Black/White America: The Real Differences

The black line plots the average percent earnings gap in each year, and the bars show the contribution to the gap from each factor; we include observable measures of differences in age, education, industry and occupation, state of residence, and part-time job status. The red portions of the bars show the part of the gaps unexplained by these factors. The results highlight a number of crucial points about the earnings gap between black and white workers.

First, a sizable portion of the black-white wage gap comes from the fact that blacks and whites work in different industries and occupations light blue bars. This sorting matters because different jobs have different levels of labor productivity, primarily related to the amount of capital that workers use in production. For example, drilling and mining, which use a lot of machines and equipment, are industries with high productivity occupations, whereas retail and other services, which rely relatively more on people than machines, have lower productivity.

Controlling for differences in the types of jobs in which blacks and whites work explains about 9 percentage points of the annual gap for men and about 5 percentage points of the annual gap for women. The importance of these differences in industry and occupation composition has declined over time, especially for women, as the distributions of black and white workers in different types of jobs have grown more equal.

That said, these differences continue to be a notable factor in the wage gap between blacks and whites. The second largest contributor is differences in educational attainment medium blue bars. For black men, the contribution of education has changed little over time, explaining about 5 percentage points of the total earnings gap. For women, the contribution from educational differences has risen in importance over time, explaining about 2 percentage points of the gap in but more than 5 percentage points of the gap in The remaining measurable variables that capture differences in age, part-time status, and state of residence have only a modest impact on the wage gaps of black men and women.

The most important fact highlighted by our decomposition is that a significant portion of the wage gap between blacks and whites is not traceable to differences in easily measured characteristics, but rather is unexplained within our model red bars. Perhaps more troubling is the fact that the growth in this unexplained portion accounts for almost all of the growth in the gaps over time. For example, in about 8 percentage points of the earnings gap for men was unexplained by readily measurable factors, accounting for over a third of the gap. By , this portion had risen to almost 13 percentage points, just under half of the total earnings gap.

A similar pattern holds for black women, who saw the gaps between their wages and those of their white counterparts more than triple over this time to 18 percentage points in , largely due to factors outside of our model. This implies that factors that are harder to measure—such as discrimination, differences in school quality, or differences in career opportunities—are likely to be playing a role in the persistence and widening of these gaps over time. Notably, our results are similar to those of Cajner et al. For example, one might worry that comparing the average black worker to the average white worker hides important gains made in the wage gaps for more highly educated black workers, which could imply in turn that policies should focus on expanding educational attainment.

To investigate this possibility, Figure 3 shows the percent shortfall in earnings for black men and women relative to white men and women by educational attainment. The bars reflect wage gaps averaged over three to year periods. The results are striking. Among men, the black-white earnings gap is now slightly higher for those with a college degree or more than it is for high school graduates.

This marks a change from earlier in the sample, when male high school graduates fared worse than male college graduates. For women, the gaps in earnings remain larger for high school graduates than for college graduates, but they have been growing for both levels of educational attainment. These results imply that the average experience of black workers, shown in Figures 1 and 2, is widespread and reflects the outcomes for black workers across the education distribution.

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Though not shown here, the consistency of the black-white wage gap across the full spectrum of subgroups in our data indicates that the forces contributing to the wage gap apply to the entire population of black workers. These results imply that time alone is unlikely to remedy the large gaps in earnings between black and white workers.

The opportunity to succeed is at the foundation of our dynamic economy. In this context, large and persistent shortfalls for African Americans, or any other group, are troubling. Individuals invest in themselves based on the payoff they expect in the labor market. Lower payoffs can result in lower investment, limiting personal and generational mobility. Moreover, disparities in labor income pass through to lower consumption, savings, and wealth, which ultimately makes individuals and families more vulnerable to economic shocks. But perhaps most crucially, there are considerable pressures weighing on U.

Inpatient hospital nights are defined as nights in acute care hospitals, excluding long-term care facilities. Expenditures, physician visits, and hospital nights have frequently been used in studies of access to or demand for medical care Blendon et al. In addition, we examine telephone contacts with physicians and contacts with nonphysician providers, with the latter defined as face-to-face contacts in any setting with nonphysician medical care providers such as chiropractors, optometrists, podiatrists, audiologists, physician assistants, nurse practitioners, physical or occupational therapists, social workers, or home health aides.

Finally, we examine several qualitative measures of utilization, including the site or setting of physician visits; whether individuals have a usual source of care; the type of usual source, if any; whether individuals usually see a particular physician at their usual source of care; and the specialty of this physician, if any. The key demographic variable in the study is race, categorized as white or black according to individuals' self-identification. Additional demographic and socioeconomic characteristics used in the analyses are age, sex, employment status, educational attainment, family income, marital status, family size, insurance coverage, and location of residence.

Age is categorized as 65 to 69, 70 to 74, 75 to 79, 80 to 84, or 85 and older. Educational attainment is categorized as no high school, some high school, high school graduate, or college graduate. Marital status is categorized as married; widowed more than 1 years; divorced or separated more than 1 year; widowed, divorced, or separated within the past year; or never married. Insurance coverage is categorized as Medicare only this category also includes a small number of patients with only private insurance or Medicaid , Medicare plus Medicaid or other public program , Medicare plus private supplementary insurance, or uninsured.

Location of residence is categorized as a large metropolitan area one of the 19 largest Metropolitan Statistical Areas , a small metropolitan area any other Metropolitan Statistical Area , or a nonmetropolitan area. A substantial body of literature confirms the association of these variables with utilization of medical care Blendon et al.

Assessing the independent effect of race on the utilization of medical care requires comprehensive adjustment for differences in health status. Further, whenever possible, the range of measured values for each component should extend beyond the absence of illness to include the degree to which positive states of health are enjoyed.

Studies of access to or demand for medical care that use multivariate analyses have found that health status is the most important determinant of medical care utilization Manning et al. However, most such studies of the elderly have used limited measures of health status. In this study, we adopt the framework developed by Manning et al. This approach has been found to substantially enhance the explanatory power of regression models for the use of medical care Manning et al. The measure of general health status used in the study was individuals' self-rating of their current health as excellent, good, fair, or poor.

Four variables were used to measure physical health: In addition, we determined whether individuals were current smokers. Although this is not a direct measure of physical health, smoking has multiple harmful effects on physical health. Mental health was measured with two variables: The measure of social health was the sum of four ordinal items that assess participation in voluntary organizations e. Assessing the independent effect of race on the utilization of medical care also requires adjusting for differences in attitudes and beliefs regarding the efficacy of medical care.

In this study, beliefs regarding health locus of control were measured with two variables: Prior studies have found such variables to be associated with the utilization of medical care Manning et al.

We conducted bivariate analyses to examine differences between black and white elders with regard to quantitative and qualitative measures of medical care utilization, demographic and socioeconomic characteristics, health status, and attitudes and beliefs. Quantitative utilization measures were adjusted for age and sex by using direct standardization Kleinbaum et al. Statistical significance was assessed through the use of t tests for continuous variables and chisquare tests for proportions.

To determine the independent effect of race on medical care utilization, we conducted multivariate regression analyses with total medical care expenditures, physician visits, and inpatient hospital nights as dependent variables. Our primary analyses employed a demand-based perspective. In these analyses, the explanatory variables included all demographic and socioeconomic characteristics and measures of health status and attitudes and beliefs described in the preceding section.

Employment status may influence the demand for medical care through its effect on the cost of care in terms of time spent. Education is expected to influence the demand for care because better educated individuals may be more efficient producers of health Grossman, Marital status and family size can affect the demand for care because family members may encourage older persons to seek care for symptoms that otherwise would be ignored or, alternatively, may serve as substitutes for formal medical care.

Insurance coverage is expected to influence the demand for care through its effect on the out-of-pocket price of care. Metropolitan versus nonmetropolitan residence may be a proxy for certain nonmonetary costs of care e. Measures of health status are expected to influence the demand for care because they reflect individuals' stock of health capital Grossman, Finally, attitudes and beliefs are expected to affect the demand for care because they shape individuals' preferences.

The explanatory variables used in our demand-based analyses are similar to those used in other studies of the demand for medical care Manning et al. In addition, we performed secondary analyses employing a need-based perspective. These analyses included as explanatory variables only age, sex, and the measures of health status and attitudes and beliefs; other demographic and socioeconomic characteristics were excluded.

Multivariate analyses were based on the two-part model of medical care utilization Manning et al. The first part of the two-part model is an equation for whether or not an individual has non-zero medical care expenditures or physician visits or hospital nights during the year and is estimated by using logistic regression.

This equation, which separates users of medical care from nonusers, assesses the factors that influence the decision to spend on care. From a statistical viewpoint, it also deals with the fact that an appreciable proportion of the population does not use any medical care during a year. The second part of the two-part model is an equation for the logarithm of medical care expenditures or physician visits or hospital nights conditional on non-zero expenditures and is estimated by using ordinary least squares. This equation assesses the factors that affect the level of use among those who use care.

The logarithmic transformation of the dependent variable deals with the marked skewness of the distribution of medical care expenditures and use, and results in more robust and efficient estimates Manning et al. Standard errors were obtained by using White's hetero-skedasticity-consistent covariance matrix estimator.

Disappointing Facts about the Black-White Wage Gap

All analyses were weighted with weights that reflect both the sample design of the Household Survey and complete and partial survey nonresponse Cohen et al. A p value of. Elderly whites constituted Table compares the demographic and socioeconomic characteristics of elderly whites and blacks. Whites and blacks were similar in age and sex distribution and had similar rates of labor force participation. However, whites had more education and higher incomes than blacks and were more likely than blacks to be married, although blacks lived in larger families.

Insurance coverage differed substantially for white and black elders. Specifically, blacks were much more likely than whites to have Medicare only—that is, without either public or private supplementary coverage—and to have Medicaid or other public coverage in addition to Medicare. In contrast, whites were much more likely than blacks to have private supplementary insurance in addition to Medicare. Blacks were more likely than whites to live in large metropolitan areas, but the proportion of individuals residing in nonmetropolitan areas was similar for both races.

As Table shows, older whites perceived themselves to be in much better general health than older blacks. White elders also had more favorable indicators of physical, mental, and social health than black elders. Compared with blacks, whites had fewer functional status limitations, chronic conditions, acute symptoms during the preceding 30 days, and disability days.

Whites also had lower degrees of emotional instability and higher degrees of positive emotional states, and were more involved in social activities. Similar proportions of white and black elders were current smokers. Elderly whites and blacks were similar in their beliefs regarding the efficacy of self-care vs.

Descriptive analyses of quantitative measures of utilization uncovered interesting patterns, as Table shows. Mean total medical care expenditures for white and black elders were not statistically significantly different. However, this finding masks racial differences in the probability of spending on medical care and in the level of use among those who use care. The findings for physician visits were similar to those for total medical care expenditures. By contrast, measures of inpatient hospital use did not differ between whites and blacks. Older whites averaged more telephone contacts with physicians than older blacks whites, 0.

An important question pertains to the burden of out-of-pocket expenditures for medical care. However, the relative burden, measured as the proportion of family income adjusted for family size devoted to the older individual's out-of-pocket expenditures, was similar in the two races whites, 5. The results presented above and in Table indicate that racial differences in quantitative measures of medical care utilization were relatively small.

Analyses of qualitative aspects of utilization, on the other hand, tell a different story. For example, as Table shows, elderly blacks saw physicians in hospital outpatient departments at more than twice the rate of elderly whites, and they saw physicians in emergency rooms nearly one and one-half times as often. Conversely, whites saw physicians in physicians' offices, group practices, or clinics or in neighborhood or family health centers at a higher rate than blacks.

Analyses of elders' usual source of medical care were consistent with these findings. Thus, whereas elderly blacks were only slightly less likely than elderly whites to have a usual source of care blacks, Whites were more likely than blacks to report a physician's office, group practice, or clinic as their usual source of care.

Whites also were more likely than blacks to report having a specialist vs. Without further information, however, it is difficult to say whether greater use of specialists is associated with higher quality of care. We used multivariate regression to assess the independent effect of race on quantitative measures of medical care utilization. Our primary analyses, which employed a demand-based perspective, adjusted for demographic and socioeconomic characteristics, health status, and attitudes and beliefs regarding medical care.

Table reports the results of logistic regression models for the first part of two-part models for medical care expenditures, physician visits, and hospital nights. However, the racial difference in adjusted probabilities of spending on care whites, Race did not have a statistically significant independent effect on the probability of having at least one physician visit or on the probability of being hospitalized. Other demographic and socioeconomic variables generally had expected effects on the decision to spend on medical care and on the likelihood of having at least one physician visit Table Thus, older age, female sex, higher income, and having private supplementary insurance in addition to Medicare were associated with a higher probability of having non-zero medical care expenditures and physician visits, while a larger family and being widowed or divorced were associated with a lower probability.

By contrast, most demographic and socioeconomic characteristics did not have a statistically significant influence on the probability of being hospitalized.

Racial and Ethnic Differences in the Health of Older Americans.

The findings for measures of health status and attitudes shown in Table are noteworthy. Worse physical health i. Interestingly, the impact of individuals' self-rating of their current health as excellent, good, fair, or poor did not achieve statistical significance in any of the logistic regression models; this suggests that the influence of general health status on the use of medical care was fully captured by the measures of physical, mental, and social health. A stronger belief in the efficacy of self-care for health problems was associated with a lower probability of non-zero expenditures, physician visits, and hospital nights.

Table reports the results of linear models for the second part of two-part models for medical care expenditures, physician visits, and hospital nights. In these equations, the sample is restricted to those who have non-zero utilization and the dependent variable is the logarithm of the level of utilization. Race did not have a statistically significant independent effect on the level of medical care expenditures among users of medical care, the number of physician visits among individuals who saw a physician, or the number of hospital nights among elders who were hospitalized.

Among those who spent on medical care, higher educational attainment, higher income, and having private supplementary insurance or Medicaid in addition to Medicare were associated with higher medical care expenditures, whereas female sex, a larger family, and living in a nonmetropolitan area were associated with lower expenditures. The findings were similar for the number of physician visits among elders who saw a physician, although nonmetropolitan residence was not associated with fewer visits. Demographic and socioeconomic characteristics had little impact on the number of hospital nights among elders who were hospitalized, with the notable exception that living in a small metropolitan area or a nonmetropolitan area was associated with fewer nights.

Turning to the findings for health status and attitudes and beliefs, we note that individuals' self-rating of their current health as poor was associated with higher medical care expenditures and more physician visits, whereas worse physical health was generally associated with higher expenditures, more physician visits, and more hospital nights. Worse mental health, as manifested by a higher degree of emotional instability, was associated with more hospital nights.

A stronger belief in the efficacy of self-care for health problems was associated with lower medical care expenditures and fewer physician visits and hospital nights. We also conducted secondary analyses, employing a need-based perspective, which adjusted only for age, sex, health status, and attitudes and beliefs. Table reports the estimated coefficients of the black race indicator variable in these analyses and compares them with the estimated coefficients from the demand-based analyses shown in Tables and Race had statistically significant and important independent effects on medical care expenditures and physician visits in the need-based analyses.

As shown in Table , elderly whites were more likely than elderly blacks to spend on medical care after adjustment for the other explanatory variables, and the racial difference in adjusted probabilities of spending on care whites, Moreover, among individuals who spent on care, adjusted medical care expenditures were Similarly, white elders were more likely than black elders to see a physician, and the racial difference in adjusted probabilities of having at least one physician visit whites, Among individuals who saw a physician, whites made 4.

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Race did not have a statistically significant impact on the utilization of inpatient hospital care. Finally, because the effect of race on the utilization of medical care may differ among population subgroups, we repeated our demand-based and need-based analyses after including interaction terms between race and sex, race and income, and race and self-rated health in the regression models. The only statistically significant interactions in these analyses were between race and self-rated health in the equations for the number of hospital nights among elders who were hospitalized.

Specifically, hospitalized older blacks who reported excellent health spent fewer nights in the hospital than their white counterparts, whereas there were no differences in the level of hospital use between hospitalized older blacks and whites who reported good, fair, or poor health. Owing to small cell sizes, however, the lack of significant interactions should be interpreted with caution. This study indicates that racial differences in the quantity of medical care received by elderly persons in the United States have largely disappeared. In particular, bivariate analyses found that in , white and black elders had similar mean annual total medical care expenditures, physician visits, and inpatient hospital nights, although whites were slightly more likely than blacks to spend on medical care and to see a physician during the year.

These data generally agree with other recent studies of medical care utilization by the elderly Kleinman et al. Interpreting descriptive findings, however, requires careful consideration of differences between older whites and blacks in demographic and socioeconomic characteristics, health status, and attitudes and beliefs regarding medical care. Consistent with prior research Manton et al. Worse health would be expected to lead to higher use of medical care among blacks.

On the other hand, compared with black elders, white elders had more education, higher incomes, and much higher rates of private insurance coverage to supplement Medicare, all of which could result in higher utilization among whites. We assessed the independent effect of race on the utilization of medical care using multivariate regression analysis. Our primary analyses employed a demand-based perspective that adjusted for all available variables that may influence the demand for care. The explanatory variables in these analyses thus included a full complement of demographic and socioeconomic characteristics, comprehensive measures of health status, and measures of attitudes and beliefs about the efficacy of medical care.

These analyses found that race generally did not have statistically significant independent effects on medical care expenditures, physician visits, or hospital nights. The only exception was that black elders were slightly less likely than white elders to spend on medical care after adjustment for other demand factors, but the racial disparity in the adjusted probabilities of non-zero expenditures was very small. The finding that race has little independent influence on the demand for medical care among the elderly, however, does not necessarily imply that the current allocation of medical care services between older whites and older blacks is socially desirable.

Policy makers in many other industrialized countries also accept this view van Doorslaer and Wagstaff, But allocation of services according to the demand for care may fall short of this criterion. In particular, features of the medical care delivery system may result in individuals of high socioeconomic status receiving more services than their lower socioeconomic status counterparts. To address this issue, we performed additional multivariate analyses employing a need-based perspective.

The explanatory variables in these analyses included only age, sex, and the measures of health status and attitudes and beliefs; educational attainment, income, insurance coverage, and indicators of family structure were excluded. Thus, the need-based analyses addressed the question of whether older whites and blacks of similar health status have the same medical care utilization irrespective of socioeconomic factors.

Ethnic differences in bone geometry between White, Black and South Asian men in the UK

The results of the need-based analyses differed strikingly from the findings of the demand-based analyses. Specifically, the need-based analyses revealed that, with adjustment made for the other explanatory variables, elderly blacks were considerably less likely than elderly whites to spend on medical care or to see physicians. Moreover, among users of medical care, black elders had lower adjusted medical care expenditures than white elders, and among individuals who saw a physician, blacks had fewer physician visits than whites.

These findings indicate that elderly blacks, as compared with whites, received less medical care and saw physicians less often than would be expected given their health status. Why are white and black older persons in equal need of medical care treated differently? Comparison of our demand-based and need-based analyses suggests that a large portion of the racial discrepancy in the use of medical care found in the need-based analyses is due to racial differences in socioeconomic variables.

Socioeconomic status may influence the allocation of medical care among elderly Americans through several mechanisms. Socioeconomic status is positively associated with elders' likelihood of having private insurance to supplement Medicare Rice and McCall, ; Nelson et al. Private supplementary coverage may influence patient behavior by reducing the out-of-pocket price of care and may influence provider behavior through more generous reimbursement. Other financial and nonfinancial barriers to care may also differ across socioeconomic groups.

For instance, individuals of low socioeconomic status are more likely than those of high socioeconomic status to travel long distances to receive care, rely on public transportation, and reside in areas where medical care providers—especially private physicians—are scarce LeGrand, ; Aday, ; Dutton, ; Ernst and Yett, High socioeconomic status may also be associated with increased ability to navigate successfully within the complex health care delivery system in the United States.

A potential objection to our analyses is that socioeconomic status, not race, is the important factor and that, consequently, our need-based analyses are misleading. However, there are at least three reasons why it is meaningful to ask whether the allocation of medical care services between elderly blacks and whites corresponds to observed racial differences in need. First, socioeconomic status and race are inextricably linked in American society, and race directly affects educational and economic opportunities through societal mechanisms such as stratification, segregation, and discrimination Wallace, Second, it is likely that a portion of the racial disparity in medical care utilization observed in the need-based analyses is due to race per se rather than to socioeconomic status Wallace, Physicians may tend to avoid areas with large minority populations when establishing private practices Ernst and Yett, Some observers believe that patient race may also have a direct influence on clinical decision making by physicians Eisenberg, ; Maynard et al.

Third, racial inequalities in the use of medical care have long interested both researchers and policy makers. Our comparison of demand-based and need-based analyses makes a novel contribution to the literature on this topic. Another potential objection to our analyses is that they overlook the full richness and complexity of the relationships among race, socioeconomic status, and health status.

A comprehensive model of these relationships, for instance, would explicitly acknowledge the impact of race on socioeconomic opportunities as well as the effects of socioeconomic factors on health and vice versa Feinstein, ; Williams et al. Such a comprehensive model is best formulated and empirically tested using a life-cycle perspective, however, and is beyond the scope of our study and our cross-sectional data.

Rather, our study focuses on the more modest goal of assessing the influence of race on medical care utilization, with health status and socioeconomic status being taken as given. Our study also is limited by imperfect measurement of health status, since it is unlikely that even our comprehensive approach captured all of the relevant racial differences in health.

But if omitted health status variables are correlated with socioeconomic characteristics, then the difference between our demand-based and need-based analyses may be partly explained by omitted variable bias. To conclude, our study indicates that the evidence that the Medicare program has substantially improved access to medical care for older blacks in the United States is subject to strong qualification. Simple descriptive analyses comparing whites and blacks and multivariate analyses employing a demand-based perspective suggest that racial differences in elders' use of medical care are small.

On the other hand, multivariate analyses employing a need-based perspective reveal that elderly blacks and whites in similar health do not receive the same amount of medical care. In particular, blacks spend substantially less on care than would be expected given their health status.

Our findings are consistent with those of van Doorslaer and Wagstaff , who performed an international comparison of equity in the delivery of medical care among industrialized nations. Their study, which also conceptualized equity as equal treatment for persons with equal medical need, found that the United States was characterized by inequity, with elderly persons of higher socioeconomic status being favored.

The findings of our study have two implications. First, the poorer health status of elderly blacks, as compared with whites, may be partially related to inadequate medical care. Older blacks do not receive the quantity of care that would be expected based on their medical need. There are also qualitative differences in care between black and whites. Although the contribution of racial differences in medical care utilization to differences in health may be small relative to the contribution of other influences, such as socioeconomic factors, elimination of the disparity in the use of care would be a positive step.

Second, additional policies to efface the relationship between socioeconomic status and the use of medical care in the United States may be helpful. Potential policies include extension of insurance coverage to low-income elderly individuals, incentives or programs to locate private physicians in undeserved areas, and development of clinical practice guidelines to mitigate the undue influence of patient race or other demographic factors on clinical decision making by physicians.

Two recent policies that may have salutary effects on the use of care by older blacks with Medicare insurance are the extension of public supplementary coverage to a higher proportion of the low-income elderly, which is expected to decrease out-of-pocket payments for this group, and the implementation of the resource-based Medicare fee schedule Health Care Financing Administration, , which is likely to reduce the difference in physician reimbursement between Medicare patients with private and with public supplementary coverage. We must acknowledge, however, that health policy has only limited ability to reverse the effects of societal mechanisms and structures that constrain black Americans' economic opportunities and adversely affect their health.

Turn recording back on. National Center for Biotechnology Information , U. Variables In this study, we make use of variables measuring utilization of medical care, demographic and socioeconomic characteristics, health status, and attitudes and beliefs about the efficacy of medical care.