Friday, 2 September 2016

Measuring global hunger: the importance of variances

is the title of a new column at by some guy John Gibson, from some out fit called the University of Waikato. Gibson argues that different survey methodologies are typically employed to produce estimates of global hunger. His column considers some of the methodological issues that arise. Short reference periods for each household lead to overstated variances and the confounding of chronic and transient welfare components. The column goes on to present a new approach to measuring chronic hunger which tackles this sampling problem by employing an intra-year panel.

About the new approach to measuring chronic hunger Gibson writes,
In a recent paper, I propose a new way to measure chronic hunger from surveys, which accounts for excess variability from just observing a snapshot of diets (Gibson 2016). This method also can identify the transient component of hunger, which is a type of welfare fluctuation that is neglected in the literature compared to the emphasis placed on transient poverty (and a type of hunger neglected by the FAO).

The proposed method needs surveys to see the same households in at least two, non-adjacent periods in the year. This survey design is rare. Dupriez et al. (2014) survey statistics offices in 100 low- and middle-income countries to obtain metadata on their food consumption surveys and find just two that use this type of intra-year panel. Many more surveys in their sample use revisits for short, adjacent, periods (e.g. every second day) to check on diary-keeping by respondents; the median diary-keeping survey has interviewers make five visits in two weeks.

However, seeing the same household repeatedly for, say, two weeks to implement a diary is less informative than seeing it for a week, and then again for another week, six months later. Seeing the same household at two or more times of the year reveals more about outcomes with low auto-correlation since a snapshot of these mis-measures their long-run average. The benefit from repeated observations has previously been noted for incomes, expenditures, and microenterprise profits, which have low auto-correlations (McKenzie 2012), and the new results show that calories are another outcome of interest with low auto-correlations.

To get a correct estimate of annual variances from snapshot surveys, the correlations between values of a living standards indicator in separate periods for the same households are needed. These correlations are implicitly assumed to be 1.0 if short reference period survey data are treated as equivalent to annual data. If correlations are only 0.7, monthly reference period surveys overstate annual variances by 40%, and by 80% if the correlations are as low as 0.5. The correlation-based method has been found to almost exactly replicate what benchmark annual data from year-long diaries show, for variance-based statistics such as inequality and poverty indices (Gibson et al. 2003) but has not previously been used to measure hunger.

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