Source: authors’ calculations based on Business Pulse Survey data.

# 3. Methodology

To investigate the effect of Russia’s invasion of Ukraine on private businesses in the Central Asia region, this paper exploits the panel dimension of our data. Our empirical analysis is divided into two main parts. First, we provide a descriptive overview of our outcomes of interest, highlighting how the distribution of these firm-level outcomes (sales and employment) changed as Russia’s invasion of Ukraine affected the region. Second, we interact this time variation with the cross-sectional heterogeneity present in our sample of firms. Specifically, we use firm-level exports to Russia as a proxy to measure the exposure of individual businesses to the negative spillovers caused by the invasion. We then analyze the interaction between this proxy and the onset of the invasion to identify the intensity of the invasion shock. Next, we examine the relative performance of firms with higher or lower exposure to the invasion compared to matching peers. This allows us to assess how firms with varying levels of exposure to the invasion were affected by the invasion.

## **Dynamics of firm-level sales and employment**

First, we analyze the dynamics of outcome variables of interest. The analysis focuses on the differences observed between 2022 when Central Asian economies were affected by the shock triggered by Russia’s invasion of Ukraine, and previous years. We are interested in two outcome variables: firm-level employment and firm-level sales. For every firm _j_ and every year _t_ , the analysis uses the following specification: (1) where is the invasion year dummy, YYjj tt = bb WWtt + αα TTtt is a vector of dummies for non-invasion BPS waves – one dummy + γγ SSjjtt + ρρ CCjjtt + θθXXjjtt + eejjtt for each of the two pre-2022 waves, is a vector of sector dummies, is a vector of country dummies tt tt – one dummy for each of the three countries covered in the analysis, WW TT is a set of controls, and is the jjtt jjtt SS CC outcome variable of interest. The analysis uses this specification to describe the evolution of the outcome jjtt jjtt variable over time. Namely, using OLS with robust standard errors, we estimate specification (1) for XX YY the mean of and for the 25<sup>th</sup> and 75<sup>th</sup> percentile of using the quantile regression approach. Analyzing jjtt YY the entire distribution of an outcome variable of interest, rather than only its mean, is particularly jjtt jjtt YY YY important when considering the effect of an aggregate crisis on individual economic agents. As highlighted earlier, large aggregate shocks can have very heterogenous impacts across individuals and firms, a pattern that can be better understood when looking into the dynamics of higher moments of the (micro)outcome distribution in response to a shock.<sup>6</sup>

## **The varied effect of Russia’s invasion of Ukraine based on a firm’s export exposure to Russia or digitization gains**

The second specification used in the analysis aims at investigating the invasion’s heterogeneous effects on firms, with special emphasis on Central Asian firms that may have been more exposed to the invasion shock. As already stressed, the analysis first proxies firms’ exposure to the invasion shock by firm-level characteristics (exports to Russia) that are likely to determine a firm’s likely intensity of exposure to the

> 6 See for example Bloom et al. (2018) and Salgado et al. (2020).

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