investment. Rampant corruption mostly goes along with weak incentives for firms to compete and hence to lower productivity growth. We can think of numerous other examples, but the point remains; the question of relative impact of reforms in one dimension against another is still very much left unanswered.

A large part of the problem is the inability of empirical macro-research to converge on methods and techniques that can attribute causal effects of interventions on outcomes. Traditionally, OLS regression methods have been used on country cross-section data to study the links between macro policy/governance interventions and outcome variables. These methods have been criticized for the lack of accuracy emanating from problems of omitted variable bias and endogeneity, model uncertainty and heterogeneity. Researchers have attempted to solve some of these problems by using instrumental variables approach, for example Acemoglu et al. (2001). These studies are important steps towards estimating causal inference in cross-country studies and yet problems remain. Strong external instruments are hard to find, and even if they exist, the effect obtained from such regressions are limited to the variation that is affected by the instrument. These problems make the instrumental variables approach limited in its ability to evaluate macro interventions. This has led some researchers to use dynamic panel data methods, which account for endogeneity issues and yet other problems remain, such as the instability of results from cross-country regressions when variables, time periods and countries in the data change.

Our goal in this paper is not to attempt to resolve the two aforementioned questions in their entirety. Such an endeavor requires a much larger and longer research agenda. We merely attempt to identify specific cases of reforms in the disparate dimensions, ranging from macroeconomic and debt dynamics to governance reforms in the last decade, and attempt to assess their impact on development indicators such as economic growth. We do this by using recently developed methods of inference, which help us attribute with larger certainty the impact on outcome variables to the interventions themselves. By selecting case studies where changes in one dimension were not accompanied by changes in other dimensions, the paper provides a window of understanding into whether reforms in certain dimensions affected development outcomes by themselves or not. We also perform robustness checks to see whether the results of these methods are sensitive to minor modeling changes. We believe that by building a larger catalogue of such results, we can attempt to start understanding the questions highlighted above.

In order to complete this task, we need to first identify significant large scale interventions in policy and governance dimensions. Chapter II of the paper explains our methodology on how to identify and measure large recent interventions on policy and governance dimensions. Chapter III explains the methodology we use to understand how successful these interventions have been in contributing towards development outcomes. We describe the results of the particular case studies in Chapters IV and V.

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