Ersa Workshop Infrastructure and Growth – Theory, Empirical Evidence and Policy Lessons Cape Town, 29 – 31 May 2006 Infrastructural Investment in Long-run Economic Growth: South Africa J. Fedderke (UCT), P. Perkins (Wits), J. Luiz (Wits)

1. Introduction  Renewed interest amongst South African policy-makers in economic infrastructural investment following an extended period of decline (from mid-1970s to 2002)  The decline coincided with poor economic growth in SA  In the literature, the empirical evidence on the infrastructure – growth relationship is relatively mixed  This paper provides a long-run, time-series investigation of the infrastructure – growth relationship in SA

2. The Role of Infrastructure in Economic Development  Labour-intensive Cobb-Douglas production function, from Barro model (1990): y = Ag  k 1-  (0 <  < 1) where y = output A (> 0) = level of technology g = government spending on productive services (e.g. infrastructure) k = private capital

 Marginal product of g :  Marginal product of k :  As g / y rises, g / k rises:  As g/y rises,  y/  k rises but  y/  g falls

 Maximize utility: where  Then steady state growth is given by:

 The impact on  of raising g / y depends on whether  y /  g is greater or less than 1:  When  y /  g > 1, d  / d ( g/y) > 0  When  y /  g = 1, d  / d(g/y) = 0  When  y /  g < 1, d  / d(g/y) < 0  The core rationale for infrastructural investment that emerges is that it raises the marginal product of other capital…  …which in turn raises the rate of economic growth, but within limits

AuthorNature of studyVariationsElasticityFindings Aschauer (1989) Cobb-Douglas, OLS, United States (national), Non-military public capital 0.39 There is a strong, positive relationship between public capital (particularly core infrastructure) and productivity. Core infrastructure (transport, power, water) 0.24 Hospitals0.06 Educational buildings-0.01 Conservation & development structures 0.02 Baffes and Shah (1998) Translog specification of a flexible production function, OLS, public- sector infrastructure, 21 countries from 4 regions, Africa (4 countries)0.03 The elasticities for labour, private capital and human capital are higher in all regions (compared with infrastructure), with the exception of labour in Latin America (0.15). Asia (8 countries)0.01 Europe / Middle East (5 countries) 0.04 Latin America (4 countries) 0.15

AuthorNature of studyVariationsElasticityFindings Easterly and Rebelo (1993) Cross-sectional pooled regression with decade averages, using 36 countries in the 1960s, 108 countries in the 1970s, and 119 countries in the 1990s. † These coefficients are not elasticities. The explanatory variables are expressed as investment/GDP ratios, so the effect of a one percentage point change in the ratio on annual GDP per capita growth is given by the coefficient/100. Total consolidated public investment to 0.04 † Results for transport and communication support Aschauer’s (1989) finding that infrastructure spending has supernormal returns, and suggest that causality runs from infrastructure to economic growth. More work is needed to investigate the surprisingly high coefficients and the direction of causality. General government investment to † Public enterprises investment to ‑ † Transport & communication to † Transport & communication (IV) 2 † General government investment (IV) 0.7 †

AuthorNature of studyVariationsElasticityFindings Garcia-Milà, McGuire and Porter (1996) Cobb-Douglas, 48 American states, , first differences with fixed state effects. Highways The elasticities are insignificant, confirming the results of Holtz- Eakin (1994). Water & sewers Other public capital Holtz-Eakin (1994) Cobb-Douglas, state and local government capital for 48 American states, No state specific effects The elasticity of private output or productivity with respect to state and local government capital is close to zero. Fixed state effects ‑ to ‑ Long differences GLS to IV Lau and Sin (1997) VAR system, multivariate stochastic cointegration method, U.S., non-military public capital, –0.11 The elasticity of 0.39 reported by Aschauer (1989) is implausibly high. They find a positive but substantially lower elasticity.

AuthorNature of studyVariationsElasticityFindings Munnell (1990a, 1990b, 1992) Cobb-Douglas, presumably OLS (not specified), United States. National, Public capital has a substantial positive impact on output, particularly at national level. 48 states, Pereira (2000) Impulse-response functions associated with estimated VAR models, United States, Aggregate public investment Public investment has a significant impact on economic growth in the United States. It also crowds in private investment and private employment. Highways & streets Power & transport Water and sewerage Hospital, educational and other buildings Conservation & development structures & civilian equipment

3. Estimation of the Structural Model  The Barro model and descriptive evidence suggest the following framework as a basis for empirical investigation [equation numbers correspond with those in the paper]: y = y(k, g) [6’] k = k(y, g) [7’] g i = g(y, g j ), i ≠ j [8]

 Employ standard VECM model in which:  Stationarity characteristics of the data: standard augmented Dickey-Fuller test statistics  All variables found to be I(1), except for total capital stock and public-sector infrastructural capital stock: both I(2)

 Parsimonious specification of [6’] – [8]: [9]  Possibility of multiple relationships between different forms of infrastructure, which may render identification of the system difficult  Choice of public infrastructure, roads and electricity rests on PSS-F tests and prior literature

 Eskom’s generating capacity: uncomfortably close to winter peak demand  Canning et al. (growth); Pereira (growth & inv.)

Maximal Eigenvalue and Trace Statistics NullAlternativeMaximal Eigenvalue Trace r =0r =155.99* (33.64) * (70.49) r ≤ 1r =236.26* (27.42) 66.62* (48.88) r ≤ 2r = (21.12) 30.36** (31.54) r ≤ 3r =49.44 (14.88) (17.86) * denotes rejection of null at the 5% level ** denotes rejection of null at the 10% level Figures in parentheses report 5% critical values

Imputed elasticities (of output, capital investment and public infrastructural investment) at variable mean values VariableCV1 (LNYPC) CV2 (DLNKPC) CV3 (DLNIFPC) LNYPC DLNKPC DLNIFPC LNTORD LNELEC0.20--

 Replace LNTORD with LNRLIN, LNRCOA, LNRPAS, LNRFRT, LNPARD, LNPASV, LNGDSV, LNPORT, LNFTEL, LNRGDS, LNRCAP  CV1: Elasticity of output wrt capital investment: 0.03 – 0.09 (prev 0.06), except for LNRGDS (0.15) Elasticity of output wrt electricity: 0.07 – 0.24 (prev 0.2), except for LNRGDS and LNRCAP (both negative)

 CV2: Elasticity of capital investment wrt output: 2.03 – 6.70 (prev 2.44), except for LNRGDS (11) and LNRCAP (11.2) Elasticity of capital investment wrt public infrastructural investment: 0.68 – 1.54 (prev 1.37), except for LNRGDS and LNRCAP (both negative)  CV3: Elasticity of public infrastructural investment wrt output: 2.73 – (prev 3.93) Elasticity of public infrastructure wrt g j : all negative (prev for total roads)

 Considering the fragility of the previous results, we estimate a more parsimonious system: [10]  Trace statistic indicates r = 2

 Results reasonably close to initial results: CV1: 0.06 (DLNKPC) and 0.20 (LNELEC) CV2: 2.44 (LNYPC) and 1.37 (DLNIFPC) VariableCV1 (LNYPC) CV2 (DLNKPC) LNYPC-4.20 DLNKPC0.05- DLNIFPC-1.38 LNELEC0.16- Imputed elasticities (of output and capital investment) at variable mean values

 An obvious concern with [10] is that the results may be sensitive to the inclusion of additional regressors  Consequently, we test this by including property rights and political instability: [11]

 Two alternative specifications of [11]: 1.Exclusion of property rights α 51 = α 52 = 0 = β 15 = β 25 2.Same identification structure with weak exogeneity restrictions β 15 = β 16 = β 24 = 0 α 31 = α 32 = α 41 = α 51 = 0

Imputed elasticities at variable mean values Specification 1Specification 2 VariableCV1 (LNYPC) CV2 (DLNKPC) CV1 (LNYPC) CV2 (DLNKPC) LNYPC DLNKPC DLNIFPC LNELEC LNPROP LNINST

4. Main findings  Investment in infrastructure appears to have led economic growth in South Africa  The impact of infrastructure is direct and indirect, the latter occurring by raising the marginal productivity of other capital  This result is robust both to the use of a parsimonious growth model and to a fuller specification incorporating institutional determinants of economic development  There is weak evidence of feedback from output to infrastructure; this is not robust

 Empirical studies using US data: wide range of estimates for the elasticity of output with respect to public capital or some type of public infrastructure  Aschauer (1989): 0.39; supported by Munnell (1990): (smaller at state level)  Holtz-Eakin (1994): elasticities ≈ 0; supported by Garcia-Milà et al. (1996)  Lau & Sin (1997): 0.11; Pereira (2000): 0.04  Econometric methodologies have generated much controversy

 SA’s economic infrastructure has developed in phases, in some cases closely linked to the development of the mining industry