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Contribution of female human capital in economic growth: an empirical analysis of Pakistan (1972–2012)

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Abstract

This study is to find out an impact of female human capital on economic growth of Pakistan. The study has therefore, used gender separate human capital as an explanatory variable along with other factors, labor force and physical capital. In this regard a composite human capital has been constructed by taking education and health as a proxy for human capital. The long run and short run dynamics among female human capital and economic growth are empirically tested on time series data spread from 1972–2012. Johanson’s co-integration approach has been applied for the long run and Vector Error Correction Model used for the short run relationship. The results show that the long run relationship between female human capital and economic growth is positive and significant. While the short run impact of female human capital on economic growth is positive but statistically insignificant.

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Authors and Affiliations

  1. National College of Business Administration (NCBA&E), Lahore, Pakistan

    Mussarat Khadija Khan

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  1. Mussarat Khadija Khan
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Correspondence to Mussarat Khadija Khan.

Appendix A

Appendix A

1.1 Human capital (H)

$$ {\text{Female human capital }} = {\text{ Female education index }} + {\text{ Female health index}}/ 2 $$
$$ {\text{Male human capital }} = {\text{ Male education index }} + {\text{ Male health index}}/ 2 $$

1.2 Education index (female and male)

According to the Pakistani education system, the number of enrolment at different levels of schooling years are taken in five categories. First one is the primary level consist at five years of schooling, second for middle stage consist at eight years of schooling. Then secondary and higher secondary consist on ten and twelve years of schooling respectively. Professional and university level of education cover sixteen years of education. The following formula has been used to construct a weighted education for male and female separately.

$$ {\text{W}}.{\text{ Education }} = \left( {5H_{1t} + 8H_{2t} + 10H_{3t} + 12H_{4t} + 16H_{5t} } \right)/POP\,_{t} $$

H t stands for enrolment (female and male) at time \( t \) and POP for total population (female and male) at time t. Weighted Education is further standardized by taking maximum and minimum values observed as goalposts in order to transform the indicator into indices from 0 to 1.

Maximums are the highest observed values in the time series as it is used by UNDP in the construction of Human Development Index (HDI) (2006, 2011). While UNDP has conceived minimum values at some subsistence level (vary in dimensions of development and over the period). However in this study maximum and minimum values are observed values in time series to transform indices from 0 to 1.

$$ {\text{Education index }} = \, \frac{{\left\{ {\left( {{\text{actual}} - {\text{minimum}}} \right){\text{ W}}.{\text{ Education}}} \right\}}}{{\left\{ {\left( {{\text{maximum }}{-}{\text{ minimum}}} \right){\text{ W}}.{\text{ Education}}} \right\}}} $$

1.3 Health index (female and male)

Life expectancy is a proxy used for health. It is further standardized by taking maximum and minimum values (goalposts) in order to transform the indicators into indices from 0 to 1.

$$ {\text{Health index}} = \;\left\{ \frac{{\left( {{\text{actual}}\,{-}\,{\text{minimum}}} \right){\text{ life expect}}}}{{\left( {{\text{maximum}}\,{-}\,{\text{minimum}}} \right){\text{ life expect}}}}\right\} $$

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Khan, M.K. Contribution of female human capital in economic growth: an empirical analysis of Pakistan (1972–2012). Qual Quant 50, 709–728 (2016). https://doi.org/10.1007/s11135-015-0172-6

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