The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Christian Bjornskov - One of the best experts on this subject based on the ideXlab platform.
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well being and entrepreneurship using establishment size to identify treatment effects and transmission mechanisms
PLOS ONE, 2020Co-Authors: Christian BjornskovAbstract:Using data from the European Value Survey, covering more than 300,000 respondents in 32 countries between 2002 and 2012, we offer new insight into the consequences for subjective well-being of self-employment. We hypothesize that the positive link between entrepreneurship and well-being is influenced by the extent to which the decision to engage in entrepreneurship reflects voluntary choice and by the ability of the entrepreneur to match entrepreneurial preferences for autonomy, task variety, and challenging tasks to task environments. While the hypotheses are confirmed by our empirical analysis, we also find—rather surprisingly—no evidence that the effects are mediated by autonomy. To handle the endogeneity and simultaneity problems that arise from the fact that the choice to become an entrepreneur is not random and which potentially threaten the validity of our findings, we rely on a novel Econometric Method which allows us to sidestep the selection problem and establish that the well-being increase associated with entering into entrepreneurial activity is at least approximately equivalent to a one-decile increase in household income.
Martin Ravallion - One of the best experts on this subject based on the ideXlab platform.
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Journal of Econometrics, forthcoming. An Econometric Method of Correcting for Unit Nonresponse Bias in Surveys
2016Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin Ravallion, Jel CAbstract:Abstract: Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. We show that this leaves a bias in the re-weighted data and we propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows us to identify a micro compliance function, which is then used to re-weight the unit-record data. An example is given for the US Current Population Surveys, 1998–2004. We find, and correct for, a strong household income effect on response probabilities
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an Econometric Method of correcting for unit nonresponse bias in surveys
Journal of Econometrics, 2005Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin RavallionAbstract:Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. The authors show that this leaves a bias in the re-weighted data and they propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows them to identify a micro compliance function, which they then use to re-weight the unit-record data. An example is given for the U.S. Current Population Surveys, 1998-2004. The authors find, and correct for, a strong household income effect on response probabilities.
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an Econometric Method of correcting for unit nonresponse bias in surveys
2005Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin RavallionAbstract:Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. We show that this leaves a bias in the re-weighted data and we propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows us to identify a micro compliance function, which is then used to re-weight the unit-record data. An example is given for the US Current Population Surveys, 1998 - 2004. We find, and correct for, a strong household income effect on response probabilities.
Xuelei Zhang - One of the best experts on this subject based on the ideXlab platform.
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impact of china s urbanization on water use and energy consumption an Econometric Method and spatiotemporal analysis
Water, 2018Co-Authors: Yan Wang, Weihua Xiao, Yicheng Wang, Yong Zhao, Jianhua Wang, Baodeng Hou, Xinyi Song, Xuelei ZhangAbstract:As important subsystems of the urban environment, water resources and energy are necessary for normal urban functions and play an important supporting role in urbanization. The rapid development of China’s economy is increasingly dependent on these two subsystems. Analyses of the relationship between urbanization and water use or energy consumption have become the focus of attention, but researchers have mainly evaluated the impact on the two subsystems separately without providing an integrated analysis, nor have they revealed the link between water use and energy consumption. We addressed this information gap by using an Econometric Method to empirically investigate the long-term equilibrium relationships and Granger causal relationships among urbanization, water use, and energy consumption in China, and by conducting a spatiotemporal analysis to identify the trends of water use intensity and energy consumption intensity under the effects of urbanization during 2005–2015. We found long-term equilibrium relationships among urbanization, water use, and energy consumption. Granger causality results reveal the presence of a unidirectional Granger causal relationship running from urbanization to energy consumption and to water use, and bidirectional causality between energy consumption and water use. Moreover, water use intensity and energy consumption intensity decreased significantly under urbanization during the study period.
D K Bhattacharyya - One of the best experts on this subject based on the ideXlab platform.
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acknowledgements for an Econometric Method of estimating the hidden economy united kingdom 1960 1984 estimates and tests
The Economic Journal, 1991Co-Authors: D K BhattacharyyaAbstract:To comply with the length requirements of the journal the author had to reduce the size of the original paper drastically. In that process a number of earlier works were deleted from the references, and the author would like to rectify those omissions with this general acknowledgement. In particular, the author would like to acknowledge the contribution of his research assistants N. Karavitis and A. Tshouhlou, who were the co-authors of an earlier version of the paper entitled 'A Robust Method of Calculating the Size of the Hidden Economy: Quarterly Estimates for the UK and the USA', which was based on the same model, and which was submitted to the ECONOMIC JOURNAL in I986 and was rejected. The subsequent extensions and revisions of the paper were done by the author without any collaboration with his coauthors of the earlier version.
Anton Korinek - One of the best experts on this subject based on the ideXlab platform.
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Journal of Econometrics, forthcoming. An Econometric Method of Correcting for Unit Nonresponse Bias in Surveys
2016Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin Ravallion, Jel CAbstract:Abstract: Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. We show that this leaves a bias in the re-weighted data and we propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows us to identify a micro compliance function, which is then used to re-weight the unit-record data. An example is given for the US Current Population Surveys, 1998–2004. We find, and correct for, a strong household income effect on response probabilities
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an Econometric Method of correcting for unit nonresponse bias in surveys
Journal of Econometrics, 2005Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin RavallionAbstract:Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. The authors show that this leaves a bias in the re-weighted data and they propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows them to identify a micro compliance function, which they then use to re-weight the unit-record data. An example is given for the U.S. Current Population Surveys, 1998-2004. The authors find, and correct for, a strong household income effect on response probabilities.
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an Econometric Method of correcting for unit nonresponse bias in surveys
2005Co-Authors: Anton Korinek, Johan A. Mistiaen, Martin RavallionAbstract:Past approaches to correcting for unit nonresponse in sample surveys by re-weighting the data assume that the problem is ignorable within arbitrary subgroups of the population. Theory and evidence suggest that this assumption is unlikely to hold, and that household characteristics such as income systematically affect survey compliance. We show that this leaves a bias in the re-weighted data and we propose a Method of correcting for this bias. The geographic structure of nonresponse rates allows us to identify a micro compliance function, which is then used to re-weight the unit-record data. An example is given for the US Current Population Surveys, 1998 - 2004. We find, and correct for, a strong household income effect on response probabilities.
