Archive for the 'Socioeconomic & Spatial Mobility/Inequality' Category

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How Mass Incarceration of Black People Skews Our Sense of Reality

Jeff Guo of Wonkblog examines how the absence of 1.6 million people from economic statistics affects the decisions politicians and policymakers make:

Though there are nearly 1.6 million Americans in state or federal prison, their absence is not accounted for in the figures that politicians and policymakers use to make decisions. As a result, we operate under a distorted picture of the nation’s economic health.

There’s no simple way to estimate the impact of mass incarceration on the jobs market. But here’s a simple thought experiment. Imagine how the white and black unemployment rates would change if all the people in prison were added to the unemployment rolls.

Predicting Crime

Maurice Chammah, writing for The Marshall Project, writes about the St. Louis police departments’ use of crime-predicting software.

HunchLab, produced by Philadelphia-based startup Azavea, represents the newest iteration of predictive policing, a method of analyzing crime data and identifying patterns that may repeat into the future. HunchLab primarily surveys past crimes, but also digs into dozens of other factors like population density; census data; the locations of bars, churches, schools, and transportation hubs; schedules for home games — even moon phases. Some of the correlations it uncovers are obvious, like less crime on cold days. Others are more mysterious: rates of aggravated assault in Chicago have decreased on windier days, while cars in Philadelphia were stolen more often when parked near schools.

H/T Flowing Data

Changes in Incarceration Rates

Keith Humphreys, writing for WonkBlog, examines recent changes in the U.S. incarceration rates:

After decades of growth, the U.S. imprisonment rate has been declining for the past six years. Hidden within this welcome overall trend is a sizable and surprising racial disparity: African-Americans are benefitting from the national de-incarceration trend but whites are serving time at increasingly higher rates.

Neighborhoods, Fatherhood and Race

Philip Cohen writes about a new paper by Raj Chetty, et al. and the role race plays, even while it is missing from the data:

The tricky thing with this data, and I don’t blame Chetty et al. for this, although I would like them to say more about it, is that they don’t know the race of the children. The data are from tax records, which allow you to know the income and marital status of the parents, but not the race. But they know where they grew up. So if they have a strong effect of the racial composition of the county kids grow up in, but they don’t know the race of the kids, you have to figure a big part of that is race of the kids — and by “you” I mean someone who knows anything about America.

Hall of Justice

The Sunlight Foundation has created a project called Hall of Justice which gathers publicly available criminal justice datasets and research.

While not comprehensive, Hall of Justice contains nearly 10,000 datasets and research documents from all 50 states, the District of Columbia, U.S. territories and the federal government. The data was collected between September 2014 and October 2015. We have tagged datasets so that users can search across the inventory for broad topics, ranging from death in custody to domestic violence to prison population. The inventory incorporates government as well as academic data.

H/T Flowing Data

Achievement Gap Between Boys and Girls

Jeff Guo of the Wonkblog writes about new research into the reasons behind the educational achievement gap between boys and girls:

A team of economists from MIT, Northwestern, and the University of Florida has been investigating the question of the female advantage using a vast trove of data collected by the state of Florida. In their preliminary research, they have found that upbringing counts for a lot. The gender gap gets wider in poorer families. Girls from disadvantaged backgrounds are much more likely to succeed than boys raised under the same circumstances.

Now, in a new paper released Monday, the economists have found additional evidence that bad schools exacerbate the differences in academic achievement between boys and girls.

Poverty and Financial Literacy

Max Ehrenfreund, writing for Wonkblog, examines research presented at the 2016 American Economic Association’s annual meeting by Anuj Shah and collaborators showing that the the poor do better on tests of financial common sense:

If you spend all your time thinking about money, chances are, you’re going to get pretty good at thinking about money. Indeed, new research suggests that the poor — for whom concerns about cash are inescapable — are not as prone to certain financial mistakes often made by the affluent.

“The poor spend a lot more time on mundane, everyday expenses. They’re focused on money,” said Anuj Shah, a psychologist at the University of Chicago and one of the authors of the research, which was published last year and presented earlier this month at the American Economic Association’s annual meeting.

Reasons People Give for Not Being in the Labor Force, 2004 & 2014

The Bureau of Labor Statistics has released a chart comparing the reasons given for not being in the labor force in 2004 and 2014.

The proportion of the working-age population reporting school attendance as the main reason for being out of the labor force rose from 5.0 percent in 2004 to 6.4 percent in 2014. The percentage who cited illness or disability as the main reason increased from 5.5 percent to 6.5 percent over that same period. The proportion citing home responsibilities declined from 6.0 percent in 2004 to 5.4 percent in 2014.

For more information, see the Beyond the Numbers article “People who are not in the labor force: why aren’t they working?,” by Steven F. Hipple.

H/T Data Detectives

Classifying Countries by Income

The World Bank has released a new working paper by Neil Fantom and Umar Serajuddin reviewing the World Bank’s classification of countries by income.

Abstract:

The World Bank has used an income classification to group countries for analytical purposes for many years. Since the present income classification was first introduced 25 years ago there has been significant change in the global economic landscape. As real incomes have risen, the number of countries in the low income group has fallen to 31, while the number of high income countries has risen to 80. As countries have transitioned to middle income status, more people are living below the World Bank’s international extreme poverty line in middle income countries than in low income countries. These changes in the world economy, along with a rapid increase in the user base of World Bank data, suggest that a review of the income classification is needed. A key consideration is the views of users, and this paper finds opinions to be mixed: some critics argue the thresholds are dated and set too low; others find merit in continuing to have a fixed benchmark to assess progress over time. On balance, there is still value in the current approach, based on gross national income per capita, to classifying countries into different groups. However, the paper proposes adjustments to the methodology that is used to keep the value of the thresholds for each income group constant over time. Several proposals for changing the current thresholds are also presented, which it is hoped will inform further discussion and any decision to adopt a new approach.

Read a summary of the findings.
Download the PDF.

Inequality and Political Turmoil

Jay Ulfelder, writing for FiveThirtyEight, argues that the widely held belief that economic inequality causes political upheaval is a difficult thing to prove:

Just because a belief is widely held, however, does not make it true. In fact, it’s still hard to establish with confidence whether and how economic inequality shapes political turmoil around the world. That’s largely because of the difficulty in measuring inequality; on this subject, the historical record is full of holes. Social scientists are busy building better data sets, but the ones we have now aren’t sufficient to make strong causal claims at the global level.

Philip Cohen’s response to the piece is on his blog, Family Inequality.