Show simple item record

dc.contributor.advisorMaclean, Johanna Catherine
dc.contributor.advisorCallaway, Brantly Mercer, IV
dc.creatorkarami, sonia
dc.date.accessioned2021-08-23T17:49:31Z
dc.date.available2021-08-23T17:49:31Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6838
dc.description.abstractThis dissertation includes three chapters. The first two chapters mainly focuson developing treatment effect set-ups in interactive fixed effects (IFE) models. The first chapter identifies and estimates distributional treatment effects on the treated in the IFE models using a small number of panel data. The set-up introduced in the first chapter is utilized to examine the effect of two tax-based investment incentives in the United States, namely bonus depreciation and section 179, on the local labor markets unemployment rate. The second chapter provides identification and estimation of average treatment effects on the treated in the IFE models using a small number of either repeated cross-sections or panel data. The approach proposed in the second chapter is employed to assess the effect of job displacement on the earnings of displaced workers in the United States. The third chapter investigates the average impact of the Affordable Care Act Medicaid expansion on employment in the local labor markets using the interactive fixed effects models. Chapter 1, tilted DISTRIBUTIONAL TREATMENT EFFECTS ON THE TREATED SET-UPS IN INTERACTIVE FIXED EFFECTS MODELS, extends the previous literature on the causal effect of a binary treatment by developing a distributional treatment effects on the treated (DTT) set-up that i) it controls for the unobservable confounding factors with either time-invariant or time-varying effects on the outcomes of interest ii) it allows for the heteroskedasticity in the error terms iii) it requires a short number of panel data and iv) it captures any potential heterogeneity in the response of outcomes to a binary treatment. In this chapter, I focus on the specific cases when some unit-specific unobserved characteristics have a time-varying impact on the outcomes of interest. These types of unobservable factors cannot be “differenced out” using panel data. Here, untreated potential outcomes are assumed to follow the IFE models; that is, the models that enter the unobservable factors with a time-varying “effect” into the regression models in an analogous way to observed covariates. Although this chapter utilizes a special case of the IFE models with only one time-varying unobservable, the set-up can be easily expanded to the case with more than one unobservable time-varying factors. Here, the distributional effects of a binary treatment are estimated through a non-parametric method built on the relationship between the probability density function of a random variable and its corresponding characteristic function. The DTT estimation set-up in this chapter is shown to be consistent when the number of time periods is finite, making this set-up applicable to a wide range of empirical questions with limited access to data over time. The set-up introduced in this chapter is utilized to investigate the effect of two tax-based investment incentives in the United States, namely bonus depreciation and section 179, on the local labor markets’ unemployment rate. The results of this chapter suggest that these two investment incentives decrease the unemployment rate of local labor markets. Also, the heterogeneity in the causal effect of these two incentives on the unemployment rate is small and almost negligible. Chapter 2, titled AVERAGE TREATMENT EFFECTS ON THE TREATED IN INTERACTIVE FIXED EFFECTS MODELS (with Brantly Callaway), extends the previous literature on the causal effect of a binary treatment by proposing an average treatment effect set-up using the IFE models that requires only a few numbers of either repeated cross-sections or panel data. In particular, we focus on the cases where untreated potential outcomes follow a special case of the IFE models with a single unobserved confounding factor whose effect is allowed to change over time, though we also allow for the time fixed effects and unobserved unit-specific heterogeneity. The models that we consider in this chapter generalize many commonly used models in treatment effects literature, including difference-in-differences (DID) and unit-specific linear trends models. Unlike most literature on the IFE models, we do not require the number of time periods approaches infinity to reach consistency in estimating the ATT. Using our approach, we show that ATT can be identified with as few as three to four time periods and with panel or repeated cross-sections data. Here, our main identification result relies on having the effect of some time-invariant covariates (e.g., race or sex) not vary over time. The developed set-up in this chapter is further used to investigate the effect of job displacement on the earnings of displaced workers in the United States. Our results suggest that the effect of job displacement on the earnings appears to be massive “on impact” but decays zero in four years. Chapter 3, titled THE EFFECT OF AFFORDABLE CARE ACT MEDICAID EXPANSION ON THE EMPLOYMENT: AN INTERACTIVE FIXED EFFECTS MODEL APPROACH, provides new empirical evidence on the effect of Affordable Care Act (ACA) Medicaid expansion on the United States local labor markets employment using the IFE models. Some evidence provided in this chapter suggests the states expanding the ACA Medicaid expansion may differ systematically from the non-expanding ones. Some observed differences between the expanding and nonexpanding states include the percentage of Medicaid coverage before implementing the policy, percentage of households below the Federal Poverty Line (FPL), and demographic characteristics. Hence, the two groups of states may differ substantially in some unobserved characteristics, such as jobs’ benefits in different industries within each local labor market (including the employer-based insurance coverage). The causal effect estimation under the DID method is biased in the presence of timevarying unobservable confounding factors. Utilizing the IFE models in treatment effects studies has an advantage over the DID in controlling both time-invariant and time-varying unobservable characteristics. Using the treatment effect set-up introduced by Callaway and Karami (2020); that is, a set-up that uses the IFE models in the identification of average treatment effects, and 2010-2018 Quarterly Census of Employment and Wages dataset, this chapter’s results suggest that ACA Medicaid expansion decreases the employment for almost 1% during 2014-2018 in the states that adopt this policy. The results of this chapter demonstrate a higher effect (by almost 2%) in some sub-populations of the original sample (e.g., industries with the highest percentage of low-paid employees).
dc.format.extent153 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectEconomics
dc.subjectEconomics
dc.titleEssays on Developing Treatment Effects Set-ups in Interactive Fixed Effects Models
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberWebber, Douglas (Douglas A.)
dc.contributor.committeememberRitter, Moritz B.
dc.contributor.committeememberCallaway, Brantly Mercer, IV
dc.contributor.committeememberSant'Anna, Pedro H. C.
dc.description.departmentEconomics
dc.relation.doihttp://dx.doi.org/10.34944/dspace/6820
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
dc.identifier.proqst14563
dc.date.updated2021-08-21T10:07:05Z
dc.embargo.lift08/17/2023
dc.identifier.filenamekarami_temple_0225E_14563.pdf


Files in this item

Thumbnail
Name:
karami_temple_0225E_14563.pdf
Embargo:
2023-08-17
Size:
2.565Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record