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Skill Mismatch and Wage Inequality in the U.S.
No full textThis dissertation is an empirical investigation into the distributive effects of overand under-education, defined as market outcomes such that some workers possess skills over or below those required at their jobs respectively. This type of market failure can arise in assignment and search equilibrium settings, as well as in the presence of asymmetric information regarding workers' performance on the job. The existence of permanent and sizable mismatch rates means that returns to education are depressed for over-educated workers and in ated for under-qualified workers. Thus, irreversible decisions to invest in human capital are made in a context of uncertainty regarding the exact outcomes that might arise. As in the Todaro model, where individuals decide whether to migrate to cities based on the expected values of the available alternatives, workers might decide it is worthwhile to keep investing in education even if the probability of finding appropriate employment is falling. The three chapters of the dissertation are entitled: Skill Mismatch and Earnings: A Panel analysis of the U.S. Labor Market," Earnings Inequality and Skill Mismatch in the U.S: 1973-2003," and Employment and Distribution Effects of Changes in the Minimum Wage." Skill Mismatch and Earnings: A Panel analysis of the U.S. Labor Market This chapter examines the effect on earnings induced by a mismatch between workers' skills and the skills actually required on the job. It uses the Current Population Survey (CPS) for the period 1983-2002. The special re-interview methodology of the CPS is used to create a large panel, so that individual heterogeneity can be controlled for. Skill requirements are estimated by the median education level for each 3-digit occupation in the 1980 census occupational classification. The analysis, including the determination of skill requirements, is conducted for males and females separately. Cross-sectional analysis confirms the findings in the recent literature. Returns to required schooling are higher than the returns to attained education in standard earnings regressions. Also, for workers with similar educational attainment, over-education reduces earnings and under-education increases them. Contrary to what other studies have found, we conclude that these results are confirmed after controlling for individual fixed effects. The chapter also investigates which groups are more exposed to mismatch. I use standard probit analysis with over-education and under-education as the respective dependent variables. Women, service sector, and non-unionized workers appear to have higher probabilities of mismatch. Earnings Inequality and Skill Mismatch This chapter shows that skill mismatch is a significant source of inequality in real earnings in the U.S. and that a substantial fraction of the increase in wage dispersion during the period 1973-2002 was due to the increase in mismatch rates and mismatch premia. Standard human capital earnings regressions that do not decompose the education variable into required, surplus, and deficit years provide biased estimates of the relative importance of education in explaining earnings inequality. In 2000-2002 surplus and deficit qualifications taken together accounted for 4:3 and 4:6 percent of the variance in earnings, or around 15 percent of the total explained variance. The dramatic increase in over-education rates and premia accounts for around 11 and 32 percent of the increase in the coeffcient of variation of log earnings during the 30 years under analysis for males and females respectively. Residual inequality is slightly diminished when the estimating equation allows the prices of surplus, required and deficit qualifications to differ but the well-studied increasing trend of within-group inequality remains otherwise unchanged. Changes in the composition of the labor force are found to be important predictors of increasing residual inequality even when skill mismatch is taken into account. The Distributive Effects of the Minimum Wage: an Effciency Wage Model with Skill Mismatch (co-authored with Peter Skott) This chapter analyzes the effect of changes in the real value of the minimum wage on the wage distribution. Changes in the minimum wage and other labor market institutions affect workers in all groups and empirically appear to be good complement to standard supply and demand arguments in explaining overall inequality. We use an effciency wage model but allow for mismatch between jobs and workers. This framework yields predictions not only on the skill premium but also on the extent of inequality within groups. To keep matters as simple as possible, we assume that high-skill workers can get two types of jobs (good and bad), whereas low-skill workers have only one type of employment opportunity (bad). As long as some matches of high-skill workers and bad jobs are sustained in equilibrium, changes in the exogenous variables will affect not only wages and employment rates but also the degree of mismatch. Thus, this paper shows that `over-education' can be generated endogenously in effciency wage models and that a fall in the real value of the minimum wage can (i) reduce total employment, (ii) lead to a simultaneous decline in both the relative employment and the relative wage of low-skill workers, and (iii) produce a rise in within-group as well as between-group inequality. Evidence from the US suggests that these theoretical results are empirically relevant.Doctor of Philosophy (Ph.D.
This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters
Get PDFI propose a relatively simple way to deploy pre-trained large language models (LLMs) in order to extract sentiment and other useful features from text data. The method, which I refer to as prompt-based sentiment extraction, offers multiple advantages over other methods used in economics and finance. In particular, it accepts the text input as is (without preprocessing) and produces a sentiment score that has a probability interpretation. Unlike other LLM-based approaches, it does not require any fine-tuning or labeled data. I apply my prompt-based strategy to a hand-collected corpus of confidential reference letters (RLs). I show that the sentiment contents of RLs are clearly reflected in job market outcomes. Candidates with higher average sentiment in their RLs perform markedly better regardless of the measure of success chosen. Moreover, I show that sentiment dispersion among letter writers negatively affects the job market candidate’s performance. I compare my sentiment extraction approach to other commonly used methods for sentiment analysis: ‘bag-of-words’ approaches, fine-tuned language models, and querying advanced chatbots. No other method can fully reproduce the results obtained by prompt-based sentiment extraction. Finally, I slightly modify the method to obtain ‘gendered’ sentiment scores (as in Eberhardt et al., 2023). I show that RLs written for female candidates emphasize ‘grindstone’ personality traits, whereas male candidates’ letters emphasize ‘standout’ traits. These gender differences negatively affect women’s job market outcomes
The effect of taxation on informal employment: evidence from the Russian flat tax reform
Get PDFThe 2001 Russian tax reform reduced average tax rates for the personal income tax and the payroll or social tax. It also made the tax structure more regressive. Because individuals in the lower income bracket were for the most part not affected, it is possible to estimate the effects
of the reform using a differences-in-differences approach. I study the effect of the reform on informal employment. Informality is defined using information on employment registration and self-employment. Applying parametric and semi-parametric techniques, I find evidence that the tax reform led to a significant reduction in the fraction of informal employees. Among the different forms of informality I study, the reform seems to have had the strongest effect on the prevalence of informal irregular activities. I also document stronger effects on individuals who benefited from the largest reductions in tax rates
The effect of taxation on informal employment: evidence from the Russian flat tax reform
Get PDFThe 2001 Russian tax reform reduced average tax rates for the personal income tax and the payroll or social tax. It also made the tax structure more regressive. Because individuals in the lower income bracket were for the most part not affected, it is possible to estimate the effects of the reform using a differences-in-differences approach. I study the effect of the reform on informal employment. Informality is defined using information on employment registration and self-employment. Applying parametric and semi-parametric techniques, I find evidence that the tax reform led to a significant reduction in the fraction of informal employees. Among the different forms of informality I study, the reform seems to have had the strongest effect on the prevalence of informal irregular activities. I also document stronger effects on individuals who benefited from the largest reductions in tax rates.informal sector; entrepreneurship; tax reform; difference-in-difference; transition; Russia
Earnings inequality and skill mismatch in the U.S.: 1973-2002
Get PDFThis paper shows that skill mismatch is a significant source
of inequality in real earnings in the U.S. and that a substantial fraction of the increase in wage dispersion during the period 1973-2002 was due to the increase in mismatch rates and mismatch premia. In 2000-2002 surplus and deficit qualifications taken together accounted for 4.3 and 4.6 percent of the variance of log earnings, or around 15 percent of the total explained variance. The dramatic increase in over-education rates and premia accounts for around 20 and 48 percent of the increase in the Gini coefficient during the 30 years under analysis for males and females respectively. The surplus qualification factor is important in understanding why earnings inequality polarized in the last decades
This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters
No full textI propose a relatively simple way to deploy pre-trained large language models (LLMs) in order to extract sentiment and other useful features from text data. The method, which I refer to as prompt-based sentiment extraction, offers multiple advantages over other methods used in economics and finance. In particular, it accepts the text input as is (without preprocessing) and produces a sentiment score that has a probability interpretation. Unlike other LLM-based approaches, it does not require any fine-tuning or labeled data. I apply my prompt-based strategy to a hand-collected corpus of confidential reference letters (RLs). I show that the sentiment contents of RLs are clearly reflected in job market outcomes. Candidates with higher average sentiment in their RLs perform markedly better regardless of the measure of success chosen. Moreover, I show that sentiment dispersion among letter writers negatively affects the job market candidate’s performance. I compare my sentiment extraction approach to other commonly used methods for sentiment analysis: ‘bag-of-words’ approaches, fine-tuned language models, and querying advanced chatbots. No other method can fully reproduce the results obtained by prompt-based sentiment extraction. Finally, I slightly modify the method to obtain ‘gendered’ sentiment scores (as in Eberhardt et al., 2023). I show that RLs written for female candidates emphasize ‘grindstone’ personality traits, whereas male candidates’ letters emphasize ‘standout’ traits. These gender differences negatively affect women’s job market outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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