HOW DOES HUMAN CAPITAL AFFECT ECONOMIC GROWTH? pdf

NATIONAL CHENG KUNG UNIVERSITY Department of Economics Master’s Thesis How Does Human Capital Affect Economic Growth? Advisor: Prof Chun-Li Tsai Student: Ming-Cheng Hung June 2009 Abstract Based on the empirical investigations and theory of endogenous growth, this paper examines the role of human capital on economic growth from 118 countries over period from 1980 to 2006 The first part of this paper classifies the education into primary secondary and tertiary ones to test whether each educational level affect economic growth differently, and attempt to uncover this different effect among different countries development Then, we focus on the composition of human capital that be categorized into agriculture(agriculture), industry(science, manufacture), service(art, humanity, health, society, service) types to find which types of human capital has the greatest impact to economic growth and add the factor of country’s industrial structure to test whether the growth effect of education depends on the coordination between country’s education fields and its’ industrial structure of local economics Empirical result indicates three conclusions First, tertiary education has the greatest contribution to economic growth for all countries development In particular, the less-developed nations need more tertiary human capital to catch up with the well-developed ones Second, we find out that only the industry skill has positive contribution to economic growth Finally, as including the factor of development and industrial organization, the human capital of industry skill only in developing countries, and the nations with highly-profession guiding in industry has significant growth effect Keyword: economic growth, human capital i ii Contents Introduction Literatures Review 2.1 The human capital to economic growth 2.2 The other variables to economic growth 14 2.2.1 Investment, Fertility rate and economic growth 14 2.2.2 Government expenditures and economic growth 15 2.2.3 Openness and economic growth 16 2.2.4 Political structure and economic growth 16 2.2.5 Inflation and economic growth 17 Methodology and Economic model .19 3.1Methodology 19 3.2 Economic model 21 3.3 Data sources and usages 26 3.4 Descriptive Statistics 28 Estimation Results 31 4.1 The human capital to economic growth 31 4.1.1 the estimation result with whole data 32 4.1.2 the estimation result in different country development 37 4.2 The human capital allocation to economic growth 46 4.2.1 the estimation result with whole data 47 4.2.2 the estimation result in different country characteristics 48 Conclusion .55 Reference 57 Appendix 62 iii List of Tables Table 2-1: the previous literatures about the growth effect of human capital 13 Table 3-1: the predicted coefficient in control variables 26 Table 3-2: the data resource 28 Table 3-3: the descriptive statistics full sample 29 Table 3-4: the descriptive statistics categorize by development .30 Table 4-1: the effect of human capital on economic growth 34 Table 4-2: the effect of human capital on economic growth full sample 35 Table 4-3: the effect of human capital on economic growth well-developed nations 36 Table 4-4: the effect of human capital on economic growth developing nations .39 Table 4-5: the effect of human capital on economic growth less-developed nations .41 Table 4-6: the effect of human caital on economic growth check robust 43 Table 4-7: the effect of human capital on economic growth include dummy variable .44 Table 4-8: the effect of human capital on economic growth different development 45 Table 4-9: the growth effect of human capital composition .51 Table 4-10: the growth effect of human capital composition different development .52 Table 4-11: the growth effect of human capital composition different industry organization(1) 53 Table 4-12: the growth effect of human capital composition different industry organization(2) 54 iv List of Figures Figure 1-1: primary, secondary and tertiary school enrollment rate (1980-2006) Figure 1-2: primary school enrollment rate (1980-2006) Figure 1-3: secondary school enrollment rate (1980-2006) Figure 1-4: tertiary school enrollment rate (1980-2006) Figure 1-5: the framework of this study Figure 4-1: cross-sectional relationship between primary education and growth (2000) 31 Figure 4-2: cross-sectional relationship between secondary education and growth (2000) 32 Figure 4-3: cross-sectional relationship between tertiary education and growth (2000) 32 Figure 4-4: cross-sectional relationship between agriculture skill and growth (2003) 46 Figure 4-5: cross-sectional relationship between industry skill and growth (2003) 47 Figure 4-6: cross-sectional relationship between service skill and growth (2003) 47 v Introduction High level of education is commonly seen as one of the major prerequisite of the world’s current wealth, and as one of the major determinants of future economic growth and development In Fig.1-1, we present the world average enrollment rates1 of primary secondary and tertiary types that recognized as the different level of human capital2, we find the enrollment rate increase continually from 1980 to 2006 It indicates that the education becomes the more and more important resource and government policy all over the world recently Education also has numerous impacts on individual and social development According to Behrman, J & Stacey, N(1997), the higher education will not only contribute to economic growth but also has lots non-market effects and social benefits such as improving people’s health, reducing mortality, decreasing crime activity and mitigating wealth inequality % The gross school enrollment rate Figure 1-1: primary, secondary and tertiary school enrollment rate (1980-2006) Total enrolment in a specific level of education, regardless of age, expressed as a percentage of the eligible official school-age population corresponding to the same level of education in a given school year primary, secondary, and tertiary education, which defined by UNESCO Regarding to the relationship between human capital and economic growth, Nelson and Lucas(1966) recognize average educational attainment as an important factor to technology, and they find the worker with high level of education has tended to adopt productive innovations easily Then according to the “new growth theory” such as Lucass(1988) and Romer(1991), they added the variable of human capital into the growth model and provided new ways of conceptualizing how human capital might contribute to self-sustaining growth of per capita incomes In term of previous literatures, the education can increase the literacy rate and the labor’s productivity that can improve the efficiency of using technology In further, the higher level of education like university produces a lot of researches that be an important resource of new idea and advances in knowledge In addition, the education also has other channels to economic growth First, a spillover effect that raises not only the productivity of those of receiving education but also the productivity of those the work and interact with Second, Katarina & Keller(2007) demonstrate the indirect effect that education can reduce fertility rate and income inequality, those can foster the economic growth deeply Empirically, many studies(Stephan,1997; Chatterji1998; Kwabena,2006) use different indexes3 of human capital and find the positive connection between higher education and economic growth However, the country’s development4 is also an important factor to the growth effect of human capital According to Fig.1-2,3,4 that present the enrollment rate in different development countries from 1980 to 2006, we find out the well-developed nations have the highest enrollment rate in the primary, secondary and tertiary education the previous studies mostly use enrollment rate, average year of school and public education expenditures as the variables of human capital According to the classification by IMF(international Monetary Funds) .However, the primary enrollment rate growths sharply in less-developed nations and catches up with the other countries’ level, but the tertiary education’s gap between welland less-developed nations becomes large gradually Some literatures (Ruth Judson1998, Petrakis,2002 ; Katarina & Keller,2007) discuss the relationship between education, development and economic growth They demonstrate the empirical work that the education and development have the positive relationship and also suggest that the role of primary and secondary education seem to be more important in LDC nations, while growth in well-developed economies depend mainly on tertiary education On the contrary, some literatures (Chatterji,1998 ; Kwabena,2006) demonstrate the different outcome that LDC nations need more tertiary education in order to foster the economic growth and catch up with the other well-developed countries Although there are no obvious theoretical issue about the link between education levels and different developments, those studies indicate that we can’t neglect the factor of country development as discussing the growth effect of human capital In contrast with previous literatures5 that always use the average-period data to discuss the issue about human capital and economic growth because of the missing value and compounding the measurement error in the data by emphasizing errors related to the timing of relationships, this paper uses unbalance panel methodology to solve the problem of missing value and include the year-fixed effect to eliminate the time shock In recently, some literatures address some new idea about the education and economic growth Richard H Mattoon(2006) finds the relationship between education and growth Barro (1998) consider the average period data, 1965-75, 1975-85, and 1985-95 in order to eliminate the missing value and accords with the growth theory, which not attempt to explain short-run business fluctuations Therefore, the application of the theories to annual or other high-frequency observations would compound the measurement error in the data by emphasizing errors related to the timing of relationships the system GMM method to solve this problem Table 4-9 shows the empirical findings in Model III, which included time dummy variables as regressors in our specifications We find out that the human capital with industrial skill has positive and significant impact to economic growth This accord with other previous literatures, which argue the innovation and technology that foster the economic growth are provided by the skill of science and engineer However, the other human capital such as service is categorized as the rent seeker, which does not accumulate the knowledge, but waste the existed resources Therefore the skill of non-science types can not have the significant growth effect In addition, we also pass the AR(2) and sargan test that confirm our instruments are well specified 4.2.2 the estimation result with different country In section I, we prove the conclusion that the science and engineer skill have positive contribution in common with the previous literatures However they don’t consider the country’s development and industrial organization According to the idea studied by Richard H Mattoon(2006), which argue the tertiary education is most successful in influencing economic growth as they are attuned to the economic structure of their local economies, our paper add two type dummy variables that represent the country’s development and industrial organization in order to find whether the human capital of S&E skill has different growth effect in different country’s characteristics Table 4-10 show the empirical finding by model IV that includes the dummy variable categorized by the country’s development, and calculates the overall effect in order to compare their outcome clearly We find out that the science and engineer skill has not all positive contribution to economic growth as classifying the country development 48 Column1~3, which check the robustness of this result, and show the human capital of S&E skill has only significant effect in developing country This result has similar conception studied by Tiago(2003), which find the nonlinear relationship between GDP per capita and S&E skill, and conclude the developing countries need more human capital of science and engineer to support their economics In addition, the skill of service has negative but not robust impact, which accords the literatures provided by Murphy et al (1991) showing the skill of lawyer obstructs the economic growth, but this conclusion we only find in developed nations Table 4-11,12 show the empirical result in model IV as considering the country industrial organization, which classified into highly- moderately- and lowly- profession guiding in industry We adopt two indexes to measure the country’s characteristic: the value-added of industry as the share of GDP and the growth rate of the value-added of industry as the share of GDP The categorization is made according to the following specification: each firm’s respective variable that include the value-added of industry as the share of GDP and it’s growth rate is defined to be “low” if it is in the bottom 33% or in the bottom 10% of the variable’s distribution, “high”if it is in the top 33% or 10%, and “medium” otherwise The left-hand-side columns(column1~2) of Table 4-11 use the bottom third of the distribution of value-added growth of industry as the share of GDP variable for a country to have a low measure, the middle third (i.e between 33% and 67%) to have a medium level, and the top third to have a high level of the variable The right hand- side column(column3~4) of Table 4-11 make a similar categorization, but using instead the 10% and 90% levels as cut-offs in order to check the sensitivity of our results All results 49 have undergone robustness checks, such as including the variable of investment and fertility rate to the estimation The results proved, however, highly robust to such changes We find the result that the human capital of S&E skill only in the country with highly profession guiding in industry has positive and robust contribution to economic growth This prove the conception provided by Richard H Mattoon(2006), which argue that the human capital’s growth depends not only on it’s composition but also on country’s industrial organization When the country with more industry to their economics, they need more skill of S&E to foster their economic growth On the other hand, the country with low or moderately value-added of industry, the human capital of science and engineer has no significant contribution Table 4-12 adopts the value-added of industry as the share of GDP variable measuring the industrial organization, and we also find the similar result However, the industry skill’s growth effect in the nations with highly-profession guiding is not robust as cutting off the data by 10% and 90% levels In conclusion, we confirm our result that when the countries with more production in industry or the growth rate on it, they need more science and engineer skill to support their economics In this section, we discuss the relationship between human capital composition and economic growth Differently to the other previous literatures, we consider the different country’s characteristics such as development and industrial organization and adopt the latest individual-period data First, we find the human capital of science and engineer has positive contribution to economic growth It accords with the previous literatures’ conclusion Second, we show the S&E skill’s growth effect is only significant in developing nations because of needing large educated workers to support their high-speed 50 Table 4-9: the growth effect of human capital composition [1] Yt-1 indt-1 ind2t-1 ind3t-1 [2] [3] [4] -0.916***[0.281] -0.017[0.077] -1.038***[0.328] -0.019[0.079] -1.058***[0.098] -0.020[0.080] -0.930***[0.097] 0.018[0.078] 0.069**[0.032] -0.025[0.022] 0.063*[0.033] -0.001[0.022] 0.056*[0.031] -0.008[0.022] 0.058**[0.030] -0.007[0.022] 0.041[0.031] 0.056[0.038] 0.035[0.029] -1.995***[0.511] -1.775***[0.499] 0.013***[0.004] -0.013[0.044] -1.786***[0.476] 0.014***[0.004] -0.036[0.036] I/Y lnf T/Y G/Y cpi pr Year2000 Year2001 Year2002 Year2003 Year2004 Year2005 Year2006 AR(1) /AR(2) Sargan -2.759**[1.187] -0.021[0.131] 0.280[0.456] 0.376[0.435] 0.290[0.433] 0.159[0.383] -1.198***[0.415] -0.815*[0.450] 0.296[0.457] -1.098***[0.398] -0.780*[0.433] 0.270[0.443] -1.120***[0.393] -0.807*[0.428] 0.270[0.436] 1.255***[0.414] 0.979**[0.399] 1.241***[0.402] 0.932**[0.388] 1.207***[0.397] 0.841**[0.385] -1.370***[0.396] -1.062***[0.373] 0.912**[0.385] -0.174[0.431] 0.646[0.402] 0.000/0.309 0.416 0.000/0.356 0.332 0.000/0.400 0.314 0.000/0.416 0.333 Note: The estimation model III is equation (3.6) G it ind 1i ,t ind i ,t ind i ,t y i ,t Z X i t it (3.7) Z ( I / Y ) i ,t (lnf ) i ,t (T /Y ) i,t (G /Y ) i,t (cpi) i,t 10 ( PR) i,t Robust standard errors, in rackets *,**,*** denote statistical significance at the 10%, 5% and 1% levels, respectively The p-value is shown in AR(1), AR(2) and sargan test in order to check the instruments that are well specified 51 Table4-10: the growth effect of human capital composition different development [1] agriculture Well-developed developing Less-developed industry Well-developed developing Less-developed service Well-developed developing Less-developed AR(1) AR(2) Sargan [2] [3] 0.011[0.528] 0.022[0.088] -0.705[5.804] -0.216[0.549] -0.014[0.089] -2.090[5.827] -0.563[0.524] 0.038[0.086] -0.792[5.459] 0.027[0.078] 0.095**[0.038] 2.391[2.366] 0.019[0.077] 0.124***[0.041] 2.272[2.308] 0.035[0.074] 0.075*[0.039] 2.391[2.222] -0.170*[0.094] -0.020[0.022] 1.567[2.752] 0.000 0.421 0.122 -0.142[0.092] -0.012[0.022] 1.428[2.753] 0.000 0.484 0.056 -0.194**[0.089] -0.001[0.021] 1.877[2.593] 0.000 0.576 0.081 Note: The estimated model is an Model II The coefficient of each column represents the overall effect to economic growth y zy y=1,2,3, z=1,3 Column1~4 respectively include investment, fertility rate to check the robust test Robust standard errors, in rackets *,**,*** denote statistical significance at the 10%, 5% and 1% levels, respectively The p-value is shown in AR(1), AR(2) and sargan test in order to check the instruments that are well specified growth Third, when categorizing the countries into highly- moderately- and lowly profession guiding in industry, the human capital of S&E skill only has positive growth effect in the countries with highly-profession guiding in industry 52 Table 4-11: the growth effect of human capital composition different industry organization(1) 33%-66% 10%-90% [1] [2] [3] [4] agriculture Highly industry Moderately industry Lowly industry 0.104[0.118] -0.082[0.148] -0.220[0.189] 0.064[0.118] -0.115[0.145] -0.151[0.176] 0.028[0.225] -0.075[0.089] -0.078[0.247] 0.103[0.219] -0.075[0.089] -0.056[0.241] industry Highly industry Moderately industry Lowly industry 0.204***[0.058] -0.030[0.075] 0.058[0.058] 0.212***[0.058] -0.013[0.073] 0.022[0.057] 0.298**[0.146] 0.041[0.036] 0.237**[0.119] 0.351**[0.148] 0.045[0.037] 0.052[0.117] 0.016[0.042] 0.050[0.041] 0.157[0.113] Lowly industry -0.091**[0.042] -0.013[0.052] -0.072*[0.040] 0.016[0.051] -0.053**[0.025] 0.023[0.079] 0.181[0.111] -0.024[0.024] 0.062[0.079] AR(1)/AR(2) 0.000/0.359 0.000/0.511 0.000/0.286 0.000/0.210 Sargan 0.339 0.271 0.117 0.060 service Highly industry Moderately industry Note: The estimated model is an Model IV The coefficient of each column represents the overall effect to economic growth y zy y=1,2,3, z=1,3 Robust standard errors, in rackets *,**,*** denote statistical significance at the 10%, 5% and 1% levels, respectively The p-value is shown in AR(1), AR(2) and sargan test in order to check the instruments that are well specified Column1~2 respectively include investment, fertility rate to check the robust test The categorization is made according to the following specification: each firm’s respective variable that include the growth rate of value-added of industry as the share of GDP is defined to be “low” if it is in the bottom 33% or in the bottom 10% of the variable’s distribution, “high”if it is in the top 33% or 10%, and “medium” otherwise 53 Table 4-12: the growth effect of human capital composition different industry organization(2) 33%-66% 10%-90% [1] [2] [3] [4] agriculture Highly industry Moderately industry Lowly industry 0.099[0.178] -0.050[0.148] 0.034[0.189] 0.068[0.168] -0.050[0.114] -0.269 [0.668] 1.386**[0.701] -0.162[0.193] 1.140[0.729] -0.026[0.237] -3.407[2.494] industry Highly industry Moderately industry Lowly industry 0.199**[0.087] 0.055[0.075] -0.027[0.058] 0.149*[0.058] 0.058[0.077] -0.003[0.085] 1.141*[0.690] -0.121[0.077] -0.013[0.090] -0.175**[0.087] -0.024[0.092] Lowly industry 0.050[0.085] -0.023[0.042] 0.042[0.052] 0.049[0.079] -0.005[0.029] 0.047[0.109] 1.129[0.701] 0.014[0.028] -0.299[0.376] 0.851[0.743] 0.007[0.029] -0.137[0.398] AR(1)/AR(2) 0.000/0.244 0.000/0.353 0.001/0.794 0.001/0.790 Sargan 0.390 0.239 0.358 0.346 service Highly industry Moderately industry -4.089*[2.391] Note: The estimated model is an Model IV The coefficient of each column represents the overall effect to economic growth 0.787[0.752] y zy y=1,2,3, z=1,3 Robust standard errors, in rackets *,**,*** denote statistical significance at the 10%, 5% and 1% levels, respectively The p-value is shown in AR(1), AR(2) and sargan test in order to check the instruments that are well specified Column1~2 respectively include investment, fertility rate to check the robust test The categorization is made according to the following specification: each firm’s respective variable that include the value-added of industry as the share of GDP is defined to be “low” if it is in the bottom 33% or in the bottom 10% of the variable’s distribution, “high”if it is in the top 33% or 10%, and “medium” otherwise 54 Conclusion This paper has reexamined the recent debate on the determinants of long-term economic growth with the simple idea that different factors matter for different country groups in relation to development and industrial organization It is also an attempt at methodological improvement in the sense that this paper has verified the hypotheses not only by cross-section estimations but also by average-time period data that used by previous literatures and we use the panel fixed effect and system-GMM estimations simultaneously in order to be safe from possible biases associated with cross-section estimations such as small sample bias, omitted variable problem, and endogeneity of explanatory variables Using the individual-period and latest data, the important findings of this study are following First, we find the secondary human capital has been replaced by tertiary education, which has positive and significant contribution to economic growth Second, the general finding that higher education seem to be more effective in generating growth for developing- and less-developed countries than for advanced ones Although this result contradicts with the most previous literatures, we can conclude that the problem of diminishing return and over-education, which cause the imbalance in labor market and obstruct the economic growth happened in well-developed nations Third, we further discuss the tertiary education by categorizing the university into the fields of agriculture, industry and service in order to demonstrate the importance of human capital composition Although we adopt the individual-period data and the latest time span, this paper has the same conclusion with previous literatures that the skill of S&E has positive and significant 55 effect to economic growth Finally, differently to the other studies, which don’t consider the country’s characteristics, we add the factor of development and industrial organization to our estimation, and find the S&E skill only in the developing nations, and the countries with highly-profession guiding in industry has significant growth effect Therefore, the favorable policy should raise tertiary enrollment rate particularly in developing and less-developed nations, and then focus on the human capital of S&E skill in university especially in developing countries and the nations with highly-profession guiding in industry However, there are three attentions and drawbacks to our study First, It is possible that our results overestimate the effects of higher education while underestimating those of primary and secondary education on economic growth even though the equation includes primary and secondary education human capital as regressors Any person who has achieved any level of higher education also has attained some years of primary and secondary education By definition, our measure of higher education includes lower levels of education, hence the coefficient of tertiary education overstate the growth effects of higher education Second, we 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