3,009 research outputs found

    An Empirical Model of Labor Supply in the Underground Economy

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    This paper uses micro data from a random survey carried out in the region of Quebec City, Canada, to estimate a model of labor supply in the underground economy. The model assumes that the individual's gross wage rate in the regular sector is parametric while his gross labor earnings in the underground sector are a concave function of hours of work. This distinction between the two sectors is used to generate a simple separation result between preferences and the magnitude of underground labor market activities. This result implies that the individual's labor supply in the underground economy is generally a negative function of his net wage rate in the regular sector. The separation result also implies a set of restrictions on the parameters of the reduced form of the model, which are imposed using minimum distance methods of estimation. Various generalized method of moments specification tests allow us to verify the validity of these restrictions. According to our results, the marginal tax rates embodied in the Quebec tax-transfer system are an important determinant of the decision to participate in the underground sector.

    Occupational Tasks and Changes in the Wage Structure

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    This paper argues that changes in the returns to occupational tasks have contributed to changes in the wage distribution over the last three decades. Using Current Population Survey (CPS) data, we first show that the 1990s polarization of wages is explained by changes in wage setting between and within occupations, which are well captured by tasks measures linked to technological change and offshorability. Using a decomposition based on Firpo, Fortin, and Lemieux (2009), we find that technological change and deunionization played a central role in the 1980s and 1990s, while offshorability became an important factor from the 1990s onwards.wage inequality, polarization, occupational tasks, offshoring, RIF-regressions

    Unconditional Quantile Regressions

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    We propose a new regression method to estimate the impact of explanatory variables on quantiles of the unconditional distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function is a widely used tool in robust estimation that can easily be computed for each quantile of interest. We show how standard partial effects, as well as policy effects, can be estimated using our regression approach. We propose three different regression estimators based on a standard OLS regression (RIFOLS), a Logit regression (RIF-Logit), and a nonparametric Logit regression (RIFNP). We also discuss how our approach can be generalized to other distributional statistics besides quantiles.Influence Functions, Unconditional Quantile, Quantile Regressions.

    Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

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    We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths

    Decomposition Methods in Economics

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    This chapter provides a comprehensive overview of decomposition methods that have been developed since the seminal work of Oaxaca and Blinder in the early 1970s. These methods are used to decompose the difference in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, our main focus is on other distributional statistics besides the mean such as quantiles, the Gini coefficient or the variance. We discuss the assumptions required for identifying the different elements of the decomposition, as well as various estimation methods proposed in the literature. We also illustrate how these methods work in practice by discussing existing applications and working through a set of empirical examples throughout the paper.

    Multi-Sine EIS for Early Detection of PEMFC Failure Modes

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    Electrochemical impedance spectroscopy (EIS) is a powerful technique that can be used to detect small changes in electrochemical systems and subsequently identify the source of the change. While promising, analysis is often non-intuitive and time-consuming, where collection times of a single EIS spectrum can reach several minutes. To circumvent the long collection times associated with traditional EIS measurements, a multi-sine EIS technique was proposed in which the simultaneous application of many frequencies can reduce the acquisition time to less than a minute. This shortened acquisition time opens the possibility to use multi-sine EIS as a real-time diagnostic tool for monitoring the state-of-health of commercial fuel cell systems. In this work, a single-cell proton exchange membrane fuel cell (PEMFC) was characterised using multi-sine EIS, by establishing steady-state impedance response under baseline conditions before systematically changing operating conditions and monitoring the dynamic changes of the impedance response. Our initial results demonstrate that full multi-sine EIS spectra, encompassing a frequency range from 50 kHz to 0.5 Hz, can be collected and analysed using simple equivalent circuit models in 50 s. It is shown that this timeframe is sufficiently short to capture the dynamic response of the fuel cell in response to changing operating conditions, thereby validating the use of multi-sine EIS as a diagnostic technique for in-situ monitoring and fault detection during fuel cell operation.publishedVersio

    Unifying prospective and retrospective interval-time estimation: a fading-gaussian activation-based model of interval-timing

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    Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried and cognitive load on participants’ judgments of interval duration. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds, with increased cognitive load leading to shorter estimates. We describe GAMIT, a Gaussian spreading-activation model, in which the sampling rate of an activation trace is differentially affected by cognitive load. The model unifies prospective and retrospective time estimation, normally considered separately, by relating them to the same underlying process. The scalar property of time estimation arises naturally from the model dynamics and the model shows the appropriate interaction between mode of query and cognitive load
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