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Lost Your Job? Call Mom and Dad

Econ Focus
Second Quarter 2017
Around the Fed

"Parental Proximity and the Earnings Consequences of Job Loss." Patrick Coate, Pawel Krolikowski, and Mike Zabek, Federal Reserve Bank of Cleveland Economic Commentary No. 2017-03, Feb. 9, 2017.

Does having your parents nearby make a difference if you lose your job? For younger workers, the answer is yes — with benefits that are both significant and long lasting, according to research from the Cleveland Fed. Analyzing a dataset of 35,000 individuals over 20 years from the Panel Study of Income Dynamics, the authors found a correlation between displaced workers' earnings recovery over time and distance to their parents. On average, young adults (age 25 to 35) who lost their jobs and lived near home (roughly speaking, a locality) did comparatively well: They almost recovered their previous hourly earnings after six years. But those who lived farther away suffered a larger drop in earnings following dislocation, and even 10 years on, their wages hadn't recovered to pre-shock levels. In contrast, for adults older than 35, the correlation didn't hold at all.

Parental help is still no cure-all for younger workers, however. All displaced workers had a lower wage trajectory than those who stayed continuously employed. Still, parental proximity may provide a safety net in tough times. This may take the form of short-term housing, food, child care, or access to a network of family friends who can help out on the job hunt. As for the reason the effect didn't hold for adults older than 35, the authors wrote that this is a question for future research to take up. But they suggested one reason may be that a greater share of older adults may choose to live closer to parents who need their help as they age.

"Minimum Wages and Consumer Credit: Impacts on Access to Credit and Traditional and High-Cost Borrowing." Lisa J. Dettling and Joanne W. Hsu, Federal Reserve Board of Governors, Finance and Economics Discussion Series 2017-010, Jan. 15, 2017.

Economists have long studied the minimum wage in the context of labor markets. Lisa Dettling and Joanne Hsu of the Federal Reserve Board of Governors used a wider lens to study the interaction between minimum-wage increases and access to credit among lower-income households, asking whether a boost in pay for the poorest workers expands their access to credit and improves their credit scores. By looking at an array of credit products — from car loans to credit cards — across states with differing minimum wage policies, they tried to determine whether higher take-home pay lowers the incidence of costly borrowing.

The authors found that a boost in the minimum wage did ease credit constraints on the poorest households, as well as shift borrowing away from more expensive varieties of credit. Raising the minimum wage helped these households secure lower-cost credit and reduced the need for payday borrowing (short-term loans with very high rates). For every $1 increase in the hourly minimum wage, poor households saw a 7 percent increase in credit card offers, while delinquency rates fell by 5 percent. More dramatically, payday borrowing dropped by 40 percent.

Borrowing volume still rose among low-paid workers who got a boost in the minimum wage, but the rate of default dropped for those loans in the medium run. As their repayment rates improved, so too did their credit scores — which, in turn, led to more favorable loan and credit card offers. Given that 19 states and 21 localities raised their minimum wage this past January, researchers will have even more data to work with as they assess the effects.

"Measuring News Sentiment." Adam Hale Shapiro, Moritz Sudhof, and Daniel Wilson, Federal Reserve Bank of San Francisco Working Paper Series No. 2017-1, Jan. 4, 2017.

Survey-based indices of consumer sentiment, such as those published by the University of Michigan and the Conference Board, are widely followed due to their reputation for predictive accuracy. The challenge for researchers is that such surveys are often expensive to conduct and confined to a small sample of individuals. So tech firms have been developing software to conduct "computational text analysis," which processes vast amounts of text quickly to analyze emotions and sentiment. Economists and other data scientists are increasingly employing this software to analyze volumes of data that couldn't be processed before.

Adam Shapiro and Daniel Wilson of the San Francisco Fed, working with Moritz Sudhof, a data scientist with the firm Kanjoya (since acquired by Ultimate Software), applied this new tool to assess how well it did compared to traditional surveys measuring consumer and economic sentiment. They employed computational text analysis of financial news articles in 16 major newspapers from 1980 to 2015 to see how closely news sentiment correlates with business cycle indicators, using an algorithm to assign "negativity" or "positivity" to particular words. They found not only a strong relationship between news sentiment and standard indicators; they also discovered that most news sentiment indicators actually outperformed the University of Michigan and Conference Board measures when it came to predicting the federal funds rate, employment, inflation, and other indicators.

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