scholarly journals A Selection Model for Panel Data: The Prospects of Green Party Support

2010 ◽  
Vol 18 (2) ◽  
pp. 172-188 ◽  
Author(s):  
Martin Spiess ◽  
Martin Kroh
Keyword(s):  
Panel Data ◽  
Panel Study ◽  
Sample Selection ◽  
Estimating Equation ◽  
Green Party ◽  
Selection Models ◽  
Binary Responses ◽  
Dependent Variables ◽  
Party Support ◽  
Frequent Problem

Although sample selection bias is a frequent problem of applied research, there has been no generalization of sample selection models with binary dependent variables of interest to data with temporal error correlations. We suggest a generalized estimating equation approach to panel data selection models, considering binary responses in both equations. We demonstrate the utility of this model by a simulation study and by analyzing highly unbalanced annual panel data taken from the German Socio-Economic Panel Study covering two decades of Green party support.

scholarly journals Assessing Unemployment Traps in Belgium Using Panel Data Sample Selection Models

2002 ◽  
Author(s):  
Anna Cristina D'Addio ◽  
Isabelle De Greef ◽  
Michael Rosholm
Keyword(s):  
Panel Data ◽  
Sample Selection ◽  
Selection Models

scholarly journals IS INDIVIDUAL ENTREPRENEURSHIP NECESSITY OR AN OPPORTUNITY IN THE KYRGYZ REPUBLIC? A PANEL STUDY

2014 ◽  
Vol 9 (1) ◽  
Author(s):  
Urmatbek Tynaliev
Keyword(s):  
Panel Data ◽  
Panel Study ◽  
Kyrgyz Republic ◽  
Global Entrepreneurship Monitor ◽  
Unemployment Rates ◽  
Independent Variables ◽  
Necessity Entrepreneurship ◽  
Dummy Variable ◽  
Dependent Variables ◽  
Global Entrepreneurship

The goal of this research has been to verify the theoretical discussions on the similarities of the Global Entrepreneurship Monitor (GEM) classified opportunity and necessity entrepreneurship across seven regions of Kyrgyz Republic (KR) and its capital, Bishkek city. Individual entrepreneurs with employees (IEWEs) and without employees (IEWOEs) were used as proxy dependent variables for opportunity and necessity entrepreneurship. The independent variables include unemployment rates, poverty rates, microcredit recipients, crime rate and a few others. The study period was from 2002 to 2012. The least squares dummy variable (LSDV) panel data method was employed, using dummies specifically for each region. The findings of this research from a macro perspective did not confirm those similarities. Proxy dependent variables for opportunity and necessity entrepreneurship, IEWEs and IEWOEs, are rarely affected by the same factors.

scholarly journals Relationship between workers’ return to work, job retention and income in industrial accidents in Korea: a longitudinal study

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e039948
Author(s):  
Suk Won Bae ◽  
Inchul Jeong ◽  
Jin-Ha Yoon ◽  
Seung Wook Lee ◽  
Tae Hyun Kim ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Return To Work ◽  
Original Work ◽  
Occupational Injury ◽  
Economic Status ◽  
Panel Study ◽  
Estimating Equation ◽  
Job Retention ◽  
Industrial Accidents ◽  
Priority Assignment

ObjectiveThis study aims to compare workers’ income before and after an occupational injury, with regard to return to work and job retention, over a period of 5 years.DesignThis study was designed as a longitudinal study.SettingThe Panel Study of Workers’ Compensation Insurance (PSWCI) survey targeted workers involved in industrial accidents for which medical care was terminated in the year 2012.ParticipantsThe panel study was conducted on a final sample of 2000 workers who were selected proportionally by region (nine regions) after priority assignment by disability rating (six levels). A total of 1458 workers were finally included in this study.MethodsThis study used data from the first to fifth PSWCI. To identify the effect on income after occupational injury considering return to work and job retention, we used the generalised estimating equation.ResultsIn regard to workers’ return to work, the OR that income after an occupational injury would be higher than that before an occupational injury was 3.17 (2.41–4.17) for those who returned to original work and 2.32 (1.81–2.97) for those re-employed as compared with who did not return to work and 1.27 (1.07–1.15) for those who retained their job as compared with those who did not. The ORs were 2.91 (2.26–3.75) for those who were re-employed and retained jobs and 2.96 (2.15–4.08) for those who returned to original work and did not retain jobs as compared with those who did not return to work and did not retain jobs.ConclusionsIt is important for accident victims to retain their jobs to maintain their economic status.

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