The authors investigate how labor market institutions such as unemployment insurance, unions, firing regulations, and minimum wages have affected the evolution of wage inequality among male workers. Results of estimations using data on institutions in eleven OECD countries indicate that changes in labor market institutions can account for much of the change in wage inequality between 1973 and 1998. Factors found to have been negatively associated with male wage inequality are union density, the strictness of employment protection law, unemployment benefit duration, unemployment benefit generosity, and the size of the minimum wage. Over the 26-year period, institutional changes were associated with a 23% reduction in male wage inequality in France, where minimum wages increased and employment protection became stricter, but with an increase of up to 11% in the United States and United Kingdom, where unions became less powerful and (in the United States) minimum wages fell.

This paper introduces a new technique for predicting latent software bugs, called change classification. Change classification uses a machine learning classifier to determine whether a new software change is more similar to prior buggy changes or clean changes. In this manner, change classification predicts the existence of bugs in software changes. The classifier is trained using features (in the machine learning sense) extracted from the revision history of a software project stored in its software configuration management repository. The trained classifier can classify changes as buggy or clean, with a 78 percent accuracy and a 60 percent buggy change recall on average. Change classification has several desirable qualities: 1) The prediction granularity is small (a change to a single file), 2) predictions do not require semantic information about the source code, 3) the technique works for a broad array of project types and programming languages, and 4) predictions can be made immediately upon the completion of a change. Contributions of this paper include a description of the change classification approach, techniques for extracting features from the source code and change histories, a characterization of the performance of change classification across 12 open source projects, and an evaluation of the predictive power of different groups of features.

Many people point to information and communications technology (ICT) as the key for understanding the acceleration in productivity in the United States since the mid‐1990s. Stories of ICT as a ‘general‐purpose technology’ suggest that measured total factor productivity (TFP) should rise in ICT‐using sectors (reflecting either unobserved accumulation of intangible organizational capital; spillovers; or both), but with a long lag. Contemporaneously, however, investments in ICT may be associated with lower TFP as resources are diverted to reorganization and learning. We find that US industry results are consistent with general‐purpose technology (GPT) stories: the acceleration after the mid‐1990s was broad‐based – located primarily in ICT‐using industries rather than ICT‐producing industries. Furthermore, industry TFP accelerations in the 2000s are positively correlated with (appropriately weighted) industry ICT capital growth in the 1990s. Indeed, as GPT stories would suggest, after controlling for past ICT investment, industry TFP accelerations are negatively correlated with increases in ICT usage in the 2000s.

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 non market 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 of the wage distribution. Models emphasizing rapid secular growth in the relative demand for skills—attributable to skill-biased technical change—and a sharp deceleration in the relative supply of college workers in the 1980s do an excellent job of capturing the evolution of the college/high school wage premium over four decades. But these models also imply a puzzling deceleration in relative demand growth for college workers in the early 1990s, also visible in a recent “polarization” of skill demands in which employment has expanded in high-wage and low-wage work at the expense of middle-wage jobs. These patterns are potentially reconciled by a modified version of the skill-biased technical change hypothesis that emphasizes the role of information technology in complementing abstract (high-education) tasks and substituting for routine (middle-education) tasks.

We offer a unified analysis of the growth of low-skill service occupations between 1980 and 2005 and the concurrent polarization of US employment and wages. We hypothesize that polarization stems from the interaction between consumer preferences, which favor variety over specialization, and the falling cost of automating routine, codifiable job tasks. Applying a spatial equilibrium model, we corroborate four implications of this hypothesis. Local labor markets that specialized in routine tasks differentially adopted information technology, reallocated low-skill labor into service occupations (employment polarization), experienced earnings growth at the tails of the distribution (wage polarization) and received inflows of skilled labor.

I analyze an economy in which firms can undertake both labor‐ and capital‐augmenting technological improvements. In the long run, the economy resembles the standard growth model with purely labor‐augmenting technical change, and the share of labor in GDP is constant. Along the transition path, however, there is capital‐augmenting technical change and factor shares change. Tax policy and changes in labor supply or savings typically change factor shares in the short run but have no or little effect on the long‐run factor distribution of income.

To monitor trends in alternative work arrangements, the authors conducted a version of the Contingent Worker Survey as part of the RAND American Life Panel in late 2015. Their findings point to a rise in the incidence of alternative work arrangements in the US economy from 1995 to 2015. The percentage of workers engaged in alternative work arrangements—defined as temporary help agency workers, on-call workers, contract workers, and independent contractors or freelancers—rose from 10.7% in February 2005 to possibly as high as 15.8% in late 2015. Workers who provide services through online intermediaries, such as Uber or TaskRabbit, accounted for 0.5% of all workers in 2015. Of the workers selling goods or services directly to customers, approximately twice as many reported finding customers through off-line intermediaries than through online intermediaries.

L’économie numérique suscite les spéculations les plus diverses. Parmi celles-ci, on prédit la fin du travail et le dépassement de l’être humain par la robotisation et l’intelligence artificielle ; on annonce de même la fin du salariat, l’ubérisation de l’économie généralisant le travail indépendant pour tous. À l’opposé de ces fantasmes, cet article décrit les métamorphoses réelles du travail tel qu’on les perçoit déjà dans une économie numérisée. Il montre comment la numérisation transforme la manière de travailler à la fois vers davantage d’autonomie mais aussi de contraintes et de contrôles technologiques. Il fait le point des connaissances sur l’évolution de l’emploi et des revenus tirés de nouvelles formes de travail. Il montre en particulier que les principales recompositions sociales tiendront à l’affaiblissement des revenus salariaux de la classe moyenne. Il invite à réfléchir sur les conséquences sociétales de ces transformations de notre façon de travailler.

Les écarts importants entre les prévisions actuelles sur l’avenir de l’emploi dans l’économie numérique reflètent des divergences profondes dans les hypothèses et les méthodologies des études existantes. L’article compare trois approches. La première repose sur une évaluation des risques de substitution des tâches réalisées par des humains au bénéfice de machines intelligentes, au niveau des profils des métiers, et conduit à des estimations très pessimistes. La deuxième prend en compte l’hétérogénéité des tâches, automatisables ou non, au sein de chaque profil de métiers. Les prévisions sont beaucoup moins pessimistes, de même que dans la troisième approche, qui repose sur une analyse des conditions de travail potentiellement exposées à l’automatisation. Selon le « niveau de la menace », les implications politiques peuvent s’avérer très différentes.