On The Relevance Of Human Capital: An Analysis Of The Mankiw, Romer, And Weil Mode

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Introduction

Proposed more than sixty years ago, the Solow Growth Model still constitutes a solid theoretical base for economic growth discussions. Solow’s capital accumulation-based model has been augmented together with other factors of productivity by several economists in the last fifty years. In line with that, Mankiw, Romer, and Weil (MRW) constructed their growth model with the Cobb-Douglas production function and constant returns to scale by including Human Capital as an input to the production function. Their augmented model managed to explain cross-country differences of per capita GDP up to 80% between 1960-1985. (Mankiw et al., 1992) This paper aims to analyze the model of Mankiw et al. by examining an altered set of countries, in a different period with different proxies of human capital to test whether human capital is still the key determinant of cross-country income per capita. To assert the relationship between human capital and growth rate analytically, findings are discussed considering other macroeconomic characteristics of countries such as their employment to population ratio.

Theoretical Background

By defining saving rates, population growth and technological progress as exogenous factors, Solow (1956) constructs his model on the balance of capital accumulation and depreciation. Therefore, in his framework, the steady-state level is exogenously determined in the long run. As Lucas (1988), Romer (1990) and Mankiw et. al (1992) argue; the exogeneity of factors limits policymakers to take action on practical matters and change the growth trajectory. In that sense, Lucas (1988) identifies human as an active contributor to the economy both via production and consumption. Thus, he includes the “average years of education” as a proxy for human capital. By including human capital with non-decreasing marginal returns to the production function, he points out that the increasing levels of human capital could bring endogenous growth. Likewise, Mankiw et. al (1992) recognize the need for endogenous factors in the classical Solow model, but they include human capital as the “the percentage of the working-age population that is in secondary school” to their model. It could be seen as a more reliable proxy to grasp the real impact of education on economic prosperity in the long run.

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Both Lucas (1988) and Mankiw et. al (1992) test their models empirically. Mankiw et. al (1992) test their model on 98 countries between 1960-1985. In the light of their empirical analysis, they indicate that including human capital to the model decreases the estimated impact of saving and population growth on income levels, which implies that Solow (1956) had overestimated the impact of saving rates on growth levels. Moreover, Mankiw et. al (1992) provide counter-evidence against convergence from different countries by adding that human capital could explain the cross-country differences in steady-state levels.

Having said that human capital has been considered as a significant factor for economic growth, a large and growing body of literature has investigated different indexes for human capital. Mankiw et al. use the “average percentage of the working-age population in secondary school” as a proxy for human capital. Working age is defined as 15-64. The World Bank has also constructed a comprehensive human capital index (HCI) including Expected Years of School and Harmonized Test Scores from different countries. However, the World Bank HCI is only calculated for 2018. Additionally, the Penn World Table (PWT) Human Capital Index (HCI) captures average years of schooling and returns to education covering 182 countries between 1950 and 2017 (Feenstra et al., 2015)

Description of Data

The analysis in this paper covers sixty countries from different income groups to test the relation between growth rate and human capital in different development levels. The period was chosen to be 1989-2014 to provide an up-to-date perspective.

World Bank World Development Indicators Data has been used for GDP per capita in current US$, Primary School Net Enrollment Rate and Secondary School Net Enrollment Rate. Growth rates are generated by logarithmic values of GDP per capita data.

Penn World Table 9.0 Human Capital Index (PWT HCI) is used as one of the proxies for human capital. To test the explanatory power of secondary and primary school enrollment rates, their relationships with Penn’s HCI are also discussed. When human capital indexes and growth rate become short of explaining the empirics, a relevant country-specific macroeconomic factor, employment to population ratio from World Bank Development Indicators Data is used to divide countries and evaluate the relative impact of human capital in different labor market conditions.

Discussion

Turning now to the empirical evidence on the impact of human capital on growth rates; Figure 1 shows the linear relationship between growth rate and PWT HCI for sixty countries. This finding, while preliminary, suggests that there is a positive relationship. Yet, the overall magnitude of the positive impact (the slope of the fitted line) remains low. The outliers lie especially on the lower levels of human capital. A plausible explanation for that is that countries with low human capital have more external factors on their growth rates. Moreover, the growth rates for high-income countries cluster at lower rates. Therefore, using additional proxies for human capital measurement or grouping countries according to other macroeconomic factors could ease the analysis.

Although Mankiw et al. use the secondary school enrollment rate as the human capital proxy in their model, it is encouraging to compare primary school enrollment rate and secondary school enrollment rate to infer a broader causal relation. Figure 2 shows the relationship between Primary School Enrollment Rate and Growth Rate. The magnitude of the positive relationship is slightly higher than the PWT HCI. It is important to note that, almost all outliers lie below the 70% primary school enrollment rate. There are two implications of that situation. First, for the countries with at least 70% primary school enrollment rate, the impact of primary school enrollment on growth is more prominent. Second, countries with less than 70% enrollment rate have diverse growth rates and unique features. Thus, they don’t follow a trend.

The relationship between the primary school enrollment rate and PWT HCI is illustrated in Figure 3 to show the explanatory power of the primary school enrollment rate as a proxy for human capital. High correlation implies that the primary school enrollment rate constitutes a relevant proxy.

Since Mankiw et al. used the “average percentage of the working-age population in secondary school” as the proxy of human capital, the main empirical test of the MRW Model should be conducted by including the secondary school enrollment rate. Consistent with the literature, the secondary school enrollment rate is found to be a powerful proxy for human development as illustrated by Figure 4.

Returning to the impact of secondary school enrollment rates, Figure 5 shows the positive relationship between secondary school enrollment and growth. As expected, high-income countries like Norway, Luxembourg or the UK record considerably small growth rates and high human capital as outliers. However, countries from the upper-middle-income group such as Argentina, Brazil, South Africa, and Venezuela have similar growth rates and very consistent distribution over the fitted line.

Overall, the magnitude and direction of the linear relationship validate the MRW theory. However, the relevance of the theory for the least developed countries(LDCs) seems quite questionable, because almost all LDCs are under the fitted line as outliers. Here, it can be intuitively argued that there are more exogenous factors for developing countries.

The main argument of MRW relies on the assumption that increased human capital would increase the growth rates via labor markets. In other words, labor with more human capital would foster productivity and raise the income per capita. However, there should be evidence for suitable market conditions and employability in the economy to assume such a causal relationship. This analysis uses the World Bank “employment to population rate” as a proxy for suitable labor market conditions. Table 1 presents the causal relationship of PWT HCI and growth rates in the countries where the employment to population ratio is higher than 60%. Here the human capital has a positive and significant impact on growth rates. On the other hand, Table 2 indicates that if the employment to population ratio is less than 60%, the human capital coefficient becomes negative. Taken together, these results suggest that there is an association between labor market conditions and the validity of human capital. Thus, countries should be grouped according to their market conditions to evaluate country-specific returns of human capital.

Conclusion

This paper has discussed the validity of human capital as a growth-enhancing factor within the MRW Growth Model. The small-Scale data analysis has supported the relevance of human capital as an endogenous factor in growth. However, in the case of LDCs, the relationship between human capital and growth rates was observed to be variant and inconsistent with the literature. In that sense, the sample has been grouped according to the employment rate. The main purpose of that is to highlight the importance of having a convenient labor market to translate human capital into production and growth. The results suggested that having a convenient labor market is significantly important for human capital to be relevant. What is now needed is a cross-national study involving labor market conditions for human capital to fulfill the promised endogenous impact in the MRW Growth Model.

References:

  1. Feenstra, R. C., Inklaar, R. and Timmer, M. P. (2015), The Next Generation of the Penn World Table American Economic Review, 105(10), 3150-3182, [Online]. Available at: www.ggdc.net/pwt (Accessed: 21 October 2019)
  2. Lucas, R. E. (1988) ‘On the mechanics of economic development.’ Journal of Monetary Economics.
  3. Mankiw, N. G., Romer, D. and Weil, D. N. (1992) ‘A Contribution to the Empirics of Economic Growth.’ The Quarterly Journal of Economics.
  4. Solow, R. M. (1956) ‘A Contribution to the Theory of Economic Growth.’ The Quarterly Journal of Economics.
  5. World Bank (2018) The Human Capital Project. [Online] Available at: https://openknowledge.worldbank.org/handle/10986/30498 (Accessed: 21 October 2019)
  6. World Bank (2018) World Development Indicators. [Online] Available at: https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (Accessed: 21 October 2019)

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