By the 1990s, the wide use of the above approaches in the Bank had declined as the extent of government involvement in the production of goods and services lessened (Little and Mirrlees 1991, p. 360). Lower levels of government‐induced distortions in the 1990s relative to the 1970s called into question the primary focus on correcting distortions to measure benefits and costs in project economic analysis. Critics noted that more attention was needed on assessing the sustainability of projects and on performance. Specifically, there was insufficient attention to identifying factors that might cause underperformance of the project including political economic consideration and other design issues that ensure resilience to economic, budgetary and policy changes (Jenkins 1997). Devarajan et al. (1995 and 1997) stressed the importance of the counterfactual (or what would happen in the absence of government engagement) and noted that with the declining involvement of government the focus should be on whether the project should be undertaken by the private or public sector. They also highlighted the challenge of fungible financing. For example, if the project being appraised would have been taken up without donor funds, then it was likely that donors were actually indirectly financing some other project that had not been appraised. As a result, they recommended sectorwide expenditure reviews that assessed the likelihood of the project succeeding and linked to the project‐specific appraisals. Sectorwide expenditure reviews would help define the private‐sector counterfactual and give a sense of the fiscal impact.

In 2010, an IEG evaluation of the quality of project economic analysis reinforced the recommendations put forth by Devarajan et al. (World Bank 2010). The IEG study documented the declining trends in the frequency of project economic analysis and suggested that this stemmed mainly from the shifts in the portfolio toward more programmatic and economywide approaches (away from traditional investment projects). However, the study also documented the within sector decline in the frequency and use of project economic analysis, particularly for those sectors where this has traditionally been strong (i.e. agriculture, energy, transport, water, etc.). This result pointed to other factors at play in determining the declining trends in the frequency of application of project economic analysis. For example, a likely key factor noted by Devarajan et al. (1995, p. 3) pertained to changes in the process for reviewing the quality of project economic analysis that had become slack over time. Drawing on the results of both the IEG evaluation and Devarajan et al., following the IEG evaluation, the Bank issued updated guidance to teams that placed emphasis on: (i) defining the counterfactual and the rationale for public provision; (ii) assessing the fiscal impact and sustainability; (iii) and examining the impact on poverty reduction.

This paper examines more recent trends in project economic analysis since the Bank’s updated guidance was issued and links these trends to project performance. A growing literature on the determinants of project performance highlights the importance of controlling for both country and project level characteristics. For example, GDP growth and GDP per capita are commonly used in these regressions and seem to be negatively associated with project outcomes when there are no controls for project level characteristics (Dollar and Leving 2005; Guillamont and Lajaj 2006; Dreher, Klasen, Vreeland and Werker 2010). In contrast, GDP growth and GDP per capita are each positively associated with project outcome measures for studies that control for both country and project level variables (Denizer, Kaufmann and Kraay 2013; Chauvet Collier Duponchel 2010; Kilby 2000). Denizer et al. estimate that country and project level variables explain around 20 percent of the total variation in project performance. In particular, Denizer et al. 2013 include a detailed set of project level variables that capture project length, size, supervision and preparation costs, whether the project was restructured and dummy variables to capture early warning of problems prior to completion. They find that if a project indicates it is problematic in the first half of its life, this is associated with a lower level of performance at the end of the project. Similarly, 4