complementarities or substitutability between these factors, as well as by the strongly non-linear effects that are at the root of NEG models. Simulations based on static estimates, or assuming only smooth linear variations around these estimates may therefore be inadequate.

Moreover, the spirit of NEG models tells us that what we observe is not that better access makes firms more efficient, but a spatial form of selection bias linked to firms locating according to the network’s characteristics. Then, if for example more efficient firms are the ones relocating in secondary centers as links are formed, the estimates of the impact of market access are likely to be biased upward. A related issue is the fact that location decisions also imply simultaneous endogenous technological choices, which are partly responsible for the differences in efficiency observed across firms, a problem very difficult to address with this type of data. Indeed, a Heckit type of procedure might help control for this if technology choices are of a 0/1 nature (i.e. deciding to produce or shut-down), but continuous choices among technologies with different levels of productivity are more difficult to address.

Finally, an additional issue relates to the potential endogenous placement of specific infrastructure. Indeed, the essence of infrastructure policy is to make investments where they are likely to have the largest effect. Therefore, evaluating the impact of past investments is unlikely to provide reliable estimates for the effect of future works. This issue, however, can be tackled provided panel data are available. Some “first nature” characteristics, such as geographical aspects, can then be used as instruments for the placement of specific infrastructure, which suitability depends on local conditions.<sup>41</sup>

# **4. Microeconometric Studies**

Some microeconometric contributions have looked into the issue of infrastructure’s impact on development outcomes. To mention a few prominent ones, Gibson and Rozelle (2003) look at the effect of access to road in Papua New Guinea on poverty at the household level, and show that reducing access time to less than three hours where it was above this threshold leads to a fall of 5.3% in the head count index. Esther Duflo and Rohini Pande (2007) study the effect of irrigation dams in India on agricultural production and poverty. Their cost-Benefit analysis suggests that dams have very low rate of return and adverse distributional effects. Using Tanzanian household survey data, Fan, Nyange and Rao (2005) look at the impact of public investment and roads on household level income and poverty and find very positive effects, with a ratio of 1 to 9 in the case of public capital investment. Ilahi and Grimard (2000) show that the development of water infrastructure has a significant impact on women’s time allocation in Pakistan. Thomas and Strauss (1992) analyze the determinants of child height in Brazil, a standard development outcome, and include electricity, water and sewerage connections as explanatory variables. They find the number of electricity connections per capita in the community to be positively correlated with height of babies, with a complementary effect of mother education. Positive results are also found for water and sanitation, with variations by

> 41 Duflo and Pande (2007) use GIS data on land inclination to instrument for the endogenous placement of dams in India.

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