© 2017 The Department of Economics, University of Oxford and John Wiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 80, 2 (2018) 0305–9049
Multiple Visits and Data Quality in Household
Goethe University Frankfurt, 60629 Frankfurt am Main, Germany,
In order to increase data quality some household surveys visit the respondent households
several times to estimate one measure of consumption. For example, in Ghanaian Living
Standards Measurement surveys, households are visited up to 10 times over a period of
1 month. I ﬁnd strong evidence for conditioning effects as a result of this approach: In
the Ghanaian data the estimated level of consumption is a function of the number of prior
visits, with consumption being highest in the earlier survey visits. Telescoping (perceiving
events as being more recent than they are) or seasonality (ﬁrst-of-the-month effects) cannot
explain the observed pattern. To study whether earlier or later survey visits are of higher
quality, I employ a strategy based on Benford’s law. Results suggest that the consumption
data from earlier survey visits are of higher quality than data from later visits. The ﬁndings
have implications for the value of additional visits in household surveys, and also shed
light on possible measurement problems in high-frequency panels. They add to a recent
literature on measurement errors in consumption surveys (Beegle et al., 2012, Gibson et al.,
2015), and complement ﬁndings by Zwane et al. (2011) regarding the effect of surveys on
Consumption data are of central importance to the study of a large range of empirical
questions that are of interest to both academics and policy makers. They are frequently
used, for example, to study levels and distributions of welfare, poverty, or vulnerability, and
their determinants. Collecting accurate data on consumption and expenditure, however, is
not straightforward, and approaches differ, sometimes substantially, across countries and
over time. Speciﬁcally, Beegle et al. (2012, p. 4) list four ‘primary dimensions [in which]
the main methods of consumption data collection’ vary: ‘diary vs. recall, the level of
aggregation or detail in the commodity list, the reference period and the level of respondent’.
Therefore, a number of papers have recently used experimental methods to assess the role
JEL Classiﬁcation numbers: C81, O12, I32, D12.
*I thank Ghana Statistical Service for sharing the data. I gratefully acknowledge very helpful discussions with
Luc Christiaensen and valuable inputs on an earlier version of this paper from Ahmed Ragab.