Unobserved Heterogeneity and Risk in Wage Variance: Does Schooling Provide Earnings Insurance?

We apply a recently proposed method to disentangle unobserved heterogeneity from risk in returns to education. We replicate the original study on US men and extend to US women, UK men and German men. Most original results are not robust. A college education cannot universally be considered an insurance against unpredictability of wages. One conclusion is unequivocally confirmed: uncertainty strongly dominates unobserved heterogeneity.wage inequality, wage uncertainty, unobserved heterogeneity, selectivity, education, replication

Reservation Wages and Starting Wages

We analyse a unique data set that combines reservation wage and actually paid wage for a large sample of Dutch recent higher education graduates. On average, accepted wages are almost 8% higher than reservation wages, but there is no fixed proportionality. We find that the difference between reservation wage and accepted wage is virtually random, as search theory predicts. We also find that most information contained in the accepted wage is included in the reservation wage, as one would predict if individuals are well informed about the wage structure that characterizes their labour market.reservation wages, starting wages, job search

How Risky is Investment in Human Capital?

The risk of investment in schooling has largely been ignored. We assess the variance in the rate of return by surveying the international empirical literature from this fresh perspective and by simulating risky earnings profiles in alternative options, choosing parameters on basis of the very limited evidence. The distribution of rates of return appears positively skewed. Our best guess of ex ante risk in university education is a coefficient of variation of about 0.3, comparable to that in a randomly selected financial portfolio with some 30 stocks. Allowing for stochastic components in earnings also markedly affects expected returns.education, return, earnings dispersion, risk

Competitiveness of Agrarian Areas in the Stavropol Region

Regional development is determined by possessing specific competitive advantages and their constant improvement. The problem how to increase competitiveness of the areas remains one of the most important for the agrarian sector. Russian economic policy aims at the stable development of the agro industrial complex (and agriculture as its central part). Therefore it is necessary to analyze competitiveness not only on the country level, but also on the regional and sub-regional scale. In this paper we have analyzed the major factors influencing the competitiveness of agricultural areas in the Stavropol regio

Competitiveness of Agrarian Areas in the Stavropol Region

Regional development is determined by possessing specific competitive advantages and their constant improvement. The problem how to increase competitiveness of the areas remains one of the most important for the agrarian sector. Russian economic policy aims at the stable development of the agro industrial complex (and agriculture as its central part). Therefore it is necessary to analyze competitiveness not only on the country level, but also on the regional and sub-regional scale. In this paper we have analyzed the major factors influencing the competitiveness of agricultural areas in the Stavropol region.Competitiveness, agrarian areas, Stavropol region, regional and sub-regional scale, Community/Rural/Urban Development, GA, IN,

Tracking and convergence of multi-channel Kalman filters for active noise control

The feed-forward broadband active noise control problem can be formulated as a state estimation problem to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output Kalman algorithm is used to perform this state estimation. To make the algorithm more suitable for real-time applications the Kalman filter is written in a fast array form and the secondary path state matrices are implemented in output normal form. The implementation was tested in simulations and in real-time experiments. It was found that for a constant primary path the Kalman filter has a fast rate of convergence and is able to track changes in the spectrum. For a forgetting factor equal to unity the system is robust, but the filter is unable to track rapid changes in the primary path. It is shown that a forgetting factor lower than unity gives a significantly improved tracking performance. Numerical issues of the fast array form of the algorithm for such forgetting factors are discussed and possible solutions are presented

Active control of time-varying broadband noise and vibrations using a sliding-window Kalman filter

Recently, a multiple-input/multiple-output Kalman filter technique was presented to control time-varying broadband noise and vibrations. By describing the feed-forward broadband active noise control problem in terms of a state estimation problem it was possible to achieve a faster rate of convergence than instantaneousgradient least-mean-squares algorithms and possibly also a better tracking performance. A multiple input/ multiple output Kalman algorithm was derived to perform this state estimation. To make the algorithm more suitable for real-time applications, the Kalman filter was written in a fast array form and the secondary path state matrices were implemented in output normal form. The resulting filter implementation was verified in simulations and in real-time experiments. It was found that for a constant primary path the filter had a fast rate of convergence and was able to track time-varying spectra. For a forgetting factor equal to unity the system was robust but the filter was unable to track rapid changes in the primary path. A forgetting factor lower than unity gave a significantly improved tracking performance but led to a numerical instability for the fast array form of the algorithm. To improve the numerical behavior, while enabling fast tracking and convergence, several variants are described in this paper. Results will be shown for a sliding window Recursive Least Squares filter in fast array form, which will later be extended to a full Kalman filter implementation by taking into account the uncertainty of the secondary path between the control sources and the error sensors. Multiple variants will be discussed in this paper. The first variant is the standard sliding window technique, which applies both updates and downdates to the filter coefficients. The second variant is an algorithm which only applies an update step to the filter coefficients and interprets the downdate step as an addition of a covariance matrix to the Riccati equation. The third variant uses an implicit forgetting factor. These implementations use a factorized form of the hyperbolic orthogonal transformation matrix. The different techniques will be applied to measured data of noise in houses near the runway of an airport. Results are given of the performance regarding tracking, convergence and numerical stability of the algorithms