The paper is organized as follows. Section 2 presents the literature review, followed by the description of the country context and data sources in Section 3. Section 4 lays out the empirical strategy and presents the results, and Section 5 concludes.

# **2. Literature Review**

Across high-income countries, there is a longstanding literature on respondent selection and labor reporting in nationally-representative surveys. In the U.S., for example, a large number of survey experiments have been conducted to better understand sources of survey measurement error in earnings by matching individual survey data with a concurrent, “validation” dataset (often administrative data from the Internal Revenue Service (IRS) or Social Security Administration, or employer’s records). Bound et al. (2001) provide a review of these validation studies, from 1950 up until 1999.

Further, several recent studies have matched individual-level survey rounds from the U.S. Census Bureau’s Survey of Income and Program Participation (SIPP) with administrative tax records from the IRS, to understand different sources of measurement error in earnings reporting. This includes proxy response, which can reduce survey costs and potentially provide less biased information on sensitive topics,<sup>2</sup> but also carries the risk of inaccurate reporting due to informational gaps, or differences in preferences and motivation between the proxy and intended respondent (see Cobb, 2018, and Bound et al., 2001). Cognitive issues related to respondents’ interpretation of questions, and ability to recall past outcomes accurately, additionally need to be considered (see Moore et al., 2000, and Bickart et al., 1990).

Tamborini and Kim (2013), for example, match the SIPP and IRS tax records at the individuallevel to find that proxy reporting leads to significant underreporting of earnings for single female workers (also see Hill, 1987). Cristia and Schwabish (2009) demonstrate that other covariates (in particular, earning higher incomes) lead to underreporting in the SIPP, and that individual characteristics that are associated with higher earnings (including age and marital status) are systematically associated with underreporting, leading to bias in estimates of these commonlyused predictors of earnings. Abowd and Stinson (2013) do not treat either approach as a “gold standard,” showing that both sources of data have measurement errors and argue for constructing a hybrid measure of earnings from survey and administrative data. Quality issues with earnings data across survey and administrative sources have also been raised. In the U.K., Britton et al. (2019) find systematically lower reporting of earnings in a dataset linking administrative tax records with student loan data, as compared to the concurrent Labor Force Survey — likely due to a combination of low survey response in the LFS from low/irregularly-paid earners, as well as a

> 2 Some studies on health outcomes in high-income countries, for example, have shown that proxy reporting can provide more accurate and detailed information on other household members’ health, including fewer issues with social desirability bias (see Mathiowetz and Groves, 1985, for a discussion using data from the U.S. National Health Interview Survey).

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