by TFP. The only exceptions to this are three infrastructure measures that prove insignificant statistically, coaching stock (column 5 of Table 8A), revenue earning freight (column 9 of Table 8A), and SAA flight passengers (column 15 of Table 8B), and three infrastructure measures which are the only measures which appear to have a positive impact on TFP growth. Goods rolling stock (column 6 of Table 8A), total roads (column 10 of Table 8B) and ports (column 14 of Table 8B).

None of the positive impacts is economically strong, with the highest elasticity reaching no more than 0.15. By contrast, the negative elasticities attaching to some of the alternative infrastructure measures, is considerably higher, notably the negative elasticity of −0.6 attaching to the railway network.

The evidence thus suggests that infrastructure is not a strong source of efficiency gains in the South African manufacturing sector as measured by TFP growth. This evidence stands in strong contrast to the pervasive and often economically powerful positive impact that infrastructure in South Africa appears to have had on manufacturing sector labor productivity.

One possible inference to draw from this evidence, is that the impact of infrastructure in South African manufacturing is directly on output per worker as a distinct factor of production - rather than indirect in fostering the productivity of private sector capital. A second possibility is that endogeneity is present as much for the idirect impact of infrastructure, as it is for the direct effect. We have already seen that accounting for potential endogeneity of infrastructure in the labor productivity estimations, strengthens the impact of the infrastructure measures both statistically as well as in economic terms. We thus investigate the impact of infrastrucutre on the TFP measure under instrumental variables estimation.

# 5.2.2 Results in the Presence of Instrumentation

Estimation is now of equation (18) by means of the PMG panel estimator for South African manufacturing sectors, but with the infrastructure measure is now replaced by the instrumented measure. The instrumentation strategy is as outlined in section 5.1.2.

Results are reported in Tables 9A, 9B and 9C.

As before, results confirm adjustment to equilibrium (see the ECM -parameters, which correspond to the φ-parameters of equation 8), and in general Hausman tests (denoted h-tests) confirm the legitimacy of the PMG estimator by failing to reject the homogeneity restriction on the long-run coefficients for South African manufacturing sectors.

As for the previous subsection, the finding for R&D expenditure, for the skills ratio (SKRAT), and for industry concentration (GINI) are symmetrical to those already reported in the preceding subsection. Furthermore, in general the net export ratio confirms the positive impact on total factor productivity growth previously reported (though there are a number of exceptions to this finding that we discuss explicitly in the following discussion). We, therefore, move directly to the discussion of the infrastructural measures.

First, the aggregate economic infrastructure measures of flow (investment: lnGFCF) and stock (lnFCS), which both proved negative and significant under the estimation in the absence of instrumentation, now test positive and significant in the case of the flow measure, and statistically insignificant in the case of the stock measure. The implication is that a 1% increase in investment in economic infrastructure, raises TFP growth by 0.04 percentage points. Results are reported in columns (1) and (2) of Table 9A.

Second, the rail transport infrastructure measures which in the absence of instrumentation had proved to be negative and statistically significant, under instrumentation reverse their signs, and with the sole exception of total rail route kilometers (lnRAIL), they all prove to be statistically significant. Nor is the economic magnitude of their impact small:

- In the case of the measures for locomotives (lnLOCO), goods stock (lnGOODS), and carrying capacity (lnCCAP), the implication is that a 1% increase in the infrastructure stock would generate approximately 0.05 percentage points of productivity growth (as measured by TFP).

19