In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

On April 1, 1992 New Jersey's minimum wage rose from $4.25 to $5.05 per hour. To evaluate the impact of the new law we surveyed over 400 fast food restaurants in New Jersey and Pennsylvania before and after the rise in the minimum. Comparisons of the changes in wages, employment, and prices at stores in New Jersey relative to stores in Pennsylvania (where the minimum remained constant at $4.25 per hour) provide simple robust estimates of the effect of the increased minimum wage. Our empirical findings challenge the conventional notion that a rise in the minimum causes employment to decline. Relative to stores in Pennsylvania, fast food restaurants in New Jersey increased employment by 2.5 employees per store. We also compare employment changes at stores in New Jersey that were initially paying $5.00 per hour or more (and were therefore largely unaffected by the new law) to the employment changes at lower-wage stores, where the new law raised wages by 10-15 percent. Stores that were unaffected by the minimum wage had the same employment growth as stores in Pennsylvania, while stores that had to increase their wages increased their employment. Finally, we evaluate theoretical models that might explain these results.

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Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

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Computational Social Science

Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This finding has shaped research agendas in a number of fields, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices -- and therefore over which producers have some direct control, at least in theory -- as well as from producers' external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward. (JEL D24, G31, L11, M10, O30, O47)

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What Determines Productivity

A recent “revisionist” literature characterizes the pronounced rise in U.S. wage inequality since 1980 as an “episodic” event of the first half of the 1980s driven by nonmarket factors (particularly a falling real minimum wage) and concludes that continued increases in wage inequality since the late 1980s substantially reflect the mechanical confounding effects of changes in labor force composition. Analyzing data from the Current Population Survey for 1963 to 2005, we find limited support for these claims. The slowing of the growth of overall wage inequality in the 1990s hides a divergence in the paths of upper-tail (90/50) inequality—which has increased steadily since 1980, even adjusting for changes in labor force composition—and lower-tail (50/10) inequality, which rose sharply in the first half of the 1980s and plateaued or contracted thereafter. Fluctuations in the real minimum wage are not a plausible explanation for these trends since the bulk of inequality growth occurs above the median ...

Trends in U.S. Wage Inequality: Revising the Revisionists

Recent research using longitudinal microeconomic databases on businesses and individuals has uncovered tremendous heterogeneity Across businesses, even within narrowly defined industrial sectors, we observe large differences in the levels and growth rates of inputs, outputs, and productivity Across workers, even controlling for demographic characteristics, wage and employment outcomes vary a great deal, and there are sizable worker flows across jobs in the economy These worker flows are closely connected to firm outcomes, reflecting in large part the ongoing shift in resources from less productive to more productive employers The primary message is that the continual process of matching and sorting as businesses and workers seek their niches is heterogeneous, with experimentation, learning, and selection all playing important roles Understanding the connection between the matching and sorting of businesses and workers is at the very heart of the overlap between industrial organization and labor economics However, due to the paucity of data linking businesses and workers at the micro level, we know relatively little about these interactions Although a large empirical literature suggests that worker outcomes are associated with firm characteristics (notable examples are firm-size wage effects, compensating wage differentials, and interindustry wage differentials), very little is known about the converse-the process by which business outcomes are associated with the characteristics of their employees There are a number of questions associated with this process Do employers choose different modes of production resulting in skill segregation, as suggested by the sorting and assignment hypotheses in Michael Kremer (1993) and Michael Sattinger (1993), or do all firms in a given industry select roughly the same workforce composition? How do employers change their workforce composition either to accommodate demand shocks or to experiment with new modes of production? In turn, what is the connection between these different decisions regarding workforce composition and observable business outcomes such as survival, growth, and productivity? In this short paper, our empirical objectives are relatively modest compared to these broad research questions Our focus is to provide an initial investigation into the connection between a key measure of firm performance, labor productivity, and the composition of the firm's workforce as measured by observable worker characteristics We exploit longitudinal matched employeremployee micro data developed under a recent pilot project at the US Census Bureau Our data set contains longitudinal information on key outcomes of the businesses, as well as many of the basic demographic characteristics for the universe of workers at these businesses Although longitudinal matched employer-employee micro data have been developed in a variety of countries over the last decade or so (see John Abowd and Francis Kramarz, 1999), ours is the first large-scale micro-level data set in the United States that allows the investigation * Haltiwanger: Department of Economics, University of Maryland, College Park, MD 20742, US Bureau of the Census, and NBER; Lane: Department of Economics, American University, Washington, DC 20016, and US Bureau of the Census; Spletzer: Bureau of Labor Statistics, 2 Massachusetts Ave, NE, Washington, DC 20212 We thank Adela Luque and Javier Miranda for superb research assistance, and Charles Brown and Marilyn Manser for comments on an earlier draft This research was conducted at the Center for Economic Studies at the US Bureau of the Census while the second author was an American Statistical Association Census Bureau fellow The analysis and results are attributable to the authors and do not necessarily reflect concurrence of the institutions they represent

Productivity Differences across Employers: The Roles of Employer Size, Age, and Human Capital

Disclosure limitation methods in use - results of a survey, F. Felso, J. Theeuwes, G.G. Wagner Information Explosion, L. Sweeney Disclosure risk assessment, M. Elliot Disclosure control methods and information loss for microdata, J. Domingo-Ferrer, V. Torra A quantitative comparison of disclosure control methods for microdata, J. Domingo-Ferrer, V. Torra Disclosure limitation methods and information loss for tabular data, G.T. Duncan, et al Disclosure risk for tabular economic data, L.H Cox Nonperturbative disclosure control methods for tabular data, S. Giessing Disclosure limitation in longitudinal linked data, J.M. Abowd, S.D. Woodcock Licensing, M.M. Seastrom Issues in the establishment and management of secure research sites, T. Dunne The potential of confidentiality and attitudes toward data sharing by federal agencies, E. Singer The privacy context of survey response - an ethnographic account, E.R. Gerber. Business perceptions of confidentiality, N. Greenia, J. Bradford Jensen, J. Lane.

Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies

We utilize a large firm-level panel dataset to explore the links between gross job flows and gross worker flows. Our findings have relevance for models of job creation and destruction, of labour reallocation and of employment adjustment costs. We find churning flows (the difference between worker and job flows at the firm) to be high, pervasive, and highly persistent over time within firms. We find the dynamic relationship between job and worker flows to be quite complex: lagged job flows raise churning flows, but the effect of churning flows on job flows is asymmetric.

Job Flows, Worker Flows and Churning

We utilize a large employer‐level panel dataset to explore the links between gross job flows and gross worker flows. Our findings have relevance for models of job creation and job destruction, and labor reallocation. We find churning flows (the difference between worker and job flows at the level of the employer) to be high, pervasive, and highly persistent within employers, suggesting that they arise as a correlate of an equilibrium personnel policy. We find the dynamic relationship between job and worker flows to be quite complex: lagged job flows raise churning flows, and lagged churning flows reduce employment growth.

https://www.jstor.org/stable/10.1086/209967

Job flows, worker flows, and churning

To capture the essence of good science, stakeholders must combine forces to create an open, sound and consistent system for measuring all the activities that make up academic productivity, says Julia Lane.

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Julia Lane

Papers

Productivity Differences across Employers: The Roles of Employer Size, Age, and Human Capital

Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies

Job Flows, Worker Flows and Churning

Job flows, worker flows, and churning

Let's make science metrics more scientific