Regis Barnichon

The unemployment rate is one of the most important business cycle indicators, but its interpretation can be difficult because slow changes in the demographic composition of the labor force affect the level of unemployment and make comparisons across business cycles difficult. To purge the unemployment rate from demographic factors, labor force shares are routinely used to control for compositional changes. This paper shows that this approach is ill-defined, because the labor force share of a demographic group is mechanically linked to that group’s unemployment rate, as both variables are driven by the same underlying worker flows. We propose a new demographic-adjustment procedure that uses a dynamic factor model for the worker flows to separate aggregate labor market forces and demographic-specific trends. Using the US labor market as an illustration, our demographic-adjusted unemployment rate indicates that the 2008-2009 recession was much more severe and generated substantially more slack than the early 80s recession.

This paper argues that a key aspect of the US labor market is the presence of time-varying heterogeneity across nonparticipants. We document a decline in the share of non-participants who report wanting to work, and we argue that that decline, which was particularly strong in the second half of the 90s, is a major aspect of the downward trends in unemployment and participation over the past 20 years. A decline in the share of “want to work” nonparticipants lowers both the participation rate and the unemployment rate, because a nonparticipant who wants to work has (i) a higher probability of entering the labor force (compared to other nonparticipants), and (ii) a higher probability of joining unemployment conditional on entering the labor force. We use cross-sectional variation to estimate a model of nonparticipants’propensity to want to work, and we …nd that changes in the provision of welfare and social insurance, possibly linked to the mid-90s welfare reforms, explain about 50 percent of the decline in desire to work among nonparticipants.

The unemployment rate is one of the most important business cycle indicators, but its interpretation can be difficult because slow changes in the demographic composition of the labor force affect the level of unemployment and make comparisons across business cycles difficult. To purge the unemployment rate from demographic factors, labor force shares are routinely used to control for compositional changes. This paper shows that this approach is ill-defined, because the labor force share of a demographic group is mechanically linked to that group’s unemployment rate, as both variables are driven by the same underlying worker flows. We propose a new demographic-adjustment procedure that uses a dynamic factor model for the worker flows to separate aggregate labor market forces and demographic-specific trends. Using the US labor market as an illustration, our demographic-adjusted unemployment rate indicates that the 2008-2009 recession was much more severe and generated substantially more slack than the early 80s recession.

This paper evaluates the flow approach to unemployment forecasting proposed by Barnichon and Nekarda (2012) for a set of OECD countries characterized by very different labor markets. We find that the flow approach yields substantial improvements in forecast accuracy over professional forecasts for all countries, with especially large improvements at longer horizons(one-year ahead forecasts) for European countries. Moreover, the flow approach has the highest predictive ability during recessions and turning points, when unemployment forecasts are most valuable.

We estimate an aggregate matching function and find that the regression residual, which captures movements in matching efficiency, displays procyclical fluctuations and a dramatic decline after 2007. Using a matching function framework that explicitly takes into account worker heterogeneity as well as market segmentation, we show that matching efficiency movements can be the result of variations in the degree of heterogeneity in the labor market. Matching efficiency declines substantially when, as in the Great Recession, the average characteristics of the unemployed deteriorate substantially, or when dispersion in labor market conditions—the extent to which some labor markets fare worse than others—increases markedly.

This paper presents a forecasting model of unemployment based on labor force flows data that, in real time, dramatically outperforms the Survey of Professional Forecasters, historical forecasts from the Federal Reserve Board’s Greenbook, and basic time-series models. Our model’s forecast has a root-mean-squared error about 30 percent below that of the next-best forecast in the near term and performs especially well surrounding large recessions and cyclical turning points. Further, because our model uses information on labor force flows that is likely not incorporated by other forecasts, a combined forecast including our model’s forecast and the SPF forecast yields an improvement over the latter alone of about 35 percent for current-quarter forecasts, and 15 percent for next-quarter forecasts, as well as improvements at longer horizons.

The negative relationship between the unemployment rate and the job openings rate, known as the Beveridge curve, has been relatively stable in the U.S. over the last decade. Since the summer of 2009, however, the U.S. unemployment rate has hovered between 9.4 and 10.1 percent in spite of firms reporting more job openings. We decompose the recent deviation from the Beveridge curve into different parts using data from the Job Openings and Labor Turnover Survey (JOLTS). We find that most of the current deviation from the Beveridge curve can be attributed to a shortfall in the vacancy yield, which measures hires per vacancy. This shortfall is broad-based across all industries and is particularly pronounced in construction, transportation, trade, and utilities, and leisure and hospitality. Construction alone accounts for more than a third of the Beveridge curve gap.

What is the relative importance of hiring and separation in driving unemployment fluctuations? This paper presents a framework to decompose the moments of unemployment and study the respective contributions of vacancy posting, a measure of firms’ hiring efforts, and separation. Separation accounts for about 40% of unemployment’s variance, compared to 60% for vacancy posting, and contributes to about 60% of unemployment steepness asymmetry, the fact that unemployment increases faster than it decreases. Further, while vacancy posting is, on average, the most important contributor of unemployment fluctuations, the opposite is true around business cycle turning points, when separation is responsible for most of unemployment movements.

The low correlation between cyclical unemployment and productivity over the postwar period hides a large sign switch in the mid-1980s: from significantly negative the correlation became significantly positive. Using a search model of unemployment with nominal rigidities and variable labor effort, I show that technology shocks can generate a positive unemployment-productivity correlation whereas non-technology shocks (i.e. aggregate demand shocks) tend to do the opposite. In this context, I identify two events that can quantitatively explain the increase in the correlation: (i) a sharp drop in the volatility of non-technology shocks in the mid-1980s, and (ii) a decline in the response of productivity to non-technology shocks, which from procyclical became acyclical in the last 25 years.

This paper builds a measure of vacancy posting over 1951–2009 that captures the behavior of total—print and online—help-wanted advertising, and can be used for time series analysis of the US labor market.

This paper develops an analytical framework that helps to quantify the optimal level of international reserves for a small open economy with limited access to foreign capital and subject to natural disasters or terms-of-trade shocks. International reserves allow the country to relieve balance of payments pressures caused by external shocks and to avoid large fluctuations in imports. The paper calibrates the model to two regions—the Caribbean and the Sahel region in sub-Saharan Africa—and assesses the sensitivity of the results. The conclusion is that popular rules of thumb, such as maintaining reserves equivalent to three months of imports, only give imprecise benchmarks.

This paper explores the sources of inflation in Sub-Saharan Africa by examining the relationship between inflation, the output gap and the real money gap. Using heterogeneous panel co-integration estimation techniques, we estimate co-integrating vectors for the production function and the real money demand function to recover the structural output and money gaps for 17 African countries. The central finding is that both gaps contain significant information regarding the evolution of inflation, albeit with a larger role played by the money gap. There is no significant evidence of asymmetry in the relationship.