Sociological Images

In analysis of Presidential pardons during the George W. Bush administration, ProPublica has found that whites were four times as likely as non-whites to be granted a pardon.  Pardons were granted to 12% of whites, 10% of Hispanics and Asians, and zero percent of Blacks and Native Americans. The disparity remained even when investigators controlled for type of crime.

ProPublica explains:

…President George W. Bush decided at the beginning of his first term to rely almost entirely on the recommendations made by career lawyers in the Office of the Pardon Attorney.

The office was given wide latitude to apply subjective standards, including judgments about the “attitude” and the marital and financial stability of applicants…

Bush followed the recommendations of the pardons office in nearly every case… President Obama — who has pardoned 22 people, two of them minorities — has continued the practice of relying on the pardons office.

Sometimes disparate decisions in pardon cases were eyebrow raising:

An African American woman from Little Rock, fined $3,000 for underreporting her income in 1989, was denied a pardon; a white woman from the same city who faked multiple tax returns to collect more than $25,000 in refunds got one. A black, first-time drug offender — a Vietnam veteran who got probation in South Carolina for possessing 1.1 grams of crack – was turned down. A white, fourth-time drug offender who did prison time for selling 1,050 grams of methamphetamine was pardoned.

ProPublica traces the disparity to age, leniency given to people who are seen as “upstanding” members of society (e.g., they’re married, have little debt), the influence of money and politics (letters from Congresspersons and donations to lawmakers by convicts’ spouses), and simple prejudice.  Nevertheless:

When the effects of those factors and others were controlled using statistical methods, however, race emerged as one of the strongest predictors of a pardon.

Recently the Pew Research Center released the results of a survey of 2,048 individuals about their perceptions of class conflict in the U.S., which are quite interesting in light of the Occupy Wall Street protests and the current attacks on Mitt Romney’s work with Bain Capital, which wouldn’t be that surprising except that they’re coming from other GOP presidential hopefuls (including Rick Perry referring to “vulture capitalism”). In the Pew survey, 2/3 of participants reported that there are “strong” or “very strong” conflicts between the rich and poor, with only 7% saying there are no conflicts:

This indicates an increase in perceived class conflict since 2009, where under half said there were “strong” or “very strong” conflicts between rich and poor. We also some difference by race, with African Americans perceiving more conflict than Whites or Hispanics, and Democrats and Independents seeing more than Republicans:

While I think these findings are interesting, I’m also struck by the language. Since Americans tend to define themselves as middle class, regardless of income, the wording here (“rich” vs. “poor”) would seem to ask Americans about their perceptions of conflict between groups that they likely do not identify with personally (though many may interpret “rich vs. poor” as shorthand for general economic inequality, of course). I just wonder what the results would be if we had a survey that asked about conflicts between the rich and the middle class, or “the rich and people like you” (and the same questions about the poor).

Regardless, increasing perceptions of class-based conflict doesn’t mean respondents necessarily think the wealthy are unfairly well-off. They were almost evenly split on whether the rich got their wealthy because of connections (family or otherwise) or because of their own hard work and effort:

The NYT has an article about the survey as well, with additional graphics. Thanks to Shamus Khan for the tip!

Cross-posted at Family Inequality.

I can’t teach my course on family sociology without these graphs, which show the rise of the unfree population, and the incredible race/ethnic and gender disparities behind them.

The Bureau of Justice Statistics has released Correctional Population in the United States, 2010, which updates my standard figures. First, the total trend toward unfreedom in the population — from less than 2 million in 1980 to more than 7 million 30 years later:

And second, to understand the disparate impact of this change on Black men in young adulthood primarily — and secondarily, Latino men — here are the rates of incarceration for men by age and race/ethnicity (Blacks here exclude Latinos; Asians and American Indians are not included in the statistics):

Just to make sure you read the scale right, that incarceration rate for Black men in their early 30s is 9,892 per 100,000, or 9.9%, or one-in-ten — more than five-times the rate for White men.

I come at this largely from its effects on families. In a nutshell: The overall trend is largely a consequence of how the U.S. has waged its drug war over this period; these policies fit into a web of practices that deny families to millions of people in the U.S. (only a minority of whom have been convicted of crimes), including by simply removing men from communities and increasing the number of single-parent families.

All that said, you may notice the little decline at the end of that long upward trend in the first figure. In fact, for the first time since 1980, there has been a decline in the incarcerated population for two years running. There has been a long-term decline in crime, but I don’t know whether that is more important than the budget crises facing so many states, or the diminished lust for locking people up. In New York, for example, seven incarceration facilities were closed in the last year, after the number of prisoners dropped about one-fifth in the past decade:

The inmate decline followed a 25 percent statewide drop in crime over the past decade and revisions in sentencing laws that allowed earlier releases and alternative programs for nonviolent drug offenders. The number of prisoners in medium-security prisons declined almost 20 percent from 2001 to 2010 while those in minimum-security facilities dropped 57 percent.

The numbers on the charts are still off the charts, meanwhile — and remember these are just those in the system now. Many more people (and their families) live lives permanently hampered by criminal records and the experience of imprisonment.

Dmitriy T.M. sent in a Census Bureau report on transportation and commuting, providing a detailed picture of how we’re getting to work. Despite constant discussions about reducing car use and encouraging mass transportation, the vast majority of people in the U.S. get to work in a car:

Not surprisingly, there are significant differences by race and ethnicity. Non-Hispanic Whites are the most likely to drive to work alone in their own car (83.5%), while only 3.2% use public transportation. Latinos are the most likely to carpool with at least one other person (16.4%) and African Americans are most likely to use public transportation (11.5%):

These differences likely reflect a variety of factors, include social class and differences in racial/ethnic concentrations in urban vs. rural areas and in different regions of the U.S., which affects how likely a worker is to have access to reliable, efficient public transportation or to realistically be able to walk to work. In fact, there were only five metro areas where at least 10% of workers use public transportation to get to work: the regions surrounding NYC, San Francisco/Oakland, Washington D.C., Boston, and Chicago.

And as anyone who has taken part in a morning commute recently won’t be shocked to hear, leaving for work is still highly concentrated in the 5 to 8:59 a.m. period for most occupations, though departure times reflect the  wider range of normal working hours in the service industry compared to other economic sectors (note that the colors do not all represent equal amounts of time):

More on mode of transportation and commuting times by region and race/ethnicity in the full report.

Sonita M. sent in a report from the Movement Advancement Project about the state of lesbian, gay, bisexual, and transgender (LGBT) families.

LGBT families are more likely to be poor than non-LGBT families.  Nine percent of married cis-gender different-sex couples live in poverty, compared to 21% of gay male couples and 20% of lesbian couples:

LGBT couples may be more likely to be in poverty in part because of wage differentials between gays, lesbians, and their heterosexual counterparts.  Research shows that gay and bisexual men earn significantly less money than heterosexual men, whereas lesbians make somewhat more money than straight women.  Gay men would be more likely than heterosexual men to be in poverty, then.  But what about women? Women in same-sex couples face the same wage disadvantage that all women face, but also are not married to the heterosexual men that are making so much money (making it so that heterosexual women can make less money than gay women, but still be less likely to live in poverty). Make sense?  I hope so.

The second reason that LGBT couples with children are more likely than cis-gendered different-sex couples with children to live in poverty is that Black and Latino LGBT people are more likely than White LGBT people to be parents, and Blacks and Latinos are disproportionately poor to begin with:

Among same-sex couples, being a parent is also correlated with immigration status, which also correlates with class.  Non-citizens are more likely to be parents than citizens:

The two million children in America being raised by LGBT parents, then, are more likely to suffer from class disadvantage.  The authors of the report go on to discuss the ways in which formal policy and informal discrimination contribute to this state of affairs.

Via Andrew Sullivan.

Jake C. sent in a good example of the racialization and gendering of jobs within the service industry. This photo shows two notices for openings at a restaurant, one in English, one in (misspelled) Spanish:

The notice in Spanish isn’t a translation of the one about the hostess job; rather, it announces that two people are needed as dishwashers. It shows the way that particular positions within a workplace are often associated with certain groups, and how organizational policies may reinforce occupational segregation by sex or race/ethnicity. The role of greeting and seating customers is explicitly gendered as a hostess, while the language difference will channel applicants into different jobs. These types of practices are one part of the process that channels individuals into different positions in the workplace, both by restricting access to information about jobs and providing subtle messages to potential applicants about which positions are the best fit for them.

Cross-posted at Jezebel.

The U.S. Census Bureau recently released a report on employment and parental leave for first-time mothers. The mean age at first birth is now 25 years. And while a few decades ago the norm was for women to quit work upon getting pregnant, from 2006 to 2008, 56.1% of women worked full time during their pregnancy, leaving work only as the due date approaches. However, this varies widely by educational level, largely because women with the lowest levels of education are less likely to be working regardless:

The graph on the left below shows how many months before the birth working women left their work; the graph on the left shows how many months after the birth they returned. As we see, over time women have stayed at their paid jobs longer and returned more quickly:

During the 2006-2008 reporting period, for the first time a majority — but a bare one, at 50.8% — of first-time mothers in the labor force used paid leave (maternity leave, sick days, etc.). Not surprisingly, access to paid leave also varied greatly by educational level, and that gap has widened significantly over time:

So nearly half of first-time mothers in the U.S. still do not have paid leave from their jobs.

PBS created an interactive program based on the data that allows you to see the patterns more clearly. You select a race/ethnicity and educational level and get a detailed breakdown of the data. For instance, here’s the info for White non-Hispanic women with a 4-year college degree or higher: