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.