"Is There a Skills Crisis? Trends in Job Skill Requirements, Technology, and Wage Inequality in the United States"

Many economists and other social scientists and policy makers believe that the growth in inequality in the last two decades reflects mostly an imbalance between the demand for and the supply of employee skills driven by technological change, particularly the spread of computers. However, the empirical basis for this belief is not strong. The growth in inequality was concentrated in the recession years of the early 1980s and any imbalance between the supply of and demand for workers with technological skills likely did not occur until later. The growth of the supply of more-educated workers decelerated during the 1980s, but any impact of that likely would not have been felt until the late 1980s and 1990s. However, inequality actually stabilized during this latter period. On the demand side, trends in occupational composition do not suggest that upgrading was particularly rapid in the 1980s and 1990s compared to the 1970s. Computers do not seem to have greatly affected employment in a number of narrow occupations that are likely to be sensitive to technological change (e.g., computer programmers, bank tellers). Computer use itself does seem to be associated with more education, even controlling for occupation, but the causal status of this relationship is uncertain and even the magnitude of the observed association does not seem large enough to have seriously compromised the ability of supply to meet the implied growth in demand. By contrast, the recession of the early 1980s coincides with a dramatic decline of traditionally better paid blue collar workers, particularly in manufacturing. This suggests a need for a closer look at other possible causes of inequality growth, such as macroeconomic forces and the decline of institutional protections for workers.

"Is There a Skills Crisis? Trends in Job Skill Requirements, Technology, and Wage Inequality in the United States"

Despite seven years of economic growth a large gap exists in the wages earned by workers at the top of the earnings scale and those at the bottom. The leading explanation for this growth in wage inequality continues to be the skills-mismatch theory. This theory in part posits that gains in technology have resulted in jobs having highly technical skill requirements that have outpaced growth in worker skills; demand for highly skilled workers therefore rises more swiftly than that for less-skilled workers, creating upward pressure on wages for those with the most skills. The empirical evidence is examined here and shows that there is little evidence to support the mismatch theory as there has been little sign of a shortage of workers with computer or general technical skills. If the analysis is correct, then policies currently used to close the wage gap, such as improved education and training, will not alone solve the inequality problem. Rather, the solution may require macroeconomic policies aimed at maintaining economic growth and full employment, and labor policies, such as the minimum wage, that support the earnings of workers at the lower end of the wage scale.

Computers and the Wage Structure

A leading explanation for the rapid growth in U.S. wage inequality in the last twenty years, consistent with both human capital and postindustrial theories, is that advanced technology has increased job skill requirements and reduced the demand for less-skilled workers. Krueger's study (1993) showing a wage premium associated with using computers at work is one of the few that seems to provide direct supportive evidence. In this paper I use previously unexamined data to suggest that measured returns to computer use are upwardly biased. In addition, I find that most of the growth of inequality since 1979 occurred in the early 1980s, which is inconsistent with a primary role for computers. Finally, computer use at work had equalizing impacts on the gender wage gap and elsewhere in the wage distribution, as well as disequalizing impacts on the wage gaps between education groups. When the contribution of computer use to all components of the variance of wages is taken into account, computers seem to have had a net equalizing impact in the period Krueger studied. This casts significant doubt on this technology-based explanation of the growth in wage inequality.

"Computers and the Wage Structure"

A leading explanation for the rapid growth in U.S. wage inequality in the last twenty years, consistent with both human capital and postindustrial theories, is that advanced technology has increased job skill requirements and reduced the demand for less skilled workers. Krueger's study (1993) showing a wage premium associated with using computers at work is one of the few that seems to provide direct supportive evidence. In this paper I use previously unexamined data to suggest that measured returns to computer use are upwardly biased. In addition, I find that most of the growth of inequality since 1979 occurred in the early 1980s, which is inconsistent with a primary role for computers. Finally, computer use at work had equalizing impacts on the gender wage gap and elsewhere in the wage distribution, as well as disequalizing impacts on the wage gaps between education groups. When the contribution of computer use to all components of the variance of wages is taken into account, computers seem to have had a net equalizing impact in the period Krueger studied. This casts significant doubt on this technology-based explanation of the growth in wage inequality.

"Is There a Wage Payoff to Innovative Work Practices?"

During the 1980s, wage inequality increased dramatically and the American economy lost many high wage, low- to medium-skill jobs, which had provided middle class incomes to less skilled workers. Increasingly, less skilled workers seemed restricted to low wage jobs lacking union or other institutional protections. Although "good" jobs for less skilled workers are unlikely to return in their previous form, a number of sociologists, economists, and industrial relations scholars have suggested that a new paradigm of work, often called "high performance," is emerging, which offers such workers more skilled jobs and higher wages. Using a unique national data set we find little evidence that high performance work systems are associated with higher wages.

Job Skill Requirements: Levels and Trends

This background paper for MIT’s Work of the Future report (2020) reviews what is known about trends in job skill requirements. The paper reviews issues related to the conceptualization and measurement of job skill requirements and the state of existing data before discussing recent rich cross-sectional data and more variable trend data for both the United States and other OECD countries. In general, the data on current levels of skill demand are at variance with the more extreme views that emphasize the prevalence of high-tech jobs or other kinds of “knowledge work.” Trends are consistently gradual on their face and often flatter in the previous ten to twenty years relative to previous decades—there is no consistent evidence that trends have accelerated markedly since 1980, and the dominant impression is continuity rather than discontinuity. Information technology changes very rapidly, but work roles and the occupational structure change gradually and will likely to continue to do so in the future. The data on current levels of job skill demands and recent trends help provide a frame of reference for evaluating predictions regarding future changes in job skill requirements

Ergodicity, Decisions, and Partial Information

In the simplest sequential decision problem for an ergodic stochastic process X, at each time n a decision u_n is made as a function of past observations X_0,...,X_{n-1}, and a loss l(u_n,X_n) is incurred. In this setting, it is known that one may choose (under a mild integrability assumption) a decision strategy whose pathwise time-average loss is asymptotically smaller than that of any other strategy. The corresponding problem in the case of partial information proves to be much more delicate, however: if the process X is not observable, but decisions must be based on the observation of a different process Y, the existence of pathwise optimal strategies is not guaranteed. The aim of this paper is to exhibit connections between pathwise optimal strategies and notions from ergodic theory. The sequential decision problem is developed in the general setting of an ergodic dynamical system (\Omega,B,P,T) with partial information Y\subseteq B. The existence of pathwise optimal strategies grounded in two basic properties: the conditional ergodic theory of the dynamical system, and the complexity of the loss function. When the loss function is not too complex, a general sufficient condition for the existence of pathwise optimal strategies is that the dynamical system is a conditional K-automorphism relative to the past observations \bigvee_n T^n Y. If the conditional ergodicity assumption is strengthened, the complexity assumption can be weakened. Several examples demonstrate the interplay between complexity and ergodicity, which does not arise in the case of full information. Our results also yield a decision-theoretic characterization of weak mixing in ergodic theory, and establish pathwise optimality of ergodic nonlinear filters.Comment: 45 page

Putting Tasks to the Test: Human Capital, Job Tasks, and Wages

Using original, representative survey data, we document that analytical, routine, and manual job tasks can be measured with high validity, vary substantially within and between occupations, are significantly related to workers’ characteristics, and are robustly predictive of wage differences between occupations and among workers in the same occupation. We offer a conceptual framework that makes explicit the causal links between human capital endowments, occupational assignment, job tasks, and wages, which motivate a Roy model of the allocation of workers to occupations. We offer two simple tests of the model’s gross predictions for the relationship between tasks and wages, both of which receive qualified empirical support.National Science Foundation (U.S.) (CAREER award SES-0239538

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Quantum Fluctuations of Coulomb Potential as a Source of Flicker Noise. The Influence of External Electric Field

Fluctuations of the electromagnetic field produced by quantized matter in external electric field are investigated. A general expression for the power spectrum of fluctuations is derived within the long-range expansion. It is found that in the whole measured frequency band, the power spectrum of fluctuations exhibits an inverse frequency dependence. A general argument is given showing that for all practically relevant values of the electric field, the power spectrum of induced fluctuations is proportional to the field strength squared. As an illustration, the power spectrum is calculated explicitly using the kinetic model with the relaxation-type collision term. Finally, it is shown that the magnitude of fluctuations produced by a sample generally has a Gaussian distribution around its mean value, and its dependence on the sample geometry is determined. In particular, it is demonstrated that for geometrically similar samples, the power spectrum is inversely proportional to the sample volume. Application of the obtained results to the problem of flicker noise is discussed.Comment: 14 pages, 3 figure