Gender & Geographical Discrimination in Economics

Gender & Geographical Discrimination in Economics ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Gender & Geographical Discrimination in Economics i need an academics literacture review for my report. My topic is the wage differential in gender and place of living in chinese labor market. Gender & Geographical Discrimination in Economics The literacture review needs relative should discuss previous research that is directly relevant to my topic (gender and geographical discrimination in economics) . the citation has to be included, the following paper is what i mainly use to write my paper purpose_of_paper.docx rest_a_00365.pdf rest_a_00356.pdf economic_transition_and_gender_differentials_in_wages_and_productivity_evidence_from_chinese_manufacturing_enterprises.pdf 1.2 Purpose of the paper There are many theoretical models have designed to study gender discrimination cross various aspects. While most of the literature is concentrated on wages, occupational attainment, or educations to measuring the gender discriminations (Dong and Zhang 2008) and (Miller 1994), and they have been proved that the existence of gender discrimination. The purpose of this paper, I would like to use replicate the founding from Edwens et al. (2011) that using a large sample correspondence study of the US rental market, the authors identify whether racial discrimination by landlords is consistent with statistical discrimination (SD) or taste-based discrimination(TBD). I use the methodology to study how the discrimination in gender and place of living by estimate the wage differences in each characteristic’s groups have been performed in the Chinese labor market. Specifically, the paper identifies how the employment will offer the wage to the interviewee differently when using the place of living and gender as a signal. The paper will analyze responses according to the yield of factors such as the education aspect, the personal aspect, and environmental aspects to find out how those factors are affecting the employment decisions. STATISTICAL DISCRIMINATION OR PREJUDICE? A LARGE SAMPLE FIELD EXPERIMENT Michael Ewens, Bryan Tomlin, and Liang Choon Wang* Abstract—A model of racial discrimination provides testable implications for two features of statistical discriminators: differential treatment of signals by race and heterogeneous experience that shapes perception. We construct an experiment in the U.S. rental apartment market that distinguishes statistical discrimination from taste-based discrimination. Responses from over 14,000 rental inquiries with varying applicant quality show that landlords treat identical information from applicants with African American– and white-sounding names differently. This differential treatment varies by neighborhood racial composition and signal type in a manner consistent with statistical discrimination and in contrast to patterns predicted by a model of taste-based discrimination. I. Introduction R ACIAL and ethnic discrimination pervades many markets in the United States. Roughly half of the discriminatory cases reported by federal agencies involve race or ethnicity, and the number of reported new incidents outpaced population growth over the past ten years.1 The literature posits two major sources of racial discrimination: taste based and statistical. Racial prejudice produces tastebased discrimination, while statistical discrimination occurs in an environment of imperfect information in which agents form expectations based on limited signals that correlate with raceGender & Geographical Discrimination in Economics .2 The result of both types of discrimination, however, is the same: similar individuals who differ only by their race experience different outcomes. A simple examination of differential outcomes sheds little light on the source of discrimination. Employing an e-mail correspondence experiment in the U.S. rental apartment market, we test whether statistical or taste-based discrimination can explain differential outcomes between white and African American rental applicants. We extend Aigner and Cain’s (1977) and Morgan and Vardy’s (2009) models of statistical discrimination to test the key feature of statistical discriminators: heterogeneous experience.3 The model posits that landlords differ in Received for publication March 18, 2011. Revision accepted for publication October 26, 2012. * Ewens: Tepper School of Business, Carnegie Mellon University; Tomlin: NERA Economic Consulting; Wang: Monash University. We benefited tremendously from the comments of Kate Antonovics, Gordon Dahl, Gordon Hanson, Pushkar Maitra, Birendra Rai, Zahra Siddique, and several anonymous referees. We also thank Eli Berman, Martin Bog, Vince Crawford, Julie Cullen, Ben Gillen, Jacob LaRiviere, Mike Price, Valerie Ramey, and Kunal Sengupta. We acknowledge the funding support from the Institute for Applied Economics at the UC San Diego. A supplemental appendix is available online at http://www.mitpress 1 For statistics on discrimination charges reported by the U.S. Equal Employment Opportunity Commission, see /statistics/enforcement. 2 Arrow (1973) and Phelps (1972) first discuss statistical discrimination, and Becker (1957) details prejudice. 3 Also see Cornell and Welch (1996) for a model of statistical discrimination where agents can better interpret signals from their own culture, race, or ethnicity. their perceptions of signals due to past experience in the screening and rental process and place a greater weight on signals from the familiar group than the unfamiliar group in making decisions. We contrast the predictions with those of taste-based discrimination, in which prejudiced landlords use information independent of race to predict expected tenant quality but derive lower marginal utility from renting to the out-group. We show that lower marginal return to signal of quality for minority groups is consistent with both statistical and taste-based discrimination, highlighting the empirical hurdle involved in attempting to separate the two explanations. The model guides our experimental design. Using vacancy listings on, an online classified ad website, across 34 U.S. cities, we send inquiry e-mails with two key components to 14,000 landlords. We use the common, racial-sounding first names employed by Bertrand and Mullainathan (2004) to associate applicants with race, and the inquiry e-mail contains differing—but limited—pieces of information about the applicants: positive, negative, and no signals beyond race. In the no-signal inquiry, landlords receive e-mails with racial-sounding names as the only signal. In the positive information inquiry, the fictional applicant informs the landlord that she is a nonsmoker with a respectable job. In the negative information inquiry, the applicant tells the landlord she has a below-average credit rating and smokes. The dependent variable codes landlords’ responses to capture an invitation to the inquiry for future contact. Although the outcome reflects only a positive response during the initial inquiry phase of a screening process, any differential treatment in screening will influence final outcomes in the same direction. Since residential locations are tied closely to characteristics associated with welfare, such as the type of job held, crime levels, and school quality, our focus on the rental apartment market is policy relevant. As the dominant source of online classifieds for apartment listings in the United States, Craigslist is frequented by one-third of the white and black U.S. adult population. The growing prevalence of online interactions in real estate, employment, finance, and auctions suggests the results extend beyond the rental apartment market. The experiment yields four major results. First, when nosignal inquiry e-mails are sent, applicants with African American–sounding names have a 9.3 percentage point lower positive response than applicants with white-sounding names. Second, using a difference-in-differences (DiD) estimator, we show that the racial gap in response rates widens in the switch from negative to positive information. Gender & Geographical Discrimination in Economics Both findings are consistent with statistical discrimination and prejudice, as they follow from landlords placing more weight on signals sent by white applicants than black appli- The Review of Economics and Statistics, March 2014, 96(1): 119–134 Ó 2014 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology 120 THE REVIEW OF ECONOMICS AND STATISTICS cants or landlords receiving a greater marginal utility of renting from white applicants than from black applicants. Thus, the research design requires further refinement to separate the explanations. Third, the model defines the notion of a ‘‘surprising’’ signal, where the no-signal base case acts as a benchmark for uninformed expectations and as a means to quantify surprise relative to the better-than-expected (positive) and worse-than-expected (negative) signals. This notion of a surprising signal is difficult to introduce in a job application setting in which re?sume?s are required, as it is impossible to provide 0 information about education or experience in a re?sume?. With differential weighting of signals by race, statistical discrimination predicts that a surprising positive signal will not necessarily shrink the racial gap in the base case, but that a negative surprise will. In contrast, the tastebased discrimination model shows that a surprising positive signal will widen the racial gap. Our empirical results are consistent with statistical discrimination. Finally, we exploit neighborhood racial composition as a source of heterogeneity in landlord experiences with, or preferences for, different racial groups. By allowing a signal’s noise to depend on race, the statistical discrimination model presents another testable hypothesis: a landlord’s relative experience with a given race increases the relative weight she places on the signals from that racial group. Conversely, taste-based discrimination predicts that if landlords exhibit out-group prejudice and that if their race and racial preferences are correlated with neighborhood racial composition, as the share of blacks in a neighborhood increases, discrimination against black applicants in the base case will attenuate. Surprising negative information will, in contrast, hurt black applicants more. As the share of blacks in a neighborhood increases, a surprising positive signal closes the racial gap observed relative to the base case, while a surprising negative signal does little to close it. More importantly, the base case racial gap persists across all types of neighborhoods and contradicts the prediction of taste-based discrimination. This paper extends the large body of research on racial discrimination. With the exception of List (2004) and Levitt (2004), past evidence of statistical discrimination is inconclusive. Altonji and Pierret (2001) and Bertrand and Mullainathan (2004) find significant racial gaps in wages and job interview callback rates, respectively, but weak support for statistical discrimination.4 The related literature on racial profiling in the context of police searches, such as Knowles, Persico, and Todd (2001) and Antonovics and Knight (2009), shows mixed evidence of racial prejudice. Audit studies, such as Yinger (1986) and Page (1995), show discrimination against minorities in U.S. housing markets. These results may suffer from the confounding factors inherent to in-per- son audit studies, as they rely on actors who often differ in many dimensions.5 In contrast, Carpusor and Loges (2006) and Ahmed and Hammarstedt (2008) pioneered the use of email correspondence design to study ethnic discrimination in rental housing markets. Our approach differs from those of previous studies and contributes to the literature in several ways. We model explicitly how and why statistical and taste-based discrimination in the screening process can each predict lower marginal return to signal for the discriminated-against group, which potentially resolves some contradictory hypotheses and findings in the discrimination literature. We extend Aigner and Cain’s (1977) and Morgan and Vardy’s (2009) nonprejudice discrimination framework and juxtapose it with a prejudice discrimination framework to permit a research design that separates statistical and taste-based explanations empirically. Gender & Geographical Discrimination in Economics Our framework also allows for landlord risk aversion, and our findings are robust to this specification. In other empirical work, Ahmed, Andersson, and Hammarstedt (2010) and Bosch, Farre, and Carnero (2010) argue that statistical discrimination implies reduced discrimination against minorities with increased positive information. They show, however, an unchanging racial gap in the likelihood of positive responses when positive information is introduced in rental e-mail inquiries and argue that the finding supports taste-based discrimination. Bertrand and Mullainathan (2004), in contrast, argue that the finding of lower or equal marginal returns to credentials for minorities is inconsistent with taste-based discrimination. On the other hand, Hanson and Hawley (2011) argue that decreased reply differences between white and African American rental applicants from improving the prose quality of e-mails suggest statistical discrimination. Because reply rates decrease with class for white applicants, however, their results may be driven by the higher fraction of negative replies to low-class e-mails. Our novel modeling and estimation framework demonstrates the difficulty of separating statistical discrimination from taste-based discrimination in most field experiments. II. We present a model to guide our research design and distinguish between statistical and taste-based discrimination in the rental apartment market.6 A landlord seeks to maximize the expected utility of interviewing each applicant, subject to a capacity constraint of M interviews and a constant marginal cost of interview c (a budget constraint of cM). The expected utility derived from each applicant depends on the stream of future rental income (tenant quality) from renting the apartment successfully. This quality is 5 4 For other environments, see Ayers and Siegelman (1995) (automobile sales), Siddique (2011) (labor market), Antonovics, Arcidiacono, and Walsh (2005) (game shows), and Doleac and Stein (2010) (online sales). Discrimination in Screening and Testable Implications See Pager and Shepherd (2008) for more examples and Heckman (1998) for a critique on audit experiments. 6 Although specific to the rental market, the model should extend to other situations of semiformal screening. STATISTICAL DISCRIMINATION OR PREJUDICE? summarized by y. Although the rent is preannounced, y may still vary as a result of default, lease renewal, and so on. Hence, the landlord forms a predicted quality ^hi (a random variable) for each applicant and maximizes the expected utility E½Uð^hi Þ. Consider the following four-stage process of matching potential tenants to apartments: 1. Inquiry: An applicant with quality y selects publicly posted rental units to send costless inquiries with signal x to landlords. 2. Screening: Given signals XT ¼ fx1 ; :::; xT g received from T independent applicants, the landlord forms a set of predicted qualities HT ¼ f^h1 ; . . . ; ^hT g and responds to n applicants. 3. Interview: Interviews, which include credit and reference checking, reveal the true quality y and cost applicants and landlords m and c, respectively. 4. Decision: The candidate with the highest true quality y is offered the apartment. This setting is similar to that of Morgan and Vardy (2009), in which employers have one vacancy, want only ‘‘competent’’ workers, and receive signals about the workers’ types. That model has an additional stage in which true type is not revealed until hiring is complete. Our experiment can distinguish only statistical and taste-based discrimination occurring at stage 2 of the process, and the results are consistent with Morgan and Vardy’s (2009) partial equilibrium, one-sided search approach. Nevertheless, we consider stages 2 through 4 in the model and discuss the implications of strategic signaling by applicants. A. Statistical Discrimination Suppose that signal x proxies the true quality y noisily with a race-specific error er:7 xr ¼ hr þ er ð1Þ where hr Nðlr ; r2h Þ, Eðer jhr Þ ¼ 0, varðer jhr Þ ¼ r2e;r , Eðxr Þ ¼ lr , and varðxr Þ ¼ r2h þ r2e;r .Gender & Geographical Discrimination in Economics The assumptions mostly correspond to those in Aigner and Cain’s (1977) model, except that we permit the signal mean to vary by race. Landlords have a sample of inquiries x and applicants’ true quality y acquired during past iterations of stages 2 and 3. Using this sample, a landlord can estimate the following forecasting regression for each race r: ^ ^Lr þ ^ cr xr ; hr ¼ l ð2Þ ^Lr is the ordinary least squares (OLS) estimator of where l the intercept term; ^cr is the estimator of the marginal effect of signal xr; and r is W for whites and B for blacks. Esti7 We may assume that x ¼ r þ ry þ e, but it does not change the model’s predictions. 121 ^Lr and c^r will vary across landlords because of mates of l their differing experiences. Equation (2) is similar to the posterior belief in Morgan and Vardy’s (2009) or Balsa and McGuire’s (2001) model. We assume, however, that landlords do not have prior beliefs about the true means and variances of quality and signal but rather form posterior beliefs using OLS estimates obtained via past samples. Indeed, ^Lr and ^cr under a joint normalBayes’s rule yields the same l 8 ity assumption. A landlord observes signal x~ and race r from an applicant in stage 2 and predicts the quality by plugging x~ into equation (2): ~hr ¼ l ^Lr þ ^cr x~: ð3Þ We call c^r the information weighting parameter for race r since it informs a landlord how much to weight a signal from an applicant of race r.9 Some applicants may reveal only their race in an e-mail inquiry.10 In this case, the landlord infers quality using the average signal ( xr ) observed among race r in past e-mail inquiries in the following forecasting regression: hr ¼ l ^Lr þ ^cr xr : ð4Þ Equation (4) is equivalent to the landlord using some average y among r to form a prediction.11 Statistical discrimination under risk neutrality and its implications. For a risk-neutral landlord with race-invariant utility, if the total number of applicants is T M, the landlord responds positively to n of the T independent applicants, where each yields E½Uð^hi Þ c. If applications exceed capacity (T > M), the landlord will sort all applicants by E½Uð^hi Þ and will invite the top M. As utility and cost are race invariant, the decision rule is in line with Morgan and Vardy’s (2009) color-blind threshold. Here, the decision rule will be some h. As statistical discrimination influences a landlord’s decision through ^hr , differential outcomes by race arise through the OLS estimators in equation (2): ^cr ¼ c^ovðhr ; xr Þ ; v^arðxr Þ ^Lr ¼ hr ^cr xr : l ð5Þ ð6Þ 8 As landlords are not interested in the causal relationship between quality and signal, but only predictions that yield the lowest variance, OLS is sensible. It can be generalized to a Bayesian framework. For instance, landlords have prior beliefs that yield the intercept and slope of equation (2) and update them as they observe more realizations of quality and signal, as in Altonji and Pierret’s (2001) example of employer learning. Our focus on the initial screening stage means that this generalization is not necessary. 9 This is not strictly a parameter, but a landlord’s estimator of the parameter of the regression model. 10 See section III for an example of such an inquiry. 11 See section IIB for the implications when applicants strategically reveal no information. 122 THE REVIEW OF ECONOMICS AND STATISTICS Here, c^ ovðhr ; xr Þ is the sample covariance between quality and signal, v^arðxr Þ is the sample variance of the signal, and hr and xr are the sample average of quality and signal of ^Lr applicants, respectively. Despite the unobservability of l and ^ cr , we can experimentally manipulate the race of applicants and signals sent by applicants to examine whether landlord responses are consistent with the model’s predictions. If each landlord’s sample of yr and xr were observable, we could average the numerator and denominator of the information-weighting parameter across the sample of landlo … Gender & Geographical Discrimination in Economics Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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