"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.

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

Inertial sensor array processing with motion models

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordBy arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this paper, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays