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Tools of the Trade: estimating correct standard errors in small sample cluster studies, another take

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For many years, researchers have recognized the need to correct standard error estimates for observational dependence within clusters. An earlier post contrasted the typical approach to this matter, the cluster robust standard error (CRSE), and various methods to cluster bootstrap the standard error.

Sadness interfering with work: depression and labor supply in developing countries

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If economists view mental health as one component of human capital, as we typically view physical health, then it’s a natural step to the corollary view that good mental health leads to productivity enhancing behaviors such as increased labor supply, greater effort, enhanced concentration, and so on. Given its productive role perhaps mental health, often neglected in the policy realm, deserves more attention. Unfortunately there are precious few studies till date that actually establish such a link between psychological health and productivity.

Job satisfaction matters … and the measurement of job satisfaction matters

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Worker job satisfaction has been linked to salient measures of performance such as productivity, absenteeism, and workforce turnover. As such it is a construct that economists care about. I’ve recently reviewed research on the determinants of job satisfaction in order to prepare for a study on pay-for-performance reforms in the health sector. And I’ve found a few surprises…

End of year review: Malaria is declining, and IE should help address the remaining challenges

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A conference on access to malaria medicine recently held at the World Bank offered many substantive studies – and I will discuss some in detail in the new year. However with my last post of 2011 I’d like to end the year on some good news (even if the news is only partially related to impact evaluation).

Tools of the Trade: Getting those standard errors correct in small sample cluster studies

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Some of the earliest posts on this blog concerned the inferential challenges of cluster randomized trials when clusters are few in number (see here and here for two examples of discussion). Today’s post continues this theme with a focus on better practice in the treatment of standard errors.

Pooling risk, saving for health, looking inside the body: what mobile phones may soon allow us to do everywhere

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On my return from a long work trip in Thailand and the Philippines, I stopped at the University of Southern California to attend the 4th global health supply chain summit. I typically enjoy attending meetings outside my immediate discipline since I get to hear about new ideas in fields far from my own. This conference was no exception.

Unique pitfalls in the analysis of networks

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Network analysis is a burgeoning sub-field in development economics as more and more attention is paid to how individual preferences and behaviors are influenced by decisions in the wider community. One example is the 2007 Kremer and Miguel paper that explores the determinants of take-up of deworming medicine by regressing take-up on the number of connections that the household has with other treated households.

Sometimes a survey interview bequeaths more than the token gift of appreciation

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When I first started field work in Indonesia (as a PhD student) I observed numerous household survey interviews. Even though I didn’t speak Javanese I was familiar with the questionnaire and so could follow the ups and downs of the household interview. These survey encounters were not trivial events for the typical household that, almost universally, would welcome a group of strangers into their house who would then probe and ask about every aspect of their lives for up to two hours.

The spoken/written word: what use for development research?

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The types of data available to development economists are proliferating – multi-topic household surveys are almost passé today but 25 years ago it was a rare privilege to be able to correlate economic measures of the household with other indicators such as health or community infrastructure. Not only are surveys more sophisticated, and arguably contain less error due to the use of field based computers, but the digital revolution has multiplied the types of data at our beck and call.