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Rural finance has long been an important tool for poverty reduction and rural development by donors and governments, but the impacts have been controversial. Measuring impact is challenging due to identification problems caused by selection bias and governments' targeted interventions, while randomised trial data are scarce and limited to contexts where little to no rural finance exists. Using an author-collected dataset, we provide insights on a large-scale long-lasting sub- sidised rice credit programme in Myanmar, one of the poorest and, until recently, most economical ly isolated countries in Asia. Identification relies on a fuzzy regression discontinuity design, exploiting an arbitrary element to the credit provision rule which is based on rice landholding size. Although we find little evidence that rice yield or output is increased, we do see that the programme has some positive effects on total household income, suggesting a positive spillover effect on other farm income activities.

AbstractAustralia supports the control of tuberculosis inPapuaNewGuinea for reasons of aid effectiveness and a desire to decrease the chance of importing tuberculosis toAustralia. This paper analyses the case for this support using both cost‐utility and cost‐benefit analysis.We reach three conclusions. First,Australia directly benefits from its investment in controlling tuberculosis inPapuaNewGuinea, with a cost of $US13 million (in 2012 prices) over 10 years earning a net present value of $US22 million.Second, the longer and more extensive the basic directly observed short course therapy, or basicDOTS, to control tuberculosis, the higher are the returns forAustralia.Finally, in addition to surpassing all commonly used benchmarks for being a cost‐effective investment forAustralia, a basicDOTSexpansion also generates a health benefit forPapuaNewGuinea that compares well as one of the 'ten best health buys' in developing countries.

This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging and popular technique for estimating time-invariant variables in panel data models with group effects. This estimator was initially motivated on heuristic grounds, and advocated on the strength of favorable Monte Carlo results, but with no formal analysis. We show that the three-stage procedure of this decomposition is equivalent to a standard instrumental variables approach, for a specific set of instruments. The instrumental variables representation facilitates the present formal analysis that finds: (1) The estimator reproduces exactly classical fixed-effects estimates for time-varying variables. (2) The standard errors recommended for this estimator are too small for both time-varying and time-invariant variables. (3) The estimator is inconsistent when the time-invariant variables are endogenous. (4) The reported sampling properties in the original Monte Carlo evidence do not account for presence of a group effect. (5) The decomposition estimator has higher risk than existing shrinkage approaches, unless the endogeneity problem is known to be small or no relevant instruments exist.

Fixed effects vector decomposition (FEVD) is simply an instrumental variables (IV) estimator with a particular choice of instruments and a special case of the well-known Hausman-Taylor IV procedure. Plümper and Troeger (PT) now acknowledge this point and disown the three-stage procedure that previously defined FEVD. Their old recipe for SEs, which has regrettably been used in dozens of published research papers, produces dramatic overconfidence in the estimates. Again PT concede the point and now adopt the standard IV formula for SEs. Knowing that FEVD is an application of IV also has the benefit of focusing attention on the choice of instruments. Now it seems PT claim that the FEVD instruments are always the best choice, on the grounds that one cannot know whether any potential instrument is correlated with the unit effect. One could just as readily make the same specious claim about other estimators, such as ordinary least squares, and support it with similar Monte Carlo assumptions and evidence.