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Neville Francis

9 April 2021
WORKING PAPER SERIES - No. 2534
Details
Abstract
We address the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restrictions are well documented, we demonstrate that the recent attempt to overcome said issues using the Max-Share approach of Francis et al. (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks, although less so than long-run specifications. We offer a new spectral methodology to improve empirical identification. This new preferred methodology offers equivalent or improved identification in a wide range of data generating processes and when applied to US data. Our findings on the bias generated by confounding shocks also importantly extends to the identification of dominant business-cycle shocks, which will be a combination of shocks rather than a single structural driver. This can result in a mis-characterization of the business cycle anatomy.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
9 April 2021
WORKING PAPER SERIES - No. 2533
Details
Abstract
Frequently, factors other than structural developments in technology and production efficiency drive changes in labor productivity in advanced and emerging market and developing economies (EMDEs). This paper uses a new method to extract technology shocks that excludes these influences, resulting in lasting improvements in labor productivity. The same methodology in turn is used to identify a stylized example of the effects of a demand shock on productivity. Technology innovations are accompanied by higher and more rapidly increasing rates of investment in EMDEs relative to advanced economies, suggesting that positive technological developments are often capital-embodied in the former economies. Employment falls in both advanced economies and EMDEs following positive technology developments, with the effect smaller but more persistent in EMDEs. Uncorrelated technological developments across economies suggest that global synchronization of labor productivity growth is due to cyclical (demand) influences. Demand drivers of labor productivity are found to have highly persistent effects in EMDEs and some advanced economies. Unlike technology shocks, however, demand shocks influence labor productivity only through the capital deepening channel, particularly in economies with low capacity for counter-cyclical fiscal policy. Overall, non-technological factors accounted for most of the fall in labor productivity growth during 2007-08 and around one-third of the longer-term productivity decline after the global financial crisis.
JEL Code
C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
O40 : Economic Development, Technological Change, and Growth→Economic Growth and Aggregate Productivity→General