The price of war: macroeconomic and cross-sectional effects of sanctions on Russia

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F23%3A00573398" target="_blank" >RIV/67985998:_____/23:00573398 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cerge-ei.cz/pdf/wp/Wp756.pdf" target="_blank" >https://www.cerge-ei.cz/pdf/wp/Wp756.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The price of war: macroeconomic and cross-sectional effects of sanctions on Russia

Popis výsledku v původním jazyce

How much do sanctions harm the sanctioned economy? We examine the case of Russia, which has faced three major waves of international sanctions over the last decade (in 2014, 2017, and 2022). In a VAR model of the Russian economy, we first apply sign restrictions to isolate shocks to international credit supply to proxy for the financial sanctions shocks. We provide a microeconomic foundation for the sign restriction approach by exploiting the syndicated loan deals in Russia. We then explore the effects of the overall sanctions shocks (financial, trade, technological, etc.) by employing a high-frequency identification (HFI) approach. Our HFI is based on each OFAC/EU sanction announcement and the associated daily changes in the yield-to-maturity of Russia’s US dollar-denominated sovereign bonds. Our macroeconomic estimates indicate that Russia’s GDP may have lost no more than 0.8% due to the financial sanctions shock, and up to 3.2% due to the overall sanctions shock cumulatively over the 2014–2015 period. In 2017, the respective effects are 0 and 0.5%, and in 2022, they are 8 and 12%. Our cross-sectional estimates show that the real income of richer households declines by 1.5–2.0% during the first year after the sanctions shock, whereas the real income of poorer households rises by 1.2% over the same period. Finally, we find that the real total revenue of large firms with high (low) TFPs declines by 2.2 (4.0)% during the first year after the sanctions shock, whereas the effects on small firms are close to zero. Overall, our results indicate heterogeneous effects of sanctions with richer households residing in big cities and larger firms with high TFPs being affected the most.

Název v anglickém jazyce

The price of war: macroeconomic and cross-sectional effects of sanctions on Russia

Popis výsledku anglicky

How much do sanctions harm the sanctioned economy? We examine the case of Russia, which has faced three major waves of international sanctions over the last decade (in 2014, 2017, and 2022). In a VAR model of the Russian economy, we first apply sign restrictions to isolate shocks to international credit supply to proxy for the financial sanctions shocks. We provide a microeconomic foundation for the sign restriction approach by exploiting the syndicated loan deals in Russia. We then explore the effects of the overall sanctions shocks (financial, trade, technological, etc.) by employing a high-frequency identification (HFI) approach. Our HFI is based on each OFAC/EU sanction announcement and the associated daily changes in the yield-to-maturity of Russia’s US dollar-denominated sovereign bonds. Our macroeconomic estimates indicate that Russia’s GDP may have lost no more than 0.8% due to the financial sanctions shock, and up to 3.2% due to the overall sanctions shock cumulatively over the 2014–2015 period. In 2017, the respective effects are 0 and 0.5%, and in 2022, they are 8 and 12%. Our cross-sectional estimates show that the real income of richer households declines by 1.5–2.0% during the first year after the sanctions shock, whereas the real income of poorer households rises by 1.2% over the same period. Finally, we find that the real total revenue of large firms with high (low) TFPs declines by 2.2 (4.0)% during the first year after the sanctions shock, whereas the effects on small firms are close to zero. Overall, our results indicate heterogeneous effects of sanctions with richer households residing in big cities and larger firms with high TFPs being affected the most.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/LX22NPO5101" target="_blank" >LX22NPO5101: Národní institut pro výzkum socioekonomických dopadů nemocí a systémových rizik</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů