The Impact of Human Capital on Growth: Evidence from West Africa
Author: Ms. Sonia Brunschwig, Mr. Emilio Sacerdoti, and Mr. Jon Tang1
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: esacerdoti@imf.org; sbrunschwig@imf.org; jwt2@acpub.duke.edu
Publication Date:
01 Nov 1998
eISBN:
9781451981148
Language:
English
Keywords:
WP; capital; country; contribution of capital accumulation; capital accumulation to growth; rate of growth
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This paper analyzes the impact of human capital on growth, on the basis of refined calculations of human capital, and with a focus on West Africa. Using a growth-accounting methodology, it distinguishes the sources of growth between the accumulation of factors of production and changes in production intensity or efficiency. Private capital is found to be particularly important to growth, but human capital appears not to be significant. The paper also identifies the terms of trade, trade openness, the government deficit, and the share of government investment in total investment as key policy variables affecting growth.

Abstract

This paper analyzes the impact of human capital on growth, on the basis of refined calculations of human capital, and with a focus on West Africa. Using a growth-accounting methodology, it distinguishes the sources of growth between the accumulation of factors of production and changes in production intensity or efficiency. Private capital is found to be particularly important to growth, but human capital appears not to be significant. The paper also identifies the terms of trade, trade openness, the government deficit, and the share of government investment in total investment as key policy variables affecting growth.

I. Introduction

Intuitively, one may expect human capital accumulation to contribute positively to economic growth. However, empirical support for this assumption appears less clear than had been previously believed. Bosworth, Collins, and Chen (1995) and Pritchett (1996) show that a positive correlation between school enrollment ratios and output growth should not be interpreted as evidence that human capital contributes positively to growth, as school enrollment is poorly correlated with the improved measure of human capital accumulation calculated by Nehru, Swanson, and Dubey (1995). However, while economists agree that enrollment ratios have no place in a production function equation, they do not share the same opinion about the way human capital is related to economic growth. Pritchett estimates the coefficient on human capital to be negative, but Nehru and Dhareshwar (1994) show that human capital contribution to growth is positive and significant.

Most recent empirical studies aimed at identifying the factors that contribute to economic growth have used a multicountry database developed at the World Bank that includes new series on human capital. However, as corresponding data on human capital are available for only few African countries, the coverage of these panel data estimations has been limited. In this paper, we extend the work done by Nehru-Swanson-Dubey to nine countries in West Africa, first by calculating the average years of schooling of the working population, and second by converting this measure of human capital into an index of labor productivity.

We then follow a growth-accounting methodology to distinguish the sources of growth between the contribution of accumulation in the quantity of factors of production and the efficiency or intensity with which these factors are used. We find that growth in physical capital, particularly privately financed, contributes strongly to output growth, but that the impact of human capital accumulation is not significant. Also, we find no evidence for conditional convergence. Moreover, we show that country-specific factors other than factor accumulation are important to understand differences in per capita income growth across countries. In an attempt to understand better the contribution to growth of factors other than the accumulation of human and physical capital, we estimate an extended growth equation that includes exogenous shocks and policy variables. We identify the terms of trade, the degree of trade openness, the government deficit, and the share of government investment in total investment as major components of country-specific effects.

II. Human capital—a comparison across west african countries

In his provocative article “Where Has All the Education Gone?” Lant Pritchett (1996) estimates the impact of the education attainment of the labor force on the rate of growth of output per worker to be consistently small and negative. In contrast to previous calculations, which used enrollment rates as a proxy for human capital growth, Pritchett’s estimations are based on the calculations of average years of schooling of the working population realized by Barro and Lee (1993) and by Nehru, Swanson, and Dubey (1995), converted into a measure of educational capital.2 Interestingly, Pritchett’s results differ from those obtained by Nehru and Dhareshwar (1994) with two alternative measures of human capital: using both average years of schooling and a measure of human capital derived from country-specific information on the wage structure, these lattter authors find human capital to contribute positively to economic growth.

Pritchett’s and Nehru-Dhareshwar’s work, as well as the calculations of human capital done by Barro-Lee and by Nehru-Swanson-Dubey, cover a wide sample of countries at diverse stages of development; however, they include only a limited number of African countries. In this paper, we extend earlier studies by constructing two series of human capital for nine countries in West Africa. Among them, five countries (Senegal, Cote d’Ivoire, Mali, Cameroon, and Ghana) are covered in Nehru-Swanson-Dubey’s study, but four countries (Niger, Guinea-Bissau, Burkina Faso, and Guinea) are not. First, we calculate human capital as the average years of schooling in the working population. Second, we measure it as a function of both the distribution of education in the working population and relative wages.

A. Human Capital Measured as Years of Schooling

Both Nehru-Swanson-Dubey and Barro-Lee identify human capital with the accumulated years of schooling present in the working-age population.3 Barro-Lee use census reports of the educational level of the population aged 25 and over and extrapolate this information with data on school enrollment. Nehru-Swanson-Dubey rely solely on school enrollment data and use the perpetual inventory method, adjusted for mortality, to estimate human capital. For a number of countries in our sample, census data are limited and do not contain sufficient information to calculate the average years of schooling in the working population. Therefore, we construct human capital following the Nehru-Swanson-Dubey methodology.

In every year, we estimate the expected years of schooling of individuals aged 15 to 64 years, which we consider as constituting the labor force. For each age group, in order to calculate the probability of having successfully completed all the years of primary school, we take into consideration the probability of having been enrolled in primary school4 and subtract the probability of repeating and dropping out to obtain the net enrollment ratio.5 We repeat the exercise for the higher education levels. The expected years of education corresponding to each age group is then defined as the sum of the years of education in primary school up to the end of the fourth grade, up to the end of the sixth grade, in secondary school, and in tertiary school, weighted by the probability of having successfully completed the corresponding years. The average human capital of the working population is then calculated as a weighted average of the expected human capital of each age group, where the weights correspond to the probability that an individual of a certain age survived to a certain date.

Our calculations differ from Nehru-Swanson-Dubey’s in a number of ways: First, we use survival probability distributions by age groups that are country specific, while Nehru-Swanson-Dubey use the same survival probability for all African countries. Second, we estimate the dropout rate using a methodology suggested by UNESCO (see Appendix II); the methodology used by Nehru-Swanson-Dubey to calculate dropout rates is unknown. Finally, in order to estimate a series of human capital stock starting in 1970, we extrapolate raw data, assuming that, before 1960, the enrollment ratio increased at a rate equal to one-third of the rate observed between 1960 and 1980; Nehru-Swanson-Dubey have chosen to maintain enrollment ratios and repetition rates constant for all years preceding the earliest available data.

Whenever possible, we compare our own estimations of human capital stock and growth rates with those obtained by other authors and find them to be rather close. Table 1 presents our results, as well as those obtained by Barro-Lee and by Nehru-Swanson-Dubey. It shows that Niger and Burkina Faso, with less than 0.5 years of schooling per worker, have the lowest levels of human capital in the region; meanwhile, Cameroon’s and Ghana’s working populations have the highest levels, at about three years of schooling on average.6 However, the growth rates of human capital in Burkina Faso and Niger (some 5 percent) are the highest. In order to facilitate comparisons across countries, Figure 1 shows gross and net enrollment ratios and Figure 2 presents human capital derived from primary education and total human capital (derived from primary, secondary, and tertiary education). The stability of human capital is striking; the evolution of this variable is influenced only marginally by recent developments in enrollment rates, as the majority of the labor force received its education many years ago.

Figure 1.
Figure 1.

Selected West African Countries: Gross and Net Enrollment in Primary School, 1960-97 1/

(In percent)

Citation: IMF Working Papers 1998, 162; 10.5089/9781451981148.001.A001

1/ Gross enrollment is defined as the number of children of any age registered in primary school, in percent of the population between the age of 6 and 11, the years in which a child should theoretically be in primary school. The net enrollment is defined as gross enrollment corrected for repeaters and drop-out.
Figure 2.
Figure 2.

Selected West African Countries: Human Capital Stock from Total and Primary Schooling, 1970-97 1/

(In years of education)

Citation: IMF Working Papers 1998, 162; 10.5089/9781451981148.001.A001

1/ Human capital measured in average years of schooling in the population.
Table 1.

Selected West African Countries: Human Capital from Education, 1970-97

(Stock as average years of schooling in the working population; and growth rate in percent)

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B. Wage-Weighted Human Capital

Years of schooling might be a good measure of educational achievement of the working population, but it may still not be the appropriate measure of human capital to incorporate in a production function. In order to capture the impact of education on the labor force production capacity, we convert information about the distribution of years of schooling in the working population into a distribution of relative wages associated with different degrees of school achievement.7 Relative wages are believed to be indicative of relative productivity as a function of education. Ideally, we would like to compare for different sectors of the economy the wage structure conditional on education. In practice however, information is available in some countries only for part of the private sector, in other countries only for the public administration. Whenever possible, we use country-specific information about the wage structure conditional on education attainment. Then, following Denison’s methodology, we normalize the income of those who just completed primary education to one, and assume that two-thirds of the reported income differential between each of the other groups and that reference group represents differences in earnings owing to differences in education, as distinguished from other characteristics.8 Appendix III describes labor market characteristics for our countries, compares them with Denison’s observations for the United State in 1960, and explains how earning weights associated with each level of education are calculated.

It is important to realize that this transformation of years of schooling into education marginal productivity is not linear. Although we assume that, for each country, the earning weights corresponding to each level of education are constant through time, the way these weights affect the measure of human capital varies over time as the distribution of education in the population changes. This point will become important when we use our series to estimate the production function equation.

Table 2 and Figure 3 report our results for the wage-weighted measure of human capital. It is immediately apparent that, for all countries, the growth rate of human capital is lower with the new measure than with average years of schooling, and that differences across countries are less important. The reason is that, with the Nehru-Swanson-Dubey methodology, individuals with no schooling are assigned a zero weight in the index of labor quality, while, with the wage-weighted human capital methodology, they are assumed to contribute positively to production in proportion to their wage. For comparison, in Table 4, we also present the growth rate of human capital calculated by Bosworth, Collins and Chen (1995) using Denison’s U.S. earning weights. Their growth rates are always higher than ours, mainly because their calculations are based on the years of schooling from Barro-Lee, which also grow faster than our own estimates.

Figure 3.
Figure 3.

Selected West African Countries: Wage-weighted Human Capital Stock from Primary and Total Schooling, 1970-97

(Index of education-related labor productivity, completion of primary school=1)

Citation: IMF Working Papers 1998, 162; 10.5089/9781451981148.001.A001

Table 2.

Selected West African Countries: Wage-Weighted Human Capital, 1970-97

(Stock as index of education-related labor productivity, completion of primary school-1; and growth rate in percent)

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