– “tail bias”, by which the poorest and richest are not adequately surveyed either they are not present in the survey at all, or they choose to either not respond or misreport about their income or consumption levels. Bollinger et al. (2019) find, indeed, a U-shaped relationship between earnings nonresponse rate in the main US household survey (CPS) and actual earnings as recorded in administrative sources. However, the non-response rates they find cannot mirror a “bunching in the middle” like the one found for subjective ranking in LiTS. For instance, in LiTS round III, corresponding to year 2016, only 0.57% of the respondents position themselves in decile 10. Under the assumption that the response rate of individuals in decile 5 -which represent 30% of the sample- is 100%, this would imply that the nonresponse rate in decile 10 is close to 98%. For decile 9, the implied nonresponse rate would be 97%. For decile 1, the poorest decile, the implied nonresponse rate would be 86%. The maximum nonresponse rate Bollinger et al. find in the top-most and bottom-most percentiles in the United States is 30% - and only for percentiles 95 and above (well into decile 10) and percentile 10 and below (decile 1). Thus, the nonresponse rates compatible with a bunching in the middle as the one observed in the subjective ranking variable are well beyond those factually observed in the extreme percentiles of the income distribution in the United States. Also, if the LiTS survey would be missing almost entirely the top two deciles and the bottom decile of the income distribution (plus sizeable portions of other deciles) of each country then survey’s claim of representativeness at the country level would be seriously in doubt. Gimpelson and Treisman (2018) analyze this point in an indirect way by contrasting LiTS round II respondents’ assets information -car and second house ownership- with their subjective ranking: in the poorest countries included in LiTS round II -Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Moldova and Ukraine- only a third of the households own a car according to official statistics, and given the average price of a car these households are among the richest. Still, they find that 60% of those who own a car in those countries position themselves in decile 5 or below. A similar pattern arises when looking at those who report owning a second house. In this sense, what emerges is that the survey does capture individuals clearly belonging to the top deciles of the distribution, but these individuals’ subjective ranking is simply different from their objective one.

**Ignorance.** A simpler reason for the “bunching in the middle” may be plain ignorance. Research in psychology has shown that individuals, when faced with _a priori_ identical choices, may systematically choose the “middle option” (Christenfeld, 1995); this has been found also in consumer product choice (Valenzuela and Raghubir, 2009) and in answer patterns to multiple-choice tests (Attali and Bar-Hillel, 2003). In this sense, if individuals are completely ignorant about the shape of the income distribution, all the choices in front of them (the ten steps of the ladder) may look the same and, therefore, may end up choosing the middle steps just as a result of a cognitive bias. As we will see later on in this

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