Human capital and growth

Transcription

1 Copyright rests with the author Human capital and growth Angel de la Fuente* Instituto de Análisis Económico (CSIC) March 2007

2 1. INTRODUCTION Many economists and policy-makers think that human capital is an important production factor, highly complementary with technological capital, and crucial in an increasingly knowledge-intensive economy. The available empirical evidence, however, has not always supported this view. I will briefly review the literature on human capital and growth and focus on recent work that provides evidence consistent with this hypothesis.

3 2. A BRIEF REVIEW OF THE EVIDENCE The literature on human capital and growth has produced mixed results. We can identify three groups of studies with conflicting conclusions: 1) Early studies focusing on cross-country growth performance over long periods, generally yielded positive results (e.g. Barro (1991), Barro and Lee (1994), Mankiw, Romer and Weil (1992)). 2) Second-round studies that used data on growth over shorter periods to increase the number of observations and control for other factors, often produced negative results (e.g. Benhabib and Spiegel (1994), Islam (1995), Caselli et al (1996), Pritchett (1995)).

4 Table 1: Coefficient of human capital in various specifications of MRW's model pooled crosssection fixed effects Mankiw, Romer & Weil (1992) 22 OECD 0.23 (2.09) 75 countries 0.23 (3.83) Lichtenberg (1992) 53 countries 0.26 (3.65) Islam (1995) 22 OECD (0.10) (0.31) 79 countries (0.53) (1.81) Caselli et al (1996) 97 countries (3.33) (2.09)

5 3) Recent studies that use improved data sets or allow for measurement error again generate positive and significant coefficients and help explain previous negative results. (e.g. Krueger and Lindhal (2001), de la Fuente and Doménech (2000, 2006), de la Fuente (2003), Cohen and Soto (2001), Bassanini and Scarpeta (2001)).

6 These studies show that a) The quality of the data used in the previous round of empirical studies was very poor. b) Measurement error tends to hide the connection between human capital accumulation and productivity growth. c) Correcting for this problem yields much larger estimates of the coefficients that measure the contribution of human capital to productivity growth. d) These estimates suggest that the economic return to investment in education is probably higher than that on physical capital.

7 3. PROBLEMS WITH CROSS-COUNTRY ATTAINMENT DATA There have been several attempts to construct international data sets on educational attainment. Even the best sources present problems: - implausible cross-section and time-series profiles. - they often do not agree with each other (Figure 1) One of the main problems is that changes in classification criteria generate sharp breaks in the series. (Figure 2)

8 Figure 1: Average years of schooling in 1985: B&L (1996) vs NSD 14 Nehru et al Ire 12 Jap US Por Fr It Sp Ost Gr Nor Ice UK Nl-Ge Bel CH Swe Fin Can Dk Aus NZ 4 Tur Barro and Lee (1996)

9 Figure 2: Evolution of university attainment levels in selected countries, Barro and Lee (1996) database Aus NZ Can changes in classification criteria generate sharp breaks in the attainment series

10 Such breaks in the schooling series will lead us to underestimate the strength of the relationship between schooling and growth, especially in panel data or firstdifference specifications, because it generates noise, i.e. spurious variability in educational attainment that will not be matched by changes in productivity. (See Figure 3)

11 Figure 3: Effects of measurement error on the estimated relationship between schooling and growth productivity growth growth of human capital these breaks generate outliers and flatten the fitted regression line

12 Since several attainment series are available, their information content can be measured using a procedure suggested by Krueger and Lindhal (2001) to measure reliability ratios (signal/signal+noise). These indicators can be used to calculate the size of the bias induced by measurement error. The results suggest that - older data sets contain substantial measurement error that can explain adverse results in the literature, - newer ones have succeeded in increasing significantly the information content of the series - but a significant downward bias still remains.

13 Figure 4: Average reliability ratios for different schooling data sets 70% 60% 50% 40% 30% 20% 10% 0% D&D (2002) C&S (2001) D&D (2000) Kyr. (1991) B&L (2000) B&L (1996) B&L (1993) NSD (1995) - Source: de la Fuente and Doménech (2002), Table 8b. - Key: NSD = Nehru et al (1995); Kyr = Kyriacou (1991); B&L = Barro and Lee (various years); C&S = Cohen and Soto (2001); D&D = de la Fuente and Doménech (various years).

14 4. CORRECTED ESTIMATES FOR THE OECD DD (2006) estimate several growth specifications using different schooling series and use the results to construct metaestimates corrected for measurement error bias. Table 1: Alternative estimates of the coefficient of human capital (α s ) in different production function specifications H data from: NSD KYR B&L93 B&L96 B&L00 C&S D&D00 D&D02 avge. levels (2.02) (2.18) (4.49) (4.82) (6.19) (7.98) (7.76) (6.92) (5.30) f. effects (0.76) (1.86) (3.30) (1.80) (3.74) (4.49) (3.99) (6.51) (3.31) differences (0.70) (0.15) (2.52) (1.47) (1.28) (2.57) (2.17) (3.10) (1.75) catch up (1.61) (0.29) (1.80) (0.11) (0.31) (3.52) (3.47) (2.89) (1.24)

15 We find that estimates of the coefficient of schooling in the production function get larger and more significant as we move to more recent data sets with higher reliability ratios. We extrapolate the observed relationship between data quality and the size of the estimated coefficient to obtain consistent estimates. The results suggest that the true value of the human capital parameter is at least 0.50 and more likely over This is over twice the size of the most widely accepted estimates from the previous literature.

16 Figure 5: Estimated human capital coefficient vs. reliability ratio 1 ˆ " s catch-up differences fixed effects levels 0.25 estimate without measurement error SUR reliability ratio catchup fe diff levels pred

17 5. SOME IMPLICATIONS Our estimates suggest that the productivity effects of human capital are substantial. In a recent report for the EU Commission, we estimate that an additional year of average school attainment raises productivity in the average EU country by 6.2% on impact and by a further 3.1% in the long run through its contribution to faster technological progress. The first of these effects is considerably higher in the cohesion countries and in Italy, reaching 9.2% in the case of Portugal.

18 Cross-country differences in educational attainment account for an important fraction of observed productivity differentials. At the EU level, education may be as important as physical capital for improving cohesion. The social return to investment in human capital is quite high. In most EU countries, it is higher than the return on physical capital. This suggests that additional investment in education would be beneficial, even if financed by a reduction in other types of capital expenditure.

19 Figure 6: Decomposition of the productivity differential with the sample average in a typical OECD country in 1960 and % 40% 30% 20% 10% 0% physical capital human capital TFP

20 Figure 7: Social rate of return to schooling in the EU 12% 11% 10% 9% 8% 7% 6% Po Sp Ir Gr UK It Nl avge Be Fr Dk Sw Ge Ost Fi baseline min

21 Figure 8: Social rate of return to schooling under different scenarios and returns on physical capital in the average EU country 12% 10% 8% 6% 4% 2% 0% only level effects + employment effects + rate effects + stdiffe correction return on physical capital baseline min/max

22 Figure 9: Social premium on human capital 4% 3% 2% 1% 0% -1% Fi It Dk Gr Nl Sp Ir avge Sw Ge Po Be Fr Ost UK -2% -3% baseline min