Author: Admin
Russia Economic Update — Brace for the Covid-19 Impact!
Russia’s oil dependence will once again contribute to an economic downturn that most certainly will follow the Covid-19 outbreak in Russia as in other countries. The decline in oil prices alone could lead to a drop in GDP of more than 8 percent. On the positive side, Russia manages its macro economy well. However, its fiscal reserves are not unlimited and the recent massive fall in oil prices has not been matched by a similar decline in the ruble exchange rate which means potential extra problems for the budget. Furthermore, monetary policy will have less of a role to play in dealing with this type of crisis. This means that Russia like other countries will face difficult trade-offs in dealing with the crisis at a time when some of the previously announced economic policy changes have not been well received by the public.
Introduction
The corona virus crisis will destroy both lives and economies as it spreads across the globe. Fortunately, the corona virus death toll in Russia so far is relatively modest compared to many other countries, but the economy is most certainly heading for very difficult times. This is (again) due to the fact that the Russian economy is too dependent on the developments of international oil prices (see e.g. Becker, 2016a,b). In recent years, Russia had to deal with two severe declines in oil prices that hit its economy, first in connection with the global financial crises 2008/09, and second, in 2014/15, when there was a fall in oil prices simultaneously with Russia being hit by international sanctions after the illegal annexation of Crimea. Although these episodes were very costly for the Russian economy, they also provided important lessons for policy makers on fiscal, monetary and exchange rate policies that come in handy today. They also contributed with data on the relationship between large movements in oil prices and the effects they had on GDP growth in Russia. This is useful at this stage to assess what can happen with the economy after the significant decline in oil prices that has followed in the course of the corona outbreak.
Dramatic Decline in Oil Prices
We still do not know when this crisis will be over, but when it comes to the fall in international oil prices the start has been far more severe than the two crises referred to above. Since the beginning of 2020, oil prices have fallen from around $60/barrel to around $15/barrel or as Figure 1 shows, a barrel is now worth around 25 percent of what it was worth three months ago. Furthermore, prices are rather volatile and will continue to be so and there will most certainly also be periods of sharp increases in oil prices going forward – but the overall result for the year compared to the previous year is most likely a very sharp fall in prices. This decline in oil prices has so far been much more dramatic than the two previous crisis episodes the Russian economy has experienced under Putin as president or prime minister.
Figure 1. Oil price developments in recent crises
Note: This graph is based on the European Brent spot price FOB published by the U.S. Energy Information Administration and the axis shows trading days, so that the graph covers the period from January 1 to March 30. Different qualities of oil of course have different prices, but the patterns shown here are similar for other oil prices as well.
Exchange Rate and Stock Market
As in previous crises, the Russian stock market and exchange rate are following the evolution of oil prices. However, neither the stock market, nor the exchange rate has fallen as rapidly as oil prices. This can be due to many factors, but one likely explanation is that investors think that the decline in oil prices will not last for as long as it has in past crises. Whether this assumption is correct remains to be seen of course, but if oil prices stay low for an extended period, we can expect to see further declines in both the exchange rate and stock market.
Figure 2. Oil prices, exchange rate and stock market
Sources: Oil prices as in Figure 1, the exchange rate from Central Bank of Russia, RTS index from Moscow Stock Exchange.
The fact that the exchange rate this time has “only” depreciated by 20 percent when oil prices have fallen by 70-80 percent means that the oil price measured in rubles has fallen much more dramatically in this crisis compared to the previous ones. In the 2008/09 global financial crisis, the oil price in ruble terms was, in the end, unchanged compared to the start of the crisis. In 2014/15 this was not the case, but the decline in the ruble oil price was a more modest 25 percent compared to the 60 percent drop right now. This has serious implications for the government’s budget which is ruble-based and highly dependent on oil revenues.
Economic Policy
The Russian government now has plenty of experience in dealing with crises. The first lesson after the crisis at the end of the 90s was to have enough fiscal resources to deal with a crisis without having to go to the IMF again. The second lesson came in the global financial crisis when the fixed exchange rate had to be abandoned to avoid depleting the central bank’s international reserves. A prudent fiscal policy backed by the National Wealth Fund and a flexible exchange rate is still the backbone of the macroeconomic policies that can help mitigate the impact of lower oil prices.
The central bank is pursuing inflation targeting and uses a 4 percent inflation rate as the target that guides its policy decisions. The main tool is setting the key interest rate at a rate that will achieve the inflation target. The key interest rate is currently 6 percent, significantly down from the high of 17 percent in January 2015. The central bank states clearly in its monetary policy documents that “Monetary policy lays the groundwork for economic development; however, it cannot be a source of a sustainable rise in economic potential” (see page 6 in Central Bank of Russia, 2020). This implies that the central bank will only lower the key interest rate if inflation falls, not to support growth or try to achieve other, potentially conflicting goals. This is good news for macroeconomic stability but may become an issue of political tension if there is a serious downturn in the economy while inflation remains higher than the target rate.
In mid-2019, the National Wealth Fund was doubled and went from $60 billion to just over $120 billion (Ministry of Finance, 2020). This was done as a one-off transfer of surplus funds from the government’s budget. However, at its peak in the global financial crisis, the combined reserve fund and wealth fund that existed then had assets of over $220 billion but by the start of 2011, the assets were down to $111 billion. In other words, a year and a half into that crisis episode, the government had used an amount from the funds that roughly corresponds to the total amount available in the National Wealth Fund today. The fiscal space is, therefore, less impressive than it may look at a first glace and just burning through the cash in the National Wealth Fund is not a sustainable fiscal policy if this crisis continues a few more months.
Instead, the government will have to plan other measures as soon as the most immediate spending to deal with the crisis is done. This will entail difficult trade-offs since the health system will need increased resources at the same time as households and companies will need support to mitigate the impact from lost jobs and closed businesses in the wake of corona-induced shut-downs rather than the decline in oil prices, so adding to the pressure coming from declining oil prices. Increasing taxes in a time of already depressed purchasing power and profits is also not an appealing option and although there are still tax increases in the pipeline, the government has announced that these will not come in effect this year. Like in many other countries, the Russian government is proposing several measures to support the economy that will be discussed in more detail in a forthcoming FREE policy brief. However, these measures will add to the costs of the government at a time of falling revenues. From an economic perspective, reallocating resources from the military and security sectors to other parts of the economy seems like an obvious choice under these circumstances, but most likely not the outcome of this process given the government’s geopolitical and domestic power ambitions. Again, the fiscal reserves will allow postponing these harder decisions, but if the crisis goes on for some time, alternative measures such as borrowing domestically or internationally will most certainly be discussed also in Russia. However, many governments will be in need of borrowing on international markets going forward and the rates required to access this type of funding may not be very attractive and still force domestic budget reallocations.
Growth Impact of the Oil Price Fall
It is of course too early in the crisis to make very precise forecasts on how the economy will fare in 2020. This will in the end crucially depend on how the Covid-19 pandemic develops and on government responses to the crisis not only in Russia but also in the rest of the world. A partial analysis of the impact of falling oil prices can however be done with the models presented in Becker (2016a) which link changes in oil prices to growth. This paper shows a few alternative specifications that differ in the GDP measure being in dollars or real rubles, and in some other dimensions. All specifications are highly statistically significant and able to explain between 60 and 90 percent of variations in GDP growth in the period 2000-2015. Focusing on the relationship between the percentage change in oil prices and growth in real ruble GDP, the estimated coefficient is 0.14. This implies that for every 10-percentage point drop of oil prices, GDP growth goes down by 1.4 percent. Currently, oil prices have declined by 75 percent since the beginning of the year. However, the model estimates are based on comparing how average oil prices change between years so this is the numbers we need to compute and compare. The average price of Brent oil (which is used in this model) was $64/barrel in 2019 but we obviously do not know what the average oil price will be this year. We therefore need to first “forecast” oil prices for the rest of the year before we can compute the impact on growth. If we make the simple assumption that oil prices stay at the current level and take into account that they were significantly higher the first couple of months this year, the average price would end up being $25/barrel. That would amount to a 60 percent decline in average oil prices between 2019 and 2020. The partial effect of this oil price decline would therefore make Russian real GDP drop by 8.5 percent in 2020. Again, this is the partial effect based on the estimated coefficient in a linear relationship between oil price changes and real GDP growth. In plainer English, we are not looking at the first order effect of closing stores etc. to avoid the virus from spreading but only the additional effect that we think will come from falling oil prices. In addition, the effect this massive decline in oil prices is assumed to have on GDP is derived by a coefficient that is estimated on smaller changes in oil prices and real GDP. Nevertheless, this exercise provides a first, and rather daunting, assessment of what can happen to GDP given the decline in oil prices alone.
Concluding Remarks with OPEC and IEA update
This brief has provided a first assessment of how the Russian economy may be impacted by the massive decline in oil prices that has followed in the course of the corona pandemic. It has shown that the economic downturn this time can be significantly worse than both the 2008/09 and the 2014/15 crises. A base line estimate suggests that GDP may fall by more than 8 percent only because of the fall in oil prices. The above calculation obviously includes neither the impact the health situation will have on companies or households, nor the government’s ability to mitigate the negative consequences. If the other problems the economy is facing as a direct result of the health crisis also lead to a significant decline in supply and demand, Russia could easily see real GDP declining by more than 10 percent in 2020.
Our estimate is an important reminder that Russia’s continued oil dependency is a risk to the economy and its citizens. Now is not the time for ambitious structural and institutional changes to generate growth, but hopefully the urgent crisis period passes without policy makers forgetting the risks the country’s oil dependence entails. They learnt the fiscal and monetary lessons well from past crises, now is the time to learn something new. The most appealing road to sustainable economic growth is still building credible property rights institutions and rule of law in a framework that would make Russia the innovative business-oriented superpower it could be.
A few days after the first version of this brief was published, oil prices started to rise as the OPEC together with Russia started discussions to cut production to support oil prices. A tentative agreement was reached which is supposed to cut production by 10 million barrels per day in May and June, the largest cut in OPEC’s history. Had this movements in prices continued, the forecast for the Russian economy would have been affected. However, this recovery in prices was soon reversed and oil prices started to fall again. The decline continued on April 15 as the International Energy Agency presented a dire forecast of oil demand and stated that this year may be the worst year ever in terms of declining demand. All in all, the price movements that have followed the OPEC meeting and the statements of the IEA do not change the baseline prediction this brief has provided.
References
- Becker, Torbjörn, 2016a. “Russia’s Oil Dependence and the EU”, SITE Working paper 38.
- Becker, Torbjörn, 2016b. “Russia and Oil — Out of Control”, FREE policy brief.
- Central Bank of Russia data on exchange rate.
- Central Bank of Russia, 2020. “Monetary Policy Guidelines for 2020–2022”.
- Ministry of Finance, 2020. Data on the National Wealth fund.
- Moscow Exchange data, 2020. Data on the RTS index.
- U.S. Energy Information Administration, 2020. Data on oil price data.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes. This brief was first published on April 6, 2020 and then revised on April 15, 2020.
Household Exposure to Financial Risks: The First Wave of Impact From COVID-19 on the Economy
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.
COVID-19 | The Case of Italy
Italy was the first European country to experience the Covid-19 pandemic on its territory, and as of today, March 30, it is the most heavily affected. Because of this, there is already ample coverage of the Italian case from multiple sources. Nevertheless, and although the country is not part of the FREE network region, we report on the covid-19 crisis in Italy, for two reasons.
Since SITE has a substantial share of Italian nationals in its staffing, following and updating the Italian statistics and measures to parallel the reporting from our core countries is relatively easy.
We intend for our report on Italy to provide a useful benchmark for the policy measures implemented by other countries, as Italy represents the first country hit in Europe and therefore the most surprised and least prepared case.
Basic Facts
Italy is a country of around 60 million people, with capital Rome, around 3 million. Around 10 million live in Lombardy, the region most heavily hit by the pandemic, and 1,3 million in Milan, its largest city. Italy is a founding member of the European Community and part of the Eurozone.
The main responsibility for health care delivery in Italy is at the level of the 20 regions and 2 autonomous provinces, although the central government, through the Health Ministry, oversees and coordinates the national strategy. The whole of the health care system, Servizio Sanitario Nazionale (SSN), which includes several national level institutes and subsidiary bodies on scientific advice plus the regional providers Aziende Sanitarie Locali (ASL) and Aziende Ospedaliere (AO), is among the best in the world for accessibility and cost efficiency, according to WHO and based on the Bloomberg Health-Care Efficiency Index. The responsibility for education is at the national level, divided between the Education Ministry and the Ministry for University and Research. Professional education is instead left to the regions. Social services to the elderly, the disabled, and needy families are dealt with by local authorities, sometimes with the assistance of volunteer associations and non-profit social service cooperatives.
Health Indicators
On January 30, the first two cases of coronavirus were reported in Italy: two Chinese tourists from Wuhan were hospitalized in Rome. They had landed 10 days before in Milan (January 23th).
On February 21, the first local infection was reported at the hospital of Codogno, in Lombardy (a 38 years old man). All the people who were in contact with him (including in the hospital) were contacted, tested and asked to isolate themselves (around 100 persons). Nevertheless, few days later hundreds of cases were reported in the area around Lodi in Lombardy, and in Veneto. The indicators on Covid-19 numbers in the table are from the newspaper Il Sole 24 ore. The numbers of hospital beds are from the NCBI as reported by the Financial Times. The OECD provides statistics on nurses and doctors. Capacity is being expanded in real time during these weeks, but this is not reported in a systematic way, as far as we could see.
Financial and Economic Indicators
As part of the EU and Eurozone, Italy does not have a sovereign monetary policy, but depends on the European Central Bank.
The stock market data is from the Italian Stock Exchange ; we focus on the performance of the main index, called FTSE MIB.
Since February 23, all layoffs of workers were put on hold for two months. There is no current reporting on this, and the latest available data is from before the pandemic and therefore can be seen as unrelated.
Short Summary of Health Crisis Measures
From January 23 (when a flight from Wuhan with 202 passengers was supposed to land in Rome) controls on passengers from Wuhan were started. These included temperature controls with scanners at major airports and mandatory submission of schedules with destinations and travel plans for all the passengers coming from Wuhan. In Rome and Milan airports, posters were put up explaining the typical symptoms of the new coronavirus, encouraging to avoid non-important travels to Wuhan and to get a flu vaccine at least two-week prior departure. The posters also gave typical hygiene recommendations such as hand washing, avoiding contact with sick people or crowded places, as well as contact with animals and raw meat, and recommendation to avoid travel if sick.
Flights to and from China were suspended as soon as the infection was detected in the two tourists, on January 30. As a precautionary measure, the same routines implemented for the SARS epidemic in 2003 were started: the Council of Ministers declared a state of emergency with a duration of 6 months starting January 31, and allocated EUR 5 million to this.
On February 22, through a decree from the central government, 10 Italian towns suspected to be outbreaks of coronavirus were put on lockdown.
On February 29, with over 1000 infected, the regions of Lombardy, Veneto and Emilia Romagna closed schools and universities. This was extended to the national territory on March 4, when also public attendance of football matches, cinemas and theatres was suspended for 1 month. The one-meter distance rule, with no hugs and no handshakes, was also introduced.
On March 7 and March 9, the lockdown was subsequently expanded to cover the national territory. On March 21, all nonessential production was stopped to halt the spread of coronavirus. As of March 30, the lockdown was prolonged two more weeks.
Government Economic Policies
Labor Market
- All layoffs started after February 23 are put on hold.
- Payments of social contributions are put on hold.
- Sick-pay restrictions are reduced (12 extra days per month allowed).
- Government funding for shortened or suspended working time.
- 500€ lump sum benefit for all free-lancers that are not part of safety nets.
- Most public and private employers must allow distance work. (Exception are allowed, and the criteria to be used have been hotly debated between workers and industry representatives.)
- Parental leave with 50% compensation for all private employees with children younger than 12 for up to 15 days since March 5. Alternatively, up to 600€ bonus for private childcare.
Tax Breaks
- Tax payments due between March and May are put on hold.
- Tax credits proportional to costs (chiefly rents and sanitation) for commercial activities.
Emergency Loans, Guarantees and Support
- Extra funding for repurposing of production towards medical needs.
- Loan guarantees, liquidity support and suspension of repayments for SMEs.
- Financial support to sports and Alitalia.
- Extra funding (400 millions) to municipalities to provide basic support (food stamps) to households with special needs (these are mostly households whose main source of income are jobs in the informal sector, which as such do not qualify for any safety net.)
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
Poland is a country of around 38 million people. The area is 312 thousand sqkm which gives a population density of 124.7persons/sqkm. The capital is Warsaw with 1.8 million inhabitants, other major cities are Kraków (0.8mn), Łódź (0.7mn), Wrocław (0,6) and Poznań (0,5). Poland has been a member of the EU since 2004, but along with some other new members has not adopted the EURO currency.
Different responses to the crisis across countries depend partly on the organization of political authority, as reflected in the level of regional decentralization of decision making in key areas of authority, and the strength and independence of public agencies. In the case of Poland, the government has four levels, the central government, 16 regions (voivodeships), 314 counties (powiaty) and 2477 municipalities (gminy). From the point of view of involvement in response to the Covid-19 pandemic, different layers of government are responsible for different public services, with counties being the most involved in the provision of healthcare and secondary education, while municipalities being in charge of social support, local transport, primary schools and other types of care.
In Poland the highest decisive body with regard to the pandemic is the Ministry of Health. The Principal Sanitary Authority (Główny Inspektor Sanitarny) deals specifically with the country’s epidemiological situation and infectious diseases, and is subordinate to the Ministry of Health.
Health Indicators
While Poland lags far behind many other developed countries in terms of the availability of medical staff (2.4 doctors and 5.1 nurses per 1000 inhabitants in 2017), the Polish health care system scores much better with regard to resources like hospital beds (6.6 beds per 1000 inhabitants) [1].
Generally, from the perspective of efficient treatment provided to large numbers of patients infected with Covid-19, the most important country statistics concern the health infrastructure related to infectious diseases. In 2018 wards devoted to infectious diseases in general hospitals had a capacity of only 2997 beds, which accounted for 1,65% of all available hospital beds [2]. As far as medical professionals are concerned, in 2020 Poland had 1120 actively working medical doctors with a specialization in infectious diseases [3]. They constituted as few as 0,75% of all specialists, which gives an indication of how small this field is in Poland. Assuming an uncontrollable dissemination of the disease, Polish health care resources would quickly face a huge overburden.
Figure 1: Nurses. Total, per 1000 inhabitants, 2018 or latest available.
Source: OECD Health Statistics.
Figure 2: Doctors. Total, per 1000 inhabitants, 2018 or latest available.
Source: OECD Health Statistics.
Figure 3: Hospital beds. Total, per 1000 inhabitants, 2018 or latest available.
Source: OECD Health Statistics.
According to official announcements, the territory of Poland was free from the Covid-19 disease until as late as March 3, when the first case was confirmed. Patient 0 came by bus from abroad after participating in the Carnival celebrations in Nordrhein Westfalen in Germany. Several other initial patients returned to Poland from Italy. Since then the disease spread throughout the whole country, (according to official statistics) having infected at least 3266 people as of one month later [4].
Financial Indicators
The Warsaw Stock Exchange belongs to the main stock markets in Central and Eastern Europe. Along with 25 other countries, it is included in the FTSE Russel list of economically developed markets. As of 2019 the Warsaw Stock Exchange had 460 listed companies, 50 of them foreign [5]. Since the emergence of the Covid-19 disease in Poland in early March, the main index of companies at the Warsaw Stock Exchange, called WIG, faced value loss exceeding 17% (Figure 2).
Poland keeps its own currency, the Polish Zloty (PLN), which is a free floating currency. According to the exchange rate data from the National Bank of Poland (NBP), which provides the average daily exchange rate of the Zloty with world’s most important currencies, during last month Poland’s currency dramatically lost value in comparison to both the Euro and the US dollar [6].
Figure 4: Volatility of one of the main indices at the Warsaw Stock Exchange (WIG).
Source: Warsaw Stock Exchange.
Figure 5: The Polish currency in March 2020.
Source: Central Bank of Poland (NBP).
In Poland, the number of newly registered unemployed is given in monthly intervals and reflects the number of people who have registered at the County Employment Agency (Powiatowy Urząd Pracy) for the first time in a particular month. However, publicly available data comes with a lag of three months, so unless statistics are provided earlier the impact of isolation policies introduced due to the pandemic will not be known publicly for some time.
Government Health Policies
The Minister of Health announced a state of epidemic emergency 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 only for 65+ [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].
Government Economic Policies
The government implemented the so called “Anti-crisis shield” which came into force on April 1. The package includes a number of broad measures to support enterprises and workers for the period of three months and includes both direct financial support as well as provisions regarding financial liquidity for companies [14]. 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 [15].
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 one-off benefit equivalent to 80% of minimum wage;
- 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%;
- Additional employment support provided to SMEs in case of higher revenue loss;
- Relaxation of work and stay permits for foreigners.
Tax breaks [14]:
- Social security contributions to be paid by the government for self-employed and employees employed in small enterprises (up to 9 employees) for three months;
- Tax payments and social security contributions on earnings and profits can be delayed.
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;
- 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 [15]:
- 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%.
- 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] Central Bank of Poland (Narodowy Bank Polski), https://www.nbp.pl/home.aspx?f=/polityka_pieniezna/dokumenty/komunikaty_rpp.html
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 Belarus
Belarus is a country with about 9.5 million citizens. The area is 207 thousand sqkm which gives a population density of 45.9 persons/sqkm. The capital is Minsk with around 2 million inhabitants, other major cities are Gomel (0.53mn), Mogilev (0.38mn), Vitebsk (0.38mn), Grodno (0.37mn), Brest (0.35mn). Belarus is a member of the Eurasian Economic Union and is part of the Union State of Russia and Belarus. The national currency is the Belarusian Ruble (BYN).
Different responses to the crisis across countries depend partly on the organization of political authority, as reflected in the level of regional decentralization of decision making in key areas of authority, and the strength and independence of public agencies. In the case of Belarus, the power is highly centralized and most decisions are made either by central government or personally by the president.
It is widely considered that the government in Belarus has a small degree of independence from the president. The authority in charge of dealing with pandemics is the Ministry of Health.
Health Indicators
Belarus had its first officially registered case of Covid-19 on February 27 and the first death on March 31. At first, the increase of the newly registered cases was slower than in most other countries, but in the beginning of April, Belarus started to catch up, reaching 351 officially registered total cases by April 3. As of April 3, officials in Belarus have performed 32000 cases and tried to trace and isolate all the close contacts in the early phase of Covid-19 spread.
Belarus has a relatively high numbers of doctors and hospital beds per capita. There are 4 doctors, 12 nurses and 8 hospital beds per 1000 citizens and 2.3 intensive care units per 10,000 citizens. Government officials claim that there are 22 lung ventilators per 100 thousand persons and that this number can be increased to 38 if necessary.
Financial Indicators
Belarus currently does not have a properly functioning stock exchange, so it is hard to provide any strong evidence on the changes in corporate valuations. The Belarusian ruble started to depreciate in late February of 2020. Figure 1 depicts the recent developments in the exchange rate with respect to US dollar. Since the beginning of 2020, the US dollar went from 2.1 BYN to 2.57 BYN.
Figure 1: USD to BYN exchange rate.
Source: National Bank of Belarus.
The developments that can be seen on Figure 1 are largely due to the depreciation of the Russian Ruble which in turn was caused by decrease in oil prices as the OPEC+ agreement have failed in early March of 2020.
Government Health Policies
The government’s strategy so far was to identify and trace all the Covid-19 cases by performing a large number of tests (32,000 as of April 3) and isolating the first-degree contacts of infected persons. Public events with international participation were forbidden, however this does not apply to other public events and gatherings including football games and music concerts. As of April 4, government officials are still planning to hold the WW2 victory parade on May 9. Borders and airports are not closed, but persons arriving from abroad are advised to self-isolate for 14 days. There is no state-wide closure of schools and universities. The only closed teaching institutions are those which had students with officially confirmed Covid-19.
There is no state-wide quarantine as government officials deem it unnecessary and President Lukashenka calls the situation “Covid hysteria”. Among the measures taken up to date is financial regulatory easing ordered by the National Bank of Belarus. The government also issued a decree that consumer prices should not increase by more than 0.5% per month. In addition to that, the government plans to spend 110 million BYN (42.5 million USD) on economic support measures.
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 Latvia
The first positive COVID – 19 case in Latvia was confirmed on March 3. By April 5, the number of positive cases grew to 533. The share of positive tests remains quite stable and is currently slightly below 3%.
State of Emergency
On March 12, the government declared the state of emergency until at least April 14. The adopted measures include suspension of all on-site education activities at schools and universities, prohibition of any public gatherings, festivals or other organized public events. People are advised to stay home, many companies are switching to remote work. There is no closure of public transportation, but, as of March 21, the number of routes and transportation frequency is being reduced because of a significant fall in the number of passengers.
On March 14, Latvia announced a national lockdown that became effective on March 17. All organized cross-border passenger traffic is closed until at least April 14. Riga airport is closed except for cargo aircrafts. Border crossing is also banned for private vehicles, except for Latvians returning to Latvia and for foreigners leaving Latvia. The government is now considering to prolong the state of emergency to three months. This implies that Latvia can remain in the state of emergency until mid-June.
As of March 28, all shopping centers are closed on weekends, except for grocery stores, pharmacies and construction shops. As of March 31, it is not allowed to stay outdoors in groups of more than 2 people (this does not apply to members of the same household). There should be at least a 2-meter distance between any groups of two people. This rule applies to staying outdoors, shops, public transport, and any other public spaces.
Fiscal Measures
The government announced a package of measures totaling approximately EUR 2 billion (equivalent to 80% of one-month Latvian GDP). The measures include financing of the sickness benefit (normally the first 10 days of the sickness leave is covered by the employer), postponement of all personal income tax advance payments, provision of up to three years of tax holidays to companies, state-guaranteed bank loan holidays and state-financed loans. Employees of the affected firms are eligible for a special compensation worth 75% of the employees’ wage (maximum 700 EUR per month). In addition, municipalities will provide additional support to the most vulnerable groups that are unable to meet their basic needs due to the crisis (e.g. the unemployed not (yet) receiving the unemployment benefit, persons in self-isolation or quarantine).
On April 2, the State Employment Agency informed that due to the COVID-19 pandemic, 20 companies in Latvia have announced collective redundancies, which will lead to 3278 employees being laid off. This mainly includes layoffs in transportation, catering and accommodation sectors.
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 Sweden
Sweden is a country of around 10 million people. The area is 450 thousand sqkm which gives a population density of 22.7 persons/sqkm. The capital is Stockholm with 1.5 million inhabitants, other major cities are Gothenburg (0.6mn), Malmö (0.3mn), and Uppsala (0.2mn). Sweden has been a member of the EU since 1995 but is not a member of the Eurozone.
Different responses across countries to the crisis depend partly on the organization of political authority, as reflected in the level of regional decentralization of decision making in key areas of authority, and the strength and independence of public agencies. In the case of Sweden, the government has three levels, the central government, 21 regions and 290 municipalities. The regions are responsible for – among other things – health care, while municipalities are in charge of elderly care and schools, all institutions which play an important role in the response to Covid-19.
Public agencies in Sweden have a significant degree of independence from the government and line ministers as long as the agency delivers on the mission and guidelines determined by the government. The agency in charge of dealing with pandemics is the Public Health Agency of Sweden (Folkhälsomyndigheten, 2020), which states on their website that “The Public Health Agency of Sweden has a national responsibility for public health issues and works to ensure good public health. The agency also works to ensure that the population is protected against communicable diseases and other health threats.” The Public Health Agency of Sweden has advised the government on which actions to take in the Covid-19 crisis. It is also Sweden’s Coordinating Competent Body for the European Centre for Disease Prevention and Control (ECDC). Other important authorities involved in health recommendations and crisis measures are Socialstyrelsen (Socialstyrelsen, 2020) and the Swedish Civil Contingencies Agency (MSB, 2020).
Health Indicators
Sweden had its first recorded case of Covid-19 on February 1, but then it took until February 27 for the next case to be registered. In the first week of March, a more significant number of people were diagnosed with the virus as people had returned to Sweden with symptoms after having been in the Italian Alps during the school winter holiday the week before. A few early cases were also related to travel to and from Iran. The indicators on Covid-19 numbers in the table are from the ECDC (ECDC, 2020). The OECD provides numbers on nurses and doctors per 1000 inhabitants.
Figure 1: Nurses. Total, per 1000 inhabitants, 2018 or latest available
Source: OECD Health Statistics.
Figure 2: Doctors. Total, per 1000 inhabitants, 2018 or latest available
Source: OECD Health Statistics.Doctors.
Financial Indicators
Sweden is highly integrated in international financial markets and has a well-developed and liquid stock market. Despite being an EU member country, Sweden keeps its own currency, the Swedish krona (SEK), which is free floating since the Swedish Riksbank is targeting inflation rather than fixing the exchange rate.
The stock market data (see below) is from Nasdaq Stockholm and is the main index of large companies called OMX30.
Figure 3: Stock market data
Source: Nasdaq Nordic
The exchange rate data is from FOREX which provides travellers with foreign currency and is the selling rate for SEK/USD.
Figure 4: Exchange rate data
Source: Forex 2020
The exchange rates are not the same as those that would be used by financial institutions and companies but easily available and movements in this exchange rate, which is what we use here, follow the institutional rates closely.
Laid off workers are the number of workers that have been given notice by their employer that they will be laid off and the numbers are reported on a monthly basis by the Swedish Public Employment Service. In the wake of the corona crisis they are now making more frequent updates to their numbers and have regular press releases to complement their standard data reporting (Arbetsförmedlingen, 2020).
Government Health Policies
In general, the government follows the recommendations of the Public Health Agency of Sweden (PHAS) and other trusted authorities.[1] However, it is also clear that the regions, which are in charge of providing health care, make their own adjustment to some of the recommendations that are issued by the PHAS. For example, the region of Stockholm has adjusted the recommended use of protective equipment to be used by medical staff, most likely in light of shortages. Testing is also not centralized and the PHAS will hold a meeting at the end of March with the different parties involved in testing.
[1] The government states “The Public Health Agency of Sweden coordinates communicable disease control at national level and provides daily updates regarding the situation in Sweden. The National Board of Health and Welfare supports and coordinates the health and medical care preparedness of the various regions. The Government is in daily contact with these agencies. The Government has issued the National Board of Health and Welfare and the Public Health Agency of Sweden several instructions on limiting the spread of SARS-CoV-2. The Government will ensure that the expert agencies and the health and medical care system have the resources necessary to limit the spread of the virus.”
Short Summary of Measures
Mobility restrictions:
- Restriction on size of public gatherings; 500 and then 50 people.
- Restrictions on bars and restaurants, no standing in line or at the bar, only service at tables.
- High schools and higher education institutions are closed and instead provide online education.
- Travel to Sweden from non-EU countries is stopped.
- Visits to nursing homes are forbidden.
Health care:
- Extra delivery of face masks.
- Coordinated efforts to procure more medical equipment.
- Provision of extra hospital beds and intensive care beds.
- Additional government funding to health care providers and related agencies.
- Information campaigns to public and social services personnel.
- Contributions to WHO emergency fund.
Government Economic Policies
In addition to the health and prevention measures, the government has announced an extensive list of measures to deal with the economic impact of the pandemic. These are implemented by several ministries as well as the central bank and the financial supervisory authority.
Short Summary of Measures
Labor market:
- Unemployment benefits extended to more people.
- Sick-pay restrictions removed.
- Government funding for shortened working time.
Tax breaks:
- Tax payments can be delayed.
Emergency loans, guarantees and support:
- Loan guarantees to SMEs.
- Capital injection to ALMI to support loans to SMEs.
- Extra funding for export credits.
- Extra funding for export guarantees.
- Financial support to culture and sports.
- Guarantees to the Nordic airline SAS.
Central Bank measures:
- Loans to banks at low interest and reduced collateral restrictions; banks that benefits from these loans pay a fine if they do not increase their credit supply significantly.
- Loans to banks in USD.
- Purchases of government and mortgage bonds.
- Purchases of commercial papers.
Financial regulator:
- Counter cyclical buffers for banks set to zero.
- Relaxed amortization requirements for households.
- Banks allowed to fall below liquidity coverage ratios.
References
- Arbetsförmedlingen, Swedish Public Employment Service. 2020. English. https://arbetsformedlingen.se/other-languages/english-engelska (2020-03-24)
- Arbetsförmedlingen, Swedish Public Employment Service. 2020. Pressmeddelanden. https://arbetsformedlingen.se/om-oss/press/pressmeddelanden (2020-03-23)
- Arbetsförmedlingen, Swedish Public Employment Service. 2020. Statistik. https://arbetsformedlingen.se/om-oss/statistik-och-analyser/statistik (2020-03-24)
- ECDC, European Centre for Disease Prevention and Control: An agency of the European Union. Download today’s data on the geographic distribution of COVID-19 cases worldwide. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide (2020-03-23)
- Folkhälsomyndigheten, 2020. Our mission – to strengthen and develop public health. https://www.folkhalsomyndigheten.se/the-public-health-agency-of-sweden/ (2020-03-23)
- Forex, 2020. Växla valuta. https://www.forex.se/valuta/usd (2020-03-24)
- MSB, The Swedish Civil Contingencies Agency. 2020. https://www.msb.se/en/ (2020-03-23)
- Nasdaqomxnordic, 2020. OMXS30, OMX STOCKHOLM 30 INDEX, (SE0000337842). http://www.nasdaqomxnordic.com/indexes/historical_prices?Instrument=SE0000337842 (2020-03-23)
- OECD, The Organisation for Economic Co-operation and Development. 2020. Doctors. https://data.oecd.org/healthres/doctors.htm (2020-03-24)
- OECD, The Organisation for Economic Co-operation and Development. 2020. Nurses. https://data.oecd.org/healthres/nurses.htm (2020-03-24)
- Government Offices of Sweden, 2020. The Government’s work in response to the virus responsible for COVID-19. https://www.government.se/government-policy/the-governments-work-in-response-to-the-virus-responsible-for-covid-19/ (2020-03-23)
- Socialstyrelsen, 2020. Learn more about Swedish Health Care. https://www.socialstyrelsen.se/en/ (2020-03-24)
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.
Recipient Type and the Effectiveness of Informational Campaigns: The Case of Meat
While global population growth has been accelerating during the last decades, the number of humans currently living on the planet is dwarfed by the amount of farm animals alive at any time, and even more by the quantity we slaughter for meat every year. According to the latest FAO statistics, this latter number is estimated at around 75 billion. Even ignoring animal welfare, this is severely affecting the health of the planet and our own. What should be done about this?
Externalities of Meat Consumption
Mankind has been butchering and eating animals for at least 3,4 million years (McPherron et al., 2010). Evolutionary biology theories claim that complementing our diet with meat contributed to the spectacular growth of our brain (Fonseca-Azevedo et al., 2012). Anthropological theories suggest that the necessity of hunting drove the development of tool building, language and social structures. The domestication of animals (and plants) around 10,000 years ago led to a jump in the history of civilization. In other words, eating meat is a large part of what made us human. However, during the last century, we took this to unsustainable levels. All in all, the agricultural sector accounts for 25 to 30% of global CO2 emissions, second only to the energy and transport sector, and 60% of non-CO2 emissions, in particular methane, which is much more efficient than CO2 at warming up the planet. A third to half of these emissions, depending on whether or not we include the share related to land use, comes from livestock production. Large scale factory farms, which cater to the ever-increasing global demand for cheap meat, are also responsible for other externalities, including distorted resource use (in particular of water and fertile land); local pollution of air and waterways, with consequences for neighbouring ecosystems and human health; abuse of antibiotics, which threatens their effectiveness with dramatic implications for the whole spectrum of modern medicine. The cheap and overabundant animal products with worsened nutritional properties, which result from these production methods are also behind the epidemic of “welfare diseases” such as diabetes, cardiovascular conditions and some types of cancer (Mozaffarian, 2016).
So, what should we do about this? Economics is very clear on this point. In the presence of externalities, market prices do not reflect social costs; therefore, the market mechanism fails, and decisions taken on the basis of these prices are suboptimal. If applied to meat consumption, this principle implies that, first of all, consumers and producers must pay for the emissions (and other externalities) they cause. Today’s carbon pricing systems, whether in the form of a tax like in Sweden or tradable emission permits like in EU, exempt the agricultural sector for various reasons. Moreover, as already mentioned there is more to meat than carbon emissions. Another FREE brief (Perrotta, 2011) makes the case for a meat (consumption) tax. Multiple teams of researchers (Wirsenius, Hedenus, and Mohlin, 2011; Edjabou and Smed, 2013; Gren, 2015; Andersson, 2019) have come as far as to compute the optimal level of such a tax, in different contexts and under different assumptions. There are also drawbacks to this approach, though. Climate-change curbing policy is in general an area where policy makers at all levels find it hard to converge to policies of strong incentives, such as taxes and regulation. Interventions targeting food production or dietary choices, in particular, are likely to face strong opposition from producers and consumers alike. It is therefore worth considering the alternative – or at least complementary – strategy of information and awareness campaigns.
The Power of Information
Given that a climate policy agenda of strong incentives is so fraught with obstacles, the potential for information to spark voluntary action would be very valuable. There is a catch here, however. Information about the benefits of an action often fails to encourage that action. Consider the case in point: for decades now, we have observed a persistence and increase of meat eating despite mounting evidence and widespread information on the ills of meat production and consumption. Indeed, this well-known weakness of informational interventions has contributed to the rising importance and application of alternative approaches. One example is the popularity of the so-called nudges (Thaler and Sunstein, 2009), modifications in the choice architecture that can subtly push agents towards an action without actually limiting the available alternatives. There is ample research on where and why the chain from information to action might get interrupted, and established evidence that the effectiveness of information depends on a variety of factors such as recipients’ prior beliefs, the sender’s credibility, and the non-informative content of the message, such as the emotional evocativeness of imagery (see a survey in DellaVigna and Gentzkow, 2010). Taking a step back to the stage before, namely the question whether information does reach the intended beneficiaries in the first place, at least three aspects of this have been investigated: limited attention, active avoidance, and selective retaining of information on the part of the recipients. In a new working paper (Berlin and Mandl, 2020), we investigate the role of individual type for selective information retention. We ask whether certain types of agents, in our case vegetarians, retain more of the information they are exposed to, even when exposed to a similar context and the same incentives to retain information as everyone else (so that hopefully the competing channels of limited attention and active avoidance can be neutralized). This has relevance for the possibility of tailoring the policy message, similar to the marketing theories of market segmentation. In contrast to well-developed marketing practices in the private sector, this potential has so far not been exploited in policy design. To the best of our knowledge, this mechanism has not been investigated in a real-life incentivized setting outside the lab before.
Natural Experiment in Class
We exploit a natural experiment in the context of higher education. A class of college students was assigned an essay about their plan for a Christmas dinner menu, after being exposed to a lecture and reading materials on the externalities of meat production, so that they could decide to make use of this information. The essays were to be written in randomly assigned groups of three, making the type combination, i.e. the presence of one or more vegetarian group members, a random group characteristic. We hypothesize that there is a difference in how carnivores and vegetarians deal with the provided information about the food industry. In particular, we test whether groups that include a vegetarian student recall a larger share of the information than groups made up only of carnivores. The essay was mandatory, and moreover it awarded study credits toward the final grade of the course (10/100 points). This constitutes a sizeable incentive and possibly provides a stronger motivation for information retention as compared to the average monetary rewards which lab experiments rely on. To measure the share of information retained, we preregistered a list of 30 words in both English and Swedish related to the learning outcomes of the lecture. We then used a script to measure how many of the 30 words appear in each essay. We call this number the essay’s score, separate and independent from the teacher’s assigned grade, which is of relevance for the student. The teacher-assigned grade, reflecting general comprehension of the topic rather than just the presence of keywords, is expected to be correlated with the score, but not perfectly. We also expect the grade to capture the ability of the students to a higher degree compared to the score, as the automatized word count fails to consider the context in which the words are mentioned.
Results
Figure 1. Group score by treatment status
Source: Berlin and Mandl (2020).
On average, groups including a vegetarian student scored higher (4.8) than groups with all meat-eaters (4.3), but not significantly so. The estimated Cohen’s d (0.347), a standard measure of effect size used to indicate the standardized difference between two means, is much smaller than the minimum detectable effect in our sample, which we estimated at 0.8. In other words, we do not have the statistical power to either accept or reject the null hypothesis. The reason is that the treated group displays larger variation in score outcomes, possibly due to the smaller than anticipated sample size: only 11 students out of almost 300 identified themselves as vegetarians or vegan (non meat-eaters), which is a much smaller proportion than what the latest survey of young adults in Sweden estimates (17%, Djurens Rätt, 2018).
Looking beyond the mean at the details of our data reveals an interesting pattern. As the Figure shows, the distribution of achieved scores among the vegetarian groups is bimodal: a lower-level concentration of scores is close to the mode of the control distribution, but there is an almost as large mass at a higher level. This might suggest that, quite understandably, (attention and) performance, in terms of recall, is affected by several factors beyond the type. In other words, not all the individuals with the relevant type display increased retention of information. While many vegetarians remain close to the mode for the meat-eating type, a large fraction obtains double the score, suggesting a substantial though heterogenous increase in the retention of information.
We also use regression analysis in order to control for potential omitted variables and net out some of the variation in the score data that is not related to our variable of interest (such as group size and ability). Robustness checks were performed with different specifications and alternative outcome variables, but the main conclusion remains the same: mean performance, in terms of information retention, is higher for the vegetarian type but not significantly so. However, these results should not be interpreted as a rejection of our original hypothesis about the importance of type for information retention, as our analysis is empirically underpowered due to the low number of vegetarians in the sample. More importantly, the method we propose is highly appropriate, easily replicable and cheap.
Conclusion
Information interventions are low-cost and can be effective. Understanding how they can be tweaked for best effect is an area of crucial research interest, in particular for such an area as climate-change curbing policy. We provide an easy and cheap method to investigate this further and hope that more future research will pursue this avenue.
References
- Andersson, Julius J., 2019. “The ‘meatigation’ of Climate Change: Environmental and Distributional Effects of a Greenhouse Gas Tax on Animal Food Products.” London School of Economics
- Berlin, Maria P. and Benjamin Mandl , 2020. “Selective attention and the importance of types for information campaigns”, SITE Working Paper Series. 53.
- DellaVigna, Stefano and Matthew Gentzkow, 2010. “Persuasion: empirical evidence.”, Annu. Rev. Econ. 2 (1), 643–669.
- Djurens Rätt, 2018. “Opinionundersökning, Våren 2018.” Novus.
- Fonseca-Azevedo, Karina and Suzana Herculano-Houzel, 2012. “Tradeoff between brain and body mass.” Proceedings of the National Academy of Sciences. 109 (45), 18571-18576
- McPherron, Shannon P. et al., 2010. “Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia.” Nature 466, 857–860.
- Mozaffarian, Dariush, 2016. “Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review.” Circulation. 133(2), 187–225.
- Perrotta, Maria, 2011. “Tax Meat to Save the Baltic Sea.” FREE Policy Brief Series.
- Säll, Sarah and Ing-Marie Gren, 2015. “Effects of an environmental tax on meat and dairy consumption in Sweden.” Food Policy. 55, 41-53.
- Thaler, Richard H. and Cass R. Sunstein, 2009. “Nudge: Improving Decisions About Health, Wealth, and Happiness.” Penguin Group.
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.
Foreign Investors on the Investment Climate in Latvia
This brief summarizes the results of an annual study on the development of the investment climate in Latvia from the viewpoint of key foreign investors – companies that have made the decision to invest in the country and have been operating here for a considerable time period. The study was initiated in 2015 and aims to assess investors’ evaluation of the government policy initiatives to improve the investment climate in Latvia. It also aims to provide an in-depth exploration of the main challenges for and concerns of the foreign investors, both by identifying problems and offering solutions. The study draws on a survey/ mini case studies of the key foreign investors in Latvia. Our findings suggest that in recent years, some progress has been achieved on a number of dimensions that are crucial for the competitiveness of the investment climate in Latvia, such as the political efforts by the government of Latvia to improve the investment climate, the overall attitude to foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.
Introduction
The study on the development of the investment climate in Latvia from the viewpoint of key foreign investors in Latvia was first launched in 2015 by the Foreign Investors’ Council in Latvia (FICIL) in cooperation with the Stockholm School of Economics in Riga (SSE Riga). This study aims to foster evidence-based policy decisions and promote a favourable investment climate in Latvia by:
- (i) Assessing how foreign investors evaluate the government’s efforts and current policy initiatives aimed towards improving the investment climate in Latvia, and
- (ii) Providing an in-depth exploration of the main challenges and concerns for the foreign investors, both by identifying problems and offering solutions.
The study draws on a survey/mini case studies of the key foreign investors in Latvia. The first 2015 wave of the survey covered 28 key foreign investors in Latvia. Our panel has gradually expanded over time, reaching 47 participating companies in 2019. From September to early November 2019, we interviewed 47 senior executives representing companies that are key investors in Latvia. Altogether, these companies (including their subsidiaries) contribute to 23% of Latvia’s total tax revenue from foreign investors, 9% of the total profit and employ 11% of the total workforce employed by foreign investors in Latvia, where by foreign investors we mean companies with above a 145 000 EUR turnover and 50% foreign capital (data form Lursoft, 2018).
All interviews were conducted by FICIL board members. The guidelines for the interviews consist of the following key parts:
- (i) Assessment of whether, according to foreign investors, the investment attractiveness of Latvia has improved during the past 12 months;
- (ii) Assessment of the work of Latvian policy-makers in improving the investment climate during 2019;
- (iii) Evaluation of progress in the major areas of concern identified by foreign investors in Latvia in 2015, including demography, access to labour, level of education and science, quality of business legislation, quality of the tax system, support from the government and communication with policy-makers, unethical or illegal behaviour on the part of entrepreneurs, unfair competition, uncertainty, the court system and the healthcare system in Latvia.
Furthermore, in the 2019 study we included questions related to some of the key issues discussed between foreign investors and policymakers during 2019, including the tax system, the stability of the financial sector and the quality of higher education and science in Latvia.
Investment Attractiveness of Latvia: Key Concerns of Foreign Investors in Latvia
The results of the 2019 study suggest that, even though the assessment of foreign investors with regards to the investment attractiveness of Latvia and the work of policy-makers to improve the investment climate in Latvia is still at the average level, it shows some positive tendencies. Namely, on a scale from 1 to 5, where ‘1’ means that there are no improvements at all, ‘3’ some positive improvements and ‘5’ significant improvements, the development of the investment climate in 2019 was evaluated as ‘2.6’ (‘2.5’ in 2018 and 2017). Furthermore, when asked to score the policy-makers’ efforts to improve the investment climate in Latvia, using a scale of 1-5, where ‘1’ and ‘2’ were fail and ‘5’ was excellent, investors responded with an average of ‘2.9’ in both the 2017 and 2018 studies, whereas in 2019, the score improved to ‘3.1’.
Foreign investors were also asked to evaluate whether there has been any progress within the key areas of concern as identified in 2015. The results of the most recent study suggest that the demographic situation, which in the long term reflects both the availability of labour and market size, is still among the key challenges for the foreign investors. Namely, on the scale from 1-5 (where an indicator value of 1 means that Latvia is not competitive and 5 means that Latvia is very competitive in this dimension), investors assessed the demographic situation of Latvia with only ‘1.5’ in 2019. Furthermore, as many as 35 (out of 47) foreign investors stated that they had not seen any progress in this area over the past 12 months. This lack of progress is, perhaps, not very surprising as demographic changes may take substantial time.
Another two key areas where investors would like to see more progress are the quality of education and science and the availability of labour. On a 5-point scale, the quality of education and science was evaluated with ‘2.7’ in 2019 (‘3.0’ in 2018, ‘3.1’ in 2017) and 30 out of the 47 investors interviewed have seen no progress in the development of education and science in Latvia over the past 12 months. The availability of labour was evaluated with ‘2.8’ in 2019 (‘2.7’ in 2018 and 2017); investors scored the availability of blue-collar labour with ‘2.4’ in 2019 (‘2.3’ in 2018, ‘2.5’ in 2017) and the availability of labour at management level with ‘3.1’ (‘3.0’ in 2018, ‘2.9’ in 2017). The majority, i.e. 39 of 47 investors have also seen no progress with regards to the access to labour during the past 12 months. In this context, however, it should be emphasised that the efficiency of labour is increasing in Latvia, according to foreign investors: in 2018, it was assessed with ‘2.9’, yet, in 2019, investors evaluated the efficiency of labour in Latvia with ‘3.4’ out of ‘5’.
The quality of health and social security as well as the quality of business legislation are yet another two indicators of the competitiveness of the investment climate in Latvia that have been evaluated around the average level of ‘3’. Further, 33 of 47 investors have seen no progress with regards to improvement of the healthcare system in Latvia over the past 12 months.
While the overall standard of living is evaluated rather positively at ‘3.8’ in 2019, there is still not much improvement in this indicator as compared to the previous three years. One encouraging result of the 2019 study is that according to foreign investors, the attitude towards foreign investors is gradually improving in Latvia: from ‘3.2’ and ‘3.1’ in 2016 and 2017 to ‘3.6’ in 2018 and reaching ‘3.7’ in 2019.
The foreign investors in Latvia who took part in the 2019 study also expressed an expert opinion with regards to whether there has been any progress during the previous 12 months in the other areas of concern. In this light, the perception of uncertainty should be highlighted. As many as 25 (out of 47 investors) have seen no progress in this area, 16 have seen partial progress and 6 stated that there has been progress in reducing uncertainty. The court system of Latvia is another area where many foreign investors have seen no progress, i.e. 22 said ‘no progress’, 23: ‘partial progress’ and only 1 that there has been progress in the development of the court system in Latvia.
Specific Issues: Tax System, Stability of the Financial System and Quality of Higher Education and Science
In the 2019 study, we also initiated an in-depth exploration related to three key issues of concern extensively discussed between foreign investors and Latvia’s government during the FICIL High Council 2019 spring meeting, and throughout the year 2019 in general. These are: (i) the tax system, (ii) the stability of the financial system, and (iii) the quality of higher education and science. Foreign investors were asked to comment on the current situation and progress over the past years, as well as to provide suggestions to the policymakers in order to improve the situation in the particular area.
(i) Tax system:
The most recent tax reform was implemented in 2018, and the newly elected government has announced that the next reform will take place in 2021. Therefore, this year we asked investors to evaluate the results of the previous tax reform in Latvia. We also asked investors to comment on whether the recent tax reform has brought any benefits to their company and the overall economy of Latvia. On average, foreign investors scored the results of the previous tax reform in Latvia with ‘3.1’, i.e. slightly above the average.
Overall, at least one part of the foreign investors who took part in the 2019 studies highlighted that the previous tax reform was a step ‘in the right direction’. In particular, the zero-rate on reinvested profit was highlighted by a large number of investors as a very positive improvement. In some cases, investors also praised the progressivity of labour tax rates. However, a number of foreign investors highlighted that the tax system has actually become more complex after the reform. Investors also expressed suggestions for further steps to improve the tax system in Latvia, and these are as follows:
Avoid uncertainty. Stability and predictability of the tax system is what the majority of the foreign investors wish to see. In essence, this means fewer changes to the tax system.
Simplify and explain. Investors highlight that paying taxes should be a “simple task” and easy to understand. According to the viewpoints of foreign investors, there is also the potential for improvement with regards to how the responsible organisations, such as the State Revenue Service, communicate changes in the tax system to the private sector.
(Continue) the shift from taxing labour to consumption. Some of the investors that took part in the 2019 studies see that the process has been initiated by the previous tax reform and recommend continuing in this direction.
(ii) Stability of the financial sector in Latvia.
On average, foreign investors evaluated the progress with regards to the effectiveness of combating economic and financial crime with 3.2, i.e. above average. We then asked foreign investors whether they have felt any negative effects on their companies with regards to the situations in the financial sector over the past 2 years. We received some positive opinions, yet the negative ones prevailed. Namely, foreign investors highlighted the reputation risks of Latvia that often impact upon the operation of their companies and create challenges when working with foreign banks.
(iii) Quality of university education and science in Latvia.
Here, foreign investors were asked to reflect upon whether they were aware of any activities that policymakers carried out during the past year to improve the situation. On a positive note, a number of investors mentioned the recent development of the University of Latvia and Riga Technical University’s campuses. Some investors also highlighted that the reform to change the governance model of higher education institutions, initiated by the Ministry of Education and Science, was a good step towards improving the quality of higher education and science in Latvia. However, we also received a number of negative opinions, such as “Nothing has been accomplished, just talking”.
When asked “What changes would you suggest to improve the quality of education and science in Latvia and why? How would this help the business environment, e.g. companies such as yours?”, foreign investors emphasised the following:
Higher education (and science) is too local, fragmented and outdated. In essence, investors pointed out that there are simply too many higher education institutions in Latvia, that they work with outdated methods and are afraid (with no good reason) to open up internationally – also by attracting top quality foreign staff.
Change the governance of higher education institutions in Latvia is another strong request from foreign investors in Latvia. Many investors believe that changes in the financing model should also follow.
Improved connection between education and science and the world of business was yet another important aspect which was highlighted during the 2019 interviews, and also strongly emphasised in the previous studies.
Further Investment Plans and Message to the Prime Minister
When asked whether they plan to increase their investments in Latvia, as many as 64% of the investors interviewed answered with ‘yes’ (in the 2018 study, 55% interviewed answered with ‘yes’), 25% said ‘no’ (35% in the 2018 study) and 11% answered that ‘it depends on the circumstances’ (10% in the 2018 study) or that they have not yet decided.
Finally, we invited foreign investors to send a message to the Prime Minister of Latvia: one paragraph on what should be done to improve the business climate in Latvia, from the viewpoint of a foreign investor. These messages closely parallel the other findings of the 2019 study, stressing a number of key concerns that foreign investors are still facing in Latvia: the situation with regards to demography, quality of education and science, availability of labour, challenges with corruption and the shadow economy as well as needs for improvements in the health care sector amongst others.
Conclusions
The findings of the 2019 study on the view of the key foreign investors of the investment climate in Latvia suggest that in recent years, some progress has been achieved on a number of dimensions, such as political effort to improve the investment climate, attitude towards foreign investors, and labour efficiency. At the same time, foreign investors see little, if any, improvement with regards to other key areas, such as the availability of labour, the quality of education, the court system, corruption and the shadow economy.
Our findings highlight the need to continue policy-makers’ efforts to improve the investment climate in Latvia and provide policymakers with better grounds for making informed policy decisions with respect to the entrepreneurship climate in Latvia. We also hope that our study will further facilitate constructive communication between foreign investors and the government of Latvia.
References
- Lursoft (2018). Official company statistics of Latvia, 2018.
- FICIL Sentiment Index (2019), https://www.sseriga.edu/centres/csb/sentiment-index
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.