components of productivity growth. They identify the dominant persistent and permanent variations in productivity. These are assumed to reflect lasting structural influences on productivity, such as technological innovation, or organizational changes. This paper uses a new spectral SVAR identification methodology that identifies technology shocks as those that drive the largest proportion of low (long-run) frequency labor productivity variation. The SVAR approach effectively filters through other less persistent changes in productivity or changes that drive only a small proportion of long-run productivity variation.<sup>3</sup> Demand shocks could also have persistent effects on productivity growth, which are not ruled out in our SVAR identification. An example of a persistent demand shock is identified using a modification of the approach for identifying technology shocks. Importantly, the persistence of the effect of the identified demand driver is found to vary across countries according to their capacity to enact counter-cyclical fiscal policy.<sup>4</sup> Finally, the literature has pointed towards evidence that a large proportion of cross-country synchronization of labor productivity growth is driven by business-cycle factors as opposed to technology spillovers (Huo et al., 2020; Imbs, 1999). Accordingly, using the identified technology measure, we assess whether these findings also apply to our expanded dataset covering EMDEs. We address the following questions:

- How can researchers identify technological and non-technological factors driving productivity developments across a wide range of countries? Are the macroeconomic responses to these shocks different across developed and developing economies?

- Can non-technology shocks have a persistent impact on labor productivity, especially in EMDEs? If yes, are there defining features associated with countries having persistent (or less persistent) impulse responses?

- After removing the cyclical and other non-technological components from labor productivity, how synchronized are the technology-driven productivity components across countries?

We make multiple contributions to the literature, which has until now focused on advanced economies. This paper is the first study to separate technology and non-technology drivers of productivity across a broad range of countries using a new spectral SVAR approach introduced by Dieppe et al. (2019, 2021). Panel and individual VARs are estimated and identified for over 100 advanced and emerging economies, an unprecedented country sample relative to the existing literature which has generally focused on the G-7 advanced economies.<sup>5</sup> This paper also uncovers non-technology shocks that may have persistent effects on advanced economy and EMDE labor productivity levels through the capital-deepening channel and country-characteristics that govern the degree of persistence. We are also the first to assess the synchronization of productivity growth across a broad range of countries for measures that remove non-technology drivers of productivity

> 3A survey of the SVAR literature has found that technology shocks account for between 1 and 55 percent of variations in output in the US (Ramey, 2016).

> 4While not explicitly modeling demand shocks, a range of studies have documented the existence of lasting negative effects on output and productivity from financial, currency, and political crises (Dieppe, 2020; Cerra and Saxena, 2008; Jord`a et al., 2013; Reinhart and Rogoff, 2014).

> 5Previous studies have focused on a small subset of advanced economies. For example, Elstner and Rujin (2019); and Gal ́ı (1999) apply long-run restriction-identified SVARs to G-7 economies only.

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