Tag: COVID19

COVID-19 | The Case of Poland II

An image of COVID-19 virus representing the COVID-19 outbreak in Sweden

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]:

  1. Increased flexibility of employee daily and weekly hours of work;
  2. Extension of childcare leave for parents with children aged 0-8;
  3. In case activities affected by revenue reduction (revenue fall by 15% year-to-year or 25% month-to-month):
    1. Self-employed or employees on non-standard contracts to receive a monthly benefit equivalent to 80% of minimum wage for up to three months;
    2. 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%;
    3. Companies to receive support equivalent to up to 40% of average wage for employees whose hours are reduced by 20%;
    4. 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%);
    5. Relaxation of work and stay permits for foreigners.

Social transfers:

  1. 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]:

  1. 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;
  2. Tax payments and social security contributions on earnings and profits can be delayed till 01.06.2020;
  3. Losses from 2020 will be deductible from the 2021 tax base.

Emergency loans, guarantees and support [14]:

  1. Small-scale loans to small companies;
  2. Reduced administrative requirements and relaxation of numerous regulatory rules;
  3. Increased liquidity of firms through channels supported by the Polish Development Fund (PFR):
    1. extension of de minimis guarantees to SMEs;
    2. subsidies to SMEs which suffered revenue losses due to the pandemic;
    3. equities and bond issues to be financed by PFR;
    4. subsidies to commercial loan interest payments from BGK;
    5. commercial turnover insurance from Export Credit Insurance Corporation (KUKE);
  4. Relaxation of regulations related to contracts with public institutions (e.g. related to delays).

Monetary policy [16]:

  1. 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.
  2. NBP announced the readiness to engage in large scale open market operations;
  3. 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

Image of numerous different coloured umbrellas photographed from above representing Belarusian SMEs

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

An image of COVID-19 virus representing the COVID-19 outbreak in Sweden

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

  1. 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

  1. 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.
  2. 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.

  1. 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.

  1. 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.
  2. 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).
  3. 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

  1. 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).
  2. 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

  1. Restructuring loans for businesses affected by the crisis;
  2. 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;
  3. Doubling the volume of VAT refunds to companies, with the aim of supplying them with working capital;
  4. 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

  1. Loan payment deferrals for three months;
  2. Personal income taxes deferred for employees in the tourism industry.

The Health Care System

  1. No new measures are planned at this point.

The Financial System

  1. Easing lending restrictions for commercial banks;
  2. NBG has not cut policy rates and is unlikely to do so given the risks of inflation.

Other Measures

  1. Boosting capital expenditure (CapEx) projects with the aim of providing additional economic incentives;
  2. 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.

IMF, 2019. “Georgia: Fourth Review Under the Extended Fund Facility Arrangement and Request for Modifications of Quantitative Performance Criteria-Press Release; Staff Report; and a Statement by the Executive Director for Georgia.”

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

Image of different age people holding hands representing older people in Poland and COVID19

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.

  1. 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

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

20200514 Covid-19 and Gender Inequality in Russia FREE Network Policy Brief Image 01

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”.

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 in LDCs: Assessing Resilience and Understanding How to Help

An image of narrow street in slum representing Covid-19 in LDCs

Poor and developing countries are now starting to be affected by the Covid-19 pandemic. Important differences in the setting need to be considered when thinking about their prospects, and the role richer countries may play in helping them face the challenge.

Introduction

Most of the focus in current analyses of the policy response to the Covid-19 crisis center on Western and East Asian countries that were hit first and hardest. Some initiatives are tracking the situation in transition countries of Eastern Europe (e.g., the FREE Network initiative and the Vienna Institute for International Economic Studies tracker).

However, poor and developing countries start also being affected by the pandemic, and richer countries have an important role in helping them face the challenge. Besides the moral obligation, in the presence of a global externality it would be extremely myopic not to do so. When thinking about this, it is important to reflect on the differences that will be relevant in these settings.

What is Happening? The Spread of the Virus

Currently, the spread of the contagion is still at substantially lower levels in low income countries (LIC) as compared to high income countries (HIC). There is not enough evidence yet to either support or reject the hypothesis that a lower spread could be due to differences in climatic zones (warmer temperatures and humidity). Younger populations might account for both a lower (observed) spread and lower mortality, but on the other hand the denser and multigenerational living arrangements with poorer hygienic conditions should be pushing in the opposite direction. Observing lower spread and lower mortality could also be put down to lower testing (and more generally, data availability and quality of information systems). Finally, we can’t exclude that this is simply a matter of timing. Many LIC are relatively less connected to global routes, and moreover were fast to close their borders: many opted for early lockdown. If this is the case, they are merely postponing the sharp increases in infections and fatalities observed in other countries. (At the time of writing, worrisome reports of a severe outbreak in Somalia are emerging.)

Figure 1: Total confirmed Covid-19 deaths.

Source: Our World in Data, downloaded on May 6, 2020.

Figure 2: Total Covid-19 tests per 1,000 vs. GDP per capita.

Source: Our World in Data, downloaded on May 6, 2020.

A number of factors related to the demographic structure as well as the public health systems are relevant as a base for our expectations on how the situation is going to evolve in these countries.  Since age plays an important role on how severely Covid-19 patients are affected by symptoms, the demographic structure of the population has consequences for the demands that will be placed on the health care system by an outbreak. This plays in favor of LICs, where only 3% of the population is above 65 years of age on average. The corresponding share is 18% in OECD countries. The state of the health care system is intuitively crucial once there is an outbreak. In Table 1, the Global Health Security Index (GHS) “Health Security Score” paints a dismal picture in terms of overall capacity “to treat the sick and protect health”, where the group of LICs (as defined by the World Bank) scores an average of 14,5 out of 100 (HIC average is 51,9).

Table 1: Public health.

Source: Over 65, share of total: WB, values for 2018 except Eritrea (2011); Health care spending % of GDP: WB, values for 2017, except Syrian Arab Republic (2012) and Yemen, Rep. (2015); Health care spending USD p/c: WB, values for 2017, except Syrian Arab Republic (2012) and Yemen, Rep. (2015); Health security score: GHS Index 2019, Health Overall Score “Sufficient & Robust Health Sector to Treat Sick & Protect Health”; Health security – response capability: GHS Index 2019, Response Overall Score, “Rapid Response to and Mitigation of the Spread of an Epidemic”.

This is clearly related to how wealthy a country is. The wealthier countries have better health care systems in general, and will do better if they experience an outbreak, while the poorer countries will do worse. Even if the average 6% of GDP devoted to health care spending in LICs looks comparable to the HIC average share (8,8%), these translate into very different figures in terms of per capita dollar spending: 40 USD per capita in the first group, to be compared to over 4,000 USD in the second. Even if costs do differ as well, a ventilator is unlikely to be two orders of magnitudes cheaper in Liberia than in Italy. Nevertheless, the “Health security – response capability” index, which includes things as emergency response plans and existing links between health and security authorities, averages 30,9 in LICs against 45,8 for HICs. The difference across income levels is much smaller in this case, reflecting both the more general lack of preparedness in this particular domain, but also the familiarity and experience of poorer countries with infectious diseases outbreaks, which might give an edge in an emergency. The World Health Organization reports over one hundred “public health events of varying magnitude and socio-economic effects” annually in Africa, for example. After the 2014-15 Ebola outbreak, an Africa Centre for Disease Control and Prevention was set up in 2017, which might have contributed to an upgrade in the index. The Centre has been quick to react in the present case, as discussed later in the policy response section.

What is Happening? Economic Impacts

It is hard for HIC to put numbers on forecasts of economic activity. For LIC, the challenge of forecasting is further compounded by the normally poor array of statistical systems and the larger informal sectors. Better indicators of economic activity and income distribution normally rely on surveys, and while surveys are still being conducted these days (see for example the relentless work of IPA affiliates  the focus at the moment is naturally on the health emergency and related behavior, rather than incomes and investments.

Even without exact numbers, we can nevertheless expect that LICs’ economies are going to be hit harder, for two main reasons:

  • They are more sensitive to the global shock(s), through commodity prices and exports, and also because of the limited access to international financial markets
  • They start from worse structural conditions, in terms of fiscal capacity and governance capacity, which makes them less resilient.

Again, a number of fiscal and macro factors are relevant for our expectations on how the situation is going to evolve, such as the trade and fiscal balance, and the composition of exports. Besides concerns for long-term growth prospects, the most immediate threat is that to people’s livelihoods, in particular poor people’s, due to the slowdown of economic activity. While this can’t be fully avoided due to the dependence on international linkages, it is made radically worse in case of domestic lockdown. The combination of large populations living below or at the margin of the poverty threshold and the slim fiscal capacity for compensation and redistribution results in much sharper trade-offs associated to different policy measures.

Some of these countries, heavily dependent on external trade and in particular on commodity exports, are at the moment facing a double shock, due to the collapse of commodity prices and the disruptions to global value chains, on top of the epidemic itself. This is dramatically reducing the fiscal space for response, which was already limited to start with. Therefore, even though a number of LICs have formulated response plans, as will be discussed in the next section, the question remains how to finance them.

Table 2: Macro factors.

Source: External Trade as % of GDP: WB, Trade (% of GDP) for 2018 except Afghanistan, Malawi, Tajikistan, Tanzania (2017); South Sudan (2015); Eritrea (2011); Commodity Exports, %: UNCTAD, 2017, Commodity exports (as a share of total merchandise exports); Population Under Poverty Line: WB; Foreign Aid % of GDP: WB, Net official development assistance received (current US$) / GDP (current US$) for 2018; Tax revenue: WB.

What is Happening? Policy Response

With few exceptions, most countries in this group were quick to react in at least two dimensions: closing borders and closing schools. While the first was probably a very wise choice and might have delayed significantly the entry of the virus in the countries, not enough thought has been given to the consequences of school closures. Less than one in four countries is providing some form of distance learning; and even where this is available, access will be very unequal, for a number of reasons: access to internet and suitable devices, need to compensate for parent’s lost income, responsibility for younger siblings are just some of the factors, in addition to the inequality in parental socioeconomic and educational background which is common also to HICs. Based on experiences from the Ebola epidemic in 2014-15 in West Africa, the protracted lack of schooling is liable to leave deep long-lasting consequences.

A quarter of the countries (8 out of 31) entered lockdown or very strict social distancing. Few of them, with help from the international community, support the enforcement of a lockdown with food distribution (for example Liberia and Uganda). This is not possible everywhere, due to financing and logistic issues, and in its absence, livelihoods are put at risk. Because of this, in many areas people defy the rules, in some cases notwithstanding enforcement by the military. Another quarter of countries opted for curfews rather than lockdown, to limit the frequency of interactions without halting completely economic activity. Very few countries explicitly chose much more limited interventions in terms of social distancing (Burundi, Mozambique, Tanzania), while most of the rest do not have the governance capacity for intervention, in some cases due to other preexisting crises (Yemen, Mali, Guinea-Bissau).

The quality of the country’s health care system and the resources that can be invested in testing will determine for how long containment measures will be needed. Two thirds of the countries have already enacted emergency interventions in the health sector, meant to strengthen the general capacity for care and in particular the infrastructure for testing.  All in all, though, half of the countries have opted for either strict public order measures or fiscal interventions. Most of the remaining half have neither, while very few have both. In most cases, the health-related emergency measures are financed by small reallocations of current spending that amount to few per-mille points of GDP. With fewer resources to cure and test, countries will need to maintain longer containment measures to avoid the spread, once the contagion reaches them. However, as mentioned above, the cost of lockdown is very different in these countries, where almost half of the population (48% on average) lives below the international poverty line. Stricter and longer lockdowns will call for broader fiscal interventions in support of households’ (food) consumption and SMEs. The few countries that planned such interventions, and/or to increase health sector spending by more than 1% of GDP, are counting on donor financing. At the same time, all are suffering contractions in their fiscal space, as noticed above, and the same can be said of most donor countries too. The question of how to finance this gap looms therefore large.

A Role for Rich Countries

In normal times, the relative importance of different financial flows entering developing countries could be phrased as follows: foreign aid is small, remittances bigger, trade and investments biggest. ODA receipt accounts for 12% of GDP in the average LIC. While almost all donor countries fall short of the pledge to give 0,7% of their annual GDP, even if they did, thus trebling the current aid bill (152,8 billion USD in 2019), this would still not reach the level of remittances flows, estimated at 551 billion USD in 2019. The FDI flows, estimated at 671 billion USD (in 2018) are more important in the aggregate, although their distributional implications are very different. The importance of trade is also substantial, as shown in Table 2.

Given the situation, though, with a global recession looming, we can expect substantial contractions in trade and FDIs at least in the short run, but more likely for a protracted period. The limitations to international mobility will also imply severe reductions in remittances flows, as migrant workers have either returned to their countries, or are more likely to lose employment in the host countries even if they stay. Clearly this implies a continued role for international support.

Without going in the merit of an optimal policy mix recommendation to developing country governments, which others have done (for example, the International Growth Centre COVID-19 guidance note), rich countries that want to play a role in this should keep in mind a few points. Aid budgets should at the very minimum not be reduced, notwithstanding the domestic fiscal squeezes. More than ever, the same amount of money has a much larger life-saving potential in a poor country than domestically. Besides quantity, the type of support will be important. During the health crisis, the priority needs to be to finance emergency expansion of health care spending, but for this to be sustainable it needs to be paired with a strong effort to limit the spread. This includes two elements: i) testing and tracing, or in absence of tests at least keeping track of the geographic spread of symptomatic outbreaks; and ii) supporting livelihoods to enable social distance or lockdown. The first includes, besides the medical material and infrastructure for the testing itself, which might not be the most cost-effective way of using resources, enabling safe and reliable public communication, which needs to go two-ways: from authorities to citizens, avoiding fake news and potential stigma attached to the contagion, and from citizens to the authorities to collect policy relevant data. Since internet is not widespread enough, and the radio only allows for one-way communication, the best shot at this is leveraging mobile telephone networks. Technical assistance in this could be valuable, as well as analytical capacity for the processing of the data.

It goes without saying that all the progress happening in rich countries, in terms of understanding of the virus spread, efficacy of different policies and behaviors, development of treatments and in due time vaccine should be promptly shared.

When it comes to consumption support, it is debatable whether cash transfers or in-kind distributions should be the preferred option. This will of course vary depending on the situation: cash is logistically easier and more flexible – but it will not help if and where the markets shut down.

In the aftermath, it is important to keep in mind that poor countries will not be able to borrow (in particular, issue domestic public debt) to finance fiscal stimuli and other recovery measures. There will be again an important role for international lenders. At the same time, a swift recovery of global economic activity must be considered as the all-over superior solution.

References

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.

The Swedish Exceptions: Early Lessons From Sweden’s Different Approach to COVID-19 – Insights From a SITE-LSE Webinar

Image of sunrise in Stockholm street representing Sweden's different approach to COVID-19 response

Sweden’s policy in the Corona crisis has been subject to a lot of discussion in international media recently. Some point to the country portraying “the Swedish way” as a valid policy alternative to the forced lock-down of society, others criticize the Swedish government for being imprudent. Given the pace with which the virus spreads and considering the volatility of current events, it is pre-mature to draw any definite conclusions. But it is certainly time to start an informed policy discussion. The webinar “The Swedish Exceptions: Early Lessons from Sweden’s different approach to COVID-19, jointly organized by the Stockholm Institute of Transition Economics (SITE) and the London School of Economics (LSE) on April 22, 2020” brought together academics from different relevant disciplines from Scandinavia, the UK and the US . The webinar allowed to discern a few of the motivations behind the Swedish policy choices as well as a number of criteria which will serve to measure the success of governments’ responses to the Covid-19 pandemic in the future.

Understanding the Swedish Approach to Covid-19

Much has been written and said about the Swedish reluctance to impose a strict lock-down on the country: the Swedish government has so far relied mostly on expert recommendations, avoiding from more stringent policies such as the strict lock-downs imposed by for instance Sweden’s neighboring countries Norway and Denmark (more on Sweden in the Covid-19 crisis here). The majority of the speakers in the webinar agree that the Swedish policy in the Corona crisis has been an outlier, even with respect to traditional Swedish policy: Peter Baldwin, historian and professor at the University of New York and the University of California, Los Angeles, argued that Sweden has had an interventionist tradition with respect to social and health policy in the past. “Native policy traditions” therefore do not explain why Sweden has chosen this policy course in his view.

While it seems difficult to pin down historical or ideological reasons behind the Swedish policy stance with respect to Covid-19, Lars Trägårdh, professor of social history at Ersta Sköndal Bräcke University College in Stockholm, pointed out that even though the legal differences may seem stark, the difference in the policy impact may be smaller than expected, the crucial factor being the degree of compliance with a certain measure or recommendation and not its legal force. Trägårdh further argued that, since it may take many months to develop a vaccine, the sustainability of a given policy strategy is essential. According to him, a policy relying on voluntary compliance as the Swedish one rather than legal obligation, may therefore yield comparable effects in the short and medium run and could even turn out to be more successful in the long run.

Trägårdh argued that the true exceptionality of the Swedish response to the global pandemic has been the choice to not close elementary schools. This policy choice can be explained above all by the concern for children’s rights: for smaller children, digital learning simply is not a valid option. As declared by the government on several occasions, another reason is that parents working in professions such as healthcare may be induced to stay at home if schools are closed. Finally, Trägårdh cited a recent study from Iceland which suggests that the effect of closing schools on limiting the spread of the virus may be relatively small.

Later in the discussion, another potential argument in favor of the Swedish strategy emerged: Professor Sara Hagemann from the LSE School of Public Policy described the difficulty of leaving a lock-down, which Denmark is currently experiencing. The question which measures are to be lifted and which sectors of the economy are to be opened first has caused considerably more controversy than imposing the initial lock-down. In contrast, the public debate in Sweden can immediately focus on dealing with the long-term consequences of the crisis according to Trägårdh.

The significance of the concept of “herd immunity” (meaning the protection from disease arising from large percentage of the population having developed immunity) for the Swedish strategy is unclear. Baldwin pointed out that even though Swedish authorities have declared not targeting herd immunity, many measures implicitly seem to be aiming for this outcome.

Results of the Swedish Approach Until Today

Tom Britton, professor of mathematics at Stockholm University, agreed that the Swedish response to the Covid-19 crisis came late and that there has been too little testing. However, he argued that the government’s policy has been consistent, focusing on reducing the spread of the virus and protecting risk groups and especially the elderly. Whether Sweden has achieved the latter goal is still up to discussion, though. As of April 2020, reported infections and deaths in nursing homes had increased, which according to Trägårdh has been the major failure of the Swedish policy response up until today. Yet, the speakers agreed that the Swedish government’s measures have received a lot of public support within Sweden so far, which is a non-negligible factor for the long-term success of the strategy.

General Policy Conclusions

Professor Ole Petter Ottersen, president of the Karolinska Institute in Stockholm, Sweden’s largest centre of medical research, stressed the speed with which the virus has been spreading: the rapid development forces policymakers to quickly take decisions based on limited information. Given the lack of data, Ottersen called for politicians to practice humility and acknowledge the uncertainty surrounding policy choices. According to him, it will take years to evaluate whether the Swedish model or the Norwegian model of a quick and strict lock-down is better suited to fight the pandemic.

Policymakers around the globe face a dilemma: for sustainable crisis management and given countries’ interdependency, measures meant to fight the spread of Covid-19 should be aligned internationally and taken cooperatively. Yet, as Hagemann pointed out, it is clear that one policy cannot fit all: countries differ for instance with respect to their socio-economic structure, health care quality and availability, demographics, and with respect to the point in time when they were hit by the virus. This is not only the case between countries, but even within countries, which could justify a differentiated approach between rural and urban areas in some instances. In other words, all models and policy recommendations have to be adapted to the specific local setting. A strategy which allows for making local adjustments while maintaining a global perspective will be a major challenge for policymakers in the coming months and, likely, years.

Britton stressed the importance of understanding the limits of the models being used. Their predictions depend on a lot of assumptions regarding for instance how individuals behave and to what extent rules and regulations are being respected. Anti-body tests will soon provide more data on the actual spread of the virus, but even then, major questions, such as how to treat a potential trade-off between preventing deaths from Covid-19 vs. the socio-economic and health costs caused by a lock-down, will remain unanswered. This trade-off is country specific as well: Hagemann argued that Sweden and the other Nordic countries have quite successfully implemented remote working and learning options. This, however, will not be feasible in most developing countries, for instance, which necessarily affects the cost-benefit analysis of the available policy options.

Further, data collection and availability undoubtedly need to improve. As long as no better instruments of analysis are available, both scientists and politicians should be transparent about the simplifying assumptions and models they base their policy recommendations and decisions on.

Finally, despite their different academic backgrounds, all experts agreed on the need to take into account the indirect consequences of both the spread of the virus and the policy measures implemented to fight it. Covid-19 is likely to reinforce social inequities. For instance, it has been shown that in Stockholm, immigrant communities have been hit the hardest. As soon as the imminent health crisis is under control, the policy focus, therefore, has to shift towards the socio-economic consequences of the crisis.

Acknowledgements

The Stockholm Institute of Transition Economics wishes to express its appreciation to the speakers for their contributions to the policy debate, to the London School of Economics for the successful cooperation in organizing the event, and to the audience for its engaging questions and interest in the topic.

List of Speakers:

  • Peter Baldwin, New York University and University of California, Los Angeles
  • Tom Britton, Stockholm University
  • Sara Hagemann, London School of Economics
  • Ole Petter Ottersen, Karolinska Institute, Stockholm
  • Lars Trägårdh, Ersta Sköndal Bräcke University College, Stockholm
  • Erik Berglöf, London School of Economics (moderator)

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.

Russia Economic Update — Brace for the Covid-19 Impact!

A view of central Moscow City at dawn representing the sudden stop of Russian economy

Russia’s oil dependence will once again contribute to an economic downturn that most certainly will follow the Covid-19 outbreak in Russia as in other countries. The decline in oil prices alone could lead to a drop in GDP of more than 8 percent. On the positive side, Russia manages its macro economy well. However, its fiscal reserves are not unlimited and the recent massive fall in oil prices has not been matched by a similar decline in the ruble exchange rate which means potential extra problems for the budget. Furthermore, monetary policy will have less of a role to play in dealing with this type of crisis. This means that Russia like other countries will face difficult trade-offs in dealing with the crisis at a time when some of the previously announced economic policy changes have not been well received by the public.

Introduction

The corona virus crisis will destroy both lives and economies as it spreads across the globe. Fortunately, the corona virus death toll in Russia so far is relatively modest compared to many other countries, but the economy is most certainly heading for very difficult times. This is (again) due to the fact that the Russian economy is too dependent on the developments of international oil prices (see e.g. Becker, 2016a,b). In recent years, Russia had to deal with two severe declines in oil prices that hit its economy, first in connection with the global financial crises 2008/09, and second, in 2014/15, when there was a fall in oil prices simultaneously with Russia being hit by international sanctions after the illegal annexation of Crimea. Although these episodes were very costly for the Russian economy, they also provided important lessons for policy makers on fiscal, monetary and exchange rate policies that come in handy today. They also contributed with data on the relationship between large movements in oil prices and the effects they had on GDP growth in Russia. This is useful at this stage to assess what can happen with the economy after the significant decline in oil prices that has followed in the course of the corona outbreak.

Dramatic Decline in Oil Prices

We still do not know when this crisis will be over, but when it comes to the fall in international oil prices the start has been far more severe than the two crises referred to above. Since the beginning of 2020, oil prices have fallen from around $60/barrel to around $15/barrel or as Figure 1 shows, a barrel is now worth around 25 percent of what it was worth three months ago. Furthermore, prices are rather volatile and will continue to be so and there will most certainly also be periods of sharp increases in oil prices going forward – but the overall result for the year compared to the previous year is most likely a very sharp fall in prices. This decline in oil prices has so far been much more dramatic than the two previous crisis episodes the Russian economy has experienced under Putin as president or prime minister.

Figure 1. Oil price developments in recent crises

Note: This graph is based on the European Brent spot price FOB published by the U.S. Energy Information Administration and the axis shows trading days, so that the graph covers the period from January 1 to March 30. Different qualities of oil of course have different prices, but the patterns shown here are similar for other oil prices as well.

Exchange Rate and Stock Market

As in previous crises, the Russian stock market and exchange rate are following the evolution of oil prices. However, neither the stock market, nor the exchange rate has fallen as rapidly as oil prices. This can be due to many factors, but one likely explanation is that investors think that the decline in oil prices will not last for as long as it has in past crises. Whether this assumption is correct remains to be seen of course, but if oil prices stay low for an extended period, we can expect to see further declines in both the exchange rate and stock market.

Figure 2. Oil prices, exchange rate and stock market

Sources: Oil prices as in Figure 1, the exchange rate from Central Bank of Russia, RTS index from Moscow Stock Exchange.

The fact that the exchange rate this time has “only” depreciated by 20 percent when oil prices have fallen by 70-80 percent means that the oil price measured in rubles has fallen much more dramatically in this crisis compared to the previous ones. In the 2008/09 global financial crisis, the oil price in ruble terms was, in the end, unchanged compared to the start of the crisis. In 2014/15 this was not the case, but the decline in the ruble oil price was a more modest 25 percent compared to the 60 percent drop right now. This has serious implications for the government’s budget which is ruble-based and highly dependent on oil revenues.

Economic Policy

The Russian government now has plenty of experience in dealing with crises. The first lesson after the crisis at the end of the 90s was to have enough fiscal resources to deal with a crisis without having to go to the IMF again. The second lesson came in the global financial crisis when the fixed exchange rate had to be abandoned to avoid depleting the central bank’s international reserves. A prudent fiscal policy backed by the National Wealth Fund and a flexible exchange rate is still the backbone of the macroeconomic policies that can help mitigate the impact of lower oil prices.

The central bank is pursuing inflation targeting and uses a 4 percent inflation rate as the target that guides its policy decisions. The main tool is setting the key interest rate at a rate that will achieve the inflation target. The key interest rate is currently 6 percent, significantly down from the high of 17 percent in January 2015. The central bank states clearly in its monetary policy documents that “Monetary policy lays the groundwork for economic development; however, it cannot be a source of a sustainable rise in economic potential” (see page 6 in Central Bank of Russia, 2020). This implies that the central bank will only lower the key interest rate if inflation falls, not to support growth or try to achieve other, potentially conflicting goals. This is good news for macroeconomic stability but may become an issue of political tension if there is a serious downturn in the economy while inflation remains higher than the target rate.

In mid-2019, the National Wealth Fund was doubled and went from $60 billion to just over $120 billion (Ministry of Finance, 2020). This was done as a one-off transfer of surplus funds from the government’s budget. However, at its peak in the global financial crisis, the combined reserve fund and wealth fund that existed then had assets of over $220 billion but by the start of 2011, the assets were down to $111 billion. In other words, a year and a half into that crisis episode, the government had used an amount from the funds that roughly corresponds to the total amount available in the National Wealth Fund today. The fiscal space is, therefore, less impressive than it may look at a first glace and just burning through the cash in the National Wealth Fund is not a sustainable fiscal policy if this crisis continues a few more months.

Instead, the government will have to plan other measures as soon as the most immediate spending to deal with the crisis is done. This will entail difficult trade-offs since the health system will need increased resources at the same time as households and companies will need support to mitigate the impact from lost jobs and closed businesses in the wake of corona-induced shut-downs rather than the decline in oil prices, so adding to the pressure coming from declining oil prices. Increasing taxes in a time of already depressed purchasing power and profits is also not an appealing option and although there are still tax increases in the pipeline, the government has announced that these will not come in effect this year. Like in many other countries, the Russian government is proposing several measures to support the economy that will be discussed in more detail in a forthcoming FREE policy brief. However, these measures will add to the costs of the government at a time of falling revenues. From an economic perspective, reallocating resources from the military and security sectors to other parts of the economy seems like an obvious choice under these circumstances, but most likely not the outcome of this process given the government’s geopolitical and domestic power ambitions. Again, the fiscal reserves will allow postponing these harder decisions, but if the crisis goes on for some time, alternative measures such as borrowing domestically or internationally will most certainly be discussed also in Russia. However, many governments will be in need of borrowing on international markets going forward and the rates required to access this type of funding may not be very attractive and still force domestic budget reallocations.

Growth Impact of the Oil Price Fall

It is of course too early in the crisis to make very precise forecasts on how the economy will fare in 2020. This will in the end crucially depend on how the Covid-19 pandemic develops and on government responses to the crisis not only in Russia but also in the rest of the world. A partial analysis of the impact of falling oil prices can however be done with the models presented in Becker (2016a) which link changes in oil prices to growth. This paper shows a few alternative specifications that differ in the GDP measure being in dollars or real rubles, and in some other dimensions. All specifications are highly statistically significant and able to explain between 60 and 90 percent of variations in GDP growth in the period 2000-2015. Focusing on the relationship between the percentage change in oil prices and growth in real ruble GDP, the estimated coefficient is 0.14. This implies that for every 10-percentage point drop of oil prices, GDP growth goes down by 1.4 percent. Currently, oil prices have declined by 75 percent since the beginning of the year. However, the model estimates are based on comparing how average oil prices change between years so this is the numbers we need to compute and compare. The average price of Brent oil (which is used in this model) was $64/barrel in 2019 but we obviously do not know what the average oil price will be this year. We therefore need to first “forecast” oil prices for the rest of the year before we can compute the impact on growth. If we make the simple assumption that oil prices stay at the current level and take into account that they were significantly higher the first couple of months this year, the average price would end up being $25/barrel. That would amount to a 60 percent decline in average oil prices between 2019 and 2020. The partial effect of this oil price decline would therefore make Russian real GDP drop by 8.5 percent in 2020. Again, this is the partial effect based on the estimated coefficient in a linear relationship between oil price changes and real GDP growth. In plainer English, we are not looking at the first order effect of closing stores etc. to avoid the virus from spreading but only the additional effect that we think will come from falling oil prices. In addition, the effect this massive decline in oil prices is assumed to have on GDP is derived by a coefficient that is estimated on smaller changes in oil prices and real GDP. Nevertheless, this exercise provides a first, and rather daunting, assessment of what can happen to GDP given the decline in oil prices alone.

Concluding Remarks with OPEC and IEA update

This brief has provided a first assessment of how the Russian economy may be impacted by the massive decline in oil prices that has followed in the course of the corona pandemic. It has shown that the economic downturn this time can be significantly worse than both the 2008/09 and the 2014/15 crises. A base line estimate suggests that GDP may fall by more than 8 percent only because of the fall in oil prices. The above calculation obviously includes neither the impact the health situation will have on companies or households, nor the government’s ability to mitigate the negative consequences. If the other problems the economy is facing as a direct result of the health crisis also lead to a significant decline in supply and demand, Russia could easily see real GDP declining by more than 10 percent in 2020.

Our estimate is an important reminder that Russia’s continued oil dependency is a risk to the economy and its citizens. Now is not the time for ambitious structural and institutional changes to generate growth, but hopefully the urgent crisis period passes without policy makers forgetting the risks the country’s oil dependence entails. They learnt the fiscal and monetary lessons well from past crises, now is the time to learn something new. The most appealing road to sustainable economic growth is still building credible property rights institutions and rule of law in a framework that would make Russia the innovative business-oriented superpower it could be.

A few days after the first version of this brief was published, oil prices started to rise as the OPEC together with Russia started discussions to cut production to support oil prices. A tentative agreement was reached which is supposed to cut production by 10 million barrels per day in May and June, the largest cut in OPEC’s history. Had this movements in prices continued, the forecast for the Russian economy would have been affected. However, this recovery in prices was soon reversed and oil prices started to fall again. The decline continued on April 15 as the International Energy Agency presented a dire forecast of oil demand and stated that this year may be the worst year ever in terms of declining demand. All in all, the price movements that have followed the OPEC meeting and the statements of the IEA do not change the baseline prediction this brief has provided.

References

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. This brief was first published on April 6, 2020 and then revised on April 15, 2020.

Household Exposure to Financial Risks: The First Wave of Impact From COVID-19 on the Economy

An areal image of households in suburbs representing household financial risk exposure to covid19

Since March 12, 2020, Poland has been under an increasing degree of quarantine due to the COVID-19 pandemic. The strict isolation-driven lockdown measures have implied significant restrictions to social interactions and economic activity. While the duration of this lockdown and the resulting overall scope of economic implications are highly uncertain at this point, in this brief we take a closer look at the possible extent of the first wave of economic consequences of the pandemic faced by Polish households. This is done by identifying sectors of the economy whose operation has been severely limited due to the lockdown, such as those involving travel, close interpersonal contact and public gatherings or those related to the retail trade. We find that about 17.2% of Polish households include members active in these sectors, and for 5.2% of households, the risk can be described as high due to the nature of the employment relationship. According to our estimates, 780K people (57% of whom are women) face a high risk of negative economic consequences as a result of the first direct wave of implications of the pandemic.

Introduction

The full scale of the socio-economic impact of the COVID-19 outbreak is incalculable today, given the uncertainty of lockdown duration and the severity of the pandemic-driven slowdown in the international economy. Still, it is possible to analyze the direct implications of the lockdown, self-isolation and quarantine measures introduced over the last few weeks in an attempt to formulate a preliminary assessment of how the outbreak will affect households in economic terms. The priority challenge now is, of course, to contain the spread of the coronavirus, but as we identify the scale of potential economic consequences associated with the pandemic, we may help calibrate the safeguards that could protect households from the impact of the imminent economic slowdown.

In this commentary paper, based on the Household Budget Survey (HBS) data, the percentage of households (HHs) whose members are most at risk of losing their job or compromising their income due to the first wave of economic consequences of the pandemic is taken as a measure of the economic impact of the COVID-19 outbreak. The analysis looks into the population of people who are economically active (through employment or self-employment) in those sectors of the economy which are most exposed to the effects of the lockdown. We discuss the HHs with a particularly high risk of income deterioration in the breakdown according to the level of household income, the place of residence, and the family type. The first part of the paper presents a detailed description of the economic sectors which were considered to be particularly exposed to the risk associated with the first wave of economic consequences of the pandemic, together with risk level definitions. Analytical findings are presented in the second part of the paper.

Households at Risk of the Negative Impact of the First Wave of Economic Consequences of the COVID-19 Pandemic

The granularity of HBS data collected annually by Poland Statistics (GUS) is not sufficient for a very precise determination of the size of risk groups in terms of individual activity on the labor market, but the data can help identify the HHs whose members have been employed in the sectors of the national economy particularly affected by the pandemic, i.e. on the first line of exposure to its economic consequences. These are, in particular, economic sectors that involve frequent interpersonal contacts and large public gatherings: following the announcement of the state of epidemiological hazard in Poland on March 14th, 2020, serious restrictions have been imposed in those sectors in an effort to prevent the rapid spread of the coronavirus.

Pursuant to the Regulation of the Minister of Health of March 13th, 2020, on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland, restrictions on doing business in the food industry, as well as in culture and entertainment, sport and recreation, hospitality and tourism have been imposed on a temporary basis (Ministry of Health 2020). The operation of large-size retail commerce facilities has also been restricted. In addition, self-isolation and social distancing result in significant decreases in the overall level of trade turnover. In view of the lockdown, we decided that the risk of economic slowdown also applies to the service sector and education (personal services included) for the purpose of this paper. The workforce from the above-mentioned sectors has been divided by type of employment contract, and those hired under a contract of employment (fixed-term or open-ended, regardless) have been ranked as less exposed to the risk of job loss or lower earnings, while all the others employed on civil law contracts (service contract, zero-hours contract, etc.) have been grouped under an elevated risk label. The elevated risk category includes all those who are self-employed in the above-mentioned sectors in Poland or abroad, regardless of whether they have employees onboard or not.

Exposure to Financial Risks in Families and Households

In accordance with the risk categories applicable to the economically active population, we can conclude that there are over 780 thousand members of the workforce (57 percent of them are women) who are particularly exposed to the negative economic consequences of the pandemic, as they work in the affected sectors of the economy on the basis of self-employment or contracts other than the contract of employment. In addition, 1.9 million people (70 percent of them are women) are employed in these sectors of the economy on contracts of employment. The status of the latter group is less precarious in the short term, but if the lockdown should continue in the long term, this population may also be affected.

The adverse impact of job loss or lower earnings will affect an entire household whose member works in a sector particularly affected by the crisis. Therefore, the risks below are presented in a breakdown by family type and by HH group aggregated according to the place of residence and income level. Moreover, the HHs were also grouped according to their members’ activity on the labor market, with analytical findings presented for all HHs and for the group of HHs with at least one economically active member in the HH.

The highest percentage of HHs whose members are particularly exposed to the negative consequences of the pandemic is reported in cities (Figure 1). For example, in cities with a population above 500,000, it is 6.6 percent of all HHs, and 9.1 percent of the HHs with at least one active member on the labor market. Additionally, in cities with a population count exceeding 500,000, 12.4 percent and 17.1 percent of the population, respectively, is employed in the affected sectors on the basis of an employment contract. In smaller cities/towns and in rural areas the percentage of HHs with the population most exposed to the crisis are slightly lower. In rural areas, it is 4.8 percent of all HHs and 6.4 percent of the HHs with at least one economically active member of the HH.

In terms of HH income levels, middle-income HHs demonstrate the highest percentage of those exposed to the negative consequences of the first wave of pandemic-driven impact on the economy (Figure 2). For example, in the 6th income decile group, in the population of HHs with at least one economically active member, 8.5 percent of HHs include a member who is economically active in an affected sector and working either on a self-employment basis or on a contract other than a contract of employment. Together with HH members who are economically active in those sectors on a contract of employment, the rate exceeds 30 percent.

Figure 1. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by place of residence

Source: Authors’ compilation based on 2018 HBS data.
Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market.

The percentage distribution of the HHs economically active in the affected sectors by family type is also uneven (Figure 3). In the group of families with at least one economically active member, the largest proportion of such HHs is reported in the group of single parents, with 31.5 percent working in the affected sectors and 6.6 percent in self-employment or on the basis of a contract other than the contract of employment. Similar percentages are reported for couples with children and at least one economically active HH member (24.2 percent and 7.8 percent, respectively.) Among working singles and couples with no dependent children, on average, one in five HHs has a HH member economically active in an affected sector. Of these HHs, 4.5 percent of the singles and 5.6 percent of the couples with no children are economically active in the affected sectors with contracts other than a contract of employment.

Figure 2. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by income decile

Source: Authors’ compilation based on 2018 HBS data.
Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market. Income decile groups are ten groups covering 10 percent of the population each, from households with the lowest disposable income to the most affluent households, on the basis of the so-called equivalent income, i.e. taking into account the differences in the size of the household using the modified OECD equivalence scale.

Figure 3. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by family type

Source: Authors’ compilation based on 2018 HBS data. Nota Bene: Economically active HHs – households with at least one member of the household active on the labor market. The following family types are distinguished: Singles – working age singles without dependent children; Single parents – working age single parents with dependent children; Couples without children – working age married couples without dependent children; Couples with children – working age married couples with dependent children.

Summary

Although our estimates of the percentage of families and households potentially exposed to the negative effects of the first wave of economic consequences of the COVID-19 pandemic do not necessarily imply that such a high share will actually be affected, the mere fact that so many families face the prospect of a deteriorating financial condition should stimulate a wide array of public policy support mechanisms. The economic support package called the “anti-crisis shield”, announced by the Government of Poland on March 18th, is a reaction to this challenge, though specific details of the announced version of the program have not been disclosed to date (Government announcement 2020). Still, the main focus of the package is on support for enterprises and entrepreneurs to help them continue business operation by postponing the due dates of business taxes and levies, and partially subsidizing employment of the workforce already on board. There is no doubt, however, that if the general economic slowdown continues for more than a few months, enterprises will be forced to start the layoffs and the self-employed will have to deregister. Therefore, the public finance system must be prepared to provide direct financial support to the households and offer a comprehensive benefit package to those who are laid off and to their families.

It is to be hoped that the economic consequences of the pandemic will be short-lived, and business activity will recover quite quickly to the pre-existing levels. For this to happen, first of all, we must keep the enterprises afloat, especially the small and medium-sized enterprises. Secondly, a fast economic reboot will be easier if the existing employment relations are preserved, even if the workload or the wages are curtailed. To that end, one solution would be to provide periodic financial support to employees in the affected sectors, even without formal termination of the contract between the employee and the employer. If the lockdown continues for more than two or three months, the financial support provided for in the “anti-crisis shield” package, representing 40 percent of the wage, may turn out to be inadequate to keep current employment levels intact.

If the pandemic-driven economic slowdown is prolonged – and there is no way this option can be ruled out today – it should be remembered that, apart from the sectors included in the analysis, the remaining sectors of the Polish economy will also be affected by the negative consequences of the recession; and the prolonged slowdown will eventually lead to a significant increase in unemployment rates. If that happens, households will need support through social transfers, both in the form of the unemployment benefit and benefits not related to a beneficiary’s track record in social security contributions paid, i.e. the housing benefit and social welfare benefits. With the expected substantial increase in public spending, the current policy of the state, focused primarily on universal public benefits, would have to be refocused on the transfers targeted at the most vulnerable households.

References

Ministry of Health (2020). Regulation of the Minister of Health of the Republic of Poland of the 13th March 2020 on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland.

Government announcement (2020). “Anti-crisis Shield” will protect companies and employees from the consequences of coronavirus epidemics.

Disclaimer

This brief was originally published as a CenEA Commentary Paper of 28th March 2020 on www.cenea.org.pl. The analyses outlined in this brief make part of the microsimulation research program pursued by CenEA Foundation. The data used in the analyses is based on the 2018 Household Budget Survey, as provided by Poland Statistics (GUS). Poland Statistics (GUS) has no liability for the results presented in the brief or its conclusions. Conclusions presented in the brief are based on Authors’ calculations based on the SIMPL model.

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. 

School Lockdown: Distance Learning Environment During the COVID-19 Outbreak

Image of an empty classroom representing distance learning environment during the COVID-19 outbreak

Students in Poland, as in many other countries, have been obliged to participate in distance learning as a result the COVID-19 pandemic and the lockdown of schools. Successful participation in this format of schooling requires some basic equipment (a computer with Internet connection) as well as adequate housing standards, in particular a separate room during online classes. Based on the data from the Household Budget Survey 2018, in this brief we take a closer look at the living conditions of schoolchildren in Polish households and their access to adequate infrastructure. Our findings indicate that in the case of 11.7 percent of households with schoolchildren aged 6-19 years housing conditions are insufficient for home schooling. Additionally, for about a quarter of households with schoolchildren distance learning can be a challenge due to inadequate technical equipment. These conditions vary significantly with household income and across urban and rural areas, which signals that prolonged distance learning in Poland is likely to exacerbate the influence of children’s socio-economic background on inequalities in education outcomes.

Introduction

In connection with the coronavirus COVID-19 outbreak, Poland’s Minister of Education, in a Regulation introduced on the 20th March 2020, postponed the end date of the lockdown of Polish schools until the 10th April 2020. Also, the regulation requires that education be organized for school-age students during this period by means of distance learning channels and methods (Ministry of Education 2020a). It is the responsibility of the principal of every educational facility to make sure that such education is provided. Furthermore, a “Guide to Education” was developed by the Ministry of Education with information and instructions on distance learning for all interested parties, such as school principals, teachers, parents and students (Ministry of Education 2020b). Due to the restrictions on the movement of people during the state of epidemic in Poland, effective as of the 20th March 2020, electronic media (the Internet and, potentially, the telephone) should serve as the main channel of communication between teachers and students/ parents.

Thus, since the 25th March 2020, 4.6M students in Poland have been studying remotely, and any decisions on reopening schools or extending the lockdown depend on the course of development of the pandemic. Even at the time of “regular” access to schooling, the discrepancies in living conditions between students, in particular in terms of their housing conditions and household infrastructure, have a substantial impact on the overall quality of learning and educational outcomes (e.g. Author et al. 2019; Guryan et al. 2008), all the more so when students have to switch to distance learning. In the current situation, substandard housing conditions and lack of access to a computer or the Internet can make it difficult or outright impossible for many students to access education in the coming weeks. Fair and equitable assessment of students’ skills and knowledge may also be affected, as well as their future academic achievements, especially for the cohorts who are about to complete their Grade 8 in the primary school and those who are preparing for their secondary school graduation examination (Polish: Matura). For a student to be able to participate in distance learning activities and benefit from online learning materials, s(he) must have access to a computer terminal with an Internet connection at home. In addition, it seems that effective distance learning requires adequate housing standards, such as a separate room for studying. The “Guide to Education” says little about the importance of these infrastructure- and housing-related factors, merely recommending that a problem, if any, should be reported to the school, and an adequate solution should be implemented in consultation with the form master.

As argued in this Policy Brief, the unexpected need for schools to switch to a distance learning environment will underscore the magnitude of inequalities among households (HHs) in terms of their access to the infrastructure required for the students to benefit from distance learning opportunities and the living conditions in which such distance learning is supposed to proceed. The findings in this Policy Brief are based on the latest data from the 2018 Household Budget Survey (HBS), as made available by Statistics Poland (GUS). Notably, while HH status regarding computer equipment and Internet access may have improved since the time the survey was conducted, it can be assumed that the living conditions reflected in survey data are an accurate representation of the present-day status.

The first part of the Policy Brief presents the living conditions of the HHs with students aged 6-19, attending schools of all levels, according to the number of rooms in a house or apartment. The analyses presented in the second part of the Policy Brief are focused on HH infrastructure required for distance learning. According to HBS data, in 11.7 percent of HHs with students the number of rooms is equal to or lower than the number of students. A total of 833K students live in those HHs. During the state of epidemic, when the adult population is also committed to the lockdown and self-isolation, the living conditions may not be optimum for home schooling. According to the 2018 HBS data, in 7.1 percent of HHs with students there is no computer or other similar device with Internet access, and in 17.3 percent of HHs the total number of such devices in the HH is lower than the number of students living in the HH. That means that for more than 1.6M students distance learning may be a serious challenge for technical reasons. In that context, it should be noted that the shortage of computer equipment in HHs varies significantly with HH financial conditions and place of residence. As discussed in the Policy Brief, the highest percentage of the HHs with inadequate supply of the equipment necessary for distance learning is reported in the bottom half of the income distribution, and in the HHs in rural areas.

1. Living Conditions of Students in Poland

The living conditions in which students are expected to continue their education over the next few weeks can affect the outcomes of distance learning and their academic achievements. Students who share a single-room dwelling unit with other members of the HH will experience particularly harsh conditions, especially in view of the lockdown also applying to adults. There are over 130K such students throughout Poland (Table 1), with top percentages reported in large cities (4 percent of HHs with students; Figure 1). Many HHs living in a two-room dwelling unit or house include only one student, but there are 490K students in two-room dwelling units or houses who share the two rooms with their school-age siblings.

In rural areas such HHs represent only 5.7 percent of the total (Figure 1), but in cities with populations exceeding 100K the figure is 7.6 percent, which means that the affected student population is 174K and 140K, respectively (Table 1). Another piece of pertinent statistics: in many of the HHs in multi-room dwelling units or houses (i.e. with three or more rooms), the number of students is equal to or greater than the number of rooms. In cities with populations exceeding 100K the figure is 1.2 percent of HHs with students, while in rural areas this ratio is 2.5 percent, with 116K students affected.

As illustrated in Figure 2, housing conditions that can be described as not conducive to distance learning vary significantly with HH income. At the bottom end of the income distribution scale, among HHs with students, there are significantly more HHs in which the number of rooms may be inadequate in relation to the number of students living there. In every fifth HH from the second and third income decile group, each of the students living there may not have a separate room at their disposal; whereas in the group of top income HHs (from the tenth decile group) with students, this ratio is only 3.7 percent.

Table 1 Student count in the breakdown according to their living conditions and place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015.)

Figure 1 Count of rooms and students in households by place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015.)

Figure 2 Count of rooms and students in households by income decile group

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).
Nota Bene: Income decile groups are ten groups covering 10 percent of the population each, from households with the lowest disposable income to the most affluent households, on the basis of the so-called equivalent income, i.e. taking into account the differences in the size of the household using the modified OECD equivalence scale.

2. Distance Learning Infrastructure in Households

To be able to use electronic educational materials available on the Internet; to participate in classes conducted by teachers on various online platforms; or even to send back homework assignments over the Internet; students need to have home access to a computer connected to the Internet (for simplicity, the term “computer” used in this Policy Brief means a computer or a similar device with Internet access).

According to 2018 HBS data, close to 330K students do not have home access to a computer connected to the Internet (Table 2). In the case of another 1.3M students, the number of such devices is lower than the number of students in the HH, so it may not be sufficient to satisfy the needs of all students undergoing parallel remote education in the HH. In other words, as many as 7.1 percent of HHs with students have no access to distance learning at all due to the lack of appropriate equipment, while for a further 17.3 percent of the HHs the shortage of relevant infrastructure may significantly impede distance learning efforts (Figure 3).

As shown in Figure 3, the challenge of inadequate infrastructure for distance learning is reported much more frequently in single parent HHs, as compared to couples with school-age children. Among students raised by a single parent, every tenth family does not have a computer with Internet access, and in every eighth family the number of such devices is insufficient for all the students living in the HH. Among married couples with children, 6.4 percent of families report no computer, and in 18.2 percent of families the number of computers is lower than the number of students in the HH.

Table 2 – Students with/without a computer with Internet access, by place of residence

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).
Nota Bene: The values shown in the Table refer to computers with an Internet connection. The total number of students is slightly different from the value shown in Table 1, because 2018 HBS survey sample for HH infrastructure has been reduced.

Figure 3 Computers with Internet access in households with students, by place of residence and family type

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015). Nota Bene: Family types are listed within HH category.

Map 1 Computers with Internet access in student population, by region of the country

a) Student has no computer with Internet access at home

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).

b) Student must share the computer with school-age siblings

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015).

According to HBS data, students living in rural areas may be particularly exposed to problems in using distance learning. Although the percentage of HHs with students that do not have a computer with Internet access in rural areas is similar to that reported for urban areas (regardless of the size of the city/town), there are visible discrepancies in the availability of a sufficient number of hardware items between different categories defined according to place of residence. In rural areas one in every five HHs reports that the number of computers in the HH is lower than the number of students, whereas in big cities (population above 100K) this issue is reported by 9.7 percent of the HH.

Inequalities in access to distance learning are also visible across Poland’s regions. As illustrated on Maps 1a and 1b, students from Lubuskie Voivodeship do not have access to a computer connected to the Internet (12.6 percent) or have to share a computer with school-age siblings (37.5 percent) much more often than students from other regions of the country. For comparison, 4.4 percent of the students from Zachodniopomorskie Voivodeship do not have a computer at home, and every fifth student does not have a computer for their personal use.

Significant differences in access to the infrastructure required for distance learning are also manifested in division by income deciles (Figure 4.) In the population of HHs with students, in the two bottom decile groups (i.e. among 20 percent of HHs with the lowest income), as many as one in ten HHs does not have a computer connected to the Internet, and another 20 percent plus cannot provide individual access to a computer for each of the school-age children. At the other end of income spectrum, only about 4.1 percent of HHs with students do not have a computer, and in the case of another 8.3 percent students do not have a computer for their personal use.

Figure 4 Computers with Internet access in households with students, by income decile group

Source: Authors’ calculations based on 2018 HBS data (weighted based on Myck i Najsztub 2015). Nota Bene: Income decile groups are ten groups covering 10 percent of the population each, from households with the lowest disposable income to the most affluent households, on the basis of the so-called equivalent income, i.e. taking into account the differences in the size of the household using the modified OECD equivalence scale.

Summary

According to 2018 Household Budget Survey data, close to 330K students do not have home access to a computer connected to the Internet; and in the case of another 1 320K students the number of computers in the HH is lower than the number of students living in the HH. Under such circumstances, distance learning on a regular basis during the COVID-19 outbreak is either outright impossible or very difficult. Due to infrastructure shortages, distance learning is particularly difficult for students living in the HHs in rural areas (30 percent of all HHs with students), but the difficulties of this nature are also reported by students living in big cities (17.1 percent of HHs). Single parent families are affected by a lack of computer equipment more frequently than married couple families (11.2 percent vs 6.4 percent); and the situation varies to a large degree depending on HH income levels. While in the HHs with students grouped in the bottom decile as much as 33.9 percent do not have access to a computer or have a computer to share with their school-age siblings, in the HHs from the top decile group the corresponding percentage is almost three times lower.

The housing conditions in which Polish students follow the curriculum are an additional impediment to distance learning. More than 130K students live in one-room dwelling units, and nearly 700K live in multi-room units where the number of rooms is the same or lower than the number of students in the HH. In terms of the housing stock, access to an adequate number of rooms for effective distance learning also varies with income level. While in the bottom two decile groups the number of rooms in relation to the number of students is insufficient for 16.6 percent and 20.7 percent of the HHs, in the top two income deciles the corresponding ratio is as low as 4.5 percent and 3.7 percent.

The longer the duration of the distance learning regime, the greater the impact of inequalities in access to distance learning for students. It may take a particular toll on the cohorts which complete their final year of each stage of education. The inequalities will be compounded by differences in support in distance learning the students can receive from their parents or guardians. A population of 720K students live in single-parent HHs, and 380K of those single parents are economically active; and speaking of the population of students living together with both parents, there are 2.6M students in whose case both parents were economically active at the point of the pandemic outbreak. Even if some parents have now been forced to cut down on their professional responsibilities, others continue working – either at the workplace or from home.

For many reasons, students as well as their parents, guardians and teachers are looking forward to students’ return to schools – it will be a long-awaited sign that the epidemic situation has stabilized. Yet, this moment will be especially important for those students for whom distance learning was a particular challenge due to their living or infrastructure-related conditions. In an effort to reduce inequalities in access to distance learning, educational facilities in cooperation with local authorities, should extend special support to the students for whom distance learning is difficult due to objective causes. It seems that the first step should be to collect specific information about the distance learning environment available to students and, if necessary, to fill in the gaps in computer equipment and Internet access. Furthermore, if the epidemic allows, it seems purposeful to introduce, to a limited extent and with appropriate security measures, direct contact between students and teachers, especially where effective distance learning turns out to be difficult or impossible to implement.

References

Disclaimer

This brief was originally published as a CenEA Commentary Paper of 28th March 2020 on www.cenea.org.pl. The analyses outlined in this brief make part of the microsimulation research program pursued by CenEA Foundation. The data used in the analysis is based on the 2018 Household Budget Survey, as provided by Poland Statistics (GUS). Poland Statistics (GUS) has no liability for the results presented in the brief or its conclusions. Conclusions presented in the brief are based on Authors’ calculations based on the SIMPL model.

CenEA is an independent research institute without any political affiliations, with main research focus on social and economic policy impact assessment, with a particular emphasis on Poland. CenEA was established by the Stockholm Institute of Transition Economics (SITE) and is a Polish partner of the FREE Network. CenEA’s research focuses on micro-level analyses, in particular in the field of labor market analysis, material conditions of households, and population ageing. CenEA is the Polish scientific partner of the EUROMOD international research project (European microsimulation model), and maintains its microsimulation model SIMPL. For more information, please visit www.cenea.org.pl.

This brief was prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). Research in the FROGEE project contributes to the discussion of inequalities in the Central and Eastern Europe with a particular focus on the gender dimension. For more information, please visit www.freepolicybriefs.com. The views presented in the brief reflect the opinions of the Authors and do not necessarily represent the position of the FREE Network or Sida.

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.