Location: Poland
Social Norms, Conspiracy Theories and Vaccine Scepticism: A Snapshot from the First Year of the Covid-19 Pandemic in Poland
In January 2022, Poland experienced the highest rate of SARS-CoV-2 transmission since the beginning of the COVID-19 pandemic. Considering the widespread consensus among experts about the efficacy of vaccines in preventing hospitalisation and death resulting from the virus, low vaccination rates and widespread anti-vaccine sentiments in Poland are of great concern. We use data from the DIAGNOZA+ Survey to demonstrate the relationship between various demographic characteristics, opinions around certain gender norms, the propensity for conspiratorial thinking, concern about the pandemic, and vaccine scepticism. While controlling for exogenous demographic characteristics, we measure the strength of the relationship between various beliefs that people hold and how they feel about the COVID-19 vaccine. Our analysis indicates that while respondents who hold more traditional views on gender roles are 6 percentage points less likely to get vaccinated, those who agree with a variety of conspiratorial statements are 43 percentage points less likely to vaccinate against COVID-19.
Introduction
As of January 2022, Europe finds itself well into the 4th wave of the COVID-19 pandemic, with some countries, including Poland, experiencing the highest rates of transmission since the virus was first detected. There are a few tools available to policymakers and healthcare professionals for combating the spread of the virus, ranging from preventative measures to strict social lockdowns aimed at reducing interpersonal interaction. A comprehensive literature review of 72 academic studies conducted by the BMJ found that the implementation of preventative measures such as hand washing, mask wearing, and social distancing decreased the risk of transmission by 53% (Talic et al., 2021). But even though such measures reduce transmission, the shortcomings in adherence and enforcement make high vaccination rates much more effective in diminishing the risk of hospitalization and death (Moline et al., 2021). With a consensus among experts reaffirming the effectiveness of vaccines in minimising the more severe cases of COVID-19 illness, the widespread availability of the vaccine has become the most effective and cost-efficient tool in limiting morbidity while avoiding future instances of economically unsustainable lockdowns. The drawbacks of the alternative scenario have already been made evident in 2020, before the development and distribution of COVID-19 vaccines. Over the course of the year, hospital capacities were overwhelmed in many countries around the world, leading to significant spikes in excess deaths. Poland saw an increase of over 18% in all-cause mortality in 2020 (OECD, 2021), the fourth-highest in the OECD and second-highest in the European Union (Eurostat, 2021).
Considering the central role that prevalent vaccination plays in combating the impact of COVID-19, it is important to understand the underlying factors and demographic characteristics of individuals who are driving the low vaccination rates in countries such as Poland. With this in mind, we use an online survey: DIAGNOZA+ (DIAGNOZA Plus, 2020-2021), conducted on a representative sample of adults in Poland throughout the pandemic, allowing for the identification of characteristics that are most strongly correlated with vaccine scepticism. This kind of analysis can provide useful indicators for the targeting of certain policies and information campaigns that encourage vaccinations, and thereby suppress future outbreaks of SARS-CoV-2, as well as any other future pandemics. Below, we first outline the key features of the DIAGNOZA+ data, describe the methodology adopted in this study, and present results on the relationship between key demographic characteristics, social norms, views of respondents, and attitudes towards COVID-19 vaccination. We show a strong correlation between traditional family values, conspiratorial views, and reservations relating to the vaccination programme. Having traditional family values (expressed by about 40% of the sample) is associated with an over 10 percentage point (p.p.) lower probability to declare a willingness to get vaccinated. This drops to about 6 p.p. when we extend the model to account for conspiratorial thinking, which strongly dominates the relationship. Individuals who express strong conspiratorial and anti-establishment views (about a quarter of the sample), conditional on other demographic characteristics, were more than 40 p.p. less likely to declare a willingness to get vaccinated.
Methodology
The following analysis is based on data from DIAGNOZA+, an online survey collected in seven waves over the course of the COVID-19 pandemic (DIAGNOZA Plus, 2020-2021). The panel survey was conducted with the purpose of assessing changes in the labour market situation of adults in Poland between April 2020 and July 2021. The survey consistently included standard questions on individual and household characteristics such as age, gender and education, as well as questions on as well as questions about the respondent’s labor market status, hours worked, and financial situation. Waves 3 and 4 included additional modules where respondents were asked to express their opinions on a variety of statements surrounding gender norms such as “In general, fathers are as well suited to look after their children as mothers”, “A pre-school child is likely to suffer if his or her mother works” and “When jobs are scarce, men should have more right to a job than women”. The questions were answered on a scale of 1 (strongly agree) to 4 (strongly disagree). For the analysis, these categorical variables are dichotomised, with a value of 1 assigned to responses 1 and 2 (strongly agree or agree) and a value of 0 assigned to responses 3 and 4 (disagree or strongly disagree). Thus, for each question, we develop a binary variable that categorises respondents as either having a progressive or traditional reaction to each particular gender norms statement.
In consecutive waves, the same respondents were asked questions surrounding their willingness to vaccinate against the virus (in wave 5) and their trust in experts and the government response to the COVID-19 pandemic (in wave 6). For this analysis, we select questions that may influence an individual’s likelihood to vaccinate, starting with their level of concern about the pandemic or their fear of the virus itself. Furthermore, we identify individuals with a high predisposition for conspiratorial beliefs based on information from wave 6. Each variable included in this module is converted into a binary measure of agreement or disagreement, as outlined above for the social norms questions. We consider seven statements from the survey related to conspiratorial views, including “Secret organisations influence political decisions” or “I trust my intuition more than the so-called experts” (see the full list of statements in Figure 2). For each of them, the variable is converted into a binary measure of agreement or disagreement, similarly to the social norms questions above. Those who agreed or strongly agreed with all seven statements are classified as having conspiratorial views.
Due to sample attrition and after dropping respondents who did not answer one (or more) of the questions needed for our analysis, the sample reduces to 726 individuals (see table A1 in the Annex). Although each wave of the DIAGNOZA+ survey is carefully weighted to ensure population representativeness of the survey, these cross-sectional weights are only relevant to each independent wave of the survey. Therefore, for our sample, we develop frequency weights by sex and age using population data from Statistics Poland (Statistics Poland, 2021), which are utilised throughout the analysis. Given the low number of participants in the oldest age groups (those above 60 years old), we limit the sample to individuals aged between 21 and 60. Unfortunately, calibrating the weights according to additional characteristics such as education and municipal population is not feasible with a sample of this size. Clearly, the requirement of consistent consecutive participation in at least three waves of the survey has implications for its representativeness. For example, after the sample of respondents that participated in wave 6 is cut to include only those who also participated in waves 3, 4 and 5, we observe a bias in favour of conspiratorial views among the remaining observations, indicating that individuals who hold these views were more likely to continue their participation in the survey. For example, while 18.1% of the total cross-sectional sample of individuals in wave 6 hold conspiratorial views, the proportion is 23.4% in the sample we analyse (falling slightly to 23.2% when weights are applied). From this perspective, while indicative of existing correlations, the results ought to be treated with some caution.
Limiting the sample to respondents who answered all sets of questions across several rounds of the survey allows us to study vaccine scepticism and respondents’ susceptibility to conspiracy theories in relation to a number of personal characteristics. Furthermore, we consider the relationship between a respondent’s attitudes towards certain social norms (asked in waves 3 and 4), their individual response to COVID-19 (asked in wave 5), and their trust in the government’s response to the pandemic (asked in wave 6). We begin the analysis by assessing the relationship between respondents’ demographic characteristics and their opinions on gender roles, their propensity to hold conspiratorial beliefs, and their concern about the pandemic. This is followed by two models measuring respondents’ willingness to vaccinate. In the first of these models, demographic characteristics and traditional family values are used as explanatory variables, while in the second model conspiratorial views are included as well. Finally, we conclude with a summary of results and policy considerations.
Survey Results
Traditional Family Values in Poland
The respondents of the DIAGNOZA+ survey vary, on average, in the ‘traditionality’ of their attitudes towards gender and family depending on the selected indicator. The shares of answers to the three questions about gender norms are presented in Figure 1. The results demonstrate that progressive views on gender norms in Poland were more common in relation to the workplace than the home and family. For example, the statement to which most respondents were opposed was “When jobs are scarce, men have more right to a job than women”, with 37.2% of respondents disagreeing and 50.3% of respondents strongly disagreeing. On the other hand, slightly fewer respondents disagreed (50.5%) or strongly disagreed (34.8%) with “In general, fathers are not as well suited to look after their children as mothers”. Finally, respondents were most ‘traditional’ in their views in reaction to the statement “A pre-school child is likely to suffer if his or her mother works”, with 28% agreeing and 10% strongly agreeing. There is a natural correlation between these different views, and in our analysis, we examine the significance of different combinations of the three indicators. Given the relatively small sample, only the last indicator proved to be significantly related to our main outcome of interest and we use this one to represent the view on the ‘progressive-traditional’ spectrum
Figure 1. Gender norms in the survey sample
Source: DIAGNOZA+ survey, waves 3 and 4. Note: Data weighted using weights generated from Statistics Poland’s data on population by sex and age. Sample limited to individuals aged 21-60. The statement “In general, fathers are as well suited to look after their children as mothers” from the questionnaire was adjusted in the graph for better readability.
Conspiratorial Views
In wave 6 of the DIAGNOZA+ survey respondents were asked seven different questions relating to trust in government, politicians, media, and the recommendations of experts. As shown in Figure 2, for five out of the seven statements, a majority of respondents agreed or strongly agreed that the government or media are dishonest, intentionally share misinformation, or have ulterior motives. Nearly three quarters of respondents agreed that “politicians and the media deliberately hide certain information”. This result supports data published by the OECD in 2020 showing that, out of the 38 member countries, Poland had the second-lowest trust in government, with only 27.3% of the population expressing confidence (OECD, 2022). However, the DIAGNOZA+ survey goes further to find that nearly half of respondents in our sample reported that they trust their own intuitions more than the experts during the pandemic, while the least widespread belief out of the seven was that “secret organisations influence political decisions”. Still, even this statement, which suggests deep-seeded nefarious behaviour behind the scenes of government, found 39.8% of respondents to be in agreement. Note that we aim to identify individuals who have a general propensity for conspiratorial thinking, rather than those who simply find some of the statements particularly compelling. To this end, we only categorise those respondents who agreed with all seven statements as having a high propensity for conspiratorial thinking, which was the case for 23.2% of our sample after reweighting.
Figure 2. Conspiratorial beliefs and trust in authority
Source: DIAGNOZA+ survey, wave 6. Note: Data weighted using weights generated from Statistics Poland’s data on population by sex and age. Sample limited to individuals aged 21-60.
Analysis
Table 1 presents regression results on the relationship between specific beliefs reported in the different waves of the survey and a number of individual characteristics. We show these results for three dependent variables: traditional family values, as defined by the opinion that a pre-school child is likely to suffer if his or her mother works; propensity for conspiratorial views, which identifies the respondents that agreed with all seven statements presented in Figure 2; and concern about the pandemic, a binary variable that identifies individuals who expressed great worry or fear about the pandemic. The results indicate that parents who live with their children are 10.1 p.p. more likely to hold traditional family values. After controlling for age, gender and education, living in a small town or village is associated with a 10.9 p.p higher probability of ascribing to more traditional gender norms, while individuals holding a tertiary degree are 18 p.p. less likely to agree that “a pre-school child is likely to suffer if his or her mother works” compared to those with primary education. Interestingly, neither age nor gender significantly correlates with family values, suggesting that the DIAGNOZA+ survey did not capture an intergenerational or gender-driven divide on these issues. This might relate to the online nature of the survey and the implied sample selection, in particular among older individuals.
Table 1. Regression results on views and attitudes
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Note: Data weighted using weights generated from Statistics Poland’s data on population by sex and age. Sample limited to individuals aged 21-60. Estimates using the linear probability model.
The results presented in Table 1 also demonstrate a relationship between some demographic characteristics and the likelihood to hold conspiratorial views (as defined by expressing agreement to the seven related statements in wave 6). A number of characteristics strongly correlate with conspiratorial thinking: being a parent living with their children aged 0-17, and living in small cities, towns and villages. Each of these characteristics is associated with a higher probability of believing in secret organisations and mistrusting experts. A number of characteristics strongly correlate with conspiratorial thinking: holding such views are 9.3 p.p. more likely among parents living with their underaged children and 10 p.p. more likely among individuals living in smaller towns or villages compared to those living in cities of over 500 thousand inhabitants. Higher education is strongly negatively correlated with the likelihood of holding conspiratorial views – those with tertiary education are 14.5 p.p. less likely to have these views compared to individuals with primary education.
One simple explanation for the increased vaccination rates among certain demographic groups in Poland could be that some segments of the population are more worried about the virus, and thus choose to take greater precautions. The analysis presented in Table 1 demonstrates that people were increasingly likely to be concerned about the pandemic in higher age groups. When asked “To what extent are you concerned about the COVID-19 pandemic?”, the probability of expressing serious concern increases progressively with age. This is an intuitive result considering the strong relationship between age and the severity of COVID-19 symptoms and the associated risk of mortality (CDC, 2021). Respondents aged between 31 and 40 were 10 p.p. more likely to report being very concerned or frightened than respondents between the age of 21 and 30, while in the age groups 41-50 (12.6 p.p.) and 51-60 (21.4 p.p.) the probability was even higher. There is also a weak but positive correlation (7.7 and 8.6 p.p.) between living in a city with a population of 10,000 to 500,000 inhabitants and expressing fear about the pandemic, as compared to respondents who lived in cities with a population of more than 500,000 people. The relationships between the remaining demographic characteristics and the probability of being seriously concerned about the pandemic are not statistically significant. Below, we use this data to examine the link between people’s beliefs and the likelihood of getting vaccinated.
Vaccine Scepticism, Demographic Characteristics and Conspiratorial Views
In light of the widespread scientific consensus on the safety and effectiveness of COVID-19 vaccines, low vaccination rates in Poland are difficult to explain. In this section, we analyse to which extent they may be driven by the underlying beliefs, on top of the socio-demographic characteristics. Overall, 54% of respondents in the selected sample from the DIAGNOZA+ survey planned to be or had already been vaccinated. Thus, the survey sample closely reflects the actual proportion of the population that was fully vaccinated in Poland as of January 2022. (ECDC, 2022). In Model A of Table 2, we present the relationship between the response to the question “Do you plan to get vaccinated against COVID-19 or are you already vaccinated?” and traditional family values, alongside the usual demographic characteristics. We find that those in the 51-60 age group were 14.5 p.p. more likely to plan to vaccinate than those aged between 21 and 30. This also reflects the higher level of concern about the virus expressed by those over the age of 50, as presented in Table 1, and the risk of serious illness associated with increasing age. However, the relationship between age and the probability of vaccination was much weaker than the relationship between age and the probability of expressing general concern about the pandemic, implying that concern does not translate directly into a willingness to vaccinate. We also find that tertiary education has a particularly strong effect, and respondents who have a university degree were much more likely (17.7 p.p.) to get vaccinated than those with less than secondary education.
Through this analysis we also discover several less intuitive relationships between individual characteristics and the propensity to vaccinate. We find that women are 11.5 p.p. less likely to plan to vaccinate against COVID-19 than men. Moreover, individuals living in a city with less than 500,000 inhabitants were much less likely to vaccinate, with the strongest correlation (-23.5 p.p.) observed for respondents living in medium-sized cities of 100,000 to 500,000 people. However, a strong relationship can also be seen for smaller cities of 10,000 to 100,000 inhabitants (-19.3 p.p.) and small towns and villages (-17.2 p.p.). Respondents’ expressions of traditional family values are also a strong predictor of their propensity to vaccinate. After controlling for gender, age, education and municipality size, those categorised as holding traditional views are 10.6 p.p. less likely to plan to vaccinate against COVID-19. Our findings demonstrate that while population density, education, age and gender, are all strong indicators of vaccine scepticism in Poland, so is the degree of traditionalism in people’s beliefs.
Table 2. Regression results on vaccination: probability of being vaccinated or planning to get vaccinated
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Note: Data weighted using weights generated from Statistics Poland’s data on population by sex and age. Sample limited to individuals aged 21-60. Estimates using the linear probability model.
A commonly cited explanatory factor for vaccine scepticism is the susceptibility to conspiratorial beliefs, as well as scepticism towards information disseminated by figures of authority (Hornsey et al., 2018). Thus, in Model B, we seek to identify a relationship between conspiratorial beliefs and scepticism towards the COVID-19 vaccine in Poland. When adding to our model a binary indicator for agreement with all seven of the conspiratorial statements included in the survey, we find that those who agreed across the board were 43.3 p.p. less likely to get vaccinated. Therefore, it seems that the propensity for conspiratorial thinking is a very strong correlate of willingness to vaccinate, and the characteristic most strongly associated with vaccine scepticism. The impact of the demographic factors goes in the same direction for both models, although the scale diminishes in Model B after controlling for conspiratorial views, reflecting the higher propensity of older individuals to hold such views. Furthermore, the effect of traditional family values is much weaker in Model B, suggesting a positive correlation between traditional family values and conspiratorial beliefs (Figure A1 in the Annex shows how values and views in the analysis views overlap with each other). This is in line with past research that ties traditional moral values and conservatism with conspiratorial beliefs, both before and during the COVID-19 pandemic (Pennycook et al., 2020; Romer and Jamieson, 2021).
One explanation for the strong relationship between conspiratorial beliefs and vaccine scepticism could be that respondents who do not trust the media and figures of authority believe that the dangers of the pandemic have been exaggerated and would thus not be concerned about its consequences. We account for this possibility in Model C by including the indicator for fear of the pandemic. We find that those who are very concerned or frightened are 21.1 p.p. more likely to vaccinate than those who are not. However, including this variable in the model has little effect on the estimates of the relationship between traditional gender views or conspiratorial thinking and the likelihood to vaccinate. Further research is needed to understand what is driving these relationships in this particular context. These findings demonstrate that while individuals that believe in conspiracies are the most susceptible to vaccine scepticism, other elements such as fear of the pandemic, education attainment, and where people live play an important role as well.
Conclusion
By January 2022 most European countries have reached a plateau in their vaccination rates, with free vaccines readily available since the summer months of 2021 to all those who are willing to take them. Not only have the high rates of hospital admissions among the non-vaccinated population proven the epidemiological models about the efficacy of vaccines in reducing hospitalisation and death to be true (a study in the United States showed a more than tenfold reduction in the risk of each measure; Scobie et al., 2021), but disparities between countries in the proportion of the population that is vaccinated have created a natural experiment that further substantiates this hypothesis. Poland, a country with a vaccination rate that is 15 p.p. lower than neighbouring Germany, had virtually the same number of cases per 100,000 people in the first two weeks of December, but almost threefold the number of deaths from COVID-19 (ECDC, 2021). Due to the burden COVID-19 related hospitalisations place on healthcare systems, the issues arising from the significant scale of vaccine scepticism are not only related to physical well-being, but also directly impact economic and fiscal stability.
Despite a fairly small sample size available for our analysis from the DIAGNOZA+ survey, a number of important correlations are identified in this study. We find that people living in cities and towns smaller than 500,000 people are less likely to vaccinate than those living in big cities. We show that women, those with less than secondary education, and young people are less likely to be vaccinated. Moreover, those believing that pre-school-aged children suffer when their mothers work are less likely to vaccinate compared to those with more progressive gender views. The most significant predictor of vaccine scepticism, however, is whether a respondent expressed low trust in authority and belief in the conspiracy theories presented in the survey, which was the case for 23.2% of the sample. These individuals are more than 40 p.p. less likely to express willingness to get vaccinated than the rest of the population. This suggests that the low rate of vaccination in Poland can, in part, be attributed to widespread distrust of government, the media, and scientific experts. Poland has already suffered the consequences of the high magnitude of anti-vaccine sentiments in the population, with the severity of the fourth wave of COVID-19 being one of the harshest in Europe (ECDC, 2021). If the government intends to prevent future outbreaks and protect the healthcare system and the economy, it must present a consistent, clear, and transparent message about the safety and efficiency of vaccines to minimise the misinformation that is driving vaccine scepticism among certain demographic groups.
References
- Centers for Disease Control and Prevention, (2021). Hospitalization and Death by Age.
- DIAGNOZA Plus, (2021). https://diagnoza.plus/
- European Centre for Disease Prevention and Control, (2021). Data on 14-day notification rate of new COVID-19 cases and deaths
- Eurostat, (2021). Excess mortality by month.
- Hornsey, M., Harris, E., &. Fielding, K., (2018). “The psychological roots of anti-vaccination attitudes:
A 24-nation investigation”, American Psychological Association. - Moline H. et al., (2021). “Effectiveness of COVID-19 Vaccines in Preventing Hospitalization Among Adults Aged ≥65 Years – COVID-NET, 13 States, February-April 2021”, Morbidity and Mortality Weekly Report.
- Organisation for Economic Co-operation and Development, (2021). “The impact of COVID-19 on health and health systems”.
- Organisation for Economic Co-operation and Development, (2022). “Trust in Government, OECD data”.
- Pennycook, G., Cheyne J.A., Koehler, D., & Fugelsang, J.(2020). “On the belief that beliefs should change according to evidence: Implications for conspiratorial, moral, paranormal, political, religious, and science beliefs”, Judgement and Decision Making
- Romer D. & Jamieson K. H., (2021). “Conspiratorial thinking, selective exposure to conservative media, and response to COVID-19 in the US”, Social Science & Medicine
- Scobie H. et al., (2021). “Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021”, Morbidity and Mortality Weekly Report.
- Statistics Poland, (2021). “Demographic Yearbook of Poland 2021”.
- Talic S. et al., (2021). “Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis”, The BMJ.
Annex is available in the PDF version.
Disclaimer
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 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.
Ukrainian Refugees in Poland: Current Situation and What to Expect
The 2022 Russian invasion of Ukraine has forced millions to flee from the war zone. This brief addresses Ukrainian refuge in Poland. It provides an overview of the current situation, discusses the ongoing solutions and potential future challenges, and stresses the key areas for urgent policy intervention. It is based on a presentation held at the FREE Network webinar Fleeing the war zone: Will open hearts be enough?, which took place on March 14, 2022. The full webinar can be seen here.
The latest data (from March 15, 2022) shows that since February 24, 1.8 million refugees have already crossed the Polish-Ukrainian border. This number represents over 60 percent of Ukrainians who have fled the country thus far. Among this group that relocated to Poland, approximately 97 percent were people with Ukrainian citizenship. Most of the foreign nationals living in Ukraine before the war, and who came to Poland after its outbreak, have already returned to their countries of origin.
Figure 1. The influx of refugees from Ukraine to Poland since February 24, 2022.
Note: The vertical axis shows the number of refugees per million. Source: Data from Polish Border Guard
Our estimates show that there are currently about 1.1 million Ukrainian war refugees in Poland. Many stay in large cities such as Warsaw, Kraków or Wrocław. The rest of those who crossed the Polish border transited to the other EU Member States or countries outside of Europe, such as Canada or the USA, reuniting with their families and friends.
In the first days after the outbreak of the war, refugee assistance in Poland was mostly provided by Polish families and households, as well as owners of guesthouses and hotels who made them available for the purpose of providing accommodation.
A similar situation took place at the border and at railway and bus stations where refugees were arriving, with a majority of support coming from volunteering citizens. This assistance largely consisted of the provision of basic necessities such as food, hygiene products, and medical or psychological first aid. The level of mobilization among non-governmental organizations, grass-roots initiatives, private citizens, and civil society, in general, is extremely commendable and should be accredited with providing the safe welcome refugees received upon arrival. For example, during the first days, Polish families sheltered several hundred thousand refugees, often in their own houses or apartments. There are currently two main Ukrainian social groups arriving in Poland: women with children and older persons over the age of 60. This is a result of Ukraine’s internal regulations, which prohibit men aged between 18 and 60 from leaving the country.
Among those who have managed to escape the war, there is a large group of people requiring very specialized support, e.g. children suffering from oncological diseases, and elderly with a high degree of disability. So far, these groups have been provided with the necessary support, but if these needs become more frequent, a review of the capacity of the Polish healthcare system and the system of support for the disabled will be needed.
In the first days after the war broke out, the situation at the border was very difficult. The waiting time for crossing reached up to 70 hours. However, this was related to problems with the information system and the limited number of border guards on the Ukrainian side. Currently, crossing the border is quick and seamless. Every day the Polish Border Police register 80 to 100 thousand individuals, a vast majority of them crossing into Poland. This is a many-fold increase compared to pre-war migration flows, which fluctuated around 12-15 thousand people per day. At the same time, over 80.000 people, mainly men, have crossed the Polish border to Ukraine in the last 20 days with the goal of joining the army or territorial defense.
For a long time, the Polish government held the position that there would be no need to build refugee centers. However, the government recently reversed this decision and decided to open a dozen centers, located in market and sports halls. Currently, over 100,000 people are staying in these types of temporary accommodation facilities. However, these centers are not sufficiently adapted for stays longer than a few days. It is necessary to prepare housing infrastructure (temporary accommodation centers equipped with habitable containers) in which refugees can stay for two or three months until they find another place to live.
So far, Poland has essentially dealt with two of three possible migratory waves. In the first, people with family members or friends living in Poland or in other EU Member States arrived. Before the war, there were already approximately 800 thousand Ukrainians working or studying in Poland. In the second wave, after the bombing of civilian facilities in large cities, people without family or friends living in Poland started arriving. They require full assistance. A third wave is possible, and this one may be much larger than the previous two. It may occur if the situation at the front worsens and the repressions by Russian troops become harsher. Such reports are already coming from eastern Ukraine. If the situation worsens, Poland could even face a couple of additional million people that would leave Ukraine. Under these circumstances, we should assume that the third wave would include young men in addition to women, children, and the elderly. This scenario is currently very unlikely, but cannot be completely ruled out.
Since the beginning of March, Poland has seen an increase in the activity of both local representatives of the government administration and the central government. Information has been gathered about vacancies in smaller cities and local communities where refugees could be accommodated. This is because large cities are on the verge of reaching their capacity for the number of refugees they are able to manage. In addition, a special law entered into force on March 13, which provides for a catalogue of support tools for refugees. The main issues are:
1. The possibility of obtaining an individual identification number, which will enable the opening of a bank account and grant access to the labor market, education, and social benefits. It will be possible to apply for the ID number from March 16. Certainly, large queues can be expected in the first days, as the procedure is complicated and rather bureaucratic. The government decided to require all the necessary information at the start of the application process, which could be complicated for some applicants and lead to additional delays. Based on recent numbers, up to 1 million Ukrainians may apply for an individual identification number in the near future.
2. Reimbursement of the costs of hosting refugees from Ukraine in Polish family homes and in private hotels. The government has agreed to cover the value of around 8 euros per day for each person. However, receiving this refund requires submitting a special application to the local administration offices, which may again cause various kinds of perturbations, and even resignation from obtaining such support.
3. Ukrainian children can be enrolled in Polish schools. It will also be possible to open school branches in temporary accommodation centers, as well as parallel Ukrainian classes inside Polish schools. At present, however, the preferred model is the inclusion of Ukrainian children in Polish classrooms. Currently, no major problems have been reported with this process, but only around 10% of Ukrainian children have entered Polish schools so far. Numerous challenges connected with this integration process are expected. Part of the solution could be distance learning or hybrid learning. The priority is to involve children in education as fast as possible so that they do not lose time while living in Poland from an educational development point of view.
4. A simplified system of qualifications recognition has been implemented for nurses and doctors. Unfortunately, contrary to the advice of experts, the act does not provide guidelines for a simplified qualification recognition of teachers, educators or psychologists from Ukraine. In his media statements, the Minister of Education and Science did not rule out introducing a simplified procedure in the near future. Such recognition could, to some extent, solve the problem of understaffing in Polish schools.
5. All adults from Ukraine who arrived after February 24 have open access to the labor market.
Until early March, the Polish government did not apply for support from other EU member states. Now, this position has changed. Over the first weekend of March alone, more than 20 trains were organized that made it possible for refugees interested in moving from Poland to countries such as Germany or other destinations within the EU. Additional relocation measures are expected in the near future. However, in contrast to the European migrant crisis in 2015, the relocation scheme of Ukrainian refugees is carried out on a voluntary, rather than a compulsory basis.
It is very difficult to predict what will happen in the next days or weeks. While it should be emphasized that Poland is managing the migration challenge well, this is not least due to the exceptional commitment of civil society. Certainly, in the coming months, Poland will not be able to cope with the integration of more than 800.000 people into the labor market and education system. Of course, it is possible to provide ad-hoc support, but that is completely different than integrating refugees into Polish society. Ukrainians are still treated as guests who are expected to return to their homes when possible. Such an assumption should not be changed until May when the situation in Ukraine will be more predictable. We must also be aware that we are dealing with dispersed families who will want to reunite as soon as possible. It is not known, however, whether this will take place in Poland or in Ukraine. It depends on how the situation develops in the weeks and months to come.
In the coming weeks, the key issue will be the relocation of Ukrainian refugees from large to smaller cities within not only Poland but also the European Union. It is absolutely necessary to coordinate activities both at the level of the Polish government and the European Commission. As far as the Polish government is concerned, a task force should be established to maintain constant contact with the European Commission and the EU Member States regarding the ability to relocate refugees from Poland to other countries. This team should be composed mainly of civil servants from the Ministry of Foreign Affairs and the Ministry of the Interior. It is also necessary to appoint a team coordinating the actions of voivodes, who are responsible for crisis management in accordance with Polish law. It is also critical to ensure the flow of information between local administrations and the government, as well as to coordinate the activities of non-governmental organizations, whose activity is key in dealing with the challenges related to the migration crisis. In the next stages, it will be necessary to adopt a systemic approach to the inclusion of Ukrainian children in the education system (Polish and Ukrainian, but functioning in Poland – remote learning), and adult refugees to the labor market.
In the end, I would like to recall my opinion, which is now popular in the media and among representatives of the central government, local governments and non-governmental organizations: “Helping refugees and managing migration crises is a marathon, not a sprint.” We must keep this in mind.
The webinar “Fleeing the war zone: Will open hearts be enough?”, was hosted by the FREE Network together with the Stockholm Institute of Transition Economics (SITE) and can be seen here.
Vaccination Progress and the Opening Up of Economies
In this brief, we report on the FREE network webinar on the state of vaccinations and the challenges ahead for opening up economies while containing the pandemic, held on June 22, 2021. The current state of the pandemic in each respective country was presented, suggesting that infection rates have gone down quite substantially recently in all countries of the network, except in Russia which is currently facing a surge in infections driven by the delta-version of the virus. Vaccination progress is very uneven, limited by lacking access to vaccines (primarily Ukraine and Georgia) and vaccine scepticism among the population (primarily in Russia and Belarus but for certain groups also in Latvia, Poland and to some extent Sweden). This also creates challenges for governments eager to open their societies to benefit their economies and ease the social consequences of the restrictions on mobility and social gatherings. Finally, the medium to long term consequences for labour markets reveal challenges but also potential opportunities through wider availability of work–from-home policies.
Background
In many countries in Europe, citizens and governments are starting to see an end to the most intense impact of the Covid-19 pandemic on their societies. Infection and death rates are coming down and governments are starting to put in place policies for a gradual opening up of societies, as reflected in the Covid-19 stringency index developed by Oxford University. These developments are partially seasonal, but also largely a function of the progress of vaccination programs reaching an increasing share of the adult population. These developments, though, are taking place to different degrees and at different pace across countries. This is very evident at a global level, but also within Europe and among the countries represented in the FREE network. This has implications for the development within Europe as a whole, but also for the persistent inequalities we see across countries.
Short overview of the current situation
The current epidemiological situation in Latvia, Sweden, Ukraine, and Georgia looks pretty similar in terms of Covid-19 cases and deaths but when it comes to the vaccination status there is substantial variation.
Latvia experienced a somewhat weaker third wave in the spring of 2021 after being hit badly in the second wave during the fall and winter of 2020 (see Figure 1). The Latvian government started vaccinating at the beginning of 2021, and by early June, 26% of the Latvian population had been fully vaccinated.
Sweden, that chose a somewhat controversial strategy to the pandemic built on individual responsibility, had reached almost 15 thousand Covid-19 deaths by the end of June of 2021, the second highest among the FREE network member countries relative to population size. The spread of the pandemic has slowed down substantially, though, during the early summer, and the percentage of fully vaccinated is about to reach 30% of the population.
Figure 1. Cumulative Covid-19 deaths
Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.
Following a severe second wave, the number of infected in Ukraine started to go down in the winter of 2020, with the total deaths settling at about 27 thousand in the month of February. Then the third wave hit in the spring, but the number of new daily cases has decreased again and is currently three times lower than at the beginning of the lastwave. However, a large part of the reduction is likely not thanks to successful epidemiological policies but rather due to low detection rates and seasonal variation.
In June 2021, Georgia faces a similar situation as Ukraine and Latvia, with the number of cumulative Covid-19 deaths per million inhabitants reaching around 1300 (in total 2500 people) following a rather detrimental spring 2021 wave. At the moment, both Georgia and Ukraine have very low vaccination coverage relative to other countries in the region(see Figure 5).
In contrast to the above countries, Russia started vaccinating early. Unfortunately, the country is now experiencing an increase in the number of cases (as can be seen in Figure 2), contrary to most other countries in the region. This negative development is likely due to the fact that the new Covid-19 delta variant is spreading in the country, particularly in Moscow and St. Petersburg. Despite the early start to vaccinations, though, the total number of vaccinated people remains low, only reaching 10.5% of the population.
Figure 2. New Covid-19 cases
Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.
In some ways similar to Sweden, the government of Belarus did not impose any formal restrictions on individuals’ mobility. According to the official statistics, in the month of June, the rise in the cumulative number of covid-19 deaths and new daily infections has declined rapidly and reached about 400 deceased and 800 infections per one million inhabitants, respectively. Vaccination goes slowly, and by now, around 8% of the population has gotten the first dose and 5% have received the second.
There were two major waves in Poland during the autumn 2020 and spring 2021. In the latter period, the country experienced a vast number of deaths. As can be seen in Figure 3, the excess mortality P-score – the percentage difference between the weekly number of deaths in 2020-2021 and the average number of deaths over the years 2015-2019 – peaked in November 2020, reaching approximately 115%. The excess deaths numbers in Poland were also the highest among the FREE Network countries in the Spring of 2021, culminating at about 70% higher compared to the baseline. By mid-June, the number of deaths and cases have steeply declined and 36% of the country’s population is fully vaccinated.
Figure 3. Excess deaths
Source: Human Mortality Database & World Mortality Dataset compiled by Our World in Data.
Turning to the economy, after a devastating year, almost all countries are expected to bounce back by the end of 2021 according to the IMF (see Figure 4). Much of these predictions build on the expectations that governments across the region will lift Covid-19 restrictions. These forecasts may not be unrealistic for the countries where vaccinations have come relatively far and restrictions have started to ease. However, for countries where vaccination rates remain low and new variations of the virus is spreading, the downside risk is still very present, and forecasts contain much uncertainty.
Figure 4. GDP-growth
Vaccination challenges
Since immunization plays such a central role in re-opening the economy and society going back to normal, issues related to vaccinations were an important and recurring topic at the event. The variation in progress and speed is substantial across the countries, though.
Ukraine and Georgia are still facing big challenges with vaccine availability and have fully vaccinated only 1.3% and 2.3% of the population by the end of June, respectively. Vaccination rates have in the recent month started to pick up, but both countries face an uphill battle before reaching levels close to the more successful countries.
Figure 5. Percent fully vaccinated
Source: Official data compiled by Our World in Data. See full source list at: https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/locations.csv
Other countries a bit further ahead in the vaccine race are still facing difficulties in increasing the vaccination coverage, though not so much due to lack of availability but instead because of vaccine skepticism. In Belarus, a country that initially had bottleneck issues similar to Ukraine and Georgia, all citizens have the opportunity to get vaccinated. However, Lev Lvovskiy, Senior Research Fellow at BEROC in Belarus, argued that vaccination rates are still low largely because many Belarusians feel reluctant towards the vaccine at offer (Sputnik V).
This vaccination scepticism turns out to be a common theme in many countries. According to different survey results presented by the participants at the webinar, the percentage of people willing or planning to get vaccinated is 30% in Belarus and 44% in Russia. In Latvia, this number also varies significantly across different groups as vaccination rates are significantly lower among older age cohorts and in regions with a higher share of Russian-speaking residents, according to Sergejs Gubins, Research Fellow at BICEPS in Latvia.
Webinar participants discussed potential solutions to these issues. First, there seemed to be consensus that offering people the opportunity to choose which vaccine they get will likely be effective in increasing the uptake rate. Second, governments need to improve their communication regarding the benefits of vaccinations to the public. Several countries in the region, such as Poland and Belarus, have had statements made by officials that deviate from one another, potentially harming the government’s credibility with regards to vaccine recommendations. In Belarus, there have even been government sponsored disinformation campaigns against particular vaccines. In Latvia, the main problem is rather the need to reach and convince groups who are generally more reluctant to get vaccinated. Iurii Ganychenko, Senior Researcher at KSE in Ukraine, exemplified how Ukraine has attempted to overcome this problem by launching campaigns specifically designed to persuade certain age cohorts to get vaccinated. Natalya Volchkova, Director of CEFIR at NES in Russia, argued that new, more modern channels of information, such as professional influencers, need to be explored and that the current model of information delivery is not working.
Giorgi Papava, Lead Economist at ISET PI in Georgia, suggested that researchers can contribute to solving vaccine uptake issues by studying incentive mechanisms such as monetary rewards for those taking the vaccine, for instance in the form of lottery tickets.
Labour markets looking forward
Participants at the webinar also discussed how the pandemic has affected labour markets and whether its consequences will bring about any long-term changes.
Regarding unemployment statistics, Michal Myck, the Director of CenEA in Poland, made the important point that some of the relatively low unemployment numbers that we have seen in the region during this pandemic are misleading. This is because the traditional definition of being unemployed implies that an individual is actively searching for work, and lockdowns and other mobility restrictions have limited this possibility. Official data on unemployment thus underestimates the drop in employment that has happened, as those losing their jobs in many cases have left the labour market altogether. We thus need to see how labor markets will develop in the next couple of months as economies open up to give a more precise verdict.
Jesper Roine, Professor at SITE in Sweden, stressed that unemployment will be the biggest challenge for Sweden since its economy depends on high labor force participation and high employment rates. He explained that the pandemic and economic crisis has disproportionately affected the labor market status of certain groups. Foreign-born and young people, two groups with relatively high unemployment rates already prior to the pandemic, have become unemployed to an even greater extent. Many are worried that these groups will face issues with re-entering the labour market as in particular long-term unemployment has increased. At the same time, there have been more positive discussions about structural changes to the labour market following the pandemic. Particularly how more employers will allow for distance work, a step already confirmed by several large Swedish firms for instance.
In Russia, a country with a labour market that allowed for very little distance work before the pandemic, similar discussions are now taking place. Natalya Volchkova reported that, in Russia, the number of vacancies which assumed distance-work increased by 10% each month starting from last year, according to one of Russia’s leading job-search platforms HeadHunter. These developments could be particularly beneficial for the regional development in Russia, as firms in more remote regions can hire workers living in other parts of the country.
Concluding Remarks
It has been over a year since the Covid-19 virus was declared a pandemic by the World Health Organization. This webinar highlighted that, though vaccination campaigns in principle have been rolled out across the region, their reach varies greatly, and countries are facing different challenges of re-opening and recovering from the pandemic recession. Ukraine and Georgia have gotten a very slow start to their vaccination effort due to a combination of lack of access to vaccines and vaccine skepticism. Countries like Belarus and Latvia have had better access to vaccines but are suffering from widespread vaccine skepticism, in particular in some segments of the population and to certain vaccines. Russia, which is also dealing with a broad reluctance towards vaccines, is on top of that dealing with a surge in infections caused by the delta-version of the virus.
IMF Economic Outlook suggests that most economies in the region are expected to bounce back in their GDP growth in 2021. While this positive prognosis is encouraging, the webinar reminded us that there is a great deal of uncertainty remaining not only from an epidemiological perspective but also in terms of the medium to long-term economic consequences of the pandemic.
Participants
- Iurii Ganychenko, Senior Researcher at Kyiv School of Economics (KSE/Ukraine)
- Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
- Natalya Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR at NES/ Russia)
- Giorgi Papava, Lead Economist at the ISET Policy Institute (ISET PI/ Georgia)
- Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
- Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
- Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
- Anders Olofsgård, Deputy Director of SITE and Associate Professor at the Stockholm School of Economics (SITE / Sweden)
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.
Five Years in Operation: the Polish Universal Child Benefit
Over the last five years, Polish families with children have been entitled to a relatively generous benefit of approximately €110 per month and child. Initially granted for every second and subsequent child in the family regardless of income and for the first child for low-income families, the benefit was made fully universal in 2019. With the total costs amounting to as much as 1.7% of Poland’s GDP, the benefit reaches the parents of 6.7 million children and significantly affects these families’ position in the income distribution. Its introduction has led to a substantial reduction in the number of children living in poverty. However, since families with children are more likely to be among households in the upper half of the income distribution, out of the total cost of the benefit, a proportionally greater share ends up in the wallets of high-income families. While the implementation of the benefit has significantly changed the scope of public support to families in Poland, there are many lessons to be learnt and some important revisions to be undertaken to achieve an effective and comprehensive support system.
Introduction
One of the principal commitments in the 2015 Polish parliamentary elections of the then-main opposition party – Law and Justice (Prawo i Sprawiedliwość, PiS), was introducing a generous child benefit. The purpose of this benefit was to support families and encourage higher fertility, which had been one of the lowest in the European Union for a long time. Following PiS’s electoral victory, the new government introduced a semi-universal child benefit of approximately €110 per month (exactly 500 PLN per month, thus the Polish nickname of “the 500+ benefit”) in April 2016. Initially, the benefit was granted for every second and subsequent child in the family regardless of income and for the first child in low-income families. Since July 2019 (nota bene three months before the next parliamentary elections), it was made universal – all parents with children under the age of 18 are entitled to 500PLN per month for every child. The benefit is relatively generous (for comparison, it accounts for 17.9% of the minimum wage in Poland in 2021), and universal coverage implies substantial costs for the government budget, totalling about 41bn PLN per year (1.7% of the Polish GDP).
Over the last five years, a number of analyses of the consequences of the benefit’s introduction have been conducted. These have encompassed a variety of socio-economic outcomes for Polish families with children – from a comprehensive assessment of these consequences (Magda et al. 2019) to analyses focused on specific effects of the benefit, such as the impact on women’s economic activity (Magda et al. 2018, Myck 2016, Myck and Trzciński 2019) or poverty (Brzeziński and Najsztub 2017, Szarfenberg 2017). The fifth anniversary of the benefit’s implementation seems to be a good opportunity for a summary and update of previous evaluations of the distributional consequences and financial gains for households resulting from this policy (an overview of all the previous CenEA analyses of the child benefit can be found in CenEA 2021). The results presented in this brief are based on analyses conducted using the Polish microsimulation model SIMPL on data from the 2019 CSO Household Budget Survey (more details in Myck et al. 2021). It should be noted that the analyses do not account for the impact of the Covid-19 pandemic on the material situation of households, as the data was collected before the outbreak. As previous studies suggest, the consequences for households of the pandemic and the series of resulting lockdowns varied greatly depending on various factors, such as the sources of income, sector, and form of employment, thus making it impossible to estimate precisely (Myck et al. 2020a).
The Child Benefit on Household Incomes
Due to its universal character, the distributional consequences of the child benefit payments are directly related to the position of households with children aged 0-17 in the income distribution relative to those without. As households with children are more likely to be in the upper half of the distribution (taking into account the demographic structure of households through income equivalisation), out of the total budget expenditure on the benefit, a proportionally greater share goes to high-income families (Table 1). Families with children in the two highest income decile groups (i.e., belonging to the 20% of households with the highest income) currently receive almost 25% of the total annual expenditure on the child benefit. On the other hand, among the 20% of households with the lowest incomes, families with children receive only 11.7% of the total annual cost of the benefit.
Table 1. Household gains resulting from the child benefit by income decile groups
Source: Myck et al. 2021. Notes: Income decile groups – ten groups each covering 10% of the population, from households (HH) with the lowest disposable income to the most affluent households, calculated on the basis of equivalised incomes.
Compared to the poorest 10% of households, families with children in the highest income decile receive 2.5 times more of the total funds allocated to the benefit.
It is also worth noting that the proportion of benefit in the disposable income is relatively evenly distributed if one considers all households in a given decile (with and without children). The proportional benefits in the first nine income deciles are in the range of 3.4% and 5.3% and only fall to 1.9% in the highest income group. A significant differentiation of the benefit in proportional terms can only be seen when accounting solely for households with children within each income decile. The benefit amounts to as much as 26.9% of the disposable income of households with children in the first decile, and the effect falls in subsequent groups – from 18.9% and 16.4% in the second and third deciles, to only 4.1% in the top decile.
The Child Benefit and the Position of Families With Children in the Income Distribution
Taking into account the magnitude of the policy, the position of families with children in the income distribution relative to other households may, to some extent, be the result of receiving the benefit itself. It is, therefore, reasonable to ask what role the benefit plays in shaping this relative position in the income distribution. Figure 1 presents the number of children under 18 in households by income decile groups when the benefit is included in total household income (left panel) and in a hypothetical scenario when the child benefit payment is withdrawn (right panel). As we can see, the withdrawal of the benefit would cause a substantial change in the relative position of families with children in the income distribution, significantly increasing the number of children in the lowest income groups. While in the current system, the poorest 10% of households include 342 thousand children aged 0-17, this number would be 553 thousand in a system without the benefit. However, the benefit also changes the relative position of high-income households with children. In the current system, the richest 10% of households include 762 thousand children. Subtracting the benefit from their household income would reduce this number to 687 thousand.
Figure 1. The child benefit and its impact on the position of families with children in the income distribution
Source: Myck et al. 2021.
Thus, even when taking into account the income distribution without the benefit, the number of children among the richest 10% of households is almost 25% higher than the number of children in the poorest 10% of households. Looking at the income distribution after including the benefit, there are more than twice as many children in the richest 10% of households than among the poorest 10%. This, in turn, inevitably means that, out of the total cost of the benefit, over twice as much money is transferred to households belonging to the richest deciles as compared to the funds transferred to families belonging to the poorest 10% of households.
Discussion
With the total costs amounting to 1.7% of Poland’s GDP, the child benefit introduced in April 2016 substantially raised the level of direct financial support for families with children. As shown in this brief, the benefit reaches the parents of 6.7 million children aged 0-17 and significantly affects the position of these families in the income distribution. While, on the one hand, a large proportion of families with children have incomes high enough to be in the highest income groups even without this support , the lowest decile group would include over 200 thousand more children in the absence of the benefit. This confirms that the child benefit alone contributes to a significant improvement in the material conditions of families with children and to a significant reduction in poverty (cf. Brzezinski and Najsztub, 2017; Szarfenberg, 2017). However, the scale of this reduction is modest given the size of the resources involved. This is not surprising given that the bulk of the total costs of the benefit comes from the 2019 program extension to cover all children regardless of family incomes, which largely ended up in the wallets of higher-income families (Myck et al. 2020b). One of the key goals of the benefit upon introduction was to increase the number of births in Poland by easing the material conditions of families with children. Yet despite a radical increase in the level of support, the number of births in Poland over the period 2017-2020 has essentially remained the same as that forecasted by the Central Statistical Office in its long-term population projection of 2014 (Myck et al. 2021). It is thus difficult to consider the benefit a success in terms of this major objective. Moreover, the withdrawal of the income threshold has largely eliminated the negative disincentive effects of the benefit with regard to employment (Myck and Trzcinski 2019). However, it is unclear whether the post-pandemic economic situation will allow for an increase in female labour force participation, which declined following the introduction of the benefit in 2016 (Magda et al., 2018).
The effects of every socio-economic programme should be assessed by comparing cost-equivalent alternatives. Despite all gains the “500+” child benefit has brought to millions of families in Poland over the last five years, the flagship programme of the ruling Law and Justice party does not fare well in this perspective. The need for change seems much broader than the reform of the benefit alone. The benefit was introduced on top of two other financial support mechanisms focused on families with children, namely family allowances and child tax credits, and the three elements have been operating in parallel since 2016. A number of suggestions on creating a streamlined, comprehensive system have been made a long time ago (e.g., Myck et al. 2016). However, a major restructuring of the entire support system with clearly defined socio-economic policy goals in mind seems all the more justified now, when many families may require additional assistance due to the difficult financial situation related to the Covid-19 pandemic.
Acknowledgement:
This Policy Brief draws on the CenEA Commentary published on 31.03.2021 (Myck et al. 2021). It has been prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). The views presented in the Policy Brief reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.
References
- Brzeziński, M., Najsztub, M. 2017. The impact of „Family 500+” programme on household incomes, poverty and inequality”, Polityka Społeczna44(1): 16-25.
- CenEA 2021. Childcare benefit 500+ in CenEA analyses. https://cenea.org.pl/2021/04/06/childcare-benefit-500-in-cenea-analyses/
- Magda, I., Brzeziński, M., Chłoń-Domińczak, A., Kotowska, I.E., Myck, M., Najsztub, M., Tyrowicz, J. 2019. „Rodzina 500+– ocena programu i propozycje zmian”. (“Child benefit 500+: the evaluation of the programme and suggestions for changes”), IBS report.
- Magda, I., Kiełczewska, A., Brandt, N. 2018. “The Effects of Large Universal Child Benefits on Female Labour Supply”, IZA Discussion Paper No. 11652.
- Myck, M. 2016. “Estimating Labour Supply Response to the Introduction of the Family 500+ Programme”. CenEA Working Paper 1/2016.
- Myck, M., Król, A., Oczkowska, M., Trzciński, K. 2021. “Świadczenie wychowawcze po pięciu latach: 500 plus ile?”(„The child benefit after 5 years – 500 plus what?”), CenEA Commentary 31/03/2021.
- Myck, M., Kundera, M., Najsztub, M., Oczkowska, M. 2016. „25 miliardów złotych dla rodzin z dziećmi: projekt Rodzina 500+ i możliwości modyfikacji systemu wsparcia” („25 billion PLN to families with children: Family 500+ programme and possible modifications of the family support system”), CenEA Commentary 18/01/2016.
- Myck, M., Oczkowska, M., Trzciński, K. 2020a. “Household exposure to financial risks: the first wave of impact from COVID-19 on the economy”, CenEA Commentary 23/03/2020.
- Myck, M., Oczkowska, M., Trzciński, K. 2020b. „Kwota wolna od podatku i świadczenie wychowawcze 500+ po pięciu latach od prezydenckich deklaracji” („Tax credit and child benefit 500+ after five years since electoral declarations”, in PL), CenEA Commentary 22/06/2020.
- Myck, M., Trzciński, K. 2019. “From Partial to Full Universality: The Family 500+ Programme in Poland and its Labor Supply Implications”, ifo DICE Report 17(03), 36-44.
- Szarfenberg, R. 2017. “Effect of Child Care Benefit (500+) on Poverty Based on Microsimulation”, Polityka Społeczna 44(1): 25-30.
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.
Regional Economic Development Along the Polish-German Border: 1992-2012
In this brief, we summarize the results of a recent analysis focused on the regional economic development in Poland and Germany along the Oder-Neisse border (Freier, Myck and Najsztub 2021a). Economic activity is approximated by satellite night-time light intensity, a comparable proxy available for regions on both sides of the frontier consistently between 1992 and 2012. This period covers the time of economic transformation and the first eight years of Poland’s membership in the European Union. We find that convergence in overall activity across the border has been complete: Polish municipalities that used to be economically much weaker have caught up with those on the German side of the Oder and the Neisse rivers.
Introduction
The question of the harmonious development of economic activity is at the heart of the European integration project (Art. 2, Treaty of Rome, 1957), and the Maastricht Treaty (1992) made economic convergence between member states an explicit objective. In a forthcoming paper (Freier et al. 2021), we take a new approach to the question of regional European integration.
This brief derives from a recent publication in Applied Economics (Freier et al. 2021a), in which we examine the degree of regional economic convergence along the German-Polish border by taking advantage of satellite night-time illumination data covering the period between 1992 and 2012. The data allows us to study detailed regional patterns of economic development along the river-delimited part of the frontier and further inland.
The seminal work by Henderson et al. (2012) was the first to use night-time light intensity data which covers the entire globe to measure economic activity. Unlike traditional regional economic indicators, light intensity data is independent of administrative border reforms and has been collected in a consistent format over the studied two decades.
Our analysis suggests that, over the analysed period from 1992-2012, there has been essentially full convergence in economic activity between municipalities on both sides of the Polish-German border. While the average value of night-time illumination in our selected group of municipalities in 1992 was 3.7 (on a scale between 0 and 63) in Poland and 7.7 in Germany, the respective values were 9.0 and 9.7 by 2012, and the latter difference is not statistically significant. This convergence suggests a much stronger rate of growth in economic activity on the Polish side of the border. Additionally, we show that within Germany, the distance to the border has much less relevance for economic activity compared to Poland, where it reflects interesting trends. In 1992, Polish towns farther from the border showed significantly higher economic performance. Within Poland, this gap has been greatly reduced over the 20 years we analyse, with regions closer to the border growing much faster compared to those farther away.
Night Lights Along the Polish-German Border
In our dataset, we include municipalities that are located within 100 km from the river delimited part of the PL-DE border. To avoid the sensitivity of the analysis to top censoring of the night-time light intensity data, we removed regional capital cities: Berlin (with surrounding municipalities), Dresden, Gorzów Wielkopolski, and Zielona Góra. This leaves us with 488 municipalities on the German side of the border and 193 municipalities on the Polish side.
The night lights data series, provided by the National Oceanic and Atmospheric Association (NOAA), starts as early as 1992 and continues in a consistent, comparable format to 2012. The data is independent of the administrative structures of local governments, which over time have changed on both sides of the border. This allows us to aggregate the night-time lights information for municipalities using the most recent available administrative borders. This data is essentially the only source of information on economic activity that is consistently available and comparable on both sides of the border over such a long period of time.
The night-time lights data has been applied widely as a proxy of economic development on the country and regional level (Henderson et al., 2012; Bickenbach et al., 2016). Clearly, the intensity of night-time lights does not capture the entire spectrum of economic activity. It has been pointed out that the relationship between night-time light intensity and conventional measures of economic development, such as GDP, is likely to differ depending on a region’s stage of economic development (Hu and Yao, 2019). However, we focus on mostly rural and sparsely populated areas (where there is little risk of top censoring of the data), and compare dynamics between regions that are similar in terms of their stage of economic development, geography, and weather. All these factors support the use of night lights as a proxy for regional development in our application (a number of technical steps are necessary to validate and calibrate the data for use in our analysis, see: Freier et al. 2021).
Economic Convergence Along the PL-DE Border
To understand the overall development of economic activity over the period of interest, we map the changes in the night-time light intensity in Figure 1. The colour scale on the map represents differences in light emissions between 1992 and 2012, with the range going from -40 to 40. A negative value indicates a reduction, and a positive value highlights an increase in light intensity. The negative values have been coloured in a blue-green scale (-40 to 0), while positive values in a red scale (0 to +40).
Figure 1. Night lights: changes in light intensity between 1992 – 2012 along the Polish-German border
Notes: municipalities along the PL-DE river border up to 100 km to the border; municipalities marked in grey treated as outliers and excluded from analysis due to high proportion of top-coded lights pixels in 1992; municipality borders as of 2013 (DE) and 2012 (PL). Source: GeoBasis-DE / BKG 2013, PRG 2012, DMSP OLS v4, OpenStreetMap, own calculations. For details see Freier et al. (2021).
As notable in Figure 1, the red areas are predominant. This exemplifies that between 1992 and 2012, nearly all municipalities in this area witnessed positive economic development as manifested in the intensity of night-time lights. We have a few areas that reflect negative dynamics on the German side of the border. This is mainly due to the regional implications of shutting down activity in agriculture and traditional industries as they were unable to compete with West-German technology and productivity. In Poland, green-blue areas are essentially non-existent, illustrating a universally positive economic development over the studied period. This difference in the pace of changes in light intensity between the German and the Polish side reflects a process of rapid convergence of economic development between municipalities on both sides of the border. These developments are represented in Figure 2 which shows the difference between the night-time light intensity in Germany and Poland by year and provides a test for its statistical significance. The estimation is done on mean log pixel values per municipality and clearly highlights the steep path of convergence. In the early nineties, the difference in mean light intensity was around 100 percent – i.e., the mean difference was as high as the mean level of lights on the Polish side of the border. Already ten years later it reduced to around 50 percent and disappeared by the end of the analysed period. It is notable that, after an initial steep convergence, the difference in light intensity had a period of stagnation between 2002 and 2008. Interestingly, the full convergence which followed coincides with Poland’s entry into the Schengen agreement in December 2007. As seen in Figure 2, the difference in the average night-time light intensity between Poland and Germany was statistically insignificant and essentially zero since 2009.
Figure 2. Difference in mean night-time lights between Germany and Poland over time
Notes: Difference in log of average pixel values per municipality; year fixed effects included, weighted by municipality area; 95% CI. Source: see Figure 1.
Regional Development and Distance from the Border
Thanks to its high degree of geographical precision, the night-time lights data allows us to study the detailed spatial patterns within each country and, in particular, the relationship between distance to the border and economic activity. This is done by looking across the years 1992 to 2012 and examining three-year windows at each end of the analysed period. Our results, which are reported in Table 1, confirm a strong positive relationship between economic activity and distance to the border on the Polish side of the Oder-Neisse rivers. Overall, Polish regions farther from the border show a greater degree of economic activity, but this relationship has substantially diminished over time. While in Germany, economic activity was higher in regions farther from the border and increasing at the average rate of about 0.3% per km, this rate was about three times higher in Poland, falling from about 1.2% per km in 1992-94 to 0.6% in 2010-2012.
Table 1. Total night-time lights along the Polish-German border, 1992-2012
Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.
Table 2 reports changes in light intensity between the beginning and the end of a specific period. Here, we find some interesting and perhaps disconcerting results on the relationship between the distance to the border and changes in light intensity. While the distance-to-border coefficient in the Polish case for the full period is negative, suggesting that regions closer to the border were catching up to the more developed regions farther away, the corresponding coefficient for the final three years is positive. This means that, in the years 2010-2012, economic development was faster in municipalities farther away from the border. Although the relationship is not very strong (the change in light intensity grows by about 0.1% per kilometre of distance to the border), it still suggests a reversal in the fortunes of municipalities close to the border on the Polish side. This result points towards the fact that homogeneity of development cannot be taken for granted and that physical distance might continue to play a role in determining the regional rate of growth in the future.
Table 2. Changes in night-time lights along the Polish-German border: 1992-2012
Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.
Conclusion
In this brief, we report results from a forthcoming paper (Freier et al. 2021) in which we evaluate regional development in municipalities on the German and Polish side of the Oder-Neisse border between 1992 and 2012, using night lights data as a proxy for economic activity. We find that driven by rapid growth in Polish municipalities and somewhat sluggish growth in German ones, the light intensity levels across the Oder-Neisse border show no significant differences by the end of our observation period. This is despite significant initial differences just 20 years earlier and the fact that municipalities on the German side also experienced increases in economic activity. In as far as economic development can be proxied by the intensity of night-time illumination, it seems that economic convergence between regions on both sides of the border was complete by 2012.
We also show interesting patterns regarding the relationship between economic activity and distance from the border. For Germany, this relationship is weakly positive and remains stable throughout the analysed period. In Poland, distance is strongly and positively correlated with light emissions at the beginning of the period, hence indicating that municipalities farther from the border show higher average economic activity. By 2012, however, the border regions have closed most of the gap and the distance to the border is a substantially weaker predictor of economic activity, suggesting a much more homogenous pattern of activity.
Acknowledgements
This brief draws on results reported in Freier et al. (2021a). The authors gratefully acknowledge the support of the Polish National Science Centre (NCN), project number: 2016/21/B/HS4/01574. For the full list of acknowledgements and references see Freier et al. (2021a).
References
- Bickenbach F, Bode E, Nunnenkamp P and Söder M (2016) Night Lights and Regional GDP. Review of World Economics 152(2): 425–47.
- Freier, R., Myck, M., Najsztub, M (2021a) Lights along the frontier: convergence of economic activity in the proximity of the Polish-German border, 1992-2012. Applied Economics, available online: doi: 10.1080/00036846.2021.1898534.
- Freier, R., Myck, M., Najsztub, M (2021b) Night lights along the PL-DE border 1992-2012. Dataset used in Freier et al. (2021a), Zenodo, DOI: 10.5281/zenodo.4600685.
- Henderson JV, Storeygard A and Weil DN (2012) Measuring Economic Growth from Outer Space. American Economic Review 102(2): 994–1028.
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.
Ahead of Future Waves of Covid-19: A Regional Perspective on Health Risks and Healthcare Resources in Germany and Poland
Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic, in this policy paper we examine the regional prevalence of a number of risk factors related to severe consequences of Covid-19. Using the examples of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak in recent months, we show that there is significant regional variation both in the distribution of health status and healthcare resources. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. The cross-country regional perspective adds a valuable angle to the analysis of challenges raised by the Covid-19 pandemic and should urgently be considered regarding any possible consequences of future outbreaks of the virus.
Introduction
In the first five months of 2020 the Covid-19 crisis has grown from a local epidemic outbreak in the Chinese city of Wuhan to a global pandemic, which by the end of May, according to official statistics, took the lives of over 370 thousand people and has been detected in nearly all countries around the world. In the initial phase of the pandemic, the healthcare systems of many countries were pushed to the brink of collapse, and in the severely hit regions even the need of “prioritizing” patients with a high chance of survival became reality. In most European countries the total number of identified cases has continued to grow throughout the month of May, but the rate of growth generally decreased, and in some countries, such as Austria or Slovenia, only a handful of cases were identified in the last two weeks of May. As a result, countries eased the social and economic lockdown, and in many parts of Europe life is beginning to portray a certain restricted semblance of pre-Covid-19 normality. At least in this part of the world, it seems that the first wave of the pandemic is behind us: the “hammer” is over, the “dance” has begun. Thus now that the spread of the virus is slowing down and we are in a phase of smaller local outbreaks, it is time to take a step back and use the information available to draw lessons before the arrival of a potential second wave, which according to many epidemiologists is likely to happen later this year.
Drawing on the most fundamental conclusions from the early research on the Covid-19 pandemic and taking a cross-country perspective, in this policy paper we examine the prevalence of a number of risk factors related to severe consequences of Covid-19 from a regional perspective. In our analysis we focus on Germany and Poland — two neighbouring countries which differ in the demographic structure of their populations as well as with respect to their healthcare infrastructure. Epidemiological research suggests that the risk of serious health complications as well as the risk of dying as a result of Covid-19 grows rapidly with age and is much higher among people with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and malignancy (Emami et al. 2020). Thus, the prevalence of these risk factors might serve as an indicator for the need of (in-hospital) health care in times of larger outbreaks. We then extend the analysis by a discussion of regional statistics on systemic features of healthcare resources reflecting the potential for addressing the pandemic. One can generally say that both in Germany and Poland the first wave of the pandemic, while placing additional heavy strain on healthcare in some regions, has not led to the collapse of healthcare provision. Yet, regions with lower level of service are at greater risk of healthcare rationing, thus further raising the likelihood of severe consequences to the local populations in the future.
We begin this policy paper with a discussion of the key demographic and epidemiological risk factors related to severe health consequences of Covid-19 (Section 1), which is followed by a presentation of the regional distribution of Covid-19 cases in Germany and Poland, as reflected in official statistics at the end of May 2020 (Section 2). We then discuss regional differences in the proportion of people aged 65+ and in the rates of the relevant comorbidities by showing regional statistics on the main causes of death (Section 3). This is complemented in Section 4 by a discussion of the regional distribution of healthcare resources as indicated by the number of hospital beds and the number of doctors. All aspects of our analysis are presented at the level of “powiat” for Poland and “Kreise” for Germany, referred to below as “counties”. There are 380 counties in Poland (including township with county status) and 401 counties in Germany, which in the international Nomenclature of Territorial Units for Statistics (NUTS) correspond to the former NUTS level 4 (former LAU 1) and NUTS level 3 respectively.
As we demonstrate, there are significant differences both across and within the two countries with respect to the relevant demographic and epidemiological risk factors. At the same time there is high heterogeneity across Germany and Poland in the resources of the respective healthcare systems. We show that the cross-country regional perspective adds an additional valuable angle to the analysis of challenges raised by the Covid-19 pandemic. Epidemiologists have modelled various scenarios of future Covid-19 waves including recurring small outbreaks, a new “monster wave” or even a persistent crisis (Moore et al. 2020). Whatever the shape of future outbreaks, the pandemic is expected to persist until “herd immunity” is reached, be it through successful vaccination or through developing immunity in response to illness. Thus, regions potentially facing more serious consequences of the pandemic need to be brought to the attention of central governments as they prepare to address the challenge of future outbreaks of the Covid-19.
1. Macro-Level Determinants of the Health-Related Consequences of Covid-19
At the initial stage of the pandemic, the WHO estimated the fatality rate of the Covid-19 disease at 3-4% (WHO 2020a). As the public health crisis developed, this general conclusion has been challenged given a high number of asymptomatic infections, low testing capacities in most countries and relatively low test accuracy for antibodies as well as PCR testing (Ghandi et al. 2020, Kandel et al. 2020, Manski & Molinari 2020). The available statistics should thus be treated more as “fatality-case” ratios, i.e. the ratios of deaths resulting from Covid-19 to the number of individuals tested positive. According to the most recent studies, this ratio differs substantially between countries, from as low as 0.04% in Qatar and 0.08% in Singapore to over 15% in Belgium or France (Oke & Heneghan 2020). Such high variation is unlikely to reflect “real” differences in the way the virus affects people in different countries, but is more likely to be a consequence of specific factors as the testing strategies, the demographic structure of the population, the characteristics of the part of the population affected (e.g. young holiday makers vs. patients of care institutions), as well as the ability of the healthcare system to deal with a sudden surge in the number of hospitalisations.
There is mounting evidence that the probability of developing severe symptoms of the infection, of hospitalisation and finally of dying, increases significantly with age. According to some early estimates the fatality-case rates grow from 1.8-3.6% among people aged 60-69, through 4.8-12.8% among those aged 70-79, up to 13-20.2% among those 80+ (Roser et al. 2020). Higher hospitalization and fatality rates are also strongly correlated with underlying health conditions, in particular with cardiac disorders, chronic lung diseases, diabetes and cancer (ECDC 2020). This further puts older individuals, among whom these health conditions are most prevalent, at much greater risk as compared to the younger population.
While the risk of severe consequences of Covid-19 substantially increases at older ages, several competing mechanisms are at play with regard to the role of the demographic structure for a potential spread of the virus. On the one hand, since levels of economic activity are generally lower among older people, their compliance with self-isolation rules is likely to be less sensitive to the intensity of economic activity at regional or country level. On the other hand, however, as social life now returns to a higher level of interaction, different forms of living arrangements of older individuals place certain groups at a particular risk. The first months of the pandemic in Europe have revealed high vulnerability of people living in long-term care facilities, many of which became Covid-19 clusters with high rates of mortality among their residents (Comas-Herrera et al. 2020; Gardner et al. 2020; McMichael et al. 2020). On the other hand, in countries characterised by low rates of institutionalization, older individuals are more likely to co-reside in households with children and younger adults (Myck et al. 2020), i.e. groups which in conditions of lifted lockdown restrictions will be exposed to the risk of infection. Studies at the early stages of the epidemic showed that intra-household transmission of the virus may be responsible for the majority of clusters (WHO 2020b). This implies that while the strategies to protect the most vulnerable groups may differ depending on the specific living arrangements, regions with a higher proportion of older people face an increased risk of severe health consequences of Covid-19 outbreaks.
Similar arguments apply to the regions where incidence of the relevant comorbidities is particularly high. Systemic constraints related to healthcare played an important role at the height of the recent Covid-19 crisis in countries such as Italy or Spain where the number of patients in need of in-hospital treatment exceeded the capacities of the healthcare systems (Pasquariello & Stranges 2020, Remuzzi & Remuzzi 2020, Verelst et al. 2020). We thus argue that regions with populations facing highest risks related to the Covid-19 pandemic ought to be particularly vigilant to the spread of the disease and ensure that their healthcare infrastructure can respond adequately to future outbreaks.
2. The Regional Spread of Covid-19 infections in Germany and Poland
The first official case of the disease in Germany was confirmed on 27 January, while the first infection in Poland dates to 4 March. Since then 183 thousand Covid-19 infections have been identified in Germany and 23 thousand in Poland by the end of May 2020. The corresponding fatality-case ratio at that point stood at the average country levels of 4.69% and 4.47% respectively. The difference in the overall number of cases relates both to the greater spread of the virus and the more extensive testing conducted in Germany as well as to a simple difference in the size of population (83 vs. 38 million inhabitants). Importantly, when we take a regional perspective on the pandemic, as we can see in Figure 1, the distribution of the infection rate is far from homogenous. In Germany, the level of infection rates is much higher in some of the southern and western regions (Bavaria, Baden-Württemberg and North Rhine Westphalia), while in Poland the region of Silesia is a clear local “hot-spot” of the pandemic.
Figure 1. COVID-19 infections per 100 thousand inhabitants by county
(as of 31 May 2020)
Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
In Germany, the first outbreaks were attributed to business travel and skiing tourism and the spread within certain communities went on via close contacts during large gatherings such as those at the time of carnival festivities and at church services, and also as a result of specific economic activities (e.g. delivery services or workers in slaughterhouses). Numerous cases have also been reported in institutionalised accommodation such as nursing and refugee homes. As Figure 1 shows, the counties with the highest rates of infections were located in Bavaria. By the end of May one of the Bavarian counties (Tirschenreuth) had an infection rate far higher than any other county – 1,568 infections per 100,000 inhabitants, when this rate was 891 and 890 in the next highest scoring counties of Straubing and Wunsiedel. At the same time the counties of Uckermark and Prignitz (in the region of Brandenburg), Friesland and Wilhelmshaven (Niedersachsen), Ostholstein (Schleswig-Holstein) and Rostock (Mecklenburg-Vorpommern) recorded infections rates of below 35 per 100,000 inhabitants.
The origins of the first reported cases in Poland were also directly related to international travel – to Germany and Italy. Further local outbreaks were reported in hospitals and social welfare homes. The virus often spread between such institutions due to a transmission via medical and care personnel working in several institutions in parallel. Initially, only Warsaw and neighbouring counties stood out with regard to the infection rate, which could be due to higher mobility and population density in the first case, and local outbreaks in social welfare homes in the latter. However, about two months after the beginning of the pandemic, a major surge in new cases was recorded in the region of Silesia where the bulk of infections concentrated among mine workers. Often asymptomatic, infections were identified as a result of extensive screening of miners and their families. By the end of May, about one third of Poland’s total infections were found in Silesia alone. Together with the cases reported in the Mazovian region (with Warsaw as capital), these two regions represented about half of the total number of infections in Poland. The highest infection rate in Poland exceeding 500 infections per 100,000 inhabitants was observed in the counties of Silesia (Bytom, Jastrzębie-Zdrój and powiat lubliniecki), Mazovia (powiat białobrzeski) and Greater Poland voivodship (powiat kępiński), while a handful of counties located throughout Poland (powiaty: bartoszycki, bieszczadzki, drawski, gołdapski, kolski, lidzbarski, międzyrzecki, sejneński, żuromiński) have not recorded any infections.
Figure 2 provides another angle on the aftermath of the epidemic in both countries – regional case fatality rates, calculated as a ratio of deaths to recorded infections and presented at a higher level of aggregation – the level of Bundesländer in Germany and Voivodship in Poland (due to the lack of comparable data on county level in Poland). Even though, as mentioned above, the country average death rates are very similar, the within-country regional differences are striking. As compared to Poland, the regional death ratios in Germany do not deviate much from the country average (4.7), with the lowest rate in the region of Mecklenburg-Vorpommern (2.6) and the highest one in the region of Saarland (6.0). On the other hand, the differences between Polish regions are substantial, with no deaths per 120 infections in the lubuskie region and the fatality rate exceeding 9.0 in the podkarpackie region. At this early stage of the pandemic such differences might reflect a number of factors and may not be systematically related to specific risks. However, as we show below, the most clearly identified risk factors are far from evenly distributed both between and within the two countries, which in cases of broader outbreaks is likely to lead to significant systematic differentiation of risks at the regional level.
Figure 2. Covid-19 death rates by region (DE: Bundesländer, PL: Voivodeships) (as of 31 May 2020)
Source: own compilation based on data from Robert Koch Institute (RKI) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and data collected individually by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
3. Demographic and Epidemiological Variation at Regional Level in Germany and Poland
There are significant differences in the age structure of the population with a substantially higher proportion of individuals in older age groups in Germany. While 17.5% of the Polish population is over 65 years old and 2.1% is aged 85+, the corresponding proportions in Germany amount to 21.4% and 2.7%. These average differences, however, conceal significant within country variation in the demographic composition, which – as we argue – is very relevant against the background of the potential consequences of the Covid-19 pandemic.
In Figure 3 we present shares of people aged 65+ in the general population by county in 2018. The counties with highest proportions of older individuals in Germany are concentrated in the east of the country. The variation in the proportion of those aged 65+ ranges between 15.7% in Frankfurt am Main (region Hessen) and Freising (region Bavaria) and 31.5% in Suhl (region Thüringen). The ‘youngest’ of German counties resemble some of the oldest ones in Poland, where we find counties with the proportion of people aged 65+ as low as 11.2% or 12.1% (powiats kartuski and gdański, region Pomerania). Only in 15 counties in Poland (less than 4% of counties), the proportion of those aged 65+ exceeds 21% – which we find in about two thirds of counties in Germany. Similar differences are found regarding the proportion of those aged 85+ (not shown here), with a distinct concentration of the “oldest-old” in the eastern parts in both countries. However, while in Poland less than half of counties have a proportion of the 85+ population higher than 2%, this is the case in all but one county in Germany.
Figure 3. Share of people aged 65+ by county, 2018
Source: own compilation based on data from Eurostat and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
When we compare the regional variation in the number of Covid-19 infections with the population’s age structure, it seems that the pandemic in both countries has so far affected the ‘younger’ regions. The spread of the virus has been relatively slow both in the eastern part of Germany and in the east of Poland. Thus, there is a negative correlation between the within-country spread of Covid-19 and the proportion of older age groups at the county level. This might be due to a higher level of travel and economic activity in younger regions of the two countries which – at least in the initial phase – limited further spread of the virus to the parts with higher proportions of older individuals.
Apart from older age several pre-existing medical conditions have also been identified as risk factors for severe consequences of Covid-19. Figure 4 displays the ratio of deaths due to a selected group of diseases in the total number of deaths among people aged 65+ to proxy the incidence of these health conditions among the living population. The causes of death are coded according to the diagnostic criteria of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) compiled by the WHO. Deaths caused by external factors such as traffic accidents are excluded from the total of fatalities due to different reporting practise in Poland and Germany. Since no clear deviations in reporting deaths due to internal causes has been found, we assume this data is comparable between the two countries and we use deaths due to internal causes as a measure of total deaths in Figure 4. Causes that are especially relevant against the background of Covid-19 include deaths due to circulatory diseases, neoplasms and respiratory diseases (the level of data aggregation does not allow to single out deaths due to diabetes). In contrast to Figure 3, which showed much higher proportions of older people in Germany than in Poland, when it comes to health risks due to the specified conditions, the country picture is reversed. While the rate of deaths resulting from the selected conditions exceeds 90% of all deaths in the 65+ population in multiple counties across Poland (over 8% of all), it does not surpass 84% anywhere in Germany. Importantly, the regional distribution of death ratios in Germany due to the chosen conditions closely reflects the proportion of the older population and is concentrated in eastern parts of the country, in particular in the southern regions of the former East Germany. Epidemiological risks related to Covid-19 seem to be lower in the more prosperous regions in southern and western Germany, as well as in bigger cities such as Hamburg. In Poland there is no apparent relation between the selected health risks and the demographic structure of the regions. The highest proportion of deaths due to the selected conditions is found in the north-western regions and in the south-east, leaving central Poland with somewhat lower incidence rates of death due to these causes – at similar levels observed in many parts of Germany. Moreover, the within-country variation in the proportion of these deaths is much higher in Poland, where in sztumski county (Pomerania region) as many as 94.5% of deaths among 65+ can be attributed to the selected conditions, while in ełcki county (Warmia-Masuria region) this number was only 66.6%.
Figure 4. Share of deaths due to neoplasms, circulatory and respiratory diseases among people aged 65+ by county, 2016
Source: own compilation based on data from European Data Portal and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
4. Healthcare Resources at the Regional Level in Germany and Poland
The initial wave of the Covid-19 pandemic in several most affected countries resulted in a significant overburden of their healthcare capacities with a sudden wave of patients in need of in-hospital intensive care. While in some hospitals in Germany and Poland the first inflow of patients placed a heavy burden on the available resources, both healthcare systems have so far not been overwhelmed to the extent that was experienced in Italy, Spain, or some states of the USA. However, there are significant differences between the healthcare resources available in Germany and Poland and these differences might become apparent if the next waves of the pandemic result in much higher rates of infections. Health expenditure accounted for 11.3% of Germany’s gross domestic product (GDP) in 2017, with an expenditure of 4,459€ per inhabitant. The spending in Poland was much lower and amounted to 6.5% of the GDP and an expenditure of 731€ per inhabitant (Eurostat 2020a). The differences are not as high in the absolute values of traditional healthcare indicators such as the number of hospital beds per 1,000 people (601.5 in Germany and 485.1 in Poland; Eurostat 2020b) or the number of doctors per 100.000 inhabitants (424.9 in Germany and 237.8 in Poland; Eurostat 2020c), but they are still notable.
We show the regional distribution of hospital beds and practising doctors in Figures 5 and 6. As in the case of the demographic structure and epidemiological conditions, there are significant regional differences in the capacity of healthcare as measured by these indicators. In the latter case the data do not allow for a direct cross-country comparison as the data in Germany only covers medical doctors who provide health services to patients with social health insurance in outpatient clinics. In Poland the data is limited to the medical doctors working directly with patients conditional on their primary workplace / main employer in case of multiple assignments (excluded if private practice is reported as such). This means that the data at hand only covers a proportion of all medical doctors – in Germany it captures 37% of all those with an active medical license (according to the German Medical Association) and in Poland 60% of licensed doctors as reported by the Polish Supreme Medical Chamber. As this data is not directly comparable across countries, the proportions in Figure 6 are presented in shades of blue and green for Germany and Poland respectively. However, the key dimension of the data we present is the high within-country variation in the level of medical staff across regions.
In both countries there is an urban-rural divide of the healthcare capacities that is most pronounced in Poland and in the south-western regions of Germany. In Poland this originates partly from the task division at consecutive levels of local administration. Although county authorities are responsible for the broad network of hospitals, the major clinical hospitals are located in the biggest cities. The north-south difference that we observe in Germany is related to the fact that in northern regions many populated cities compose a county together with neighbouring municipalities, while in the southern and central parts they constitute an independent county. This brings out the contrast between cities and the localities around them, which is also noticeable in the case of Poland. For many areas this means that their inhabitants have to travel or be transported relatively long distances when in need for medical treatment, in particular in cases of specialised interventions. In 2016 there were four counties in Germany and as many as 24 counties in Poland with no hospitals.
Figure 5. Number of hospital beds per 1,000 inhabitants by county, 2016
Source: own compilation based on data from Federal Statistical Office and Statistical Offices of the Länder and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
The rural-urban divide is even more evident in Poland when we look at the number of medical doctors, as doctors are clustered in the biggest cities or counties with clinical hospitals (Figure 6). In 2018, three counties had 20 or less medical doctors per 100,000 inhabitants (powiat łomżyński in Podlaskie region, średzki in Lower Silesia and siedlecki in Mazovia), and in 30% of counties this number was below 100. Almost 10% of counties (all big cities and regional capitals) had at the same time 400 or more doctors per 100,000 inhabitants, two counties in South-East Poland – Lublin (Lubelskie region) and Rzeszów (Podkarpackie region) reported over 770 doctors. Thus, the striking feature of several regions in Poland is that besides a strong medical centre, there is a high number of municipalities around them with very low number of doctors. This is the case for example in Olsztyn in the north-east of Poland (region Warmia-Masuria) or Poznań in the west (Greater Poland region).
Since for Germany we only considered doctors working in outpatient clinics and excluded doctors working solely in hospitals and thus concentrated in major regional cities, the medical workforce seems spread out more equally (Figure 6) compared to the availability of hospital beds (Figure 5). However, in particular since in Germany the data covers a much lower proportion of medical doctors compared to Poland, even in the German counties with lowest statistics, the numbers of doctors are still much higher than in many rural areas throughout Poland.
Figure 6. Number of doctors per 100,000 inhabitants by county, 2018
A) in Germany: doctors working in outpatient clinics B) in Poland: doctors working directly with patients in primary workplace
Source: own compilation based on data from Federal Medical Registry (KBV) and Federal Agency for Cartography and Geodesy (BKG) in case of Germany and Central Statistical Office (GUS) and Head Office of Geodesy and Cartography (GUGiK) in case of Poland.
Note: Class 3, 4 and 5 cover 20% of the observations, the other classes 10% each.
Conclusion
The early evidence suggests that people over the age of 65 and those with pre-existing health conditions such as cardiovascular conditions, diabetes, hypertension, chronic pulmonary disease and cancer are at the highest risk of severe consequences of Covid-19. A well-equipped healthcare system is required to respond appropriately to increases in demand for healthcare in order to safeguard the population against the worst outcomes of the disease in potential future waves of the pandemic. This regards the issue of preventing Covid-19 related fatalities, but it also refers to the continued need to provide other general types of healthcare which are constantly required alongside the cases directly related to the pandemic.
Such a combination of health risks related to demographic, epidemiological and systemic factors results in potentially high regional variation of the scale of consequences of the spread of the Covid-19 pandemic. Using the example of Germany and Poland, two neighbouring countries which have generally dealt relatively well with the outbreak of Covid-19 in recent months, this policy paper shows that there is significant regional variation both in the distribution of health risks and healthcare resources. These regional inequalities should be considered regarding the consequences of future outbreaks of the virus. The regional analysis of the first wave of the pandemic – with data until 31 May 2020 – suggests that in both countries the virus spread mainly in ‘younger’ regions (with low proportions of people aged 65+) with lower incidence of the relevant comorbidities. At the same time the number of cases in the two countries was low enough so that both the German and the Polish healthcare systems, notwithstanding the differences between them, were not overwhelmed by the inflow of Covid-19 patients.
Such a situation is by and large not guaranteed in the case of future waves of the pandemic. The virus is likely to spread beyond the best connected and most mobile regional populations, which has been the case so far in Germany and Poland. With respect to the demographic structure of the population, the places most at risk for severe health consequences due to Covid-19 are the counties of the former East Germany and those in the east of Poland, where we observe an outstandingly large proportion of people aged 65+. Similarly – looking at the incidence of relevant comorbidities, the northern and southern counties clearly stand out in Poland, and in this respect the health of the German 65+ population presents a much lower risk compared to the health status of the Polish counterparts.
How these two critical risk factors translate into health outcomes in future waves of Covid-19 depends on the readiness of the local healthcare system to provide support to patients requiring in-hospital and intensive care. Using regional data on the number of beds and medical doctors we have shown that in both countries there is a significant variation in healthcare resources. This variation is particularly visible in Poland with a substantial urban-rural divide and high concentration of healthcare resources and staff in larger cities. A rapid spread of the disease in future months could be devastating in Polish rural areas with poor medical infrastructure and high proportions of the population at risk.
The differences between and within the countries regarding the healthcare infrastructure lead to two crucial conclusions with regard to the potential consequences of future waves of Covid-19. First of all, it is clear that the German healthcare system – with the better hospital infrastructure and higher number of doctors, is overall better prepared to face a surge in Covid-19 cases. Secondly, there is a much higher proportion of counties in Germany with high level of medical resources and few localities standing out with much lower levels of hospital capacity or doctors compared to those with the highest values. This is not the case in Poland where the majority of counties have very low capacities of both hospital beds and doctors. While such inequalities in medical resources may be of less concern in ‘normal times’ when individuals from areas with poorer infrastructure might find a place in their nearest relevant hospital, in the case of a sudden increase in demand for hospitalisations such local medical centres might rapidly become overwhelmed. Additionally, moving patients to distant hospitals would place significant additional demand on medical transportation. In cases of rapid increases in the numbers of infected people problems are also likely to occur at the level of the basic diagnosis before the patients are classified for hospitalisation.
As shown in this policy paper the variance in the demographic structure of the population as well as in the main causes of death at older ages between Germany and Poland and within each of the two countries is substantial. In many regions these underlying demographic and epidemiological factors overlap with relatively low general capacities of the healthcare system to deal with a sudden surge of hospitalisations (Kandel et al. 2020). Thus, the analysis presented in this policy paper points towards the need for a disaggregated regional level risk-management approach to future waves of the Covid-19 pandemic. Highly differentiated demographic and epidemiological risks related to the pandemic between as well as within Germany and Poland call for a decentralised evaluation of risks and point out the need to consider an application of regionally focused policy reactions such as lockdowns and social distancing regulations. If risks and the ability to respond to them vary significantly at the regional level, policies should consider and account for such variation to prepare for potential next outbreaks later this year or next year.
Acknowledgement
The authors wish to acknowledge the support of the German Science Foundation (DFG, project no: BR 38.6816-1) and the Polish National Science Centre (NCN, project no: 2018/31/G/HS4/01511) in the Beethoven Classic 3 funding scheme. We are grateful to Vera Birgel for research assistance.
References
- Comas-Herrera, A., Zalakaín, J., Litwin, C., Hsu, A.T., Lane, N., Fernández, J.-L. (2020) Mortality associated with COVID19 outbreaks in care homes: early international evidence. LTCcovid.org, CPEC-LSE. https://ltccovid.org/wp-content/uploads/2020/05/Mortality-associated-with-COVID-21-May-6.pdf
- ECDC – European Centre for Disease Prevention and Control (2020) Disease background of COVID-19. https://www.ecdc.europa.eu/en/2019-ncov-background-disease
- Eurostat (2020a): Healthcare expenditure statistics. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_expenditure_statistics
- Eurostat (2020b): Healthcare resource statistics – beds. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_resource_statistics_-_beds
- Eurostat (2020c): Health care personnel statistics – physicians. https://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_personnel_statistics_-_physicians#Healthcare_personnel
- Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A. (2020) Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch Acad Emerg Med, 8, e35. https://www.ncbi.nlm.nih.gov/pubmed/32232218
- Gardner, W., States, D., Bagley, N. (2020) The Coronavirus and the Risks to the Elderly in Long-Term Care. J Aging Soc Policy, 1‐6. https://pubmed.ncbi.nlm.nih.gov/32245346/
- Ghandi, M., Yokoe, D. S., Havlir, D. V. (2020) Asymptomatic transmission – the achilles’ heel of current strategies to control Covid-19. N Engl J Med, 382, 2158-2160. https://www.nejm.org/doi/full/10.1056/NEJMe2009758
- Kandel, N., Chungong, S., Omaar, A., Xing, J. (2020) Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries. Lancet, 395, 1047-1053. https://www.sciencedirect.com/science/article/pii/S0140673620305535
- Manski, C. F., Molinari, F. (2020) Estimating the COVID-19 infection rate: Anatomy of an inference problem. JoE (Online first). https://www.sciencedirect.com/science/article/pii/S0304407620301676
- McMichael, T., Currie, D., Clark, S., Pogosjans, S., Kay, M., Schwartz, N., Lewis, J., Baer, A., Kawakami, V., Lukoff, M., Ferro, J., Brostrom-Smith, C., Rea, T., Sayre, M., Riedo, F., Russell, D., Hiatt, B., Montgomery, P., Rao, A., Chow, E., Tobolowsky, F., Hughes, M., Bardossy, A., Oakley, L., Jacobs, J., Stone, N., Reddy, S., Jernigan, J., Honein, M., Clark, T., Duchin J. (2020) Epidemiology of Covid-19 in a long-term care facility in king county, Washington. N Engl J Med. https://www.ncbi.nlm.nih.gov/pubmed/32220208
- Moore, K. A., Lipsitch, M., Barry, J. M., Osterholm, M. T. (2020) COVID-19: The CIDRAP viewpoint. Part 1: The future of the COVID-10 pandemic: Lessons learned from pandemic influenza. https://www.cidrap.umn.edu/sites/default/files/public/downloads/cidrap-covid19-viewpoint-part1_0.pdf
- Myck M., Oczkowska M., Trzciński K. (2020) Safety of older people during the Covid-19 pandemic: Co-residence of people aged 65+ in poland compared to other European countries. FREE Policy Paper. https://freepolicybriefs.org/2020/05/18/safety-older-people-covid-19/
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Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
COVID-19 | The Case of Poland II
Poland in the FREE Network Covid-19 Project (May 26, 2020)
Current Health Situation in Poland
Poland noted its first coronavirus infection in early March 2020. After the initial rapid spread of the disease throughout the country and spike in the total number of registered infections, since early April the infection curve stabilized at a relatively low level (compared to other European countries) of 250-350 new daily cases. The flattening of the curve was a result of drastic health and social restrictions gradually imposed on society (more details below). Since the first reported case, the testing capacity has also been substantially improved, with the number of tests conducted daily increasing from 2K to 15-20K in late April, and holding steady since then.
Figure 1. Number of Covid infections per 100K inhabitants in districts in PL (as of May 25)
Source: own compilation based on data collected by Michał Rogalski (https://www.micalrg.pl/) from Voivodeship Offices, Voivodeship and Powiat Epidemiological-Sanitary Stations, media and materials sent on request. Note: first/last class covers 10% lowest/highest obs., other classes – 20% obs.
Even though Poland has not yet reached an apparent decrease in the number of new daily infections, since the end of April the government introduced a strategy of a slow, four-step re-opening of the economy (more details below). As of 26 May 2020, the total number of Covid infections in Poland approached 22K, with the number of fatalities as high as 1K, and cases reported in all but 7 districts of the country (out of over 300 – see Figure 1). At this point in time, Poland also found itself at the third phase of the lifting of restrictions on economic activity.
Government Health Policies
Lockdown Introduction
The Minister of Health announced a state of epidemic risk in the territory of Poland on March 14 [7], raising it further to a state of epidemic 6 days later [8]. Measures counteracting the epidemic were introduced centrally in Poland by the Minister of Health, and were gradually extended:
- Restriction on the size of public gatherings: since 14.03.2020 limited to 50 [7]; since 25.03.2020 – 2 people (except for families and funerals up to 5 people) [9],
- Ban on all non-essential mobility since 25.03.2020 [9]; since 01.04.2020 limitations on access to public spaces like parks, playgrounds and recreational areas; distance of 2 meters between people in public places; further restrictions for minors [10],
- Bars and restaurants closed and allowed only to provide take-away food since 14.03.2020 [7],
- Childcare institutions, all schools and higher education institutions closed on 12.03.2020, formally online education provided since 25.03.2020 [11, 12],
- Since 15.03.2020 foreigners banned from travelling into Poland (with exceptions), while all Poles arriving from abroad quarantined for 14 days after arrival [7],
- Shopping malls, sports and recreation centers, sports events, cinemas, theatres, etc. closed since 14.03.2020 [7]; since 01.04.2020 – hairdressers, beauty salons, physiotherapy, hotels etc. [10],
- Restrictions on the number of people using public transport since 25.03.2020 [9],
- Since 01.04.2020 restrictions on the number of people in shops and designated shopping hours for 65+ only [10], since 02.04.2020 obligation to wear disposable gloves [10],
- Restrictions in workplaces since 02.04.2020: distance between coworkers, access to protective equipment [10],
- Since 16.03.2020 certain hospitals devoted exclusively to patients with (suspicion of) Covid-19 [13],
- Since 16.04.2020 mandatory covering of mouth and nose in all public places, inside and outside [17].
Gradual Ease of Restrictions
On March 16, 2020, the Minister of Health announced a gradual strategy of lifting the restrictions imposed on social life and economic activity. The plan is divided into four steps. The first stage was implemented on 20.04.2020 [18]:
- increase in the limit of customers in shops,
- public spaces like parks and recreational areas (except playgrounds) open,
- mobility restrictions lifted for minors over 13 y.o.
The second stage was introduced on 04.05.2020 [19, 20, 21]:
- shopping malls open with restrictions on the number of customers, shopping hours for 65+ cancelled,
- museums, libraries, physiotherapy, hotels open,
- sports facilities open with restrictions on the number of users,
- 14-day quarantine for workers from neighbouring countries cancelled,
- since 06.05.2020 some nurseries and kindergartens open.
The third stage started on 18.05.2020 [22, 23]:
- mobility restrictions lifted for minors under 13 y.o.
- hairdressers, beauty salons, outdoor cinemas open, restaurants and bars – with restrictions on the number of customers,
- increase in the number of people using public transport,
- sport trainings allowed with restrictions,
- some classes (practical or individual) in post-secondary schools allowed,
- since 25.05.2020 classes for children from the 1st – 3rd grade in primary schools and final-year graduates allowed,
- since 01.06.2020 consultations with teachers at schools allowed.
The fourth stage is planned for the near future, without a specific date. It involves the opening of cinemas and sports centers.
Government Economic Policies
The government implemented several stages of the so called “Anti-crisis shield”, the first of which came into force on April 1. The overall package includes a number of broad measures to support enterprises and workers for a period of three months and covers both direct financial support as well as provisions regarding financial liquidity for companies [14, 15]. In March the National Bank of Poland decreased interest rates and announced that it will support access to credit through targeted longer-term refinancing operations and if necessary will provide monetary stimulus through large scale open market operations [16].
Short Summary of Measures
Labor market [14]:
- Increased flexibility of employee daily and weekly hours of work;
- Extension of childcare leave for parents with children aged 0-8;
- In case activities affected by revenue reduction (revenue fall by 15% year-to-year or 25% month-to-month):
- Self-employed or employees on non-standard contracts to receive a monthly benefit equivalent to 80% of minimum wage for up to three months;
- Companies to receive support equivalent to 50% of the minimum wage for inactive employees due to the stoppage, provided individual salaries are not reduced by more than 50%;
- Companies to receive support equivalent to up to 40% of average wage for employees whose hours are reduced by 20%;
- Alternative support to employment provided to SMEs (up to 249 employees) in case of revenue loss from the Labour Fund: depending on the level of revenue loss (>30%, >50%, >80%) support to employees expressed as ratio of the Minimum Wage (respectively: 50%, 70% and 90%);
- Relaxation of work and stay permits for foreigners.
Social transfers:
- No specific measures have been implemented but the government is considering:
- a tourism voucher of 1000 PLN paid to employees with a 90% contribution from the government (10% paid by employers); paid to employees on wages below the national average wage;
- additional support to housing benefit for those who become eligible to housing benefits due to the economic slowdown;
Tax breaks [14]:
- 100% of social security contributions to be paid by the government for self-employed and employees employed in micro enterprises (up to 9 employees) and 50% paid by the government in small enterprises (10-49) for three months;
- Tax payments and social security contributions on earnings and profits can be delayed till 01.06.2020;
- Losses from 2020 will be deductible from the 2021 tax base.
Emergency loans, guarantees and support [14]:
- Small-scale loans to small companies;
- Reduced administrative requirements and relaxation of numerous regulatory rules;
- Increased liquidity of firms through channels supported by the Polish Development Fund (PFR):
- extension of de minimis guarantees to SMEs;
- subsidies to SMEs which suffered revenue losses due to the pandemic;
- equities and bond issues to be financed by PFR;
- subsidies to commercial loan interest payments from BGK;
- commercial turnover insurance from Export Credit Insurance Corporation (KUKE);
- Relaxation of regulations related to contracts with public institutions (e.g. related to delays).
Monetary policy [16]:
- On 17.03.2020 NBP lowered the main reference interest rate by 0.5 pp and reduced the rate of obligatory reserves from 3.5% to 0.5%. The main reference rate was lowered further to 0.5% on 08.04.2020.
- NBP announced the readiness to engage in large scale open market operations;
- Targeted longer-term refinancing operations to allow credit refinancing by commercial banks.
References
[1] OECD Health Statistics, https://stats.oecd.org/viewhtml.aspx?datasetcode=HEALTH_REAC&lang=en.
[2] Central Statistical Office in Poland (GUS), bdl.stat.gov.pl.
[3] Supreme Medical Chamber (Naczelna Izba Lekarska), https://nil.org.pl/rejestry/centralny-rejestr-lekarzy/informacje-statystyczne.
[4] Ministry of Health, https://twitter.com/mz_gov_pl?lang=pl.
[5] Warsaw Stock Exchange (Giełda Papierów Wartościowych), https://www.gpw.pl/gpw-statistics.
[6] Central Bank of Poland (Narodowy Bank Polski), https://www.nbp.pl/home.aspx?f=/kursy/kursya.html.
[7] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/433.
[8] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/491.
[9] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/522.
[10] ministry of Health, http://dziennikustaw.gov.pl/DU/2020/566.
[11] Ministry of Science and Higher Education, http://dziennikustaw.gov.pl/DU/2020/405.
[12] Ministry of National Education, http://dziennikustaw.gov.pl/DU/2020/410.
[13] https://www.gov.pl/web/koronawirus/lista-szpitali.
[14] Polish Development Fund (Polski Fundusz Rozwoju Przewodnik Antykryzysowy dla Przedsiębiorców 02.04.2020), https://pfr.pl/tarcza.
[15] Polish Development Fund (Polski Fundusz Rozwoju Przewodnik Antykryzysowy dla Przedsiębiorców 05.05.2020), https://pfr.pl/tarcza.
[16] Central Bank of Poland (Narodowy Bank Polski), https://www.nbp.pl/home.aspx?f=/polityka_pieniezna/dokumenty/komunikaty_rpp.html.
[17] Ministry of Health, http://dziennikustaw.gov.pl/DU/2020/673.
[18] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/697.
[19] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/792.
[20] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/780.
[21] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/779.
[22] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/878.
[23] http://dziennikustaw.gov.pl/DU/rok/2020/pozycja/871.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Safety of Older People During the Covid-19 Pandemic: Co-Residence of People Aged 65+ in Poland Compared to Other European Countries
Bearing in mind that the estimated fatality rates related to Covid-19 infections are substantially higher among older people, in this Policy Paper we focus on the demographic composition of households of people aged 65+ as one of the social risk factors that influence the consequences of the pandemic. In light of plans of easing isolation restrictions and a gradual return to higher economic activity, a key challenge for the coming weeks is to ensure the safety of those most at risk. Although lifting the lockdown mainly affects the lives of the working population and children, attention should be paid to the channels that could enhance transmission of the coronavirus among older people. This includes the prevalence of co-residence with those who will get back to their workplaces or schools once they are open again. Compared to other European countries, Poland has the highest rates of people aged 65+ sharing their households with younger adults and children with nearly 40% living together with people aged up to 50 years old (excluding partners). On the other hand, Nordic countries, the Netherlands, Belgium and Germany report far lower rates of co-residence among the older population. In these countries however, older people commonly reside in formal care facilities, which, in turn, have proved vulnerable to outbreaks of infections. This emphasizes that each country has to carefully determine its own strategy on the way to recovery. Among other factors, the pace at which restrictions on social distancing are lifted should take into account the prevalence of co-residence among the older population.
Introduction
According to the WHO, at the early stage of the Covid-19 epidemic, the fatality rate among coronavirus-infected people was estimated at about 3-4% (WHO 2020a), although estimates based on the data from European countries suggest that the rate is lower and is closer to 1.5% (ECDC 2020). The rate is quite varied from country to country; it also fluctuates over time. To a large extent, the figure depends on the number of tests conducted and, consequently, the reliability of information on the number of people infected (Roser et al. 2020). Nevertheless, both the risk of experiencing serious symptoms of the coronavirus infection and the risk of death from complications arising from the disease increase significantly with the age of the infected person. Furthermore, the risk is definitely higher for the patients with underlying conditions, in particular cardiovascular diseases, diabetes, or hypertension (Emami et al. 2020). The highest risk is observed among older persons, with the fatality rate of people infected fluctuating from 1.8%-3.5% in the 60-69 cohort, to 13.0%-20.2% in the 80+ cohort (Roser et al. 2020). Therefore, a major challenge in the area of health and socio-economic policy measures in the coming months is to keep the older population safe and contain the spread of coronavirus in that population.
This Policy Paper presents an analysis of the housing situation of people aged 65+ in Europe. Co-residence may be one of the relevant social risk factors that determine the probability of being infected with viruses which, like SARS-Cov-2, are spread through droplet transmission. As shown by research on intra-household transmission at the early stages of the epidemic in China, the majority (75%-85%) of clusters (group illnesses) were observed within households (WHO 2020b). Depending on the data, the coronavirus secondary attack rate within households is estimated at 7.6%-15.0% (Bi et al. 2020; KCDC 2020b), and from this perspective it is important to note that the incidence rate is the highest in the 20-29 age group, with most of them showing no symptoms of the disease while being able to infect others (KCDC 2020a).
Given the limited scope of labor market activity in the 65+ population, compliance with the self-isolation regime by this group will not interfere much with the gradual easing of socio-economic restrictions. Things look different among younger people due to their work or study, and among the youngest members of the population due to their school or pre-school attendance. In line with the regulations introducing the state of epidemic in Poland, since March 23rd, 2020, many workplaces have been operating on a remote basis, with their labor force doing work from home, and many companies and organizations having been closed. Similarly, the nurseries, kindergartens, schools and universities have been closed since the 16th of March this year. However, the government has already announced a plan to ease some of the restrictions to pave the way for a phased return to more intensive social contacts and economic activity (Council of Ministers 2020). Because of the shortcomings of distance learning and serious inequalities in access to education in this system (Myck et al. 2020), and considering the adverse impact of closed schools and kindergartens on the working parents, it seems imperative to resume the operation of these facilities as soon as possible.
A key challenge for the coming weeks will therefore be to reconcile the socio-economic benefits of lifting the lockdown with the risk of health implications arising from less stringent social distancing restrictions. Those implications may be particularly severe for older people. Thus, this Policy Paper discusses structural determinants of the well-being of older people, with a focus on the housing situation in European societies and the rate of co-residence with the younger population. The analyses outline the status in Poland in comparison to other European countries, pointing to a great diversity of health risks for older people. One factor is the difference in the prevalence of co-residence between the older and younger populace, and another is the prevalence of formalized care facilities. Next to disease statistics, these differences should be taken into account in any decisions on lockdown easing or a detailed design of policy measures.
In Poland, the percentage of people aged 65+ in co-residence with other members of the household aged 50 or below (excluding a spouse or partner) is 37.4% for the female population and 38.6% for the male population, i.e. the highest in Europe. In Poland, 12.0% of people aged 65+ share a household with school-age children (aged 7-18), and 7.7% live together with children aged 0-6. Co-residence with minors usually means, for obvious reasons, that the adult parents of the minors live under the same roof as well. However, Poland also reports one of the highest percentages of co-residence with other adults without minors. For example, 7.6% of people aged 65+ live in one household with people aged 19-30, and 17.3% share a household with adults aged 31-50 who are not their spouses or partners. It is worth noting, however, that in the European countries considered here a high percentage of co-residence is negatively correlated with the prevalence of collective dwelling facilities that deliver formalized care for older persons. In Poland, the supply of such institutions – whether public or private – has been very limited, with only 1.6% of people aged 80+ living in those facilities. In contrast, in Belgium, almost every fourth person of that age is a resident of such a facility. When it comes to the pandemic, it must be underscored that although in such institutions the interactions with younger people can be quite easily limited, the experience of many countries has shown that they have been quite vulnerable to coronavirus clusters and epidemic outbreaks.
Considering that Poland reports the highest percentage of co-residence among people aged 65+, particular attention should be paid to the challenges for health and socio-economic policy measures introduced in Poland to manage the intensity of social contacts during the pandemic. This, in particular, applies to the regulations on students returning to schools and the easing of social distancing rules for students and working adults. Therefore, in countries such as Poland, the restoration of frequent social contacts, which is necessary, inter alia, to put the economy back on track, will have to be accompanied with adequate safeguards for those who are most heavily exposed to negative health effects of Covid-19.
The first section of this Policy Paper reviews co-residence percentage data for the 65+ population, based on data for Europe (the European Union member states and Norway, Switzerland and the United Kingdom, for the remaining European countries the data is not available), from the 2017 European Union Statistics on Income and Living Conditions study (EU-SILC.) The second section presents data on older people living in long-term care facilities in a number of European countries, collected in recent years by the OECD.
1. Older People in Co-Residence With Other Members of the Household
In the analytical discussions below, the terms “co-residence” or “shared household” refer to a situation where persons aged 65+ live in one household with adults who are not their spouse or a partner, or with children under 19 years of age. In Poland, the percentage of households shared by people aged 65+ and children aged 18 or younger is one of the highest in Europe. Of all the older people in Poland that live in a household setting on a permanent basis (i.e. excluding those living in formalized care facilities), as many as 16.9% of women and 16.6% of men aged 65+ share a household with persons under 19 years of age (cf. Figure 1). With the exception of Slovakia and Romania, other countries report a much lower rate. In countries such as Norway, Sweden, Denmark, or the Netherlands, the rate is between 0.1% and 0.6% for women, and between 0.5% and 1.2% for men (65+ population).
Figure 1. Population aged 65+ in co-residence with persons other than their spouse/partner, by the age of the youngest member of the household
a) Male
b) Female
Source: Authors’ compilation based on the 2017 EU-SILC data.
Nota Bene: Share of 65+ population not living in formalized care facilities.
In Poland, approximately 12% of women and men aged 65+ share a household with students aged 7-18. In other words, more than 460k women and 280k men aged 65+ in Poland have direct, daily interactions with students attending schools (Table 1). In addition, 13.9% of women and 14.7% of men aged 65+ (530k and 360k, respectively) share a household with persons aged 19-30, who – according to research findings from other countries – demonstrate the highest incidence of coronavirus disease (KCDC 2020a). On top of that, these proportions are significantly higher in rural areas, and over 40% of the 65+ population in Poland live in rural areas. Compared to other countries in Europe, it is especially in the rural areas that Poland reports a significantly higher percentage of older people in co-residence with younger people (Figure 2). For example, while in Poland 19.0% share a household with children aged 7-18, and 21.1% with people aged 19-30, in Sweden in the 65+ population in rural areas those percentages are 0.4% and 1.0%, respectively, and in Belgium 1.9% and 1.5%. In urban areas the disparities in the demographic structure of households between Poland and other European countries are less pronounced, but still the share of the 65+ population in co-residence with younger people is among the highest in Europe; with 7.2% sharing a household with school children and 9.5% with adults aged 19-30. In Sweden these percentages are 0.7% and 1.7%, respectively, and in Belgium 1.2% and 3.8%.
Table 1: Population aged 65+ in Poland in co-residence with other members of the household (other than a partner/spouse).
| Urban | Rural | Total | |||||
| Male | Female | Male | Female | Male | Female | Total | |
| Population aged 65+ (in thousands) | 1 435 | 2 268 | 1 007 | 1 508 | 2 441 | 3 776 | 6 218 |
| People in co-residence with a person aged (in thousands): | |||||||
| – 0-6 | 82 | 107 | 117 | 175 | 199 | 282 | 481 |
| – 7-18 | 91 | 174 | 190 | 288 | 281 | 462 | 743 |
| – 19-30 | 142 | 210 | 216 | 315 | 359 | 525 | 883 |
| – 31-50 | 353 | 546 | 446 | 681 | 799 | 1227 | 2026 |
| People in co-residence with a person aged (in %): | |||||||
| – 0-6 | 5.7% | 4.7% | 11.6% | 11.6% | 8.1% | 7.5% | 7.7% |
| – 7-18 | 6.4% | 7.7% | 18.9% | 19.1% | 11.5% | 12.2% | 12.0% |
| – 19-30 | 9.9% | 9.2% | 21.5% | 20.9% | 14.7% | 13.9% | 14.2% |
Source: Authors’ compilation based on the 2017 EU-SILC data.
Nota Bene: Share of 65+ population not living in formalized care facilities.
Figure 2. Population aged 65+ in co-residence with other members of the household (other than a partner/spouse), by age of the other members of the household.
- Urban
Rural
Source: Authors’ compilation based on the 2017 EU-SILC data. Nota Bene: Countries: SE – Sweden, BE – Belgium, IT – Italy, HU – Hungary, ES – Spain, SK – Slovakia, PL – Poland. Share of 65+ population not living in formalized care facilities.
2. Residents of Formalized Care Facilities for Older Persons
Households where people aged 65+ live under one roof with younger people (usually they are all family members) reflect the financial status of the family on the one hand, but on the other they offer care to those who might need it to due to their age or health status. In that respect, unlike many other countries in Europe, Poland has a very low share of older people who, due to barriers to independent living, decide to relocate to a formalized care facility or a similar setting. In 2017, less than 1% of the 65+ population in Poland lived in formalized care facilities; and for the 80+ population the share was only slightly higher and reached 1.6% (Figure 3). One reason is the low number of vacancies in such facilities: in 2017 in Poland there were, statistically, 12 beds per 1000 inhabitants aged 65+. For comparison, in Nordic countries (Denmark, Finland, Norway, Sweden) more than 12% of the 80+ population live in formalized care facilities for older people; in Luxemburg and Switzerland the rate is close to 16%, and in Belgium it is 24%. These countries also report a much higher availability: from 50 beds per 1000 people aged 65+ in Denmark to over 80 beds in Luxembourg. The share of older people living in formalized care facilities is also relatively high in countries such as Slovenia (12.6% for the 80+ population) or Estonia (9.9%).
Figure 3. Long-term care facilities – resources and utilization.
Source: Authors’ compilation based on the OECD data.
Nota Bene: According to the latest 2017 data available, with the exception of: Spain, Portugal – 2018 data; the Netherlands, Slovenia – 2016 data; Belgium, Denmark – 2014 data. The figure includes the European countries for which the data has been available. For Italy, only the data on the number of beds has been available, and for Portugal, only the data on the number of facility residents.
The isolation regime introduced to restrict the frequency of visits, side by side with a system of appropriate checks and controls for the staff, are relatively simple ways to reduce the risk of external coronavirus infection in formalized care facilities. Yet, as we have learnt from numerous examples in Poland and internationally, infection transmission between the residents or between the residents and the staff has been a frequent source of infection clusters and outbreaks. For example, in South Korea, even more than 30% of new coronavirus cases could be the result of transmission between hospital patients or nursing home residents (KCDC 2020a). In connection with a coronavirus outbreak in a formalized care facility in the USA, more than half of the residents had to be hospitalized and, eventually, 33.4% died (McMichael 2020). It seems that keeping the residents of formalized care facilities safe from the infection should be a priority in an epidemic control policy. However, the pace at which social distancing restrictions are lifted so that students can get back to schools and the lockdown in public spaces can be removed, should not have a vital impact on the safety of those living in the facilities, in contrast to the situation of older persons who share a household with younger persons.
Summary
The well-being of the groups with the biggest exposure to the grave outcomes of coronavirus infection deserves special attention when lifting the lockdown introduced in connection with COVID-19 pandemic. In this context, the housing situation of older people and the nature of the underlying social contacts are among important aspects to take into account in developing detailed regulations. As outlined in this Policy Paper, different countries in Europe report different status in that respect. Of all the countries in Europe, Poland has the highest share of the 65+ population co-residing with younger people. On the other hand, less than 1% of the 65+ population live in formalized care facilities. In Europe, the lowest share of co-residence is reported in the Nordic countries, the Netherlands, Germany and Belgium. At the same time, the share of the 65+ population residing in formalized care facilities in those countries fluctuates from 4% to 8%, reaching over 10% in the 80+ population.
In formalized care facilities, lockdown lifting will not have material impact on the safety of the residents or the risk of coronavirus transmission. In contrast, the households where older people live side by side with the younger populace may actually represent a significant risk factor in terms of the spread of the epidemic and infection transmission to those who are most heavily exposed to the grave complications of Covid-19.
In general in Poland, 37.4% of women and 38.6% of men aged 65+ share a household with people under 50 other than their spouse or partner. This is the highest rate of co-residence with younger people for this age cohort in Europe. In Denmark, this percentage is 1.3% for women and 3.3% for men. Even in Spain it is much less common for people aged 65+ to share a household with younger family members (the rates being 28.0% for women and 26.6% for men, respectively). Additionally, in Poland, especially in rural areas, many people aged 65+ live under one roof with school-age children (7-18 years of age: 19.1% of women and 18.9% of men in this age group, respectively); and even more (20.9% of women and 21.5% of men) share a household with adults aged 19-30, which is the age group where coronavirus infection is the most prevalent (KCDC 2020a).
In view of major discrepancies in the demographic structure of households between countries, it seems necessary to differentiate the social distancing rules and the pace with which these rules are to be eased, if one of the objectives is to protect the people exposed to the most serious consequences of coronavirus infection. Especially in such countries as Poland, the policy of gradual opening of schools and other institutions and phased recovery of economic activity should be accompanied by a broad-based communication campaign on how to protect the most vulnerable household members. It seems advisable that the campaign be conducted both in the mass media and in schools, workplaces, and public spaces.
References
- Bi, Q., Y., Wu, S., Mei, Ch,., Ye, X., Zou, Z., Zhang, X., Liu, L.,Wei, S., Truelove, T., Zhang, W., Gao, C., Cheng, X., Tang, X., Wu, Y., Wu, B., Sun, S., Huang, Y., Sun, J., Zhang, T., Ma, J., Lessler, T., Feng (2020). “Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts.” medRxiv 2020.03.03.20028423
- ECDC – European Centre for Disease Prevention and Control (2020). “Coronavirus disease 2019 (COVID-19) in the EU/EEA and the UK – eighth update.”
- Emami, A., Javanmardi, F., Pirbonyeh, N., Akbari, A. (2020).”Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis.” Arch Acad Emerg Med. 8(1): e35.
- KCDC – Korea Centers for Disease Control & Prevention (2020a). “The updates on COVID-19 in Korea.”
- KCDC (2020b). “Coronavirus Disease-19: Summary of 2,370 Contact Investigations of the First 30 Cases in the Republic of Korea.” Osong Public Health Res Perspect. 2020 Apr; 11(2): 81–84.
- McMichael T., Currie D., Clark S., Pogosjans S., Kay M., Schwartz N., Lewis J., Baer A., Kawakami V., Lukoff M., Ferro J., Brostrom-Smith C., Rea T., Sayre M., Riedo F., Russell D., Hiatt B., Montgomery P., Rao A., Chow E., Tobolowsky F., Hughes M., Bardossy A., Oakley L., Jacobs J., Stone N., Reddy S., Jernigan J., Honein M., Clark T., Duchin J. (2020). “Epidemiology of Covid-19 in a Long-Term Care Facility in King County”, Washington. N Engl J Med. 2020 Mar 27.
- Myck, M., Oczkowska, M, Trzciński, K. (2020). “School lockdown: distance learning environment during the COVID-19 outbreak.” CenEA Commentary Paper.
- Oke, J., Heneghan, C. (2020). “Global Covid-19 Case Fatality Rates“.
- Rada Ministrów (2020). “Rozporządzenie Rady Ministrów z dnia 10 kwietnia 2020 r. w sprawie ustanowienia określonych ograniczeń, nakazów i zakazów w związku z wystąpieniem stanu epidemii” [Regulation of the Council of Ministers of 10 April 2020 on establishing certain restrictions, orders and prohibitions in connection with the introduction of the state of the epidemic].
- Roser, M., Ritchie, H., Ortiz-Ospina, E. (2020). “Coronavirus Disease (COVID-19) – Statistics and Research“.
- WHO (2020a). “Coronavirus disease 2019 (COVID-19)“. Situation Report – 46.
- WHO (2020b). “Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)“.
Disclaimer
This Policy Paper was originally published as a CenEA Commentary Paper of 21st April 2020 on www.cenea.org.pl. The analyses outlined in this Policy Paper make part of the microsimulation research program pursued by CenEA. The analyses are based on EU-SILC 2017 data as part of microsimulation research using the EUROMOD model and have been provided by EUROSTAT, and on publicly available OECD data. EUROSTAT, the European Commission, the National Statistical Institutes in each country, or the OECD have no liability for the results presented in the Policy Paper or its conclusions.
This Policy Paper was prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). FROGEE papers contribute to the discussion of inequalities in the Central and Eastern Europe. For more information, please visit www.freepolicybriefs.com. The views presented in the Policy Paper reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.
Household Exposure to Financial Risks: The First Wave of Impact From COVID-19 on the Economy
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
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
- Beacháin Stefańczak, K.Ó. and Connolly, E.(2015), ‘Gender and political representation in the de facto states of the Caucasus: women and parliamentary elections in Abkhazia’. Caucasus Survey, 3(3), pp.258-268.
- Author, D., Figlio, D., Karbownik, K., Roth, J., Wasserman, M. (2019) Family Disadvantage and the Gender Gap in Behavioral and Educational Outcomes, American Economic Journal: Applied Economics, 11(3), 338–381.
- Guryan, J., Hurst, E., Kearney, M. (2008) Parental Education and Parental Time with Children, Journal of Economic Perspectives, 22(3), 23–46.
- Ministry of Education (2020a) Regulation of the Minister of Education of the Republic of Poland of the 20th March 2020 on special measures applicable at the time of temporary restrictions in the operation of educational facilities in connection with the efforts to prevent, counteract and combat the COVID-19.
- Ministry of Education (2020a) Guide to education.
- Myck, M., Najsztub, M. (2015) Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey, International Journal of Microsimulation, 8(1), 33-66.
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.