Infrastructure and Long Run Economic Growth David Canning Infrastructure and Growth: Theory, Empirical Evidence and policy Lessons Cape Town May 2006

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

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

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

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

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.

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

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

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.

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

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

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

Table 4 Tests for Presence of Long Run Effects Null Hypothesis: No Long Run Effects from Infrastructure to Income –Joint Test TEL to Y325*** (67) EGC to Y164*** (43) PAV to Y211*** (42)

Table 5 Tests of Parameter Homogeneity for Long Run Effects Across Countries Null Hypothesis: Homogeneity of parameters across countries Test of Wald Test TEL to Y232***101***(67) EGC to Y124***46(43) PAV to Y 153***57*(42)

Table 6 Sign of the Effect Group MeanPercentage of Countries Rejecting Alternative: TEL to Y *16.4**16.4** N=67(0.023) EGC to Y * N=43(0.028) PAV to Y *21.4***9.5 N=42(0.061)

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.

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.

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.

Estimation of the Productivity of Infrastructure 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).

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

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

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

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

Estimating Equation Differencing the production function Estimating Equation

Interpretation If = 0 we have production function in first differences as in Krueger and Lindahl 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.

Panel 89 countries with growth in five year intervals between 1960 and 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

Results - Base Line Coefficientt-statisitc Capital 0.272***(2.83) Labor 0.742***(6.60) Schooling0.152 ** (2.34) Life expectacny0.051 ***(3.42) Catch up 0.146*** (3.84)

Adding Infrastructure Coefficientt-statisitc Telephones 0.195* (1.69) Electricity (0.15) Paved raods BR (0.98)

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