Drivers of productivity: Technology vs. demand shocks

G L O B A L P R O D U C T I V I T Y C H A P T E R 6 365

Methodology. A new SVAR approach, before now applied only in studies of a few advanced economies, allows a decomposition of labor productivity into long-term drivers and drivers that operate at business cycle frequencies (Angeletos, Collard, and Dellas 2018a; Dieppe, Francis, and Kindberg-Hanlon 2019). The SVAR includes the log level of labor productivity, the log of employment per capita, consumption as a share of GDP, investment as a share of gross domestic product (GDP), consumer price inflation, and monetary policy interest rates where available (Francis et al. 2014).

9

For illustrative purposes, TFP is also included to show how labor productivity and TFP individually react to a technology shock.

10

Panel estimations for advanced economies and EMDEs were run with country fixed effects as well as a series of country-specific estimations. Technology shocks are defined as shocks that explain the largest share of the variance of labor productivity at the horizon of more than 10 years; demand shocks are those that explain the largest share at horizons of 2-8 years (annex 6A).

11 Chapter 3

offers some examples of such demand shocks.

Data. This chapter uses a data set broad enough to capture global productivity developments. Data on capital services and human capital are taken from the Penn World Table 9.1, and data on other macroeconomic aggregates such as GDP and employment are from the World Bank’s World Development Indicators (WDI) database and The Conference Board’s Total Economy Database (TED). Consistent annual data are available for 1980-2018 for 103 economies, of which 74 are EMDEs and 29 are advanced economies, for labor productivity and as a basis for estimates for TFP and capital services (chapter 1). Labor productivity is measured as output per worker. Data requirements to estimate SVAR technology shocks require additional variables, resulting in an unbalanced, but broader, panel of 30 advanced economies and 96 EMDEs.12 The average sample length is 40 years for EMDEs and 45 years for advanced economies.

The productivity surge that peaked in 2004 and 2007 in advanced economies and EMDEs, respectively, was the largest since at least 1980 in EMDEs (World Bank 2020).

9 Checks on robustness to the inclusion of exchange rate and cyclically adjusted primary balance are shown in annex 6A. They do not materially affect impulse response functions (IRFs) but do result in shorter and more unbalanced data. 10 TFP estimates are taken from chapter 1. An alternative approach to identify the long-run drivers of productivity takes into account changes in the utilization of labor and capital in the calculation of TFP growth. Basu, Fernald, and Kimball (2006), Comin et al. (2019), Duval et al. (2020), and Levchenko and Pandalai-Nayar (2018) have implemented this approach for advanced economies other than the United States, but not for EMDEs. 11 Typically, business cycles are assumed to last two to eight years (Christiano and Fitzgerald 2003; Sargent 1987). 12 Typically, technology-identifying SVARs have been applied to quarterly data sets. Data shortcomings for EMDEs—with typically less than 20 years of quarterly data on employment or productivity—impose severe constraints. Hence, annual data are used to estimate the SVARs. This choice significantly lengthens the time period over which the SVAR is estimated for many EMDEs.