Attrition and Follow‐Up Rules in Panel Surveys: Insights from a Tracking Experience in Madagascar

Most longitudinal surveys recontact households only if they are still living in the same dwelling, producing very high attrition rates, especially in developing countries where rural–urban migration is prevalent. In this paper, we discuss the implications of the various follow‐up rules used in longitudinal surveys in the light of an original tracking survey from Madagascar. This survey attempted in 2005 to search and interview all individuals who were living in the village of Bepako in 1995, the baseline year of a yearly survey, the Rural Observatories. The tracking survey yielded an individual recontact rate of 78.8 percent, more than halving attrition compared to a standard dwelling‐based follow‐up rule. The tracking reveals a very high rate of out‐migration (38.8 percent) and household break‐ups, as three‐quarters of recontacted households had divided between 1995 and 2005. The average income growth of the sample over the period increases by 28 percentage points when follow‐up is extended to those who moved out of their household or village, suggesting that dwelling‐based panels give a partial view of the welfare dynamics of the baseline sample. A higher baseline income per capita is associated with a higher probability of staying in Bepako and of being found in the tracking if one moved out. The hardest people to find are the poorest and most isolated. Special attention should be paid to collecting data that enable the identification and follow‐up of individuals, without which attrition is likely to remain a source of bias even after a tracking procedure is carried out.     

Cross‐Country Income Differences Revisited: Accounting for the Role of Intangible Capital

This paper develops a new intangible investment database that is consistent and internationally comparable for a set of 60 economies over the period 1995–2011. I find that over time a growing share of total investment consists of intangible assets, rather than investment in tangible assets, like machinery and buildings. Across countries, the level of economic development of a country is positively associated with its investment intensity in intangibles. By including intangible capital as an additional production factor, this paper finds that we can account for substantially more of the variation in cross‐country income levels. Depending on the assumptions regarding the output elasticities of factor inputs, the observed differences in intangible capital can account for up to 16 percentage points of the cross‐country income variation.     

Self‐rated Health Status of Japanese and Europeans in Later Life: Evidence from JSTAR and SHARE

Using panel data from two surveys in Japan and Europe, we examine the comparability of the self‐rated health of the middle‐aged and elderly across Japan and European countries and across the survey periods. We find that a person's own health is evaluated using different standards (thresholds) across the different countries and survey waves. When evaluated using common thresholds, the Japanese elderly are found to be healthier than their counterparts in the European countries. Reporting biases causing discrepancies between the changes in individuals' self‐rated health and their actual health over the survey waves are associated with education and country of residence.     

The Indian Public Distribution System as provider of food security: Evidence from child nutrition in Andhra Pradesh

We study whether a sudden increase of the price of rice supplied by the Indian Public Distribution System in Andhra Pradesh, a large Indian state, had a negative impact on child nutrition. A few months after the price increase, a health survey started to record weight for a large sample of children. The data collection continued for several months, so that children measured later lived for a longer period of time in a less favorable price regime. Using different estimation techniques we find that longer exposure to high prices are not accompanied by worse nutritional status, as measured by weight-for-age.