Ioana Marinescu

Informality and the expansion of social protection programs: Evidence from Mexico

Many countries are moving from employer-based to universal health coverage, which can generate crowd out. In Mexico, Seguro Popular provides public health coverage to the uninsured. Using the gradual roll-out of the system at the municipality level, we estimate that Seguro Popular had no effect on informality in the overall population. Informality did increase by 1.7% for less educated workers, but the wage gains for workers who switch between the formal and the informal sector were not significantly affected. This suggests that marginal workers do not choose between formal and informal jobs on the basis of health insurance coverage.

HIV, Wages, and the Skill Premium

The HIV epidemic has dramatically decreased labor supply among prime-age adults in Sub-Saharan Africa. Using within-country variation in regional HIV prevalence and a synthetic panel, I find that HIV significantly increases the capital-labor ratio in urban manufacturing firms. The impact of HIV on average wages is positive but imprecisely estimated. In contrast, HIV has a large positive impact on the skill premium. The impact of HIV on the wages of low skilled workers is insignificantly different from 0, and is strongly dampened by competition from rural migrants. The HIV epidemic disproportionately increases the incomes of high-skilled survivors, thus increasing inequality.

The Labor Market Impacts of Universal and Permanent Cash Transfers: Evidence from the Alaska Permanent Fund

What are the effects of universal and permanent cash transfers on the labor market? Since 1982, all Alaskan residents have been entitled to a yearly cash dividend from the Alaska Permanent Fund. Using data from the Current Population Survey and a synthetic control method, we show that the dividend had no effect on employment, and increased part-time work by 1.8 percentage points (17 percent). Although theory and prior empirical research suggests that individual cash transfers decrease household labor supply, we interpret our results as evidence that general equilibrium effects of widespread and permanent transfers tend to offset this effect, at least on the extensive margin. Consistent with this story, we show suggestive evidence that tradable sectors experience employment reductions, while non-tradable sectors do not. Overall, our results suggest that a universal and permanent cash transfer does not significantly decrease aggregate employment.

Concentration in US Labor Markets: Evidence from Online Vacancy Data

Using data on the near-universe of online US job vacancies collected by Burning Glass Technologies in 2016, we calculate labor market concentration using the Herfindahl-Hirschman index (HHI) for each commuting zone by 6-digit SOC occupation. The average market has an HHI of 3,953, or the equivalent of 2.5 recruiting employers. 54% of labor markets are highly concentrated (above 2,500 HHI) according to the DOJ/FTC guidelines. Highly concentrated markets account for 17% of employment. All plausible alternative market definitions show that more than 33% of markets are highly concentrated, suggesting that employers have market power in many US labor markets.

Anticompetitive Mergers in Labor Markets

Mergers of competitors are conventionally challenged under the federal antitrust laws when they threaten to lessen competition in some product or service market in which the merging firms sell. Mergers can also injure competition in markets where the firms purchase. Although that principle is widely recognized, very few litigated cases have applied merger law to buyers. This article concerns an even more rarefied subset, and one that has barely been mentioned. Nevertheless, its implications are staggering. Some mergers may be unlawful because they injure competition in the labor market by enabling the post-merger firm anticompetitively to suppress wages or salaries. To the best of our knowledge no court has ever condemned a merger for this reason. This paper examines a number of issues that are relevant to merger challenges in employment markets, focusing on the traditional rationale for challenging horizontal mergers – namely, that increased market concentration in labor markets threatens to facilitate coordinated interaction among employers that could lead to lower output and wage suppression in employment markets. Because most mergers are challenged prior to their occurrence, the threat is not of observed coordinated interaction, but rather of an “appreciable danger” that it may occur if the merger is permitted to proceed. We outline the major issues that enforcers are likely to encounter in assessing mergers threatening competitive harm in labor markets. Mergers affecting the labor market require some rethinking of merger policy, although not any altering of its fundamentals. For example, mergers that threaten wage suppression are horizontal when the merging firms compete in the labor market, and this may be true even if they are not competitors in any product market. One useful way to think of the extent of horizontal competition in the market for employees is to look at the participants in the several “anti-poaching” cases that involve agreements among firms not to hire one another’s employees. This is quite consistent with the general principle of market definition in merger cases that a market consists of a grouping of firms that, if unified by a cartel, would have market power, or more specifically, an ideal collusive group. Horizontal mergers threatening labor market competition present a significant competition problem but also unique legal issues. On significance, labor market concentration – measured by a small number of employers – is very high, perhaps as high or higher than product market concentration. This suggests that a mature policy of pursuing mergers because of harmful effects in labor markets could yield many cases, although prima facie we cannot predict how many. Also significant is that some of these mergers might be horizontal in the labor market but not in the product market in which the merging firms sell their goods or services. Once again, we do not predict the extent to which this is true, but it does suggest that those reviewing mergers cannot simply assume that lack of competition in the product market entails the same for the labor market. So to say that merger analysis focusing on labor will take evaluators into uncharted territory seems clear, and perhaps even more clearly for courts.

Special Issue Editorial

This issue of Data Science and Engineering (DSE) contains a collection of four extended papers from the following Workshop: “The First International Workshop on Data Science for Human Capital Management (DSHCM).” Besides these four papers, this DSE issue also has one invited paper and one regular research paper. The first DSHCM workshop was held on November 18, 2017, in New Orleans, LA, USA, collocated with the 17th Edition of IEEE International Conference on Data Mining (ICDM). The second DSHCM workshop was held on September 14, 2018, with ECML (European Conference of Machine Learning) conference in Dublin, Ireland. The theme of DSHCM workshop is human capital management, which refers to the set of practices and systems that facilitate talent acquisition and management in labor market. Specifically, this workshop solicits research works that discuss application of data science and machine learning methodologies in all areas of human capital management, both pre-hire and post-hire, including talent and labor market analytics, job advertising and distribution, professional social networks, candidate sourcing, tracking, onboarding, employee training, benefits administration and compliance. There are many recent successful applications of data mining and data science techniques to problems in the HCM domain. For example, text classification techniques are used for job posting classification; sequence labeling and statistical modeling approaches find application in resume and job parsing; near-deduplication algorithms in concert with big data pipelines power many job aggregators; predictive analytics models employee flight risk; ontology mining techniques help build knowledge graphs of human capital entities; personalized search and semantic search help job seekers by demystifying searchers’ intent and contextual meaning of terms in the recruitment domain; and recommender systems have been used for expertise search and job recommendations. To this date, DSHCM is the only venue that provides a platform for publishing research works for solving problems in HCM domain by applying data science and engineering methodologies. The 2017 DSHCM workshop was very successful. About 17 papers were submitted in this workshop, of which 11 were selected for publications in the workshop proceedings. The selected papers have been published and archived by IEEE digital library [1]. DSE Guest editors, Mohammad Hasan, Faizan Javed and Ioana E. Marinescu, have selected four papers from the accepted papers based on the relevance of the journal and the reviews of the workshop version of the papers. The authors were asked to revise their workshop paper for journal publication and in accordance with customary practice to add 30% new materials. The revised paper again went through the normal journal-style review process and was finally presented to the readers in the present form. We appreciate the willingness of the authors to help in organizing this special issue. The four papers in this special issue cover both pre-hire and post-hire data analytics. The first paper titled, “Talent Flow Analytics in Online Professional Networks,” belongs to pre-hire data analytics. In this paper, the authors study job preferences and career progression of an individual by using data from online professional networks. Authors also perform connectivity analysis at job and organization levels to derive insights on talent flow as well as on job and organizational competitiveness. The second paper, “Automatically Detecting Errors in Employer Industry Classification using Job Postings,” also belongs to pre-hire data analytics. This work is important for online job portals, such as careerbuilder.com and monster.com. In this work, authors show how job posting data can be used for the classification of employer industry so that the jobs can be organized and displayed based on the employer industry. Such classification also helps effective job recommendation to potential job seekers. The remaining two papers belong to post-hire * Mohammad A. Hasan alhasan@iupui.edu

Labor Market Concentration

A product market is concentrated when a few firms dominate the market. Similarly, a labor market is concentrated when a few firms dominate hiring in the market. Using data from the leading employment website CareerBuilder.com, we calculate labor market concentration for over 8,000 geographic-occupational labor markets in the US. Based on the DOJ-FTC horizontal merger guidelines, the average market is highly concentrated. Using a panel IV regression, we show that going from the 25th percentile to the 75th percentile in concentration is associated with a 17% decline in posted wages, suggesting that concentration increases labor market power.