Panels of data from each of the odd-numbered years between 2000 and 2010 -- 2001. 2003, 2005, 2007, and 2009 -- were used in this research, and though these datasets included many of the same individuals from year to year each individual was counted separately in each separate year. This was considered appropriate for two reasons: first, as this research was not longitudinal in nature, and is attempting to determine specific influences on female labor force participation currently rather than how these forces might change over time, an analysis that controls for individual factors over an extended time period was not deemed necessary; second, because individual circumstances can change, the decision of each individual in each year can be seen as the result of the changing factors, which are for all intents and purposes unique (as is the decision-making process itself) each year. This created a pooled dataset that allowed for a more detailed and comprehensive analysis of the relationship between the dependent variable of employment status and the independent variables of age, health, marital, status as a student, and two variables measuring educational level.
The research included several dummy variables, including the dependent variable of employment status/labor force participation that is the key focus of this research. health, marital status, and status as a student were also dummy variables assigned either a zero or a one depending on whether the condition was met or not (e.g. A health person would receive a "1" for health as opposed to a "0" for an unhealthy person; a married individual has a "1" while an unmarried individual has a "0," etc.). The existence of dummy variables, and particularly of a dummy variable for the dependent variable, had a direct impact on the design of the research and the statistical tests that were eventually selected as appropriate and capable of providing meaningful and reliable results (Fernandez & Rodriquez-Poo, 1997). Income was also considered as a potential variable, yet did not allow for the creation of a clear model but rather clouded the data patterns found in the research. Basic descriptive statistics were calculated, but were essentially meaningless given the performing of Logit rather than a standard regression analysis on the data (regression was rejected due to the dummy variable status of the dependent variable) (Fernandez & Rodriquez-Poo, 1997).
Section 4: Analysis
LOGIT Pooled tests were conducted on the data from each year of the panel survey utilized as the dataset in this research, with different models used in the LOGIT analysis to enable a comparison of different data configurations and interactions. After a host of such testing and analysis, the Model 4 Logit test for 2009 was selected for further analysis, interpretation, and discussion because a) 2009 was the most recent year for which data was available, and b) Model 4 contained the highest level of significance across all variables and provided the most comprehensive model of best fit for the observed data. Employment was the sole dependent variable in all models; in Model 1, independent variables included were age, health, and status as a student, Model 2 used these same independent variables with the addition of marital status; Model 3 added the first of two education measures; and finally Model 4 included all identified potential independent variables: age, health, status as a student, marital status, and two different measures of education. As the most comprehensive model and the model with the highest level of significance across the independent variables, Model 4 was deemed to be especially...
The independent variables had both positive and negative influences on levels of labor force participation, with a positive health status, and the second measure of education both tending to influence greater levels of female participation in the labor force, while increases in age, being married, being a student, and the other educational measure all had a negative effect on participation levels. More specifically, an increase in age of one year was correlated with a reduction of 0.3% in the likelihood that a woman would be employed, while being a current student had a much more dramatic effect, reducing the likelihood of employment by more than thirty percent. This is more than twice the impact that marital status had; women who are married are just under fifteen percent less likely to be employed than their unmarried counterparts, and finally the first of two educational measures led to a reduced likelihood of female participation in the labor force of just over eight percent. A positive health status, on the other hand, increased the likelihood of female labor force participation by twenty-eight percent, while the second educational measure increased labor participation by 0.5%.
This research has some direct and profound implications for future economic research in the area of influencing greater equality in the workforce and in labor force participation in South Korea. Clearly, there are many factors at work that inhibit the rate of female participation in the labor force, some for purely cultural reasons. While being a student or having poor health might lead to practical problems in seeking regular employment, marriage and education should not be barriers to employment, whether of a directly practical or a more perception-driven cultural type. Further research must take these factors into account when developing assessments of the labor pool, productivity, and programs meant to enhance equality.
Section 6: Conclusion
This research did not identify any surprising new variables or trends in the rate of female participation in the labor force in South Korea, nor did it set out to do so. The variables that contribute to labor force participation rates have been fairly well-established by previous research, yet remained poorly understood in the modern context of the South Korean economy and culture. This research sought to bring existing knowledge of potential influences and models for labor force participation rates into the modern era with a more thorough quantitative analysis than has been recently conducted, and in this endeavor it was largely successful: a fairly accurate and comprehensive model for the decision of whether or not to participate in the labor force was created, supported both by previous research in the area and theoretical/analytical frameworks utilized by the research community. Student status, marital status, health, education, and age all play important roles in influencing the rate of female labor force participation, and the pooled tests show that these influences remain in play from year to year. Initial research also suggests that the significance of each variable is evolving, however the dataset, period, and analysis conducted are not enough to provide conclusive results in this regard. Future research is certainly called for in this area in order to determine what evolutions are taking place, if there are other factors that have a significant impact on female labor force participation rates, and how policies might be implemented or changed to encourage greater inclusivity of females in the labor force.
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