By Jessica Cohen, William Easterly
What Works in improvement? brings jointly prime specialists to deal with essentially the most uncomplicated but vexing matters in improvement: what will we quite find out about what works- and what does not - in scuffling with worldwide poverty? The participants, together with some of the world's most useful financial improvement analysts, concentrate on the continued debate over which paths to improvement actually maximize effects. should still we emphasize a big-picture method - targeting the function of associations, macroeconomic regulations, development options, and different country-level elements? Or is a extra grassroots strategy how to pass, with the focal point on specific microeconomic interventions resembling conditional money transfers, mattress nets, and different microlevel advancements in provider supply at the floor? The publication makes an attempt to discover a consensus on which method might be more desirable. The members comprise Nana Ashraf (Harvard company School), Abhijit Banerjee (MIT), Nancy Birdsall (Center for international Development), Anne Case (Princeton University), Jessica Cohen (Brookings),William Easterly (NYU and Brookings),Alaka Halla (Innovations for Poverty Action), Ricardo Hausman (Harvard University), Simon Johnson (MIT), Peter Klenow (Stanford University), Michael Kremer (Harvard), Ross Levine (Brown University), Sendhil Mullainathan (Harvard), Ben Olken (MIT), Lant Pritchett (Harvard), Martin Ravallion (World Bank), Dani Rodrik (Harvard), Paul Romer (Stanford University), and DavidWeil (Brown).
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Extra info for What Works in Development?: Thinking Big and Thinking Small
That implies that the researcher may have to contend with various sources of omitted-variable bias. In addition, there may not be enough variation over time, so that equation 2-1 may need to be estimated as a pure cross section: (2-2) Yi = ␣ Pi + ⌺ j ␤j Pi Xi + ⌺ j ␥j Xi + ⑀t . j j Since one cannot control for time-invariant regional unobservables in this specification, any potential problem of omitted-variables bias becomes that much more severe. A second problem is how to code and create a quantitative index for the type of policy in place in different regions or countries.
Cost sharing significantly reduced the number of ITNs that ended up in the hands of recipients without increasing actual usage among those who did receive the bed nets. Furthermore, there was no evidence of selection benefits from cost sharing: women who paid a positive price were no sicker than women in the control group. Under reasonable assumptions on private and social benefits, Cohen and Dupas show that free distribution is more cost-effective than cost sharing: the benefits of greater use more than offset additional budgetary costs.
I do not deal here with the criticism that randomized evaluations typically entail very little theorizing, except insofar as this renders extrapolation to other settings more problematic. Even though this may be a legitimate complaint in practice, I do not think it is a fundamental issue. There is nothing in the nature of randomized trials that precludes either theory testing or more explicit use of theory. As I explain later, good use of experimentation in fact relies on explicit theoretical framing.