[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-109491-en":3,"doc-seo-109491-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},109491,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Nonparametric Pseudo-Event Study Designs - Estimating the Motherhood Earnings Penalty in El Salvador - Background Note 4","We propose a nonparametric approach to pseudo-event studies to estimate motherhood earnings penalties. The method combines nonparametric matching with pseudo-event designs to address common support gaps between men and women across periods and to handle heterogeneity in treatment assignment within observable cells via inverse probability weighting. It yields a more efficient pseudo-event estimator than existing approaches. Using El Salvador data, the study finds a motherhood earnings penalty that is lower than standard estimates suggest, despite prior evidence indicating a comparatively high penalty.","Public Disclosure Authorized Public Disclosure Authorized  \n BACKGR ONUONDTE 4  \nNonparametric Pseudo-Event Study Designs:  \nEstimating the Motherhood Earnings Penalty in El Salvador  \nBackground Note 4  \nNonparametric Pseudo-Event Study Designs:  \nEstimating the Motherhood Earnings Penalty in El Salvador  \nAbstract  \nWe propose a nonparametric approach to pseudo-event studies to examine motherhood earnings penalties. By combining nonparametric matching and pseudoevent studies, we address limitations highlighted in the literature, such as the lack of common support between men and women between periods and over time, and treatment assignment heterogeneity within cells of observables using inverse probability weighting. Our methodology also provides a more efficient pseudo-event estimator compared to the extant approach. We demonstrate the advantages of our methodology using data from El Salvador, where previous studies have found a comparatively high motherhood earnings penalty. Contrary to these findings, we find that the motherhood earnings penalty is lower than what standard estimates suggest.  \nJEL codes: D63, J13, J16, J22, J31  \nKeywords: Motherhood, labor outcomes, nonparametric, pseudo-panels, event study  \n~~ ~~ 2  \nBackground Note 4  \nNonparametric Pseudo-Event Study Designs:  \nEstimating the Motherhood Earnings Penalty in El Salvador  \nI.  \nIntroduction  \nHaving children has important implications for various individual-level outcomes, especially for women. Empirical evidence suggests that the birth of the first child leads to a decline in labor earnings for women, a phenomenon referred to as the motherhood earnings penalty—which also extends to other labor market outcomes. In this note, we propose a methodology to estimate this earnings penalty using repeated cross sections, employing a combination of nonparametric matching and a pseudo-event study design, building on recent research by Kleven (2023), and apply it to El Salvador.  \nOur methodology presents advantages over current approaches. First, analyzing the earnings penalty relies on the Blinder-Oaxaca decomposition, which estimates the earnings penalty net of the effect of sociodemographic characteristics using a form of inverse probability weighting. However, Blinder-Oaxaca often underestimates the earnings penalty by not properly restricting comparisons to the common support of observable characteristics of men and women. Furthermore, as markets and societies change over time, the distribution of observable characteristics may also change, leading to a lack of common support when comparing cross sections over time.  \nSecond, while the gender wage gap can be interpreted as an average treatment effect, the controls included in these parametric regressions are often post-treatment, as almost all observable characteristics used as controls are often a function of gender and birth choices. Our methodology circumvents these issues to some extent through the use of nonparametric matching: there is evidence that weighting by the inverse of the propensity score for the entire treatment history as a function of time-varying  \nconfounders, at the cell level, can improve ‘allelse-equal’ comparisons without introducing post-treatment bias to recover more structural estimates. Our methodology allows us to compute the difference in means within cells in the distribution of observables as an average of directed controlled effects restricted to the common support. Further, it allows us to address distributional variations due to differences in the distribution of observables between men and women as well as changes in sampling frames over time through proper reweighting. However, the size of the common support decreases with the number of observable characteristics included in the nonparametric matching procedure, that is, the curse of dimensionality. Thus, the researcher must balance internal and external validity concerns.  \nFinally, our matching estimates are additively ","cbCaitrjMt6YHhoQ","https://ap.wps.com/l/cbCaitrjMt6YHhoQ","pdf",1922707,6,1,27,"English","en",105,"# Introduction\n## Proposed methodology\n## Comparison with Blinder-Oaxaca decomposition\n## Addressing post-treatment controls and heterogeneity\n## Application to El Salvador","[{\"question\":\"What does the proposed nonparametric pseudo-event study approach estimate?\",\"answer\":\"It estimates the motherhood earnings penalty, focusing on how having children affects women’s earnings using repeated cross sections and a combined nonparametric matching plus pseudo-event design.\"},{\"question\":\"How does the methodology address common support and treatment heterogeneity?\",\"answer\":\"It restricts comparisons to the common support of observable characteristics and uses inverse probability weighting to account for heterogeneity in treatment assignment within cells defined by observables over time.\"},{\"question\":\"What do the El Salvador results imply about standard estimates?\",\"answer\":\"The study finds the motherhood earnings penalty is lower than what standard estimates suggest, contrary to earlier findings of a comparatively high penalty.\"}]","Nonparametric Pseudo-Event Study Designs - 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