sharply in the mid-1990s, following debt crises affecting many commodity-exporting economies in Latin America and the Caribbean and Sub-Saharan Africa in the 1980s, further compounded by volatility following the breakup of the Soviet Union (Federal Deposit Insurance Corporation, 1997; Kaminsky and Pereira, 1996). As these crises faded, the contribution of technology grew rapidly, partially resulting from institutional reforms and rapid global trade and production integration, particularly in Asia (Baldwin, 2013; Subramanian and Kessler, 2013; World Bank, 2019). The contribution of technology post-crisis has remained strong relative to contributions in the 1980s and 1990s, although the contribution of non-technology factors has been zero or negative for much of the post-crisis period.

# **7.2 Synchronization of Productivity: Technology versus Non-Technology Drivers**

The decomposition of productivity growth into technology-driven and non-technology driven factors allows a deeper exploration of the drivers of global labor productivity synchronization. Productivity growth has declined in both advanced economies and EMDEs since the global financial crisis, and in all EMDE regions (World Bank, 2020). This broad-based fall suggests the presence of common factors or spillovers. A large body of literature has already documented the co-movement of output across economies.<sup>14</sup> The strong correlation between output growth and labor productivity growth (70 percent on average in our sample) suggests the possibility of common determinants of productivity developments across economies. The cross-country synchronization of labor productivity growth, and the extent to which it is driven by structural factors captured by the SVAR-identified measure of technology, or business-cycle factors, has been so far underexplored. The literature that does exist has focused on advanced economy synchronization and has found some co-movement in cyclical drivers of productivity but little in structural measures.

In advanced economies, utilization-adjusted TFP, a similar measure to SVAR-identified technology, has been found to be uncorrelated across countries, while unadjusted measures of TFP are correlated (Huo et al., 2020; Imbs, 1999). Structural VARs point to the presence of cointegration between TFP in the United States and other economies but with slow and limited spillovers (Mandelman et al., 2011; Miyamoto and Nguyen, 2017). In a broader dataset, utilization-adjusted U.S. TFP has been found to have spillover effects on TFP growth in other advanced economies but only at very gradual rates (Adler et al., 2017). Using data for patents and R&D spending as proxies for productivity-enhancing technology adoption, some cross-country spillovers have been identified (Keller, 2010). Finally, in a factor modeling framework, TFP growth has been found to be one of the most important correlates of common developments in G7 GDP growth (Crucini et al., 2011) and a GDP factor estimated for a broader range of 117 economies (Abate and Serven, 2019).

An alternative and growing strand of the literature has highlighted the role of slow technological diffusion between leading and lagging firms across advanced economies (Organisation for Economic Co-operation and Development, 2015; Andrews et al., 2015; Cirera and Maloney, 2017). Long lags in the adoption and intensity of use of new technologies have been found to explain

> 14See, for example, Francis et al. (2017), Francis et al. (2019), and Kose et al. (2003).

20