TITLE: Student Mean Income based on College Tiers and Household Income Hierarchy
DATE: 04/17/2022
Kwame Darko-Mensah
INTRODUCTION
The degree to which an individual’s position in the income distribution continues or changes from one generation to the next is referred to as intergenerational income mobility (Stuhler, J. (2018). Policymakers are increasingly worried about intergenerational economic mobility since income inequality has risen in many nations in recent decades (Deutscher, et. al (2021). This study aims predict a kid’s percentile using their parents’ income percentile and other factors.
To do this analysis, a cross-sectional data obtained from the data repository https://opportunityinsights.org/data/ was used. The dataset contains information of 1515 and has 21 variables. Our most interesting variables are k_mean and par_pctile whereby we need to check if there exists any relationship between k_means and par_pctile. In the research, our dependent variable is kid’s percentile income while our independent variable is parent’s percentile income. Other variables such as age and sex which are likely to have some impacts on our analysis will be included in the analysis.
SCHOLARLY REVIEW
While there are several approaches to conceptualizing and quantifying intergenerational mobility, a common issue is that in societies with low levels of mobility, poverty and economic disparities are more likely to persist, exacerbated by the long-term effects of growing inequality. Furthermore, the so-called “Great Gatsby” curve demonstrates a substantial link between levels of inequality and intergenerational persistence across nations (Corak et al., 2013). This section explores many effective models for forecasting children’s earnings based on their parents’ earnings and other factors. It looks at how these models are employed in various case studies.
CASE STUDIES
For instance, according to Stanford University research on intergenerational income mobility, children in the United States receive approximately half of their parents’ wage advantages, which is one of the lowest estimates of economic mobility ever recorded. The study also found that the extent to which parental economic advantages are passed on to children differs throughout the income spectrum, with children from higher-income families benefiting more than those from lower-income homes. The data reveals that opportunities for economic success are not uniformly distributed. (Murniati et. al. Internationalization at Home: Designing International General Education Curricula.)
This study is the most comprehensive to date on the intergenerational transmission of economic gain. It uses a new data collection gathered from tax returns and other administrative sources to address limits that hampered previous investigations. These findings show that children raised in homes with a wide range of income levels can have a wide range of economic outcomes.
“A family’s economic circumstances have a huge impact on a child’s economic opportunities later in life,” said (Erin Currier 2011), director of Pew’s financial stability and mobility project. “For example, children raised in the 90th percentile can expect their family’s income to be three times that of children raised in the 10th percentile.” These outcomes are diametrically opposed to our country’s egalitarian goals.”
In this study, the intergenerational elasticity (IGE) is used to calculate the percentage of economic benefit that is passed down to descendants. The IGE is normally between 0 and 1, with a value of 0 indicating that children from various socioeconomic origins are expected to earn the same amount, with no inherited income advantage or disadvantage.
Furthermore, (Deutscher and Mazumder, 2021) provide three study approaches for assessing intergenerational income mobility in their paper. To begin, linear regression analysis can be used to assess income elasticity. This figure, which is normally between 0 and 1, represents the relationship between the parents’ income and the income of the adult child. The larger the elasticity, the lower the income mobility. The second strategy deals with transitions between parental and child income groups (transition matrices). This method involves dividing the population into equal-sized groups and reporting the distribution of parents and children in each group in order of income. The third method, logistic regression analysis, is useful for examining income mobility in specific segments of the income distribution, such as the upper middle class.
The report also uses the Income panel study to look at income mobility between generations. By comparing parental earnings in 1985 to the earnings of their now-adult children in 2008. It does so by combining total family income and personal earnings. By comparing elasticity values, they may infer that when parental household income is utilized, mobility is somewhat greater. In other words, (adult) children’s earnings are more dependent on both parents’ incomes than on only one parent’s earnings. Transition matrices are useful for revealing nonlinearities in the link between parents and children’s earnings. The findings of the Income panel research, for example, revealed that wages mobility is substantially higher towards the bottom of the income distribution than at the top. In general, wealth is handed down to the following generation in higher proportion than poverty.
However, one disadvantage of transition matrices is that a nonlinear pattern may represent ceilings and floors at the top and bottom of the matrix: upward mobility for those born at the top is impossible, while downward mobility for those born at the bottom is impossible (Atkinson, Maynard and Trindler, 1983). Consequently, the level of inertia at the top may be overstated. This is not the case for linear regression analysis as a method of measuring income mobility. Using a two-year average of wages to lessen the impact of the transitory component of earnings has a minimal effect on earnings elasticity, according to our findings. According to (Corak and Heisz, 2013), it is required to adopt at least a three-year average, and a five-year horizon should be large enough to avoid the bias induced by transitory income variations, such as in the projected elasticity. The Income Panel Study is still insufficient to create such a long-term income across a period: in fact, we would have to average, for example, the father’s income from 1989 to 1994 and the child’s income from 2004 to 2009. Then the difference between parent and kid life phases would be considerably wider. In addition, the adult offspring, particularly those with higher education, would be too young to have established a job. As a result, their average salary would be grossly undervalued.
Finally, (Stuhler, 2018) examines research on intergenerational mobility, which evaluates how independent children’s labor market results are from their parents’ outcomes. The research begins with data on descriptive questions, demonstrating that mobility levels differ significantly across nations and even within countries. Mobility seems to be much lower in low-income nations than in high-income ones, although major variances may be detected even within countries with comparable income levels. Furthermore, in nations or locations where financial differences are greater, children’s results are more heavily influenced by parental outcomes. The fundamental determinants of intergenerational mobility are a major focus: what factors and policies lead to greater mobility? According to a recent study, ability inequalities begin at an early age and grow throughout life. Maintaining modest skill gaps throughout infancy, for example, by boosting maternal and infant health, giving universal access to high-quality childcare, and making the educational system comprehensive and integrated, seems to be critical.
However, the labor market is as crucial since it determines the economic and social repercussions of these childhood disparities. According to a recent study by UNESCO, discrepancies in mobility across nations are partially attributable to differences in skill inequality, but they are also related to how labor markets reward talents and redistribute earnings. Intergenerational inequalities may be mitigated by reducing wage disparities and promoting employment for everyone. However, there is a paucity of causal data on labor-market conditions and policies and how they affect intergenerational mobility. Evidence suggests measures that help to expand the middle class and enable more broad and equitable access to well-paying employment and appealing businesses. To accomplish the latter, it seems that reducing the role of family and social networks, as well as combating workplace discrimination, is critical.
Conclusion.
The previously highlighted studies have managed to successfully prescribe means to measure intergenerational income mobility using various variables/parameters. In addition, they have identified effective means to bridge the income disparity between generations whenever present/needed. Finally, this research paper adds on to the findings of the discussed research publications in of the fact that wealth is passed from one generation to the next.
METHODS
Participants
The study used a cross-sectional data obtained from the data repository https://opportunityinsights.org/data/ . The dataset contains information of 1515students and has 21 variables.
Materials
R-studio to run some analysis on the data to determine the best variables to be used in the model then proceed to perform the statistical analysis.
Procedure
The mean, standard deviation, median, skewness, kurtosis, range, minimum, and maximum of the independent variables were calculated using the psych package, which displayed the mean, standard deviation, median, skewness, kurtosis, range, minimum, and maximum of the independent variables.
The data distribution for the parent’s income and the kid’s income was shown using a histogram.
A correlation test was used to determine whether there is a significant correlation between our dependent and independent variables, as well as the sort of correlation that exists.
RESULTS
In the summary of our data there were no missing data. All entries had a value.
Descriptive statistics
Output of summary statistics
Output of summary statistics
The table of summary statistics shown above gave a summary of the average, maximum, minimum, standard deviation, skewness and kurtosis. It indicates that some of the independent variables were negatively skewed while all others were positively skewed. The table also shows some variables had a positive kurtosis indicating that their distribution had heavier tails (leptokurtic distribution) while other variables had light tails. They had a negative kurtosis thus a platykurtic distribution.
A histogram of Kids earning
The plot indicates that the distribution of kid’s income is skewed to the left with most earnings ranging between 0 and 100000.
Scatter plot of Kid’s earnings and parent’s earnings
The scatter plot indicates the trend in the data. As parent’s income increases , kid’s income also increases.
Scatter plot of kid’s income and kids rank
The plot shows that as kid’s rank increases, the kid’s income also increases.
Correlation
A correlation test was run to evaluate if there was a significant correlation and the type of correlation that exists between the dependent variable and independent variables. Kid’s earnings and parent’s earnings show a weak positive correlation. with a value of 0.1867327 and a p-value of 2.535e-13 which is less than 0.05 an indication that they had a significant correlation. The following variables were also significantly correlated to the dependent variable (kid’s earnings) and might be considered in the regression model; k_rank, par_mean, k_median, k_top, tier and k_q. These variables were used as control variables in our study since they were significantly correlated to the dependent variable k_means.
Regression analysis
The regression summary show that the overall p-value of the model is < 2.2e-16 which is less than 0.05 implying the study is significant.
Also, also all the variables except par_pctile are significant with p-values less than 0.05. The variable par_pctile is not significant in the study of factors that affect mean kid’s earning with a p-value of 0.4105 which is less than 0.05.
The adjusted r-squared is 0.9321 which means that 93.21% of the dependent variable is explained by the independent variables.
REFERENCES
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Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. Journal of Economic Perspectives, 27(3), 79-102.
Deutscher, N., & Mazumder, B. (2021). Measuring Intergenerational Income Mobility: A Synthesis of Approaches.
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