# Figure 2: Reported Earnings and Employment Before Childbirth—Heterogeneity

## (a) Probability of Large Wage Increase

- (b) Change of Probability of New Employment by Job Type, Conditional on New Employment

<!-- Start of picture text --> Wage relative to MW Wage relative to MW<br>2+ 2+<br>1.5-1.99 1.5-1.99<br>1-1.49 1-1.49<br>Occupation Occupation<br>Unskilled Unskilled<br>Assembler, machine opSkilled blue collar Assembler, machine op<br>Other white collar Skilled blue collar<br>Professional Other white collar<br>Manager Professional<br>Manager<br>Firm size<br>51+ Firm size<br>1-50 51+<br>TFP quartile 1-50<br>Q4 (highest) TFP quartile<br>Q3 Q4 (highest)<br>Q2 Q3<br>Q1 (lowest) Q2<br>Firm ownership Q1 (lowest)<br>DomesticForeign Firm ownership<br>Industry Services DomesticForeign<br>Transportation, storage Industry<br>Accommodation, food Services<br>Trade Transportation, storage<br>Manuf., construction Accommodation, food<br>Agriculture Trade<br>Sex of manager Manuf., construction<br>Male manager Agriculture<br>Female manager Sex of manager<br>Male manager<br>Age at childbirth 31-40 Female manager<br>20-30<br>Living area -.2 -.1 0 .1 .2<br>Budapest Share of job categories in<br>Not Budapest non-employment to employment transitions<br>Drug spending during pregn.<br>Non-zero<br>Zero Childbirth in January 2012-December 2014<br>Drug spending before pregn. Childbirth in October 2015-December 2017<br>Non-zero<br>Zero<br>0 .05 .1 .15 .2<br>Share with major wage increase<br>Childbirth in January 2012-December 2014<br>Childbirth in October 2015-December 2017<br><!-- End of picture text --> _Note:_ Figure shows heterogeneity in reported earnings and employment before giving birth by individual, firm, and job characteristics. Panel (a) shows variation in the probability of a pre-childbirth top-percentile wage increase by individual and firm characteristics (average probability and 95% confidence interval). Panel (b) shows changes in non-employment to employment transition probabilities before giving birth, compared to thirty to ten months before childbirth. The panel shows estimated _β_ parameters and 95% confidence intervals from a modified version of equation (1), with a binary indicator of pregnancy period (one to nine months before childbirth versus ten to thirty months before childbirth) replacing the event time dummies, and using an indicator for transitioning from non-employment to employment indicators as the dependent variable. Control variables include age dummies, county dummies, and calendar month date dummies. The sample is restricted to women who enter formal employment from non-employment, and the outcome is formal employment in a specific job category. In both panels, results for 2012-2014, before reporting incentives were strengthened, are shown by blue circles and results for 2015-2017, after reporting incentives were strengthened, are shown by red squares. We calculate the value added-based TFP. When doing so, we apply the estimation procedure of Wooldridge (2009) and use the `prodest` Stata package by Rovigatti and Mollisi (2020). TFP quartiles are calculated based on the year-specific distribution of TFP, using the entire linked employer-employee database. The heterogeneity indicators refer to twelve months before giving birth, except for the drug spending indicators, which refer to _−_ 24 to _−_ 13 months (before pregnancy) or to _−_ 9 to _−_ 4 months (during pregnancy) before childbirth. The ownership, TFP, and industry indicators are not observed for the smallest (single-bookkeeping) firms. The sample is women employed in the private sector who gave birth between age 20 and 40 and have non-zero wage between thirty and four months before giving birth. (Number of individuals in panel (a): childbirth 2012-2014— 8,019; childbirth 2015-2017—5,823. Number of individuals in panel (b): childbirth 2012-2014—46,801; childbirth 2015-2017—33,289.) 17