frequency by cross-country regressions (21%), cost function estimations (9%) and growth accounting techniques (3%). On the other hand, micro-level specifications using either household data or firm-level data sum up to approximately 18% of the total.

What is the global picture coming out of the research surveyed here? Overall, 63% of the specifications find a positive and significant link between infrastructure and some development outcome, while 31% find no significant effect and only 6% find a negative and significant relationship. Before discussing in more details the characteristics of these studies, two facts are worth noting.

First, contrary to what might be expected, there does not seem to be a bias towards publishing positive results, as the frequency of positive and significant results is actually lower in the sub-sample of published paper (58%). Of course, the possibility that this results from a selection bias when including papers in this review cannot be totally excluded, although no such explicit selection rule was used.

Second, the type of infrastructure proxies used by researchers interested in infrastructure has evolved in the last two decades. While most of the papers in the field at the end of the 1980s and beginning of the 1990s used some form of public capital figures (public investment), the limitations of this type of data, together with the growing availability of alternative infrastructure proxies such as physical indicators, has led to a move towards this second type of data. Indeed, while in the period 1989 to 1999 (65 specifications), the proportion of papers using public capital proxies is 72% against 28% using physical indicators, between 2000 and 2007 (75 specifications), the figures are reversed to only 24% using public capital data and 76% using some form of physical indicators.<sup>29</sup> Looking now in more details, Table 2 shows the distribution of results from the studies under review depending on a number of characteristics. Historically, the literature has first been oriented to developed countries studies, mostly because this was where data were available and of better quality.<sup>30</sup> Where infrastructure problems are more acute and policy lessons more needed, however, is clearly in developing countries. This has led to a research drive to develop and use developing countries data. As a result, while in the period 1989-1999, only 29% of specifications were using specific developing countries data (and 20% mixed developed/developing samples), between 2000 and 2007, this figure went up to 47% (and 35% of mixed sample specifications).

Do results differ depending on the type of sample used? In our sample review, developing country data do lead to positive results slightly more often, but the difference is small (5%), while mixed sample are more often inconclusive, an outcome probably related to some fundamental heterogeneity across observational units (most of these studies use country-level data).

> 29 The suitability and limitations of each type of data is discussed in more details in the next section. 30 A large part of this literature corresponds to the US cross-state public capital literature.

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