We have updated our paper “The Cyclicality of the Price-Cost Markup”. You can download the revised version directly here or on my research page.
Tag Archive for 'CPS'
Valerie Ramey and I have posted a draft of our new working paper, “The Cyclical Behavior of the Price-Cost Markup.” In it we present considerable evidence that markups are significantly procyclical, contrary to the stylized fact that markups are countercyclical.
Here is the abstract:
Countercyclical markups constitute the key transmission mechanism for monetary and other “demand” shocks in New Keynesian models. This paper tests the foundation of those models by studying the cyclical properties of the markup of price over marginal cost. The first part of the paper studies markups in the aggregate economy and the manufacturing sector. We use Bils’ (1987) insights for converting average cost to marginal cost, but do so with richer data. We find that all measures of markups are either procyclical or acyclical. Moreover, we show that monetary shocks lead markups to fall with output. The second part of the paper merges input-output information on shipments to the government with detailed industry data to study the effect of demand changes on industry-level markups. Industry-level markups are found to be decidedly procyclical in response to demand changes.
You can download a copy of the paper from the link above or on my Research page.
Connor Dougherty discusses a dramatic decline in geographic mobility during 2008 (via Economist’s View):
U.S. Migration Falls Sharply, by Conor Dougherty, WSJ:
Migration around the U.S. slowed to a crawl last year, especially for this decade’s boom towns, as a weak housing market and job insecurity forced many Americans to stay put.
Demographers say the dropoff in migration, shown in Census data to be released Thursday, is among the sharpest since the Great Depression. It marks the end of what Brookings Institution demographer William Frey calls a “migration bubble.”
As asset values rose fairly steadily in the past decade, Americans young and old moved around the country in search of jobs or better weather. In many cases, people living in higher-cost housing markets such as San Francisco and New York cashed in their real-estate winnings and moved to outlying counties, or to states like Florida and Nevada, hoping to find a cheaper house and pocket the difference. Now, “people are hanging tight; they’re too scared to do anything,” said Mr. Frey.
The data, covering the one-year period until July 1, 2008, show this effect across U.S. counties and metropolitan areas — another sign of how this recession has spared few industries or regions.
Migration typically slows during recessions. But in past downturns, the slowdown has been more regional in scope, with workers fleeing weaker job markets for places where companies were still hiring. In the deep 1980s recession, for instance, laid-off auto workers fled the industrial Midwest for energy-rich states in the South with more plentiful jobs.
What’s unique this time is migration has slowed almost everywhere. The sharpest year-to-year changes were among what demographers call “domestic migrants,” people who moved within the U.S. That doesn’t count population changes that result from births, deaths or immigration.
Although I agree with the trend behavior described above, Dougherty is incorrect about the cyclicality of geographic mobility. In fact, geographic mobility is moderately countercyclical—that is, more people move during recessions than during booms (relative to trend). This may seem counter-intuitive but makes economic sense.
Geographic mobility is a means of reallocating resources, in this case labor, to more efficient uses. In the past, 70 percent of people who move indicated having moved for economic reasons and up to 50 percent of those moves occurred because of a job separation [Lansing and Morgan (1967); Bartel (1979)]. In particular, there is a significant positive relationship between unemployment and geographic mobility [Bartel (1979); Schlottmann and Herzog Jr. (1981, 1984)]. Thus, countercyclical mobility is consistent with reallocation of idle workers across space.
I assess the cyclicality of geographic mobility in a recent working paper. I the measure the rate of geographic mobility as one minus the share of persons living at the same address one year later reported by the U.S. Census Bureau. These data come from the March supplement to the Current Population Survey, so the 2007 data do not reflect much of the distress in mortgage markets—and any concomitant effects on mobility—that began later in 2007.
Removing the low-frequency trend is important because it represents structural changes—such as demographic changes or, say, innovations in mortgage finance—that are unassociated with the business cycle. I isolate the component of the time series that moves at business cycle frequency using an unobserved components model (see paper for details). The figure below plots the cyclical component of the mobility rate together with that of the unemployment rate for comparison.
The cyclical component of mobility tends to follow the unemployment rate, indicating that more people move during recessions than during booms. This is consistent with geographic mobility as a means for reallocating idle labor to more productive uses. The contemporaneous correlation of the cyclical component of the mobility rate with the unemployment rate is 0.50, indicating moderate countercyclicality. Also note that mobility is substantially less volatile over the business cycle than unemployment.
Of course, the problems in the housing market beginning in 2007, notably the dramatic decline in prices, will undoubtedly reduce geographic mobility during this recession. This will further slow recovery because unemployed persons cannot move to areas with more favorable labor markets as easily or quickly as before.
I posted a revised version of my paper on the cyclical bias of geographic mobility, “A Longitudinal Analysis of the Current Population Survey: Assessing the Cyclical Bias of Geographic Mobility.” You can download the paper from my research page or directly by clicking on the title. Here is the abstract:
This paper assesses the implications of geographic mobility for the measurement of U.S. labor market dynamics using the Current Population Survey (CPS). Because the CPS does not follow individuals that move, estimates may be biased if the labor market behavior of movers differs systematically from that of nonmovers. I create a new database, the Longitudinal Population Database (LPD), that utilizes all longitudinal information in the CPS to form a panel data set. I use the LPD to identify persons who move and therewith estimate a bound on the bias from geographic mobility. I find that the cyclical bias arising from geographic mobility is small. At business cycle frequencies, the difference between the separation hazard rate calculated from the entire CPS sample and from a subset that are known not to have moved never exceeds 4 percent. There is little effect of mobility on the job finding hazard rate. I conclude that geographic mobility does not significantly affect CPS labor market dynamics.
I posted a revised version of my job market, “Understanding Unemployment Dynamics: The Role of Time Aggregation.” Here is the abstract:
This paper uses weekly data from the Survey of Income and Program Participation (SIPP) to estimate the role of time aggregation in measuring gross labor force flows and unemployment dynamics. Time aggregation is substantial: gross flows estimated from monthly data understate the true number of transitions by 15–24 percent. Time aggregation in both separations to unemployment and accessions from unemployment comoves positively with the business cycle. The effect from time aggregation on separations is roughly offset by its effect on accessions, however, creating no meaningful cyclical bias in measured gross flows or hazard rates. Contrary to claims by Hall (2006) and Shimer (2007), separation hazard rates calculated from the SIPP and the Current Population Survey are strongly countercyclical and remain so after adjusting for time aggregation. In addition, the separation hazard rate contributes fully one-half of the cyclical variance of the steady-state unemployment rate after adjusting for time aggregation.
Part of my research involves examining the “flow” of persons among three labor force classifications: employed, unemployed, and not in the labor force (NILF). These flows are calculated by observing the change in a person’s labor force status from one month to the next. For example, a person who was unemployed last month but had a job this month would have made a UE transition. The sum of all persons making such transitions that month is called the UE flow.
We use the term gross flows to distinguish such measures from net flows, which are the change in the stock from one month to the next. It is the net change that gets reported in government statistics. For example, if 250,000 persons found a job this month and 100,000 lost a job, the net change is +150,000 persons. The total gross flow, however, is 350,000 persons.
Gross flows are considerably larger than net flows. Over the last 30 years the U.S. economy has added about 150,000 jobs a month (net) on average. During that same period the average gross flow into and out of employment is over 10 million persons a month! To put that figure in perspective, 5.4 percent of the U.S. working-age population moves into and out of employment every month. Accordingly, the net changes reported in the monthly Employment Situation release do not capture the true dynamism in the U.S. labor market.
Although not directly germane to my research, I recently became interested in the long term trend in gross worker flows. The figure below plots the trend in worker flows for 1976-2007. The series shown is the sum of all flows into and out of employment, expressed as a share of population. Since I am interested in only the trend in the series, I remove the substantial month-to-month variation by seasonally adjusting the data and then smoothing the seasonally-adjusted data using a local weighted least squares regression. Shaded bars indicate recessions as determined by the NBER.
There has been a dramatic decrease in gross flows over the past 30 years. This decrease is consistent with evidence from Bleakley, Ferris, and Fuhrer (1999) and Fallick and Fleischman (2004), who also observe declines in gross flows.
In the late 1970s, flows averaged about 5.8 percent of the population a month. This fell more or less steadily throughout the 1980s and early 1990s until bottoming at 5 percent at the end of 1996. Flows grew to just over 5.3 percent a month in 2001 after which they fell off a cliff, stabilizing briefly in 2004-05. By the end of 2007, the total gross flow was below 5 percent of the population. To put a number to this trend, the decline in gross flows means that about 1.7 million fewer persons a month move into and out of employment today compared to 1977.
Identifying an empirical regularity is only a first step in research. Without understanding the causes of this trend it is impossible to draw any meaningful implications from the data. In fact, it is difficult to say whether the secular decline in total gross flows into and out of employment is “good” or “bad”. On the one hand, if the decline represents decreased employment volatility for U.S. workers — increased job stability — then it may be a positive development. On the other hand, if the observed decline in gross flows results from decreasing allocative efficiency — labor market “sclerosis” — then the trend may be worrisome. Other possible explanations include “job lock,” where employees cannot easily change jobs because of employer-provided health insurance, and increased efficiency in employer-employee job matching, resulting in fewer low-quality matches and thus less job turnover.
The examples above show that both explanations and implications can be conflicting and contradictory. This is why economists write models. Also why Truman wanted a one-armed economist.