % Change in Abortion Rate ● 18−21 ● 30−35 ● 16−40 Notes: This figure presents difference-in-difference results for the effect of the 2014 policy on abortion rates. Each row presents the results from a different specification. In each row, the dot represents the treatment effect (δ·P ostt ×Tit) and the lines mark the 95% confidence interval around the point estimate. The dashed vertical line is at 0, indicating an insignificant result. The effect is estimated separately in three samples based on womens’ age groups: 16-40 (green), 18-21 (orange) and 30-35 (blue). The estimates are divided by the mean abortion rate of each group prior to the policy change and represent the percentage change relative to the baseline mean. 4 Figure A3: Effect of the Policy by SES and Ethnicity Below Median Earnings (Father) N: 14,651 Above Median Earnings (Father) N: 6,508 0.0 2.5 5.0 Change in Abortion Rate (p.p.) (a) Stronger Effect for Women From Low SES Households(?) Secular−Jew ● N: 8,423 USSR Migrants ● N: 2,414 Religious−Jew ● N: 2,450 Arab ● N: 2,188 −5 0 5 10 15 Change in Abortion Rate (p.p.) (b) Stronger Effect for Women From More Religious Background Notes: The figure presents the heterogeneous difference-in-difference results, where we split the sample by pop- ulation groups. Panel (a) is split by yearly earnings of the father of the woman who concieved (our proxy for household SES level); whereas Panel (b) is split by ethnic groups. In each row, the dot represents the percentage change of the treatment effect (δ · P ostt × Ti), and the lines mark the 95% confidence interval around the point estimate. The dashed vertical line is at 0, indicating an insignificant result. The sample includes all unmarried women in the country aged 18-21 from 2009-2016 who conceived. Treated women are those aged 20-21. 5 Figure A4: Baseline Yearly Earning by Religious Level and SES Socially Socially Socially Socially Acceptable Unacceptable Acceptable Unacceptable (Secular−Jew) (Religious−Jew) (Secular−Jew) (Religious−Jew) Financially Unconstrained Financially Unconstrained (High SES) $2848 $2944 (High SES) 6283 1769 Credit Constrained Credit Constrained (Low SES) $2793 $2895 (Low SES) 13922 4629 Yearly Earnings Prior to Conception Number of Observations 2800 2850 2900 5000 10000 (a) Woman’s Earnings (b) Number of Observations Socially Socially Socially Socially Acceptable Unacceptable Acceptable Unacceptable (Secular−Jew) (Religious−Jew) (Secular−Jew) (Religious−Jew) Financially Unconstrained Financially Unconstrained (High SES) $63519 $58593 (High SES) $84165 $75528 Credit Constrained Credit Constrained (Low SES) $14576 $13895 (Low SES) $24521 $22253 Yearly Father's Earnings at Conception Yearly Household Earnings at Conception 2000030000400005000060000 400006000080000 (c) Father’s Earnings (d) Household’s Earnings Notes: This figure presents the baseline yearly earning while splitting the population across two dimensions: religiosity level and SES background (based on parental earnings). Panel (a) presents the mean yearly earnings of the woman who conceived, one year prior to conception; Panel (b) presents the number of observations in each cell; Panel (c) presents the mean yearly earnings of the woman’s father at the year of conception; Panel (d) presents the mean yearly earnings of the woman’s household (sum of her parents earnings) at the year of conception. Darker blue correspond to higher levels. 6 Figure A5: Effect on Abortions vs. Births (Levels) Pregnancies ● Abortions ● Births ● −10 −5 0 5 Change per Monthly Pregnancies Notes: This figure presents difference-in-difference results for the effect of the 2014 policy on abortion vs birth levels. Each row presents the results from a different specification. In each row, the dot represents the treatment effect (δ · P ostt × Ti) and the lines mark the 95% confidence interval around the point estimate. The dashed vertical line is at 0, indicating an insignificant result. The sample includes all unmarried women in the country aged 18-21 from 2009-2016. Treated women are those aged 20-21. The estimates are percentage point changes that can be interpreted as the relative change per 100 pregnancies. 7 Figure A6: Employment and Education Before Conception (Unmarried Religious Women) 60 40 % Women 20 0 Studying College Working Part−time Notes: This figure presents the employment and education mean among young (18-21), religious, unmarried women who concieved at baseline (one year prior to conception). ‘Studying’ equals to one if a woman did any of the following: obtained a high-school diploma, took the Israeli-SAT, enrolled in college, or participated in some form of professional training. ‘College’ equals to one if a woman enrolled in college. ‘Working’ equals to one if a woman had worked during that calendaric year. ‘Part-time’ equals to one if a woman’s average monthly earnings that year were below the minimum monthly wage (unconditional on working). 8 Figure A7: Abortion Access Increased Human Capital Investment 30 5 20 ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● 0 Change in Studying 0 Change in Teacher Training Change in Teacher −10 −3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 Years from potential birth Years from potential birth (a) Studying - Event-Study x DiD (b) Teacher Training - Event-Study x DiD 2 10 ● 1 5 ● ● ● ● ● ● 0 ● ● ● ● ● ● 0 ● Change in High−School Plus Change in Retook the Bagrut −1 −5 −2 −3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 Years from potential birth Years from potential birth (c) Matriculation - Event-Study x DiD (d) Non-academic Training - Event-Study x DiD Notes: This Figure presents the results on four sets of outcomes: Panels (a) present the results on the probability a woman will be be enrolled in either: teacher training, take the matriculation exam, academic or non academic training; Panels (b) present the results on the probability a woman will be enrolled in teacher training; Panels (c) present the results on the probability a woman will take the matriculation exam; Panels (d) present the results on the probability a women will be enrolled in non-academic training. Each panel presents results for the reduced form effect of the 2014 policy relative
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