Reliability of recall in agricultural data

Beegle, Kathleen; Carletto, Calogero; Himelein, Kristen

doi:10.1016/j.jdeveco.2011.09.005

Cited by 144 publications

(86 citation statements)

References 9 publications

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“…Bardasi et al, 2011 and Beegle et al, 2012). WISE includes a number of internal validations to ensure data quality.…”

Section: Datamentioning

confidence: 98%

“…This is converted to person days in order to be comparable with hired-in labor assuming a 9.5 hour work day. 8 Accurately measuring labor supply in informal markets is a substantial challenge in any survey setting (e.g., Bardasi, Beegle, Dillon, and Serneels (2011), Beegle, Carletto, and Himelein (2012)). WISE includes a number of internal validations to ensure data quality.…”

Section: Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Farms, Families, and Markets: New Evidence on Completeness of Markets in Agricultural Settings

LaFave¹,

Thomas²

2016

Econometrica

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The farm household model has played a central role in improving the understanding of small-scale agricultural households and non-farm enterprises. Under the assumptions that all current and future markets exist and that farmers treat all prices as given, the model simplifies households’ simultaneous production and consumption decisions into a recursive form in which production can be treated as independent of preferences of household members. These assumptions, which are the foundation of a large literature in labor and development, have been tested and not rejected in several important studies including Benjamin (1992). Using multiple waves of longitudinal survey data from Central Java, Indonesia, this paper tests a key prediction of the recursive model: demand for farm labor is unrelated to the demographic composition of the farm household. The prediction is unambiguously rejected. The rejection cannot be explained by contamination due to unobserved heterogeneity that is fixed at the farm level, local area shocks or farm-specific shocks that affect changes in household composition and farm labor demand. We conclude that the recursive form of the farm household model is not consistent with the data. Developing empirically tractable models of farm households when markets are incomplete remains an important challenge.

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“…Bardasi et al, 2011 and Beegle et al, 2012). WISE includes a number of internal validations to ensure data quality.…”

Section: Datamentioning

confidence: 98%

Section: Identificationmentioning

confidence: 99%

Farms, Families, and Markets: New Evidence on Completeness of Markets in Agricultural Settings

LaFave¹,

Thomas²

2016

Econometrica

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“…Differences in recall period (through questionnaire design or timing of interview) or differences in recall ability for different activities (e.g., rare, “salient” events vs. common ones) can lead to differences in household responses (Beegle et al, 2012, Bound et al, 2001). The possibility of measurement error in the constructed labor supply aggregates is addressed in Section 4 of this paper.…”

Section: Data and Variable Constructionmentioning

confidence: 99%

Labor productivity and employment gaps in Sub-Saharan Africa

McCullough

2017

Food Policy

157

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Drawing on a new set of nationally representative, internationally comparable household surveys, this paper provides an overview of key features of structural transformation – labor allocation and labor productivity – in four African economies. New, micro-based measures of sector labor allocation and cross-sector productivity differentials describe the incentives households face when allocating their labor. These measures are similar to national accounts-based measures that are typically used to characterize structural change. However, because agricultural workers supply far fewer hours of labor per year than do workers in other sectors in all of the countries analyzed, productivity gaps shrink by half, on average, when expressed on a per-hour basis. Underlying the productivity gaps that are prominently reflected in national accounts data are large employment gaps, which call into question the productivity gains that laborers can achieve through structural transformation. Furthermore, agriculture’s continued relevance to structural change in Sub-Saharan Africa is highlighted by the strong linkages observed between rural non-farm activities and primary agricultural production.

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“…Beegle et al (2012a) tested for recall bias in agricultural data, submitting questionnaires with different time spans between harvest and interviews in three African countries. An assessment of whether and how modalities of data collection in agricultural production may affect results is also provided by Deininger et al (2012).…”

Section: Testing Alternative Survey Instrumentsmentioning

confidence: 99%