Acemoglu, D., 1996, “A Microfoundation for Social Increasing Returns in Human Capital Accumulation,” Quarterly Journal of Economics, 111, 779-804.
Acemoglu, D., and J. D. Angrist, 2000, “How Large Are Human-Capital Externalities? Evidence from Compulsory Schooling Laws,” NBER Macroeconomics Annuals, 9-59.
Alesina, A., S. Danninger, and M. Rostagno, 2001, “Redistribution Through Public Employment: The Case of Italy,” IMF Staff Papers, 48, 447-473.
Angrist, J. D. and A. B. Krueger, 2001, “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments,” Journal of Economic Perspectives, 15, 69-85.
Bartel, A. P. and N. Sicherman, 1999, “Technological Change and Wages: An Interindustry Analysis,” Journal of Political Economy, 107, 285-325.
Benabou, R., 1996, “Heterogeneity, Stratification, and Growth: Macroeconomic Implications of Community Structure and School Finance,” American Economic Review, 86, 584-609.
Brunello, G., S. Comi, and C. Lucifora, 2001, “The Returns to Education in Italy: A New Look at the Evidence,” in H. Colm, I. Walker, and N.W. Nielsen (editors), The Returns to Education in Europe, Edward Elgar, Cheltenham.
Cannari, L., and G. D’Alessio, 1995, “II rendimento dell’istruzione: alcuni problemi di stima,” Bank of Italy, Temi di Discussione No. 253.
Cannari, L., F. Nucci, and P. Sestito, 2000, “Geographic Labor Mobility and the Cost of Housing: Evidence from Italy,” Applied Economics, 32, 1899-1906
Cannari, L., and Signorini L. F., 2000, “Nuovi strumenti per la classificazione dei sistemi locali,” in Signorini (ed.), Lo sviluppo locale, Meridiana Libri, Roma.
Card, D., 1999, “The Causal Effect of Education on Earnings,” in Ashenfelter and Card (eds.) Handbook of Labor Economics, North-Holland.
Card, D., and A. B. Krueger, 1992a, “Does School Quality Matter: Returns to Education and the Characteristics of Public Schools in the United States,” Journal of Political Economy, 100, 1-40.
Card, D., and A. B. Krueger, 1992b, “School Quality and Black-White Relative Earnings: A Direct Assessment,” Quarterly Journal of Economics, 107, 151-200.
Card, D., and A. B. Krueger, 1996, “Labor Market Effects of School Quality: Theory and Evidence,” in Burtless (ed.) Does Money Matter? The Effects of School Resources on Student Achievement and Adult Success, Brooking Institution, Washington D.C..
Checchi, D., A. Ichino, and A. Rustichini, 1999, “More Equal but Less Mobile? Education Financing and Intergenerational Mobility in Italy and the US,”Journal of Public Economics, 14, 351-393.
Ciccone, A., and G. Peri, 2002, “Identifying Human Capital Externalities: Theory with an Application to US Cities,” mimeo, Universitat Pompeu Fabra, Barcelona.
Colussi, A., 1997, “II tasso di rendimento dell’istruzione in Italia: un’analisi cross-section,” Politico Economica, 13, 273-294.
De la Fuente, A., and R. Domenech, 2001, “Schooling Data, Technological Diffusion and the Neoclassical Model,” American Economic Review, Papers and Proceedings, 323-327.
Duranton, G., and V. Monastiriotis, 2002, “Mind the Gaps: The Evolution of Regional Earnings Inequalities in the U.K., 1982-1997,” Journal of Regional Science, 42, 219-256.
Faini, R., G. Galli, P. Gennari, and F. Rossi, 1997, “An Empirical Puzzle: Falling Migration and Growing Unemployment Differentials,” European Economic Review, 41, 571-579.
Glaeser, E., H. D. Kallal, J. A. Scheinkman, and A. Shleifer, 1992, “Growth in Cities,” Journal of Political Economy, 100, 1125-1152.
Goldin, C., and L. F. Katz, 1998, “The Origins of Technology-Skill Complementarity,” Quarterly Journal of Economics, 113, 693-732.
Haveman, R., and B. Wolfe, 1984, “Scooling and Economic Well-Being: The Role of Nonmarket Effects”, Journal of Human Resources, 19, 377-407.
Haveman, R., and B. Wolfe, 2002, “Social and Nonmarket Benefits from Education in an Advanced Economy,” mimeo, Institute for Research on Poverty, University of Wisconsin-Madison.
Mankiw, G., D. Romer, and D. Weil, 1992, “A Contribution to the Empirics of Economic Growth,” Quarterly Journal of Economics, 107, 407-437.
Manning, A., 2002, “The Real Thin Theory: Monopsony in Modern Labour Markets”. Adam Smith Lecture, EALE Conference, Paris. Forthcoming Labour Economics.
Mauro, P., E. Prasad, and A. Spilimbergo, 1999, “Perspectives on Regional Unemployment in Europe,”IMF Occasional Paper 111, International Monetary Fund, Washington.
Moretti, E., 2002, “Estimating the Social Returns to Higher Education: Evidence from Longitudinal and Repeated Cross-Sectional Data,” NBER Working Paper 9108, forthcoming Journal of Econometrics.
Rauch, J. E., 1993, “Productivity Gains from Geographic Concentration of Human Capital: Evidence from the Cities,” Journal of Urban Economics, 34, 380-400.
Rudd, J., 2000, “Empirical Evidence on Human Capital Spillovers,” Finance and Economics Discussion Paper 2000-46, Federal Reserve Board, Washington.
Schultz, T. P., 1994, “Human Capital and Economic Development,” Economic Growth Center Discussion Paper No. 711, Yale University, Yale.
University of Siena and Bank of Italy, respectively. The authors thank Luigi Cannari, Antonio Ciccone, Piero Cipollone, Bob Haveman, Massimo Omiccioli, and Athanasios Vamvakidis for helpful discussions on earlier versions of this paper. They are also grateful to Masahisa Fujita, Vernon Henderson, Will Strange, and the other participants in the Center for Economic Policy Research conference “The Economics of Cities” (London, 6-8 June 2003) for their comments. The views expressed herein are those of the authors and not necessarily those of the Bank of Italy. Mr. de Blasio was a member of the IMF staff when the project on which this paper is based was initiated.
According to Weisbrod (1962, p. 106): “[Education] benefits the student’s future children, who will receive informal education at home; it benefits neighbors who may be affected favorably by the social values developed in children by the schools and even by the quietness of the neighborhood while the schools are in session. Schooling benefits employers seeking a trained labor force; and it benefits the society at large by developing the basis of an informed electorate.”
The Mincerian approach evaluates social returns to education by merely looking at wage differences across areas. This strategy has two main limits, which tend to bias the size of actual spillovers downwards. First, average human capital may have effects that go largely beyond the boundaries of the local labor market. For example, research at the Massachusetts Institute of Technology can have nationwide, or even worldwide, effects while affecting differential productivity in the Boston area only marginally. Second, Haveman and Wolfe (1984, 2002) have argued that wage differences capture only a portion (and possibly only a small portion) of the full “social” effects of education. For example, reductions in criminal activity owing to schooling may generate both higher average productivity and nonmarket effects, such as higher social cohesion. The Mincerian approach will only capture the productivity effect of less criminality on wages.
As noted by Bils (2000, p.60), “particularly for models based on externalities in production, it is not clear if the state of residence is the relevant economy.”
Full detail is provided in /locali/studi/misf/areaind/INDAGINI/ibf/pubblica/shiw00.pdfltalian Household Budgets, Supplements to the Statistical Bulletin, Banca d’ltalia, various years.
Workers who did not report their age when taking the first job are therefore dropped from the sample. Our measure of experience is more accurate than the most widely used measure of seniority (Experience = Age - Years of Schooling – 6), which attributes “waiting unemployment” after school to work experience.
We use the 1981 Census to derive our demographic instruments; information from the 1993 Company Account Data Service for a measure of LLMA capital per worker; and data from the real estate private agency “II Consulente Immobiliare” to check our index of housing costs.
Acemoglu and Angrist (2000) and Ciccone and Peri (2002) point out that, unless the elasticity of substitution is infinite, the effect of the average level of local education on the average local wage-level is positive for any CES technology even in the absence of spillovers. Ciccone and Peri (2002) tackle this issue by adopting a “constant-composition approach” which is designed to measure pure human capital externalities.
The inclusion of controls for the branch of activity and firm-size may be criticized on the ground that such variables are determined together with the labor market outcomes. However, the exclusion of these controls from equation (1) does not change our results.
We also estimate a model in which private returns to education are non-linear in the years of schooling. For this purpose, we replace the categorical variable INDIVIDUAL EDUCATION with dummies for each year of schooling as suggested by Heckman, Layne-Farrar and Todd (1996). This has negligible effects on the estimates of average human capital returns.
Acemoglu and Angrist (2002, p.20) address the same problem by distinguishing between State-of-Birth and State-of-Residence across individuals.
The F-test on the instruments displays a P-value equal to 0.000.
Instrumenting with the 1971 demographic structure delivers similar results.
The first-stage F-test on the 1962 Mandatory Middle School Reform has a low predictive power in our sample when used as the only instrument. Similarly, Brunello, Comi, and Lucifora (2001) use compulsory schooling laws to augment family background variables when estimating private returns to schooling for the 1995 SHIW sample.
Migration flows in Italy have limited size, compared with the U.S. Internal migration from the South of Italy to Northern regions, a salient feature of the Italian development process during the fifties and the sixties, died out in the first half of the seventies: see Faini, Galli, Gennari, Rossi (1997).
In order to control for endogenous sorting, Moretti (2002) includes a set of Individual×City dummies in the 1979-1994 NLSY panel, so that variation coming from migrants is lost and identification is based on stayers only. He concludes that unobserved ability is not a major source of bias. By contrast, Ciccone and Peri (2002) find some evidence that cities with higher average schooling do attract better workers. However, Ciccone and Peri (2002) restrict the definition of stayers to (i) those who lived in the same house over a 20-year period, and (ii) those who had been living in the same city five years before their wages were observed and that were born in the state where they reside.
Since we can match the area of residence with the area of birth for each individual during the nineties, our identifying assumption fails to capture “comebacks”, such as individuals who migrated in youth and returned to the place of birth later on. However, “comebacks” are a small percentage of the internal migration rates and are mostly confined to retired workers, who are not included in our sample: see Bonifazi (1999).
Census variables (such as human capital in 1991 and population structure in 1981 and 1971) may reflect the changes in the labor-composition structure induced by migration from Southern to Northern Italy, a phenomenon which largely died out after 1975. However, it must be noted that movers and stayers were broadly characterized by similar skills in Italy: see Cannari, Nucci, and Sestito (1997). Moreover, we also consider local human capital effects limited to young cohorts, which is, those born after 1959 (3.607 individuals, whose age were less than 15 in 1975), and after 1964 (1.592 individuals, who were less than 10 in 1975). Our Census variable tends to measure more accurately the human capital level to which the younger cohorts were actually exposed. The estimated social returns, obtained under G2SLS with both average and individual education instrumented, are respectively equal to 0.033 and 0.036.
We thank Antonio Ciccone for suggesting this procedure to us.
This observation is relevant especially for firms that produce traded goods: see Acemoglu and Angrist (2000, p.19, Note 7). To this regard, our estimates for the manufacturing sector in the Section III.C are particularly useful.
Glaeser and Maré (2001) also use housing prices to measure for cost-of-living differentials, a partial measure of local price levels. Another limit of the analysis is the inability to measure for local “amenities”.
Results based on the index for rents are very similar.
Restricting the sample to stayers leads to somewhat higher effects on living standards, but the main conclusion does not change.
Local unemployment and public infrastructure do not specifically refer to manufacturing. The inclusion of such controls in specifications (4.1)-(4.9) does not lead to any difference in our results.
Estimates are unadjusted for cost-of-living differentials.
We have also used the 1993 Company Account Data Service index of capital per worker, which was calculated at the LLMA level by Fabiano Schivardi. The results do not differ from the those reported above.
In a search model of skill acquisitions, Jovanovic and Robb (1989) suggest that productivity depends on both the overall level and spatial concentration of human capital in a local market.
Cingano and Schivardi (2001) find that this measure of agglomeration affects total factor productivity in Italian manufacturing. To account for differences in labor market participation rates, we also replaced this measure with the LLMA share of manufacturing workers over total employment. Results did not change.
This two-group separation is quite natural in the Italian case, given that mandatory school covers up to 8 years of schooling.
Our sample confirms this 1 percentage point difference between North and Center-South.