Literature review: Economic growth determinants and models

CHAPTER 2 Literature Review

2.0 Introduction

Economic growth is one of the most important fields in economics. Since sustained economic growth is the most important determinant of living standards, there is no more important issue challenging the research efforts of economists than to understand the causes of economic growth. Human capital has been identified as a key stimulus of economic growth.

In fact, it can never be overemphasized that human capital is the engine of growth of an economy. No nation can develop beyond its investment in education in particular. Growth economists in affirmation have explained that the differences in the per capita income of countries cannot be explained in isolation from the differences in human capital development.

Health and education are both components of human capital and contributors of human welfare. Numerous economists research their relevance in the economic growth and tried to incorporate human capital in the growth model. While some researchers take a Keynesian route and stress on the demand factors, other researchers follow the neoclassical route and emphasis the role of factor supplies in growth.

2.1 Human Capital in the form of education

It is equally important to effectively and efficiently measure the human capital with the perceiving importance of human capital. Since, human capital is considered as a synonym of knowledge embedded in all levels such as an individual, an organizations and a nation, education is the primary element in the measurement of human capital.

Some economists attempted to measure the stock of human capital utilizing “school enrollment rates” as a proxy of human capital. Through the study of 129 countries for a time period 1960 to 1985, Barro and Lee, 1993 concluded that female education stimulates the acquisition of human capital through children. A fact is in accordance with the findings of De Tray, 1773 and Becker and Lewis, 1973. Barro and Lee reconcile their findings with the conclusion of De Long and Summers (1992) with the belief that “perhaps the true key is to have educated women working with machines”. (Barro and Lee, 1991, p29). However, the study of Kyriacou in 1991 concluded a negative and insignificant correlation between years of schooling in labour force and future growth. One of the possible explanations for this result is the link between human capital and subsequent growth of technology was ignored. The method of using school enrollment rates is criticized as student’s effectiveness can be recognized after participating in production activities.

Nehru, Swanson, and Dubey (1993) attempted to measure relationship between human capital and students‟ “accumulated years of schooling” in the employable age as educational attainment. Their approach to measure human capital is similar to that of Lau, Jairison, and Louat( 1991), Psacharopolous and Arriagada (1986,1992). The results show a positive relationship between education stock and its influence on income per capita. They also concluded that there is a high correlation between education stock and other human capital indicators and hence justify the usage of this variable as a proxy for human capital. Nevertheless, they note that there is a problem with the estimates of education stock due to repeaters and dropout rates. The weakness in the study pertains to education stock estimation as they are “based on sparse data of uneven quality”( Nehru, Swanson, and Dubey,1993, p8). Romer (1990) suggested the ratio between skilled-adults and total adults to measure the stock of human capital in the national economy.

Another approach to measure human capital is through the returns which an individual obtains from a labour market throughout education investment. Mulligan and Sala-i-Martin (1995) defines that aggregate human capital is the sum of quality adjustment of each individual’s labor force, and presents the stock of human capital utilizing an individual’s income. Their belief was that the “quality of a person would be related to the wage rate he receives in the marketplace”( Mulligan and Sala-i-Martin, 1995, p.2). This measure called the Labour –Income –Based is a measurement of human capital calculated through wage rate. Though this study, it was noted that the usage of average years of schooling as a measurement could be misleading since economists could interpret the increase in income in 1980s independent of human capital accumulation due to the dispersion of average years of schooling.

2.2 Human capital in the form of Health

A large body of literature has established that investment in education pay off in the form of higher future earnings. However, the demerit of the conventional measurement of the human capital is the disregard to qualitative benefits of human capital such as health, fertility rate, child mortality. Given the importance of “health capital” for education and earnings (Grossman, 2000; Case, Fertig, and Paxson, 2005; Currie and Madrian, 1999; Smith, 1999), it is possible that poor health has an impact on education and hence on economic status. Many health shocks can affect human capital and productivity, both in the short-run (Strauss and Thomas, 1998; Currie and Stabile, 2006) and the long-run (Cunha and Heckman, 2007; Currie and Hyson, 1999)( Joshua Graff Zivin and Matthew Neidell, 2013). The World Health Organization’s Commission on Macroeconomics and Health (2001) claims the following. “Improving the health and longevity of the poor is an end in itself, a fundamental goal of economic development. But it is also a means to achieving the other development goals relating to poverty reduction. The linkages of health to poverty reduction and long-term economic growth are powerful, much stronger than is generally understood.”

Despite the importance of health capital, the empirical literature of the effects of health on economic growth is relatively thin. Recent experimental or quasi-experimental studies, such as Thomas and Frankeberg (2002) and Thomas et al. (2003) have found that specific health sector interventions help recipients raise earnings significantly, and general indicators of health and nutrition status are significant predictors of economic success. At macroeconomic level, several researches support the positive contribution of health on economic growth. Barro (1996b), Bloom and Canning (2003), Bloom, Canning, and Sevilla (2004) and Gyimah-Brempong and Wilson (2004) find that health capital indicators have desirable influence on aggregate output. For the countries in their sample, about one-fourth of economic growth was attributable to improvements in health capital, and improvements in health conditions equivalent to one more year of life expectancy are associated with higher growth of up to 4 percentage points per year.

The following table summarises the finding of macroeconomic studies with health.

Source: J. Hartwig / Journal of Macroeconomics 32 (2010) 314–325

According to Weil (2007, p. 1295 and 2005, pp. 153–161), health’s positive effect on GDP is strongest among poor countries. The existing evidence on whether health capital formation has an impact on economic growth gives a mixed response. Some papers such as Heshmati (2001), Rivera and Currais (1999a, 1999b, 2003, 2004) accept the significance of health capital formation for economic growth in OECD countries. However, Knowles and Owen (1995, 1997) as well as McDonald and Roberts (2002) reject the hypothesis that life expectancy is a statistically significant explanatory variable for productivity growth in high income countries. IN fact, Bhargava et al. (2001) and Acemoglu and Johnson (2007) estimated a negative effect of adult survival rate on economic growth for US, France and Switzerland.

Some studies have associated fertility rate and child mortality with human capital. The best known study between population growth and development is Kuznets (1967). His study found a positive correlation between growth rates of population and income per capita within broad country groupings, which he interpreted as evidence of a lack of a negative causal effect of population growth on income growth. However, Kelley (1988) found no correlation between population growth and growth of income per capita, and similarly no relationship between population growth and saving rates. Summarizing many other studies, he concluded that the evidence documenting a negative effect of population growth on economic development was weak or nonexistent.

Becker et al. (1990) associated endogenous fertility and a rising rate of return on human capital as the stock of human capital increases. Their analysis discusses the importance of investment of human capital and the impact of family sizes and birth rates. They concluded that “societies with limited human capital choose large families and invest little in each member; those with abundant human capital do the opposite ” ( Becker et al., 1990, p.35). Weil et al.(2012) found that a reduction in fertility rate will increase GDP per capita income by an economically significant amount. This result is similar to the findings of Bloom and Canning (2008) who have regressed the growth rate of income per capita on the growth rate of the working-age fraction of the population, and have gotten a positive and significant coefficient. The high growth of working age fraction is the result of fertility reductions; it can be seen as showing the economic benefits of reduced fertility.

2.3 Growth Models

Being one of the most important determinants of living standards, economic growth is among the most important issue challenging the research efforts of economists. Many adopted the neoclassical growth approach to study economic growth. The neoclassical growth model emphasizes the role of factor supplies in growth as it seeks to undermine the long-run economic growth rate determinant through the accumulation of factor inputs such as physical capital and labour.

Over time, human capital was introduced in the growth model. The concept of capital in the neoclassical model has been broadened from physical goods to include human capital in the form of education, training and experience. In the early 1960s, Schultz initiated the human capital revolution in economic thought. He claimed that “This knowledge and skill are in great part the product of investment and, combined with other human investment, predominantly account for the productive superiority of the technically advanced countries. To omit them in studying economic growth is like trying to explain Soviet ideology without Marx.”(Schultz, 1961, p.3).

2.3.1 Exogenous growth model

In general, there are two basic frameworks that seek to understand the relationship between human capital and economic growth. The first approach is through the exogenous growth model adopted by Nelson and Phelps (1966). The exogenous growth model has its origin form the Solow growth model.

The crux of this model is the aggregate production function written in the general form:

Y = F (A, K, L),

Where output is explained as being a function of technology, A in addition to capital (K) and labour (L).

In 1957, after a study of 40 years of growth, Robert Solow concluded that “it is possible to argue that about one-eighth of the total increase is traceable to increased capital per man hour, and the remaining seven-eighths to technical change” (Solow 1957, p316).

The Solow growth model assumes a constant growth rate of productivity, g

Y = A e^gt K^α L^1-α.

This implies that the growth in income in income is determined by productivity growth, g and growth of capital per worker. However, Solow left technological progress unspecified. Moreover, the model assumption of market competitiveness, constant returns to scale lead to further study of the model.

In his seminal paper, Nelson and Phelps (1966) related how level of human capital stock is an indirect determinant of economic growth. They concluded that “the usual, straightforward insertion of some index of educational attainment in the production function may constitute a gross misspecification of the relation between education and the dynamics of production.” (Nelson and Phelps, 1966, p.75) They believe that stock of human capital determines the economic capacity of a nation to innovate, which in turn lead to economic growth. Education and training facilitate the implementation and usage of new techniques makes an economy technologically progressive and more productive. Henceforth, incentives to innovate and market structures necessary for research and development have become important in theories for growth. The Schumpeterian growth literature revived this doctrine. The Schumpeterian theory explains that “current innovators exert positive knowledge spillovers on subsequent innovators as in other innovation-based models, but where current innovators also drive out previous technologies-, generates predictions and explains facts about the growth process that could not be accounted for by other theories.”(Aghion et al, 2013, p.35)

The empirical literature on technical diffusion has been growing. The role of human capital in facilitating technological is supported by Welch (1975), Bartel and Lichtenberg (1987) and Foster and Rosenzweig (1995). The significant spill-overs are documented by the survey of Griliches (1992). Benhabib and Spiegel (1994), using cross-country data, investigate the Nelson-Phelps hypothesis and conclude that technology spillovers flow from leaders to followers, and that the rate of the flow depends on levels of education. As a matter of fact, a great deal of study seeks to analyse the relationship between level of education and technological diffusion and this affects economic growth. Some examples will be Islam (1995), Temple (1999), Krueger and Lindahl (2001), Pritchett, Klenow and Rodriguez-Clare (1997), Hall and Jones (1999), Bils and Klenow (2000), Duffy and Papageorgiou (2000), and Hanushek and Kimko (2000). (Jess Benhabib and Mark M. Spiegel, 2002)

2.3.1 Endogenous growth model

The second approach is the endogenous growth model inspired by Gary Becker’s human capital theory (1964) which directly links human capital to economic growth. The basic idea behind Becker’s view is that growth is driven by human capital accumulation. Nobel laureate Robert Lucas presented an endogenous growth model in which the engine of growth is the human capital. He added “what Schultz (1963) and Becker (1964) call ‘human capital’ to the model, doing so in a way that is very close technically lo similarly motivated models of Arrow (1962), Uzawa (1965)and Romer (1986)” ( Lucas, 1988. p.17). He assumed that individuals choose to allocate time to current production or schooling based on increases in productivity and wages in the future due to the current investment of time in education.

Lucas model can be summarized in

Y = K^ß(UH)^1-ß,

Where H represents the current human capital stock of the individual and U is the fraction of time allocated to current production and K is the per capita stock of physical capital.

2.3.3 Human capital growth model

Over time, with numerous studies on human capital, different variables were included in the growth equation as a measurement of human capital. Drawing upon Mankiw et al. (1992), Barro (1996a, 1996b), Bassanini and Scarpetta (2001), Bloom et al. (2004) and Gyimah-Brempong and Wilson (2004), the following growth equation was modelled in the Baldacci, Clements, Gupta and Cui (2008) paper on Social Spending, Human Capital, and Growth in Developing Countries.

The growth equation is based on the framework of neoclassical growth augmented by the inclusion of education capital, ed, health capital, he, investment ratio, s_k and denotes the set of macro and institutional control variable such as the fiscal balance, inflation rate, trade openness, and governance that augment the baseline specification of the model. Moreover, it is assumed that there is a relationship between the initial stock and increment in human capital with per capita GDP growth, g.

The baseline growth model was as follows:

Where g_it is real capita per income growth,

_1i and _1t denote the country-specific effect and period-specific effect, respectively,

Ln (y_it-1) is the lagged logarithm of per capita income to control for the expected reduction in growth rates as per capita incomes rise and there is convergence to steady growth rates;

S_kit denotes the investment ratio,

Ed_it refers to the stock of education capital, which is proxy by the sum of the gross primary and secondary enrollment rate,

Ed refers to changes in education capital,

He_it refers to the stock of health capital, and he refers to changes in health capital,

^m_it consists of control variables and u_it is the error term.