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Macro, Growth and Welfare Effects of Policy Interventions

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The changes in individual behavior induced by a program can, at scale, have impacts on the macroeconomy — creating feedback loops whose effects may manifest over time. For instance, imagine that scaling up a program changes aggregate labor productivity and savings rates. Measuring those changes alone gives an incomplete view of the program, as they can alter the behavior of firms, lenders, and governments, leading to structural shifts and welfare impacts that may emerge only in the medium – or long term. For these questions, the combination macroeconomic theory and rigorous evidence from RCTs can be used to predict the effects of large-scale programs on the structure and trajectory of the macroeconomy.

 Sometimes programs can be randomized at a large enough scale to detect these effects (Muralidharan et al. 2017); however, it is both infeasible and undesirable to capture the full range of macroeconomic consequences solely through large-scale experiments.  Macroeconomic theory makes several important contributions to the study of scale up. First, theory can predict the medium- and long-run changes that are rarely observed in an experimental setting.  Second, it can account for sectors or agents that sometimes go unstudied in an experiment. Finally, theory can estimate welfare impacts that are difficult to measure directly.

Y-RISE network conveners Joe Kaboski and David Lagakos combine high-quality microdata with macroeconomic models to generate insights about the aggregate and welfare consequences of prominent anti-poverty programs. Kaboski and coauthors, in their work on microfinance, validate the partial equilibrium results of a theoretical model using evidence from existing randomized evaluations of microfinance. They then use the calibrated model, which accounts for the general equilibrium effects of large-scale microfinance, to estimate the long-term effects of economy-wide microfinance regimes on aggregate productivity, capital accumulation, and welfare (Buera, Kaboski, Shin, 2017).  David Lagakos and coauthors use the random assignment of migration subsidies to estimate the net welfare effects of migration, including non-monetary disutility that migrants may experience (Lagakos et al, 2018). Such methods can be used to answer difficult normative questions about welfare tradeoffs that are important for policy decisions.

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