689 research outputs found
Selection of workers and firm heterogeneity
A model based on differences between workers regarding their preferences for wage and leisure drives the heterogeneity of firms result. The more industrious workers are driven to small firms due to free riding in large firms. An industry consisting of small and large firms turns out to produce more output than an industry consisting of only large firms. Some comparative statics results are derived with respect to the size of large firms, the productivity difference between firms, and monitoring capabilities
A theoretical and empirical investigation of nutritional label use
Due in part to increasing diet-related health problems caused, among others, by obesity, nutritional labelling has been considered important, mainly because it can provide consumers with information that can be used to make informed and healthier food choices. Several studies have focused on the empirical perspective of nutritional label use. None of these studies, however, have focused on developing a theoretical economic model that would adequately describe nutritional label use based on a utility theoretic framework. We attempt to fill this void by developing a simple theoretical model of nutritional label use, incorporating the time a consumer spends reading labels as part of the food choice process. The demand equations of the model are then empirically tested. Results suggest the significant role of several variables that flow directly from the model which, to our knowledge, have not been used in any previous empirical work
Financing Direct Democracy: Revisiting the Research on Campaign Spending and Citizen Initiatives
The conventional view in the direct democracy literature is that spending against a measure is more effective than spending in favor of a measure, but the empirical results underlying this conclusion have been questioned by recent research. We argue that the conventional finding is driven by the endogenous nature of campaign spending: initiative proponents spend more when their ballot measure is likely to fail. We address this endogeneity by using an instrumental variables approach to analyze a comprehensive dataset of ballot propositions in California from 1976 to 2004. We find that both support and opposition spending on citizen initiatives have strong, statistically significant, and countervailing effects. We confirm this finding by looking at time series data from early polling on a subset of these measures. Both analyses show that spending in favor of citizen initiatives substantially increases their chances of passage, just as opposition spending decreases this likelihood
International Coercion, Emulation and Policy Diffusion: Market-Oriented Infrastructure Reforms, 1977-1999
Why do some countries adopt market-oriented reforms such as deregulation, privatization and liberalization of competition in their infrastructure industries while others do not? Why did the pace of adoption accelerate in the 1990s? Building on neo-institutional theory in sociology, we argue that the domestic adoption of market-oriented reforms is strongly influenced by international pressures of coercion and emulation. We find robust support for these arguments with an event-history analysis of the determinants of reform in the telecommunications and electricity sectors of as many as 205 countries and territories between 1977 and 1999. Our results also suggest that the coercive effect of multilateral lending from the IMF, the World Bank or Regional Development Banks is increasing over time, a finding that is consistent with anecdotal evidence that multilateral organizations have broadened the scope of the “conditionality” terms specifying market-oriented reforms imposed on borrowing countries. We discuss the possibility that, by pressuring countries into policy reform, cross-national coercion and emulation may not produce ideal outcomes.http://deepblue.lib.umich.edu/bitstream/2027.42/40099/3/wp713.pd
Regression toward the mean – a detection method for unknown population mean based on Mee and Chua's algorithm
<p>Abstract</p> <p>Background</p> <p>Regression to the mean (RTM) occurs in situations of repeated measurements when extreme values are followed by measurements in the same subjects that are closer to the mean of the basic population. In uncontrolled studies such changes are likely to be interpreted as a real treatment effect.</p> <p>Methods</p> <p>Several statistical approaches have been developed to analyse such situations, including the algorithm of Mee and Chua which assumes a known population mean <it>μ</it>. We extend this approach to a situation where <it>μ </it>is unknown and suggest to vary it systematically over a range of reasonable values. Using differential calculus we provide formulas to estimate the range of <it>μ </it>where treatment effects are likely to occur when RTM is present.</p> <p>Results</p> <p>We successfully applied our method to three real world examples denoting situations when (a) no treatment effect can be confirmed regardless which <it>μ </it>is true, (b) when a treatment effect must be assumed independent from the true <it>μ </it>and (c) in the appraisal of results of uncontrolled studies.</p> <p>Conclusion</p> <p>Our method can be used to separate the wheat from the chaff in situations, when one has to interpret the results of uncontrolled studies. In meta-analysis, health-technology reports or systematic reviews this approach may be helpful to clarify the evidence given from uncontrolled observational studies.</p
Bayesian inference of accurate population sizes and FRET efficiencies from single diffusing biomolecules.
It is of significant biophysical interest to obtain accurate intramolecular distance information and population sizes from single-molecule Förster resonance energy transfer (smFRET) data obtained from biomolecules in solution. Experimental methods of increasing cost and complexity are being developed to improve the accuracy and precision of data collection. However, the analysis of smFRET data sets currently relies on simplistic, and often arbitrary methods, for the selection and denoising of fluorescent bursts. Although these methods are satisfactory for the analysis of simple, low-noise systems with intermediate FRET efficiencies, they display systematic inaccuracies when applied to more complex systems. We have developed an inference method for the analysis of smFRET data from solution studies based on rigorous model-based Bayesian techniques. We implement a Monte Carlo Markov chain (MCMC) based algorithm that simultaneously estimates population sizes and intramolecular distance information directly from a raw smFRET data set, with no intermediate event selection and denoising steps. Here, we present both our parametric model of the smFRET process and the algorithm developed for data analysis. We test the algorithm using a combination of simulated data sets and data from dual-labeled DNA molecules. We demonstrate that our model-based method systematically outperforms threshold-based techniques in accurately inferring both population sizes and intramolecular distances.This is the final published version. It's also available from ACS in Analytical Chemistry: http://pubs.acs.org/doi/pdf/10.1021/ac501188r
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