Brookings Papers on Economic Activity

Spring 2016 Brookings Panel on Economic Activity

The Spring 2016 Brookings Panel on Economic Activity will take place March 10-11, 2016 at the Brookings Institution in Washington, DC. The conference will be live-tweeted under the Chatham House rule using hashtag #BPEA. Read summaries of all six papers below, and learn more by following the conversation on Twitter. 

Measuring income and wealth at the top using administrative and survey data

In “Measuring income and wealth at the top using administrative and survey data,” Jesse Bricker, Alice Henriques, and John Sabelhaus of the Federal Reserve Board and Jacob Krimmel of the University of Pennsylvania reconcile widely divergent estimates of increases in wealth and income, demonstrating how specific choices in data sets and methodological decisions affect the levels and trends. They find that while the concentration of wealth and income of the top 1 percent has indeed increased since 1992, it increased far less than prior research has found. A widely-cited estimate from Saez and Zucman (2016) estimates the share of wealth held by the top 1 percent increased 13 percentage points, from 29 percent in 1992 to 42 percent in 2013. The new, more robust Bricker et al. research shows income shares rising only 6 points over that same time period, to just under 34 percent in 2012. The mismeasurement has implications for policies designed to benefit families in the middle and bottom of the wealth and income distribution, the authors note. “The failure to properly measure the effects of government policies and market practices that disproportionately benefit families in the middle and bottom of the wealth or income distribution leads directly to overstatement of top wealth and income shares."

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Does the United States have a productivity slowdown or a measurement problem?

David M. Byrne of the Board of Governors of the Federal Reserve System, John G. Fernald of the Federal Reserve Bank of San Francisco, and Marshall B. Reinsdorf of the International Monetary Fund find that underlying macroeconomic trends—not mismeasurement of IT-related innovations—are responsible for the slowdown in U.S. labor productivity and total factor productivity (TFP) since the early 2000s. The authors assert that measurement errors have not gotten worse in the data that underlie productivity growth. Furthermore, they find that the productivity slowdown hasn’t been concentrated in industries that are traditionally hard to measure. The authors also address the impact of omitting or miscalculating economic gains from free digital services. “The major ‘cost’ to consumers of Facebook, Google, and the like is not the broadband access, the cell phone service, or the phone or computer; rather, it is the opportunity cost of [the users’] time. They note the similarity between free digital services and free TV services, which are counted as providing advertising services, not final consumption services. The authors suggest that because the slowdown predated the Great Recession, and growth was similar in the 1970s and 1980s to what it’s been since 2004, it was the fast-growth of 1995-2004 period that was the anomaly—a one-time upward shift in the level of productivity rather than a permanent increase in its growth rate—thanks to the Internet, the reorganization of distribution sectors, and the like.

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Learning from potentially biased statistics: Household’s inflation perceptions and expectations in Argentina

When forming expectations, households may be influenced by the possibility that the information they receive is biased. Alberto Cavallo of MIT Sloan and NBER, Guillermo Cruces of CEDLAS-FCE-UNLP CONICET and IZA, and Ricardo Perez-Truglia of Microsoft Research study how individuals learn from potentially-biased statistics using data from both a natural and a survey-based experiment obtained during a period of government manipulation of inflation statistics in Argentina (2006-2015) .The authors’ findings suggest that rather than ignoring biased statistics or naively taking them at face value, households react in a sophisticated way, as predicted by a Bayesian learning model, effectively de-biasing the official data to extract all its useful content.

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Income inequality, social mobility, and the decision to drop out of high school

Greater income gaps between those at the bottom and middle of the income distribution lead low-income boys to drop out of high school more often than their counterparts in higher inequality areas, suggesting that there is an important link between income inequality and reduced rates of upward mobility, according to Brookings Nonresident Senior Fellow and University of Maryland economics professor Melissa S. Kearney and Wellesley economics professor Phillip B. Levine. The authors propose a channel through which income inequality might lead to less upward mobility – often assumed to be the case but not yet fully proven. They focus on income inequality in the lower half of the income distribution, as measured by income gaps between the 10th and 50th percentiles of the income distribution rather than income gaps between the the top and bottom of the income distribution, which has been more of a focus in popular culture. The authors look specifically at high school drop-out rates through a geographic lens, noting the link between highly variable rates of high school completion and income inequality across the country. Digging into reasons students themselves give for dropping out, they find that low-income students from more unequal places are more likely to give up on their educational pursuits. The finding suggests that economic despair could play an important role: if a student perceives a lower benefit to remaining in school, then he or she will choose to drop out at a lower threshold of academic difficulty. “There are important policy implications for what types of programs are needed to improve the economic trajectory of children from low-SES backgrounds,” they write. “Successful interventions would focus on giving low income youth reasons to believe they have the opportunity to succeed.

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Credit policy as fiscal policy

Federal credit programs like Fannie Mae, Freddie Mac and students loans, among others, had just as much power to stimulate the economy during the economic recovery as did the program specifically designed to do so. In “Credit Policy as Fiscal Policy,” MIT economist Deborah Lucas examines the fiscal effects of the over 150 federal credit programs in the U.S. and finds that they yield stimulus in 2010 of roughly $344 billion, similar to the amount the Congressional Budget Office has attributed to the economic impact of the ARRA. New loans originated under traditional federal direct loans and loan guarantees for housing, education, agriculture, small businesses, energy, trade and other private activities totaled $584 billion in 2010. In addition, she finds that these programs had a big “bang-for-the-buck”—a large amount of stimulus per dollar of taxpayer cost. Lucas points out that structural changes to the larger federal credit programs thus could have macroeconomic and fiscal policy implications, given the programs’ effectiveness, particularly in an economic downturn. A further question she raises is whether credit policy should be classified as fiscal policy, monetary policy or as a third category of its own. Lucas concludes by comparing the U.S. economic recovery to that of Europe through the lens of these credit programs.

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Understanding declining fluidity in the U.S. labor market

Raven Molloy, Christopher L. Smith, and Riccardo Trezzi of the Federal Reserve Board of Governors and Abigail Wozniak of the University of Notre Dame find that overall fluidity of the U.S. labor market has been declining since at least the 1980s. Furthermore, the decline has been occurring along a number of dimensions—including the rate of job-to-job transition, hires and separations, and geographic movement across labor markets—since at least the 1980s, and the declines are all related. Investigating the pattern of declining fluidity by geography, the authors use state-level panel data to identify factors that correlate with larger declines. The analysis reveals substantial variation in declines across states, with noticeably larger decreases in many Western states. The authors rule out several likely explanations for the decline in fluidity and examine the hypothesis that improved matching between firms and workers could be contributing to the decline in dynamism. While the ultimate cause of the decline in fluidity remains unclear, the authors note several promising avenues for future research, including the role of increasing firm size and age, and changes in how worker pay is adjusted to reflect changing productivity and outside offers.

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