# **1 Introduction**

Productivity growth in advanced economies (AEs) and emerging and developing economies (EMDEs) has undergone many surges and declines historically, usually coinciding with global recessions and slowdown events (Dieppe, 2020). Productivity growth has been less volatile in advanced economies but has followed a similar series of rapid growth gains and slowdowns (Fernald, 2007).

A wide range of factors can affect productivity growth. This paper will disentangle the influences of lasting changes to productivity, “technology”, from temporary or demand-side influences for a large number of economies.We use ’technology’ as a catchall phrase for the _most_ persistent driver of productivity. Improvements in productivity due to factors such as better technologies, or organizational and institutional changes, are important drivers of sustained productivity improvements. Non-technology factors can also affect productivity growth. For example, demand-led changes in productivity are likely to have played a role in the pre-2008 surge and the subsequent decline in global productivity growth during the Global Financial Crisis (GFC), and are once again expected to operate during the recent COVID-19 pandemic. Both types of shock are of interest to policymakers and are explored in this paper.

To identify the dominant driver of lasting productivity changes, it is necessary to abstract from temporary and business-cycle influences. There are two established approaches to removing these influences from labor productivity and total factor productivity (hereafter, TFP): first, a utilization-adjustment of TFP, and second, structural vector-autoregressions (VARs) that identify the dominant drivers of long-run influences on labor productivity. The latter approach is the focus of this paper.

A standard growth accounting analysis of productivity developments only provides a partial insight into the drivers of large swings in productivity growth or slower-moving trend changes. One component of labor productivity, TFP, will reflect demand-driven cyclical influences such as changing labor and capital utilization as well as technological and organizational changes (Basu et al., 2006; Fernald and Wang, 2016).<sup>1</sup> To account for the intensive margin of labor inputs, researchers typically scale the factor inputs using observable proxies for factor utilization. For example, average hours per worker, electricity usage, and surveys of capacity utilization can provide insight, individually and collectively, into how labor effort and capital utilization vary (Burnside et al., 1996; Imbs, 1999; Shapiro, 1993). This approach has also been extended using industrylevel data to account for industry heterogeneity. However, data requirements for this approach—in particular, annual data on the sectoral distribution of hours-worked, employment, and capital—are prohibitive for many advanced economies and most EMDEs, which makes it impossible to conduct a broad-based analysis across many economies.<sup>2</sup> Structural VARs (SVARs) take an alternative approach to removing cyclical or demand-led

> 1In the United States, one-half of TFP growth variability has been attributed to non-technology factors Basu et al. (2006).

> 2Basu et al. (2006), Huo et al. (2020), and Comin et al. (2019) have implemented these for advanced economies, but not for EMDEs. A second difficulty with this approach is the possible presence of a wide range of structural relationships between different inputs to production, preventing the broad-based application of this methodology. For example, an inflexible labor market around the number of hours worked makes it a poor proxy for utilization.

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