Tall or taller, pretty or prettier: Is discrimination absolute or relative?

Hamermesh, Daniel S. Article Tall or taller, pretty or prettier: Is discrimination absolute or relative? IZA Journal of Labor Economics Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Hamermesh, Daniel S. (2012) : Tall or taller, pretty or prettier: Is discrimination absolute or relative?, IZA Journal of Labor Economics, ISSN 2193-8997, Springer, Heidelberg, Vol. 1, pp. 1-17, https://doi.org/10.1186/2193-8997-1-2 This Version is available at: https://hdl.handle.net/10419/92271 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/2.0/ REVIEW Open Access Tall or taller, pretty or prettier: is discrimination absolute or relative? Daniel S Hamermesh * Correspondence: hamermes@eco. utexas.edu Department of Economics, University of Texas, Austin, TX 78712, USA Abstract Using microeconomic data sets from the United States and the Netherlands, this study considers how agents perceive characteristics that are discriminated against. It uses the examples of beauty and height to examine whether: 1) Absolute or relative differences in a characteristic affect labor-market and other outcomes; and 2) The effects of a characteristic change when all agents acquire more of it. Decision-makers seem to respond more to absolute than to relative differences among individuals. Weaker results show that an increase in the mean of a characteristic’s distribution does not alter market responses to differences in it. JEL codes: J71, J78 Keywords: Beauty, Height, Discrimination, Market responses 1. Introduction The literature on the economics of discrimination is immense, going back at least to Becker (1957). While research in the area has mostly been empirical—concerned with measuring the ceteris paribus impact of an ascriptive characteristic on some economic outcome, often earnings or wages, a small theoretical literature has made additional fundamental contributions (see the summaries by Cain 1986; Altonji and Blank 1999). With only one exception (Fryer and Jackson 2008), however, the theoretical literature appears to have been unconcerned about how agents form their views of the characteristic against which they discriminate—how they organize their impressions of the characteristic that in turn affect their treatment of members of other groups. The lack of concern with this question in the empirical literature seems to have been complete. That the question is generally important seems clear. How do wage differences respond to differences in height in the work force if new cohorts of workers are taller than their predecessors? How would earnings differentials that arise from differences in workers’beauty be altered if workers generally became better-looking? How does the impact of looks on electoral success change if the distribution of candidates’looks changes? Persico et al. (2004), Case and Paxson (2008), Hamermesh and Biddle (1994), Möbius and Rosenblat (2006), Benjamin and Shapiro (2009) and Berggren et al. (2010) have studied the market responses of these outcomes to differences in the characteristics. None of these studies, nor any other, has considered the general question of how perceptions of the characteristic affect the outcome. With Americans, and especially northern Europeans, becoming taller, the treatment of height as an earnings-enhancing © 2012 Hamermesh; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hamermesh IZA Journal of Labor Economics 2012, 1:2 http://www.izajole.com/content/1/1/2 labor-market characteristic may change. To the extent that the distribution of looks is changeable by an increasingly affluent and beauty-obsessed public, how those possible changes would affect the returns to beauty is also important. In this study I examine these issues on a number of data sets covering several different characteristics and outcomes, with the data coming from the United States and the Netherlands. In several cases I run a “horse race”between models specifying the characteristic as absolute and those specifying it as relative—in percentiles. Where possible I estimate the kernel density of the characteristic and use kernel estimation to obtain a nonparametric representation of its impact on the outcome, thus obviating spurious results that might arise from the imposition of a particular functional form on the relationship. This approach seems a sensible way of introducing the empirical examination of how perceptions of differences in ascriptive characteristics affect what we view as discriminatory outcomes. No doubt there are other methods of doing so. Whether there is a general answer—a consistent way in which agents form the perceptions that affect how a characteristic alters labor- and other market outcomes—is not clear. But by examining several characteristics in a variety of contexts I may be able to shed a bit of light on how perceptions of differences in characteristics (in the empirical examples here, in beauty and in height) affect outcomes that have previously been examined without attention to the nature of the apparent discrimination. 2. Modeling the nature of responses to personal characteristics In this section I describe the questions of interest generally to provide a guide for future studies, illustrating the general points with the examples I use in subsequent sections. Write a general statistical relationship between some characteristic X~f(X) and an outcome Y as: Y¼gXðÞ;ð1Þ where I ignore the error term and any conditioning variables in a vector Z that might also affect Y. In many of the examples here X is a measure of beauty that affects some outcome whose desirability increases in Y, for example, the likelihood of electoral success, Throughout I assume that X is solely ascriptive and that the response of Y to it reflects discrimination. I do not inquire (and, indeed, the literature only very rarely considers) whether the response of Y to X reflects market discrimination or the productivity-enhancing effects of X. I follow the literature and assume the former. The focus throughout is on @Y/@X and how it changes in response to changes in f(X). In particular, I first examine: @@Y=@XðÞ=@σXμX; jð2Þ that is, how the responsiveness of Y to changes in X is altered when there is a mean-preserving spread in X. All of the examinations of this phenomenon relate to beauty. In them the question is how an outcome responds when there is more dispersion in people’s looks, e.g., when the spread between the looks of people at the 90 th and 10 th percentiles of looks widens. The world does not appear to have Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 2 of 17 http://www.izajole.com/content/1/1/2 generated any exogenous shocks on which we can obtain data that would allow examining such changes directly. I thus need to formulate proxies for the shock in (2) that can capture the change. One way to do this is to note that one could estimate a linear (or log-linear) specification of (1) to obtain (@Y/@X), as is standard practice. If a mean-preserving spread in X leaves @Y/@X unchanged, a re-specification of (1) with X defined as C(X), the centiles of X, would be an inferior description of g(X) compared to the linear (or log-linear) specification. In other words, does the relationship g(X) describe the responses of Y to absolute changes in X or to changes in an agent’s rank in the distribution of X? Taking the beauty example again, I am thus asking whether beauty is better characterized by some absolute scale or by rank in the distribution of beauty. In the empirical sections, absent the desired exogenous shocks to f(X), I follow this approach to infer the shape of (2). The second issue is the estimation of: @@Y=@XðÞ=@μXσX; jð3Þ that is, how the responsiveness of Y to X changes when there is a variance-preserving increase in the mean of X. Using the beauty example, one of two that I use to examine this issue, the question is equivalent to asking whether a given absolute difference in people’s looks affects some outcome equally if the average person is bad-looking, average-looking or good-looking. In the case of height, the issue is whether an increase in average height in a population alters the responsiveness of some outcome (earnings is the example here) to absolute differences in height. In this case there are three examples in which shocks unrelated to Y generated such changes. It is a simple matter either to compare estimates of (1) obtained from times when μ X differs or to examine (1) in the presence of simultaneous exogenous shocks to μ X across groups. The difficulty with this entire approach is that g(X) may not be linear, not based on centiles or any other simple transformation of X, but may instead be some high-powered, non-monotonic and perhaps even discontinuous function of X. In the example of beauty, one needs to distinguish between inherently highly nonlinear responses of outcomes to differences in beauty and apparently nonlinear responses that arise because relative differences in looks matter more than absolute differences. While I cannot solve this difficulty generally, in some of the examples the distribution of X has sufficient support to allow kernel estimation of g(X) and thus to enable me to examine these potential problems. 3. The impact of changing variance of a characteristic In this Section I examine how changes in the distribution of beauty affect the impacts of looks on the success of fund-raisers for a charity, on retention as a participant in a television game show and on electoral success in a professional organization. All three independent examples suggest that absolute differences in looks have bigger effects on outcomes than do relative differences. The superior “performance”of absolute differences is not always large, but taken together the results indicate that future research on the impact of discriminatory tastes should at least proceed from the assumption that discriminating agents care more about Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 3 of 17 http://www.izajole.com/content/1/1/2 absolute than about relative differences in the characteristic against which they discriminate. 3.1 The beauty of charitable solicitors Landry et al. (2006) conducted a field experiment in which solicitors for a charity went door-to-door seeking funds, with different treatments applied randomly to potential target households. As part of the experiment the impacts of solicitors’characteristics, including their physical attractiveness, BMI and personality traits, were also assessed (separately, not by those being solicited). In their published study the authors estimated linear regressions over all the households surveyed, of which over two-thirds contributed nothing. Here I estimate probits describing whether or not a household contributed, then tobits describing that and the amount donated. In the first set of estimated equations I use exactly the same variables as Landry et al., a vector that includes solicitors’beauty as evaluated by ten raters whose assessments were normalized and then averaged. (Thus the mean beauty was 0.06, the standard deviation 0.61 1 .) In the second set of estimates I replace each male solicitor’s beauty by his percentile in the distribution of male solicitors’looks, and similarly for the female solicitors. Table 1 presents the estimates of the two sets of equations, with Columns (1) and (3) containing the estimates that follow Landry et al. by including absolute beauty measures, Columns (2) and (4) reporting the results with the beauty variables re-specified as percentiles in the distribution of own-sex beauty. The probit estimates present derivatives showing the impacts of one-unit increases in the independent variables. The estimated standard errors are clustered on the solicitors’identification numbers. As in the original study, male beauty has negative, but quite insignificant effects, while female beauty has significant and quite large positive impacts 2 . (E.g., a two-standard deviation increase in female beauty from the mean in this sample raises the probability that a household contributes to the charity from 0.30 to 0.46.) The explanatory power of the equations containing the absolute measures of beauty exceeds that of the equations Table 1 Charitable donations, field experiment, probit and tobit estimates, N = 1754 a Contribute? Contributed Amount Beauty: Absolute: Male Beauty -0.0263 -0.6005 (0.042) (0.936) Female Beauty 0.1353 2.6563 (0.034) (0.789) Rank: Male Beauty/100 -0.0739 -1.6554 (0.076) (1.838) Female Beauty/100 0.2748 5.3858 (0.070) (1.599) Pseudo-R 2 0.0765 0.0760 0.0227 0.0225 a The data are from Landry et al. (2006). All equations include as controls a vector of attidudinal variables, indicators of the arm of the experiment and indicators for whether the solicitor was overweight or obese. Standard errors in parentheses below the parameter estimates are clustered on solicitor identification numbers. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 4 of 17 http://www.izajole.com/content/1/1/2 containing percentiles, although the differences are small. In this example using market-wide data there is some weak evidence that the absolute effects of the relevant characteristic dominate its relative impacts. 3.2 Beauty in a Dutch game show, 2002 Belot et al. (2012) describe a television game show in which groups of five people answer questions posed by the quizmaster. At the end of the first round of questions the contestant who earned the most points selects one of the other four group members for expulsion from the group (and from further participation in the show). Belot et al. demonstrate that, holding “productivity”(questions answered) constant, those contestants who were rated (on a 7 down to 1 scale, averaged over ten raters) as being worselooking were more likely to be expelled. Using these data we can analyze whether the likelihood of expulsion in this round of five players was greater if being relatively worse-looking had the same effect on the probability of expulsion regardless of absolute differences in looks among the players. The first column in Table 2 shows the average absolute rating of the beauty of contestants who did not “win”in the first round of the game and were thus eligible for expulsion. The average beauty ratings of these players ranged from 5.73 down to 1.70 on the seven-point scale. Since productivity and/or the “winner’s”preferences for expelling other players may depend on characteristics other than beauty, I control for each player’s score in the round of questions, a quadratic in the player’s age, and an indicator of gender. Column (2) of this table lists conditional logit estimates of the impact of a one-unit increase in the absolute beauty rating on the probability of expulsion. Column (3) shows the estimated impact of moving up one in the ranking of beauty among the four “losers”in the round. There is a substantial difference in the explanatory power of the absolute as opposed to the relative differences in the “losing”contestants’looks on their likelihood of expulsion. The pseudo-R 2 is noticeably higher in the conditional logits based on the specification of differences in beauty as absolute. Column (4) presents estimates with both measures (and the controls) included. A likelihood-ratio test comparing this to the Table 2 Descriptive statistics and conditional logit estimates, Dutch game show, N = 276 (dependent variable is “sent away”) a Mean Parameter Std. Dev. estimates Range Beauty: Absolute 3.52 -0.4911 -0.8525 (0.69) (0.2551) (0.5343) [1.70, 5.73] Rank in round 2.50 -0.1346 0.1627 (1.42) (0.1002) (0.2082) Pseudo R 2 0.1678 0.1571 0.1710 a The conditional logits include as controls a vector showing the rank of the person’s score in the round, a quadratic in age and an indicator for gender. Standard errors in parentheses below the parameter estimates here and in Tables 3, 5 and 6. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 5 of 17 http://www.izajole.com/content/1/1/2 conditional logit in Column (2) yields χ 2 (1)=0.62 (p=0.43); the same test compared to the estimates in Column (3) yields χ 2 (1)=2.66 (p=0.10). Although not highly significant statistically, the estimates clearly show that the “losers’” absolute beauty, not their standing in a ranking of beauty, determines how the “winner”of a round in this game treats them. Figure 1 shows the kernel density of the average beauty ratings of the “losing”contestants in the first round of the game, and Figure 2 presents the kernel estimates of its effects on the probability of expulsion. The density is slightly right-skewed, due entirely, as the points in Figure 2 show, to the presence of one outlier whose beauty was one standard deviation above that of the second best-looking among the 276 “losing”contestants. That this outlier contestant was expelled from the show explains the strange upturn in the expulsion-beauty kernel estimate in Figure 2. Ignoring this individual, the kernel estimate implies an especially large penalty to being very bad-looking, so that it is unsurprising that absolute differences in looks describe the relationship better than relative differences. 3.3 Economists’beauty and AEA elections, 1966–2004 Another example that allows examining the roles of absolute position and rank in the distribution of a characteristic is provided by the data collected and analyzed by Hamermesh (2006). The looks of each candidate in the 78 elections for office (vice-presidents and members of the Executive Committee) in the American Economic Association that were held from 1966 to 2004 were rated by a panel of four incoming economics graduate students, with the average for each rater normalized and then averaged across the four raters. In each four-person election the two candidates obtaining the most votes from the Association’s membership won the election. Since candidates’pictures were mailed out with the ballots, the voters at least had the opportunity to choose (discriminate?) on the basis of the candidates’looks. The question is whether they did, and whether any impact 0.1 .2 .3 .4 .5 1 2 3 4 5 6 attractiveness kernel = epanechnikov, bandwidth = 0.3000 Kernel density estimate Figure 1 Kernel of beauty density, Dutch game show, 2002. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 6 of 17 http://www.izajole.com/content/1/1/2 of looks worked through the candidate being among the better-looking of the four in his/ her election, or whether the absolute extent of differences among the candidates’looks is what mattered for voters’electoral choices. Column (1) in Table 3 shows the mean, standard deviation and range of the average ratings of each candidate. That the range of the average beauty ratings is large shows that the raters were able to make fairly sharp distinctions among the candidates’looks. Figure 3 presents the kernel density of the distribution of the average ratings and suggests that the distribution of the averages (of the four standardized ratings) has an extended right tail. As in the previous sub-section, Column (2) of the table shows the conditional logit estimates of the impact of a one standard-deviation increase in absolute beauty, in this 0.2 .4 .6 .8 1 Sent away 2 3 4 5 6 attractiveness 90% CI Sent away lpoly smooth kernel = epanechnikov, degree = 0, bandwidth = .3, pwidth = .45 Local polynomial smooth Figure 2 Kernel estimation of the effect of beauty on expulsion probability, Dutch game show, 2002. Table 3 Descriptive statistics and conditional logit estimates, AEA elections, N=312 (dependent variable is “elected”) a Mean Parameter Std. Dev. estimates Range Beauty: Absolute 0.00 0.3623 0.5300 (0.71) (0.2043) (0.4184) [−1.80,2.71] Rank in election 2.50 0.1385 -0.0981 (1.12) (0.1038) (0.2123) Pseudo R 2 0.1846 0.1795 0.1853 a The conditional logits include as controls a quadratic in the candidate’s lifetime citations up through the year before the election, and indicators for gender, whether the person had previously held a high-level government position, was in a Top 5 economics department, was not an academic, was African-American or had won a Nobel Prize. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 7 of 17 http://www.izajole.com/content/1/1/2 case on the probability of winning the election; Column (3) shows the estimated impact of a one-unit increase in the rank of the beauty distribution in the election; and Column (4) includes both of these variables. Also contained in each equation is a set of controls including, most importantly, the candidate’s rank in scholarly productivity among the candidates (measured by lifetime citations in the Social Science Citation Index up to the election year), an indicator of gender and whether the candidate had previously held or currently holds high public office 3 . The results are qualitatively remarkably similar to those in the previous subsection. Again the specification of beauty as absolute describes the outcome better than does the candidate’s position in the ranking of beauty. The differences are not, however, as large as in the previous example. A likelihood-ratio test comparing the conditional logit in Column (4) to the estimates in Column (2) yields χ 2 (1)=0.21 (p=0.64); the same test compared to the estimates in Column (3) yields χ 2 (1)=1.64 (p=0.20). That the results indicate that voters respond to absolute differences in the candidates’looks is also suggested by the kernel estimates shown in Figure 4. The response is monotonically increasing over theentirerangeoftheaveragebeauty ratings. It is especially strong, however, as a response to increases in beauty as one approaches the upper tail of looks. This result suggests that here too, and more generally than is possible in the conditional logits, absolute beauty rather than relative position in the distribution of looks determined the outcome. 4. The impact of a variance-preserving increase in a characteristic’s mean In this Section I examine three natural experiments that allow inferring the impact of an increase in the average of some characteristic that occurs without any change in its variance. I use these to examine whether and how the agents’treatment changes in order to estimate the cross-partial derivative in (3). The first 0.1 .2 .3 .4 .5 Density -2 -1 0 1 2 3 Average rating of i kernel = epanechnikov, bandwidth = 0.3000 Kernel density estimate Figure 3 Kernel of beauty density, AEA elections, 1966–2004. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 8 of 17 http://www.izajole.com/content/1/1/2 The differences between the ability of absolute and relative differences in the characteristic to characterize behavior were not large, but absolute differences in the characteristic consistently described agents’discriminatory responses better than did relative differences. On the second question the results are somewhat more ambiguous. The weak conclusion, however, is that the evidence indicates that responses to remaining differences are not changed when the average of a characteristic increases. In terms of our examples, being equally more attractive than one’s competitors enhances positive outcomes by the same amount whether the competitors are bad- or good-looking. Being a few inches taller than other workershasthesamepositiveeffecton earnings whether the others are 5’9”or 6’1”. I have shown that, in the context of choices that discriminating agents make between well-defined small sets of individuals among whom they make simultaneous distinctions, i.e., in the studies of the impacts of beauty, the conclusions are unambiguous. The results suggest that the nature of responses to differences in ascriptive characteristics is discernible when we can examine the explicit comparisons that discriminating agents make among those against whom they discriminate. So too, the effects are fairly well determined when the distribution of a characteristic shifts with no change in its variance. Is there any way to distinguish between the alternatives in these two questions in the more interesting context of labor markets generally rather than in the narrower contexts of charitable solicitations, a game show, elections in a professional organization or the unusually large and rapid increases in height that occurred in one country? Studies based on secondary data describing large national random samples of workers cannot make the required distinctions, as a number of attempts not reported here demonstrate 10 . One possibility would be to construct audit studies in which the characteristics of various lists of job applicants are manipulated to allow inferences about these two issues 11 . Another alternative would be to create laboratory experiments in which groups of agents with appropriately manipulated different characteristics confront other “buying”agents (although the generalizability of any results would be questionable). Yet a third possibility would be econometric case studies of promotion choices (tournaments) in which small numbers of candidates who differ along one of the dimensions analyzed here are included. Overall, given the importance of such characteristics in determining labor-market outcomes, it would be worthwhile to understand more about how the structure of perceptions of differences and changes in these characteristics alter individuals’labor-market success. Endnotes 1 The mean differs slightly from zero because better-looking solicitors appear to have contacted more households, and each observation is a household. 2 There is no effect of solicitors’beauty on the size of the contribution conditional on it being positive. The impact of female solicitors’beauty in these data works through its inducements to make some contribution. 3 Estimates of these equations that did not include these controls, and other estimates that include the share of citations instead of the rank in this measure, yield the same conclusions about the relative importance of absolute and relative differences in beauty. Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 15 of 17 http://www.izajole.com/content/1/1/2 4 See Komlos and Lauderdale (2007) for evidence, and http://www.thedailyshow.com/ watch/thu-June-21-2007/stature-of-liberty for a humorous popular presentation of this phenomenon. 5 We restrict the analysis here to men to avoid concerns about the changing labor force participation of Dutch women. Suffice it to note, however, that Dutch women’s heights in these samples also rose significantly and sharply over these periods. We use men 25–59 to avoid including those who may not have reached their full adult height or who may have begun to shrink. 6 In the four years of the POLS that are used here this restriction excludes seven men. It excludes seven men from the DNB in 1995, and one man from the DNB in 2010. I supplement the 2010 sample going backward through 2006 with men who did not appear in the 2010 wave. No individual appears more than once in the 2006–10 sub-sample that we use here. Of the 872 men used in that sub-sample, 449 are observed in 2010, 136 in 2009, 83 in 2008, 109 in 2007 and 95 in 2006. 7 We can reject the hypothesis that the means of the two distributions are the same (t=23.54). Given the sample sizes, we cannot reject the hypothesis that the variance of the distribution was unchanged over this period. 8 Re-estimating the model for the DNB 1995 sample only on workers ages 29 through 45 (presumably a random sample of those who would reach 43 through 59 in 2009, the average year in which members of the 2006–10 sample were observed), the estimated impact of height is even larger than it was for this cohort in 2009. 9 Since the average probability of being sent away is always 0.25, and that of election is always 0.5, the main effect of average looks in a game or an election cannot be included in the specification of the conditional logits. 10 I examined the impacts of absolute and relative rankings of heights on earnings in the Dutch data and the American Time Use Survey, with nearly identical results for specifications of height as absolute or relative. Similarly ambiguous results were produced for the impact of beauty on earnings in recent German data, and for estimates of the impact of immigrants’skin color on earnings (using data on skin color previously used by Hersch 2008). 11 See, however, Heckman (1998) for a discussion of audit studies and some of their difficulties. Despite concerns about whether asking businesspeople to spend time evaluating resumés of phony applicants is even ethical, there seems to be no shortage of researchers willing to undertake this type of study. Competing interests The IZA Journal of Labor Economics is committed to the IZA Guiding Principles of Research Integrity. The author declares that he has observed these principles. Author information Sue Killam Professor in the Foundations of Economics, University of Texas at Austin, and professor of labor economics, Maastricht University, research associate, IZA and NBER. I am indebted to Michèle Belot, Lex Borghans, Joni Hersch and Michael Price, each of whom provided one of the data sets used here and explained its idiosyncrasies, and to the CentER of Tilburg University for making the DNB data available. I thank Jason Abrevaya, V. Bhaskar, John Komlos, Steve Trejo, a referee and an editor, and participants in seminars at several institutions for helpful comments. Acknowledgements Responsible Editor: V. Joseph Hotz Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 16 of 17 http://www.izajole.com/content/1/1/2 Received: 23 May 2012 Accepted: 25 June 2012 Published: 9 October 2012 References Altonji J, Blank R (1999) Race and gender in the labor market. In: Ashenfelter O, Card D (eds) Handbook of labor economics, vol 3C. Elsevier, Amsterdam, pp 3143–3259 Becker G (1957) The economics of discrimination. University of Chicago Press, Chicago Belot M, Bhaskar V, van de Ven J (2012) Beauty and the sources of discrimination. J Hum Resour 47:851–872 Benjamin D, Shapiro J (2009) Thin-slice forecasts of gubernatorial elections. Rev Econ Stats 91:523–536 Berggren N, Jordahl H, Poutvaara P (2010) The looks of a winner: Beauty and electoral success. J Publ Econs 94:8–15 Cain G (1986) The economic analysis of labor market discrimination: A survey. In: Ashenfelter O, Layard PRG (eds) Handbook of labor economics, vol 2. North-Holland, Amsterdam, pp 693–785 Case A, Paxson C (2008) Stature and status: Height, ability and labor-market outcomes. J Pol Econ 116:499–532 Fryer R, Jackson M (2008) A categorical model of cognition and biased decision making, Berkeley Electronic J Theoretical Econs,Contributions 8: Article 6 Hamermesh D (2006) Changing looks and changing ‘discrimination’: The beauty of economists. Econ Lttrs 93:405–412 Hamermesh D, Biddle J (1994) Beauty and the labor market. Am Econ Rev 84:1174–1194 Heckman J (1998) Detecting discrimination. J Econ Perspect 12:101–116 Hersch J (2008) Profiling the new immigrant worker: The effects of skin color and height. J Labor Econ 26:345–386 Komlos J, Lauderdale B (2007) Underperformance in affluence: The remarkable relative decline in U.S. heights in the second half of the 20 th century. Soc Sci Qtrly 88:283–305 Landry C, Lange A, List J, Price M, Rupp N (2006) Toward an understanding of the economics of charity: Evidence from a field experiment. Qtrly J Econ 121:747–782 Möbius M, Rosenblat T (2006) Why beauty matters. Am Econ Rev 96:222–235 Persico N, Postlewaite A, Silverman D (2004) The effect of adolescent experience on labor market outcomes: The case of height. J Pol Econ 112:1019–1053 doi:10.1186/ 2193-8997-1-2 Cite this article as: Hamermesh: Tall or taller, pretty or prettier: is discrimination absolute or relative? IZA Journal of Labor Economics 2012 1:2. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com Hamermesh IZA Journal of Labor Economics 2012, 1:2 Page 17 of 17 http://www.izajole.com/content/1/1/2