_5_

# **III. Data and Variable Definitions**

The surveys used are listed in Table 1. Surveys are nationally representative except for Bangladesh, Brazil, China, Guatemala, Indonesia and the Philippines whose coverage is regional. Survey years range from 1987 to 1997. In selecting countries and surveys, we wanted to achieve a degree of geographic heterogeneity. In addition, however, the surveys included also needed to include (i) data on consumption or income, (ii) anthropometric data to measure malnutrition (height, weight, age and gender of children under 5), and (iii) an acceptably large sample size of children. There are 20 countries and surveys in total, of which 11 are Living Standards Measurement Study (LSMS) surveys, 9 are similar multi-topic surveys, which satisfy the above criteria. However, we need to keep in mind that quality standard, sampling method and variable definitions may not be uniform across surveys, especially when conducting agencies are different.

## **MEASURING LIVING STANDARDS**

As the main focus of this survey is to see the poor-nonpoor inequalities in malnutrition, choosing a variable according to which households are ranked is of paramount importance. Consumption is usually considered a better indicator of living standards than income, as ranking by the latter is generally more unstable than the former over the years. This reflects the fact that households can smooth out their consumption by saving and dissaving, while income fluctuates yearly depending on factors such as the household's employment situation and agriculture yield [22]. Therefore, whenever possible, consumption was used to rank households. Consumption variables were available for all countries except for China and the Philippines.

In addition to the choice between consumption and income, different methods by which consumption/income is aggregated reduce data transparency and comparability. It is therefore ideal to use consumption/income aggregates constructed using the same method. Due to lack of such data in some cases, however, we aggregated various consumption components, following the methodology proposed by Hentschel and Lanjouw [23] and Deaton and Zaidi [22].2 Even in cases where consumption data exist, their comprehensiveness varies across surveys. For example, the LSMS is the most comprehensive of all, including consumption items such as home-produced food, the imputed rental value of the household's dwelling, and the annual service value of the durable goods, as well as spending on food, non-food items, health services and education. By contrast, some surveys-such as that for Guatemala-offer only a very limited range of consumption categories with only food and certain non-food items. Readers are advised to bear the above caveats in mind when interpreting the results presented in this paper.

We equivalize household consumption to take into account inter-household differences in household size. The two extreme positions on equivalization are (a) to assume that there are no economies of scale in household consumption (it costs two people twice as much to live as one) and (b) to assume that there are maximum economies of scale (two can live as cheaply as one). These two extremes, and the various possible intermediate positions, can be represented by the following relationship between equivalent consumption and actual consumption: 2 This was the case for the following countries: Guatemala, Philippines, Russia and Zambia.