contributor to economic growth and poverty reduction. However, little consideration has been given to possible discrepancies arising from differences in the survey method used.

A growing body of evidence indicates that survey methods matter for the labor statistics they generate. This is well illustrated for OECD countries, especially for the US (see Bound et al 2001), and recent evidence confirms this for developing countries (see Bardasi et al 2011).<sup>4</sup> Survey methods may impact estimates of structural parameters if measurement error varies with survey design in a systematic way. Non‐random measurement error in a continuous left hand side variable would normally not bias OLS point estimates (although it may reduce precision).<sup>5</sup> However, it may lead to bias if the measurement error comes from respondents strategically trying to guess the true value of a variable on which they have imperfect information, and making a systematic error in this judgment. Response by proxy may result in this type of error as it is potentially prone to strategic guessing. A common example is that husbands may be systematically under‐reporting earnings of their wives (or vice versa), for instance because they are not aware of all activities of the latter, and the earnings related to them.<sup>6</sup> Similarly, the wording and detail of questions may affect how respondents categorize themselves, and thus the labor statistics generated from the data. These differences, for instance in labor force participation and occupational distribution, may result in distinct subsamples on which estimations are carried out. This is especially relevant for returns to schooling, which are typically estimated for wage workers alone. Yet little is known how factors of survey design affect estimated returns to education.

To address this gap, this study implemented a field experiment that creates variation in two key dimensions of survey design: the level of detail of the questions ‐ whether including or excluding employment screening questions ‐ and the respondent selected: self or proxy – and investigates whether these differences in survey design yield distinct estimates of the returns to education using a common econometric approach and identification strategy.<sup>7</sup>

> 4 While work on survey measurement error in high income countries often focuses on how survey replies deviate from true values using validation studies – comparing for instance in the case of wages between employee survey replies and employer pay records ‐  this approach is typically less fruitful in low income countries where records are often missing, incomplete or less reliable.  As a result, it is more fruitful to compare replies obtained from different survey methods.  (See de Mel et al (2009) for a comparable approach to measure small business profit.)

> 5 In contrast, in the case of discrete dependent variables, such as labor force participation, both classical measurement error and measurement error in the dependent variable biases the point estimates of the coefficients of right‐hand‐side variables (Hausman, Abrevaya, and Scott‐Morton, 1998; Hausman, 2001.)

> 6 This is especially relevant when husbands and wives have separate sources of income stemming from individual activities; which is often the case in both urban and rural developing country contexts.

> 7 Note that the paper does not aspire to improving upon commonly used estimation strategies, but rather takes this as given, to focus on the impact of survey methods when using existing estimation methods.

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