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Can Central Banks Always Influence Financial Markets? Evidence from Russia
In many financial markets, including the UK and US, central banks are able to influence asset prices through unexpected interest rate changes (so-called indirect channel of monetary policy). In our paper (Shibanov and Slyusar 2019) we study the Russian market in 2013-2019 and measure policy shocks by the difference between the key rate and analysts’ median forecast. We show that in the short-term, the Central Bank of Russia does not significantly influence the general stock market or the ruble exchange rate outside December 2014 and January 2015, while some sectoral stock indices react to the changes opposite to what theoretical models predict. Overall, the Russian case is more similar to the ECB and the case of the German economy than to results from the UK or the US. This may mean that the Bank of Russia has more influence through the direct channel on the interest rates of credits and deposits.
Asset Price Reaction to Policy Changes
What should we expect from a general stock market or a national currency reaction to the central bank interest rate policy? This indirect effect may lead to changes in the collateral available in the economy, or in imports and exports of a country. Theoretical models predict that an expected decrease in the key rate would have no impact on asset prices, while unexpected increases in the key rate may have a negative impact on asset prices (Kontonikas et al. 2013). If the interest rate increases more than the markets or analysts expect, we would see prices decrease as discount rates most probably increase; the opposite happens when the interest rate decreases more than expected.
The results of testing this presumption on different countries are not uniform. While in the US (Kontonikas et al. 2013) and in the UK (Bredin et al. 2009) the impacts of key rate policy surprises are significant, the ECB influences neither the UK nor the German stock markets (Breidin et al. 2009).
Regarding the exchange rate (Hausman and Wongswan, 2011), there is evidence that unexpected changes in the US interest rate have a strong impact on floating currencies.
The Case of Russia
Russian monetary policy has changed a lot since 2013. The introduction of the “key rate” as the main policy tool, switch to the floating ruble and inflation targeting in November 2014 all lead to a new framework used by the Bank of Russia. Therefore, it is of interest to check what happens with the indirect channel of policy transmission (through asset prices and financial markets).
There is at least one paper that precedes our research. Kuznetsova and Ulyanova (2016) study the impact of verbal interventions by the Bank of Russia (Central Bank of Russia) on both the returns and the volatility of the Russian stock market index (RTS) in 2014-2015. Their findings suggest that returns do react to the Bank of Russia communications, while volatility does not.
In our paper (Shibanov and Slyusar 2019) we study the period of 2013-2019, that is the time of Elvira Nabiullina as governor of the Bank of Russia. Our approach is based on the assumption that news are incorporated in the stock market reasonably fast, no later than 4 trading days after the day of announcement. For the exchange rate we take short-term movements 30 minutes before and after the time of publication (like in Hausman and Wongswan 2011). Monetary policy surprise is measured as the difference between the realized key rate and the median expectations of analysts in Thomson Reuters. Abnormal returns are computed using an index model.
Figure 1 shows that the surprises are close to zero except for two dates: December 2014 and January 2015. In the first period the key rate was increased to 17%, while in the second it was reduced to 15%. In the paper we show that these two days are clear outliers that bias the results, so we study the relationship without them.
Results for the Stock Market
The stock market reaction in the symmetric window of four days before the announcement and four days after is muted (see Table 1). While the main index (MICEX) does not react significantly, two sectors (MM – metals and mining, and chemistry) react positively to the unexpected increase in the key rate. This result seems to contradict what we would expect from the market. The bond index does not significantly react to the changes.
Table 1. Cumulative effect, sample with no shocks (days from -4 to +4).
| Sector | Estimate | t-statistic | P-value | Significance | |
| MICEX | 1.6192 | 0.6803 | 0.4999 | 0.041 | |
| OG | 0.2511 | 1.125 | 0.2668 | 0.005 | |
| Finance | -1.2933 | -1.080 | 0.2860 | 0.024 | |
| Energy | -0.4513 | -0.7145 | 0.4787 | 0.004 | |
| MM | 2.2876 | 3.326 | 0.0018 | *** | 0.113 |
| Telecom | -0.2534 | -0.2844 | 0.7774 | 0.001 | |
| Consum. | 0.2178 | 0.4191 | 0.6772 | 0.001 | |
| Chemistry | 2.9787 | 2.642 | 0.0114 | ** | 0.132 |
| Transport | 0.3200 | 0.1548 | 0.8777 | 0.001 | |
| Bonds | 1.4080 | 1.048 | 0.3002 | 0.037 |
Source: Shibanov and Slyusar (2019), Thomson Reuters, Moscow Stock Exchange and Bank of Russia data.
Results for the Ruble Exchange Rate
The exchange rate should react with a depreciation to the unexpected key rate decrease. If there is an unexpected increase, the return on the ruble-denominated bonds rises and so the currency becomes more attractive to the international investors.
However, we do not observe any significant difference between the cases of expected and unexpected changes (see Table 2). All the movements are quite noisy and do not show any stable pattern.
Table 2. Exchange rate reaction to the key rate changes.
| Key rate increase | Key rate decrease | |
| Unexpected | -1.05% | -0.04% |
| Expected | 0.65% | 0.003% |
Source: Shibanov and Slyusar (2019), Thomson Reuters and Bank of Russia data.
Figure 1. Deviations of the actual key rate from median expectations (key rate surprises), percentage points.

Source: Shibanov and Slyusar (2019), Thomson Reuters and Bank of Russia data.
Conclusion
As we see from our analysis, the Bank of Russia’s impact on financial markets is similar to the one observed in Germany after ECB policy changes. There is almost no sizeable and stable effect neither on asset prices nor on the exchange rate.
The results do not mean, however, that monetary policy in Russia is irrelevant. The direct channel – i.e. the impact of the central bank’s decisions on the interest rates of credits and deposits works well. Moreover, we only consider short-term effects concentrated around the announcement date. Longer-term effects may be more pronounced.
References
- Bredin, D. et al. (2009) ‘European monetary policy surprises: the aggregate and sectoral stock market response’, International Journal of Finance & Economics. Wiley Online Library, 14(2), pp. 156–171.
- Hausman, J. and Wongswan, J. (2011) ‘Global asset prices and FOMC announcements’, Journal of International Money and Finance. Elsevier Ltd, 30(3), pp. 547–571. doi: 10.1016/j.jimonfin.2011.01.008.
- Kontonikas, A., MacDonald, R. and Saggu, A. (2013) ‘Stock market reaction to fed funds rate surprises: State dependence and the financial crisis’, Journal of Banking and Finance, 37(11), pp. 4025–4037. doi: 10.1016/j.jbankfin.2013.06.010.
- Kuznetsova, O. and Ulyanova, S. (2016) ‘The Impact of Central Bank’s Verbal Interventions on Stock Exchange Indices in a Resource Based Economy: The Evidence from Russia’, Working Paper, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2876617.
- Shibanov, O. and Slyusar A. (2019) ‘Interest rate surprises, analyst expectations and stock market returns: case of Russia’, Working Paper.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
How to Liberalise EU-Ukraine Trade under DCFTA: Tariff Rate Quotas
This policy brief focuses on trade relations between Ukraine and the EU amid preparations for the review of the Deep and Comprehensive Free Trade Agreement (DCFTA) due in 2021. In particular, it analyses Ukraine’s utilization of the DCFTA tariff rate quotas (TRQs) over 2016-2019. According to the results, Ukraine has been steadily increasing the level of TRQs usage – in terms of the number of utilized TRQs and export volumes within and beyond TRQs. For some DCFTA TRQs, total exports to the EU far outweigh quota volumes, while for other TRQs supply is limited by quota volume. The brief provides arguments and recommendations for the DCFTA TRQs update to increase Ukraine’s duty-free access to the EU market.
Why Update DCFTA TRQs for Ukraine?
EU-Ukraine trade under the Deep and Comprehensive Free Trade Agreement (DCFTA, in effect since January 1, 2016) progressed considerably. Ukraine’s exports of goods to the EU reached $20.8 billion in 2019 – a 54% increase compared to 2016 and a 24% increase compared to pre-crisis 2013.
According to the EU-Ukraine Association Agreement/DCFTA, the parties may initiate a review of its provisions in five years from its implementation – in 2021. So far, both governments confirmed their readiness to start such negotiations next year.
Ukraine advocates for further trade liberalisation with the EU through reducing the existing tariff and, most importantly, non-tariff barriers. This is an imperative for maintaining positive trade dynamics and providing new impetus to deepening bilateral economic integration.
Updating duty-free tariff rate quotas (TRQs) under the DCFTA is at the top of the EU-Ukraine 2021 negotiations agenda. Current quota volumes are based on outdated statistics, as it has been 10 years since the DCFTA negotiations (2008-2011).
Many TRQs are too low in terms of Ukraine’s current export and production capacities. For example, Ukraine’s total exports of grains (annual averages) increased from 19 million tons in 2008-2010 to 42.3 million tons in 2016-2018. Honey exports increased from 5.9 thousand tons in 2008-2010 to 58 thousand tons in 2016-2018. As a result, some TRQs are fully exhausted in the first days or months of the year.
High competition for access to duty-free quota volumes is a barrier first of all for SMEs that cannot compete effectively for it with large companies, while out-off-quota tariffs may be too restrictive for them.
Ukraine’s TRQs Utilisation During 2016-2019
DCFTA TRQs grant partial liberalisation of market access to the EU. Zero tariff rates are only applied to a specified quantity of imported goods inside a TRQ, while beyond TRQ imports to the EU are dutiable on a regular basis (subject to third-country tariff rates).
The EU applies TRQs for 36 groups of agro-food products originated in Ukraine plus 4 additional TRQs for certain product groups (in total 40 TRQs under DCFTA) – see Table 1. Ukraine applies TRQs for 3 groups of products plus 2 additional TRQs.
By the level of utilisation, TRQs fall into three groups: 1) fully utilised. They, in turn, can be divided into TRQs with and without over-quota supply; 2) partially utilised; and 3) not utilised.
The data indicate a general upward trend in Ukraine’s utilisation of TRQs under the DCFTA. In general, Ukrainian exporters utilised 32 TRQs in 2019 (80%) comparing to 26 TRQs in 2016 (65%).
Figure 1. Number of DCFTA TRQs utilized by Ukraine during 2016-2019.

Table 1 shows Ukraine’s utilization of 40 DCFTA TRQs over 2016-2019 – in tons and %. The main findings include:
The number of fully exhausted TRQs has been increasing. In 2019, Ukraine filled up 12 TRQs including honey; processed tomatoes; wheat; maize; poultry meat; barley groats and flour, other cereal grains; sugars; grape and apple juice; butter and dairy spreads starches; starch processed; as well as malt-starch processed products. For 9 of them, Ukraine’s supplies exceeded TRQs volumes.
The number of partially utilized TRQs increased from 16 in 2016 to 20 in 2019. In 2018-2019, Ukraine began using new TRQs such as fermented-milk processed products; malt-starch processed products; sugar syrups. High TRQs utilization rates (over 80%) in 2019 were observed for malt and wheat gluten; cereal processed products; eggs (main); barley, barley flour and pellets.
Moreover, Ukraine increased utilisation of TRQs for processed products. For example, utilisation of a TRQ for cereal processed products increased from 2.7% in 2016 to 99.5% in 2019. This signifies the growing ability of Ukrainian producers to comply with the EU food safety requirements and standards for processed products. Exports of processed starch increased significantly in 2019 and exceeded TRQ volume by a lot.
Ukraine’s utilisation of some TRQs has decreased. For example, a TRQ for oats gradually decreased from 100% in 2016 to 31% in 2019 due to a decrease in total exports and domestic production of oats in Ukraine during this period. Low utilisation of other TRQs may also be attributed to high price competition and quality requirements in the EU, complex quota allocation procedure, etc.
The number of not utilized TRQs decreased from 14 in 2016 to 8 in 2019. For instance, no exports within TRQs were observed for beef, pork, sheep meat, as Ukraine has not yet been authorized to export these meat products to the EU.
Moreover, since October 2017, Ukraine has been able to use provisional TRQs that were granted by the EU as autonomous trade measures (ATM) for 3 years. They increased duty-free access for 8 groups of Ukrainian products – in addition to the relevant DCFTA TRQs. So far, Ukraine fully utilises 5 ATM TRQs including honey; processed tomatoes; barley groats and meal, cereal grains otherwise worked; wheat, flour and pellets; maize, flour and pellets.
Total Exports to the EU vs Duty-Free Exports Within TRQs
For most fully utilized DCFTA TRQs, Ukraine’s total exports of the covered products exceeded TRQ volumes during 2016-2019. Considerable over-quota supply occurred for: honey; processed tomatoes; barley groats and meal, cereal grains; apple and grape juice; maize, flour and pellets; poultry meat; wheat, flour and pellets; sugars; butter and dairy spreads; starch processed.
For instance, over-quota exports of processed tomatoes from Ukraine to the EU in 2019 (31.2 thousand t) more than doubled the quota volumes (10,000 t of the DCFTA TRQ and 3,000 t of the provisional ATM TRQ). See Figure 2 for more examples.
Figure 2. Ukraine’s exports to the EU within and beyond certain TRQs, 2016-2019.

Increasing exports beyond TRQs indicate significant demand for these Ukrainian products in the EU, and their competitiveness in terms of price and quality on the EU market.
It also signifies that volumes of these fully utilised DCFTA TRQs with increasing over quota exports are rather low in terms of Ukraine’s export and production potential. Therefore, these TRQs are the primary candidates for updating.
At the same time, for certain DCFTA TRQs (malt-starch processed products; starch, malt and wheat gluten), exports to the EU were about 100% of TRQ volume but did not go far beyond. This may indicate a significant restrictive impact of those TRQs and out-of-quota tariffs for Ukrainian exports. These TRQs also need to be further analysed and revised.
Тable 1. Utilisation of DCFTA tariff rate quotas by Ukraine, 2016-2019.
| 2016 | 2019 | |||||
| Quota name | Quota volume | Utilised | Quota volume | Utilised | ||
| t | t | % | t | t | % | |
| “First-come, first-served” method for TRQ allocation | ||||||
| Sheep meat | 1500 | 0 | 0,0% | 1950 | 0 | 0,0% |
| Honey | 5000 | 5000 | 100% | 5600 | 5600 | 100% |
| Garlic | 500 | 49 | 9,8% | 500 | 393 | 78,6% |
| Oats | 4000 | 4000 | 100% | 4000 | 1239 | 31,0% |
| Sugars | 20070 | 20070 | 100% | 20070 | 20070 | 100% |
| Other sugars | 10000 | 5929 | 59,3% | 16000 | 1006 | 6,3% |
| Sugar syrups | 2000 | 0 | 0,0% | 2000 | 7 | 0,4% |
| Barley groats and meal, cereal grains otherwise worked | 6300 | 6300 | 100% | 7200 | 7200 | 100% |
| Malt and wheat gluten | 7000 | 7000 | 100% | 7000 | 6319 | 90,3% |
| Starches | 10000 | 1898 | 19,0% | 10000 | 10000 | 100% |
| Starch processed | 1000 | 0 | 0,0% | 1600 | 1600 | 100% |
| Bran, wastes and residues | 17000 | 7286 | 42,9% | 20000 | 14467 | 72,3% |
| Mushrooms main | 500 | 0 | 0,1% | 500 | 0 | 0,0% |
| Mushrooms additional | 500 | 0 | 0,0% | 500 | 0 | 0,0% |
| Processed tomatoes | 10000 | 10000 | 100% | 10000 | 10000 | 100% |
| Grape and apple juice | 10000 | 10000 | 100% | 16000 | 16000 | 100% |
| Fermented-milk processed products | 2000 | 0 | 0,0% | 2000 | 866 | 43,3% |
| Processed butter products | 250 | 0 | 0,0% | 250 | 0 | 0,0% |
| Sweetcorn | 1500 | 13 | 0,9% | 1500 | 23 | 1,5% |
| Sugar processed products | 2000 | 340 | 17,0% | 2600 | 417 | 16,0% |
| Cereal processed products | 2000 | 55 | 2,7% | 2000 | 1989 | 99,5% |
| Milk-cream processed products | 300 | 73 | 24,4% | 420 | 9 | 2,2% |
| Food preparations | 2000 | 5 | 0,3% | 2000 | 65 | 3,2% |
| Ethanol | 27000 | 1889 | 7,0% | 70800 | 6083 | 8,6% |
| Cigars and cigarettes | 2500 | 0 | 0,0% | 2500 | 0 | 0,002% |
| Mannitol-sorbitol | 100 | 0 | 0,0% | 100 | 0 | 0,0% |
| Malt-starch processed products | 2000 | 0 | 0,0% | 2000 | 1998 | 99,9%* |
| Import licensing method for TRQ allocation | ||||||
| Beef meat | 12000 | 0 | 0,0% | 12000 | 0 | 0,0% |
| Pork meat main | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Pork meat additional | 20000 | 0 | 0,0% | 20000 | 0 | 0,0% |
| Poultry meat and preparations main | 16000 | 16000 | 100% | 18400 | 18400 | 100% |
| Poultry meat and preparations additional | 20000 | 8552 | 42,8% | 20000 | 9174 | 45,9% |
| Eggs and albumins main | 1500 | 232 | 15,5% | 2400 | 2027 | 84,5% |
| Eggs and albumins additional | 3000 | 0 | 0,0% | 3000 | 1891 | 63,0% |
| Wheat, flours, and pellets | 950000 | 950000 | 100% | 980000 | 980000 | 100% |
| Barley, flour and pellets | 250000 | 249460 | 99,8% | 310000 | 249250 | 80,4% |
| Maize, flour and pellets | 400000 | 400000 | 100% | 550000 | 550000 | 100% |
| Milk, cream, condensed milk and yogurts | 8000 | 0 | 0,0% | 9200 | 250 | 2,7% |
| Milk powder | 1500 | 450 | 30,0% | 3600 | 560 | 15,6% |
| Butter and dairy spreads | 1500 | 690 | 46,0% | 2400 | 2400 | 100% |
Source: European Commission, own calculations * Note: We consider 99.9% usage rate as fully utilized TRQ.
Conclusion
The EU and Ukraine confirmed their readiness to initiate the update of the DCFTA due in 2021. Ukraine is interested in increasing duty-free trade under DCFTA with the EU in line with the current state of Ukraine’s production and export capacities, as well as EU-Ukraine bilateral trade developments.
Although many DCFTA TRQs did not limit over-quota exports, Ukraine wants to revise DCFTA TRQs to secure permanent broader duty-free access to the EU market and reduce access barriers for SMEs (as SMEs are more affected by TRQs and other non-tariff barriers). So far, the EU temporarily increased certain TRQs in 2017 for three years as autonomous trade preferences for Ukraine. The primary candidates for the update should include DCFTA TRQs demonstrating high utilization rates, with or without over-quota supply (honey; processed tomatoes; barley groats and meal, cereal grains; apple juice; sugars; butter and dairy spreads; starch processed, etc.).
Amid future DCFTA update negotiations, Ukraine should conduct a detailed analysis for each DCFTA TRQ (taking into account temporary ATM quotas) to prepare its suggestions how and to what extent to liberalise them. It is worth considering different options of such liberalisation – by either increasing TRQs’ volumes or setting up preferential tariff rates for Ukraine instead, etc.
In the framework of the future negotiations with the EU, a special emphasis should be placed on increasing duty-free access for Ukrainian processed goods to promote their exports to the EU – as stipulated in the Export Strategy of Ukraine. For this purpose, Ukraine may explore possibilities for modifying the structure of certain TRQs (such as wheat, flour and pellets; maize, flour and pellets; barley, flour and pellets) to separate primary and processed products and to ensure more duty-free volumes for processed products.
References
- European Commission, 21.04.2020. DG Agriculture and Rural Development. “AGRI TRQs – Allocation Coefficients and Decisions”.
- European Commission, 12.02.2020. Remarks by Commissioner Várhelyi at a press conference with Prime Minister of Ukraine, Oleksiy Honcharuk.
- European Commission, DG Taxation and Customs Union, 21.04.2020. Tariff quota consultation.
- European Commission, 21.04.2020. “Trade Helpdesk Statistics.”
- OECD, 2018. “Fostering greater SME participation in a globally integrated economy”.
- Official Journal of the European Union, 2014. “EU-Ukraine Association Agreement”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Addressing the Covid-19 Pandemic: Policy Responses Across Eastern Europe
The world holds its breath as Covid-19 continues to spread and challenge local health care systems as well as local economies. The focus of international media has mostly been on China and then Western Europe and the US. However, countries around the Baltic Sea, Eastern Europe and the Caucasus differ from the West with respect to their socio-economic development, trade integration, and political systems. The webinar “Addressing the Covid-19 Pandemic in Eastern Europe: Policy Responses Across Eastern Europe” hosted by the the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network on May 28, 2020 aimed to fill this gap in the current discourse and give voice to experts from Latvia, Russia, Georgia, Belarus, Poland, Ukraine as well as Sweden, in order to contextualize their countries’ policy choices and experiences in the crisis. Policy recommendations can only be of preliminary nature at this point of time. Yet, it becomes clear that even though transition countries have fared relatively well during the health crisis, they will not be spared from the ensuing economic crisis and will require policy tools which are adapted to the local context.
Introduction
Less than six months after the outbreak of the Covid-19 crisis in China, the pandemic has spread across the globe. The epicenter has moved from Asia to Europe and the US, and in late May 2020 some voices are warning that it is now shifting towards Latin America. While the world´s eyes have been on Milan and Paris, little has been said about how the new EU member states and countries to the East of the European Union cope with the pandemic. Some observers have claimed the emergence of a new “iron curtain” in the corona crisis; Eastern Europe, the Baltic States and the Caucasus having been relatively unscathed compared to the West. Persisting differences in trade and travel patterns, demographic and socio-economic differences, as well as differences in trust levels could account for such an observation.
Yet, the most recent statistics suggest that this may be a premature interpretation and the overall picture is much more heterogeneous. Infections in Russia seem to be rising quickly, Georgia by contrast has turned out to be one of the top students.
Figure 1: Total confirmed Covid-19 cases vs. deaths per million.

Source: Our World in Data, 2020. • CC BYa.
Note: Data includes the most recent numbers as of May 25, 2020. Both measures are expressed per million people of the country’s population. The confirmed counts are lower than the totals. The main reason for this is limited testing.
On May 28, 2020, the Forum for Research on Eastern Europe and Emerging Economies (FREE) Network hosted a webinar with its member institutes: BEROC in Belarus, BICEPS in Latvia, CEFIR@NES in Russia, CenEA in Poland, ISET-PI in Georgia, KSE in Ukraine, and SITE in Sweden to discuss how their countries have fared in the corona crisis so far. The webinar provided an opportunity to share experiences and to add some interpretations and insights to the crude statistics, which often become unintelligible in the current overflow of information.
Figure 2: FREE Network Countries.

Source: SITE 2020.
The webinar started with Torbjörn Becker, director of SITE, introducing recent developments in terms of health statistics in the region and the research being done within the framework of the FREE Network.
SITE on Sweden
Jesper Roine, Professor at the Stockholm School of Economics and SITE, then presented the case of Sweden, the country which – with regards to death rates – has surpassed all other FREE Network countries by far. The Swedish case has been very controversially discussed in international media throughout the pandemic. Yet, the common claim that in Sweden everything was “business as usual” is not true, according to Roine. Compared to its direct neighboring countries Finland, Denmark and Norway, Sweden has chosen a relatively lenient approach to Covid-19, but high schools and universities have moved to distance learning since March and working from home is highly encouraged. Mobility reports show that Swedes have reduced their movement a lot, but less so than their Scandinavian neighbors. Roine confirmed that the Swedish health policy has been dominated by the public health agency, Folkhälsomyndigheten. Even though this is the default option in Swedish law, Roine stressed that this does not mean that the government’s hands are tied.
He presented two preliminary conclusions regarding the impact of the Swedish strategy: first, Sweden’s mitigation strategy has worked relatively well; the public health system is seriously strained but not overwhelmed. Yet, Roine said that the “lack of testing [remained] a mystery”, even for advocates of the current mitigation strategy. Second, in Roine’s opinion the attempt to protect the elderly has failed. The virus has spread to numerous nursing homes and excess death rates indicate that mortality has increased sharply for citizens above 65 years of age, much less for other age groups. Geographically, Stockholm has been the center of the epidemic. Other parts of the country have been affected to a much lesser degree.
BICEPS on Latvia
Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS) presented the Latvian experience of the corona crisis. A small country of about 2 million inhabitants, Latvia currently presents the second lowest Covid-19 mortality rate within the EU. Gubins related this to the Latvian government’s quick and determined policy reaction. After the first cases were reported in early March, schools and universities were closed, public gatherings forbidden, international travel halted, and a two-meter social distance rule imposed. Given the success of this strategy, Latvia has started to loosen its restrictions. A “Baltic Schengen area” was announced very recently and travel among the Baltic states is now possible again. The economic support package announced by the government amounts to 45 percent of GDP and includes a large equity investment in the airline airBaltic as well as important investments in infrastructure. According to Gubins, the current policy discussion focuses on the accessibility and size of help funds, widely deemed insufficient. Furthermore, the economic outlook of the country in terms of unemployment rates and GDP growth is bleak despite its success in containing the virus.
CEFIR on Russia
According to Natalia Volchkova, Director of the Centre for Economic and Financial Research (CEFIR) at the New Economic School in Moscow, Russia has pursued a “standard European strategy” in its fight against Covid-19. Two new hospitals exclusively for Covid-19 patients were created in Moscow, the current epicenter of the pandemic, and nearby. Most money spent on health care went to these new facilities, less was transferred as bonuses to medical workers. Russia has emphasized testing: around 10 million tests were performed; close to 400,000 cases of Covid-19 were confirmed. On May 27, free antibody testing was started in Moscow and is to be extended to other parts of the country. State-financed testing will serve to measure the potential degree of immunization of the population. While cases have started to decline in Moscow, other regions of Russia lag behind and are still expected to peak.
Volchkova stressed the role of the Russian shadow economy, which has been severely hit by the crisis. The size of the informal sector makes it difficult for the Kremlin to pass efficient support packages for the economy. Another policy problem lies in the weakness of the social security net, particularly unemployment benefits are hard to obtain. Therefore, most policy measures have focused on companies. Family allowances are the government’s second heavily used tool, which to Volchkova’s mind is an efficient policy choice. She concluded that the current help measures may already amount to 3 percent of GDP.
ISET-PI on Georgia
As of May 28, 2020, Georgia had only reported 12 corona deaths. According to Yaroslava V. Babych, Lead Economist at ISET Policy Institute in Tbilisi, the key explanation for Georgia’s relative success in the corona crisis is that, as in Latvia, testing started very early. She explained that even before Georgia’s neighbor Iran confirmed an outbreak of Covid-19, passengers’ temperatures were taken at the border crossing. The government in Tbilisi then soon imposed harsh quarantine measures, local quarantines in regional hotspots, a shutdown of public transport, an evening curfew and very high fines. Compliance with the measures was very high. Orthodox Easter celebrations were allowed to take place under strict hygiene measures and did not result in a spike in infection rates.
The country, largely reliant on tourism and agriculture, is now focusing on the economic consequences of the crisis. According to Babych, Georgia holds the ambition to become the first European country to open up to international tourism again from July 1, 2020. The government is also determined to avoid another meltdown of the important construction sector, as happened in 2008 – 2009. However, similar to the Russian case, Babych identified two factors which crucially weaken the Georgian economy: the lack of automatic stabilizers in the form of unemployment benefits and the large informal sector. Policymakers have therefore resorted to monthly cash payments to those who stopped paying income tax around March and fixing prices for specific food products. While the effectiveness of these measures still has to be evaluated, the policy discourse in Georgia has moved on to the socio-economic consequences of the crisis.
BEROC on Belarus
Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC), provided an overview of the Belarusian policy measures. According to Lvovskiy, Belarus has a high number of nurses and doctors and a relatively efficient “Soviet style of fighting pandemics”. There have been hardly any restrictions to public gatherings and events, both the Orthodox and the Catholic Easter festivities were maintained, as were soccer games and the national Victory parade. Initially, the official policy was to trace and isolate cases, but this did not prove to be very efficient, supposedly due to poor enforcement. Lvovskiy said that testing is rare which is why statistics on the spread of the virus and its effects remain of questionable quality.
While Belarus disposed of a solid health care system, it was not well prepared economically, which explains why the government has not been very proactive in Lvovskiy’s opinion. The Belarusian industrial production decreased by 7 percent in April 2020 compared to the same month the year before; unemployment has started to increase, yet, there are no significant unemployment benefits. Increasing the height of unemployment pay is the key policy issue under discussion in Minsk but in the absence of international loans, the government´s hands are tied. The issue is urgent: the most recent BEROC survey suggests that 46% of individuals living in urban areas have already seen their income decrease. Lvovskiy’s preliminary conclusion is that the Belarusian policy response to the Covid-19 crisis was not as bad as expected by many international observers: the health crisis has mostly been contained. But like in the Georgian case, the socio-economic implications of the crisis are becoming more pressing now.
CenEA on Poland
Michal Myck, Director of the Centre for Economic Analysis (CenEA) in Szczecin, explained that Poland also successfully avoided a spike in infection rates thanks to a quick policy response. Poland was one of the first countries to impose international travel restrictions and very harsh social distancing measures, yet, infection rates remain higher than in other FREE Network countries. Since the second half of April, most measures have been lifted and the spread of the virus seems under control and concentrated in the region of Silesia.
All limitations were implemented without invoking a state of emergency. Myck suggested that the government may have made this choice because the presidential elections would have been automatically postponed otherwise, an outcome the government wanted to avoid. The elections were eventually postponed, but doubts persist with regards to the constitutional validity of the way this decision was taken. Myck stressed the persisting political uncertainty. Economic policy in Poland has focused on protecting jobs and providing liquidity to enterprises. State loans have been primarily directed to SMEs and will be partly written off, conditional on continued activity and employment. In Myck’s opinion, the economic outcome for Poland will depend on whether investments from and exports to Western Europe quickly resume or not.
KSE on Ukraine
Tymofiy Mylovanov, President of the Kyiv School of Economics and former Minister of Economic Development, Trade and Agriculture, stressed that in the first few weeks of the pandemic, Ukraine enforced harsher policy measures than its neighbors. The lock down was almost complete, with only grocery stores and pharmacies allowed to open. Compliance was high during the first few weeks but then started to decline.
The government allocated 3 percent of GDP to a Covid-19 support fund, there has been a lot of deregulation on the labor market, but the central bank’s key interest rate remains at 8 percent. Pressure for a looser monetary policy increases according to Mylovanov, as GDP has fallen by 1.2 percent and unemployment is expected to reach up to 10 percent by the end of the year.
Mylovanov’s thoughts about Ukraine’s economic prospects are mixed: average salaries continue to grow during the crisis which may be explained by the fact that low-skilled employees get laid off first, suggesting a potentially long-lasting change of the composition of the workforce. At the same time, the political situation is volatile with local elections coming up in October 2020 and public pressure mounting. As Poland, Ukraine did not declare a state of emergency. While Mylovanov thinks that the policy response could have been better, he is optimistic that Ukraine was better prepared to Covid-19 than to previous crises and will not have to resort to international loans.
Preliminary Conclusions
It is too early to draw any definite conclusions, but undoubtedly, a lot can be learned from the very diverse experiences of the corona crisis in the region. The former Soviet countries have a different historical and political legacy than Western European countries and accordingly, have found different ways of handling the crisis. Some have been more successful than their Western neighbors. But even those countries which have not faced a large health crisis have been severely hit economically and are likely to suffer economic hardship in the future.
The lack of a strong tradition of unemployment benefits and automatic stabilizers renders countries like Georgia, Belarus and Russia particularly vulnerable to the economic crisis which will inevitably follow the Covid-19 outbreak. In some countries, the corona shock may also accelerate or trigger political changes. In the view of this, the FREE Network will organize a series of follow-up webinars and briefs on more specific corona-related topics, with the aim of contextualizing statistics and providing a wider picture of the socio-economic consequences and policy implications of the crisis.
Please find a full recording of the webinar below. Updates on further events will be posted on the FREE website and on social media channels (Facebook, Twitter).
List of Speakers
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE)
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Addressing the Covid-19 Pandemic in Eastern Europe
The Covid-19 pandemic is affecting everyone around the globe and leaves none untouched. However, much of the focus in international media has been on the most affected countries and richer countries in East Asia, the European Union and the United States with less attention given to countries around the Baltic Sea, Eastern Europe and the Caucasus.
Since the FREE Network includes research and policy institutes in Belarus (BEROC), Latvia (BICEPS), Russia (CEFIR@NES), Poland (CenEA), Georgia (ISET), Ukraine (KSE) and Sweden (SITE), experts from the FREE Network institutes discuss the regional perspective on the pandemic with examples of very different strategies implemented in the countries concerned.
Speakers
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS / Latvia)
- Natalia Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR@NES / Russia)
- Yaroslava V. Babych, Lead Economist at ISET Policy Institute (ISET / Georgia)
- Tymofiy Mylovanov, President at the Kyiv School of Economics (KSE / Ukraine)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC / Belarus)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
Chair/Moderator
- Torbjörn Becker, Director of the Stockholm Institute of Transition Economics (SITE / Sweden)
The Social Impacts of Covid-19 – Case for a Universal Support Scheme?
Beyond its impact on the healthcare system, the Covid-19 pandemic has already reached labor markets throughout every economy via economic shocks. As of 1 April 2020, ILO estimates indicate a substantial rise in global unemployment, leading to a 6.7% decline in working hours in the second quarter of 2020, which is equivalent to 195 million full-time workers.[1] In this policy note we will draw the reader’s attention to the potential scale of the impact on the labor market and the respective social consequences in Georgia. We will identify a wide variety of groups affected by the Covid-19 crisis, with a special emphasis on the labor market, and provide our judgement on the possible extent of the repercussions. The current crisis affects almost every segment of the population, including members of the following large social groups:
- Labor market participants face high risk of job loss. Fewer employment opportunities and broad scale layoffs force a large section of self-employed and salaried workers into challenging circumstances.
- Recipients of Targeted Social Assistance (TSA) are at great risk of slipping deeper into poverty. While members of this group mostly rely on social assistance layouts, the supplementary income that they receive, often from informal sources, could be cut. In addition, the increased prices on food and other essential goods could be particularly detrimental to this group of people.
- Senior citizens are extremely exposed to the danger of the virus and struggle with greater health risks.
Our analysis starts with an overview of the Georgian labor market and the short-term impacts of Covid-19 on workforce displacement throughout the various sectors. The impact is not gender neutral, as it affects men and women differently depending on the sector. Therefore, we will further provide the decomposition of the impacts on the labor market and propose gender-responsive solutions to the pandemic. To mitigate adverse effects across various vulnerable groups, we will review the existing theoretical and practical evidence on targeted and universal support schemes. An overview of international social support programs is moreover provided in this note. We will further analyze the relative merits and drawbacks of our pre-defined policy options based on a multi-criteria assessment in the context of the Covid-19 crisis and thereafter provide recommendations for policy implementation.
Covid-19 – Impact Across Sectors and an Overview of the Labor Market
Unemployment in Georgia is expected to experience a large-scale increase in the short-term, leading to massive social problems. Workers have been told to remain at home because of the broad virus containment measures taken during the outbreak. Those with the opportunity to work from home are relatively well-off, unlike the large variety of vulnerable groups affected by the lockdown. Low levels of economic activity impact almost all industries, and the most vulnerable sectors include accommodation and food services, most wholesale and retail trade and entertainment and recreation. These difficulties place hundreds of thousands at risk, either by downward adjustments to income or working hours, or by completely losing their jobs.
In order to evaluate Covid-19’s potential short-run effect on employment across various economic sectors, we have qualitatively assessed the strength of the impact at the sub-sectoral level,[2] taking into account the following: (1) list and scale of economic activities prohibited during the ‘lockdown’; (2) restrictions imposed on transportation; (3) drop in consumer demand; (4) fall in intermediate input use.
In Table 1 we present our assessment of the Covid-19 impact across sectors, coupled with the corresponding labor market statistics.[3]
Table 1: Covid-19 impact on possible workforce displacement across sectors.

Source: Authors’ sectoral assessments and calculations based on Geostat Labor Force Survey (LFS 2018).
The key findings from the labor market assessment include:
- Close to 30 percent of hired workers face a high risk of job displacement, mostly driven by an expected fall in economic activity in the trade, construction, manufacturing, and accommodation and food services sectors;
- The least impacted industries are projected to be education, public administration and defense, utilities, and health;
- The majority of self-employed are active in the agricultural sector, which faces a moderate impact for several reasons: the closedown of open food markets, restrictions on transportation, and a partial decline in demand (mostly from the food service sector). Although agriculture is not projected to be severely affected, a substantial number of the self-employed (mostly subsistence farmers) in this sector, considering their significantly lower than average baseline earnings, may require special policy emphasis within this group.
Finally, it should be emphasized that the severity of impacts across sectors will further depend on the longevity of the lockdown measures and the sequence in which they may be lifted for different economic activities.
In addition to the assessments in Table 1, Annex 1 presents a correlation between our estimates weighted by sub-sectors and the ILO’s assessment of the current global impact of the crisis on economic output across the sectors. It should be further noted that, in most cases, the scale of impacts coincide, and the remaining differences are due to: (1) our approach being based on more detailed sub-sectoral data; (2) the ILO looks at the global impact, whereas we focus solely on Georgia.
Short-term Workforce Displacement Risks in Vulnerable Sectors
To alleviate social problems stemming from the labor market shock during the strict, short-term quarantine measures, the clear need for safety net programs has raised the important questions of how they should be designed and who the recipients of support should be.
The discussion of social program designs requires a thorough analysis of the potential target groups. As mentioned in the previous section, after drastic quarantine and lockdown measures, many people in Georgia are at risk of finding themselves without jobs or with decreased salaries and earnings, which, in turn, is a main cause of social problems, like the inability to provide food and other necessities. The highly affected groups, as outlined in Table 1, can be clustered across the following sectors of economy:
- The accommodation and food service sector is currently the most directly and highly affected sector. Hotels and restaurants are completely closed for an uncertain period, except for the food delivery business. However, even this is constrained to certain periods of the day, since according to the state’s emergency rules after 21:00 all movement, including delivery, is forbidden.
Most people hired within accommodation businesses face temporary job loss. This group includes hotel administration staff, housekeeping staff, people working in hotel restaurants, etc. Similarly affected are employees in restaurants and cafés, faced with cutbacks in salaries, if not complete job loss.
Another significant group within this sector are the self-employed. Owners of small family hotels and restaurants, typically dependent on tourism expenditure, now find themselves without any cashflow.
- A significant portion of the wholesale and retail trade sector also faces major shutdowns. To begin with, employees of trade centers and individual stores are now out of work for an indefinite period. These include consultants in clothing stores, hardware stores, household appliance stores, etc. A very limited number of shops that continue to work via online sales have retained several employees on decreased salaries.
The reality is also harsh for the self-employed in retail trade. Open marketplaces, including construction materials shops and farmers’ markets have been shut, and such people are left without a vital income source. It should also be noted that most of these workers are members of a lower social strata and are less likely to have enough, if any, savings for the quarantine period.
- As for the relatively small, but equally affected, arts, entertainment and recreation sector, art galleries, museums, night clubs, theatres, movies, and sports and spa facilities, have all been closed down due to their ‘non-vital’ function. Salaried as well as self-employed workers in these sectors found themselves without employment soon after the state emergency was announced.
- Additional highly affected groups are those hired and self-employed in the transportation sub-sectors. The closing of public transportation has left hired bus, metro, and minibus drivers entirely without work.
Other than hired employees, self-employed drivers for intercity transportation are now left without work since intercity commuting is now forbidden under the state of emergency. Comparatively less affected are self-employed taxi drivers, who are still allowed to work, however only between 06:00-21:00. The fact that many drivers previously worked night shifts, combined with declined daytime demand, results in significant cutbacks in daily earnings for taxi drivers.
- Another significantly affected group are those workers employed in households. These include housemaids, nannies, private tutors, handymen, etc. Since everyone is being cautious and following social distancing instructions, many households have dismissed their hired help for an indeterminate period, and even those still employed have a hard time getting to work due to the suspension of public transport, and are therefore left without vital daily income.
- The agriculture, forestry, and fishing sector, the largest in terms of employment, remains less affected relatively, though it is facing restrictions since restaurants and cafés require fewer agricultural products than before. Moreover, as farmers’ markets have closed, their access to marketplaces has become significantly constrained. Farmers are now supplying only supermarket chains and restaurants with delivery services, a significant economic decrease compared to the normal environment. It should also be noted that self-employed small farmers are in the majority in the sector. Such workers are likely without strong links to supermarket chains or restaurants, and therefore, they will be more noticeably affected by the economic impact.
An important specificity of self-employed and domestic workers is that many are also informally employed, thus their identification by official sources (i.e. in tax returns or small business registers) is extremely problematic. Thus, the existence of a large variety of potentially affected groups, as well the inability to correctly estimate the severity of impacts across groups, highlights the need for a temporary social protection mechanism that will cover all affected parties, particularly since the people included in the groups above are not typically the main recipients of social assistance programs.
Decomposition of Labor Market Impacts by Gender
In this section, we present the gender decomposition of labor market impacts, and conclude that unemployment-driven assistance may benefit men considerably more than women.
Chart 1 summarizes the distribution of self-employed and salaried men and women across low, medium, and highly affected industries, based on the sub-sectoral assessments previously described and using gender-disaggregated employment data.
Figure 1: COVID-19 impact on possible workforce displacement, by gender

Source: Authors’ sectoral assessments and calculations based on Geostat’s Labor Force Survey (LFS, 2018).
It is evident that the proportion of employed men is significantly higher in the most vulnerable sub-sectors. Such a picture is highlighted by the high male-employment ratios in construction, transportation, and parts of manufacturing, as well as the high female-employment in the minimally affected education and healthcare industries[4].
To summarize, during the current crisis men are more susceptible to job displacement, and if a social assistance policy is solely based on labor market outcomes, they will yield higher benefits. Such social support mechanisms will deepen existing gender inequalities[5] in the country as women face disproportionate and increasing burden of care work (in situation of lockdown).
Social Assistance Policy Objectives in a Crisis
Considering the diversity of groups influenced by the lockdown, any assistance program should have several main policy objectives:
- Maximizing the reach of a policy to those in need and minimizing their risk of impoverishment – a large part of the population is affected by the lockdown, thus there is a substantial risk of increasing poverty directly from job loss and indirectly via job losses within families. Social assistance should, in a best-case scenario, reach the maximum number of disadvantaged people, while avoiding providing assistance to the affluent.
- Minimizing fiscal pressure – social assistance can create substantial pressure on the budget, especially in the current situation as revenues have decreased due to the lockdown. Furthermore, people who do not require support should not receive assistance, thus, decreasing unjustified pressure on the budget.
- Progressivity and gender responsiveness – an assistance program should provide proportionally larger support to those in greater need and aim to balance support by gender.
To mitigate the negative social impact of the economic lockdown, the government will have to provide significant and effective social assistance. And this is where all governments face a key dilemma, as they decide between providing targeted versus universal assistance.
An Overview of Targeted vs. Unconditional Universal Assistance
Targeted assistance is based on the methodology to define target groups, this could be under a points-based system (similar to current targeted social assistance available in Georgia) or a certain criterion defining affected groups. Under any targeting approach, two major challenges exist: (i) missing certain affected people (exclusion error), where defining an ideal criterion is impossible; and (ii) supporting those who do not require any assistance (inclusion error). Hanna and Olken (2018)[6] show that targeted programs have the potential to maximize welfare, however, they require a substantial amount of data and effort to minimize errors in the inclusion and exclusion of recipients. They further illustrate that, under normal circumstances, to reach 80% of poor people, the inclusion error will be around 22-31%. Deciding on a targeting methodology can also be costly and time consuming. Klasen and Lange (2016)[7] highlight that there is little difference between simple targets, such as demography or geography, and more complex asset-based measures, and both make poor proxies as they do not capture poverty effects in great enough detail.
In contrast, a universal support scheme can also be considered; defined as an unconditional transfer to every member of society. From the administrative perspective it is substantially easier to organize and administer, as it will not require the formation of targeting methodology or identification of target groups. Compared to targeted assistance, universal support will simply not have exclusion errors. However, the universality of the scheme would be associated with large inclusion errors. Nevertheless, considering the current situation in Georgia, with a large variety of affected groups, the inclusion error need not be as high as in normal circumstances. As previously noted, due to the lockdown, the number of vulnerable groups will have increased substantially.
Unlike targeted support schemes, there is limited practical evidence behind the implementation of universal programs (Banerjee et al., 2019).[8] However, some of the impacts can be identified from existing pilot case studies, impact assessments of existing targeting schemes, and an analysis of theoretical knowledge. The key here is that the expected impacts depend substantially on the duration and type of the support scheme (i.e. direct cash transfers, provision of vouchers or coupons, tax credit).
For our purposes we assume that the duration of the support scheme will be relatively short-term (related to the length of the lockdown). Furthermore, there is nearly no practical evidence on the impact of the long-lasting universal support schemes (Banerjee et al. 2019). Theoretically, long-lasting universal support can have a negative impact on labor force participation. Moreover, Banerjee et al. (2017)[9] finds no evidence that unconditional transfers discourage work. Considering the characteristics of the crisis, labor market participation is already limited because of the lockdown.
In addition, direct unconditional cash transfers could serve the progressivity purpose well, as households in greater need will receive a larger portion of their income, compared to those who require less assistance. Progressivity will depend on whether the recipient of a cash transfer is a household or an individual. Providing a cash transfer to households might have a disproportionate impact on larger households, requiring them to sustain themselves with less money per capita. Another important point to consider is whether money should be provided to everyone or only to the working age population (those above 15 years of age).
Coupons and Vouchers vs. Direct Cash Transfer
The type of support scheme can have a substantial influence on its impacts from the welfare and macroeconomic perspectives. One form of support scheme is the provision of vouchers or coupons to help households with utility payments or to purchase essential goods. Utility vouchers will disproportionally support more well-off households that use more appliances. The universality of such vouchers is also questionable, as some households are not connected to the utility networks (for instance the natural gas network), and thus will not benefit at all from vouchers. Considering the situation, the positive impact of vouchers is that during such a lockdown utility companies will not face liquidity problems that may otherwise arise from increased delinquency rates.
On the other hand, cash transfers allow recipients to rationalize between the consumption of different types of goods. As opposed to the provision of coupons and vouchers, transfers could further increase welfare by allowing individuals to self-rationalize (Ghatak & Maniquet, 2019).[10]
A Review of Social Support Programs Internationally
In this section, we discuss various governments’ (Table 2) social protection measures during the Covid-19 crisis. The actions taken cover the different functions of social protection, such as unemployment benefits; special social assistance or direct cash transfers; wage subsidies; deferrals of tax payments; pensions and pension fund adjustments; sickness and childcare benefits; etc.
In order to promote income security and stimulate aggregate demand, several countries have introduced either universal or quasi-universal direct cash payments (e.g. Australia, Hong Kong, Singapore, Serbia, Greece, the US). In order to further ease liquidity constraints on individuals and enterprises, some countries have announced the deferral of certain tax payments, social security contributions, rent, and utility payments (e.g. Bulgaria, Estonia, Spain, Canada). In addition, several governments are providing grants and wage subsidies to SMEs, start-ups, and other hard-hit businesses to avoid the drop in revenues and safeguard employment. In most cases, these measures were supplemented by extended unemployment benefits.
Table 2: Covid-19 social protection measures, by country
| Central, South, and Eastern European Countries | Certain Social Protection Measures Taken
|
| Estonia |
|
| Poland |
|
| Latvia |
|
| Serbia |
|
| Bulgaria |
|
| Albania |
|
| Ukraine |
|
| Asia-Pacific | |
| Hong Kong, China |
|
| Australia |
|
| New Zealand |
|
| Singapore |
|
| Western Countries | |
| United States of America |
|
| Canada |
|
| Germany |
|
| Greece |
|
| Spain |
|
| Norway |
|
Source: Policy Responses to Covid-19, IMF policy tracker, April 2020; Social protection responses to the Covid-19 crisis, ILO, March 2020; Countries’ public announcements of Covid-19 economic responses.
Alternative Policy Options
Considering the existing social challenges, policy objectives, and possible alternatives implemented around the world, we propose the following five policy options:
Option 1 – Targeted Assistance
Considering the current situation in Georgia, the state’s capacity to implement a targeted exercise is extremely limited. This is largely due to the lockdown and the complexity of matching the current economic challenges and general characteristics of target groups. One way for the government to target different groups would be to use its administrative resources and revenue service databases to identify affected unemployed people no longer receiving salaries. However, using these resources, it will be hard to identify the majority of self-employed and informal workers who have also lost their income (fully or partially) and are facing hardships; examples of these individuals may include a small business owner working at the Eliava construction materials market, a self-employed tourism sector worker, a domestic worker – a nanny or cleaning lady, etc. Under normal circumstances, such individuals do not require any social assistance, however due to the lockdown they may not have enough cash inflow to sustain their families.
Furthermore, targeted assistance can create perverse incentives for some employees. Depending on the amount of the assistance, employees (that are still allowed to work) whose net salaries are close to the assistance threshold, might be discouraged from work. For example, if targeted assistance is 200 GEL, a grocery store worker with a gross salary of 300 GEL might prefer to leave their job temporarily (as unpaid leave for example).
Furthermore, the government could target following socially vulnerable groups that are easier to identify, such as:
- Receivers of targeted social assistance, adults – 297,094 individuals;
- Receivers of targeted social assistance, under 18 – 161,374 individuals;
- Pensioners – 765,911 individuals.
Providing additional support to these groups will mean indirectly covering some self-employed individuals and informal workers. Many of such socially vulnerable groups work informally or are self-employed. Furthermore, some individuals could potentially have family members that are either informally or self-employed.
To calculate the total number of people subject to the targeted scheme, we consider the above listed individuals and add the group of hired employees that may lose the job or may have to take unpaid leave. Based on our estimates, around 200,000 hired workers may lose their income. Adding this to the number of TSA recipients (458,468) and pensioners not receiving TSA payments (692,431) brings the total number of beneficiaries of a targeted assistance scheme to 1,350,899 individuals. Assuming, 150 GEL in assistance per adult, and 75 GEL for under 18s, this will bring the cost of targeted assistance to approximately 191 mln. GEL per month.
Option 2 – Income Tax Breaks
The second policy option to consider is a variation on a tax break (tax credit, lowering income, or other taxes)[11]. Such an assistance mechanism will not be universal and only benefit the taxpayers. Furthermore, it is not a fact that tax relief will be transferred from employers to employees. Thus, essential social assistance may not be provided to a large proportion of the population. In addition, due to the lockdown, opportunities for investments have shrunk and hence, most tax saving will not influence economic growth. Finally, a decrease in tax rates will create additional pressure on government revenues, already negatively influenced by the lockdown, which may potentially create fiscal problems.
Aside from the costs of tax breaks, one should also bear in mind that this policy option is only intended for income tax payers who managed to retain their jobs. In an optimistic scenario, about 200,000 of hired employees will be left jobless, thus, about 640 thousand people will be aided by tax breaks. If income tax for all these employees would be reimbursed, the cost of tax breaks would amount to approximately GEL 136 mln. (monthly). It should also be mentioned that if companies are not paying income tax to the government, they might fail to reimburse this money to their employees, leaving some people without any assistance.
Option 3 – Unconditional Universal Cash Transfers
The third policy option is unconditional universal cash transfers. In this case, the government would make an unconditional cash transfer to every member of society. From a practical perspective there are two important questions to be answered: (i) should cash transfers be provided to individuals or to households?; and, (ii) should cash transfers only be made to the working age population or to children as well?
To minimize the potential negative consequences stemming from the possible negative gender impacts, individual payments are the preferred system. This may be as men are more often than not considered to be heads of their households, and if assistance is household-based women may not be able to take full advantage of it.
Furthermore, to ensure the progressivity of a universal cash transfer, it should not be limited to the working age population. A common approach would be to give guardians of children a decreased amount of a standard Universal Basic Income (UBI) payment (Ghatak & Maniquet, 2019). The progressivity of such a scheme is an important advantage, as it ensures support to those people who are not participants of the labor market and dependents of employed family members. Thus, the universal system helps mitigate the substantial indirect impacts on poverty resulting from job losses.
A major drawback of the unconditional universal cash transfer is its expense. This is primarily due to the large inclusion error, which accompanies this system by its very definition. However, alternatively, in a targeted program the vast majority of the affected self-employed and domestic workers (in total, close to 50% of all employment) are nearly impossible to identify. Furthermore, due to the lockdown, the potential group under risk of impoverishment is greater than under normal conditions. Consequently, compared to a perfectly targeted system (without any inclusion or exclusion errors) an unconditional universal cash transfer would be only marginally costlier.
However, with imperfect targeting, an unconditional cash transfer would be substantially costlier compared to targeted assistance. Assuming 150 GEL assistance for all working age population (2,968,964 individuals) and 75 GEL for children (754,500 individuals), the total cost of unconditional universal cash transfers would be 502 mln. GEL per month.
Option 4 – An Opt-out/Opt-in Unconditional Universal Transfers
As previously mentioned, the significant cost of a universal support scheme is a notable challenge, particularly because budgetary fiscal pressure is already high due to decreased economic activity and tax revenues. Thus, implementing a potentially costly assistance program will be hard from a public finance perspective. To partially alleviate this problem and decrease the inclusion error of universal cash transfers, the government could implement it in the following ways:
- The government could offer unconditional transfers to all individuals whose income is impossible to identify, while providing an opt-out option in case they do not deem the assistance necessary (for example, individuals and their families with savings or those unaffected by non-labor income);
- The government may assist employed workers based on their income using the following two principles:
- Offer assistance using an opt-out option to everyone whose income is below a certain threshold (for example, 700 GEL gross salary for the month of March);
- Offer assistance using an opt-in option to everyone whose income is above the threshold.
Opt-out/opt-in universal cash transfers have the potential for governmental savings. To evaluate the expected cost of this option we assume that half of all employees (i.e. 430,000) with a salary of over 700 GEL gross would opt-in into the system. In this case, the total cost of opt-out/opt-in universal cash transfers would be up to GEL 470 mln. Furthermore, in the better-case scenario, where no employees with a gross salary over 700 GEL would opt-in into the system, the total cost of the cash transfer scheme would be up to GEL 437 mln. Thus, our expected cost of the opt-out/opt-in universal cash transfer will be an average of GEL 454 mln[12].
Option 5 – Conditional Cash Transfers
To decrease the fiscal pressure associated with unconditional universal cash transfers, the government could use relatively simpler methods to minimize inclusion errors in the system. In this case, the government could potentially exclude employees who may not face an urgent need for assistance. Firstly, the government could exclude individuals who received an income of over 40,000 GEL in 2019 from the program. Secondly, those workers with an average monthly income of 1,200 GEL in 2020 could also be left outside the assistance scheme. This will allow the government to limit the inclusion error of the cash transfer system, while keeping similar overall impacts.
We evaluate the expected cost of the conditional cash transfer assuming 30% of the hired workers (258,048) having monthly income above 1,200 GEL. Based on the same population data, as for calculation of the cost of the unconditional cash transfer, the expected cost for conditional cash transfer will be roughly GEL 463 mln.
Multi-Criteria Analysis of Policy Options
To summarize these options, we have created a multi-criteria assessment of the different possibilities for social assistance using our pre-defined policy objectives. We assess each policy option on a 5-point scale, with 1 representing the worst performance, while 5 showing perfect performance. The overall efficiency of the policy option is a simple average of points in each criterion.
Table 3: Multi-Criteria Assessment of different social assistance systems during Covid-19
| Assessment Criteria | Option 1 – Targeted Assistance | Option 2 – Income Tax Break | Option 3 – Unconditional Universal Cash Transfer | Option 4 – Opt-out/opt-in Unconditional Universal Cash Transfer | Option 5 – Conditional Cash Transfer
|
| Monthly Cost of the assistance Scheme (mil. GEL) | 191 | 136 | 502 | 454 | 463 |
| 1. Minimization of Exclusion Error (minimization of impoverishment risk) | 3 | 1 | 5 | 5 | 4 |
| 2. Minimization of Inclusion Error (minimization of fiscal cost) | 4 | 2 | 2 | 3 | 3 |
| 3. Ease of implementation | 2 | 5 | 5 | 4 | 4 |
| 4. Progressivity | 4 | 1 | 4 | 5 | 5 |
| 5. Gender responsiveness | 3 | 2 | 5 | 5 | 5 |
|
Overall Efficiency |
3.2 | 2.3 | 4.2 | 4.4 | 4.2 |
Summary and Recommendations
In this policy note, we have summarized the potential social impacts of Covid-19 and the subsequent lockdown caused by the pandemic. Our assessment of the sub-categories of employment show that there is a large group of mid to highly affected individuals among the employed populace. Around 30% of hired employees will be significantly influenced, while 22% will suffer a medium impact. The impact on the self-employed will also be substantial, roughly 15% of the group will be highly affected, where 84% of self-employed individuals will feel a medium impact from the lockdown. The impacts are also disproportionate from a gender perspective, posing a risk of unemployment-driven assistance benefitting men more so than women.
Having reviewed international responses to the Covid-19 crisis from 17 selected countries, the evidence compiled has helped to form possible designs for a social assistance program. We believe that direct cash transfers to individuals are preferable to providing assistance for the purchase of specific goods or services, as individuals can self-rationalize.
Our multi-criteria assessment shows that an opt-out/opt-in unconditional universal cash transfer is marginally better compared to other universal cash transfer schemes. It has the best performance in minimizing the risk of impoverishment. Furthermore, our analysis shows that under the current conditions, the government’s ability to correctly design a targeted program that is able to reach all affected individuals is limited. This is primarily due to the relatively high percentage of self-employed on the Georgian labor market. Consequently, a targeted program would have a limited impact on minimizing the risk of impoverishment. This is even more true for possible tax breaks. The greatest merit of a targeted program is that it imposes less fiscal pressure and is thus substantially less costly compared to a universal support scheme.
Annex 1 – Comparison of Sectoral Impact Assessments by ILO (globally) and ISET-PI (for Georgia)

Annex 2 – Summary of the assumptions used for calculating costs of different support schemes
| Indicator | Amount | |
| Population | ||
| A | Working Age Population (>15) | 2,968,964 |
| B | Population Below Working Age (<15) | 754,500 |
| C | Total Population | 3,723,464 |
| Hired Workers | ||
| D | Total Hired Workers | 860,161 |
| E | Hired Workers with salary above GEL 700 | 430,081 |
| F | Share of hired workers with salary above GEL 1,200 | 30% |
| G | Total number of hired workers who lose labor income | 200,000 |
| H | TSA Recipients (>18) | 297,094 |
| I | Pension Recipients | 692,431 |
| J | TSA Recipients (<18) | 161,374 |
| Cash Transfer | ||
| K | Cash transfer per adult (GEL) | 150 |
| L | Cash transfer per child (GEL) | 75 |
- [1] ILO Monitor 2nd edition: COVID-19 and the world of work, April 2020.
- [2] NACE 2 classification system, 4-digit level
- [3] Based on the Labor Force Survey, Geostat (2018)
- [4] One has to note that the working environment for frontline health workers has changed and they are exposed to higher health risk and psychological stress, which regardless of relatively stable labor market positions makes them more vulnerable physically and psychologically.
- [5] For example, more women live in poverty as demonstrated by the fact that 55% of social assistance recipients are women.
- [6] Hanna, R. & Olken, B. (2018). Universal basic incomes vs. targeted transfers: anti-poverty programs in developing
- countries. J. Econ. Perspect. 32(4):201–26.
- [7] Klasen, S. & Lange, S. (2016). How narrowly should anti-poverty programs be targeted? Simulation evidence from Bolivia and Indonesia. Discuss. Pap. 213, Courant Res. Cent., Göttingen, Ger.
- [8] Banerjee, AV., Niehaus, P. & Suri T. (2019). Universal basic income in the developing world. Annu. Rev. Econ. 11:961–85.
- [9] Banerjee, AV., Hanna, R., Kreindler, G. & Olken B. (2017). Debunking the stereotype of the lazy welfare recipient: evidence from cash transfer programs. World Bank Res. Obs. 32:155–84
- [10] Ghatak M. & Maniquet F. (2019). Some theoretical aspects of a universal basic income proposal. Annu. Rev. Econ.11.
- [11] For the purposes of this policy option we will concentrate solely on income tax breaks.
- [12] These scenarios do not consider additional potential saving from individuals with an opt-out option utilizing this opportunity.
Disclaimer
This policy brief was first published as an ISET policy note on April 17, 2020 under the title “The Social Impacts of COVID-19 – Case for a Universal Support Scheme?”.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
COVID-19 | The Case of Poland II
Poland in the FREE Network Covid-19 Project (May 26, 2020)
Current Health Situation in Poland
Poland noted its first coronavirus infection in early March 2020. After the initial rapid spread of the disease throughout the country and spike in the total number of registered infections, since early April the infection curve stabilized at a relatively low level (compared to other European countries) of 250-350 new daily cases. The flattening of the curve was a result of drastic health and social restrictions gradually imposed on society (more details below). Since the first reported case, the testing capacity has also been substantially improved, with the number of tests conducted daily increasing from 2K to 15-20K in late April, and holding steady since then.
Figure 1. Number of Covid infections per 100K inhabitants in districts in PL (as of May 25)

Source: own compilation based on data collected by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request. Note: first/last class covers 10% lowest/highest obs., other classes – 20% obs.
Even though Poland has not yet reached an apparent decrease in the number of new daily infections, since the end of April the government introduced a strategy of a slow, four-step re-opening of the economy (more details below). As of 26 May 2020, the total number of Covid infections in Poland approached 22K, with the number of fatalities as high as 1K, and cases reported in all but 7 districts of the country (out of over 300 – see Figure 1). At this point in time, Poland also found itself at the third phase of the lifting of restrictions on economic activity.
Government Health Policies
Lockdown Introduction
The Minister of Health announced a state of epidemic risk in the territory of Poland on March 14 [7], raising it further to a state of epidemic 6 days later [8]. Measures counteracting the epidemic were introduced centrally in Poland by the Minister of Health, and were gradually extended:
- Restriction on the size of public gatherings: since 14.03.2020 limited to 50 [7]; since 25.03.2020 – 2 people (except for families and funerals up to 5 people) [9],
- Ban on all non-essential mobility since 25.03.2020 [9]; since 01.04.2020 limitations on access to public spaces like parks, playgrounds and recreational areas; distance of 2 meters between people in public places; further restrictions for minors [10],
- Bars and restaurants closed and allowed only to provide take-away food since 14.03.2020 [7],
- Childcare institutions, all schools and higher education institutions closed on 12.03.2020, formally online education provided since 25.03.2020 [11, 12],
- Since 15.03.2020 foreigners banned from travelling into Poland (with exceptions), while all Poles arriving from abroad quarantined for 14 days after arrival [7],
- Shopping malls, sports and recreation centers, sports events, cinemas, theatres, etc. closed since 14.03.2020 [7]; since 01.04.2020 – hairdressers, beauty salons, physiotherapy, hotels etc. [10],
- Restrictions on the number of people using public transport since 25.03.2020 [9],
- Since 01.04.2020 restrictions on the number of people in shops and designated shopping hours for 65+ only [10], since 02.04.2020 obligation to wear disposable gloves [10],
- Restrictions in workplaces since 02.04.2020: distance between coworkers, access to protective equipment [10],
- Since 16.03.2020 certain hospitals devoted exclusively to patients with (suspicion of) Covid-19 [13],
- Since 16.04.2020 mandatory covering of mouth and nose in all public places, inside and outside [17].
Gradual Ease of Restrictions
On March 16, 2020, the Minister of Health announced a gradual strategy of lifting the restrictions imposed on social life and economic activity. The plan is divided into four steps. The first stage was implemented on 20.04.2020 [18]:
- increase in the limit of customers in shops,
- public spaces like parks and recreational areas (except playgrounds) open,
- mobility restrictions lifted for minors over 13 y.o.
The second stage was introduced on 04.05.2020 [19, 20, 21]:
- shopping malls open with restrictions on the number of customers, shopping hours for 65+ cancelled,
- museums, libraries, physiotherapy, hotels open,
- sports facilities open with restrictions on the number of users,
- 14-day quarantine for workers from neighbouring countries cancelled,
- since 06.05.2020 some nurseries and kindergartens open.
The third stage started on 18.05.2020 [22, 23]:
- mobility restrictions lifted for minors under 13 y.o.
- hairdressers, beauty salons, outdoor cinemas open, restaurants and bars – with restrictions on the number of customers,
- increase in the number of people using public transport,
- sport trainings allowed with restrictions,
- some classes (practical or individual) in post-secondary schools allowed,
- since 25.05.2020 classes for children from the 1st – 3rd grade in primary schools and final-year graduates allowed,
- since 01.06.2020 consultations with teachers at schools allowed.
The fourth stage is planned for the near future, without a specific date. It involves the opening of cinemas and sports centers.
Government Economic Policies
The government implemented several stages of the so called “Anti-crisis shield”, the first of which came into force on April 1. The overall package includes a number of broad measures to support enterprises and workers for a period of three months and covers both direct financial support as well as provisions regarding financial liquidity for companies [14, 15]. In March the National Bank of Poland decreased interest rates and announced that it will support access to credit through targeted longer-term refinancing operations and if necessary will provide monetary stimulus through large scale open market operations [16].
Short Summary of Measures
Labor market [14]:
- Increased flexibility of employee daily and weekly hours of work;
- Extension of childcare leave for parents with children aged 0-8;
- In case activities affected by revenue reduction (revenue fall by 15% year-to-year or 25% month-to-month):
- Self-employed or employees on non-standard contracts to receive a monthly benefit equivalent to 80% of minimum wage for up to three months;
- Companies to receive support equivalent to 50% of the minimum wage for inactive employees due to the stoppage, provided individual salaries are not reduced by more than 50%;
- Companies to receive support equivalent to up to 40% of average wage for employees whose hours are reduced by 20%;
- Alternative support to employment provided to SMEs (up to 249 employees) in case of revenue loss from the Labour Fund: depending on the level of revenue loss (>30%, >50%, >80%) support to employees expressed as ratio of the Minimum Wage (respectively: 50%, 70% and 90%);
- Relaxation of work and stay permits for foreigners.
Social transfers:
- No specific measures have been implemented but the government is considering:
- a tourism voucher of 1000 PLN paid to employees with a 90% contribution from the government (10% paid by employers); paid to employees on wages below the national average wage;
- additional support to housing benefit for those who become eligible to housing benefits due to the economic slowdown;
Tax breaks [14]:
- 100% of social security contributions to be paid by the government for self-employed and employees employed in micro enterprises (up to 9 employees) and 50% paid by the government in small enterprises (10-49) for three months;
- Tax payments and social security contributions on earnings and profits can be delayed till 01.06.2020;
- Losses from 2020 will be deductible from the 2021 tax base.
Emergency loans, guarantees and support [14]:
- Small-scale loans to small companies;
- Reduced administrative requirements and relaxation of numerous regulatory rules;
- Increased liquidity of firms through channels supported by the Polish Development Fund (PFR):
- extension of de minimis guarantees to SMEs;
- subsidies to SMEs which suffered revenue losses due to the pandemic;
- equities and bond issues to be financed by PFR;
- subsidies to commercial loan interest payments from BGK;
- commercial turnover insurance from Export Credit Insurance Corporation (KUKE);
- Relaxation of regulations related to contracts with public institutions (e.g. related to delays).
Monetary policy [16]:
- On 17.03.2020 NBP lowered the main reference interest rate by 0.5 pp and reduced the rate of obligatory reserves from 3.5% to 0.5%. The main reference rate was lowered further to 0.5% on 08.04.2020.
- NBP announced the readiness to engage in large scale open market operations;
- Targeted longer-term refinancing operations to allow credit refinancing by commercial banks.
References
[1] OECD Health Statistics, https://stats.oecd.org/viewhtml.aspx?datasetcode=HEALTH_REAC&lang=en.
[2] Central Statistical Office in Poland (GUS), bdl.stat.gov.pl.
[3] Supreme Medical Chamber (Naczelna Izba Lekarska), https://nil.org.pl/rejestry/centralny-rejestr-lekarzy/informacje-statystyczne.
[4] Ministry of Health, https://twitter.com/mz_gov_pl?lang=pl.
[5] Warsaw Stock Exchange (Giełda Papierów Wartościowych), https://www.gpw.pl/gpw-statistics.
[6] Central Bank of Poland (Narodowy Bank Polski), https://www.nbp.pl/home.aspx?f=/kursy/kursya.html.
[7] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/433.
[8] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/491.
[9] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/522.
[10] ministry of Health, http://dziennikustaw.gov.pl/DU/2020/566.
[11] Ministry of Science and Higher Education, http://dziennikustaw.gov.pl/DU/2020/405.
[12] Ministry of National Education, http://dziennikustaw.gov.pl/DU/2020/410.
[13] https://www.gov.pl/web/koronawirus/lista-szpitali.
[14] Polish Development Fund (Polski Fundusz Rozwoju Przewodnik Antykryzysowy dla Przedsiębiorców 02.04.2020), https://pfr.pl/tarcza.
[15] Polish Development Fund (Polski Fundusz Rozwoju Przewodnik Antykryzysowy dla Przedsiębiorców 05.05.2020), https://pfr.pl/tarcza.
[16] Central Bank of Poland (Narodowy Bank Polski), https://www.nbp.pl/home.aspx?f=/polityka_pieniezna/dokumenty/komunikaty_rpp.html.
[17] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/673.
[18] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/697.
[19] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/792.
[20] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/780.
[21] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/779.
[22] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/878.
[23] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/871.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Supporting Measures for Belarusian SMEs: the Context of the Covid-19 Pandemic
In the context of the evolving global economic crisis, governments are “competing” with each other in the complexity and scale of measures to support the economy and, in particular, small and medium-sized enterprise (hereafter SMEs). The main goal of these measures is, on the one hand, to prevent a significant increase in unemployment and a consequent social strain, and, on the other hand, to ensure economic recovery driven by the most efficient enterprises.
Belarusian SMEs, which currently employ more than 1.3 million people, usually respond faster and more extensively than the state companies to the downturn in the economy by laying off employees. At the same time, they are also expected to be more sensitive reacting to governmental support policies. In this regard, the policy brief discusses the role and response of SMEs in the period of crises and delineates short- and medium-term measures.
Why are SMEs in the Focus During Economic Downturns?
SMEs often become the focus of state policy in a period of adverse and unstable economic situations and the recent pandemic is not an exception. This special attention can be motivated by the following basic assumptions:
1) SMEs are more flexible and respond faster to both negative and positive trends in the economy (Muller at al., 2018);
2) the activity of SMEs is more labor-intensive compared to large enterprises (Beck et al., 2005; Cravo et al., 2012);
3) a period of economic uncertainty creates new opportunities (new niches, exits of competitors from the market) that can be used by the most proactive SMEs (Cowling et al., 2015).
Based on these assumptions, a large share of SMEs on the one hand makes the economy more resilient in crises and, on the other hand, contributes to the volatility of unemployment. As a result, governments try to support SMEs to prevent a rapid increase in unemployment due to staff cuts and bankruptcy and, simultaneously, to maintain a competitive environment that creates incentives for innovation.
Typically, governments have substantial experience and proven tools to uphold large public and too-big-to-fail private enterprises, while supporting a heterogeneous population of SMEs requires additional study and field tests.
At the same time, the design, the scope, and the coverage of support policies should be introduced having in mind the possible reactions of various types of SMEs to the economic hardship. Indeed, during an economic decline even in the worst hit sectors, businesses and SMEs in particular may react by implementing three basic strategies:
1) reducing costs by firing employees, cutting wages and by increasing productivity;
2) increasing revenue by introducing innovations (product, process, organizational, marketing), diversification, and entering new markets;
3) suspension of activities or liquidation of an enterprise (OECD, 2009).
Definitely, any government aims for the largest possible share of enterprises that pursue the second strategy leading to job creation and significant added value.
Policy Responses in the Period of the Pandemic
Due to the urgency of adoption and the weak predictability of the epidemiological situation, most of the proposed SME-support packages around the world are designed for the short term and are poorly targeted. Based on the study of already announced measures, the OECD (2020) has developed a comprehensive classification and sequence of SME-support measures undertaken by governments:
1. Health measures, and information for SMEs on how to adhere to them;
2. Measures to address liquidity by deferring payments (taxes, social security & pension contribution, rental, utilities);
3. Measures to provide extra and more easily available credit to strengthen SME resilience;
4. Measures to mitigate the consequences of lay-offs by extending possibilities for temporary redundancies and wage subsidies;
5. Structural policies (digitalization, training and education for SMEs, support in finding and entering new markets etc.).
Unfortunately, the government of Belarus has started discussing and implementing some of these measures only partially and in a rather non-specific way. Instead of this, we argue that all the measures should be targeted and adjusted to different sectors. To further expand and analyze our point, BEROC developed and commissioned an express random-sample survey of 100 Belarusian SMEs on April 13-27 in order to elaborate and justify relevant support measures (BEROC, 2020).
Belarusian SMEs in the Pandemic
The financial situation of Belarusian SMEs by sector and their response to the crisis manifested in implementing innovative approaches and new business models are illustrated in Figure 1.
Figure 1. Decrease of revenues and response of SMEs

Note: Area of circles is proportional to the number of SME employees in a sector.
Source: Own elaboration based on the survey.
SMEs operating in hotels, restaurants, catering (HoReCa), education, sport & leisure as well as transportation (the right lower rectangle) are characterized by a substantial decrease of revenues and low adaptability. At the same time SMEs in the communication and IT sector and scientific, technological and consulting sectors demonstrate a high degree of adaptability that may be related to some extend to managerial competencies and human capital in general which is concentrated in these sectors.
As an implication for policy makers and SMEs’ leaders, possible support measures (based on OECD classification) and business strategies are summed up in Table 1.
Table 1. Support measures and business strategies for Belarusian SMEs
| Group | Sectors | Recommended strategy | Relevant Measure (number in the OECD classification) |
| A. Decrease of revenues + slow adaptation | Construction,
wholesale trade & retail manufacturing |
Re-configuring supply chains, entering new niches, business process optimization | 2,3,5 |
| B. Decrease of revenues + active adaptation | Communication & IT
Scientific, technological, consulting services |
Focusing on development of anti-crisis solutions in B2B and B2C segments | 2,4 |
| C. Substantial decrease of revenues + slow adaptation | Transportation
HoReCa Education Leisure, beauty & sport |
«Conservation» or liquidation of a business | 2,3,5 |
| D. Substantial decrease of revenues + active adaptation | Not identified in the survey | Diversification to adjacent market segments | 2,4,5 |
| E. No changes or growth of revenue | Agriculture & Forestry
E-commerce, pharmacy, online services, online games… |
Expansion to new markets while competitors are on quarantine. | 5 |
Source: Own elaboration based on the survey.
The main measure to support SMEs in the short term (items 2-4 in the OECD classification) can be:
- Deferral, reduction or suspension of contributions to the social security fund (groups B, C) – this will save jobs in the short term;
- Wage subsidies that will allow paying minimum wages and keeping staff (groups A, C)
- Rent and utility deferrals or at least payment in arrears – for groups A, C – combined with the support of building owners. This will significantly reduce costs in the face of falling revenues instead of reducing labor costs;
- Loan holidays and preferential conditions for SMEs (group D). This will provide liquidity for enterprises that according to banks’estimates will be able to develop in the medium term;
- Temporary repeal of fines for late payment of taxes and contribution to the social security fund (groups A-D).
As for the medium-term measures, the most relevant ones are as follows:
- Expanding the coverage and improving the quality of business education (including digitalization of business) by means of providing vouchers and/or grants;
- Subsidies to unemployed people for starting up a business combined with basic training on entrepreneurship;
- Export support by developing infrastructure for certification and international marketing as well as providing export loans (Marozau et. al., 2020).
Conclusion
The Belarusian government is substantially restricted in terms of financial resources, fiscal and external debt opportunities to extensively support businesses suffering from the economic crisis. Therefore, formal and economically justified criteria for selecting sectors, as well as individual businesses and individual entrepreneurs should be developed. Meanwhile, the beneficiaries of the state support should not be the most affected businesses, but rather the most forward-looking ones. This so-called “picking winners” approach (Gonzalez-Pernia et al., 2018) would conduce to faster economic recovery and job creation driven by the private sector and, particularly, by SMEs. This is probably the main argument in favor of supporting small and medium-sized businesses in the crisis.
References
- Beck, T., Demirguc-Kunt, A., Levine, R. (2005). “SMEs, Growth and Poverty: Cross- country evidence.” Journal of Economic Growth, 10(3), 199-229.
- BEROC. (2020). “SME Survey Results”, Access mode http://covideconomy.by/business. Access date: May 19, 2020).
- Cowling, M., Liu, W., Ledger, A., & Zhang, N. (2015). “What really happens to small and medium-sized enterprises in a global economic recession? UK evidence on sales and job dynamics.” International Small Business Journal, 33(5), 488-513.
- Cravo, T.A., Gourlay, A., Becker, B. (2012). “SMEs and Regional Economic Growth in Brazil.” Small Business Economics, 38 (2), 217-230.
- González-Pernía, J. L., Guerrero, M., Jung, A., & Pena-Legazkue. (2018). “Economic recession shake-out and entrepreneurship: Evidence from Spain.” BRQ Business Research Quarterly, 21(3), 153-167.
- Marozau, R., Akulava, M., Aginskaya, H., (2020). “Measures to support small and medium-sized businesses in Belarus in the context of the pandemic and global recession.” BEROC Policy Paper Series, PP no.89.
- Muller, P., Mattes, A., Klitou, D., Lonkeu, O., Ramada, P., Ruiz, F.A., Devnani, S., Farrenkopf, J., Makowska, A., Mankovska, N., Robonn, N., Steigertahl, I. (2018). Annual report on European SMEs 2017/2018. The 10th Anniversary of the Small Business Act. European Commission.
- OECD. (2020). “COVID-19: SME Policy Responses.” OECD Centre for Entrepreneurship, SMEs, Regions and Cities (CFE). Access mode https://read.oecd-ilibrary.org/view/?ref=119_119680-di6h3qgi4x&title=Covid-19_SME_Policy_Responses. Access date: May 19, 2020.
- OECD. (2009). “The Impact of the Global Crisis on SME and Entrepreneurship Financing and Policy Responses.” OECD – Centre for Entrepreneurship, SMEs and Local Development, Paris.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
COVID-19 | The Case of Georgia
Introduction
Georgia has close to 4 million inhabitants. It borders Russia, Azerbaijan, Armenia and Turkey, which are also its main trading partners. The capital and largest city is Tbilisi with about 1,5 million inhabitants. Agriculture and the tourism sector dominate the local economy.
Georgia reported its first case of Covid-19 on February 27, 2020 and its first deaths on April 6, 2020. The government reacted quickly, banning direct flights from China in late January 2020 and imposing severe travel restrictions even within the country in March 2020. Schools and universities were closed on March 11, 2020. The government banned all larger public gatherings on March 21, 2020, the same day when the country declared the state of emergency. The four major cities of Georgia – Tbilisi, Batumi, Kutaisi and Rustavi – were put under lockdown on April 15, 2020.
As of May 8, 2020, Georgia reported a total of 9 fatalities, suggesting that the virus has quite successfully been contained so far. A breakdown of the healthcare system seems unlikely at the moment. Economically, the situation is more heterogenous. Georgia’s public finances are in a tolerable enough shape to handle a crisis. The public debt to GDP ratio is not very high (44.9% in 2018), and the government budget deficit is also below 3% of GDP. Georgia’s financial system has been praised as one of the strongest among in the ECA region. However, annual inflation in January-February was 6.4%, which is significantly higher than the target level of 3%. Georgia is facing uncertainties in terms of inflationary expectations, and this limits the National Bank of Georgia’s (NBG) ability to stimulate the economy under the current circumstances. Most probably, NBG will not cut the policy rate to avoid provoking further currency depreciation and stoking inflationary expectation even further. Moreover, a major weakness in the Georgian economic system lies in its lack of a broad social safety net infrastructure, which could help support afflicted groups during downturns. Finally, another risk is the substantial informal sector: workers in these sectors are hard to reach via conventional policy measures.
Below, we outline how the Georgian economy has been affected by Covid-19 and what the policy responses have been so far. We will also discuss several economic scenarios and explain which further policy options are thinkable.
How Does the Covid-19 Crisis Affect the Georgian Economy?
Demand Side Effects
- A decline in domestic consumption resulting from behavioural and policy changes is to be expected on the demand side – i.e. people staying home as a precaution or because they are required to. In addition, currency depreciation and possible price spikes (due to herding behaviours and potential disruptions in supply chains) are also expected to have a negative effect on consumption and investment.
Household consumption accounts for 66.7% of the Georgian GDP (Geostat, 2018). A significant reduction in household consumption (e.g. spending on transportation, clothing, electronics, and domestic services) would therefore result in an overall slowdown of GDP growth. A slowing of internal demand would hit people working in the informal sector particularly hard; namely, those without a regular salary (e.g. temporary workers, taxi drivers, and other self-employed service sector workers) and small and micro business-owners. Their situation is worsened still because the government’s fiscal stimulus and assistance is unlikely to reach them directly. They are also not expected to benefit from the extra liquidity injected into the financial system, as they will not qualify for bank loans to cover temporary income losses. Another vulnerable group are the formal sector workers employed in companies that face a dramatic decline in their usual economic activities (restaurants, hotels, the entertainment industry, transport, etc.). These companies are likely to put their workers on unpaid leave or simply fire them. Moreover, the slump in household demand will also be made worse by the fact that most families are likely to have limited savings and, therefore, their capacity to smooth consumption is limited. Hence, the crisis may cause a significant drop in well-being and, possibly, further deterioration in individuals’ physical and mental health, alongside the direct impacts of Covid-19
- A decline in domestic investment because uncertainty and deteriorating business sentiments will stall business investment decisions. Expectations of a global recession could become self-fulfilling if ‘business-as-usual’ does not resume in the next few months. If companies expect a slowdown in demand, they will also delay investment, and GDP will decline further. Investment (gross fixed capital formation) accounts for approximately 28% of Georgia’s GDP. Thus, the Georgian government has announced capital spending to combat the expected drop in private investment.
- A decline in tourism and related business seems inevitable as tourism arrivals and receipts are expected to decrease sharply as a result of the numerous travel bans, and due to precautionary behavior. According to our preliminary calculations, the Georgian economy lost between 3-9% of potential tourism revenue in February. Since the tourism sector accounts for 6% of Georgia’s GDP (GNTA 2018), a direct hit to the industry will substantially impact GDP. In table 1, we work out GDP losses associated with the following scenarios:
Table 1: Net effect of the coronavirus crisis on tourism in Georgia

Note: after each period indicated in the scenarios, tourism is assumed to immediately recover to 2019 levels.
Source: Geostat, NBG, authors’ calculations.
- The spillover effect on other sectors: a drop in demand for goods and services in the region, in China, the EU, and the US – will affect the overall economy via trade and production linkages.
While it is difficult to predict how Georgia’s economy will react to a global shock of such magnitude, some preliminary estimations may already be made. Georgia’s growth rate over the last 20 years correlates notably to several neighboring economies. One of the greatest correlations is, unsurprisingly, with Russian economic growth. Russia’s growth is also highly correlated with other countries, reflecting global economic linkages. These correlations are reported in table 2 below:
Table 2: Correlations of growth rates
| Table 2 | Georgia | Russia | Armenia | Turkey | China | Kazakhstan | Italy | Germany | France | US | Israel | Ukraine |
| Georgia | 1.00 | 0.87 | 0.88 | 0.66 | 0.58 | 0.81 | 0.67 | 0.74 | 0.85 | 0.69 | 0.77 | 0.73 |
| Russia | 1.00 | 0.90 | 0.60 | 0.73 | 0.83 | 0.64 | 0.67 | 0.82 | 0.63 | 0.79 | 0.91 |
Source: World Bank, authors’ calculations.
In order to explore how a slowdown across major world economies will affect Georgia, we have followed three economic scenarios relating to major world economies, as reported by Orlik et al. (2020). The numbers reflect growth rate changes relative to the baseline (no virus outbreak).
Table 3: Coronavirus effect on growth rates.
| Table 3. Coronavirus effect on growth rates | Real GDP annual growth change in 2020 compared to the baseline scenario, pp | Real GDP growth, % in 2020, assuming a 5% baseline | |||
| Russia | Germany | US | Georgia | Georgia | |
| Scenario A: Outbreak causes localized disruption | -0.9 | -1.2 | -0.2 | -1.09 | 3.91 |
| Scenario B: Widespread contagion | -3 | -2.8 | -1.3 | -3.09 | 1.91 |
| Scenario C: Global pandemic | -4.8 | -3.6 | -2.4 | -4.55 | 0.45 |
Source: Orlik et al. (2020); authors’ calculations.
- A decline in trade is likely and it is possible to find certain similarities between the current situation and the economic slowdown in the Eastern Europe and Central (EECA) region in 2014-2017, caused by a drop in oil prices and global appreciation of the US dollar. The latter resulted in a sharp decline of external demand, falling commodity prices and regional currency crises, which equally affected the Georgian economy. The country’s goods exports fell by 23%, while imports contracted by 15% in 2015. Trade was only restored to the 2014 level by 2018. While, the forthcoming crisis is expected to not only have stronger negative impacts on external demand, but also disruptions in the production value chains, affecting Georgia’s trade in more severe ways. Trade of all commodities, except food and medicine, is projected to decline, depending on the duration of the shock.
- A decline in Foreign Direct Investment (FDI) is to be expected since foreign investors prefer to invest in safe assets. Additionally, currency depreciation expectations will negatively affect FDI. The FDI in Georgia amounted to 1,267.7 mln. USD in 2019 (7.1% of GDP).
- A decline in remittance inflows seems likely: since all countries will suffer economically in the aftermath of the health and oil price crises, we expect significant slowdown in remittance inflows from the rest of the word. The remittances decline will hit Georgia particularly hard as it is among the top receiver countries of foreign transfers. For instance, in 2019, money transfer inflows accounted for 9.8% of GDP. Various scenarios for just how much Georgia is set to lose in monetary inflows is presented in table 4 below:
| Table 4. Net change in money transfers inflow in 2020 due to coronavirus (Mln. USD) | ||
| Scenario 1: 10% decrease of net money transfers in the remaining months of the year (March-December) | Scenario 2: 30% decrease of net money transfers in the remaining months of the year (March-December) | Scenario 3: 50% decrease of net money transfers in the remaining months of the year (March-December) |
| -114 | -372 | -629 |
| Net change in consumption spending due to money transfers decline* | ||
| -570 | -1,857 | – 3,146 |
| Net change as a share of household total real consumption spending** | ||
| +0.3% | -2.6% | -5.5% |
* $1 of transfers is assumed to become $0.8 equivalent of consumption spending.
** USD/GEL exchange rate is assumed to equal to the official exchange rate as for March 20th (3.1818) in the remaining months of the year (March-December). Inflation is assumed to be 6% in 2020.
Source: Geostat, NBG, authors’ calculations.
Supply Side Effects
- Production disruptions may occur on the supply side. Domestic production suffers as a result of forced business closures and the inability of workers to get to work, as well as disruptions to trade and business as a result of border closures, travel bans, and other restrictions on the movement of goods, people, and capital (in the PRC as a whole fell to 50%–60% of normal levels but is now normalizing, after the introduction of extremely restrictive measures that – so far – no country in the West has been able/willing to mimic. However, in the absence of such restrictions, the crisis may be prolonged, and production might be hard to restart quickly). The overall impact on production may be mitigated by the fact that in some sectors (particularly in manufacturing) production can be ramped up in later periods to compensate for lower production (providing closures do not last too long).
- Long-term economic effects need to be taken into account. Covid-19 will impact health via mortality and morbidity, and through changes in (and the diversion of) healthcare expenditure.
Currency Depreciation
The expected decline of tourist inflows, remittances, and exports as a result of reduced foreign demand from Georgia’s trading partners and low world oil prices have already affected the lari exchange rate (mostly through expectation channels). On the other hand, due to restrictions on air travel, the outflow of currency from Georgia to foreign countries will be reduced (the import of tourism services will be lower), which will have a positive effect on the exchange rate. Another positive factor may be that Georgia’s reliance on remittances from oil-exporting countries (like the Russian Federation) has been significantly reduced in recent years.
What Has Been Done to Address the Covid-19 Crisis?
The Government of Georgia timely started applying measures to address dramatic impacts on various market participants:
Businesses
- Restructuring loans for businesses affected by the crisis;
- Companies that operate in the tourism industry: hotels and restaurants, travel agencies, passenger transportation companies, site-seeing companies, arts and sports event organizers, etc., will have their property and personal income taxes deferred by the Georgian government for four months;
- Doubling the volume of VAT refunds to companies, with the aim of supplying them with working capital;
- Designing a state program to co-finance interest payments on bank loans by hotels with 4-50 rooms, throughout the country, for the next six months.
Workers
- Loan payment deferrals for three months;
- Personal income taxes deferred for employees in the tourism industry.
The Health Care System
- No new measures are planned at this point.
The Financial System
- Easing lending restrictions for commercial banks;
- NBG has not cut policy rates and is unlikely to do so given the risks of inflation.
Other Measures
- Boosting capital expenditure (CapEx) projects with the aim of providing additional economic incentives;
- Governmental price fixing for specific products (rice, pasta, sunflower oil, flour, sugar, wheat, buckwheat, beans, milk powder and its products) by subsidizing corresponding businesses.
Will the Current Measures Be Sufficient?
Given the rapidly changing scope of the crisis, the short answer is simple – probably not. As the forecast seems pessimistic, it is the role of the fiscal stimulus and, where possible, the monetary policy to help soften the economic shock.
It is evident that the measures adopted by the government as well as private commercial banks in Georgia will not be able to directly reach a sizeable group of the population affected by the shock – i.e. those unemployed due to Covid-19; those working in the informal sector; people with low income; or households that are very reliant on remittances transfers. It is important for the government to connect with these groups quickly, not only for humanitarian reasons, but also in the interest of a broader development agenda. In case of relatively prolonged quarantine sizable part of the population will no longer be able to support themselves and their families in coming months.
What More Can Be Done?
We broadly outline the additional monetary and fiscal policy measures that may be considered:
More Forceful Fiscal Intervention:
As previously mentioned, Georgia’s systemic weakness lies in its lack of a broad social safety net infrastructure, which could help target and support afflicted groups during downturns. An unemployment benefits system, which in other countries acts as an “automatic stabilizer” and reduces and mitigates the effect of economic downturns, simply does not exist in Georgia. Yet even with an unemployment benefits system in place, the sizeable informal economy would prevent such a system from effectively easing labor market tensions. In the current situation, the government should attempt to provide cash relief for workers in the informal sector, for the low-income self-employed, and for independent contractors. These groups of workers are the most vulnerable to income flow reduction during the crisis, furthermore, they are unlikely to have access to sick leave benefits or to take advantage from cheaper bank credit.
Based on the experience of other countries, the government perhaps should consider the following measures in addition to current measures:
- Providing low interest emergency loan/cash advances to affected adults, or direct cash payments to affected households, in particular households with the elderly and children. These measures are valuable as they can quickly reach afflicted groups. Unfortunately, this solution is not well-targeted and risks wasting government funds on those who are not disadvantaged.
- Simply providing “helicopter money”, or cash transfers to households below a certain income threshold (similar measures are being considered in the US) may be an option, but this measure is subject to the same concerns as above. However, the advantage is that cash transfers allow households to optimize their expenditure and do not distort consumption choices.
- Another form of wide-reaching support could be state subsidies to help support utility payments for a limited time. These measures, equally, are not well-targeted, nevertheless there may be methods to direct them towards the households which need them the most.
- Measures to encourage companies to not cut employment in the months following the crisis: following the example of other countries, Georgia may support salary payments for companies, on the condition that they do not reduce employment or force workers to take unpaid leave.
Naturally, none of the proposed measures are perfect as they cannot specifically target those most affected by the crisis, yet they may act as a short-term second-best solution. As these examples show, Georgia should consider to develop a targeted social safety net system in the future. Such a system can make the country more resilient in the face of future crises and unexpected emergencies.
Monetary Policy
While other countries push for fiscal stimulus and monetary expansion, Georgia is facing uncertainties in terms of inflationary expectations. As discussed, this limits NBG’s ability to stimulate the economy under the current circumstances. Annual inflation in January-February was at 6.4%, significantly higher than the 3% target. Going forward, a sharp decline in aggregate demand would reduce the pressure on inflation, while a depreciating nominal effective exchange rate will exert upward pressure. Therefore, the possibility to reduce the monetary policy rate depends on which effect will dominate in the future. In the meantime, NBG has approached the IMF to increase access to funding under its Extended Fund Facility program (NBG). Alongside the additional funds from other international donors, this will positively affect the economy, strengthen the nominal effective exchange rate and, consequently, curb inflation.
In addition to the measures already announced, NBG has the option of decreasing the minimum reserve requirements for deposits attracted in a foreign currency. This will stimulate FX lending and economic activity, without creating depreciation or inflationary expectations.
Overall, the Georgian government responded very timely and efficiently to contain the virus outbreak, earning well-deserved plaudits from the international community and approval from the general public. However, as the scope of the crisis continues to change rapidly, additional measures might soon be needed. As the economic landscape becomes more uncertain, the government needs to ensure that emergency economic stimulus measures directly reach the people most affected by the crisis.
Disclaimer
This policy brief was first published as an ISET policy note on March 25, 2020 under the title “The Economic Response to COVID-19: How is Georgia Handling the Challenge?“. This brief is an adaption of the original note and is published with the consent of the authors.
References
CIA World Fact Book, 2020. “Georgia”.
The Guardian, 2020. “How UK government could support people as coronavirus spreads”.
Imeson, Michael, 2019. “Georgian banks gather rewards for resilience”. The Banker.
Lomsadze, Giorgi, 2020. “Georgia gets rare plaudits for coronavirus response“. Eurasianet.
Migration Policy Institute, 2020. “Global Remittances Guide”.
Orlik, Tom; Jamie Rush; Maeva Cousin and Jinshan Hong, 2020. “Coronavirus Could Cost the Global Economy $2.7 Trillion. Here’s How”. Bloomberg.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Safety of Older People During the Covid-19 Pandemic: Co-Residence of People Aged 65+ in Poland Compared to Other European Countries
Bearing in mind that the estimated fatality rates related to Covid-19 infections are substantially higher among older people, in this Policy Paper we focus on the demographic composition of households of people aged 65+ as one of the social risk factors that influence the consequences of the pandemic. In light of plans of easing isolation restrictions and a gradual return to higher economic activity, a key challenge for the coming weeks is to ensure the safety of those most at risk. Although lifting the lockdown mainly affects the lives of the working population and children, attention should be paid to the channels that could enhance transmission of the coronavirus among older people. This includes the prevalence of co-residence with those who will get back to their workplaces or schools once they are open again. Compared to other European countries, Poland has the highest rates of people aged 65+ sharing their households with younger adults and children with nearly 40% living together with people aged up to 50 years old (excluding partners). On the other hand, Nordic countries, the Netherlands, Belgium and Germany report far lower rates of co-residence among the older population. In these countries however, older people commonly reside in formal care facilities, which, in turn, have proved vulnerable to outbreaks of infections. This emphasizes that each country has to carefully determine its own strategy on the way to recovery. Among other factors, the pace at which restrictions on social distancing are lifted should take into account the prevalence of co-residence among the older population.
Introduction
According to the WHO, at the early stage of the Covid-19 epidemic, the fatality rate among coronavirus-infected people was estimated at about 3-4% (WHO 2020a), although estimates based on the data from European countries suggest that the rate is lower and is closer to 1.5% (ECDC 2020). The rate is quite varied from country to country; it also fluctuates over time. To a large extent, the figure depends on the number of tests conducted and, consequently, the reliability of information on the number of people infected (Roser et al. 2020). Nevertheless, both the risk of experiencing serious symptoms of the coronavirus infection and the risk of death from complications arising from the disease increase significantly with the age of the infected person. Furthermore, the risk is definitely higher for the patients with underlying conditions, in particular cardiovascular diseases, diabetes, or hypertension (Emami et al. 2020). The highest risk is observed among older persons, with the fatality rate of people infected fluctuating from 1.8%-3.5% in the 60-69 cohort, to 13.0%-20.2% in the 80+ cohort (Roser et al. 2020). Therefore, a major challenge in the area of health and socio-economic policy measures in the coming months is to keep the older population safe and contain the spread of coronavirus in that population.
This Policy Paper presents an analysis of the housing situation of people aged 65+ in Europe. Co-residence may be one of the relevant social risk factors that determine the probability of being infected with viruses which, like SARS-Cov-2, are spread through droplet transmission. As shown by research on intra-household transmission at the early stages of the epidemic in China, the majority (75%-85%) of clusters (group illnesses) were observed within households (WHO 2020b). Depending on the data, the coronavirus secondary attack rate within households is estimated at 7.6%-15.0% (Bi et al. 2020; KCDC 2020b), and from this perspective it is important to note that the incidence rate is the highest in the 20-29 age group, with most of them showing no symptoms of the disease while being able to infect others (KCDC 2020a).
Given the limited scope of labor market activity in the 65+ population, compliance with the self-isolation regime by this group will not interfere much with the gradual easing of socio-economic restrictions. Things look different among younger people due to their work or study, and among the youngest members of the population due to their school or pre-school attendance. In line with the regulations introducing the state of epidemic in Poland, since March 23rd, 2020, many workplaces have been operating on a remote basis, with their labor force doing work from home, and many companies and organizations having been closed. Similarly, the nurseries, kindergartens, schools and universities have been closed since the 16th of March this year. However, the government has already announced a plan to ease some of the restrictions to pave the way for a phased return to more intensive social contacts and economic activity (Council of Ministers 2020). Because of the shortcomings of distance learning and serious inequalities in access to education in this system (Myck et al. 2020), and considering the adverse impact of closed schools and kindergartens on the working parents, it seems imperative to resume the operation of these facilities as soon as possible.
A key challenge for the coming weeks will therefore be to reconcile the socio-economic benefits of lifting the lockdown with the risk of health implications arising from less stringent social distancing restrictions. Those implications may be particularly severe for older people. Thus, this Policy Paper discusses structural determinants of the well-being of older people, with a focus on the housing situation in European societies and the rate of co-residence with the younger population. The analyses outline the status in Poland in comparison to other European countries, pointing to a great diversity of health risks for older people. One factor is the difference in the prevalence of co-residence between the older and younger populace, and another is the prevalence of formalized care facilities. Next to disease statistics, these differences should be taken into account in any decisions on lockdown easing or a detailed design of policy measures.
In Poland, the percentage of people aged 65+ in co-residence with other members of the household aged 50 or below (excluding a spouse or partner) is 37.4% for the female population and 38.6% for the male population, i.e. the highest in Europe. In Poland, 12.0% of people aged 65+ share a household with school-age children (aged 7-18), and 7.7% live together with children aged 0-6. Co-residence with minors usually means, for obvious reasons, that the adult parents of the minors live under the same roof as well. However, Poland also reports one of the highest percentages of co-residence with other adults without minors. For example, 7.6% of people aged 65+ live in one household with people aged 19-30, and 17.3% share a household with adults aged 31-50 who are not their spouses or partners. It is worth noting, however, that in the European countries considered here a high percentage of co-residence is negatively correlated with the prevalence of collective dwelling facilities that deliver formalized care for older persons. In Poland, the supply of such institutions – whether public or private – has been very limited, with only 1.6% of people aged 80+ living in those facilities. In contrast, in Belgium, almost every fourth person of that age is a resident of such a facility. When it comes to the pandemic, it must be underscored that although in such institutions the interactions with younger people can be quite easily limited, the experience of many countries has shown that they have been quite vulnerable to coronavirus clusters and epidemic outbreaks.
Considering that Poland reports the highest percentage of co-residence among people aged 65+, particular attention should be paid to the challenges for health and socio-economic policy measures introduced in Poland to manage the intensity of social contacts during the pandemic. This, in particular, applies to the regulations on students returning to schools and the easing of social distancing rules for students and working adults. Therefore, in countries such as Poland, the restoration of frequent social contacts, which is necessary, inter alia, to put the economy back on track, will have to be accompanied with adequate safeguards for those who are most heavily exposed to negative health effects of Covid-19.
The first section of this Policy Paper reviews co-residence percentage data for the 65+ population, based on data for Europe (the European Union member states and Norway, Switzerland and the United Kingdom, for the remaining European countries the data is not available), from the 2017 European Union Statistics on Income and Living Conditions study (EU-SILC.) The second section presents data on older people living in long-term care facilities in a number of European countries, collected in recent years by the OECD.
1. Older People in Co-Residence With Other Members of the Household
In the analytical discussions below, the terms “co-residence” or “shared household” refer to a situation where persons aged 65+ live in one household with adults who are not their spouse or a partner, or with children under 19 years of age. In Poland, the percentage of households shared by people aged 65+ and children aged 18 or younger is one of the highest in Europe. Of all the older people in Poland that live in a household setting on a permanent basis (i.e. excluding those living in formalized care facilities), as many as 16.9% of women and 16.6% of men aged 65+ share a household with persons under 19 years of age (cf. Figure 1). With the exception of Slovakia and Romania, other countries report a much lower rate. In countries such as Norway, Sweden, Denmark, or the Netherlands, the rate is between 0.1% and 0.6% for women, and between 0.5% and 1.2% for men (65+ population).
Figure 1. Population aged 65+ in co-residence with persons other than their spouse/partner, by the age of the youngest member of the household
a) Male

b) Female

Source: Authors’ compilation based on the 2017 EU-SILC data.
Nota Bene: Share of 65+ population not living in formalized care facilities.
In Poland, approximately 12% of women and men aged 65+ share a household with students aged 7-18. In other words, more than 460k women and 280k men aged 65+ in Poland have direct, daily interactions with students attending schools (Table 1). In addition, 13.9% of women and 14.7% of men aged 65+ (530k and 360k, respectively) share a household with persons aged 19-30, who – according to research findings from other countries – demonstrate the highest incidence of coronavirus disease (KCDC 2020a). On top of that, these proportions are significantly higher in rural areas, and over 40% of the 65+ population in Poland live in rural areas. Compared to other countries in Europe, it is especially in the rural areas that Poland reports a significantly higher percentage of older people in co-residence with younger people (Figure 2). For example, while in Poland 19.0% share a household with children aged 7-18, and 21.1% with people aged 19-30, in Sweden in the 65+ population in rural areas those percentages are 0.4% and 1.0%, respectively, and in Belgium 1.9% and 1.5%. In urban areas the disparities in the demographic structure of households between Poland and other European countries are less pronounced, but still the share of the 65+ population in co-residence with younger people is among the highest in Europe; with 7.2% sharing a household with school children and 9.5% with adults aged 19-30. In Sweden these percentages are 0.7% and 1.7%, respectively, and in Belgium 1.2% and 3.8%.
Table 1: Population aged 65+ in Poland in co-residence with other members of the household (other than a partner/spouse).
| Urban | Rural | Total | |||||
| Male | Female | Male | Female | Male | Female | Total | |
| Population aged 65+ (in thousands) | 1 435 | 2 268 | 1 007 | 1 508 | 2 441 | 3 776 | 6 218 |
| People in co-residence with a person aged (in thousands): | |||||||
| – 0-6 | 82 | 107 | 117 | 175 | 199 | 282 | 481 |
| – 7-18 | 91 | 174 | 190 | 288 | 281 | 462 | 743 |
| – 19-30 | 142 | 210 | 216 | 315 | 359 | 525 | 883 |
| – 31-50 | 353 | 546 | 446 | 681 | 799 | 1227 | 2026 |
| People in co-residence with a person aged (in %): | |||||||
| – 0-6 | 5.7% | 4.7% | 11.6% | 11.6% | 8.1% | 7.5% | 7.7% |
| – 7-18 | 6.4% | 7.7% | 18.9% | 19.1% | 11.5% | 12.2% | 12.0% |
| – 19-30 | 9.9% | 9.2% | 21.5% | 20.9% | 14.7% | 13.9% | 14.2% |
Source: Authors’ compilation based on the 2017 EU-SILC data.
Nota Bene: Share of 65+ population not living in formalized care facilities.
Figure 2. Population aged 65+ in co-residence with other members of the household (other than a partner/spouse), by age of the other members of the household.
- Urban

Rural

Source: Authors’ compilation based on the 2017 EU-SILC data. Nota Bene: Countries: SE – Sweden, BE – Belgium, IT – Italy, HU – Hungary, ES – Spain, SK – Slovakia, PL – Poland. Share of 65+ population not living in formalized care facilities.
2. Residents of Formalized Care Facilities for Older Persons
Households where people aged 65+ live under one roof with younger people (usually they are all family members) reflect the financial status of the family on the one hand, but on the other they offer care to those who might need it to due to their age or health status. In that respect, unlike many other countries in Europe, Poland has a very low share of older people who, due to barriers to independent living, decide to relocate to a formalized care facility or a similar setting. In 2017, less than 1% of the 65+ population in Poland lived in formalized care facilities; and for the 80+ population the share was only slightly higher and reached 1.6% (Figure 3). One reason is the low number of vacancies in such facilities: in 2017 in Poland there were, statistically, 12 beds per 1000 inhabitants aged 65+. For comparison, in Nordic countries (Denmark, Finland, Norway, Sweden) more than 12% of the 80+ population live in formalized care facilities for older people; in Luxemburg and Switzerland the rate is close to 16%, and in Belgium it is 24%. These countries also report a much higher availability: from 50 beds per 1000 people aged 65+ in Denmark to over 80 beds in Luxembourg. The share of older people living in formalized care facilities is also relatively high in countries such as Slovenia (12.6% for the 80+ population) or Estonia (9.9%).
Figure 3. Long-term care facilities – resources and utilization.

Source: Authors’ compilation based on the OECD data.
Nota Bene: According to the latest 2017 data available, with the exception of: Spain, Portugal – 2018 data; the Netherlands, Slovenia – 2016 data; Belgium, Denmark – 2014 data. The figure includes the European countries for which the data has been available. For Italy, only the data on the number of beds has been available, and for Portugal, only the data on the number of facility residents.
The isolation regime introduced to restrict the frequency of visits, side by side with a system of appropriate checks and controls for the staff, are relatively simple ways to reduce the risk of external coronavirus infection in formalized care facilities. Yet, as we have learnt from numerous examples in Poland and internationally, infection transmission between the residents or between the residents and the staff has been a frequent source of infection clusters and outbreaks. For example, in South Korea, even more than 30% of new coronavirus cases could be the result of transmission between hospital patients or nursing home residents (KCDC 2020a). In connection with a coronavirus outbreak in a formalized care facility in the USA, more than half of the residents had to be hospitalized and, eventually, 33.4% died (McMichael 2020). It seems that keeping the residents of formalized care facilities safe from the infection should be a priority in an epidemic control policy. However, the pace at which social distancing restrictions are lifted so that students can get back to schools and the lockdown in public spaces can be removed, should not have a vital impact on the safety of those living in the facilities, in contrast to the situation of older persons who share a household with younger persons.
Summary
The well-being of the groups with the biggest exposure to the grave outcomes of coronavirus infection deserves special attention when lifting the lockdown introduced in connection with COVID-19 pandemic. In this context, the housing situation of older people and the nature of the underlying social contacts are among important aspects to take into account in developing detailed regulations. As outlined in this Policy Paper, different countries in Europe report different status in that respect. Of all the countries in Europe, Poland has the highest share of the 65+ population co-residing with younger people. On the other hand, less than 1% of the 65+ population live in formalized care facilities. In Europe, the lowest share of co-residence is reported in the Nordic countries, the Netherlands, Germany and Belgium. At the same time, the share of the 65+ population residing in formalized care facilities in those countries fluctuates from 4% to 8%, reaching over 10% in the 80+ population.
In formalized care facilities, lockdown lifting will not have material impact on the safety of the residents or the risk of coronavirus transmission. In contrast, the households where older people live side by side with the younger populace may actually represent a significant risk factor in terms of the spread of the epidemic and infection transmission to those who are most heavily exposed to the grave complications of Covid-19.
In general in Poland, 37.4% of women and 38.6% of men aged 65+ share a household with people under 50 other than their spouse or partner. This is the highest rate of co-residence with younger people for this age cohort in Europe. In Denmark, this percentage is 1.3% for women and 3.3% for men. Even in Spain it is much less common for people aged 65+ to share a household with younger family members (the rates being 28.0% for women and 26.6% for men, respectively). Additionally, in Poland, especially in rural areas, many people aged 65+ live under one roof with school-age children (7-18 years of age: 19.1% of women and 18.9% of men in this age group, respectively); and even more (20.9% of women and 21.5% of men) share a household with adults aged 19-30, which is the age group where coronavirus infection is the most prevalent (KCDC 2020a).
In view of major discrepancies in the demographic structure of households between countries, it seems necessary to differentiate the social distancing rules and the pace with which these rules are to be eased, if one of the objectives is to protect the people exposed to the most serious consequences of coronavirus infection. Especially in such countries as Poland, the policy of gradual opening of schools and other institutions and phased recovery of economic activity should be accompanied by a broad-based communication campaign on how to protect the most vulnerable household members. It seems advisable that the campaign be conducted both in the mass media and in schools, workplaces, and public spaces.
References
- Bi, Q., Y., Wu, S., Mei, Ch,., Ye, X., Zou, Z., Zhang, X., Liu, L.,Wei, S., Truelove, T., Zhang, W., Gao, C., Cheng, X., Tang, X., Wu, Y., Wu, B., Sun, S., Huang, Y., Sun, J., Zhang, T., Ma, J., Lessler, T., Feng (2020). “Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts.” medRxiv 2020.03.03.20028423
- ECDC – European Centre for Disease Prevention and Control (2020). “Coronavirus disease 2019 (COVID-19) in the EU/EEA and the UK – eighth update.”
- Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A. (2020).”Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis.” Arch Acad Emerg Med. 8(1): e35.
- KCDC – Korea Centers for Disease Control & Prevention (2020a). “The updates on COVID-19 in Korea.”
- KCDC (2020b). “Coronavirus Disease-19: Summary of 2,370 Contact Investigations of the First 30 Cases in the Republic of Korea.” Osong Public Health Res Perspect. 2020 Apr; 11(2): 81–84.
- McMichael T., Currie D., Clark S., Pogosjans S., Kay M., Schwartz N., Lewis J., Baer A., Kawakami V., Lukoff M., Ferro J., Brostrom-Smith C., Rea T., Sayre M., Riedo F., Russell D., Hiatt B., Montgomery P., Rao A., Chow E., Tobolowsky F., Hughes M., Bardossy A., Oakley L., Jacobs J., Stone N., Reddy S., Jernigan J., Honein M., Clark T., Duchin J. (2020). “Epidemiology of Covid-19 in a Long-Term Care Facility in King County”, Washington. N Engl J Med. 2020 Mar 27.
- Myck, M., Oczkowska, M, Trzciński, K. (2020). “School lockdown: distance learning environment during the COVID-19 outbreak.” CenEA Commentary Paper.
- Oke, J., Heneghan, C. (2020). “Global Covid-19 Case Fatality Rates“.
- Rada Ministrów (2020). “Rozporządzenie Rady Ministrów z dnia 10 kwietnia 2020 r. w sprawie ustanowienia określonych ograniczeń, nakazów i zakazów w związku z wystąpieniem stanu epidemii” [Regulation of the Council of Ministers of 10 April 2020 on establishing certain restrictions, orders and prohibitions in connection with the introduction of the state of the epidemic].
- Roser, M., Ritchie, H., Ortiz-Ospina, E. (2020). “Coronavirus Disease (COVID-19) – Statistics and Research“.
- WHO (2020a). “Coronavirus disease 2019 (COVID-19)“. Situation Report – 46.
- WHO (2020b). “Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)“.
Disclaimer
This Policy Paper was originally published as a CenEA Commentary Paper of 21st April 2020 on www.cenea.org.pl. The analyses outlined in this Policy Paper make part of the microsimulation research program pursued by CenEA. The analyses are based on EU-SILC 2017 data as part of microsimulation research using the EUROMOD model and have been provided by EUROSTAT, and on publicly available OECD data. EUROSTAT, the European Commission, the National Statistical Institutes in each country, or the OECD have no liability for the results presented in the Policy Paper or its conclusions.
This Policy Paper was prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). FROGEE papers contribute to the discussion of inequalities in the Central and Eastern Europe. For more information, please visit www.freepolicybriefs.com. The views presented in the Policy Paper reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.
Covid-19 and Gender Inequality in Russia
Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.
Introduction
Gender inequality is a complex phenomenon characterized by significant and persistent differences in social and economic indicators for women and men. These may be differences in access to education and medicine, labor market participation, wages, entrepreneurship, participation in politics and public administration, and the distribution of domestic unpaid labor within the family. Reducing gender inequality (like any other form of inequality) correlates with increases in GDP.
The prevalence and scale of gender inequality is, on average, lower in developed countries than in developing countries, and although there is a general tendency for gender gaps to narrow over time, this does not happen simultaneously and equally in all countries. According to the Global Gender Gap Index (2020), which ranks more than 150 countries, the five countries with the best indicators include Iceland, Norway, Finland, Sweden, and Nicaragua, while Congo, Syria, Pakistan, Iraq, and Yemen are in the very bottom. As of 2020, Russia is located approximately in the middle, being the 81st, right between El Salvador and Ethiopia.
In connection with the Covid-19 pandemic and unprecedented quarantine measures around the world, economists are thinking not only about the obvious global consequences for the global economy but also about the indirect effects, including those through gender-related changes in the labor market. A study of World War II, for example, shows that even short-term gender differences in the labor market can have long-term consequences (Goldin and Olivetti, 2013). What will the consequences of this crisis on the labor market be in the long run, especially on its gender and family-related components? In this brief, we look at the potential effects of the Covid-19 epidemic and the associated quarantine on gender inequality in Russia.
Heterogeneous Cross-Sectoral Effects
Economists are now discussing two main channels that can influence gender inequality (Alon et al., 2020). The first one works through differential risk of losing jobs and salaries for women and men due to the disproportionate impact of the epidemic and quarantine on sectors which predominantly employ each gender. The direction of this effect is not easy to predict. On the one hand, the current crisis differs from ordinary recessions in that the service sector, where more women are traditionally employed, is now suffering more than usual. However, it is very important to emphasize what kind of services we are talking about: restaurants and salons are not the whole of the Russian economy. According to the Russian Statistical Agency (Rosstat) 49% of all employed women in 2019 worked in three sectors – trade, healthcare, and education. At the same time, hotels, restaurants, and other services (which include hair and beauty salons) provided less than 8% of women’s employment.
Therefore, from the point of view of assessing the risk of job loss, it makes sense to consider state-financed sectors, where employees are likely to be retained, separately. Among the private businesses, two (non-mutually exclusive) types of sectors are likely to suffer the least. First, the critical ones that do not stop their activity during quarantine (for example, food retail, private medical centers). And second, those that are characterized both by a high ability to work “remotely” and continue to have sufficient demand for their goods and services – either directly or through value chains (see e.g. Volchkova, 2020). For example, agriculture, manufacturing and hotels are worse off in this combination than the financial sector, science, administration, and some types of online education. At the level of the individual characteristics of the employee, even when comparing the same occupations, the possibility of remote work positively correlates with the level of education, wealth, working for a company (rather than self-employment), and being female (according to Saltiel, 2020, for developing countries).
According to the same data from Rosstat, it turns out that about 49% of all women and 40% of all men worked in the “state-financed” and “remote-work” sectors (or 69% against 52%, if we add the trade sector). This is of course an overestimate, since not every job within a sector is characterized by state-financing or remoteness, but it likely represents the relative propensity across genders, which is of our interest. This relative propensity is mostly due to the much higher employment of women compared to men in health and education (approximately 4 to 1 in both sectors). In general, this may mean that the risk of job loss is now higher for men, and not for women as was predicted using US data by Alon et al. (2020), given the gender structure of employment by industry in the US. This rough assessment does not account for different opportunities for women and men to quickly find a new job, especially in the areas of high demand. For example, if the need for delivery workers has increased, and men are more likely to take this job, then it may be easier for them to quickly find a new job. This adaptive effect would unlikely overturn the original difference, because the number of such jobs is also limited.
The Effect of Childcare Facilities Closure
The second channel, likely having a multiplicative effect on the first, operates through the unexpected closure of children’s educational institutions (kindergartens and schools). These effects may be different depending on family composition. While before the pandemic, working parents could send their children to kindergarten and school, this opportunity is now completely unavailable. In the case of online education, not all children are independent enough to learn at home, especially primary school students. At the same time, other childcare support (e.g. from nannies, grandparents and other relatives, etc.) can also be significantly limited due to social distancing and self-isolation, although Russia is in a better position in this regard compared to many developed countries because grandparents traditionally help more in raising children. (It is interesting that in developed countries, the possibility of outsourcing household chores – childcare, cleaning, etc. – is one of the important explanatory factors for higher fertility among more educated women, compared with less educated ones, (see Hazan and Zoabi, 2015)).
Naturally, the situation with closed childcare and educational institutions will not affect the productivity of people without young children. According to the latest census in 2010, about 88 million people, which is as much as 75% of the total adult population of the country, do not live together with children under 18 years old. Also, most likely there will not be a big negative effect on families with children where one of the parents (most often the mother) or another individual in the household (a grandparent) took care of the child at home before the quarantine.
For all other families, the critical problem is juggling childcare with work. The most vulnerable categories of the population here are single mothers and single fathers (and there are about 5 and 0.6 million in Russia, respectively), especially those who do not have any outside help.
Among families with small children where both parents work, several important factors can be identified. On the one hand, according to developed countries, even in families where both parents work, women spend more time on household chores and childcare than men (Doepke and Kindermann, 2019). If one believes that the initial factors that affected this distribution of domestic work (such as traditional norms and role models or the relative income of spouses) have not disappeared, then the sharply increased burden of household chores will disproportionately fall on women. This can lead to a decrease in the relative productivity of women compared to men in the labor market and a greater risk of dismissal. In the long run, this can also negatively affect gender inequality, as even a temporary exit from the labor market may be accompanied by human capital losses and a worse career path in the future.
The Interaction of Both Effects
On the other hand, the opposite situation is also possible. If, due to the disproportionate effect of quarantine on various sectors of the economy, which has been discussed above, women have a lower risk of losing their jobs, then it is possible that at least temporarily, a significant part of the childcare will fall on men. This situation can also happen in families where the woman works in critical sectors of the economy (especially in healthcare) and the man works remotely from home.
Economists have suggested several mechanisms for the effect of short-term additional interaction between fathers and children on long-term participation in their upbringing: there is more information about children’s needs, learning-by-doing, and greater attachment to children. For example, the data from Canada shows that the introduction of 5 weeks of parental leave for fathers led to a more even distribution of domestic labor in households and a greater likelihood of the mother’s participation in the labor market, even 1-3 years after the fact (Patnaik, 2019). Moreover, even if there are not many families like this in the country, the new social norms can gradually spread in society through so-called “peer effects”. Dahl et al. (2014), for example, show using Norwegian data that the brothers and colleagues of men who took parental leave were 11-15% more likely to take it in the future, relative to brothers and colleagues of men who did not take such leave.
Other Hypotheses
Another major consequence of the epidemic and quarantine is the potential upsurge in domestic violence. Several European countries have already noticed an increase in such crimes (European Parliament, 2020), and some crisis centers in Russia have also reported an increase in calls to helplines. Economists identify different triggers for this behavior (Peterman et al., 2020). This may be a direct consequence of quarantine, which increases the time spent by the potential victim and abuser in a closed space, and the inability to seek immediate help, both psychological and medical. Indirect effects can also work through an increased risk of depression and post-traumatic stress syndrome, which were well documented for previous epidemics such as SARS and swine flu. and that may happen due to job loss, reduced income, general economic uncertainty, or a direct fear of getting sick.
These effects disproportionately affect women (and children); therefore, additional resources should be dedicated to identifying such crimes, strengthening support structures for women, and increasing the availability of reporting options without attracting the attention of an abuser (for example, such a warning system may be installed in pharmacies – a place where a woman can go to alone).
Economists have yet to accurately measure and test all these mechanisms, which interact with each other in complex combinations, but it is now clear that very different scenarios are possible, including the positive ones – of a long-run decrease in gender inequality.
References
- Alon T., Doepke M., Olmstead-Rumsey J., and Tertilt M. “The impact of Covid-19 on gender equality”, Covid Economics, Issue 4, 14 April 2020.
- Dahl G.B., Løken K.V., Mogstad M. “Peer Effects in Program Participation”, American Economic Review 104(7): 2049–2074 (2014).
- Doepke M. and Kindermann F. “Bargaining over Babies: Theory, Evidence, and Policy Implications”, American Economic Review, 109(9): 3264–3306 (2019).
- Goldin C. and Olivetti C. “Shocking Labor Supply: A Reassessment of the Role of World War II on Women’s Labor Supply”, American Economic Review, 103(3): 257-262 (2013).
- Hazan M. and Zoabi H. “Do highly educated women choose smaller families?” Economic Journal, 125(587): 1191-1226 (2015).
- Patnaik A. “Reserving Time for Daddy: The Consequences of Fathers’ Quotas”, Journal of Labor Economics, 37(4): 1009-1059 (2019).
- Peterman A., Potts A., O’Donnell M., Thompson K., Shah N., Oertelt-Prigione S., and van Gelder N. “Pandemics and Violence Against Women and Children”, Center for Global Development working paper, 1 April 2020.
- Saltiel F. “Who can work from home in developing countries?” Covid Economics, Issue 6, 17 April 2020.
- Volchkova N. “Who should receive government support during Covid-19 crisis”, in “Economic Policy during Covid-19”, April 2020.
- European Parliament. “COVID-19: Stopping the rise in domestic violence during lockdown”, Press Release 7 April 2020.
- Rosstat, “Russian census 2010”.
- Rosstat, “Russian labor force survey 2019”.
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