Manan Mehta Development Formative PDF

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Critically examine the role of human capital as a major factor in promoting the economic growth and development of poor countries. We endeavour to identify some transmission channels of how human capital impacts progress in developing countries using economic growth as the measure of said progress. Naturally, the first hurdle will be determining what ‘human capital’ is in the context of convergence specifically. Concerning the empirical critique, there will be a discussion of how human capital should feature in the production function given the literature following the results of the empirical studies exploring these avenues. It seems necessary to define ‘human capital’ (H) firstly. Traditionally, the literature has considered schooling and education to be the sole of source of human capital, however the turn of the last century has all incorporated the improvement of health and nutrition (Bleakley, 2010) . The concept of a ‘poor country’ may be defined as being deficient of ‘capital’ in the spirit of the neoclassical growth model (Solow, 1957). Empirically, human capital has been linked to attracting namely, physical capital that which Solow uses to define a poor country. (Lucas, 1990) Hereby we shall refer this ‘capital’ as Solow’s capital. Here the fascinating aspect to be dealt with lies in rationalising how it should enter the production function. By and large, we examine here two strands of literature. Solow’s back-of-the-envelope calculation to measure Total Factor Productivity (TFP) raises the question of whether human capital - defined in whichever capacity - contributes to TFP or if it should enter the production function as an independent input as in the AK model (Romer, 1986). For this section of the analysis, note that human capital is defined in the traditional sense of education. Empirical studies to compare the above two transmission mechanisms reveals wildly different conclusions about the role of human capital. The general presumption in endogenous growth theory is that a more educated work force is arguably better at adopting and subsequently implementing new technologies from abroad - technology diffusion - to generate economic growth. Eventually, if said human capital accumulates and also boosts Solow’s capital accumulation process, convergence is what one might expect. Using a human-capital augmented Solow model (Mankiw et al, 1992) where human capital enters independently, we find the role of human capital in economic growth can be shown to have a negative point estimate, or at least lack significance (Benhabib et al, 1994). The alternative transmission via TFP however finds attractive results to establish human capital as significant. The conclusion in this 1994 paper was that human capital generates growth via two channels, namely, the aforementioned attraction one physical variables and as explained above through technology diffusion contributing to the Solow residual. The critique against the disappointing results from the Mankiw regression has been expressed as a failure to distinguish between schooling and skill. To use teacher absenteeism in India as an example of the ‘school attendance’ variable failing to have a sufficient impact on the skills of the upcoming workforce (Duflo et al, 2012), it has been established the measurement of skill differences in cross-country analysis eliminates the issue of ascertaining the varying quality of schooling across different countries.

If skill or ‘learning’ is the variable which has the most significant impact on the Solow residual or indeed on income directly as a standalone variable, we must now introduce connections established between health and the level of learning and skill gain in schools (Bundy, 2005). The rationale here is that ill health and malnutrition has a direct impact on attendance, participation and ultimately achievement which slows down human capital accumulation in poor countries. The question to ask ourselves is whether health should be a part of the human capital calculation - thereby having a first-hand impact on income - or should it feature in the ‘educational production function’ as a variable which ultimately impacts income but not directly. The key finding is often that human capital is not simply impact by health or skill but a whole range of ancillary variables are required. This has given rise to empirical propositions such as :

Y = f (R, F, Z, A, ϵ) Here, Y represents income ; R represents an assortment of school inputs (Hanushek, 2013) ; F is related to families, Z is related general institutions in the neighbourhoods ; A captures the level of accountability of the State following Hanushek (2002). We can clearly tell the immediate empirical critique all academic strides taken in rationalising the role of human capital in generating growth will face a dichotomy. Since defining these already ill-defined variables remains a matter of delicacy and finding a plausible measurement of variable remains even more delicate, specifications will often fall into the traps of multicollinearity as there is bound to be some interaction. The specifications which might avoid this trap of interaction will be critiqued on their omission of significant variables having treated them as ‘ancillary variables’. Since the empirical literature often fails to address this and given Benhabib and Spiegel’s disappointing finding that human capital fails to enter growth rates in their growth accounting regressions, we must review the rationale behind Romer’s postulation to include human capital as a factor of production. As Solow explains a lower capital stock should imply a greater rate of return to capital investment via the non-linear production function exhibiting diminishing returns, we may apply this to human capital too - for now, considering it a separate factor completely. In SubSaharan Africa however, arguably a dataset of developing countries, the empirical literature finds that the low level of skilled labour is not coupled with a high rate of return to education, which seems puzzling at first. (Bigsten et al, 2000) One explanation to this puzzle has been the significance of parental background in the countries within the dataset - in this case, it was the family norm for even educated children to join the manufacturing sector as opposed to facilitate the shift to high productivity sectors which leads to the technology diffusion previously discussed. To provide some evidence, from three studies, we find that including the parental background variable reduces returns to schooling by approximately 20% (Behrman, 1983; Lan, 1993; Heckman, 1986). Quite predictably, the critique for Bigsten’s findings involves the omission of key variables, but what we have learned in response to our initial question is that human capital accumulation requires a specific set of social institutions to successfully impact Y. So policy recommendations such as Bundy’s in 2005 require complementing policies surrounding the inner dynamic of society to achieve the high returns to human capital that empirical literature has implied. Having compared the incorporation of human capital in production functions across time periods and schools of thought, it seems natural to conclude that there is huge variation in how human capital is defined within the literature. This makes the task to dissect and compare studies to produce meaningful inference truly

challenging feat. However, if we momentarily ignore the technical flaws with the empirics or in fact account for the bias due to parental background or poor accountability, a recurrent theme remains the transmission from education to skill to technological adoption to economic growth. The ambiguity in the role of human capital arises from precisely the number of steps the transmission contains and so naturally sparks the questions we have tried to address in this essay. Bibliography Benhabib, J. and Spiegel, M. (1994). The role of human capital in economic development evidence from aggregate cross-country data. Journal of Monetary Economics, 34(2), pp.143-173. Bigsten, A., Isaksson, A., Söderbom, M., Collier, P., Zeufack, A., Dercon, S., Fafchamps, M., Gunning, J., Teal, F., Appleton, S., Gauthier, B., Oduro, A., Oostendorp, R. and Pattillo, C. (2000). Rates of Return on Physical and Human Capital in Africa's Manufacturing Sector. Economic Development and Cultural Change, 48(4), pp.801-827 Bleakley, H. (2010). Health, Human Capital, and Development. Annual Review of Economics, 2(1), pp.283-310. Bundy, D. (2005). School Health and Nutrition: Policy and Programs. Food and Nutrition Bulletin, 26(2_suppl2), pp.S186-S192. Duflo, E., Hanna, R. and Ryan, S. (2012). Incentives Work: Getting Teachers to Come to School. American Economic Review, 102(4), pp.1241-1278. Hanushek, E. (2013). Economic growth in developing countries: The role of human capital. Economics of Education Review, 37, pp.204-212. Lucas, R. (1990). Why Doesn't Capital Flow from Rich to Poor Countries? The American Economic Review, 80(2), 92-96. Retrieved from www.jstor.org/stable/2006549 Mankiw, N., Romer, D. and Weil, D. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), pp.407-437....