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How The Economy Affects Education
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How The Economy Affects Education In Central Florida
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By Paravee Maneejuk Paravee Maneejuk Scilit Preprints.org Google Scholar View Publications 1, 2, * and Woraphon Yamaka Woraphon Yamaka Scilit Preprints.org Google Scholar View Publications 1, 2
Received: December 5, 2020. / Revised: January 2, 2021 / Accepted: January 5, 2021 / Published: January 7, 2021
U.s. Labor Markets Require A New Approach To Higher Education
This study analyzed the non-linear effects of education, especially higher education, on economic growth in the ASEAN-5 countries (i.e. Thailand, Indonesia, Malaysia, Singapore and the Philippines) between 2000 and 2018. The impact of education on economic growth is measured using a variety of education indicators, including public spending on higher education per student, enrollment rates in primary, secondary, and tertiary education, the educated labor force, and a new measure of graduate unemployment. This study constructs non-linear regression models – time series kink regression and panel regression – to examine the impact of educational attainment on economic growth of a single country and the ASEAN-5 region, respectively. There are three main conclusions. First, the non-linear effect of government spending per college student on economic growth in the ASEAN-5 region is confirmed. However, the liability is not subject to the law of diminishing returns. Second, our results show that rising unemployment among high-skilled workers can have positive or negative effects on economic growth, requiring appropriate measures to counteract these negative effects. Finally, secondary and tertiary education can contribute to the economic growth of the ASEAN-5 (both at the individual level and at the regional level). However, the regional analysis shows that the effect of higher education doubles when the enrollment rate exceeds a certain level (exclusion point). It can be concluded that the number of secondary schools has a positive effect on economic growth; However, higher education is the key to future growth and sustainability.
In recent years, economists have shown renewed interest in the role of human capital in economic growth. Previous studies have generally considered education as a simple measure of human capital and have attempted to examine the impact of education on economic growth. Although the relationship between education and growth is widely debated, most recent studies focus on higher education and attempt to examine its impact on economic growth. Because higher education is considered one of the main factors of economic growth and competitiveness of any country. Basic education (primary and secondary) may be sufficient to simply produce goods and services and enable workers to use technology in the workplace. On the other hand, higher education institutions can become workforces that can produce graduates capable of inventing new technologies and help transform the country into a knowledge-based economy. Higher education provides technology and innovation and allows highly skilled workers to enter the labor market, contributing to economic growth.
Many economists have presented evidence showing the potential impact of higher education in both developing and developed countries (eg [1, 2, 3]). However, relatively little research has focused on how higher education is expanded and used [4, 5]. According to the literature, some previous studies measure the impact of higher education through research and development (see [6, 7, 8]). However, this study reexamines the relationship between higher education and economic growth. We attempt to fill a gap in the literature by including the college-educated workforce as a proxy for higher education expansion in our analysis. This study also introduces a new measure of higher education utilization: high-skilled unemployment, as we argue that the contribution of higher education to the economy may be related to the jobs available to graduates. Employment is a platform where highly skilled workers can demonstrate their potential to drive the economy towards future growth. On the other hand, if those who invest in skills do not have access to good jobs, this can lead to losses.
Beyond the measurement issue, this study also sheds light on how the effects of education on growth are modeled. Most of the existing literature uses a linear model, which is generally a good approximation and consistent with theories. However, the relationship between education and economic growth may not be linear . Research on the non-linear relationship between these two variables is very limited. Only a few studies focus on nonlinearity [9, 10, 11, 12, 13]. One of the most recent works on this topic, Maneejuk, Yamaka, and Sriboonchitta  shows that the linear model may miss important nonlinear dependencies. Therefore, this study investigates potential nonlinear relationships using fractional regression.
Chart: Studying Covid’s Impact On Education
In the regression discontinuity model, the regression function is continuous, but there is a discontinuity at the breakpoint of the slope. This inflection point is a turning point that occurs when the relationship between variables changes structurally. Our analysis shows that the effects of education on several indicators may be nonlinear or invariant over time. The effect may increase above a certain level of development or decrease with decreasing income. Therefore, this study can show the economic perspective that arises from structural changes after educational performance peaks. We need to go beyond the classical linear model to explain the impact of education on economic growth in different educational systems in a new way. This study also shows whether education positively affects or hinders economic growth. To estimate this model, ridge estimation is performed to address multicollinearity and sample size issues in the educational growth model. The discussion and reasons for using the Ridge Estimator are explained in the next section.
This study is of particular interest to the ASEAN-5 countries, which include Thailand, Indonesia, Malaysia, Singapore and the Philippines. These five countries are considered leaders of the group due to the size of GDP and relatively high level of economic development. They have different cultures, lifestyles and languages. However, the ASEAN-5 countries emphasize education to boost economic growth. Therefore, they planned to improve the education system and overall quality, and tried to bring the level of education of each member country to the same standard. However, the quality of education still varies greatly and is inconsistent from country to country. In the relevant literature, there are few studies that have examined the role of education, especially higher education, in the ASEAN-5 countries. Therefore, it can be difficult to gather evidence on this topic and draw conclusions about the impact of higher education on economic growth in this area.
The purpose of this paper is to analyze the nonlinear effect of higher education on economic growth in ASEAN-5 countries. The main contribution of our study to the existing literature is mainly threefold. First, this study examines the expansion of higher education as well as education and investment. Second, although the impact of higher education on economic growth has been studied, there are few studies that use a nonlinear model to analyze this issue. However, there are no studies that have examined the negative impact of each educational measure on economic growth. Therefore, this study uses a kink regression model to examine the impact of fluctuations in educational performance on economic growth in ASEAN-5 countries and to fill a gap in the literature. Third, this is the first attempt to use the ridge estimator to fit a kink regression model to address data limitation and multicollinearity issues in our empirical model.
The structure of this document is as follows. After the introduction, Section 2 reviews the literature on education and the measurement of its impact on economic growth. Section 3 then explains the econometric methodology. Section 4 describes the data. Section 5 presents empirical results and discussions. Finally, Section 6 provides conclusions and policy recommendations.
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Schultz’s  modern theory of human capital fundamentally supports the analysis
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