Human Capital Composition and Economic Growth at the Regional Level Fabio Manca IPTS / JRC - European Commission Lisbon-Coinvest Conference Introduction (1): Inter-regional Technology differences In a recent paper, Acemoglu and Dell (2009) argue how "between-municipality (regional) differences in labor income are about twice the size of between-country differences“ Differences in physical capital cannot account for the whole observed differences in productivity levels and for the large growth differentials. Capital moves freely and its PRODUCTIVITY (or technology) to drive economic differentials. Since, at the regional level the available technology frontier is the same for all, what matters for regional economic growth is, hence, the relative efficiency with which economic agents in each region are capable of implementing the available technology and taking advantage of it profitably (technology implementation/adoption—full exploitation). The discussion about technology differences is usually accompanied by the statement that human capital (HK) should be considered as one of the main factors boosting catch-up (Abramovitz (1986), Nelson and Phelps (1966), Benhabib and Spiegel (1994, 2005)). Introduction (2): Is Human Capital Composition ? Average measures of HK are sometimes proven to be weakly correlated to economic growth in cross section and panel analysis (see Krueger and Lindhal (2001), de la Fuente and Domenech (2002) or Pritchett (1996)) Other strands of literature point to the uneven (non-lineal) impact that HK may have on economic growth and catch-up (Vandenbussche, Aghion and Meghir (2006)). This depends on the actual development stage of the region/country taken into consideration since the engine of growth can be dual: 1) Innovation at the frontier 2) Technology adoption (for the followers) The idea is that HK proxies for the ability of regions (and broadly of economic agents) to absorb or adopt the available technology. How can we link HK to Technology Spillovers (adoption/absorption)? Introduction (3): Human Capital and Technology Adoption Basic assumption of our Technology catch-up model: Technology Adoption is a COSTLY ACTIVITY (Technology adoption is not a free lunch at all!) Adoption of a technology need the correct skills in order to: 1) Motivate the adoption (managerial and entrepenurship skills, …why to adopt this technology rather than the other) 2) Knowledge of the Market Potential of a new Technology to be adopted Maskus (2000): Imitation usually takes the form of adaptations of existing technologies to new markets” 3) Skills, necessary in order to make the new technology operative Mansfield, Schwartz and Wagner (1981): over 48 different products in chemical, drug, electronics and machinery U.S. industries, the costs of imitation lied between 40% and 90% of the costs of innovation Teece (1977) estimated the cost of technology transfer across regions to be equal, on average, to 19% of total project expenditure Introduction (4): Human Capital and Technology Adoption 4) Organizational and Productive changes, usually need trained workforce in order to implement a process innovation. Nelson and Phelps (1966) "it is clear that the farmer with a relatively high level of education has tended to adopt productive innovations earlier than the farmer with relatively little education [...] for he is better able to discriminate between promising and unpromising ideas [...] The less educated farmer, for whom the information in technical journals means less, is prudent to delay the introduction of a new technique until he has concrete evidence of its profitability". The idea is therefore that, if TWO (LAGGING) REGIONS DIFFER IN THEIR HK, THE ONE WITH BETTER TRAINED WORKFORCE WILL BE ABLE: 1) To adopt the same technology…YES, but faster 2) To adopt MORE technologies 3) To discern among profitable and unprofitable ideas (technologies) 4) To find the correct market niche for the new technology in the local market 5) To better organize the work (when the technology has been adopted) 6) To CATCH-UP FASTER In one word, the cost of adoption will be lower for those who are endowed with highly educated workforce (while there is no apparent reason to believe that unskilled workers will be better suited than skilled ones in the adoption of technology) Aims of the paper, twofold Theory We build a catching-up model where follower regions adopt innovations from the technological frontier. Leader and follower are endowed with different HK levels and Institutional Quality. More importantly, the Composition of the HK is different across regions and determines imitation possibilities and finally economic growth (catch-up or divergence). The high skilled margin of the workforce (the fraction of population with higher education) rather than “average” HK is expected to lead to GDP convergence Empirics We test the model using dynamic panel for Spanish Regions and Provinces for the period 19601997. We address endogeneity by making use of Robust System GMM estimations Basic Setup For simplicity we assume only 2 regions exist named i=1,2 1 is Leader and 2 is Follower The two regions produce the output Y by means of a Spence/Dixit-Stiglitz (1977) production function as follows: where 0 1 X ij Quantity of jth nondurable intermediate good Ni Number of types of intermediates available in country i, (proxy for TECHNOLOGICAL LEVEL of the region) Lyi Fraction of labor used in the production of goods, (Unskilled) Ai Institutional/government quality of each region Human Capital composition As in Romer (1990) labor is used in the production of goods and in the R&D sector in order to produce new blueprints L ri is the fraction of population with a higher schooling degree working in the R&D sector. (skilled workers) Lyi is the fraction with low schooling working in manufacturing sector. (unskilled workers) The fraction of highly-educated people is higher in the Leader Region than in the Follower one ...and viceversa Human capital composition and innovation and imitation costs Imitation cost is assumed to be also an inverse function of the skills of the workforce cost of imitation is considerable and lies in between 40% and 90% of innovation costs: (Teece (1977), Mansfield et al. (1981), Maskus (2000) Imitation is a “human capital demanding” activity (Nelson and Phelps (1966), Benhabib and Spiegel (1994, 2005)) If two regions perform imitation, the one with better skills will imitate faster and better, being able to do more reverse engineering or adaptations of technologies discovered at the frontier. When a new intermediate is invented the innovator in region 1 retains monopoly power over the use of it for production, hence: profit to innovator Maximization of profits yelds country 1 total output Rate of return Growth rate of the Leader Economy as a function of human capital composition Similarly, for the follower region it will be that the process of adoption leads to: region 2 total output Region 2 growth rate as a function of its human capital composition. Results of the theoretical model (2) Proposition 2: The long-run technological proximity between region 1 and 2 depends on the relative differences in the composition of human capital, on differences in institutional and bureaucratic quality… Proposition 4: A rise in the fraction of population with a higher level of education is growth enhancing. Conversely, a rise in the fraction of population with a lower degree of education is growth diminishing. The result applies in imitation or innovation for both region 1 and region 2 economies. The result holds as long as basic education is positive (and not everyone is highly skilled!) Empirical results Data: •Regional Panel for Spain (1960-1995, 17 regions) •Provinces Panel for Spain (1965-1997, 50 provinces) GVA (BBVA foundation) GVA (BBVA foundation) Human capital data (de la Fuente, Domenech, Jimeno 2006) Human capital data (IVIE, Insituto Valenciano de Investigaciones Economicas) •HK1 (primary educ.) •HK21 (lower secondary) •HK22 (upper secondary) •HK31 (higher educ., first) •HK32 (higher educ., second) •HK1 (no education) •HK2 (primary educ.) •HK3 (vocational training) •HK4 (pre-university degrees) •HK5 (higher education) Econometric model and estimations Endogeneity: In dynamic panel we make use of: First-difference GMM estimators such as those proposed by Arellano and Bond (1991) However, when working with very persistent series (close to random walk) as in the case of educational variables the lagged realizations of these variables convey little information so: System GMM better perform as proposed by Arellano-Bover(1995)/Blundell-Bond (1998) These estimators allow to build internal instrumental sets relying on the moment conditions produced by exploiting lagged (and differenced) realizations of the variables in the model (both dependent and exogenous/endogenous variables are used) Issues and warnings: i. Instruments count can overfit the endogenous variable leading to unrealiable estimates of the Hasen-test for Over-Identification (usually overlooked in empirical works!) i. Two-step System GMM Standard errors are downward biased with many instruments. A correction is proposed by Windmeijer (2005) and we apply it i. Control for any pattern of heterosckedasticity and autocorrelation within the panel Regional Panel (1960-1995) Higher education reduces GDP gap across regions Lower education enters negatively and insignificantly Intermediate educational levels show a mixed result: i) HK31 (negative) Higher education: general bachelor degree+Peritaje ii) HK22 (positive) Upper secondary: Vocational Training+lower technical diploma Instruments proliferation affects over-id test Regional Panel (1960-1995) Higher education reduces GDP gap across regions When we aggregate Intermediate education the effect of different (lower) types of HK averages out and the coefficient turns out not to be statistically significant Lower secondary levels are not significant but negatively related to GDP catch-up Primary education is positive and significant pointing to the need of “some” degree of education as in the theoretical model Provinces Panel (1965-1997) Again, higher education reduces GDP gap across regions Pre-University Degrees: Humanities and Social Science (mainly) Lower secondary: Vocational Training Social Capital index (IVIE) is inserted to control for institutional quality. It is positive (as expected) and statistically significant Provinces Panel (1960-1995) Different aggregations for the intermediate educational levels do not change the results Intermediate educational levels show a negative coeff with very strong significance even after the aggregation Higher Education reduces the GDP gap across provinces Conclusions (Theory) 1. Recent theoretical (and empirical) literature takes imitation to be a “no-skill” demanding activity better performed by large fractions of unskilled workers. This leads to assume that follower regions may simply “seat and wait” for the higher returns to imitation to make them grow faster and catch-up with the leaders. 1. Hovewer the reality seems to be that follower regions have to make huge efforts in order to catch-up 1. This is because imitation is itself a costly activity (in a range of 60-90% of innovation costs). Hence, it requires skills to be performed (engineers to do reverse engineering, technicians to replicate the blueprints and so on) 1. Hence, it is the high-skill margin of the workforce to push for catch-up at the regional and country level. 1. When we insert costly imitation and HK composition differences into a Catch-up model this predicts convergence for those regions which more than other raise their educational attainment levels (even if they rely on imitation to grow!) 1. Our results argue for investments in “education excellence” rather than simply raising average educational attainments. Good few universities are better than many and mediocre ones (the same for research centres!) Conclusions (Empirics) 1. Regions which show the highest shares of highly educated workforce are those closing the GDP gap with the frontier. Imitation is itself a costly activity (in a range of 60-90% of innovation costs). Hence, it requires skills to be performed (engineers to do reverse engineering, technicians to replicate the blueprints and so on) 1. Vocational Training seems to show a (weak) but positive impact on Technology catchup while more generalist diplomas a negative one (finer data disaggregation would be needed to better analyze this phenomena) 3. We propose System GMM estimation robust to heterosckedasticity, autocorrelation and instruments proliferation (as in Roodman (2006). When endogeneity is correctly dealt with we are able to show how highly skilled workforce is actually driving GDP catch-up across regions. Other empirical works do not fully account for “endogeneity issues” and find different results 4. The results are robust to the use of different aggregation levels (regions and provinces), the use of different datasets and the insertion of institutional quality controls as well as aggregations of the HK variables.