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Infrastructure and Long Run Economic Growth

Infrastructure and Long Run Economic Growth. David Canning Infrastructure and Growth: Theory, Empirical Evidence and policy Lessons Cape Town 29-31 May 2006. Theory. Public goods Raises issues of level of provision This argument is weakening with new technology

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Infrastructure and Long Run Economic Growth

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  1. Infrastructure and Long Run Economic Growth David Canning Infrastructure and Growth: Theory, Empirical Evidence and policy Lessons Cape Town 29-31 May 2006

  2. Theory • Public goods • Raises issues of level of provision • This argument is weakening with new technology • Externalities to Infrastructure • Price may be less than the marginal social benefit

  3. Externalities • Extent of the market • Specialization • Contestability and pricing • Intermediate goods • Specialization • The big push – escape the poverty trap • Power and industrialization

  4. Marginal social benefit– look at the effect on aggregate output Estimation problems • Measurement • capital stock • Reverse causality • Income leads to investment • Omitted Variable bias • Proxy for K or industrialization • Bottlenecks/Threshold Effects • Functional form

  5. Two approaches • Estimate the marginal product of infrastructure using an aggregate production function and compare with the cost • Test for the direction of causality between infrastructure and economic growth

  6. Estimating The Effect of Infrastructure on Aggregate Output • Flexible functional form to allow for infrastructure “shortages”. • Double counting effect since infrastructure is already included in physical capital. • Effect estimated is of reallocating capital from other sources to infrastructure.

  7. Causality - Theory • Infrastructure has a cost and diverts resources form other activities • Growth effect of extra infrastructure depends on whether it is above or below its growth maximizing level – Barro 1990

  8. Causality- estimation • Granger Causality • Do innovations in infrastructure lead to growth? • Income and Infrastructure are Non-stationary • Causality in non-stationary series

  9. First Differences • We could estimate relationship between infrastructure and income in first differences – produces stationarity • But the long run effect depends on the infinite sum of the responses – high standard error.

  10. Co integration and error correction • We have a long run relationship • We can write the system as a set of error correction mechanisms

  11. Causality • Long run causality depends only on the signs on the error correction terms • No causality from g to y if • sign of effect in the long run is the same as the sign of

  12. Infrastructure Physical Measures • Paved Roads • Electricity Generating Capacity • Telephone main lines (to 1992) • Using value of investment may be misleading due to price differences across countries

  13. Table 4Tests for Presence of Long Run Effects • Null Hypothesis: No Long Run Effects from Infrastructure to Income –Joint Test TEL to Y 325*** (67) EGC to Y 164*** (43) PAV to Y 211*** (42)

  14. Table 5Tests of Parameter Homogeneity for Long Run Effects Across Countries Null Hypothesis: Homogeneity of parameters across countries Test of Test of Wald Test Wald Test TEL to Y 232*** 101*** (67) (67) EGC to Y 124*** 46 (43) (43) PAV to Y 153*** 57* (42) (42)

  15. Table 6Sign of the Effect Group Mean Percentage of Countries Rejecting Alternative: TEL to Y -0.014 14.9* 16.4** 16.4** N=67 (0.023) EGC to Y 0.024 14.0 9.3 16.3* N=43 (0.028) PAV to Y 0.027 16.7* 21.4*** 9.5 N=42 (0.061)

  16. Conclusion • Evidence that Income has a long run effect on Infrastructure • Evidence that Infrastructure has a long run effect on Income • Evidence of Heterogeneity in the sign of the effect • Many countries appear to be near the growth maximizing infrastructure level while some have too much and some have tool little.

  17. Reverse Causality • Estimation must take account of reverse causality. • We use cointegration techniques and find significant results. • Results with more standard instrumental variables methods are similar in pattern but estimates of infrastructure effect are not statistically significant.

  18. Results • In general, the rate of return to road infrastructure in most countries is the same or lower than of capital in general. • A few fast growing economies (e.g. South Korea) exhibit infrastructure shortages and very high rates of return to roads. • Rates of return are somewhat higher in middle income countries where the cost of road building is low.

  19. Estimation of the Productivity of Infrastructure 1960-2000 • Estimate the Productivity Effect • Aggregate Production function • Includes capital, labor, education and health, as well as infrastructure (paved roads, electricity generating capacity, telephone main lines).

  20. Old Approach • Estimate co integration relationship – identify it as the production function • Significant effects for infrastructure – excess returns relative to other capital • Problem – cointegrating relationship is likely to be an average of the production function and infrastructure investment equations and the parameters are not indentified

  21. New approach • Identify the production function as an error correction mechanism for income • Allows for other cointegrating relationships in the data • Can be derived from a model of technological diffusion

  22. Total Factor Productivity and Economic Growth • Production function in logs • We need a model of total factor productivity • Steady state level of TFP

  23. Value of Lagged TFP • Proxy lagged TFP with lagged income per worker • Baumol 1986 • Dowrick and Rogers 2002 • Fagerberg 1994 • It seems better to use actual lagged TFP • Bloom, Canning, and Sevilla 2002 • Blundell and Bond 2000 • De La Fuente and Domenech 2001 • Griliches and Mairesse 1998

  24. Estimating Equation • Differencing the production function • Estimating Equation

  25. Interpretation • If = 0 we have production function in first differences as in Krueger and Lindahl 2001. • We can add factors that might affect steady state TFP - similar to growth regressions. • Catch up term is productivity growth, not convergence of capital to its steady state level with a fixed saving rate.

  26. Panel • 89 countries with growth in five year intervals between 1960 and 2000 -364 observations • Instrument current growth rates of inputs with lagged growth rates (over-identifying restriction test of validity not rejected) • Impose same sort run and long run parameters (restriction tested and not rejected) • Include time dummies and a range of factors that affect TFP – geography and institutions

  27. Results - Base Line Coefficient t-statisitc Capital 0.272 *** (2.83) Labor 0.742 *** (6.60) Schooling 0.152 ** (2.34) Life expectacny 0 .051 *** (3.42) Catch up 0 .146*** (3.84)

  28. Adding Infrastructure Coefficient t-statisitc Telephones 0.195* (1.69) Electricity -0.010 (0.15) Paved raods BR -0.082 (0.98)

  29. Conclusion • Infrastructure is already included in capital • We are testing for excess returns to infrastructure • Some evidence of excess returns to telephones • No evidence of excess returns to roads and electricity • Results are averages – country specific effects are likely to differ

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