In this article, we study under what circumstances a gas station is more likely to commit fuel fraud. Using a new and hitherto unexploited list of fuel fraud detections, we find evidence that stations under less favorable economic conditions -- more competitors, lower retail fuel price, or higher operating costs -- engage in fraudulent activity more often, while the reputational incentives for product credibility is stronger for chain stations than independent ones. Also, fuel fraud tends to cluster among nearby stations, suggesting that illicit activity may be propagated from one station to others nearby. As for pricing behavior, in general gas stations seem to keep price constant and take higher price-cost margins when selling adulterated fuel, suggesting that consumers are harmed by this kind of fraud.
{"title":"Who Commits Fraud? Evidence From Korean Gas Stations","authors":"Christian Ahlin, I. Kim, Kyoo il Kim","doi":"10.2139/ssrn.3548326","DOIUrl":"https://doi.org/10.2139/ssrn.3548326","url":null,"abstract":"In this article, we study under what circumstances a gas station is more likely to commit fuel fraud. Using a new and hitherto unexploited list of fuel fraud detections, we find evidence that stations under less favorable economic conditions -- more competitors, lower retail fuel price, or higher operating costs -- engage in fraudulent activity more often, while the reputational incentives for product credibility is stronger for chain stations than independent ones. Also, fuel fraud tends to cluster among nearby stations, suggesting that illicit activity may be propagated from one station to others nearby. As for pricing behavior, in general gas stations seem to keep price constant and take higher price-cost margins when selling adulterated fuel, suggesting that consumers are harmed by this kind of fraud.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75657892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Russian Abstract: Целью настоящего исследования является проведение анализа распространённости и динамики депривационной бедности в разрезе различных социально-демографических групп домохозяйств и регионов. В работе определены социально-демографические группы с наиболее высоким уровнем депривационной бедности, выявлены регионы с высоким и низким уровнем депривационной бедности, а также регионы, имеющие благоприятную и неблагоприятную динамику показателей депривационной бедности. English Abstract: The purpose of this study is to analyze the prevalence and dynamics of deprivation poverty in the context of various socio-demographic groups of households and regions. The work identifies socio-demographic groups with the highest level of deprivation poverty, identifies regions with high and low levels of deprivation poverty, as well as regions that have favorable and unfavorable dynamics of deprivation poverty indicators.
{"title":"Распространённость и динамика депривационной бедности (The Prevalence and Dynamics of Deprivation Poverty)","authors":"E. Grishina, Elena Tsatsura (Kovalenko)","doi":"10.2139/ssrn.3594487","DOIUrl":"https://doi.org/10.2139/ssrn.3594487","url":null,"abstract":"Russian Abstract: Целью настоящего исследования является проведение анализа распространённости и динамики депривационной бедности в разрезе различных социально-демографических групп домохозяйств и регионов. \u0000 \u0000В работе определены социально-демографические группы с наиболее высоким уровнем депривационной бедности, выявлены регионы с высоким и низким уровнем депривационной бедности, а также регионы, имеющие благоприятную и неблагоприятную динамику показателей депривационной бедности. \u0000 \u0000English Abstract: The purpose of this study is to analyze the prevalence and dynamics of deprivation poverty in the context of various socio-demographic groups of households and regions. The work identifies socio-demographic groups with the highest level of deprivation poverty, identifies regions with high and low levels of deprivation poverty, as well as regions that have favorable and unfavorable dynamics of deprivation poverty indicators.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78771328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a general framework for Maximum Likelihood (ML) and Bayesian estimation of income distributions based on grouped data information. The asymptotic properties of the ML estimators are derived and Bayesian parameter estimates are obtained by Monte-Carlo-Markov-Chain (MCMC) techniques. A comprehensive simulation experiment shows that obtained estimates of the income distribution are very precise and that the proposed estimation framework improves the statistical precision of parameter estimates relative to the classical multinomial likelihood. The estimation approach is finally applied to a set of countries included in the World Bank database PovcalNet.
{"title":"Classical and Bayesian Inference for Income Distributions using Grouped Data","authors":"Tobias Eckernkemper, Bastian Gribisch","doi":"10.2139/ssrn.3713891","DOIUrl":"https://doi.org/10.2139/ssrn.3713891","url":null,"abstract":"We propose a general framework for Maximum Likelihood (ML) and Bayesian estimation of income distributions based on grouped data information. The asymptotic properties of the ML estimators are derived and Bayesian parameter estimates are obtained by Monte-Carlo-Markov-Chain (MCMC) techniques. A comprehensive simulation experiment shows that obtained estimates of the income distribution are very precise and that the proposed estimation framework improves the statistical precision of parameter estimates relative to the classical multinomial likelihood. The estimation approach is finally applied to a set of countries included in the World Bank database PovcalNet.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90475737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper exploits exogenous variation in the date of transition from analogue to digital television signal in the UK across more than 40,000 geographical units to investigate the causal impact of television on employment probabilities and potential mechanisms. Using a large individual panel survey data set and a difference-in-differences model that compares the outcomes of adults living in regions where the switchover occurred in different years, I find that the digital transition increases employment probabilities. The impact is driven by mothers and is due to an increase in part-time and self-employment. The effect increases with the number of children in a household and when the parent does not cohabit with a partner. A possible explanation for these results is that television keeps children busy, reducing the amount of housework that parents need to do and allowing them to focus on their careers. I test whether the digital transition reduces the time that individuals dedicate to housework and show that this is the case for mothers but not for fathers and non-parents. I find no effect on time allocation other than via housework.
{"title":"Television and the Labour Supply: Evidence from the Digital Television Transition in the UK","authors":"Adrian Nieto Castro","doi":"10.2139/ssrn.3587398","DOIUrl":"https://doi.org/10.2139/ssrn.3587398","url":null,"abstract":"This paper exploits exogenous variation in the date of transition from analogue to digital television signal in the UK across more than 40,000 geographical units to investigate the causal impact of television on employment probabilities and potential mechanisms. Using a large individual panel survey data set and a difference-in-differences model that compares the outcomes of adults living in regions where the switchover occurred in different years, I find that the digital transition increases employment probabilities. The impact is driven by mothers and is due to an increase in part-time and self-employment. The effect increases with the number of children in a household and when the parent does not cohabit with a partner. A possible explanation for these results is that television keeps children busy, reducing the amount of housework that parents need to do and allowing them to focus on their careers. I test whether the digital transition reduces the time that individuals dedicate to housework and show that this is the case for mothers but not for fathers and non-parents. I find no effect on time allocation other than via housework.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77003167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the impact of the 2018-2019 trade war on U.S. local labor markets, distinguishing between regional exposure to foreign tariffs on U.S. exports, U.S. import tariffs, and U.S. tariffs on intermediate inputs. We find foreign retaliatory tariffs on U.S. exports have led to an increase in local unemployment rates, and this effect is magnified for regions specialized in non-manufacturing tradable goods (e.g. agriculture). U.S. import tariffs, on the other hand, have had an impact on local labor market conditions primarily through input-output linkages, leading to a decline in the employment share in the manufacturing sector and a decline in regional earnings.
{"title":"The Impact of the 2018-2019 Trade War on U.S. Local Labor Markets","authors":"Felipe Benguria, Felipe E. Saffie","doi":"10.2139/ssrn.3542362","DOIUrl":"https://doi.org/10.2139/ssrn.3542362","url":null,"abstract":"We study the impact of the 2018-2019 trade war on U.S. local labor markets, distinguishing between regional exposure to foreign tariffs on U.S. exports, U.S. import tariffs, and U.S. tariffs on intermediate inputs. We find foreign retaliatory tariffs on U.S. exports have led to an increase in local unemployment rates, and this effect is magnified for regions specialized in non-manufacturing tradable goods (e.g. agriculture). U.S. import tariffs, on the other hand, have had an impact on local labor market conditions primarily through input-output linkages, leading to a decline in the employment share in the manufacturing sector and a decline in regional earnings.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78217984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We look into the reasons for the significant deceleration of college wage premium growth since 2000 with a planner decision model in which the supply of college workers is endogenously determined. By counterfactual simulations, we find that: (1) the slower skill-biased technological change and faster skill-neutral productivity progress in both routine and manual task occupations account for two-third of the deceleration; (2) the slower skill-biased technological change in cognitive task occupations only explains less than one-tenth of the deceleration prior 2014; (3) the change in cost shifter of college worker supply attributes about a quarter of the deceleration. Furthermore, we show that the decline in college workers’ mean quality is a mechanism with a moderate impact on the deceleration. Our findings suggest that even the overall technological change was becoming more biased in favor of skills, the average college wage premium growth could still slow down. Also, if the demand reversal and automation in non-cognitive task occupations are the necessary directions of technological change, then the deceleration is somewhat an inevitable outcome of technological progress.
{"title":"Why Did College Wage Premium Growth Slow Down? An Analysis with Endogenous Supply of College Workers","authors":"Yiheng Huang, K. Tsui","doi":"10.2139/ssrn.3538610","DOIUrl":"https://doi.org/10.2139/ssrn.3538610","url":null,"abstract":"We look into the reasons for the significant deceleration of college wage premium growth since 2000 with a planner decision model in which the supply of college workers is endogenously determined. By counterfactual simulations, we find that: (1) the slower skill-biased technological change and faster skill-neutral productivity progress in both routine and manual task occupations account for two-third of the deceleration; (2) the slower skill-biased technological change in cognitive task occupations only explains less than one-tenth of the deceleration prior 2014; (3) the change in cost shifter of college worker supply attributes about a quarter of the deceleration. Furthermore, we show that the decline in college workers’ mean quality is a mechanism with a moderate impact on the deceleration. Our findings suggest that even the overall technological change was becoming more biased in favor of skills, the average college wage premium growth could still slow down. Also, if the demand reversal and automation in non-cognitive task occupations are the necessary directions of technological change, then the deceleration is somewhat an inevitable outcome of technological progress.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Perceptions of social mobility in society are one of the most important determinants of individuals' preferences for redistribution and tolerance for economic inequalities. What shapes these perceptions is however so far little understood. In this paper, I propose and empirically test a behavioural model of social mobility perceptions based on the self-serving bias, using the ISSP Social Inequality Cumulative. The self-serving bias states that people blame external circumstances for their failures and take excessive credit for their successes. The results of my analysis indicate, in line with the expectations of the self-serving bias, that personal experiences with social mobility only influence people's perceptions of societal mobility if the experiences were negative or stagnating. Conversely, those who experienced positive mobility overestimate their personal contribution and, therefore, do not extrapolate from their experience onto perceptions of society at large. Instead, their perceptions are primarily related to their political orientation, with those towards the right of the political spectrum being more optimistic about social mobility and those towards the left being more pessimistic.
{"title":"Experience and Perception of Social Mobility – A Cross-Country Test of the Self-Serving Bias","authors":"N. Weber","doi":"10.2139/ssrn.3536890","DOIUrl":"https://doi.org/10.2139/ssrn.3536890","url":null,"abstract":"Perceptions of social mobility in society are one of the most important determinants of individuals' preferences for redistribution and tolerance for economic inequalities. What shapes these perceptions is however so far little understood. In this paper, I propose and empirically test a behavioural model of social mobility perceptions based on the self-serving bias, using the ISSP Social Inequality Cumulative. The self-serving bias states that people blame external circumstances for their failures and take excessive credit for their successes. The results of my analysis indicate, in line with the expectations of the self-serving bias, that personal experiences with social mobility only influence people's perceptions of societal mobility if the experiences were negative or stagnating. Conversely, those who experienced positive mobility overestimate their personal contribution and, therefore, do not extrapolate from their experience onto perceptions of society at large. Instead, their perceptions are primarily related to their political orientation, with those towards the right of the political spectrum being more optimistic about social mobility and those towards the left being more pessimistic.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89353829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: This article explores how labor and employment laws shape workplace technological change. It focuses on emerging data-driven technologies such as machine learning, the branch of artificial intelligence that has sparked widespread concern about the future of work. The article argues that labor and employment laws shape employers’ technological choices in two ways. First, those laws help to facilitate technological development by granting employers broad rights to gather workplace data, to develop new technologies using that data, and to implement those technologies into the workplace, typically regardless of workers’ preferences. Second, those laws channel technological development in certain directions, in particular by encouraging companies to use technologies to exert power over workers and therefore cut labor costs. This analysis has policy implications. Among other things, it suggests that ensuring a decent future of work may require reforms to guarantee workers a voice in the development and deployment of workplace technologies.
{"title":"The Law & Political Economy of Workplace Technological Change","authors":"Brishen Rogers","doi":"10.2139/SSRN.3327608","DOIUrl":"https://doi.org/10.2139/SSRN.3327608","url":null,"abstract":"Abstract: This article explores how labor and employment laws shape workplace technological change. It focuses on emerging data-driven technologies such as machine learning, the branch of artificial intelligence that has sparked widespread concern about the future of work. The article argues that labor and employment laws shape employers’ technological choices in two ways. First, those laws help to facilitate technological development by granting employers broad rights to gather workplace data, to develop new technologies using that data, and to implement those technologies into the workplace, typically regardless of workers’ preferences. Second, those laws channel technological development in certain directions, in particular by encouraging companies to use technologies to exert power over workers and therefore cut labor costs. This analysis has policy implications. Among other things, it suggests that ensuring a decent future of work may require reforms to guarantee workers a voice in the development and deployment of workplace technologies.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90795859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Government Shutdown of 2018-2019 ran for 35 days, from December 22, 2018 – January 25, 2019. It was the longest shutdown in the history of the United States. Most all Federal agencies were affected, and 800,000 federal workers were directly impacted by furloughs and delayed paychecks. The solidarity of Federal unions working synergistically with private sector unions, especially aviation sector unions, played a key role in ending the Shutdown. Private sector unions can strike; in the federal sector, it is illegal. Federal Unions: The National Federation of Federal Employees (NFFE), the American Federation of Government Employees (AFGE) , and the National Treasury Employees Union (NTEU) and the National Air Traffic Controllers Association (NATCA) coordinated efforts in solidarity with private sector Association of Flight Attendants (AFA-CWA), and the Air Line Pilots Association (ALPA). Televised interviews featuring Union leaders and federal workers, along with rallies at the AFL-CIO headquarters in Washington, DC featuring AFL-CIO President Richard Trumka, union leaders, and members of Congress were followed by demonstrations at the Senate Hart Office Building and arrests outside Leader Mitch McConnell’s office. These efforts brought national attention to the shutdown’s effect on workers. A specific leader emerged during the Shutdown – Sara Nelson. Union President and professional flight-attendant Sara Nelson of the AFA-CWA would ultimately throw down the gauntlet to Leader McConnell and the Administration, by threatening to call a strike if an agreement was not reached, citing mounting safety factors to the flying public. TSA workers had been stretched thin – 33 days without pay – some forced to sleep in their cars for lack of gas money. The AFA-CWA, National Air Traffic Controllers Association(NATCA), AirLine Pilots Association (ALPA) joined in. She called a strike on Jan. 25; a ground stop was ordered by the FAA at New York’s LaGuardia airport; and the shutdown ended later that day, approximately a week before the Superbowl. The dynamics of the union solidarity during the shutdown will be detailed in the presentation.
{"title":"Union Solidarity in the 2018-19 Government Shutdown","authors":"Gregory George Guthrie","doi":"10.2139/ssrn.3535911","DOIUrl":"https://doi.org/10.2139/ssrn.3535911","url":null,"abstract":"The Government Shutdown of 2018-2019 ran for 35 days, from December 22, 2018 – January 25, 2019. It was the longest shutdown in the history of the United States. Most all Federal agencies were affected, and 800,000 federal workers were directly impacted by furloughs and delayed paychecks. The solidarity of Federal unions working synergistically with private sector unions, especially aviation sector unions, played a key role in ending the Shutdown. Private sector unions can strike; in the federal sector, it is illegal. Federal Unions: The National Federation of Federal Employees (NFFE), the American Federation of Government Employees (AFGE) , and the National Treasury Employees Union (NTEU) and the National Air Traffic Controllers Association (NATCA) coordinated efforts in solidarity with private sector Association of Flight Attendants (AFA-CWA), and the Air Line Pilots Association (ALPA). Televised interviews featuring Union leaders and federal workers, along with rallies at the AFL-CIO headquarters in Washington, DC featuring AFL-CIO President Richard Trumka, union leaders, and members of Congress were followed by demonstrations at the Senate Hart Office Building and arrests outside Leader Mitch McConnell’s office. These efforts brought national attention to the shutdown’s effect on workers. A specific leader emerged during the Shutdown – Sara Nelson. Union President and professional flight-attendant Sara Nelson of the AFA-CWA would ultimately throw down the gauntlet to Leader McConnell and the Administration, by threatening to call a strike if an agreement was not reached, citing mounting safety factors to the flying public. TSA workers had been stretched thin – 33 days without pay – some forced to sleep in their cars for lack of gas money. The AFA-CWA, National Air Traffic Controllers Association(NATCA), AirLine Pilots Association (ALPA) joined in. She called a strike on Jan. 25; a ground stop was ordered by the FAA at New York’s LaGuardia airport; and the shutdown ended later that day, approximately a week before the Superbowl. The dynamics of the union solidarity during the shutdown will be detailed in the presentation.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85341011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous research suggests that minimum wages induce heterogeneous treatment effects on wages across different groups of employees. This research usually defines groups textit{ex ante}. We analyze to what extent effect heterogeneities can be discerned in a data-driven manner by adapting the generalized random forest implementation of Athey et al (2019) in a difference-in-differences setting. Such a data-driven methodology allows detecting the potentially spurious nature of heterogeneities found in subgroups chosen ex-ante. The 2015 introduction of a minimum wage in Germany is the institutional background, with data of the Socio-economic Panel serving as our empirical basis. Our analysis not only reveals considerable treatment heterogeneities, it also shows that previously documented effect heterogeneities can be explained by interactions of other covariates.
{"title":"The German Minimum Wage and Wage Growth: Heterogeneous Treatment Effects Using Causal Forests","authors":"Patrick Burauel, Carsten Schroeder","doi":"10.2139/ssrn.3415479","DOIUrl":"https://doi.org/10.2139/ssrn.3415479","url":null,"abstract":"Previous research suggests that minimum wages induce heterogeneous treatment effects on wages across different groups of employees. This research usually defines groups textit{ex ante}. We analyze to what extent effect heterogeneities can be discerned in a data-driven manner by adapting the generalized random forest implementation of Athey et al (2019) in a difference-in-differences setting. Such a data-driven methodology allows detecting the potentially spurious nature of heterogeneities found in subgroups chosen ex-ante. The 2015 introduction of a minimum wage in Germany is the institutional background, with data of the Socio-economic Panel serving as our empirical basis. Our analysis not only reveals considerable treatment heterogeneities, it also shows that previously documented effect heterogeneities can be explained by interactions of other covariates.","PeriodicalId":18085,"journal":{"name":"Macroeconomics: Employment","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87787293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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