geophysical (earthquakes, tsunamis, volcanoes) disasters. Wars include internal (intra-state) and external (inter- and extra-state) armed conflicts that are identified using the Correlates of War (COW) and Peace Research Institute Oslo (PRIO) databases. Financial crises include banking, currency and debt crises and are based on the Laeven and Valencia (2018) database. For additional details on the definition of the events and data, we refer the reader to **Annex A** .

# **3.2 Methodology**

To assess the effects of adverse events on productivity, the local projection method (LPM) is used, with country productivity level, measured as output per worker, as the dependent variables (Jord`a et al., 2013; Jord`a, 2005).<sup>25</sup> The LPM approach provides an estimate of the response of labor productivity (and TFP) to adverse events over various horizons. It also helps to identify key transmission channels, assess how countries’ resilience to adverse events has changed over time and analyze the role of policies in mitigating their effects. The advantage of this approach is that it avoids the problem of dimensionality inherent in other approaches such as vector autoregressions.<sup>26</sup> However, it does not directly take into account the severity of the adverse event.

The dependent variable is the cumulative change in labor productivity, TFP or output levels between horizons _t −_ 1 and _t_ + _h_ , measured as difference in the natural logarithms _yt,j_ . The explanatory variables include the event dummy and controls. For a specific type of event, the explanatory variable of interest is an episode which equals 1 if the event occurred at least once in a particular country in a year and 0 otherwise. The baseline model is given by where _h_ = 0 _,_ 1 _,_ 2 _, · · ·_ is the horizon, _α_ ( _h_ ) _,j_ and _τ_ ( _h_ ) _,t_ are country j and time fixed effects, and _u_ ( _h_ ) _t,j_ is an error term. The coefficient of interest _β_ ( _h_ ) captures the dynamic multiplier effect (impulse response) of the dependent variable with respect to the event dummy variable _Et,j_ . The number of lags for each variable is denoted by p and set to 1 for the estimation. The specification controls for (i) country-specific trends, (ii) lagged event dummies, (iii) future values of the event dummy between time _t_ and _t_ + _h −_ 1 to correct for possible forward bias (Teulings and Zubanov, 2014), and (iv) past changes ∆ _yt−s,j_ .

The event dummies are lagged to help attenuate the potential endogeneity bias caused by contemporaneous interactions between productivity (TFP or output) and crises. In some cases, weak productivity accompanied by a sharp decline in output can trigger financial crises and wars. To guard against such possible endogeneity or reverse causation between productivity and the event,

> 25Labor productivity is measured as output per worker, with the number of employees used as the unit of labor input. This has the advantage of wide availability across countries. Its disadvantage rests in the failure to account for the quality and intensity of labor input.

> 26Vector autoregressions approaches entail modeling and estimating a large number of time series, whereas LPM focuses on the dynamics of the variable of interest—productivity in this case.

16