Reliability of recall in agricultural data

Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on the quality of the underlying data. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons that took place a number of months prior to the interview. This gap can lead to recall bias in reported data on agricultural activities. The problem is further complicated when interviews are conducted over the course of several months, thus leading to recall of variable length. To test for such recall bias, the length of time between harvest and interview is examined for three African countries with respect to several common agricultural input and harvest measures. The analysis shows little evidence of recall bias impacting data quality. There is some indication that more salient events are less subject to recall decay. Overall, the results allay some concerns about the quality of some types of agricultural data collected through recall over lengthy periods.Crops&Crop Management Systems,Educational Sciences,Rural Development Knowledge&Information Systems,Regional Economic Development,Rural Poverty Reduction

Frame-of-reference bias in subjective welfare regressions

Past research has found that subjective questions about an individuals'economic status do not correspond closely to measures of economic welfare based on household income or consumption. Survey respondents undoubtedly hold diverse ideas about what it means to be"poor"or"rich."Further, this heterogeneity may be correlated with other characteristics, including welfare, leading to frame-of-reference bias. To test for this bias, vignettes were added to a nationally representative survey of Tajikistan, in which survey respondents rank the economic status of the theoretical vignette households, as well as their own. The vignette rankings are used to reveal the respondent's own scale. The findings indicate that respondents hold diverse scales in assessing their welfare, but that there is little bias in either the economic gradient of subjective welfare or most other coefficients on covariates of interest. These results provide a firmer foundation for standard survey methods and regression specifications for subjective welfare data.Rural Poverty Reduction,Housing&Human Habitats,Economic Theory&Research,Poverty Lines,Agricultural Knowledge&Information Systems

A map of the poor or a poor map?

This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study (LSMS) surveys. The estimation methods considered are that of Elbers, Lanjouw and Lanjouw (2003), the empirical best predictor of Molina and Rao (2010), the twofold nested error extension presented by Marhuenda et al. (2017), and finally an adaptation, presented by Nguyen (2012), that combines unit and area level information, and which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared error (MSE). Results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias is unknown ex ante, methods relying only on aggregated covariates should be used with caution, but may be an alternative to traditional area level models when these are not applicable.This research was funded by Ministry of Economy, Industry and Competitiveness grant numbers MTM2015-69638-R (MINECO/FEDER, UE) and MTM2015-72907-EXP

Frame-of-reference bias in subjective welfare regressions

"Past research has found that subjective questions about an individuals' economic status do not correspond closely to measures of economic welfare based on household income or consumption. Survey respondents undoubtedly hold diverse ideas about what it means to be "poor" or "rich." Further, this heterogeneity may be correlated with other characteristics, including welfare, leading to frame-of-reference bias. To test for this bias, vignettes were added to a nationally representative survey of Tajikistan, in which survey respondents rank the economic status of the theoretical vignette households, as well as their own. The vignette rankings are used to reveal the respondent's own scale. The findings indicate that respondents hold diverse scales in assessing their welfare, but that there is little bias in either the economic gradient of subjective welfare or most other coefficients on covariates of interest. These results provide a firmer foundation for standard survey methods and regression specifications for subjective welfare data. "--World Bank web siteKathleen Beegle; Kristen Himelein; Martin RavallionLiteraturverz. S. 33 - 3

Semi-automatic mapping of pre-census enumeration areas and population sampling frames

Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts

Using gridded population and quadtree sampling units to support survey sample design in low-income settings

BackgroundHousehold surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames.MethodsPreviously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous.ResultsThe quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and pre-war regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach.ConclusionOur approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized selection of a household in each block mitigated potential selection bias by enumerators. The approach shows labour, time and cost-saving and points to the potential use in wider contexts