Monthly Archives: September 2016

White Prejudice Affects Black Death Rates

Health, Prejudice and discrimination, ,

Dr. Jordan Leitner of the University of California at Berkeley and three colleagues have published a study of the relationship between White racial attitudes and the health outcomes of Black Americans. Here are some things we already know:

  • African-Americans have a higher death rate from cardiovascular diseases (e.g., heart attacks, strokes) than White Americans. (Other diseases as well, of course.)
  • The perception by Blacks (and others) that they are being discriminated against (e.g., being followed by store employees, being pulled over by the police for a minor offense) is associated with physiological stress responses known to cause circulatory problems, and with increased mortality. However, since these studies measure perceived rather than actual discrimination, a skeptic could argue that Blacks only imagined that Whites were biased against them.
  • African-Americans have higher death rates in locations where national surveys show that anti-Black attitudes are greater. But since these surveys include both Black and White respondents, it could be argued that the results were influenced by the attitudes of Black people who hate themselves.

Social psychologists distinguish between two types of prejudice. Implicit bias refers to automatic responses that are unintentional, and of which people may not be aware. Implicit bias was not related to any of the outcomed measured in this study. Explicit bias refers to responses that are deliberate and intentional. In this study, explicit bias was defined as the difference between how warmly (on a 10-point scale) participants said they felt toward White and Black Americans.

Leitner and his colleagues used a data base from Project Implicit consisting of the scores of about 1.4 million White Americans on the Implicit Association Test (IAT), a measure of implicit bias, collected between 2003 and 2013. When filling out the IAT, the participants indicated their race, age and gender, and completed the measure of explicit bias. The county in which their computer was located was determined from their Internet protocol address. Although it is large, this is not a representative sample of Americans, since the participants were younger than the average resident of their county. To correct this bias, the researchers weighted the responses of older participants more heavily. The results were the same with or without this correction.

In Study 1, racial bias was correlated with data from a 2012 telephone survey by the Centers for Disease Control (CDC), in which both race and county of residence were identified. Two questions were of interest. Access to affordable health care was measured by asking respondents whether they had ever, in the past year, needed to see a doctor but did not because of the cost. Coronary disease diagnosis was indicated by whether they reported being told by a health professional that they had a heart attack or heart disease.

In Study 2, racial bias was related to county-level statistics, also from the CDC, indicating the age-adjusted death rates from circulatory diseases of Blacks and Whites from 2003 through 2013. To control for alternative explanations, the data analyses of both studies statistically eliminated the effects of the following county-level characteristics: population, education, income, residential segregation, housing density and geographical mobility.

Below are scatterplots showing the outcomes of the two studies. Each dot represents a county and the lines indicate the statistical averages.

  1. Blacks overall reported less access to affordable medical care. More importantly, as explicit racial bias among the county’s Whites increased, Blacks had less access to affordable medical care. Explicit bias did not affect Whites’ access to medical care.
  2. However, explicit bias had no significant effect on coronary disease diagnosis among either Blacks or Whites.
  3. In the second study, they found that the higher the explicit racial bias among Whites, the more likely both Blacks and Whites were to die of circulatory diseases. However, this relationship was stronger for Blacks than it was for Whites. For example, among counties in which Whites were high in explicit racial bias, the difference between Blacks’ and Whites’ death rates from circulatory diseases was 62 per 100,000. Among counties low in explicit bias, the difference was 35 deaths per 100,000.

According to the authors, this is the first large-scale study to demonstrate that White prejudice increases the death rate due to coronary disease of African-Americans living in the same counties. However, racial bias did not affect Black death rates due to cancer. Thus, physiological stress due to discrimination and its effects on the cardiovascular system appears to be critical in producing this effect.

The results of Study 1 imply that these increased deaths were also due in part to Blacks’ reduced access to affordable medical care. The failure of prejudice to affect coronary disease diagnosis among Blacks could be related to their difficulties in obtaining health care. Diagnosis and treatment require doctor visits, but death does not.

The fact that explicit racial bias predicted Black outcomes but implicit bias did not suggests that these health outcomes were an result of conscious bias on the part of the White majorities in these counties. Failure to provide adequate health care for poor people and minorities is an outcome of social policy decisions made by politicians and by corporate executives such as the managements of hospitals and clinics. Although the present data were collected prior to the Affordable Care Act, it would not be surprising if many of these same counties were located in states that failed to take advantage of the federal government’s offer to expand Medicaid in 2014.

I suspect that White prejudice at the community level has many other effects on the lives of African-Americans in addition to limiting access to health care. Black-White wage inequality and criminal justice policies affecting Blacks would seem to be obvious topics for future research.

You may also be interested in reading:

Outrage

The Implicit Association Test: Racial Bias on Cruise Control

Old-Fashioned Racism

The Cost of Climate Inaction

Economics, Environment, , ,

A recent headline says that climate change will cost the millennial generation $8.8 trillion. But from where does this number come? The trail leads to a 2015 study by Marshall Burke of Stanford University and two colleagues from the University of California at Berkeley in which they attempted to measure the relationship between temperature and economic productivity.

We know that global temperatures are increasing, and we can estimate how much they will increase if nothing is done to mitigate climate change (the “business-as-usual” scenario). How can you measure the relationship between temperature and economic productivity? You can’t do it simply by comparing the economies of warmer and cooler countries, since there are many cultural and environmental differences between, for example, Sweden and Nigeria. But if you compare the productivity of each country during warmer- and cooler-than-usual years, each country serves as its own control group.

However, other variables that influence the economy may take on different values during warmer and cooler years. For example, a global trade agreement may have increased productivity in certain countries in certain years, and those years may also have happened to be warmer (or cooler). These confounding variables have to be measured and statistically removed from the data.

Burke and his colleagues gathered data from 166 countries over the 50-year span between 1960 and 2010. They used multiple regression to calculate the relationship between temperature and productivity, while eliminating the effects of “common contemporaneous shocks,” such as global price changes or technological innovations, “country-specific . . . trends in growth rates,” such as those produced by changing political institutions or economic policies, and the lagged effects of previous years’ temperature and rainfall. Their final curve is an average of the impact of temperature on productivity in the 166 countries, weighted by the countries’ population size.

They found that the relationship between temperature and productivity is a curve which peaks at 55 degrees Fahrenheit (13 degrees Celsius). That is, countries are most productive when their average annual temperature is 55 degrees, and their productivity declines the more the average deviates from that temperature in either direction. The curve is shown below, along with the average yearly temperatures of selected countries. The blue shaded area represents the 90% confidence interval around their best estimate. At right are separate breakdowns for rich and poor countries, years of measurement, and agricultural and non-agricultural productivity.

figure2

Next, they used this relationship to calculate the effects of expected future climate change, assuming business-as-usual, on future global income and the incomes of each country. The model predicts that global productivity will decline approximately 23% by 2100, as compared to the same future without global warming. While some cooler-than-average countries, such as Canada and Russia, will see their economies improve, the majority (77%) will see declines in income, especially those countries near the Equator. Since the countries that can anticipate the worst effects are already poorer than average, the result will be an increase in global inequality. Here is a brief presentation of their findings by Dr. Burke.

How can these results be explained? The authors found that agricultural productivity peaks at around the same temperature (see the chart above). They also mention increased energy costs and declines in health at warm and cool temperatures. Finally, they cite research showing that human cognitive errors and interpersonal conflicts increase at warmer temperatures.

Can we trust these predictions? An optimist might note that there is a danger of overestimating the damage climate change will cause if the peak in productivity at 55 degrees is actually due to confounding variables unrelated to temperature that are not controlled in their analysis. However, it’s difficult to think of phenomena not caused by temperature that would still produce a productivity curve peaking at 55 degrees.

The authors also point out that between 1960 and 2010 annual temperatures fluctuated fairly randomly. This provided little incentive for people to adapt to warmer or cooler temperatures. However, future temperatures are expected to increase consistently, which may instigate successful efforts to adapt to these warmer temperatures.

Optimists might also argue that the assumption of no climate action at all between now and 2100 is unrealistic. To the extent that effective action is taken to mitigate climate change, the loss of productivity will not be as great.

On the other hand, a pessimist could think of reasons why their analysis might underestimate climate change’s damage to the economy. The authors note that their model focuses only on the effects of temperature and those other phenomena that are directly influenced by temperature. But climate change will affect other things besides temperature, such as sea level rise and extreme weather events. If these other effects reduce productivity, the harm due to climate change will be greater than they predict.

They also note that their model predicts the effects of annual temperatures only within the range that they have been observed between 1960 and 2010. But if global temperatures increase substantially, the future may not be predictable from the past. For example, if temperature increases cause sustained droughts over large areas, the cumulative effects on agricultural productivity may be much greater than the effects of any known previous droughts. In reality, we probably have little idea of what future catastrophes await us.

We can now return to the effect of climate change on the incomes of millennials. Two nonprofits, Demos and NextGen Climate, have published an analysis of the lifetime cost of climate change to American millennials, using the data from Burke and his colleagues. The Burke analysis predicts that, in the absence of climate action, the United States economy will shrink 5% by 2050 and 36% by 2100—slightly more than the global average of 23%.

Millennials are typically defined as people born between the early 1980s and the early 2000s. The Demos/NGC paper calculated the lifetime earnings lost by Americans who turned 21 in 2015 (born in 1994) and those born in 2015. This is simply a matter of arithmetic, and the formulas are given in their appendix. Using these formulas, you can calculate the cost of climate change to any birth cohort. Obviously, the later the birth year, the greater the cost. The $8.8 trillion figure is the aggregated cost to all millenials.

The chart below illustrates the average cost of climate change to Americans turning 21 in 2015, calculated separately for college graduates and non-graduates.

nextgen-figure-3

The second chart compares wealth lost by 2015 college graduates due to climate change to two other drains on the income of their generation—college debt and the lingering effects of the Great Recession.

lifetime-lost-wealth

Of course, the accuracy of these figures depends entirely on the validity of the analysis by Burke and his colleagues.

You may also be interested in reading:

Snow Job

Deep Background