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Job Satisfaction of Self-employed Workers

Hansson, Emil LU (2024) NEKP01 20241
Department of Economics
Abstract
Individuals transitioning from paid employment into self-employment has increased
in industrialized countries, and currently, a large share of the labor force consists of self-employed individuals. Self-employed individuals play a vital role in the economy by creating jobs and by promoting new innovations. This paper aims to obtain a causal effect on job satisfaction of self-employed individuals using data from correspondents of the National Longitudinal Survey of Youth 1979. The paper implements a propensity score matching framework where propensity scores are estimated using a baseline Probit specification and two machine learning algorithms, the Random Forest, and Gradient Boosting algorithm. To obtain a causal effect, three distinct... (More)
Individuals transitioning from paid employment into self-employment has increased
in industrialized countries, and currently, a large share of the labor force consists of self-employed individuals. Self-employed individuals play a vital role in the economy by creating jobs and by promoting new innovations. This paper aims to obtain a causal effect on job satisfaction of self-employed individuals using data from correspondents of the National Longitudinal Survey of Youth 1979. The paper implements a propensity score matching framework where propensity scores are estimated using a baseline Probit specification and two machine learning algorithms, the Random Forest, and Gradient Boosting algorithm. To obtain a causal effect, three distinct propensity score matching procedures and one Inverse Probability Weighting procedure is implemented. The paper finds a slight significant increase in job satisfaction for self-employed individuals. However, the paper does not find evidence of an enhanced analysis by estimating propensity scores with machine learning algorithms. (Less)
Please use this url to cite or link to this publication:
author
Hansson, Emil LU
supervisor
organization
course
NEKP01 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Propensity Score Matching, Self-employment, Job Satisfaction, Machine Learning, ATT
language
English
id
9156456
date added to LUP
2024-10-01 13:18:21
date last changed
2024-10-01 13:18:21
@misc{9156456,
  abstract     = {{Individuals transitioning from paid employment into self-employment has increased
in industrialized countries, and currently, a large share of the labor force consists of self-employed individuals. Self-employed individuals play a vital role in the economy by creating jobs and by promoting new innovations. This paper aims to obtain a causal effect on job satisfaction of self-employed individuals using data from correspondents of the National Longitudinal Survey of Youth 1979. The paper implements a propensity score matching framework where propensity scores are estimated using a baseline Probit specification and two machine learning algorithms, the Random Forest, and Gradient Boosting algorithm. To obtain a causal effect, three distinct propensity score matching procedures and one Inverse Probability Weighting procedure is implemented. The paper finds a slight significant increase in job satisfaction for self-employed individuals. However, the paper does not find evidence of an enhanced analysis by estimating propensity scores with machine learning algorithms.}},
  author       = {{Hansson, Emil}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Job Satisfaction of Self-employed Workers}},
  year         = {{2024}},
}