The Role of Education and Health in Economic Growth: A Regressional Analysis
Introduction
Education and health sectors are considered key economic indicators in any economy. The characteristics of the education sector and those of the health sector determine the overall outlook of the economy. This implies that whenever there are changes in the education and/ or the health sectors, there are corresponding changes in the national economy to reflect these indicators. Countries considered to be under- developed and/ or relatively poor have poorly performing health and education sectors. On the other hand, economically performing countries have strongly performing health and education sectors. The characteristics of health and education sectors and their impacts on the economy are reviewed using regression analysis of data from the U.S across three years. The objective of the study is to determine the empirical relation between education, health and the economy.
The education sector results in increased productivity and specialization of skills. This implies that as more people are educated, they possess greater capacity to contribute to economic growth hence countries with more educated people have higher GDPs than those with fewer educated people (Benos and Zotou 3). Similarly, where most of the citizens are healthy, the productivity is increased significantly, hence resulting in higher GDPs. The impacts of the education and health sectors are thus compared through analysis of data across years as well as across two countries. In terms of years, the study looks at data from 2000, 2003, 2006, 2009 and 2012. The cross- country evaluations have been conducted on the U.S and India. This gives the opportunity to compare not only the education and health sectors with respective economic performance of a particular country across the years but how the performances trended across the countries.
Data Description
The data sets used for the study comprised of historical economic data for the U.S and India. The data was obtained from various past literatures reporting on the economies of the two countries across the years. For instance, Acharya and others (1- 130) provided a lot of data on India’s economy. Similarly, most of the data for the U.S was obtained from Jacobsen and Mather (2-10). The Organization for Economic Cooperation and Development also provided substantial information on the country expenditures in health and education sectors as percentages of the GDP. The tables below show some of the data to be analyzed.
| National Expenditures on Education as a Percentage of the GDP | |||
| Year | India | United States | STD. DEV |
| 2000 | 4.26 | 4.85 | 0.417193 |
| 2003 | 3.55 | 5.55 | 1.414214 |
| 2006 | 3.09 | 5.39 | 1.626346 |
| 2009 | 3.99 | 5.26 | 0.898026 |
| 2012 | 3.87 | 5.2 | 0.940452 |
| Mean | 3.752 | 5.25 | 1.059246 |
Table 1: National expenditures on education as a percentage of GDP (Source: OECD.org)
| Country expenditures on Health as a Percentage of the GDP | |||
| Year | India | United States | STD. DEV |
| 2000 | 4.26 | 12.5 | 5.82656 |
| 2003 | 4.29 | 14.5 | 7.21956 |
| 2006 | 4.24 | 14.7 | 7.396337 |
| 2009 | 4.38 | 16.3 | 8.428713 |
| 2012 | 4.39 | 16.4 | 8.492352 |
| Mean | 4.312 | 14.88 | 7.472704 |
Table 2: National Expenditures on Health as a Percentage of GDP (Source: data.worldbank.org)
| National GDP Per Capita | |||
| Year | India | United States | Std. Dev |
| 2000 | 452.4 | 36449.9 | 25454.08 |
| 2003 | 557.9 | 39677.9 | 27662.02 |
| 2006 | 816.7 | 46437.1 | 32258.49 |
| 2009 | 1090.4 | 47001.6 | 32464.12 |
| 2012 | 1447.5 | 51433 | 35345.09 |
| Mean National GDP | 872.98 | 44199.9 | 30636.76 |
Table 3: GDP Per Capita in India and the United States (Source: knoema.com)
The scatter plot for the above GDP table is as shown below
From the data above, it is clear that the performance of India in terms of GDP per capita and the average expenditures on the education and health sectors are way below those of the U.S in the years considered. The data to be used for regression analysis was extracted as country specific data. The objective was to determine the function that relates the national GDP to the health and education expenditures in the two countries. As such, the independent variables in the two country relations were set to be the education and health expenditures while the dependent variables were the GDP per capita records. From the data tables, regression analysis was conducted to determine the statistical importance of the different variables in the data sets. The tables below show the analyzed data by country.
| Year | Education Expenditure | Health Expenditure | GDP |
| 2000 | 4.26 | 4.26 | 452.4 |
| 2003 | 3.55 | 4.29 | 557.9 |
| 2006 | 3.09 | 4.24 | 816.7 |
| 2009 | 3.99 | 4.38 | 1090.4 |
| 2012 | 3.87 | 4.39 | 1447.5 |
Table 4: India Education and Health Expenditures versus the GDP per capita
Similarly, the data for the U.S is also shown in the table below.
| Year | Education Expenditure | Health Expenditure | GDP |
| 2000 | 4.85 | 12.5 | 36449.9 |
| 2003 | 5.55 | 14.5 | 39677.9 |
| 2006 | 5.39 | 14.7 | 46437.1 |
| 2009 | 5.26 | 16.3 | 47001.6 |
| 2012 | 5.2 | 16.4 | 51433 |
Table 5: Education and Health Expenditures in comparison to the US GDP
Regression Analysis
For the regression analysis, the data set was first organized according to specific country statistics. Tables comprising of the data for India and that for the United States were created respectively to represent the data for easy analysis. The first stage of regression analysis involved gaining access to the analysis tool pack under the data menu. From the analysis tool pack the regression tool was selected. This provided the opportunity to input data ranges for the X and Y categories as well as to select the most appropriate output ranges. For output, a new data sheet was selected.
The input ranges for the Y axis comprised of only the columns containing the country GDPs per capita while the input ranges for the X axis had the columns for health and education expenditures. This is because of the consideration of health and education as the independent variables while the GDP was considered to be the dependent variable. In each case, the data exploration began by reviewing the data for acceptance into the regression model. The different indicators given by the analysis tool pack provided insight into the relevance of the selected data set for the model. For instance, there was no statistical relationship between health expenditures of the two countries based on logical reasoning. Based on the findings from the different models examined, it was established that the relationship between the GDP and the economic indicators also depended on the country economy under consideration.
Empirical Results
The empirical results for the two countries based on regression analysis present limited relationships between the education sector expenditures and the GDP per capita growth in the countries. For instance, the table below depicts the statistical results from the analysis of India’s data.
| Coefficients | Standard Error | t Stat | P-value | |
| Intercept | 0 | #N/A | #N/A | #N/A |
| Education Expenditure | -110.0205937 | 527.0661141 | -0.208741543 | 0.84802 |
| Health Expenditure | 299.1793257 | 461.1923947 | 0.648708281 | 0.562758 |
In the table above, the coefficients provide the relationship between particular indicators and the GDP. For instance, the education expenditure coefficient represents the magnitude of change in the GDP per unit change in the education expenditure as a percentage of the GDP. From the results above, both education and health expenditures are indications of GDP growth. The p- value indicates the statistical significance of each of these variables. Each p-value above the average 0.05 depicts lack of statistical significance. As such, the results for India are an indication that both education and health expenditures have limited statistical significance on the GDP of the country. This is probably because in developing countries such as India, the GDP affects the allocations for education and health as well as the other sectors. As such, it is more probable for the GDP to be statistically significant to the health and education expenditures that the two indicators to impact the GDP of the country. From the regression analysis, the error limits are also observable to be wide. This can be explained by the low probability depicted by the dependent variable. According to the regression results, the regression statistics are as shown in the table below. The R square value represents the percentage of the dependent variables that have been accounted for by the observed independent variable values. The low values of R square thus indicate that only a limited percentage of the dependent variable is accounted for by the independent variables. From these statistics, it can be deduced that the impacts of the health and education expenditures on the GDP of India are limited. This just supports the findings based on the p- values for the analysis.
| Regression Statistics | |
| Multiple R | 0.929090661 |
| R Square | 0.863209456 |
| Adjusted R Square | 0.484279275 |
| Standard Error | 451.3177954 |
| Observations | 5 |
The results from the regression analysis therefore represent the GDP per capita in India to be a function of the Health and education expenditures through the relationship below. In the equation, G is the GDP of the country; H is the health expenditure while E is the education expenditure.
G = 110.02E + 299.18H = 0 —— Equation 1
The economic growth of India is linked to the growth in various economic indicators including the health and education sectors. The period leading into 2000 was characterized by growth in these sectors, particularly in terms of public funding for the same. The industry growth was however hampered by the limited availability of public funding for both sectors through the years. As such, most of the growth in the education and health sectors came about as a result of the growth in the private sector supported health and education initiatives. After 2000, the slow growth in the education and health sectors can be linked to the same challenges of poor economic conditions in the country. Moreover, Acharya and others (34) opine that the gendered education in India also contributes immensely to the slow growth in the sector. This is based on the argument that illiteracy among women results in high rates of school drop- out among their children. The allocations of public funds and poverty levels also constrain access to education across the nation. It is thus arguable that the education sector in India is controlled by the low national GDP per capita and not the other way round.
In the health sector, Acharya and others (32) explain the changes that occurred in terms of infant mortality, fertility and life expectancy rates in the country in the period leading into 2000. The observed changes arising from increased access to vaccinations are also linked to the support from the private sector. Contrary to the developed countries where access to quality healthcare enhances national productivity, the case presented in India is that in which the low national productivity limits public access to quality healthcare.
The U.S gives a perfect example of a developing country where the health expenditure impacts positively on the national GDP. The regression results for the U.S are depicted in the table below.
| Coefficients | Standard Error | t Stat | P-value | |
| Intercept | 0 | #N/A | #N/A | #N/A |
| Education Expenditure | -2092.95 | 2865.41 | -0.73042 | 0.518006 |
| Health Expenditure | 3707.605 | 1007.364 | 3.680503 | 0.034745 |
As in the regression analysis for India, the p-values in the analysis for the U.S indicate limited statistical significance on the education expenditures. On the other hand, the health expenditures have significant statistical values in terms of their influence on the GDP of the country. The relationship is also depicted by the regression statistics and the R square values as depicted in the table below.
| Regression Statistics | |
| Multiple R | 0.998756 |
| R Square | 0.997513 |
| Adjusted R Square | 0.663351 |
| Standard Error | 2866.571 |
| Observations | 5 |
In comparison to the R square values for India, the U.S values are higher, portraying higher percentages of accounting between the independent and the dependent variables. From the U.S results obtained, the economy of the country can be said to be performing effectively, subject to the human resources in the country. Productivity in the U.S depends somewhat on the health allocations of the country. Higher health allocations depict greater access to quality healthcare and subsequently greater productivity across the nation. While this appears to be the case based on the regression results, Jacobsen and Mather (3) report that the unemployment rate in the U.S from 2000 to 2009 was increasing steadily. The explanation of this could be linked to the unprecedented population growth rates through these periods. The improved healthcare results in an improvement of the individual health status of each of the citizens. Child mortality reduces significantly and life expectancy increases. The combination of such health effects and immigration results in higher population growth and lower employment opportunity growth rates.
On the other hand, the education sector and the public funding for the same in the U.S have increased immensely. Lack of discrimination in the sector and enhanced education quality, both result in higher productivity among the citizens hence the observed increase in the GDP. With greater literacy levels and high levels of specialization, the U.S builds a work force that is capable of taking care of the less productive and the unemployed members of the society through social service, unlike in India where the poor and the uneducated cannot be supported by public funding. The U.S GDP, represented as a function of the health expenditures and the education expenditures can be depicted through the formula:
G = 3707.61H – 2092.25E —- 0 (Equation 2)
The negative coefficient on the education expenditure indicates that the education sector is considered as a cost rather than an investment in the U.S economy. This could be due to two factors. The first probability is that the provision of universal free primary education has been pursued by the U.S as a millennium development goal hence the monies are indicated as negatives rather than positives. On the other hand, it could also mean that the contemporary literacy levels in the country are so high so that any further investment in the education sector brings about no additional GDP benefits for the country. In this way, the education expenditures reduce rather than increasing the national productivity. This may be the most probable case as the level of specialization in the U.S has grown significantly while opportunities for specialized roles are limited in the country.
Summary and Discussion
The regression analysis conducted on the U.S and India data for the years 2000, 2003, 2006, 2009 and 2012 reveal important information about the economic status and growth of the two countries. The main objective of the study was to determine the relationship between the education and health expenditures for the two countries with respect to the national GDPs. Regression analysis of the data pertaining to India reveals that both the health and the education expenditures of the country have no significant statistical implications for the national GDP. This is contrary to the results for the U.S which indicates that the statistical significance of the health sector expenditure on the GDP is high. Based on the economic growth reports for the two countries, it has been established that access to quality education and healthcare in India is limited by the lack of public funds to support the same. On the other hand, the support provided to the two sectors by the U.S is significant. The GDP of the U.S thus depends to a large extent on the health of the people. The health sector expenditures are thus an important reflection of the country’s productivity in terms of GDP per capita.
Regression analysis provides an essential measure of the relationships that exist between a given independent variable and a dependent variable. However, the main limitation of the method is that it provides the statistical information without the logical explanations for the same. For instance, while the objectives of the study have been achieved, the results cannot be applied universally not only because they are country specific but also because they do not take into consideration many other factors that are indications of the economic status of a country. Factors such as high life expectancy rates, agriculture and industrial sector growth as well as the exchange rates are not considered in the study. The results obtained cannot therefore be concluded to be reliable since there is the probability that interactions between several factors bring about different sets of interaction information. Apart from this, the changes viewed are from historical data. This implies that the data may not depict the specific interactions between the different variables at any particular time.
The limitations of the study notwithstanding, it has been instrumental in achieving the study objectives and also acts as an indication of how different countries allocate funds for different purposes depending on the national GDP. The low GDP countries such as India have to allocate public spending under constraints. As such, the benefits reaped from the allocations to different sectors are limited. On the other hand, countries such as the U.S with sufficient national revenues can allocate funds for continued economic growth of the country. They therefore reap greater benefits from the allocations and continue growing economically.
Works Cited
Acharya, Shankar, Ahluwalia, Isher, Krishna, K.L and Patnaik, Ila. India, economic growth 1950- 2000. Global Research Project on Growth, 2003. Retrieved from www.researchgate.net/profile/Ila_Patnaik2/publication/234109500_India_Economic_Growth_1950-2000/links/0912f50f3abfd6da2c000000/India-Economic-Growth-1950-2000.pdf
Benos, Nikos and Zotou, Stefania. Education and economic growth: A meta-regressional analysis. Munich Personal RePEc Archive, (2013). Retrieved from www.mpra.ub.uni-muenchen.de/46143/1/MPRA_paper_46143.pdf
Jacobsen, Linda A. and Mather, Mark. U.S economic and social trends since 2000. Population Bulletin 65, 1(2010). Retrieved from www.prb.org/pdf10/65.1unitedstates.pdf
Knoema. GDP by country – statistics from the World Bank, 1960- 2015. Retrieved from www.knoema.com/mhrzolg/gdp-by-country-statistics-from-the-world-bank-1960-2015?country=United%20States
OECD. Health expenditure and financing. Retrieved from www.stats.oecd.org/index.aspx?DataSetCode=SHA
The World Bank. Health expenditure, total (% of GDP). Retrieved from www.data.worldbank.org/indicator/SH.XPD.TOTL.ZS?locations=IN