Index

Abstract

This paper investigates the causal relationship between government spending on education and economic growth in eight selected Latin American countries by using panel unit root test and panel cointegration analysis for the period 2000-2014. A three-variable model was formulated with trade volume as the second independent variable. The findings conclude that government spending on education and economic growth in the selected countries is positively and significantly associated, in the long and short-run, with evidence of a bidirectional Granger causal relationship between the dependent and the variable of interest, a unidirectional Granger causal relationship between trade volume and economic growth. The implication of our results shows that government secondary school spending on education has a positive impact on the selected countries, and our analysis can be replicated with other countries.

Keywords: Government spending on Education, Economic growth, Economic development, Latin America, Panel cointegration, Eductaion.

JEL Classification: O11, O15, I22, I256, E10, E12.

Received: 20 July 2020 / Revised: 28 August 2020 / Accepted: 16 September 2020/ Published: 1 October 2020

Contribution/ Originality

The paper's main contribution to the existing literature by investigating the cointegration and Granger causal relationship between human capital and economic growth for eight selected Latin American countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru, and El Salvador. The study is one of the few studies which have examined this relationship, which makes it unique and of great value to the field of economics and economic development.


1. INTRODUCTION

The concept of human capital emerged from recognizing that the investment in human capital by an individual or a firm has an increasing return to scale on productivity. Human capital can be split into three concepts: talent (natural given ability), acquired qualification(s), and expertise.  The term human capital was first used in the late '50s and early '60. Before the '50s and '60s, the term was a suggestive phrase in economics and played no role in the decision-making algorithm when it came to recommending, passing, and implementing educational policies. Upon empirical and practical evidence that there was a high return on quality education and it helped promote a nation's national goals, new ideas on public spending on education as a form of a nation's domestic investment was advocated by academics, policymakers, and practitioners.

This paper contributes to the research of economic growth, that is, human capital and how it fosters economic growth following Lucas Jr (1988); Barro (1991); Mankiw, Romer, and Weil (1992).

1.1. Understanding Human Capital

For this paper, human capital will be defined as human labor expertise used to produce other goods and services. Schultz (1961) defined human capital as a value used for measuring human potential. Smith (1776) "[stated that] the improvement to human capital through training, education, and experience makes the individual enterprise more profitable while adding to the collective wealth of the nation. Human capital can be seen as the collective wealth of a society in terms of judgment, skills, training, knowledge, experiences, and talent for a population (Schultz, 1961; Schultz., 1960).

1.2. Return on Human Capital

In a standard growth economic model (Mankiw et al., 1992; Romer, 1989, 1990; Romer, 1996; Romer, 1994) the accumulation of human capital is seen as a (private and public) investment undertaken to promote economic growth and development. The principle of opportunity cost is implemented in the model where the individual trades (some initial proportion of) his/her current income during their education and training period in return for and the hopes of higher future earnings. This trade-off will only be done if the additional schooling or training (i.e., investment in human capital) that translates to higher future earnings compensate the current costs (tuition and training course fees, forgone earnings while at school, and reduced wages during the training period) of the sacrifices.

2. LATIN AMERICA

In the last two decades, our SLAC and Latin America, in general, have achieved remarkable socio-economic progress. The lower and middle class has grown to historic levels; access to education and health care has expanded; poverty has been cut almost in half; property rights are recognized, and prosperity is being shared (World Economic Forum, 2016). As a result, most Latin American countries have now achieved "middle-income" and emerging nation's status, but the work(s) is far from done. If our SLAC is to move onto a path of first-world countries, achieving sustained socio-economic growth, these nations' will have to address numerous challenges–beginning with its lack of high-quality human capital (World Economic Forum, 2016). Today, Latin America's young population has enormous potential, with 67% of the region's total population being counted in the labor force. Population aging is not yet a significant concern, as it is in the developed economies – many workers lack the skills required to fulfill the demand for labor (World Economic Forum, 2016).
Unskilled human capital makes much-needed productivity growth challenging to come by in these regions. Companies in the productive sectors in the areas – which should be creating more and better-quality jobs – struggle to grow due to this human capital crisis, much less compete in the global economy (World Economic Forum, 2016). The continued worldwide technological advancement threatens to increase the existing gap between available skills and those demanded at the worldwide market. If our SLAC and other Latin American countries are to or want to compete effectively and efficiently with those based in developed or emerging economies, the nations' must remedy this by raising the skill level of its workforce (World Economic Forum, 2016).

2.1. Previous Works

Kögel and Prskawetz (2001) analyzed how the advancement of human capital affected the industrialized world and escaping the Malthusian trap characterized by low economic and high population growth to the post-Malthusian regime characterized by high economic and low population growth. The authors' examined the transition between these regimes by constructing a growth model with two types of consumer goods (an agricultural and manufacturing product), endogenous fertility, endogenous technological progress in the manufacturing sector.

Gibbons and Waldman (2004) in their paper, that built on Becker (1964) expressed the economic implications of the third type of human capital Task-Specific, which is as essential as the general-purpose and firm-specific.  The team Task-Specific human capital is a situation when a person is acquiring the skills for a particular job as opposed to the firm or the industry. This type of human capital is based on the simple—plausible ideology that most human capital accumulation is due to Task-Specific learning by doing. The authors' concluded by discussing how the concept of human capital can explain the cohort effects and provide an essential perspective regarding job-design issues. 

Becker (1964) work on human capital focused on the presupposition on general-purpose and firm-specific human capital. Becker (1964) developed one of the most significant strands of research that focused on human capital and the economic approach to human capital. Teixeira (2014) explored (Becker, 1964) early work on human capital, which Becker considered being a method of analysis rather than an assumption about human emotions, because, an attempt to explain various facets of human behavior through a set of simplified assumptions regarding human behavior, a result of individual choices characterized by utility maximization a forward-looking stance, consistent rationality, and stable and persistent preferences (Becker, 1964).  Asserting that the decisions were constrained by income, calculating capabilities, time, opportunities, and imperfect memory (Becker, 1993).

Nerdrum and Erikson (2001) analyzed intellectual capital and found complementary capacities of competence and commitment. Based on theoretically and empirically robust human capital theory, Nerdrum and Erikson (2001)  found that intellectual capital generates added value and creates wealth. The authors viewed resources to be perceived to be both tangible and intangible; and an extension of the human capital theory to be included in the intangible capacities of people.

3. METHODOLOGY, MODEL, AND RESULTS

As the world transitions from the millennial generation (1980 – 1995) to generation Z (1996 – 2010) in colleges, it is safe to conclude that the increasing enrollment rate can be attributed to the primary and secondary school net favorable educational policies implemented by educational policymakers. Over the decades, education, which for this paper will be defined as the successful completion of a formal primary school system, has led to the effective use of physical and financial capital leading to the efficient use of units of labor in the production process (Smith, 1776) an overall increase in production.  

3.1. Data Definition and Source

The study employs panel data between 2000 – 2014 for eight SLAC: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru, and El Salvador. These countries were selected due to data availability. The data was collected from  (World Penn Tables, 2019) Real Gross Domestic Product-per capita-purchasing power parity

3.2. Correlation analysis

3.3. Estimation Concerns

3.5. The Dynamics of the Model

Where equation seven represent the Cobb-Douglas production function, showing physical and human capital that is defined as

3.6. Empirical Results

3.6.1. Panel Unit Root Test

Conventionally, macroeconomics time series are non-stationary (Nelson, 1982). It is necessary to test the stationary properties of the data. This requires examining the order of integration of the data set, which is the unit root test. A time series is stable if its mean, variance, and autocovariance are independent of time (Gujarati, 2012).

The panel data technique referred to above has appealed to the researchers because of its weak restrictions. It captures the country-specific effects and allows for heterogeneity in the direction and magnitude of the parameters across the panel and provides a high degree of freedom in the model selection. Following the methodology used in earlier works (Al-Yousif, 2002), we test for the trend and intercept stationary for our variables. With a null of non-stationary, the test is a residual-based test that explores the performance of three different statistics. These three statistics reflect a combination of Levin et al. (2002); Breitung (2000) and Im et al. (2003).

3.6.3. Panel Cointegration Test

Our study identifies two kinds of test statistics the pooling residuals within the dimension of the panel and the other without the dimension. The long-run equilibrium equations are as follows:
Panel V-Statistic

Table-4. Pedroni panel cointegration test.
Panel Group Statistics
Statistic
Prob
Panel V-Statis tic
5.90
0.00***
Panel rho-Statis tic
3.33
0.99
Panel PP-Statis tic
-2.64
0.00***
Panel ADF-Statis tic
-2.84
0.00***
Group rho-Statis tic
4.01
1.00
Group PP-Statis tic
-5.74
0.00***
Group ADF Statis tic
-3.03
0.00***

Note:
* indicates significance at 10%
** indicates significance at 5%
*** indicates significance at 1%

4. SUMMARY STATEMENT, CONCLUSION, AND EDUCATIONAL POLICY RECOMMENDATIONS

Further studies need to be examined using different methodologies to investigate the effect of how spending in education translates to higher economic growth, community development, and higher productivity. Notwithstanding, specific government spending on different tiers of education (higher education) needs to be investigated. The policy implications of this research involve the following: first, Our SLAC will provide incentives that would promote academic spending in the primary and secondary education levels and overall educational advancement in the region.

In conclusion, this paper adopted the Mankiw et al. (1992) technique and supports that education is imperative for economic growth and development for the SLAC. Despite this widespread belief that investment in education is a key determinant of economic growth and shortly will lead to economic development, the empirical estimations, especially focusing on low-income countries, are less than conclusive. Quiggin (2002); Devarajan, Swaroop, and Zou (1996); Benhabib and Spiegel (1994). This can be attributed to how schooling, investment, and successes are measured.

Funding: This study received no specific financial support.  

Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper.

REFERENCES

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. Available at: https://doi.org/10.1109/TAC.1974.1100705.

Al-Yousif, Y. K. (2002). Financial development and economic growth: Another look at the evidence from developing countries. Review of Financial Economics, 11(2), 131-150. Available at: https://doi.org/10.1016/S1058-3300(02)00039-3.

Barro, J. R. (1991). Human capital and growth in cross-country regressions. The Quarterly Journal of Economics, 106(2), 407-443. Available at: https://doi.org/10.2307/2937943.

Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago, IL: University of Chicago Press.

Becker, G. S. (1993). Nobel lecture: The economic way of looking at behavior. Journal of Political Economy, 101(3), 385-409. Available at: https://doi.org/10.1086/261880.

Benhabib, J., & Spiegel, M. M. (1994). The role of human capital in economic development evidence from aggregate cross-country data. Journal of Monetary Economics, 34(2), 143-173. Available at: https://doi.org/10.1016/0304-3932(94)90047-7.

Breitung, J., & Das, S. (2005). Panel unit root tests under cross-sectional dependence. Dutch Statistics, 59(4), 414-433. Available at: https://doi.org/10.1111/j.1467-9574.2005.00299.x.

Breitung, J. (2000). The local power of some unit root test for panel data. JAI, 15, 161-178. Available at: https://doi.org/10.1016/S0731-9053(00)15006-6.

Devarajan, S., Swaroop, V., & Zou, H.-f. (1996). The composition of public expenditure and economic growth. Journal of Monetary Economics, 37(2), 313-344. Available at: https://doi.org/10.1016/S0304-3932(96)90039-2.

Edrees, A. (2016). Human capital, infrastructure, and economic growth in Arab world: A panel granger causality analysis.

Engle, R. A. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica, 55(2), 251-276. Available at: Co-integration and error correction: representation, estimation, and testing.

Feinstein, L., Robertson, D., & Symons, J. (1999). Pre-school education and attainment in the national child developement study and british cohort study. Education Economics, 7(3), 209-234. Available at: https://doi.org/10.1080/09645299900000019.

Gibbons, R., & Waldman, M. (2004). Task-specific human capital. American Economic Review, 94(2), 203-207. Available at: https://doi.org/10.1257/0002828041301579.

Gujarati, N. P. (2012). Basic econometrics.

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74. Available at: https://doi.org/10.1016/S0304-4076(03)00092-7.

Khembo, F., & Tchereni, B. H. (2013). The impact of human capital on economic growth in the SADC Region. Developing Country Studies, 3(4), 144-152.

Kögel, T., & Prskawetz, A. (2001). Agricultural productivity growth and escape from the Malthusian trap. Journal of Economic Growth, 6(4), 337-357. Available at: https://doi.org/10.1023/A:1012742531003.

Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24. Available at: https://doi.org/10.1016/S0304-4076(01)00098-7.

Lucas Jr, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3-42. Available at: https://doi.org/10.1016/0304-3932(88)90168-7.

Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407-437. Available at: https://doi.org/10.2307/2118477.

Mehrara, M., & Musai, M. (2013). The relationship between economic growth and human capital in developing countries. International Letters of Social and Humanistic Sciences, 5(55), 55-62. Available at: https://doi.org/10.18052/www.scipress.com/ILSHS.5.55 .

Nelson, R. C. (1982). Trends and random walk in macroeconomics time series. Journal of Monetary Economics(10), 139-162. Available at: https://doi.org/10.1016/0304-3932(82)90012-5.

Nerdrum, L., & Erikson, T. (2001). Intellectual capital: A human capital perspective. Journal of Intellectual Capital, 2(2), 127-135. Available at: https://doi.org/10.1108/14691930110385919.

Osiobe, E. U. (2020). Understanding Latin America's educational orientations: Evidence from 14 nations. Education Quarterly Review, 249-260. Available at: https://doi.org/10.31014/aior.1993.03.02.137.

Pedroni, P. (2002). Critical value for cointegration test in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1). Available at: https://doi.org/10.1111/1468-0084.61.s1.14.

Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series test with an application to the PPP hypothesis. Economic Theory, 20(3), 325-597. Available at: https://doi.org/10.1017/S0266466604203073.

Phillips, C. P. (1995). Fully modified least squares and vector autoregression. Econometrica, 63(5), 1023 - 1078. Available at: www.jstor.org/stable/2171721 .

Quiggin, J. (2002). Human capital theory and education policy in Australia. Australian Economic Review, 32(2). Available at: https://doi.org/10.1111/1467-8462.00100.

Rahman, M. M. (2011). Causal relationship among education expenditure, health expenditure and GDP: A case study for Bangladesh. International Journal of Economics and Finance, 3(3), 149-159. Available at: https://doi.org/10.5539/ijef.v3n3p149.

Romer, P. (1989). Human capital and growth: Theory and evidence. NBER Working Paper No. 3173.

Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98(5), 71-102. Available at: https://doi.org/10.1086/261725.

Romer, D. (1996). Advanced macroeconomics. New York: Mc Graw Hill Education.

Romer, P. M. (1994). The origins of endogenous growth. The Journal of Economic Perspective, 8(1), 3-22. Available at: https://doi.org/10.1257/jep.8.1.3.

Schultz, T. (1961). Investment in human capital. American Economic Review, 51(1), 1-17.

Schultz., T. W. (1960). Capital formation by education. Journal of Political Economy, 68(6), 571-583. Available at: https://doi.org/10.1086/258393.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. Available at: https://doi.org/10.1214/aos/1176344136.

Sharma, P., & Sahni, P. (2015). Human capital and economic growth in India: A cointegration and causality analysis. Ushus Journal of Business Management - Journals, 14(2), 1-18. Available at: https://doi.org/10.12725/ujbm.31.1.

Shibata, R. (1976). Selection of the order of an autoregressive model by akaike's Information criterion. Biometrika, 63(1), 117-126. Available at: https://doi.org/10.1093/biomet/63.1.117.

Sims, A. C. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48. Available at: https://doi.org/10.2307/1912017.

Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. Available at: https://doi.org/10.1093/oseo/instance.00043218.

Teixeira, P. N. (2014). Gary becker’s early work on human capital–collaborations and distinctiveness. IZA Journal of Labor Economics, 3(1), 1-20. Available at: https://doi.org/10.1186/s40172-014-0012-2.

Vieira, F. H. (2013). Growth and exchange rate volatility: A panel data analysis. Applied Economics, 45(26). Available at: https://doi.org/10.1080/00036846.2012.730135.

World Economic Forum. (2016). Skilling up: Human capital and Latin America. Retrieved from World Economic Forum: https://www.weforum.org/agenda/2016/12/skilling-up-human-capital-and-latin-america .

World Penn Tables. (2019). Retrieved from: http://datacentre2.chass.utoronto.ca/pwt/alphacountries.html .

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