A Comparison of CAPI and PAPI Through a Randomized Field Experiment
56 Pages Posted: 8 Feb 2011
Date Written: November 2010
Abstract
This paper reports on a randomized survey experiment among 1840 households, designed to compare pen-and-paper interviewing (PAPI) to computer-assisted personal interviewing (CAPI). We find that PAPI data contain a large number of errors, which can be avoided in CAPI. We show that error counts are not randomly distributed across the sample, but are correlated with household characteristics, potentially introducing sample bias in analysis if dubious observations need to be dropped. We demonstrate a tendency for the mean and spread of total measured consumption to be higher on paper compared to CAPI, translating into significantly lower measured poverty, higher measured inequality and higher income elasticity estimates. Investigating further the nature of PAPI’s measurement error for consumption, we fail to reject the hypothesis that it is classical: it attenuates the coefficient on consumption when used as explanatory variable and we find no evidence of bias when consumption is used as dependent variable. Finally, CAPI and PAPI are compared in terms of interview length, costs and respondents’ perceptions.
Keywords: CAPI, PAPI, electronic surveys, measurement error
JEL Classification: C42, C88, C81, C93, O12
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?
By John Bound and Alan B. Krueger
-
Intertemporal Labor Supply and Long Term Employment Contracts
By John M. Abowd and David Card
-
Macroeconomic Analysis and Microeconomic Analyses of Labor Supply
-
Generating Equality and Eliminating Poverty, the Swedish Way
-
Are Earnings Inequality and Mobility Overstated? The Impact of Non-Classical Measurement Error
By Peter Gottschalk and Minh Huynh
-
Explaining the Recent Divergence in Payroll and Household Employment Growth
By Chinhui Juhn and Simon Potter
-
Measurement Error and Misclassification: A Comparison of Survey and Register Data
By Arie Kapteyn and Jelmer Ypma