Call Me Maybe: Experimental Evidence on Using Mobile Phones to Survey Microenterprises

Garlick, Robert; Orkin, Kate; Quinn, Simon

doi:10.2139/ssrn.2816064

Cited by 10 publications

(15 citation statements)

References 52 publications

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“…Thus, it is unlikely that the omission of a single household member would necessarily result in the omission in their plots. ensure the continued participation of respondents, to keep track of respondents who relocate, or to collect data requiring high-frequency contacts or a quick turnaround (Dillon 2012;Garlick, Orkin, and Quinn 2015).…”

Section: Resultsmentioning

confidence: 99%

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Not your average job: Measuring farm labor in Tanzania

Arthi

Beegle

Weerdt

et al. 2018

Journal of Development Economics

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Understanding the constraints to agricultural growth in Africa relies on the accurate measurement of smallholder labor. Yet, serious weaknesses in these statistics persist. The extent of bias in smallholder labor data is examined by conducting a randomized survey experiment among farming households in rural Tanzania. Agricultural labor estimates obtained through weekly surveys are compared with the results of reporting in a single end-of-season recall survey. The findings show strong evidence of recall bias: people in traditional recall-style modules reported working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. Recall bias manifests both in the intensive and extensive margins of labor reporting: while hours are over-reported in recall, the number of people and plots active in agricultural work are under-reported. The evidence suggests that this recall bias is driven not only by failures in memory, but also by the mental burdens of reporting on highly variable agricultural work patterns to provide a typical estimate. All things equal, studies suffering from this bias would understate agricultural labor productivity.

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Section: Resultsmentioning

confidence: 99%

“…Similar recent work has used phone surveys to collect high-frequency data on economic activity. SeeGarlick, Orkin, and Quinn (2015) for a review of the literature on phone-based strategies for collecting household and enterprise data.…”

mentioning

confidence: 99%

Not your average job: Measuring farm labor in Tanzania

Arthi

Beegle

Weerdt

et al. 2018

Journal of Development Economics

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mentioning

confidence: 99%

Location, Search Costs and Youth Unemployment: Experimental Evidence from Transport Subsidies

Franklin

2017

The Economic Journal

107

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Do high search costs affect the labour market outcomes of jobseekers living far away from jobs? I randomly assign transport subsidies to unemployed youth in urban Ethiopia. Treated respondents increase job search intensity and are more likely to find good, permanent, jobs. Subsidies also induce a short‐term reduction in temporary work. I use a high‐frequency phone call survey to track the trajectory of search behaviour over time to show that the subsidies significantly increased job search intensity and the use of formal search methods. The evidence suggests that cash constraints cause young people to give up looking for good jobs too early.

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“…Telephone surveys have been demonstrated in other contexts to generate data that is statistically indistinguishable from in-person interviewing (Garlick et al 2016, Bloom et al 2012a).…”

Section: What Explains Management?mentioning

confidence: 99%

School Management and Public–Private Partnerships in Uganda

Crawfurd

2017

Journal of African Economies

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Can the quality of school management explain differences in student test scores? In this paper I present the first internationally benchmarked estimates of school management quality in Africa (based on the "World Management Survey"). The level and distribution of management quality is similar to that found in other low and middle-income countries (India and Brazil). I combine this data with individual student panel data, and demonstrate that differences in school management quality matter for student value-added-a standard deviation difference in management is associated with a 0.06 standard deviation difference in test scores. Finally I contribute to understanding the role of the private sector in education in a low-income setting. Contrary to common perception, I find no difference between the quality of school management in government, private, or public-private partnership (PPP) schools (despite the higher level of autonomy available to them). An exception is an internationally-owned chain of PPP schools, which are as well managed as schools in the UK.

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Call Me Maybe: Experimental Evidence on Using Mobile Phones to Survey Microenterprises

Cited by 10 publications

References 52 publications

Not your average job: Measuring farm labor in Tanzania

Not your average job: Measuring farm labor in Tanzania

Location, Search Costs and Youth Unemployment: Experimental Evidence from Transport Subsidies

School Management and Public–Private Partnerships in Uganda

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