Statistical Discrimination

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 31494 Experts worldwide ranked by ideXlab platform

Benjamin Hansen - One of the best experts on this subject based on the ideXlab platform.

  • the unintended consequences of ban the box Statistical Discrimination and employment outcomes when criminal histories are hidden
    Journal of Labor Economics, 2020
    Co-Authors: Jennifer L. Doleac, Benjamin Hansen
    Abstract:

    Jurisdictions across the United States have adopted “ban the box” (BTB) policies preventing employers from asking about job applicants’ criminal records until late in the hiring process. Their goal is to improve employment outcomes for those with criminal records, with a secondary goal of reducing racial disparities in employment. However, removing criminal history information could increase Statistical Discrimination against demographic groups that include more ex-offenders. We use variation in the timing of BTB policies to test BTB’s effects on employment. We find that BTB policies decrease the probability of employment by 3.4 percentage points (5.1%) for young, low-skilled black men.

  • The Unintended Consequences of “Ban the Box”: Statistical Discrimination and Employment Outcomes When Criminal Histories Are Hidden
    SSRN Electronic Journal, 2017
    Co-Authors: Jennifer L. Doleac, Benjamin Hansen
    Abstract:

    Jurisdictions across the United States have adopted “ban the box” (BTB) policies preventing employers from asking about job applicants’ criminal records until late in the hiring process. Their goal is to improve employment outcomes for those with criminal records, with a secondary goal of reducing racial disparities in employment. However, removing criminal history information could increase Statistical Discrimination against demographic groups that include more ex-offenders. We use variation in the timing of BTB policies to test BTB’s effects on employment. We find that BTB policies decrease the probability of employment by 3.4 percentage points (5.1%) for young, low-skilled black men.

Alan M. Zaslavsky - One of the best experts on this subject based on the ideXlab platform.

  • testing for Statistical Discrimination by race ethnicity in panel data for depression treatment in primary care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

  • Testing for Statistical Discrimination by Race/Ethnicity in Panel Data for Depression Treatment in Primary Care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

John Z. Ayanian - One of the best experts on this subject based on the ideXlab platform.

  • testing for Statistical Discrimination by race ethnicity in panel data for depression treatment in primary care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

  • Testing for Statistical Discrimination by Race/Ethnicity in Panel Data for Depression Treatment in Primary Care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

Thomas G. Mcguire - One of the best experts on this subject based on the ideXlab platform.

  • testing for Statistical Discrimination by race ethnicity in panel data for depression treatment in primary care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

  • Testing for Statistical Discrimination by Race/Ethnicity in Panel Data for Depression Treatment in Primary Care
    Health Services Research, 2008
    Co-Authors: Thomas G. Mcguire, John Z. Ayanian, Daniel E. Ford, Rachel Mosher Henke, Kathryn Rost, Alan M. Zaslavsky
    Abstract:

    In the Institute of Medicine (IOM) (2003)Unequal Treatment report, “Discrimination” refers to a health care provider's treatment of patients with similar health care needs differently due to the patient's race or ethnicity. Investigators have studied three types of Discrimination—affective bias, cognitive stereotyping, and Statistical Discrimination (Hilton and von Hippel 1996; Fiske 1998; Dovidio 1999; Balsa and McGuire 2003; IOM 2003; Escarce 2005; Fennell 2005), noting that each type of Discrimination has distinct implications for appropriate remedial actions (Balsa and McGuire 2003). Statistical Discrimination appears to be a potent (if more difficult to observe) source of Discrimination in health care use (Balsa, McGuire, and Meredith 2005; Lutfey and Ketcham 2005; Werner 2005). In Statistical Discrimination, providers apply correct information about a group to reduce their clinical uncertainty about an individual patient. While Discrimination stemming from affective bias and cognitive stereotyping is generally expected to reduce treatment of minorities in comparison with whites, Statistical Discrimination may or may not exacerbate race/ethnicity gaps in treatment (Balsa and McGuire 2001). Statistical Discrimination can be in the minority patient's best interest if physicians use reliable group differences in the absence of reliable data about the individual. Nonetheless, if physician must rely less on individual level information, treatment for minority patients is less likely to be matched well to their individual needs. When poor communication exacerbates clinical uncertainty, providers give undue weight to group-based generalizations, taking the form of diminished responsiveness to the circumstances of an individual patient. Prior research has documented worse communication between white providers and minority patients compared with white patients (see, for example, Cooper-Patrick et al. 1999). Because providers primarily rely on patient report to judge the severity of depression, effective communication is particularly important for the physician to allocate severity-appropriate treatment resources for this condition. The objective of this paper is to test the hypothesis that providers employ Statistical Discrimination in treating depressed patients over time. Specifically, we assess whether treatment intensity changes as depression severity changes comparably over 2 years for minority and white patients.

  • Testing for Statistical Discrimination in Health Care
    Health Services Research, 2005
    Co-Authors: Ana I. Balsa, Thomas G. Mcguire, Lisa S. Meredith
    Abstract:

    In the U.S. context, a “disparity” refers to the unfair treatment of patients on the basis of race or ethnicity. In its recent report, “Unequal Treatment,” the Institute of Medicine (2002) defines a racial disparity as a difference in treatment provided to members of different racial (or ethnic) groups not justified by the underlying health conditions or preferences about treatment of the patient. Disparities by this definition have many sources. They can stem from an array of social factors, such as the patients' socioeconomic status, insurance, or geography, with the key one probably being that minorities1 are much more likely than whites to be uninsured or to be in plans with restrictive payment policies (Monheit and Vistnes 2000; Phillips, Mayer, and Aday 2000). Social factors do not, however, fully account for all of the unjustified differences between whites and minorities. Disparities also emerge from the face-to-face decisions of doctors when insurance and other social factors can be ruled out: such differences have been referred to as Discrimination. An increasing body of literature has documented and condemned disparities originating at the clinical encounter (Bach et al. 1999; Schulman et al. 1999; Mayberry, Mili, and Ofili 2000; Geiger 2001). Also, conceptual research has identified potential sources of health disparities within the clinical encounter (Einbinder and Schulman 2000; Balsa and McGuire 2001; Balsa and McGuire 2003; Bloche 2001; van Ryn 2001). However, we are aware of no paper trying to measure empirically the magnitude and importance of these different mechanisms. In this paper, we intend to shed light on some of the processes behind the observed discriminatory patterns in the provision of health care to patients of different racial and ethnic groups. The Institute of Medicine (2002) identifies three mechanisms that may explain Discrimination at the medical encounter2: simple prejudice against members of a minority group, stereotypes that a doctor holds about the health-related behavior of minorities (such as, “blacks do not comply with treatment recommendations”), or the rational application of probabilistic decision rules when uncertainty surrounds the doctor's estimate of a patient's health status. In the same way the term is applied in labor economics and related fields, we refer to this latter form of Discrimination as “Statistical.” The basic idea of Statistical Discrimination in the health context is that uncertainty about the patient's severity of illness can induce the doctor to behave differently with otherwise identical members of different race/ethnic groups. If the underlying prevalence of the illness is associated with race, the doctor might take race into account in deciding about the diagnosis and treatment of a particular patient. Or, race/ethnicity might be associated with poor doctor–patient communication (Balsa and McGuire 2001), interfering with the doctor's ability to discern and respond appropriately to the patient's health status (Cooper and Roter 2001). Our objective in this paper is to test whether Statistical Discrimination can explain a race effect in clinical decisions and the extent to which this mechanism can account for the observed racial differentials in health care data. Our tests of Statistical Discrimination revolve around whether a race effect can be interpreted as working through the route of information in its influence on the physician's decision to diagnose a certain condition. Methodologically, our paper integrates the normative literature on clinical decisionmaking (Weinstein et al. 1980) with economic literature on Statistical Discrimination. Decision-theoretic approaches to diagnosis are common in the medical literature (Mushlin et al. 1997 or Fendrick et al. 1995), although the connection with Discrimination and disparities appears not to have been made previously. In economics, attempts to distinguish Statistical Discrimination from prejudice (or “taste Discrimination”) have been made in the area of wage differentials (Altonji and Pierret 2001) and differences in vehicle detention rates (Knowles, Persico, and Todd 2001). Our paper is in the same spirit as these investigations. Disparities and Discrimination are complex, with multiple sources and operating through multiple mechanisms. In this paper, we study the diagnostic phase of a clinical encounter, chosen by us to be the domain of decision making where information-related forces on the clinician are most likely to play out. Most of the literature on disparities is concerned, by contrast, with decisions about treatment. The role of Statistical Discrimination in relation to other possible causes of Discrimination may well differ in other parts of the decision-making sequence.

  • Statistical Discrimination in health care
    Journal of Health Economics, 2001
    Co-Authors: Ana I. Balsa, Thomas G. Mcguire
    Abstract:

    This paper considers the role of Statistical Discrimination as a potential explanation for racial and ethnic disparities in health care. The underlying problem is that a physician may have a harder time understanding a symptom report from minority patients. If so, even if there are no objective differences between Whites and minorities, and even if the physician has no discriminatory motives, minority patients will benefit less from treatment, and may rationally demand less care. After comparing these and other predictions to the published literature, we conclude that Statistical Discrimination is a potential source of racial/ethnic disparities, and worthy of research.

Andrea Moro - One of the best experts on this subject based on the ideXlab platform.

  • Testing for Asymmetric Employer Learning and Statistical Discrimination
    2018
    Co-Authors: Andrea Moro, Beibei Zhu
    Abstract:

    We test the implications of a Statistical Discrimination model with asymmetric learning. Firms receive signals of productivity over time and may use race to infer worker’s productivity. Incumbent employers have more information about workers productivity than outside employers. Using data from the NLSY79, we find evidence of asymmetric learning. In addition, employers Statistically discriminate against non-college educated black workers at time of hiring. We also find that employers directly observe most of the productivity of college graduates at hiring, and learn very little over time about these workers.

  • Asymmetric Employer Learning and Statistical Discrimination
    2016
    Co-Authors: Beibei Zhu, Andrea Moro
    Abstract:

    This paper develops a simple model of Statistical Discrimination in which firms learn about worker productivity over time and may use race to infer worker productivity. The framework we propose nests both symmetric and asymmetric employer learning by allowing outside employers to learn about workers productivity with noisier signals relative to current employers. We derive testable hypotheses on race-based Statistical Discrimination under different processes of employer learning. Testing the model with data from the NLSY79, we find that employers Statistically discriminate against black workers at time of hiring in the non-college market where learning appears to be mostly asymmetric. For college graduates, employers directly observe most of the productivity of potential employees at hiring and learn very little over time. A series of sensitivity tests provide further support for our main findings

  • Theories of Statistical Discrimination and Affirmative Action: A Survey
    Handbook of Social Economics, 2011
    Co-Authors: Hanming Fang, Andrea Moro
    Abstract:

    This chapter surveys the theoretical literature on Statistical Discrimination and affirmative action. This literature suggests different explanations for the existence and persistence of group inequality. This survey highlights such differences and describes in these contexts the effects of color-sighted and color-blind affirmative action policies, and the efficiency implications of discriminatory outcomes.

  • Theories of Statistical Discrimination and Armative Action: A Survey
    2010
    Co-Authors: Hanming Fang, Andrea Moro
    Abstract:

    This chapter surveys the theoretical literature on Statistical Discrimination and afrmative action. This literature suggests dierent explanations for the existence and persistence of group inequality. This survey highlights such dierences and describes in these contexts the eects of color-sighted and color-blind armative action policies, and the eciency implications of discriminatory outcomes.

  • A General Equilibrium Model of Statistical Discrimination
    Journal of Economic Theory, 2004
    Co-Authors: Andrea Moro, Peter Norman
    Abstract:

    We consider a general equilibrium model with endogenous human capital formation in which ex ante identical groups may be treated differently in equilibrium due to informational externalities. Unlike earlier models of Statistical Discrimination, group inequalities may arise even if the corresponding model with a single group has a unique equilibrium. The dominant group gains from Discrimination, rationalizing why a majority may be reluctant to eliminate Discrimination. The model is also consistent with "reverse Discrimination" as a remedy against Discrimination since it may require to decrease the welfare of the dominant group to achieve parity.