How changing spending in Education would help to decrease income inequality?

How Changing Spending in Education Would Help to Decrease Income Inequality
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How Changing Spending in Education Would Help to Decrease Income Inequality
Introduction
In the past 20 years, China has experienced tremendous economic growth which was spearheaded by industrialization. During this period, the country managed to lift millions of people out of poverty. However, this era also created a certain problem: it led to an increase in income inequality in China. China’s income inequality is quite high, and Jain-Chandra (2018) notes that it might continue to rise if the country’s regime does not adopt new policies and measures to help alleviate the issue. Inequality is a major problem in China and Jain-Chandra et al. (2018) note that this problem could as well lead to other problems which include “suboptimal investment in health and education, which weighs on growth.” They continue to say that “widening inequality can weaken the support for growth-enhancing reforms and may spur governments to adopt populist policies and weaken reform prospects.” In his 2018 article, Trivedi does acknowledge that some inequality should be expected when industrialization happens. However, he does acknowledge that China’s inequality may soon be a problem for the country.
Education has always been considered as the appropriate answer to decreasing poverty as well as income inequality in a country. However, is this practical and can it be done especially in a country like China? Well, this question leads to the first economic policy, the expansionary policy, that I believe is relevant in this study. The expansionary policy recommends that the government spends more money to enhance make education accessible to everyone. Economic growth cannot be evenly distributed if education is not evenly distributed.
The second question concerns fiscal policies. Fiscal policies are mainly concerned with the measures governments to adopt that seek to stimulate their economies. For China to experience wholesome growth, it must incur certain costs, and these might involve or include the costs to make education accessible to everyone in the country especially students from low-income households. Chinese policymakers ought to understand the goal and impact of enacting measures and investing in the education of the country. The country will never achieve wholesome growth if it fails to invest in the education of everyone. Bright students from low-income families often end up left out from the country’s expansion schemes because they cannot afford to finance their education. So, policymakers need to step up investment in education to spur the other sectors and regions to develop.
In this article, the focus is on whether the government of China could spur growth through increased spending on education and in the end reduce income inequality. The world is not for the rich and those who can afford the pleasures of life. The world is for everyone, and the government of China needs to invest heavily in its education system if the goal of reducing income inequality is to become a reality. This article draws from three sources which have analyzed the matter at length and have sought for ways through which education appears to impact or influence income inequality.
Article 1:
Title: The impact of education on income inequality and intergenerational mobility
Yang, J. & Qiu, M. (2016). The impact of education on income inequality and intergenerational mobility. China Economic Review,37, 110-125. doi:10.1016/j.chieco.2015.12.009
This research was meant to find out how innate ability, compulsory education, and noncompulsory education affects intergenerational mobility of income and income inequality. The researchers sought to understand the reasons why Chinese income inequality has been increasing in the past despite more investment in education. The researcher constructed a four-period overlapping-generation model to find out the relationship between different variables in the education system. The research gravitated to generational mobility because most economic models assume random assignments of ability and other market factors determine income inequality which makes them overlook the impact of intergenerational mobility income. Through secondary research, the researchers found out that parent’s inheritance largely determines the assignment of high levels of ability and therefore more research was necessary to determine the relationship between parent’s inheritance with education and its implications on reducing intergenerational income mobility and income inequality.
The researchers hypothesized that innate ability and educational investment are the most important factors that determine the equilibrium model of income inequality and intergenerational income mobility though other factors such as tax rates and public education policy have a significant impact. The researchers theorized that each individual is either a child, juvenile, young parent or an old parent. The categorization of an individual into these classes was important to determine the variables which are pertinent to each group and their impact on intergenerational mobility of income and income inequality. The assumption made by the researchers is that a child lives two periods (child &juvenile) with his/her parents and the latter two periods (young parent & old parent) as a parent (caring for his/her child). Thus, the model encompassed an often-overlooked concept; the stage of an individual as a parent or child and the nature of the family he/she comes from. The other assumption made for the study is that children inherit innate abilities from their parents and parents determine their child’s education level for compulsory and non-compulsory schooling. The researchers defined compulsory education/early education as the 9-year education from grade one to grade nine and the latter as non-compulsory education.
The researchers derived several mathematical formulas to simulate the impact of variables on the education of an individual. The formulas were based on several assumptions about the families and the children. The calculation of the impact of variables necessitates several assumptions for the researchers to calibrate the variables and results. The researchers observed that there are different circumstances for young and old families in relation to their investment in education. Since the quality of education in China is proportional to the cost of private cost (the higher the private cost, the better the quality of education) and the government bears the rest of the cost. The researchers also assumed that the families could not borrow and do not have savings, and thus all their expenditure of their children’s education affect their recurrent expenditure. The researchers also accounted for the college enrollment test which is administered in China and derived a formula to explain account for the probability of admission. The researchers also utilized data from secondary sources such as Educational Statistical Yearbook of 1996 to find the mean value of senior high school enrolment rate during 1990-1995. Thus, the mathematical formulas were calibrated to give the results they collected from several other credible secondary sources. For example, in the case of the mean value of senior high school enrolment rate during 1990-1995, the answer ought to be 13%. The model they employed did not involve any observations but the calibration of various mathematical formulae to reflect the findings of empirical studies conducted by other researchers on the respective variables they deduced the formulas for. The results for the calibration were fairly very similar to the real data, but it had few disparities probably because of the inherent influence of the assumptions made by the researchers. For example, the real data showed that college enrollment rates were higher in wealthier families and lowered for low-income families than the data from the model. However, both the model and real data showed an increasing trend of college and senior high school enrollment rate with increasing income.
The researchers also used a calibrated model to study the sources of income inequality and intergenerational income mobility. From the data, the researchers concluded that innate ability is an important source of income inequality. The researchers also normalized the levels of three variables to establish the level of innate ability and found that children from wealthier families are only 1.36 times ‘smarter’ than children from poorer families and the gap increases in favor of children from wealthier families to 2.35 by the end of early education and 2.89 at the end of compulsory education. Such findings show that family backgrounds and their educational choices are critical in determining the inequality of human income and thus their income. If the human capital level is low, the individual earns less, and when they start a family, they would also influence their education such that their development of human capital is lower than those from higher income earners leading to a vicious cycle of generational mobility and income inequality. This phenomenon can explain partly why the richer families continue becoming richer while poorer families continue to suffer in poverty.
The research further sought to test the model under new parameters that influence the acquisition of human capital; innate ability and college. The researchers hypothesized that education expands the innate ability difference and decreases the mobility between adjacent generations. The researchers also found out that there is a higher probability for children from poorer families to be innately intelligent though they face budgetary constraints to develop and realize their human capital potential. The researchers also found out that the contribution of compulsory education to intergenerational income persistence declines while cross-sectional income disparity increases. The study also uncovered that wages increased the senior high school enrollment rate. The parents likely spend the surplus money earned from their jobs on the education of their children thus high school enrollment rate increased when parents earned more.
The calibrated models were used to design and test public education investment policies to find the most effective approach. The first policy was entailed increasing the expenditure in compulsory education for all children holding all the other factors constant. The second policy to be tested against the calibrated models was to designed to channel all incremental expenditure by the government to compulsory education of low-income families. The third policy was designed to channel incremental government education expenditure to juveniles from low-income families who attend senior high school. The fourth policy was designed to test the efficacy of policies structured to devoted to higher education of juveniles in low-income families. The researchers found out that the most effective and efficient way to reduce income inequality is to subsidize compulsory education for children from low-income families (policy experiment2).
The article tries to show the overarching elements that can potentially affect inequality and stem from issues not directly related to education such as family income. The researchers coined a model that when calibrated against real data were very similar and used it to test education policies that could help reduce intergenerational mobility of income and income inequality. By creating a model that mirrors the real results and determining the constants and variables for calculating the impact of certain policies, the researchers helped develop a framework that could help decisionmakers in the education system and the government make more informed decisions on strategically investing in developing human capital. The article has been instrumental in helping structure research questions on economics; the article helps show that different education spending behaviors can influence the final capacity and potential of human potential as a factor of production and affect income inequality. The article cements the argument that the society can holistically benefit by more investment into compulsory education for children.
The article also has some inherent weaknesses and strengths. One of the internal strengths of the researchers is that they calibrated their models against real-world data such that their models mirrored the results of real-world data. The calibration validates the efficacy and objectivity of the models, and from the calibration, they became reliable models that can be used to simulate other scenarios and give reliable and objective results. The inherent weakness of the article is that it only featured information from China and pegged on the societal structure which is very different from many western countries and other parts of the world. The assumptions made for the study are only based on the Chinese way of life such as the categorization of stages in an individual’s life where he/she is a child living with a young parent or a juvenile living with an old parent. This approach is blurred in the western world because in a capitalist society most juveniles live alone in campus or rented apartments as they pursue their education. The circumstances and approaches to education cost are also relatively different in China and many other countries especially the western countries thus making the models inapplicable at least without review and revision for other countries. Other elements that are specific to China and they were included in the modelling are the college entrance exam which is administered in China. The modelling incorporated the probability of college admission in the modelling, and these issues is only pertinent in China as the other countries there are other alternatives if an individual is unable to get admission in the college. Such issues make the models developed in the study un-mappable to other countries.
One internal weakness is that the models were tested with limited data collected in a span of a few years. Thus, the reliability of the modelling cannot be defended beyond the span of years of calibration. More data from the past and data as recent as the publication of the article in 2016 ought to have been used to calibra...