Author: Cecilia Smitt Meyer

Reforming Financial Support in Widowhood: The Current System in Poland and Potential Reforms

Woman in a blue jacket sitting on a bench with a cat in autumn, overlooking a city landscape representing Widowhood support Poland

In this policy paper, we discuss the material conditions of widows and widowers compared to married couples in Poland, and analyse the degree to which the current support system to those in widowhood in Poland limits the extent of poverty among this large and growing share of the population. The analysis is set in the context of a proposed reform recently discussed in the Polish Parliament. We present the budgetary and distributional consequences of this proposal and offer an alternative scenario which limits the overall cost of the policy and directs  additional resources to low income households.

Introduction

According to the National Census in 2021 there were about 2.2 million widows and 450 000 widowers in Poland. In the following year over 123 000 women and about 47 000 men became widowed. Apart from the severe consequences for mental health and psychological well-being, losing a partner typically has implications also for material wellbeing, in particular in cases of high income differentials between the spouses and in situations when the primary earner – often the man – dies first. Material conditions of the surviving spouse in widowhood depend on the one hand on the couple’s accumulated resources, and, on the other hand, on the available  support system. Many countries have instituted so-called survivors’ pensions, whereby the surviving spouse continues to receive some of the income of her/his deceased partner alongside other incomes. The systems of support differ substantially between countries and they often combine social security benefits and welfare support for those with the lowest incomes.

In this policy paper we discuss the material situation of widows and widowers versus married couples in Poland and analyse the degree to which the current Polish support system for  people in widowhood limits the extent of poverty within this group. We compare the current system of survivors’ pension with a proposed reform discussed lately in the Polish Parliament;the introduction of a ‘widow’s pension’. We present the budgetary and distributional consequences of the announced scheme and offer an alternative scenario which limits the overall cost of the policy and focuses additional resources on low income households. Our results show significant income gains for  widows/widowers from the implementation of the recently proposed widow’s pension. The policy however, would come at a substantial cost to the public purse, and the most significant benefits would be accrued by surviving partners at the top of the income distribution. Our proposed alternative scenario is better targeted at poorer households and achieves the objective of limiting poverty in widowhood at a substantially lower cost.

The Material Situation of Widows and Widowers in Poland

Numerous research papers show a strong impact of losing a spouse on mental health and overall well-being (Blanner Kristiansen et al., 2019; Lee et al., 2001; Ory & Huijts, 2015; Sasson & Umberson, 2014; Schaan, 2013; Siflinger, 2017; Steptoe et al., 2013). Adena et al. (2023) use a comprehensive dataset on older women observed a number of years before and after the death of their spouses. The study finds a sharp deterioration in mental health among widows after their partner’s death, displayed as a higher likelihood of crying (Figure 1a) or an increased probability of depression (Figure 1b). The authors provide evidence that, in comparison to similar women who remained partnered, widows suffer from poorer mental health and experience worsened quality of life for several years after their partners’ death.

Figure 1. Women’s mental health before and after their partners’ death.

Charts comparing the share of widowed women who cried and those at risk of depression over five years representing policy brief that covers widowhood support Poland.

Source: Adena et al. (2023). Notes: The control group consisted of women from statistical “twin” marriages with an identical distribution of selected characteristics; Figure 1b) Risk of depression defined as 4 or more depression symptoms according to the EURO-D scale. For methodological details see Adena et al. (2023).

While the impact of spouse’s death on widows mental health is largely undisputed, the impacts on their material situation are ambiguous (Ahn, 2005; Bíró, 2013; Bound et al., 1991; Corden et al., 2008; Hungerford, 2001).The differences across countries in the material situation of widowed versus partnered elderly people undoubtedly reflect countries’ various social security systems for those in widowhood. At the same time, these differences may also stem from variations in other factors that widows and widwers can rely on such as the prevalence of property ownership or accumulation of wealth and savings. It should be noted though, that in contrast to the immediate effects of spouse’s death on mental health, the consequences for widows’ and widowers’ material situation may unfold over a number of years. This is reflected in the results from poverty surveys which often point to the poorer material standing of widows and widowers (Panek et al., 2015; Petelczyc & Roicka, 2016; Timoszuk, 2017, 2021).

Similar conclusions can be derived from subjective evaluations of households’ material situation reflected in the Central Statistical Office’s Polish Household Budget Survey (HBS). In Figure 2a we present the percentage of people aged 65 and over who declared a ‘bad’ or ‘rather bad’ material situation of their household between 2010 and 2021, split between widows, widowers and married couples.. Throughout the analysed period, the share of both widows and widowers reporting a rather bad material situation was significantly higher than for married couples aged 65+. While in 2010 30 percent of widows and 20 percent of widowers reported a rather bad material standing, this share amounted to just above 10 percent among married couples. In all social groups the ratio of those in a rather bad material situation declined significantly over the analysed decade. A particularly significant drop was observed among widows; in 2021 the share of widows declaring a rather bad material situation declined to the level observed for married couples eleven years earlier.

Data capturing the risk of poverty from Eurostat, based on the EU Statistics on Income and Living Conditions Survey (EU-SILC), also display significantly worse material conditions of older individuals living alone compared to those living with another adult (Figure 2b). While this data does not explicitly allow us to divide the sample based on marital status, it is highly likely (and assumed hereafter) that the majority of single-person households 65+ cover widows or widowers, while two-person households aged 65+represent married couples. As compared to Figure 2a, the dynamics of the poverty levels among people aged 65+ in Figure 2b differ from the dynamics of the assessment of the overall material situation. Among two-person households, the risk of poverty in Poland declined between 2010 and 2013, and then remained relatively stable at about 15 percent until 2020. Among one-person households the poverty rate also declined during the first five years (from 33 percent in 2010 to 25 percent in 2015), however, it then increased to 37 percent in 2020. Consequently, the gap in poverty risk between two-person and one-person households increased substantially, from 8 percentage points in 2010 to 22 percentage points in 2020.

Figure 2. Material situation among households with individuals aged 65 and over.

Charts comparing material situation and poverty risk among widows, widowers, married couples, and households in Poland, Germany, Czech Republic, and Italy representing policy brief that covers widowhood support Poland.

Source: Own compilation based on: a) HBS; b) Eurostat. Notes: a) Widows and widowers aged 65+ living in one-person households; married couples living in two-person households with at least one spouse aged 65+; b) Eurostat data does not allow for division by gender or marital status. In two-person households both persons are adults, at least one is aged 65+. At-risk-of-poverty rate is defined as 60 percent of the median equivalized income of the entire population.

When analyzing poverty risk information, it should be noted that this indicator is based on income thresholds calculated separately for each year, accounting for the whole population. Poverty risk threshold may therefore increase as a result of income boosts among other groups and in consequence raise the risk of poverty of older people even if their real incomes are stable or grow. Thus the substantial increase in o the poverty risk share among Polish individuals 65+ and living alone after 2015, is related to the sharp rise in income of families with children and wage dynamics, which, in turn raised the poverty threshold considered in the analysis. Based on Figure 2b it is also worth noting that in comparison to Poland the risk of poverty among single-person households 65+ grew even faster in the Czech Republic (though the situation among two-person households 65+ was stable there). The  relative position of these households deteriorated also in Germany (the share at risk of poverty increased from 24 percent in 2010 to 31 percent in 2020). It is therefore clear that even though absolute material conditions may have improved among widowed households in Poland over the last decade, their relative position in the income distribution – as in many other countries – places them at a significantly greater risk of poverty compared to partnered older individuals. Questions regarding the level of state support directed towards widowed older individuals are therefore highly relevant for government policy.

Figure 3. The living situation of widows, widowers and married couples aged 65 and over, in Poland.

Charts showing share of owner occupiers and dwelling size in square meters per person among widows, widowers, and married couples in Poland representing policy brief that covers widowhood support Poland.

Source: Own compilation based on HBS. Notes: Widows and widowers aged 65+ living in one-person households. Married couples in two-person households with at least one spouse aged 65+.

To better understand the broader context of material conditions in widowhood, and to try to address the discrepancy between the trends in subjective evaluation and widows’ relative position in the income distribution, it is also worth examining other aspects of material well-being. In Figure 3a we present some statistics on property ownership. As we can see, the majority of individuals aged 65+ in Poland, both widowed and married, owned the house or flat they lived in. For example, in 2010 62 percent of all widows and 68 percent of all widowers owned their dwelling, and these shares increased to 72 percent for both groups by 2021. Moreover, among older owner occupiers, the size of the house or apartment per person living in it was on average two times larger for widows and widowers (50 m2) as compared to married couples (25 m2), as depicted in Figure 3b. The high share of widows and widowers owning housing assets may therefore be one of the most important explanations to the discrepancies between the dynamics of income poverty and the declarations about the overall material situation observed in recent years. Although the risk of relative income poverty among widows and widowers have increased since 2016 (after a period of decline between 2010 and 2015), widowhood in Poland is not unequivocally associated with poor material conditions. While some widowed individuals clearly face a challenging material situation, for many the current system of survivor’s pension seems to offer adequate protection against the risk of a significant financial deterioration following the loss of a spouse. This suggests that any additional support through a new social security instrument should be directed principally to a relatively narrow group of widows and widowers in order to help particularly those in a difficult financial situation.

Survivor’s Pension, Widow’s Pension and an Alternative Solution

In this part of the paper we present simulations of changes in the level of household income and the relative position in the income distribution among widows under different scenarios of support through the social security system. In the first step we use the 2021 HBS data (uprated to 2023 income levels) to calculate disposable incomes of the entire sample of nearly 31 000 households under the 2024 Polish tax-benefit system using the SIMPL tax and benefit microsimulation model (henceforth the ‘baseline’ system; more details on the SIMPL model: Myck et al., 2015, 2023a; Myck & Najsztub, 2014). Based on the baseline system, we divide the households into ten income decile groups according to their disposable income (equivalised, i.e. adjusted for household composition). In the second step we focus on the sample of 4188 married couples aged 65 and over, representing 1.7 million Polish households (almost 13 percent of the total population). 65 percent of these couples lived in two-person households and the remaining 35 percent cohabited also with other people. In the baseline system, the incomes received by these households placed 9.5 percent of them in the lowest (1st) income decile group and 4.4 percent in the highest (10th) group (see Table 1).

Table 1. Relative position of households with married couples aged 65+ in the income distribution.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: The baseline system for calculating the equivalised income thresholds was the January 2024 system; the thresholds for the income decile groups were calculated on the basis of a full sample of households.

Figure 4a shows a comparison of men’s and women’s gross retirement pensions in our sample of married couples 65+ in the baseline system. Every dot corresponds to one married couple and a combination of the spouses’ pensions. The greater concentration of combinations of these values above the 45-degree line indicates that in most marriages , the husbands’ retirement pensions are higher than the wives’. The differences are also apparent in Figure 4b, which presents the percentages of individuals receiving a pension benefit within the given value range of the pension. The share of women are greater than the share of men at lower benefit values (below 3000 PLN gross per month), and the opposite is true for higher pension amounts. Overall, for 65 percent of all couples, the husband received a higher retirement pension than his wife. There are also older people who did not receive retirement benefits – either because they continued to work or because they were not entitled to a retirement pension (this is the case for 9 percent of husbands and 10 percent of wives), as illustrated by the first column in Figure 4b. It is worth noting that for 2 percent of the couples only the husband received a retirement pension (the wife had never worked and was not eligible for retirement pension or she still worked). In the current Polish system of support for surviving spouses, the amount of own and spouse’s retirement pension is crucial for the choice of the benefit one makes when a spouse dies. A widowed person can choose to continue receiving their own full retirement pension or to receive a survivor’s pension, which is equivalent to 85 percent of the pension of the deceased spouse. Given the differences between men’s and women’s pensions, many women choose the latter option, either because their own retirement pension is significantly lower than the survivor’s pension or because they are not entitled to their own retirement pension.

Figure 4. Retirement pension amounts received by husbands and wives aged 65+

Comparison of men's and women's retirement pension gross amounts and percentage of individuals receiving retirement pensions in value brackets representing policy brief that covers widowhood support Poland.

Source: Own compilation based on HBS 2021. Notes: Both spouses aged 65 and over; gross monthly retirement pensions; in less than 1 percent of the marriages at least one spouse received a retirement pension higher than 10000 PLN (not included in the Figure). 1PLN~0.23EUR.

We treat the sample of married couples aged 65 years or more as a reference sample in our analysis of the consequences from the implementation of various support schemes within the social security system, in the case of widowhood. The calculations presented below reflect the financial situation of the analyzed sample after the hypothetical death of husbands. We focus on widows, as they represent the vast majority of widowed individuals (due to, e.g., longer life expectancy of women and age differences between spouses). We simulate four support scenarios:

I) a system with no support for widowed individuals – this would be the situation without the current survivor’s pension, in which widows would need to rely fully on their own social security incomes (pensions);

II) the current system of survivor’s pension: in which the widow must choose between 100 percent of her own pension or the survivor’s pension (85 percent of her deceased husband’s gross pension)

III) a system with the widow’s pension (currently debated in the Polish Parliament): the widow must choose between: a) 100 percent of her own pension + 50 percent of the survivor’s pension (42,5 percent of the deceased husband’s gross pension), b) 50 percent of her own pension + 100 percent of the survivor’s pension (85 percent of her dead husband’s gross pension);

IV) an alternative system in which the widow chooses between: a) 100 percent of her own pension + 50 percent of a minimum pension if her husband received at least minimum retirement pension (50 percent of the husband’s pension if it was lower than the minimum pension), b) 100 percent of the survivor’s pension (85 percent of the husband’s pension) increased to the minimum pension if the husband received at least minimum retirement pension.

While the simulations are based on a hypothetical death of a husband, they provide a realistic picture of the financial situation of households in which women face widowhood. It is also important to note that the simulations of the financial conditions of ‘widowed’ households take into account other potential forms of public social support such as housing benefits and social assistance for low-income households. The results thus include the most relevant forms of financial support individuals might receive from the Polish government.

Figure 5 shows the results of the four aforementioned scenarios in the form of flow charts between income decile groups. The starting point (the left-hand side of each chart) are the income groups of households with married couples aged 65+, i.e. before the simulated widowhood. The transition to the income deciles on the right hand side of each chart is the result of a change in equivalised disposable income in the widowhood simulation, under different support scenarios (I – IV). Thus, on the right hand side we observe the income groups in which the women would find themselves after the death of their husbands, conditional on the assumed system of support: without the survivor’s pension (system I, Figure 5a), with the survivor’s pension (system II, figure 5b), with the widow’s pension (system III, Figure 5c) and under the alternative system (system IV, Figure 5d).

Figure 5a shows that without any additional support the financial situation of older women would significantly deteriorate in the event of the death of their spouses (Figure 5a). The share of women whose income would place them in the lowest two decile groups would be as high as 54.7 percent (compared to 17.5 percent of married couples), and 82.8 percent of the widows would be in the bottom half of the income distribution (compared to 57 percent of married couples). The current survivor’s pension seems to protect a large proportion of women (Figure 5b), although the proportion of those who find themselves in the lowest two income decile groups still more than doubles relative to the situation of married couples, to 38.3 percent. Further, 74.9 percent of the widows would find themselves in the bottom half of the distribution. The proposed widow’s pension (Figure 5c) offers much greater support with a very high share of new widows remaining in the same decile or even moving to a higher income group. For example, with the widows’ pension 8.0 percent of women would be in the 9th income decile group and 5.3 percent in the 10th group, while, in comparison, 7.0 percent and 4.4 percent of married couples found themselves in these groups, respectively. 

Figure 5. Change in income decile among women aged 65+, following a hypothetical death of their husbands.

Comparison of income decile groups under different widowhood pension systems in Poland, highlighting income shifts across four scenarios and representing policy brief that covers widowhood support Poland.

Source: Own calculations based on HBS 2021 using SIMPL model; graphs were created using: https://flourish.studio/

The proposed alternative system (Figure 5d) raises widows’ incomes compared to the current survivor’s pension system, but it is less generous than the system with the widow’s pension. Importantly however, it increases the incomes of widows in the lower income groups, which means that, compared to the current system, the number of women dropping to the poorest income groups following their husband’s death would be significantly reduced (24.0 percent would be in the lowest two deciles). At the same time 4.6 percent and 3.4 percent of the widows would be placed in the 9th and the 10th decile groups, respectively.

Table 2 shows the change in the poverty risk among the women in five considered scenarios, i.e. before they become widowed and after the hypothetical death of their husband under the considered four systems of support. 10.5 percent of married couples aged 65+ had equivalised disposable incomes which placed them below the poverty line calculated in the baseline system. After the simulated death of a husband, in a scenario without the survivor’s pension, the poverty rate among widows would increase to 35.3 percent, while the current survivor’s pension limits it to 20.7 percent. Poverty would be further reduced in the two systems with considered reforms: to 11.0 percent the widow’s pension system and to 11.8 percent in the alternative system.

Table 2. At-risk-of-poverty rates in the analysed scenarios.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: The at-risk-of-poverty threshold is set at 60 percent of median equivalised disposable income in the baseline system.

Total Costs of the Considered Schemes

As mentioned above, the presented simulations take into account the conditions of current older couples. Therefore, we cannot directly calculate the consequences of the two suggested systems (the widow’s pension system and the alternative system) for those who are already widowed. This applies in particular to the present-day cost from the suggested changes to the widowhood support schemes to the public budget . In order to accurately estimate the changes in already widowed people’s incomes, we would have to have the information on the values of widow’s pensions and of pensions that their deceased spouses received when they were still alive, information that is not available in the HBS.

Nevertheless, our simulations allow us to compare the aggregated costs of support for women in the simulated widowhood scenarios under different support systems. Such calculations suggest that an implementation of the widow’s pension would increase the gross benefits received by widows by 34.2 percent compared to the current survivor’s pension system., while the alternative system would raise them by 14.7 percent. Applying these growth rates to the social security benefits currently received by widows and widowers (from the HBS data) implies additional annual costs of 24.1 bn PLN (5.6 bn EUR) under the widow’s pension system, and 10.5 bn PLN (2.5 bn EUR) under the alternative system.

Who Gains the Most?

From a distributional perspective, the simulated outcomes of the two suggested systems of support in widowhood can be compared to the baseline situation. In Figure 6 we show average changes in widowed women’s disposable income resulting from a change from the current system with survivor’s pension to the system with widow’s pension, and to our alternative design. Gross monthly survivor’s pensions of the widows are divided into seven groups, starting from 0-500 PLN up to 5501 PLN and more. One can clearly see that women who would, on average, gain the most from the implementation of the widow’s pension are those who already have a relatively high survivor’s pension in the current system. The average rise in disposable income (net) among those with gross monthly pensions between 4501 and 5500 PLN would be 1200 PLN, if widow’s pension was implemented. In contrast, women who receive 501-1500 PLN (gross) per month under the current survivor’s pension, would see a net monthly gain of about 350 PLN. These women would benefit slightly more under the alternative system – on average about 390 PLN, while much lower increases (on average about 220 PLN per month) would be faced by women in the 4501-5500 PLN group. Women in the last group, with gross monthly pensions of 5501 PLN and more under the current survivor’s pension system, would additionally gain even less in the alternative system – on average about 170 PLN. Thus overall, greater gains would accrue to those with lower current benefits in the alternative system.

Figure 6. Average increase in disposable income among widows by current survivor’s pensions’ value group.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR.

In Figure 7 we categorise the sample of widows in terms of the range of their gains resulting from the two analysed reforms. The gains are calculated as changes in disposable income between the current system of support and the modelled reforms. We see that 20 percent of widows would gain over 1000 PLN extra per month as a result of the widow’s pension’s reform, while a further 24 percent would gain between 801 to 1000 PLN and 28 percent could expect to see a gain of between 601-800 PLN per month. The reform would leave the incomes of only about 12 percent of the widows unchanged – most of them are women who are not eligible for their own retirement pensions. In the alternative system the incomes of 34 percent of the analysed widows would remain unaffected. This group of women includes not only those without their own retirement pensions, but also those whose husbands received much higher pensions than themselves. This means that even if a widow’s retirement pension were to increase by 50 percent of the minimum pension, it would still be lower than 85 percent of her spouse’s retirement pension (see Figure 4a). In the alternative system about 17 percent of women in the sample would increase their disposable income by less than 400 PLN per month. For 28 percent, the increase would be in the range of between 400 and 600 PLN per month. While 21 percent would receive increased benefits under the alternative system, none of the hypothetical widows would receive more than 800 PLN per month.

Figure 7. Share of women by ranges of increases from the widow’s pension and the alternative scenario.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR.

Figure 8 presents the average effect of the modelled reforms on disposable incomes of women in the sample, divided by income decile groups. Households were assigned to one of ten income groups based on their equivalised disposable income in the baseline system (i.e. according to the joint income of the couples). Figure 8 reflects the distribution of gains from the implementation of the widow’s pension or the alternative system. In the first case, the highest gains would be concentrated among the richest households. While women in the 8th and 9th income decile would, on average, receive an increase in their disposable income of about 1100 PLN per month, those in the 2nd decile group would, on average, receive only an additional 470 PLN per month. The distribution under the alternative system is far more concentrated on low income households. The highest average additional gain of about 420 PLN per month would be granted to widows from the 3rd income decile group, and benefits to women in the upper half of the income distribution would be significantly lower. Women in the top decile would gain, on average, only about 280 PLN per month. In many of the poorest households in our sample of couples, neither partner qualifies for a retirement pension. As a result, widows in this group would experience significantly lower average gains under both analyzed systems compared to those in higher income brackets.

Figure 8. Average gains due to the implementation of widow’s pension and the alternative system, by income decile group.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR. Assignment to the income group was done prior to the hypothetical death of husbands.

Conclusion

In 2021 only 10 percent of the Polish widows and 8 percent of the Polish widowers aged 65 and more evaluated their material situation as rather bad, percentages that had dropped significantly since 2010. According to the HBS the majority of widowed individuals in Poland are also owners of the dwelling they live in. At the same time, income poverty among older persons living alone has increased in Poland since 2015, suggesting that despite the subjective evaluations, incomes of these older individuals – many of whom are widowed – have not managed to keep up with the dynamics of earnings and social transfers aimed at other demographic groups in Poland. As showed in our simulations, the current widowhood support system in Poland substantially limits the risk of poverty following the death of one’s partner. However, while the current survivor’s pension decreases the poverty risk from 35.3 percent (in a system without any support) to 20.7 percent, the risk of poverty among widows is still significantly higher compared to the risk faced by married couples.

The simulations analysed in this Policy Paper has covered the proposal of a support system reform, thewidow’s pension, which is currently discussed in the Polish Parliament. The simulations also covered an alternative alternative proposal putting more emphasis on poorer households. Both of these reforms would provide additional support to individuals affected by widowhood. In the case of the widow’s pension the average value of social security benefits would increase by 34.2 percent, whereas the alternative scenario would increase these benefits by 14.7 percent. If the pensions of current widows and widowers were to be increase by these proportions, the total annual cost to the public sector would amount to 24.1 bn PLN (5.6 bn EUR) and 10.5 bn PLN (2.5 bn EUR) per year, respectively. As shown above, the impact of these two reforms on poverty levels among widowed individuals would be very similar – the reforms would reduce it to 11.0 and 11.8 percent, respectively. The substantial difference in the total cost of these two alternatives is mainly due to the fact that the bulk of the additional benefits from the implementation of the widow’s pension is concentrated among high-income widows and widowers, while the highest profits in the modelled alternative system are targeted at households at the bottom of the income distribution.

If the aim of the potential legislative changes is to support widows and widowers in a difficult material situation and to reduce the extent of poverty, the widow’s pension currently discussed in the Polish Parliament seems to be far from ideal. As demonstrated in this Policy Paper, additional support addressed to widows and widowers in Poland can be designed in a way that substantially reduces the risk of poverty, with limitations on benefit increases to those already in a favourable financial situation. Our proposed alternative system would generate higher incomes for the poorest widows and widowers similar to the widow’s pension, while its cost to the public budget would be less than half of the cost of the discussed widow’s pension reform.

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  • Schaan, B. (2013). Widowhood and Depression Among Older Europeans—The Role of Gender, Caregiving, Marital Quality, and Regional Context. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 68(3), 431–442. https://doi.org/10.1093/geronb/gbt015
  • Siflinger, B. (2017). The Effect of Widowhood on Mental Health—An Analysis of Anticipation Patterns Surrounding the Death of a Spouse. Health Economics, 26(12), 1505–1523. https://doi.org/10.1002/hec.3443
  • Steptoe, A., Shankar, A., Demakakos, P., & Wardle, J. (2013). Social isolation, loneliness, and all-cause mortality in older men and women. Proceedings of the National Academy of Sciences, 110(15), 5797–5801. https://doi.org/10.1073/pnas.1219686110
  • Timoszuk, S. (2017). Wdowieństwo a sytuacja materialna kobiet w starszym wieku w Polsce. Studia Demograficzne, nr 2(172), 121–138. http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ekon-element-000171500466
  • Timoszuk, S. (2021). Wdowieństwo w starszym wieku. O sytuacji finansowej wdów w Polsce.

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.

Nuclear Energy Renaissance: Powering Sweden’s Climate Policy

Cooling towers of a nuclear power plant releasing steam into a clear blue sky representing nuclear energy in Sweden.

Sweden’s Nuclear Energy Expansion: Is It the Key to Net-Zero Emissions by 2045?

The Swedish government has placed nuclear energy at the forefront of its climate policies, aiming for two new reactors to be operational by 2035 and a total of ten new reactors by 2045. This policy brief explores whether the large-scale expansion of nuclear energy in Sweden is an environmentally and economically viable solution to achieve the nation’s goal of net-zero emissions by 2045. To assess this, we examine three critical factors: potential emission reductions, the cost-effectiveness of nuclear power, and the feasibility of the proposed construction timelines.

As a case study, we compare Sweden’s approach to nuclear energy with the successful nuclear build-out in France during the 1970s. France significantly reduced its carbon dioxide (CO2) emissions while reaping economic benefits, with an average reactor construction time of about six years. However, the situation in Sweden’s nuclear energy sector today differs from France in the 1970s. Sweden already has a low-carbon electricity grid, and the costs of alternative zero-carbon energy sources, such as wind and solar, have dropped considerably. Additionally, the construction costs and timelines for nuclear reactors in Sweden have increased compared to historical norms.

Thus, while nuclear energy in Sweden may contribute to modest emission reductions, the abatement costs are high, and reactor construction is expected to take much longer—up to two or three times longer than France’s build-out. This raises questions about whether Sweden’s nuclear expansion can effectively support the country’s ambitious climate goals.

A Renewed Focus on Nuclear Energy

When the current government in Sweden, led by Prime Minister Ulf Kristersson, came into power in 2022, they swiftly made changes to Sweden’s environment and climate policies. The Ministry of Environment was abolished, transport fuel taxes were reduced, and the energy policy objective was changed from “100 percent renewable” to “100 percent fossil free”, emphasizing that nuclear energy was now the cornerstone in the government’s goal of reaching net zero emissions (Government Office 2023, Swedish Government 2023). This marked a new turn in Sweden’s relationship with nuclear energy: from the construction of four different nuclear power plants in the 1970s – of which three remain operational today – to the national referendum on nuclear energy in 1980, where it was decided that no new nuclear reactors should be built and that existing reactors were to be phased-out by 2010 (Jasper 1990).

Today’s renewed focus on nuclear energy, especially as a climate mitigation policy tool is, however, not unique to Sweden. As of 2022, the European Commission labels nuclear reactor construction as a “green investment”, the US has included production tax credits for nuclear energy in their 2023 climate bill the Inflation Reduction Act, and France’s President Macron is pushing for a “nuclear renaissance” in his vision of a low-carbon future for Europe (Gröndahl 2022; Bistline, Mehrotra, and Wolfram 2023; Alderman 2022).

France As a Case Study

In the 1970s, France conducted an unprecedented expansion of nuclear energy, which offers valuable insights for Sweden’s contemporary nuclear ambitions. Relying heavily on imported oil for their energy needs, France enacted a drastic shift in energy policy following the 1973 oil crisis. In the subsequent decade, France ordered and began the construction of 51 new nuclear reactors. The new energy policy – dubbed the Messmer Plan – was summarized by the slogan: “All electric, all nuclear” (Hecht 2009).

To support the expansion of new reactors, the French government made use of loan guarantees and public financing (Jasper 1990). A similar strategy has recently been proposed by the Swedish government, with suggested loan guarantees of up to 400 billion kronor (around $40 billion) to support the construction of new reactors (Persson 2022).

France’s Emissions Reductions and Abatement Costs

To make causal estimates of the environmental and economic effects of France’s large-scale expansion of nuclear energy, we need a counterfactual to compare with. In a recent working paper – titled Industrial Policy and Decarbonization: The Case of Nuclear Energy in France – I, together with Jared Finnegan from University College London, construct this counterfactual as a weighted combination of suitable control countries. These countries resemble France’s economy and energy profile in the 1960s and early 1970s, however, they did not push for nuclear energy following the first oil crisis. Our weighted average comprises five European countries: Belgium, Austria, Switzerland, Portugal, and Germany, with falling weights in that same order.

Figure 1 depicts per capita emissions of CO2 from electricity and heat production in France and its counterfactual – ‘synthetic France’ – from 1960 to 2005. The large push for nuclear energy led to substantial emission reductions, an average reduction of 62 percent, or close to 1 metric ton of CO2 per capita, in the years after 1980.

Figure 1. CO2 emissions from electricity and heat in France and synthetic France, 1960-2005.

Andersson and Finnegan (2024).

Moreover, Figure 1 shows that six years elapsed from the energy policy change until emission reductions began. This time delay matches the average construction time of around six years (75 months on average) for the more than 50 reactors that were constructed in France following the announcement of the Messmer Plan in 1974.

Table 1. Data for abatement cost estimates.

Andersson and Finnegan (2024).

Lastly, these large and relatively swift emission reductions in France were achieved at a net economic gain. Table 1 lists the data used to compute the average abatement cost (AAC): the total expenses incurred for the new policy (relative to the counterfactual scenario), divided by the CO2 emissions reduction.

The net average abatement cost of -$20 per ton of CO2 is a result of the lower cost of electricity production (here represented by the levelized cost of electricity (LCOE)) of new nuclear energy during the time-period, compared to the main alternative, namely coal, – the primary energy source in counterfactual synthetic France. LCOE encompasses the complete range of expenses incurred over a power plant’s life cycle, from initial construction and operation to maintenance, fuel, decommissioning, and waste handling. Accurately calculated, LCOE provides a standardized metric for comparing the costs of energy production across different technologies, countries, and time periods (IEA 2015).

Abatement Costs and Timelines Today

Today, more than 50 years after the first oil crisis, many factors that made France’s expansion of nuclear energy a success are markedly different. For example, the cost of wind and solar energy – the other two prominent zero-carbon technologies – has plummeted (IEA 2020). Further, construction costs and timelines for new nuclear reactors in Europe have steadily increased since the 1970s (Lévêque 2015).

Figure 2 depicts the LCOE for the main electricity generating technologies between 2009 and 2023 (Bilicic and Scroggins 2023). The data is for the US, but the magnitudes and differences between technologies are similar in Europe. There are two important aspects of this figure. First, after having by far the highest levelized cost in 2009, the price of solar has dropped by more than 80 percent and is today, together with wind energy, the least-cost option. Second, the cost of nuclear has steadily increased, contrary to how technology cost typically evolves over time, meriting nuclear power the “a very strange beast” label (Lévêque, 2015, p. 44). By 2023, new nuclear power had the highest levelized cost of all energy technologies.

Regarding the construction time of nuclear reactors, these have steadily increased in both Europe and the US. The reactor Okiluoto 3 in Finland went into commercial operation last year but took 18 years to construct. Similarly, the reactor Flamanville 3 in France is still not finished, despite construction beginning 17 years ago. The reactors Hinkley Point C in the UK were initiated in 2016 and, after repeated delays, are projected to be ready for operation in 2027 at the earliest (Lawson 2022). Similarly, in the US, construction times have at least doubled since the first round of reactors were built. These lengthened constructions times are a consequence of stricter safety regulations and larger and more complex reactor designs (Lévêque, 2015). If these average construction times of 12-18 years are the new norm, Sweden will, in fact, not have two new reactors in place by 2035. Further, it would need to begin construction rather soon if the goal of having ten new reactors by 2045 is to be achieved.

Figure 2. Levelized Cost of Electricity, 2009-2023.

Source: Bilicic and Scroggins (2023).

Sweden’s Potential Emission Reductions

The rising costs and extended construction times for new reactors are notable concerns, yet the crucial measure of Sweden’s new climate policy is its capacity to reach net zero emissions across all sectors. Figure 3 depicts per capita emissions of CO2 from electricity and heat production in Sweden and OECD countries between 1960 and 2018.

Figure 3. Sweden vs. the OECD average.

Source: IEA (2022).

In 2018, the OECD’s per capita CO2 emissions from electricity and heat averaged slightly over 2 metric tons. In comparison, Sweden’s per capita emissions at 0.7 metric tons are low and represent only 20 percent of total per capita emissions. Hence, the potential for substantial emission cuts through nuclear expansion is limited. By contrast, Sweden’s transport sector, with CO2 emissions more than two times larger than the emissions from electricity and heat, presents a greater chance for impactful reductions. Yet, current policies of reduced transport fuel taxes are likely to increase emissions. The electrification of transportation could leverage the benefits of nuclear energy for climate mitigation, but broader policies are then needed to accelerate the adoption of electric vehicles.

Conclusion: Sweden’s Nuclear Energy Renaissance and Its Impact on Climate Policy

As Sweden rewrites its energy and climate policies, nuclear energy is placed front and center – a position it has not held since the 1970s. Yet, while nuclear energy may experience a renaissance in Sweden, it will not be the panacea for reaching net zero emissions the current government is hoping for. Expected emission reductions will be modest, abatement costs will be relatively high and, if recent European experiences are to be considered an indicator, the aspirational timelines are likely to be missed.

Considering these aspects, it’s imperative for Sweden to adopt a broader mix of climate policies to address sectors such as transportation – responsible for most of the country’s emissions. Pairing the nuclear ambitions with incentives for an accelerated electrification of transportation could enhance the prospects of achieving net zero emissions by 2045.

References

  • Alderman, L. (2022). France Announces Major Nuclear Power Buildup. The New York Times. February 10, 2022.
  • Andersson, J. and Finnegan, J. (2024). Industrial Policy and Decarbonization: The Case of Nuclear Energy in France. Working Paper.
  • Bilicic, G. and Scroggins, S. (2023). 2023 Levelized Cost of Energy+. Lazard.
  • Bistline, J., Mehrotra, N. and Wolfram, C. (2023). Economic Implications of the Climate Provisions of the Inflation Reduction Act. Tech. rep., National Bureau of Economic Research.
  • Government Office. (2023). De första 100 dagarna: Samarbetsprojekt klimat och energi. Stockholm, January 25, 2023.
  • Gröndahl, M-P. (2022). Thierry Breton: ’Il faudra investir 500 milliards d’euros dans les centrales nucléaires de nouvelle génération’.  Le Journal du Dimanche January 09, 2022.
  • Hecht, G. (2009). The Radiance of France: Nuclear Power and National Identity after World War II. MIT Press.
  • IEA. (2015). Projected Costs of Generating Electricity: 2015 Edition. International Energy Agency. Paris.
  • IEA. (2020). Projected Costs of Generating Electricity: 2020 Edition. International Energy Agency. Paris.
  • IEA. (2022). Greenhouse Gas Emissions from Energy (2022 Edition). International Energy Agency. Paris.
  • Jasper, J. M. (1990). Nuclear politics: Energy and the state in the United States, Sweden, and France, vol 1126. Princeton University Press.
  • Lawson, A. (2022). Boss of Hinkley Point C blames pandemic disruption for 3bn delay. The Guardian. May 20, 2022.
  • Lévêque, F. (2015). The economics and uncertainties of nuclear power. Cambridge University Press.
  • Persson, I. (2022). Allt du behöver veta om ’Tidöavtalet. SVT Nyheter. 14 October, 2022.
  • Swedish Government. (2023). Regeringens proposition 2023/24:28 Sänkning av reduktionsplikten för bensin och diesel. State Documents, Sweden. Stockholm, October 12, 2023.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

The Impact of Technological Innovations and Economic Growth on Carbon Dioxide Emissions

Power plant emitting smoke, illustrating the economic impact of emissions on the environment.

This policy brief offers an examination of the interplay between economic growth, research, and development (R&D), and CO2 emissions in different countries. Analysing data for 83 countries over three decades, our research reveals varying impacts of economic and R&D activities on CO2 emissions depending on country income level. While increased economic growth often leads to higher emissions due to greater industrial activity, our model indicates that increased GDP levels, when interacted with enhanced investments in R&D, is associated with reduced CO2 emissions. Our approach also recognizes the diverse economic conditions of countries, allowing for a more tailored understanding of how to tackle environmental challenges effectively.

Technological Innovation and CO2 Emissions

Human activity has over the past few decades significantly contributed to environmental problems, in particular CO2 emissions. The consequences from increased CO2 emissions, such as global warming and climate change, have motivated extensive research focused on understanding their impact and finding potential solutions to associated issues.

Economic growth, and research and development (R&D) can serve as differentiating factors between countries when it comes to their pollution levels, specifically measured by CO2 emissions per capita. Higher levels of economic growth are associated with increased industrial activity and energy consumption, which may lead to increased CO2 emissions. At the same time, countries that invest more in R&D often focus on developing cleaner technologies and implementing sustainable practices, which may result in reduced CO2 emissions.

In this policy brief, we analyse CO2 emissions’ dependencies on technological innovation and economic growth. For our analysis we group the considered 83 countries into three wealth levels: High, Upper Middle, and Lower Middle income levels. This grouping facilitates a better understanding of the complex interplay between wealth, innovation and growth and their projection into emissions. Considering each wealth level group separately also allows us to account for varying economic and developmental contexts.

Data

Based on data availability, we analyse 83 countries, spanning from 1996 to 2019, inclusive. We follow current research trends and use R&D intensity as a proxy for technological innovation (see Chen & Lee, 2020; Petrović & Lobanov, 2020; Avenyo & Tregenna, 2022).

Data on energy use originate from Our World in Data. R&D data from after 2014 are based on figures from the UNESCO Institute for Statistics. All other indicators come from World Development Indicators (WDI).

Table 1 presents an overview of the variables considered in our empirical model. Our response variable is CO2 emissions per capita. We include several covariates (i.e. urban population, renewable energy, trade), found to be significant in previous studies where CO2 emissions was considered the dependent variable (Avenyo & Tregenna, 2022; Wang, Zeng & Liu, 2019; Petrović & Lobanov, 2020; Chen & Lee, 2020).

Table 1. Variable description.

Additionally, we include quadratic terms for GDP and R&D to account for nonlinearity and non-monotonicity. Also, we incorporate the interaction term between GDP and R&D (see Table 3). This allows us to evaluate whether the impact of technological innovations on CO2 emissions is dependent on the GDP level, or vice versa.

Wealth Level Classification

Existing literature highlights significant variation between countries in terms of economic growth and income levels, particularly in relation to R&D expenditure and CO2 emission levels (see Cheng et al., 2021; Chen & Lee, 2020; Petrović & Lobanov, 2020; Avenyo & Tregenna, 2022). Given this we deployed the Mclust method (Scrucca et al., 2016; Fraley & Raftery, 2002), and classified our considered countries into three distinct groups based on their median Gross National Income (GNI) over a specified range of years for each country. Following this methodology, we obtained three groups of countries: High, Upper Middle and Lower Middle. The list of countries categorized by their respective wealth level is presented in Table 2.

Table 2. Countries within each wealth group.

Low-income countries, (as categorized by the World Bank in 2022) were not included in the analysis as the study focuses on the impact of technological innovations on CO2 emissions, innovations which are less frequent in such economies. Limited infrastructure, financial resources, and access to technology often result in lower levels of R&D activities in low-income countries, which reduces the number of measurable innovations.

The Hybrid Model

Our leading hypothesis is that country income levels (measured by GDP) mediates the relationship between innovation (measured by R&D expenditures) and CO2 emissions. To test this, one could estimate this relationship for each group of countries separately. This policy brief instead estimates the relationship for the whole sample of countries accounting for group differences via interaction effects. Specifically, our estimation allows for interaction terms between some or all covariates and the wealth level. This approach, which we refer to as the hybrid model, thus combines elements of both pooled and separate models. It is a great alternative to separate models as it allows for estimation of both group-specific and sample-wide effects, and as it contrasts differential impacts across wealth level groups.

We test two versions of the hybrid model, one full and one reduced. The full model incorporates interactions with all covariates while the reduced model includes some indices without interactions, resulting in a relationship shared across all wealth levels. The reduced model assumes that the variables Renewable energy consumption, Energy use and Trade exhibit the same relationship with CO2 emissions across all wealth levels.

Both the reduced and full hybrid models have similar coefficients for the variables and interactions that they share. While the coefficients share signs in both the full and reduced hybrid models, they are smaller, in absolute values, in the reduced hybrid model. In Table 3 we present the estimates from the reduced hybrid model.

Table 3. Results from the reduced hybrid model with CO2 emissions as dependent variable, by wealth group level.

Note: The upper part of the table (denoted “interaction variables”) depicts the coefficients for the interaction term between the variable in the respective row and the income group in the respective column. * denotes a 0.05 significance level. ** denotes a 0.01 significance level. ***denotes a 0.001 significance level.

Several things are to be noted from Table 3. First, for High and Upper Middle wealth level countries there is a significant positive association between innovation (as proxied by R&D) and CO2 emissions. However, the significance levels of the interaction term for R&D and GDP reveal that the relationship between R&D and CO2 is not constant across wealth levels even within each group. Specifically, it appears that relatively high values of GDP and R&D are associated with a decrease in CO2 emissions in High and Upper Middle wealth level countries. This suggests that in wealthier countries, advancements in technology and efficient practices derived from R&D are likely contributing to reduced emission levels. Interestingly, GDP has no direct effect on emissions for countries in these two wealth groups. Rather, GDP only affects emissions through the interaction term with R&D.

In turn, for the Lower Middle wealth level countries, R&D has no impact on CO2 emissions, whether directly or via interaction with GDP. Instead, higher GDP leads to a significant increase in emissions. This suggests that for these countries economic growth entail CO2 emissions while R&D activities are too small to have a mediating effect.

Second, medium and high-technology industry value added manufacturing is only significant for countries within the Upper Middle wealth level. This is in line with previous literature (see Avenyo & Tregenna, 2022, Wang, Zeng & Liu, 2019). A higher proportion of medium and high-technology industry value added is often negatively associated with CO2 emissions due to the adoption of cleaner and more environmentally sustainable technologies and practices within these industries. Additionally, these industries are often subject to stringent environmental regulations. As a result, these industries can contribute to reduced emission levels, becoming key drivers of sustainable economic growth and environmental protection (Avenyo & Tregenna, 2022). Interestingly, in our estimation, this result is evident only for Upper Middle wealth level countries.

Third, urban population is only significantly increasing emissions for High wealth level countries. Such positive relationship can be attributed to several factors. There is often a higher concentration of industrial and manufacturing activities in urban areas, leading to increased emissions of pollutants as urbanization increases (Wang, Zeng & Liu, 2019). Additionally, urban areas tend to have higher energy consumption and transportation demands, further contributing to higher emission levels.

When it comes to the factors jointly estimated across wealth groups, the positive relationship between renewable energy consumption and CO2 emissions is well-documented within the literature (Chen & Lee, 2020) which emphasizes the need for sustainable energy practices and efficient resource management to mitigate adverse environmental impacts. In line with this, the significant negative relationship between renewable energy consumption and CO2 emissions suggests that an increase in renewable energy usage is associated with a reduction in CO2 emissions. This is in line with previous findings demonstrating that technological progress helps reduce CO2 emissions by bringing energy efficiency (Akram et al., 2020; Sharif et al., 2019).

Conclusion

This policy brief analyses the effects of GDP and technological innovations on CO2 emissions. The theoretical channels linking economic development (and technological innovations) and CO2 emissions are multifaceted, warranting the need for an econometric assessment. We study 83 countries between 1996 and 2020 in a setting that allows us to disentangle the effects across countries with different income levels.

Our findings underscore the importance of considering the various income levels of the considered countries and their interplay with R&D expenditures in environmental policy discussions. Countries with Lower Middle income levels exhibit insignificant effects from R&D expenditures on CO2 emissions, while for Upper Middle and High wealth level nations, increased R&D expenditures incurs higher emissions.

The moderating role of GDP adds complexity to this relationship. At sufficiently high wealth levels, GDP weakens the effect of R&D on emissions. This alleviating effect becomes stronger as GDP increases until reaching a turning point, at which the impact reverses and R&D expenditures instead decrease emissions.

Our results on the significant nonlinear relationship between R&D, GDP and CO2 emission levels highlights the complexity of addressing environmental challenges within the context of macroeconomics. It suggests that policies promoting both R&D and economic growth simultaneously can foster more sustainable development paths, where economic expansion is accompanied by a more efficient and cleaner use of resources, leading to lower CO2 emissions. This decoupling of economic growth from emissions is likely to be further enhanced by governments incentivising research and development focused on improved energy efficiency and emission reduction.

References

  • Akram, R., Chen, F., Khalid, F., Ye, Z., & Majeed, M. T. (2020). Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: Evidence from developing countries. Journal of cleaner production, 247, 119122.
  • Avenyo, E. K., & Tregenna, F. (2022). Greening manufacturing: Technology intensity and carbon dioxide emissions in developing countries. Applied energy, 324, 119726.
  • Chen, Y., & Lee, C. C. (2020). Does technological innovation reduce CO2 emissions? Cross-country evidence. Journal of Cleaner Production, 263, 121550.
  • Cheng, C., Ren, X., Dong, K., Dong, X., & Wang, Z. (2021). How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. Journal of Environmental Management, 280, 111818.
  • Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association, 97/458, pp. 611-631.
  • Petrović, P., & Lobanov, M. M. (2020). The impact of R&D expenditures on CO2 emissions: evidence from sixteen OECD countries. Journal of Cleaner Production, 248, 119187.
  • Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R journal, 8(1), 289.
  • Sharif, A., Raza, S. A., Ozturk, I., & Afshan, S. (2019). The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations. Renewable energy, 133, 685-691.
  • Wang, S., Zeng, J., Liu, X., (2019). Examining the multiple impacts of technological progress on CO2 emissions in China: a panel quantile regression approach. Renew. Sustain. Energy Rev. 103, 140–150.

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.

Can Farmland Market Liberalization Help Ukraine in its Reconstruction and Recovery?

20240319 Farmland Market Liberalization Ukraine Image 01

The Russian full-scale invasion of Ukraine has inflicted massive damages and losses on Ukraine, already amounting to more than 2.5 times Ukraine’s 2023 GDP. Despite substantial and continuing international political and financial support to help Ukraine in its recovery and reconstruction, it is becoming increasingly clear that it will need to mobilize its own resources and private financing as well – not just for the country’s reconstruction but also for its long-term development. From a government perspective, it is important for Ukraine to leverage scarce public and donor resources and to undertake necessary reforms to facilitate and crowd in private financing. Farmland market liberalization is one of the key reforms in this respect. Its scale, with farmland accounting for more than 70 percent of Ukraine’s territory, and capacity for private financing generation for agriculture and rural areas is, however, often underestimated.  

An Unbearable War Toll and the Need for Private Financing

The raging Russian war on Ukraine enters its third year, imposing an immense toll in terms of human life, economic stability, and regional security. About 20 percent of Ukraine’s territory has been occupied. More than 10 million Ukrainians have left their homes, including 6.45 million refugees that have resettled across Europe (UNHCR, 2024). Ukraine’s military casualties are reported to be approaching 200,000 (The New York Times, 2023) and at least 10,000 civilians have been killed (United Nations, 2023). Ukraine’s GDP plunged by 30 percent in 2022, and the documented total damages to Ukraine’s economy have reached US$ 155 billion, as of January 2024 (KSE, 2024). Similarly, economic losses amount to around US$ 500 billion (as of December 2023). At the same time Ukraine’s reconstruction and recovery needs are estimated at about US$ 486 billion (World Bank, 2024). This immense number make up more than 2.5 times Ukraine’s 2023 GDP.

While there is a substantial and continuing international political and financial support for Ukraine’s defense, recovery, and reconstruction, this will not be enough (World Bank, 2023). Ukraine needs to mobilize its own resources and private financing, not just for its reconstruction but also for its long-term development. The Ukrainian government must leverage scarce public and donor resources and undertake necessary reforms to facilitate and crowd in private investments. One of the crucial reforms in this regard is the ongoing liberalization of the farmland market. The scale of its impact and capacity to generate private financing for agriculture and rural areas is frequently undervalued.

Ukraine’s Farmland Market and Reform

Almost 71 percent of Ukraine’s territory (or 42.7 million ha, including occupied territories) is farmland and 33 million ha is arable. This is far more than in the largest countries in the EU. Ukraine also has one-third of the world’s most fertile black soils. This resource has however been heavily underutilized for agricultural and overall economic development (KSE, 2021). Over the last two decades, Ukraine has turned into an increasingly important global supplier of staple foods (von Cramon-Taubadel and Nivievskyi, 2023), but this has largely happened without a full-fledged farmland market in Ukraine capable of facilitating even further agricultural productivity growth.

The farmland sales market was virtually non-existent for over three decades, instead rental transactions dominated. The farmland sales market began operating only in July 2021, and in a very limited format. Only individuals could purchase farmland plots and with a 100-ha cap per person. The minimum price was set at the normative monetary land value, and tenants had pre-emptive purchase rights while foreigners and legal entities were excluded; state and communal farmland remained under the 2001 sales ban. The farmland sales market opening was part of a large-scale land reform to support an efficient and transparent farmland market. This included a legislation package aimed at preventing land raiding, decentralizing land management, introducing electronic land auctions, establishing tools for land planning and use, creating a national infrastructure for geospatial data, establishing institutions for supporting small scale farmers, and empowering small scale farmers capacity to compete for land (KSE, 2021).

In general, there are two broad benefits of sales and lease transactions. First, the farmland market, via transactions, sorts out more efficient farms from less efficient ones, thus increasing the overall sector value added. Another important benefit, specifically linked to the farmland sales market, is that a functioning farmland sales market makes farmland a collateral which can generate productive investments in increased agricultural and non-agricultural productivity growth (Deininger and Nivievskyi, 2019).

Early Reform Outcomes

Almost two out of the first two and a half years of the reform phase unfolded amidst the profound shock from Russia’s full-scale invasion of Ukraine. Following this, nearly 20 percent of Ukraine’s farmland has been occupied (Mkrtchian and Mueller, 2024), almost a third of the agricultural sector has been ruined – the total damages and losses to the agricultural sector amount to US$ 80 billion (Neyter at al., 2024). As a result, a very restrictive first-phase format of the market, on top of the war challenges, effectively limited the expected benefits of the market liberalization.

The war has put a sizable drag on the farm-land sales market development, effectively slashing the transacted volume almost by half (see Figure 1).

Figure 1. Cumulative market transactions and the effect of the war.

Source: Nivievskyi and Neyter, 2024.

Overall, about 1.1 percent of total farmland area, or about 1.3 percent of Ukraine’s total controlled farmland (equivalent of 200,000 sales transactions or 444,300 ha) has been traded since the opening of the market. Regionally, the outcome is quite diverse (see Figure 2).

This is nonetheless an encouraging outcome as it is quite comparable to developed countries benchmarks where, on average, roughly 1 percent (and up to 5 percent) of the total agricultural land area is transacted annually (Nivievskyi et al., 2016). Another important outcome is that the transacted farmland has remained in agricultural production.

Farmland price development is also positive, especially for commercial farmland (see Figure 3). Since the commencement of the farmland sales market in Ukraine, the capitalization has increased by US$ 5.5 billion (KSE Agrocenter, 2024).

In fact, farmland market capitalization might be even greater. There are indications that the actual market price should be much higher, on average, than the officially registered one, as transacting parties may try and evade fees and taxes (Nivievskyi and Neyter, 2024).

Figure 2. Transacted area as share of total oblast (administrative region) area.

Source: The Center for Food and Land Use Research at Kyiv School of Economics (KSE Agrocenter), 2024.

Continued Farmland Market Liberalization and Associated Expectations

As of January 1, 2024, legal entities gained the right to acquire farmland that had, from 2001, been under sales ban. Also, in this second stage, the farmland accumulation cap per beneficiary increased to 10,000 hectares. Other restrictions remain, including that legal entities with a foreign beneficiary still cannot purchase farmland.

The first results of the second stage are premature, and firm conclusions cannot be drawn, yet the preliminary results are quite encouraging. The new market participants have already increased the volume of transactions and corresponding price by 13 percent, on average (see Figure 3).

Figure 3. Average farmland prices, in thousands UAH.

Source: KSE Agrocenter (2024). Note: Demonstration and estimations are based on the State GeoCadaster Data.

Another encouraging result highlights that legal entities bring further transparency into the market. For half of the transactions involving individuals, the sales price did not exceed the minimum price by more than 1.5 percent, while in half of the farmland transactions with legal entities, the price exceeded the minimum one by more than 44 percent.

These early results provide insight into the market’s direction and the associated benefits. The expected economic benefits from liberalizing the farmland market for legal entities could amount to an annual increase of 1-2.7 percent of GDP over the next three years.  The scale depends on many factors, including the availability of financing and financial support for small farmers (KSE Agrocenter, 2023).

Rural and agricultural financing is of particular interest as land is generally considered a high-quality collateral which could be utilized to attract loans and investments. This is particularly important during the current wartime period, as agricultural producers are facing significant collateral damage and severe financial difficulties for the third consecutive year. Currently, despite its potential, only a meager share of all farming loans is secured by farmland – far below global benchmarks.

Under current registered farmland prices, the total farmland market capitalization is equivalent to roughly US$ 35.5 billion. This could potentially generate an additional US$ 12.4 billion of loans (under the current low liquidity risk ratio of 0.35), already much greater than the current agricultural debt of about US$ 3.5 billion. Adding legal entities to the pool of farmland buyers (as of January 2024), is expected to increase farmland prices by an additional 40 percent. Thus, the farmland market will grow to almost US$ 50 billion, and the volume of land-secured financing could amount to US$ 17.5 billion. Further liberalization of the farmland market, such as a strengthening of its transparency, boosting the market liquidity, and accumulating necessary market statistics, may allow the National Bank of Ukraine to reconsider the liquidity risk ratio for farmland – potentially considering it as collateral similar to other types of real-estate (see the National Bank of Ukraine Resolution #351, June 30, 2016). A liquidity risk ratio at the level of developed countries (0.6-0.8) could further increase the volume of potential land-secured financing available to agriculture and rural areas/landowners to at least US$ 35 billion. This would, in turn, close the more than US$ 20 billion current financing gap for agricultural reconstruction, recovery and development. It would also contribute to Ukraine’s nearly US$ 500 billion reconstruction and recovery needs.

Further significant strides toward liberalizing Ukraine’s farmland sales market are anticipated as part of the country’s journey towards EU membership (European Commission, 2024), aligning with Chapter 4 ‘Free Movement of Capital’. Specifically, this pertains to allowing foreigners (EU citizens and legal entities) the right to purchase Ukrainian farmland (Nivievskyi and Neyter, 2024).

Conclusion

Russia’s full-scale invasion of Ukraine have inflicted massive damages and losses to Ukraine, already amounting to more than 2.5 times Ukraine’s 2023 GDP. The recently estimated reconstruction and recovery needs measure at nearly US$ 500 billion. This is an unbearable burden for Ukraine alone. Despite substantial and continuing support from international partners and donors, Ukraine will need to heavily draw on its own resources and capacity to generate private financing, not just for the country’s reconstruction, but also for its long-term development. It is therefore essential, from the Ukrainians government’s perspective, to focus on necessary reforms and optimize policy decisions to leverage the scarce public and donor resources and facilitate and crowd in private investments. Continued farmland market liberalization is one such critical reform, providing hope to generate substantial private investment in the agricultural sector and rural areas.

The size of the farmland market is immense (with farmland accounting for more than 70 percent of Ukraine’s territory). The first two years following the opening of the farmland sales market demonstrate a substantial potential for private financing generation for agriculture and rural areas. The results from regular market monitoring and the early findings, as discussed above, suggest that further farmland market liberalization and increased transparency could generate about US$ 35 billion of financing for agricultural producers and rural areas/landowners. That could, in turn, close the current agricultural financing gap of more than US$ 20 billion for rebuilding and recovery, as well as partially close the nearly US$ 500 billion financing gap for Ukraine’s overall reconstruction and recovery. The expected economic benefits from liberalizing the farmland market for legal entities are estimated at 1-2.7 percent of GDP annually, over the next three years. A further liberalization of the farmland market, and a step towards EU membership, would include granting foreigners (EU citizens and legal entities) the right to buy Ukrainian farmland – expected to bring even further benefits.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Sanctions on Russia: Getting the Facts Right

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The important strategic role that sanctions play in the efforts to constrain Russia’s geopolitical ambitions and end its brutal war on Ukraine is often questioned and diminished in the public debate. This policy brief, authored by a collective of experts from various countries, shares insights on the complexities surrounding the use of sanctions against Russia, in light of its illegal aggression towards Ukraine. The aim is to facilitate a public discussion based on facts and reduce the risk that the debate falls prey to the information war.

Sanctions are a pivotal component in the array of strategies deployed to address the threat posed by Russia to the rule-based international order. Contrary to views minimizing their impact, evidence and research suggest that sanctions, particularly those targeting Russian energy exports, have significantly affected Russia’s macroeconomic stability [1,2,3]. Between 2022 and 2023:

  • merchandise exports fell by 28 percent,
  • the trade surplus decreased by 62 percent,
  • and the current account surplus dropped by 79 percent (see the Bank of Russia’s external sector statistics here).

Although 2022 represents an extraordinarily high baseline due to the delayed impacts of energy sanctions, the $190 billion decrease in foreign currency inflows during this time has already made a significant difference for Russia. This amount is equivalent to about two years of Russia’s current military spending, or around 10 percent of Russia’s yearly GDP, depending on the figures. Our estimates suggest that Russia’s losses due to the oil price cap and import embargo alone amount to several percent of its GDP [3,4]. These losses have contributed to the ruble’s continued weakness and have forced Russian authorities to sharply increase interest rates, which will have painful ripple effects throughout the economy in the coming months and years. Furthermore, the international sanctions coalition’s freezing of about $300 billion of the Bank of Russia’s reserves has significantly curtailed the central bank’s ability to manage the Russian economy in this era of war and sanctions.

Sanctions Enforcement

Addressing the enforcement of sanctions, it is crucial to acknowledge the extensive and continuous work undertaken by governments, think tanks, and the private sector to identify and close loopholes that facilitate sanctions evasion. Suggesting that such efforts are futile, often with arguments that lack solid evidence, potentially undermines these contributions, and furthermore provides (perhaps unintended) support to those advocating for a dismantling of the sanctions regime. We do not deny that several key aspects are facing challenges, from the oil price cap to export controls on military and dual-use goods. However, the path forward is to step up efforts and strengthen the implementation and enforcement – not to abandon the strategy altogether. Yes, Russia’s shadow fleet threatens the fundamental mechanism of the oil sanctions and, namely its reliance on Western services [4,5,6]. However, recent actions by the U.S. Treasury Department have shown that the sanctioning coalition can in fact weaken Russia’s ability to work around the energy sanctions. Specifically, the approach to designate (i.e., sanction) individual tankers has effectively removed them from the Russian oil trade. More vessels could be targeted in a similar way to gradually step-up the pressure on Russia [7]. While Russia continues to have access to many products identified as critical for the military industry (for instance semiconductors) [8], it has been shown that Russia pays significant mark-ups for these goods to compensate for the many layers of intermediaries involved in circumvention schemes. Sanctions, even when imperfect, thus still work as trade barriers. In addition to existing efforts and undertakings, companies which help Russia evade export controls can be sanctioned, even when registered in countries outside of the sanctioning coalition. Furthermore, compliance efforts within, and against, western companies, who remain extremely important for Russia, can be stepped up.

The Russian Economy

Many recent newspaper articles have been centered around the theme of Russia’s surprisingly resilient economy. We find these articles to generally be superficial and missing a key point: Russia is transitioning to a war economy, driven by massive and unsustainable public spending. In 2024, military spending is projected to boost Russia’s GDP growth by at least 2.5 percentage points, driven by a planned $100 billion in defense expenditures [9]. However, seeing this for what it is, namely war-spending, raises significant concerns about the sustainability of this growth, as it eats into existing reserves and crowds out investments in areas with a larger long-term growth potential. The massive spending also feeds inflation in consumer prices and wages, in particular as private investment levels are low and the labor market is short on competent labor. This puts pressure on monetary policy causing the central bank to increase interest rates even further, to compensate for the overly stimulating fiscal policy.

Further, it is important to bear in mind that, beyond this stimulus, the Russian economy is characterised by fundamental weaknesses. Russia has for many years dealt with anaemic growth due to low productivity gains and unfavourable demographics. Since the first round of sanctions was imposed on Russia, following its illegal annexation of Crimea in 2014, growth has hovered at around 1 percent per year on average – abysmal for an emerging market with catch-up potential. More recently, current sanctions and war expenditures have made Russia dramatically underperform compared to other oil-exporting countries [10]. Moreover, none of the normal (non-war related) growth fundamentals is likely to improve. Rather, the military aggression and the ensuing sanctions have made things worse. Hundreds of thousands of Russians have been killed or wounded in the war; many more have left the country to either escape the Putin regime or mobilization. Those leaving are often the younger and better educated, worsening the already dire demographic situation, and reinforcing the labor market inefficiencies. Additionally, with the country largely cut off from the world’s most important financial markets, investments in the Russian economy are completely insufficient [11].

As a result, Russia will be increasingly dependent on fossil fuel extraction and exports, a strategy that holds limited promise as considerations related to climate change continue to gain importance. With the loss of the European market, either due to sanctions or Putin’s failed attempt to weaponize gas flows to Europe, Russia finds itself dependent on a limited number of buyers for its oil and gas. Such dependency compels Russia to accept painful discounts and increases its exposure to market risks and price fluctuations [12].

The Cost of Sanctions

Sanctions have not been without costs for the countries imposing them. Nonetheless, the sanctioning countries are in a much better position than Russia. Any sanction strategy is necessarily a tradeoff between maximizing the sanctioned country’s economic loss while minimizing the loss to the sanctioning countries [9], but there are at least two qualifications to bear in mind. The first is that some sanctions imply very low losses – if any – while others may carry limited short term losses but longer term gains. This includes the oil-price cap that allows many importing countries to buy Russian oil at a discount [3], and policies to reduce energy demand, which squeezes Russia’s oil-income [13]. These policies may also initially hurt sanctioning countries, but in the long term facilitate an investment in energy self-sufficiency. Similarly, trade sanctions also imply some protection of one’s own industry, meaning that such sanctions may in fact bring benefits to the sanctioning countries – at least in the short run. The second qualification is that, in cases where sanctions do imply a cost to the sanctioning countries, the question is what cost is reasonable. Russia’s economy is many times smaller than, for instance, the EU’s economy. This gives the EU a strategic advantage akin to that in Texas hold’em poker: going dollar for dollar and euro for euro, Russia is bound to go bankrupt. Currently, Russia allocates a significantly larger portion of its GDP to its war machine than most sanctioning countries spend on their defense. That alone suggests sanctioning countries may want to go beyond dollar for dollar as it is cheaper to stop Russia economically today than on a future battlefield. This points to the bigger question: what would be the future cost of not sanctioning Russia today? Many accredit the weak response from the West to the annexation of Crimea in 2014 as part of the explanation behind Putin’s decision to pursue the current full-scale invasion of Ukraine. Similarly, an unwillingness to bear limited costs today may entail much more substantial costs tomorrow.

When discussing the cost of sanctions, one must also take into account Russia’s counter moves and whether they are credible [14]. Often, they are not [3, 15]. Fear-inducing platitudes, such that China and Russia will reshape the global financial system to insulate themselves from the West’s economic statecraft tools, circulate broadly. We do not deny that these countries are undertaking measures in this direction, but it is much harder to do so in practice than in political speeches. For instance, moving away from the U.S. dollar (and the Euro) in international trade (aside from in bilateral trade relations that are roughly balanced) is highly challenging. In such a trade, conducted without the U.S. dollar, one side of the bargain will end up with a large amount of currency that it does not need and cannot exchange, at scale, for hard currency. As long as a transaction is conducted in U.S. dollar, the U.S. financial system is involved via corresponding accounts, and the threat of secondary sanctions remains powerful. We have seen examples of this in recent months, following President Biden’s executive order on December 22, 2023.

One of Many Tools

Finally, we and other proponents of sanctions do not view them as a panacea, or an alternative to the essential military and financial support that Ukraine requires. Rather, we maintain that sanctions are a critical component of a multi-pronged strategy aimed at halting Putin’s unlawful and aggressive war against Ukraine, a war that threatens not only Ukraine, but peace, liberty, and prosperity across Europe. The necessity for sanctions becomes clear when considering the alternative: a Russian regime with access to $300 billion in the central bank’s reserves, the ability to earn billions more from fossil fuel exports, and to freely acquire advanced Western technology for its military operations against Ukrainian civilians. In fact, the less successful the economic statecraft measures are, the greater the need for military and financial aid to Ukraine becomes, alongside broader indirect costs such as increased defense spending, higher interest rates, and inflation in sanctioning countries. A case in point is the West’s provision of vital – yet expensive – air defense systems to Ukraine, required to counteract Russian missiles and drones, which in turn are enabled by access to Western technology. Abandoning sanctions would only exacerbate this type of challenges.

Conclusion

The discourse on sanctions against Russia necessitates a nuanced understanding of their role within the context of the broader strategy against Russia. It is critical to understand that shallow statements and misinformed opinions become part of the information war, and that the effectiveness of sanctions also depends on all stakeholders’ perceptions about the sanctioning regime’s effectiveness and long run sustainability. Supporting Ukraine in its struggle against the Russian aggression is not a matter of choosing between material support and sanctions; rather, Ukraine’s allies must employ all available tools to ensure Ukraine’s victory. While sanctions alone are not a cure-all, they are indispensable in the concerted effort to support Ukraine and restore peace and stability in the region. The way forward is thus to make the sanctions even more effective and to strengthen the enforcement, not to abandon them.

References

[1] “Russia Chartbook”. KSE Institute, February 2024

[2] “One year of sanctions: Russia’s oil export revenues cut by EUR 34 bn”. Center for Research on Energy and Clean Air, December 2023

[3] “The Price Cap on Russian Oil: A Quantitative Analysis”. Wachtmeister, H., Gars, J. and Spiro, D, July 2023

[4] Spiro, D. Gars, J, and Wachtmeister, H. (2023). “The effects of an EU import and shipping embargo on Russian oil income,” mimeo

[5] “Energy Sanctions: Four Key Steps to Constrain Russia in 2024 and Beyond”. International Working Group on Russian Sanctions & KSE Institute, February 2024

[6] “Tracking the impacts of G7 & EU’s sanctions on Russian oil”. Center for Research on Energy and Clean Air

[7] “Russia Oil Tracker”. KSE Institute, February 2024

[8] “Challenges of Export Controls Enforcement: How Russia Continues to Import Components for Its Military Production”. International Working Group on Russian Sanctions & KSE Institute, January 2024

[9] “Russia Plans Huge Defense Spending Hike in 2024 as War Drags”. Bloomberg, September 2023

[10] “Sanctions and Russia’s War: Limiting Putin’s Capabilities”. U.S. Department of the Treasury, December 2023

[11] “World Investment Report 2023”. UNCTAD

[12] “Russia-China energy relations since 24 February: Consequences and options for Europe”. Swedish Institute of International Affairs, June 2023

[13] Gars, J., Spiro, D. and Wachtmeister, H. (2022). “The effect of European fuel-tax cuts on the oil income of Russia”. Nature Energy, 7(10), pp.989-997

[14] Spiro, D. (2023). “Economic Warfare”. Available at SSRN 4445359

[15] Gars, J., Spiro, D. and Wachtmeister, H., (2023). “Were Russia’s threats of reduced oil exports credible?”. Working paper

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Russian Wheat Policies and Georgia’s Strategic Trade Policies

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Russia is known for periodically halting its grain exports to impact global wheat prices. This has become a significant policy concern in recent years, most notably during the Covid-19 pandemic and in the wake of Russia’s war in Ukraine. Georgia heavily depends on wheat imports, and over 95 percent of its wheat has historically been sourced from Russia. Despite Russia’s periodic bans and restrictions on wheat exports occurring every 2-3 years, Georgia is yet to effectively diversify its sources of wheat imports. This policy brief analyses the impact of Russia’s most recent wheat policies on Georgia’s wheat market, examines Georgia’s response, and provides policy recommendations in this regard.

In June 2023, the Georgian government introduced a temporary import duty on wheat flour imported from Russia in response to requests from the Georgian Flour Producers Association. The association began advocating for an import duty after Russia, in 2021, imposed a so-called “floating tariff” on wheat which made it relatively more expensive to import wheat in comparison to wheat flour. As a result of the “floating tariff” on wheat, wheat flour imports skyrocketed and almost fully substituted wheat imports. Eventually, many Georgian mills shut down and local wheat producers struggled to sell domestically produced wheat. Such an increase in flour imports raises the risk of completely replacing domestically produced flour with flour imported from Russia.

To address the above, the government has implemented a temporary import duty of 200 GEL (75 USD) per ton on wheat flour imported from Russia (the average import price ranges between 225 USD/ton and 435 USD/ton). In turn, millers have agreed to purchase 1 kilogram of wheat from Georgian farmers for 0.7 GEL (0.3 USD). This policy measure is in effect until March 1, 2024.

The Georgian Flour Producers Association advocates for an extension of the temporary import duty beyond March 1, 2024, to uphold fair competition in the wheat and flour market. According to the Georgian Flour Producers Association, an extension is desirable due to the following (Resonance daily, 2024):

  • Under the import duty, fair competition between wheat flour and wheat has been restored, and Georgian mills have resumed their operations.
  • Following the government intervention, farmers have successfully sold over 50,000 tons (on average half of the annual production) of domestically produced wheat. The Ministry of Environmental Protection and Agriculture has reported a 60 percent increase in local wheat production over the past two years, with expectations of sustained growth.
  • Wheat imports have resumed, with Georgia importing 20,000 to 25,000 tons of wheat monthly, while prior to the government intervention, the average monthly wheat imports amounted to 15,337 tons (in 2022). Additionally, 8,000 to 12,000 tons of wheat flour, on average, are also imported monthly, while in the absence of government intervention, wheat flour imports surged to over 15,000 tons (in 2022).
  • Post-intervention, the price of 100 kilograms of first-quality flour has remained stable, ranging from 45 to 49 GEL. Consequently, the price of bread has not increased but remains steady.
  • The import duty has generated an additional 20 million GEL in government revenue.
  • Through the efforts of the mills, the country now enjoys a steady and strategically managed supply of wheat, in accordance with UN recommendations. Coupled with the seasonal harvest of Georgian wheat, this ensures complete food security in any unforeseen critical scenario.

While many arguments support the decision to preserve the import duty on wheat flour, in order to make an informed decision on that matter, it is essential to thoroughly assess production, trade and price dynamics in the wheat market in Georgia. Additionally, to design adequate trade policy measures, one has also to consider the issue in a broader perspective and assess the risks associated with a high dependency on Russian wheat, especially given Russia’s history of imposing wheat export restrictions.

Russian Policy on the Wheat Market

Russia has long been one of the dominant players on the global wheat market, and its periodic decisions to halt grain exports have heavily affected international wheat prices (see Table 1). This concern became especially stringent in recent years, during the Covid-19 pandemic and Russia’s war in Ukraine.

Table 1. Russia’s policy interventions in the wheat market and their estimated impact on wheat prices, 2007-2023.

Source: United States Department of Agriculture, 2022.
The Government of the Russian Federation.
The Kansas City Wheat Futures, The U.S. Wheat Associates.

One of Russia’s most recent interventions in the wheat market is its withdrawal from the Black Sea Grain Initiative – an agreement between Russia, Ukraine, Turkey, and the United Nations (UN) during the Russian invasion of Ukraine on the Safe Transportation of Grain and Foodstuffs from Ukrainian ports. While Georgia doesn’t directly import wheat from Ukraine and isn’t immediately threatened by famine, Russia’s export policies regarding wheat have raised significant food security concerns in the country. Georgia heavily depends on wheat imports from Russia, with over 95 percent of its wheat historically being sourced from there. Despite Russia’s recurrent bans and restrictions on wheat exports every 2-3 years, Georgia is yet to successfully diversify its import sources.

The Georgian Wheat Market in Figures

Domestic Production

Historically, Georgia’s agricultural sector has struggled to achieve a large-scale and sufficient wheat production due to the prevalence of small-sized farms. However, over the past decade, Georgian domestic wheat production has shown significant growth (see Figure 1). This growth has been particularly sizeable in recent years, with production increasing by 32 and 53 percent in 2021 and 2022, respectively, as compared to 2020.

Figure 1. Wheat production in Georgia, 2014-2022.

Source: Geostat, 2024.

Such increase in local production positively contributes to the self-sufficiency ratio, which increased from 7 percent in 2014 to 22 percent in 2022, in turn implying higher food security levels.

Wheat Imports

Before the introduction of Russia’s floating tariff on wheat, wheat flour imports to Georgia were almost non-existent. However, after the floating tariff was imposed on wheat, imports of wheat flour increased more than 20 times – from 743 tons in January 2021 to 15,086 tons in May 2023 – peaking at 23,651 tons in August 2022 (see Figure 2). At the same time wheat imports declined by almost 60 percent, from 29,397 tons in January 2021 to 12,133 tons in May 2023, with the smallest import quantity being 2,743 tons in May 2022 (as depicted in Figure 2).

Figure 2. Georgian wheat and wheat flour imports, 2021-2023.

Source: Geostat, 2024. Note: Imports include meslin (a mixture of wheat and rye grains).

After the introduction of the temporary import duty on wheat flour in June 2023, wheat imports have picked up, although not reaching the levels seen in 2021. Similarly, wheat flour imports have declined while remaining at higher levels than in 2021. This indicates a change in Georgia’s wheat market dynamics. Historically, Georgia predominantly imported wheat; now it imports both wheat and wheat flour. This shift must be considered in future policy design, as it has implications for domestic wheat farmers and mills.

The continued wheat flour imports, despite the temporary import duty imposed by the Georgian Government can likely be attributed to a smaller price gap between wheat and wheat flour import prices (see Table 2).

Table 2. Average import prices of wheat and wheat flour in Georgia, 2021-2023.

Source: Geostat, 2024.

In 2021, prior to Russia’s introduction of a floating tariff on wheat, the import price of wheat flour in Georgia was 24 percent higher than the import price of wheat. After the introduction of the floating tariff, importing wheat became more expensive, and the import price gap between wheat flour and wheat decreased to 22 percent by the end of 2021. Subsequently, in 2022, this gap further narrowed, and by the first half of 2023, the import price of wheat flour was 5 percent lower than the import price of wheat. This significant decrease in the price gap resulted in nearly full substitution of wheat imports with wheat flour imports. After the introduction of the import duty on wheat flour and as international wheat prices declined, a marginal positive price gap has reappeared, amounting to just 1 percent. As it stands, importing wheat flour remains more advantageous than importing wheat.

Price Effects

Russia’s floating tariff on wheat led to increased bread and wheat flour prices in 2021-2022. In June 2022, bread prices experienced the most significant surge, increasing by 36 percent, while wheat flour prices reached their peak in September 2022 with a year-on-year increase of 41 percent (see Figure 3). The primary reason for this was the record increase in wheat prices, leading to a corresponding surge in wheat flour prices in 2022. This spike occurred as the world price of wheat reached its highest point in five years.

Figure 3. Annual change in bread and wheat flour prices, 2021-2023.

Source: Geostat, 2024.

Nevertheless, in 2023 bread and wheat flour prices decreased, indicating that the import duty on wheat flour did not lead to increased prices. This could partially be explained by the fact that mills pay farmers 0.5 GEL/kg, which is lower than agreed price of 0.7 GEL/kg. Another and more crucial factor is the decline in global wheat prices. They began their descent in June 2022 and have since maintained a downward trajectory. This decrease, combined with increased local production, has so far acted as a barrier to any new bread and wheat flour price increases.

The Way Forward

The question that must be addressed is whether the import duty on wheat flour imported from Russia should be extended.

The import duty may have contributed to increased local production as higher import duties can incentivize local businesses to invest in expanding their production capacity or improving their technology to meet an increased demand. It is however essential to note that the impact of import duties on local production varies depending on the level of domestic competition, the availability of inputs (high quality seed, fertilizer etc.), technological capabilities, and government policies beyond import duties (such as investment incentives, infrastructure development, and regulatory environment). Additionally, import duties can also lead to retaliatory measures from trading partners, affecting overall trade dynamics – potentially incurring unintended consequences. Therefore, while import duties can contribute to an increased local production under certain conditions, it is just one of many factors influencing production dynamics.

Secondly, as previously detailed, the import duty has so far not resulted in increased bread prices. However, the effect of an import tariff on retail prices depends on various factors, including elasticity of demand and supply, market, competitiveness, and the extent to which the tariff is passed on to consumers by importers and retailers. Since demand for bread is inelastic, one has to keep in mind that the importers and retailers can fully pass on the increased cost from an import tariff to consumers.

Given that the floating tariff and the import duty make wheat and wheat flour imports to Georgia more expensive, one should expect future bread price increases. This unless international wheat prices continue to decline and/or producers agree to reduce their profit margins or make supply chain changes. Therefore, an extension of the import duty might be a suitable solution in the short and medium-term, but it should not be seen as a permanent solution.

To limit the risks of food scarcity in Georgia in the long run, it is essential to design strategies helping the country to reduce its dependency on Russian wheat and wheat flour. Some measures to achieve this objective may include:

Further supporting local production. Encourage investment in domestic agriculture to increase the productivity and quality of wheat production in Georgia. This can be achieved through subsidies, incentives for modern farming techniques, and access to credit for farmers.

Improving the quality of local production. Currently, most of the domestically produced wheat is unsuitable for milling into wheat flour. A significant portion of domestically produced wheat is of poor quality and instead used for feeding livestock. It is essential to invest in research and development to improve the quality of domestically produced wheat. This includes developing wheat varieties that are resistant to diseases and better suited for local growing conditions.

Seeking alternative markets for import diversification. One alternative for Georgia may be to focus on the Kazakh and Ukrainian markets (once the war is over) and negotiate possible ways to decrease the cost of transporting wheat to Georgia with state and private sector representatives.

Reducing the Georgian dependence on Russian wheat imports requires a multifaceted approach that addresses various aspects of agricultural policy, trade diversification, and domestic production capacity.

References

Resonance daily. (2024). The Association of Wheat and Flour Producers of Georgia requests an extension of the import tax on imported flour. https://www.resonancedaily.com/index.php?id_rub=4&id_artc=197847

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.

Closing the Gender Data Gap

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High-quality data plays a crucial role in enhancing our comprehension of evolving social phenomena, and in designing effective policies to address existing and future challenges. This particularly applies to the gender dimension of data, given the profound impact of the pervasive so-called “gender data gap”. In recent decades, data recovered from archives, high quality surveys, and census and administrative data, combined with innovative approaches to data analysis and identification, has become pivotal for the progress of documenting structural gender differences. Nonetheless, before we can close the gender gaps on the labour market, within households, in politics, academia and other areas, researchers and policy-makers must first ensure a closure of the gender data gap.

Policy Brief | EN langauge version

Policy Brief | GE language version

Introduction

Any progress in our understanding of social phenomena hinges on the availability of data, and there is no doubt that recent advances in economics and other social sciences would not have been possible without countless high quality data sources. As we argue in this policy brief, this applies also, and perhaps particularly, to the documentation of different dimensions of gender inequalities and the analysis to identify their causes. Over the last few decades innovative ways to document historical developments, combined with improvements in the access to existing data, as well as new approaches to data collection, have become cornerstones in the progress made in our understanding of the various expressions of gender inequality. In the economic sphere this has covered themes such as labor market status,  earning and income levels, wealth accumulation over the life course, education investments, pensions, as well as consumption patterns and time allocation – in particular caregiving and household work. Researchers have also been able to empirically study gender inequalities in politics, culture, crime, the justice system and in academia itself.

Groundbreaking studies in gender economics, including those by Claudia Goldin, the recent Nobel Prize laureate, would not have been possible without high quality data and innovative ways aimed at closing the “gender data gap”, a term coined by Caroline Criado Perez, in her bestseller “Invisible women” (Criado Perez, 2020). In the introduction to the book she notes that “(…) the chronicles of the past have left little space for women’s role in the evolution of humanity, whether cultural or biological. Instead, the lives of men have been taken to represent those of humans overall.” (p. XI). The gender data gap is the result of deficits of informative data sources on women, which has been augmented by frequent lack of differentiation of information by sex/gender in available sources. Closing the gender gaps along the dimensions already identified in existing studies will require a continuous monitoring of evidence, thus closing the gender data gap in the first place. New studies focused on greater equality and on the effectiveness of various implemented policies will continue to rely on good data. Thankfully, few new datasets currently ignore the gender of the respondents. However  as our understanding of the biological and cultural aspects of sex and gender grows, the way data is collected will need to be modified.

As we prepare for the new challenges ahead of those designing data collection efforts and examining the data, we believe it is important to give credit to the authors of some of the groundbreaking studies that paved the way to the current pool of evidence on gender inequality. Around the time of the International Women’s Day, we recall several empirical studies in gender economics that, in our opinion, merit special attention due to either their innovative approaches to data collection, their unique access to original data sources, or their methodological novelty. These studies bring valuable insights into specific dimensions of gender inequality. This short list is naturally a subjective choice, but we believe that all of these studies deserve credit not only among researchers within gender economics, but also among those more broadly interested in the recent progress in the understanding of different aspects of gender inequality.

From Data to Policy Recommendations

Over the last few decades substantial efforts have been made to provide empirical evidence concerning historical trends in inequalities between men and women on the labor market. Seminal work in this field was conducted by Claudia Goldin in the 1970s and 80s, culminating in the publication of the path-breaking book Understanding the Gender Gap: An Economic History of American Women (Goldin, 1990). The book fundamentally changed the view of women’s role in the labor market. Empirically Goldin shows that female labor force participation has been significantly higher in historical times than previously believed. Before Goldin, researchers mainly studied twentieth century data. Based on this it looked as if women’s participation in the labour market is positively correlated with economic growth. Goldin’s work showed instead that women were more likely to participate in the labour force prior to industrialization, and that early expansion of factories made it more difficult to combine work and family. Seen over the full 200 year period, from before industrialization to today, the pattern of women’s labour market participation is in fact U-shaped, pointing to the importance of various societal changes that alter incentives and possibilities for women’s work. Goldin’s contribution is however not just about getting the empirical picture right. At least equally important is the recognition of women as individual economic agents, who make forward looking decisions under various institutional constraints and limitations related to social norms about identity and family, as well as education opportunities and labor market options. While some decision can be modeled as taken by “the economic man”, others by households, it may seem surprising that studying women’s decisions was for so long neglected.

Institutional, cultural and economic factors behind historical trends have become the focus of much of the literature trying to identify the forces driving gender disparities. Some of the most original work considers the role that “chance” plays in determining individual decisions related to gender – how having a first-born son (e.g. Dahl and Moretti, 2008) or having twins (Angrist and Evans, 1998), both of which can be considered random, – affect choices related to partnership, future fertility and the labor market. Others examin the influence of gender imbalances caused by major historical events. Brainerd (2017) investigates the consequences of extremely unbalanced sex ratios in cohorts particularly affected by the massive loss of lives during World War II in the Soviet Union. By exploiting a unique historical data source derived from the first postwar census, combined with statistics registry records from archives, Brainerd provides evidence that the war-induced scarcity of men profoundly affected women’s outcomes on the marriage market. Women were more likely to never get married, give birth out of wedlock and get divorced. On top of that, unbalanced sex ratios affected married women’s intrahousehold bargaining power and resulted in lower fertility rates and a higher rate of marriages with a large age gap between spouses. The post-war institutional setup increased the cost of divorce and withdrew legal obligations to support children fathered out of wedlock, which exacerbated the consequences from the shortage of men by further reducing the rates of registered marriages and increasing marital instability.

The examples above highlight how conditions beyond individuals’ control can contribute to social gender imbalances, or shed light on existing gender biases. How these ‘exogenous’ circumstances translate into economic inequalities and what additional factors drive disparities has been the focus of much academic work on gender inequalities. One of the most challenging questions has been that of demonstrating that discrimination of women, rather than women’s characteristics or choices, are behind the growing body of evidence on economic gender inequality. In this respect Black and Strahan (2001) provide important convincing conclusions by using significant changes in the level of regulation in the US banking sector. Increasing competition between banks lowered banks’ profits, and led to a reduced ability of managers to ‘divide the spoils’, and thus to discriminate between different types of employees. The authors used information on wages within specific industries (including banking) from one of the oldest ongoing surveys in the world – the US Current Population Survey (CPS). By exploiting detailed individual data covering a period of several decades the authors show that higher levels of banking sector regulations (prior to deregulation) facilitated greater premia paid out to male compared to female employees. Thus, increased competition in the banking sector brought favorable changes to women’s pay conditions as well as their position in banks’ management.

While long running surveys such as the CPS continue to serve as invaluable sources of information on the relative conditions of men and women, the growing availability of administrative data has opened new opportunities for documentation of inequalities and identification of the reasons behind these. For instance, the ability to track individuals throughout their work history before and after the arrival of their first child has allowed researchers to compare the trajectories of women’s and men’s earnings, wages and working hours. This comparison has revealed the existence of the so-called “child penalty”, with women experiencing a drop in their labor market position relative to their male partners after the birth of their first child, and with the gap persisting for many years. Strikingly, this penalty has been estimated in some of the most gender-equal countries in the world, such as Sweden (Angelov et al., 2016) and Denmark (Kleven et al., 2019), two countries which have spearheaded collecting and making rich administrative data available to researchers.

Another area where individual register data has proven invaluable is in the study of the so-called “glass ceiling”, i.e., the sharply increasing differences between men and women when it comes to pay as well as representation in the very top of the income distribution. In a seminal study by Albrecht et al. (2003), individual earnings for men and women were compared and differences were found to be markedly higher (with men earning much more) when comparing men in the top of the male income distribution with women in the top of the female income distribution. Also making use of Swedish registry data, Boschini et al. (2020) study a related question, namely the evolution of the share of women in the top of the income distribution. In line with other glass-ceiling results, they demonstrate that the share of women in the top is small, and that it gets smaller the higher one looks, , although it has increased over time. Decomposing incomes into labor earnings and capital income they also show that while women seem to be catching up in the labor income distribution, they clearly lag in the capital income distribution. Also, the income profile of the partners of high-income men and high-income women are strikingly different. Most high-income women have high-income partners, while the opposite is not true for high-income men.

Differences in the economic position of men and women reflected in the above examples can have their origin much before the time individuals enter the labor market. They can be driven by differences in schooling opportunities, as well as other forms of early life investments, to the extent that even much of what is perceived as choices or preferences later in life are in fact results of these subtle early life disadvantages for women. While these have largely diminished in the global North, there is a growing number of studies documenting these differences in the global South. Jayachandran and Pande (2017) examine the impact of son preference, a widespread cultural practice for example in India, on child health and development. The study leverages a simple, standardized, and broadly available indicator – the height of children – which is measured at routine health checks and included in many population surveys, such as the Demographic and Health Surveys (DHS). Additionally, their use of a natural experiment, based on the birth order of children, helps to establish a causal relationship between eldest son preference and nutritional disparities that have long-term developmental consequences among subsequent children, not only for girls but for Indian children on average. Findings like these underscore the importance of gender equality not only as a fundamental value but also as a crucial factor in promoting growth and development at the societal level.

The social costs of gender inequality have also motivated the growing research interest in gender-based violence and crime. Given the specific challenges associated with these topics – such as the clandestine and underreported nature of these acts but also the consideration for victims’ confidentiality and safety – studies in this area has required researchers to develop and apply innovative tools and data collection methods. In this framework list experiments have emerged as a methodology allowing respondents to disclose sensitive or socially undesirable attitudes indirectly, reducing the likelihood of the so-called social desirability bias in survey reporting. In a list experiment, respondents are presented with a set of statements or behaviors and asked to indicate their agreement or engagement with these. Among listed items, one is considered “sensitive” and is included only for a randomly selected subset of respondents. By comparing the average number of items agreed with by the entire sample to a control group that did not get the sensitive item, researchers can estimate the proportion of respondents who agreed with or engaged in the sensitive behavior or opinion. Kuklinski et al. (1997) is one of the pioneering contributions in this area, estimating the proportion of voters who harbored racial prejudices but who may have been unwilling to admit it in a direct survey question. List experiments have since become a widely used tool in political science and economics and have helped in the advancement of our understanding of gender-based violence (Peterman et al., 2018). Given the strong assumptions underlying the analysis the method has not become the ”statistical truth serum” it was at some point considered to be. However, list experiments have broadened the analytical opportunities in an area plagued by significant informational and data challenges.

While worldwide gender gaps in economic opportunities and especially in education and health have rapidly declined (and sometimes reversed) in the last decades, larger differences remain in political empowerment (see e.g., WEF Gender Gap Report 2023). Another Nobel Prize laureate in economics, Esther Duflo, in her joint work with Raghabendra Chattopahyay (2004), have pioneered a highly prolific area of research on the impacts of women as policymakers. In their study, they leverage a unique policy experiment in India  that randomized the gender of the leader of Village Councils, and a detailed dataset based on extensive surveys administered to both Village Council leaders and villagers. The surveys allowed for estimation of the investments in different public goods in 265 Village Councils, as well as the preferences over each of these public goods among female and male villagers. Combining the randomization and this rich dataset, the authors establish that political leaders prioritize public goods that are more relevant to the needs of their own gender, suggesting that women’s under-representation in politics might result in women’s and men’s preferences being unequally represented in policy decisions.

Conclusions and Recommendations

The narrowing gender gap in political representation across various levels of government, the growing influence of women in other areas such as public institutions, administration etc., and the heightened awareness of the crucial role gender equality plays in socio-economic progress all bode well for improvements in access to high-quality gender-differentiated data sources. Before we can recognize and close gender gaps identified from high-quality data, the gender data gap needs to firstly be closed. Governments and public institutions should make their  increasing amounts of digitized information available for research purposes. Funding should be available to collect data through surveys, and these could in turn be combined with details available in administrative sources to take advantage of the breadth of survey data and the precision of official statistics. Information needs to be collected on a frequent and regular basis to make sure that the consequences of various major developments, such as legal changes, conflicts or natural disasters, can be identified. Innovative data sources, for instance information from mobile apps or social media, can provide additional useful insights into socio-economic trends, old and new dimensions of inequalities and regular timely updates on different aspects of gender disparities. These new data sources can become the basis for future innovative studies on gender inequalities, contributing to a better understanding of the mechanisms behind these inequalities, and providing evidence for policies and other efforts to effectively close the remaining gaps. Already now there is enough evidence to conclude that closing these gaps is not only just but that it also constitutes a fundamental basis for continued inclusive economic development.

Post Scriptum

Contributing to the existing pool of data sources we are happy to share a regional dataset with information on gender norms and gender-based violence: the FROGEE Survey 2021. The data was collected using the CATI method (phone interviews) in autumn 2021 in Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. In each country interviews were conducted with between 925 and 1000 adults. The survey covered areas such as: basic demographics, material conditions, labor market status, gender norms, attitudes towards harassment and violence, awareness of violence against women and awareness of legal protection for gender violence victims.

The data collection was funded by the Swedish International Development Cooperation Agency (SIDA) as part of the FREE Network’s FROGEE project. The dataset and supporting materials are freely available for research purposes. For more information see: FROGEE Survey on Gender Equality.

References

  • Angrist, D. J., and Evans, N. W. (1998). Children and their parents’ labor supply: Evidence from exogenous variation in family size. American Economic Review, 88(2), 450-477.
  • Albrecht, J., Björklund, A., and Vroman, S. (2003). Is there a glass ceiling in Sweden? Journal of Labor Economics, 21(1), 145-177.
  • Angelov, N., Johansson, P., and Lindahl, E. (2016). Parenthood and the gender gap in pay. Journal of Labor Economics, 34(3), 545-579.
  • Black, S. E., and Strahan, P. E. (2001). The division of spoils: Rent-sharing and discrimination in a regulated industry. American Economic Review, 91(4), 814-831.
  • Boschini, A., Gunnarsson, K., and Roine, J. (2020). Women in top incomes: Evidence from Sweden 1971–2017. Journal of Public Economics, 181, 104-115.
  • Brainerd, E. (2017). The lasting effect of sex ratio imbalance on marriage and family: Evidence from World War II in Russia. The Review of Economics and Statistics, 99(2), 229-242.
  • Chattopadhyay, R., and Duflo, E. (2004). Women as policymakers: Evidence from a randomized policy experiment in India. Econometrica, 72(5), 1409-1443.
  • Criado Perez, C. (2020). Invisible women. Vintage, London.
  • Dahl, G. B., and Moretti, E. (2008). The demand for sons. Review of Economic Studies, 75(4), 1085-1120.
  • Goldin, C. (1990). Understanding the Gender Gap: An Economic History of American Women. Oxford University Press.
  • Kleven, H., Landais, C., and Søgaard, J. E. (2019). Children and gender inequality: Evidence from Denmark. American Economic Journal: Applied Economics, 11(4), 181-209.
  • Kuklinski, J. H., Sniderman, P. M., Knight, K., Piazza, T., Tetlock, P. E., Lawrence, G. R., & Mellers, B. (1997). Racial prejudice and attitudes toward affirmative action. American Journal of Political Science, 402-419.
  • Jayachandran, S., and Pande, R. (2017). Why are Indian children so short? The role of birth order and son preference. American Economic Review, 107(9), 2600-2629.
  • Peterman, A., Palermo, T. M., Handa, S., Seidenfeld, D., and Zambia Child Grant Program Evaluation Team (2018). List randomization for soliciting experience of intimate partner violence: Application to the evaluation of Zambia’s unconditional child grant program. Health Economics, 27(3), 622-628.

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.

Alcohol-Related Costs and Potential Gains from Prevention Measures in Latvia

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Latvia has the highest per capita registered alcohol consumption rate among EU and OECD countries (OECD, 2024). In this brief, we show that the total budgetary (direct) and non-budgetary (indirect) costs associated with alcohol consumption in Latvia in 2021 amounted to 1.3–1.8 percent of the GDP. Non-financial costs from alcohol abuse amounted to a loss of nearly 90 thousand years spent in good health and with a good quality of life. We assess the potential effects of five alcohol misuse prevention measures, all recognized by the World Health Organization (WHO) as effective in reducing harmful alcohol consumption – especially when implemented together. Our analysis focuses on the individual effects of each measure and shows that raising the minimum legal age for alcohol purchases and enforcing restrictions on alcohol advertising and marketing are likely to yield the largest reductions in alcohol-related costs, although these effects will take time to fully materialize.

Introduction

Alcohol consumption is an important risk factor for morbidity and premature death worldwide. It is associated with over 200 diagnoses recorded in the International Statistical Classification of Diseases and Related Health Problems (CDC, 2021), including liver diseases, injuries, malignancies, and diseases of the heart and circulatory system (WHO, 2018). Alcohol consumption at any level is considered unsafe (Burton & Sheron, 2018).

Globally, an average of 3 million people die each year due to alcohol-related harm, accounting for 5.3 percent of all deaths (Shield et al., 2020). In 2019, alcohol consumption was the main risk factor for disease burden in people between 25 and 49 years of age and the second most important risk factor in people aged 10-24 years (GDB, 2019).

Alcohol use is associated not only with health problems but also with social issues, posing risks to people’s safety and well-being. It causes harm not only to the individual but also to family members and society at large (Rehm & Hingson, 2013). Various sectors, including health, justice, home affairs, and social care agencies, are involved in preventing the consequences of alcohol misuse and reducing the harm this causes. This demonstrates the multiple negative impacts of alcohol use on public health and well-being (Flynn & Wells, 2013).

Latvia has the highest per capita registered alcohol consumption rate among the EU and OECD countries (OECD, 2024), and no clear trend of declining levels has been observed in recent years. Moreover, the consumption of spirits, which can potentially cause more harm than other alcoholic beverages (Mäkelä et al., 2011), is steadily increasing. According to WHO data (WHO, 2024), the high per capita consumption of registered absolute alcohol in Latvia, compared to other countries, is largely due to the consumption of spirits. In Latvia, the share of spirits in total consumption is around 40 percent. By comparison, in the Czech Republic and Austria, where total per capita alcohol consumption is similar to Latvian levels, spirits account for only 25 and 16 percent of total consumption, respectively, while the proportions of beer and wine are higher.

This policy brief reports the estimated costs related to alcohol use in Latvia in 2021, based on the study Alcohol Use, its Consequences, and the Economic Benefits of Prevention Measures (Pļuta et al., 2023). It also provides an overview of the expected benefits from implementing preventive measures, such as raising the minimum legal age for buying alcohol and restricting alcohol advertisements.

Costs of Alcohol Use in Latvia

We estimate three types of costs associated with alcohol consumption:

  • Direct costs: These include budgetary costs related to alcohol consumption, such as healthcare, law enforcement and social assistance costs, as well as expenses for public education.
  • Indirect costs: These costs represent unproduced output in the economy and arise from the premature deaths of alcohol users, as well as their reduced employment or lower productivity.
  • Non-financial welfare costs: This type of cost arises from the compromised quality of life of alcohol users, their families, and friends.

We estimate direct costs by utilizing detailed disaggregated data on alcohol-related budget costs in the healthcare sector, law enforcement institutions (including police, courts, and prisons), costs of public education (e.g., educating schoolchildren about the consequences of alcohol consumption), costs of awareness-raising campaigns, and social assistance costs. For cost categories that are only partially attributable to alcohol consumption, we classify only a fraction of these costs as attributable to alcohol use (e.g., liver cirrhosis is attributable to alcohol usage in 69.8 percent of the cases, so only this fraction of the budget costs on compensated medicaments is attributable to alcohol use). To estimate social assistance costs, including public expenditure on social services, sobering-up facilities, social care centres, orphanages, and specialized care facilities for children and out-of-family care, we conduct a survey among social assistance providers.

To estimate non-budgetary costs, we construct a counterfactual scenario where alcohol is not being overly consumed, ensuring higher productivity, a lower rate of unemployment, and lower mortality within the labour force. Finally, non-financial welfare costs are estimated by measuring the reduction in quality of life or QALYs lost (quality-adjusted-life-years) (for details, see the methodology section in Pļuta et al. (2023)).

The total direct and indirect costs of alcohol abuse in 2021 amounted to 1.3–1.8 percent of Latvia’s GDP. In comparison, revenues from the excise tax on alcoholic beverages in 2021 accounted for 0.7 percent of the GDP.

Direct costs, which entail expenses directly covered by the state budget, comprised 0.45 percent of the GDP. Among these costs, healthcare expenses were the largest component, constituting 37.8 percent  of total direct costs and 2.7 percent of general government spending on healthcare. Nearly half of these healthcare costs were attributed to the provision of inpatient hospital treatment for patients diagnosed with alcohol-related conditions. Another significant component of budgetary costs is associated with addressing alcohol abuse and combating illicit trade through law enforcement, accounting for 31.9 percent of total direct costs and 6.5 percent of general government spending on public order and safety.

Alcohol-related indirect costs amount to 0.9-1.3 percent of Latvia’s GDP. Despite not being directly covered by the state budget, they represent unproduced output and thus entail economic losses. The primary components of these indirect costs are linked to decreased output resulting from higher unemployment and reduced economic activity (0.6-0.8 percent of the GDP), as well as decreased output due to premature death among heavy drinkers (0.2-0.4 percent of the GDP). Notably, indirect costs attributed to alcohol misuse by males constitute almost two-thirds of the total indirect costs.

Finally, the non-financial costs from alcohol abuse in 2021 are estimated to reach 88 620 years spent in good health and with a good quality of life. These losses primarily stem from the distress experienced by household members from alcohol users, the decline in the quality of life among alcohol users themselves, and the premature mortality of such individuals.

The Effects of Preventive Measures

We consider five alcohol misuse preventive measures, all of which are included in the list of WHO “best buys” policies that effectively reduce alcohol consumption (WHO, 2017):

  • Reducing the availability of retail alcohol by tightening restrictions on on-site retail hours
  • Raising the minimum legal age for alcohol purchase from 18 to 20 years
  • Increasing excise tax on alcohol
  • Lowering the maximum allowed blood alcohol concentration limit for all drivers from 0.5 to 0.2 per mille (currently 0.2 for new drivers and 0.5 for all other drivers)
  • Restricting alcohol advertising and marketing

Our estimates of the expected reduction in alcohol-related costs resulting from these measures are based on two main components:

  • (1) our own estimates of alcohol-related costs in Latvia, as described above, and
  • (2) external estimates of the impact of the five misuse preventative measures on alcohol consumption derived from existing literature on other countries.

We then apply these external estimates to the calculated alcohol-related costs and Latvian data on alcohol consumption to determine the estimated impact for Latvia (for further details, see the methodology outlined in Pluta et al. (2023)).

Our findings indicate that the most substantial reduction in direct costs attributed to alcohol misuse is anticipated through raising the minimum alcohol purchase age to 20 years (yielding an 11.4-15.8 percent estimated cost reduction). Previous literature has shown that early initiation of alcohol use significantly increases the likelihood of risky drinking, and that risky drinking during adolescence significantly increases the risk of heavy drinking in adulthood (Betts et al., 2018; McCarty, 2004). Hence, raising the minimum legal age for alcohol purchase represents an effective tool to reduce alcohol consumption also among the adult population.

Another highly effective measure to reduce alcohol consumption is imposing restrictions on advertising, which results in a 5.0-8.0 percent estimated reduction of direct costs. There is a large body of literature indicating that alcohol advertising increases alcohol consumption among young people, as well as significantly increases the likelihood of alcohol initiation among adolescents and young adults (Noel, 2019; Jernigan et al., 2017). Also, among the adult population, alcohol consumption decreases with stricter advertising restrictions (see Casswell, 2022; Rossow, 2021).

However, it is important to emphasize that the full impact of both above discussed preventative measures will only manifest in the long run.

The Effect of Illicit Markets

It is often argued that illicit alcohol markets, which provide access to cheaper alternative alcohol than registered commercial markets, can limit the effectiveness of preventive measures on overall alcohol consumption (Rehm et al., 2022).

To explore the interplay between illicit alcohol circulation and alcoholism prevention measures we conduct semi-structured interviews with experts regarding the prevalence of illicit alcohol circulation in Latvia and strategies to mitigate it.

While our main findings emphasize the inherent challenge of precisely quantifying the size of the illicit alcohol market, our analysis suggests that the share of illicit alcohol in total alcohol consumption in Latvia is relatively low. We also conclude that the size of the illicit alcohol market has been diminishing in recent years, and that public interest in engaging with illicit alcohol is declining. Given these findings, the current scope of the illicit market is unlikely to substantially undermine the efficacy of alcohol control measures. This is especially true as the consumers of illicit alcohol represent a specific group minimally affected by legal alcohol control measures in the country.

Conclusion

Our findings underscore the substantial costs associated with the large alcohol consumption in Latvia. In 2021, budgetary (direct) and non-budgetary (indirect) costs reached 1.3–1.8 percent of Latvia’s GDP. Furthermore, non-financial costs from alcohol abuse represent a loss of nearly 90 thousand years spent in good health and with a good quality of life.

Furthermore, non-financial costs from alcohol abuse represent a loss of nearly 90 thousand years spent in good health and with a good quality of life. This stems primarily from the distress experienced by alcohol users’ household members, and the decline in life quality and premature mortality among users themselves.

Latvia stands out as a country with exceptionally high levels of absolute alcohol consumption per capita compared to other countries. Policy makers should implement effective preventive measures against alcohol consumption to maintain the sustainability of a healthy and productive society in Latvia.

Acknowledgement

This brief is based on a study Alcohol Use, its Consequences, and the Economic Benefits of Prevention Measures completed by BICEPS researchers in 2023, commissioned by the Health Ministry of Latvia (Pļuta et al., 2023).

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Trending? Social Media Attention on Russia’s War in Ukraine

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Russia’s invasion of Ukraine is one of the most important geopolitical events of the 21st century. For almost two years, international news outlets have been covering the war, often providing daily or even hourly updates. But what is the level of public interest and public engagement in countries around the world? When does the war capture an international audience’s attention and what are the events that supplant it? This brief uses data on X (formerly Twitter) trends in 62 countries to address these questions.

The competition for attention is a defining feature of our information landscape. From the relentless stream of social media updates to the myriad of news articles vying for our clicks, individuals are constantly bombarded with information, each competing for a slice of their limited attention. Amidst this cacophony of voices, certain topics rise to the forefront, capturing the collective consciousness and dominating public discourse.

Russia’s war in Ukraine has, for obvious reasons, commanded significant media coverage over the past two years. It has been described as a hybrid war, where conventional military tactics are increasingly combined with non-traditional methods. This includes an information war, fought with narratives to manipulate people’s perceptions, spread falsehoods, or enlist support. To a large extent, this information war has taken place on social media. On the one hand, social media platforms have been used to spread disinformation and propaganda. For example, we’ve seen the spread of false narratives about the causes of the war, the actions of the different parties involved, and the suffering of the Ukrainian people. But on the other hand, social media has also been used to counter this disinformation, with fact-checking initiatives and grassroots efforts to promote accurate information.

This policy brief analyses the prominence of the war in social media discourse. While the content on traditional media outlets provides a snapshot of the supply of information, platforms like X/Twitter offer a unique window into the broader population’s demand for that information and how they evolve over time. Whether or not hashtags related to Russia’s war in Ukraine are trending in a given country, depends not just on the public’s interest in the war relative to other events in the news, but also on the level of interest relative to sport, music, television, and cats. By tracking the prevalence of trending hashtags, we can gain insights into the public’s engagement with Russia’s war in Ukraine, going beyond traditional media narratives and high-level governmental discussions to uncover the conversations and sentiments that shape broader public opinion.

The X/Twitter data suggest that in most countries, social media attention in the Russian war on Ukraine has been short-lived and sporadic. On February 24, 2022, Ukraine-related hashtags were trending in 100 percent of the countries in our dataset. Two weeks later, on March 9, 2022, they were trending in only 3 percent of the countries. We find that geographical proximity to the conflict is a strong predictor of social media interest. Related hashtags trend most frequently in Eastern, Central and Northern Europe. We also document spikes in interest around events that link a country to the war in Ukraine: announcements of military assistance or visits by Ukraine’s President Volodymyr Zelenskyj. Finally, we compare the hashtags trending in NATO countries to those trending in countries that either sided with Russia or abstained from voting in a critical UN resolution and show significant differences between the two groups.

Data and Methodology

The source for our dataset is archive.twitter-trending.com – a website that records trending hashtags on X/Twitter across countries and over time. We scrape this website to collect (i) the five highest volume topics in each country on each day and (ii) the five longest-trending topics in each country on each day (these two categories can overlap). Our sample consists of the 62 countries available on the website and covers the timeframe July 2021 to December 2023. From this, we construct a country-by-day panel dataset with 55,862 observations.

We identify 11 topic categories that collectively account for the overwhelming majority of trending topics related to Russia’s war in Ukraine. These topics and their relative frequency are shown in Figure 1. The three dominant categories are “Ukraine”, “Russia” and ”Putin”. We use Google’s translation software to translate non-English tweets which account for a significant fraction of the dataset.

Figure 1. Frequency of hashtags in 11 category topics.

Note: This chart shows the number of times topics assigned to our 11 war-related categories were among the top five longest trending topics (in orange) or the top five highest volume topics (in blue) in any country on any day in our dataset. The source are data scraped from archive.twitter-trending.com

Figure 1 shows that it is more common for war-related topics to be among the highest volume topics on a given day than among the longest trending topics. This suggests that these topics attract a lot of interest in a narrow timeframe (e.g. when news breaks) but are relatively less likely to remain prominent over a whole day. Despite this difference, we find that the distinction between highest-volume and longest-trending does not affect any of the patterns we observe when comparing across countries or time. For simplicity, the results shown below all use the highest-volume measure.

It is important to acknowledge the limitations of the X/Twitter data. Firstly, the population actively using X/Twitter is not representative of the overall population. Secondly, the composition of users may differ across countries which complicates cross-country comparisons. Trending hashtags provide an indicator of public interest that is informative only because we do not have high frequency, nationally representative surveys that are comparable across countries. Finally, we are only able to observe the top-five hashtags in a country on any given day. In principle, a war-related topic could increase in absolute volume from one day to the next, while still being crowded out of the top five.

Geographic Variation in Attention

Social media attention to the war in Ukraine varies greatly across countries. The map in Figure 2 shows the proportion of days when any hashtag from the considered categories was among the top-five most tweeted, for each country in the database since the start of the war. Interest has, on average, been higher in Europe as well as in Anglo-Saxon countries. In contrast, other regions of the world exhibited less sustained interest, as indicated by the lower frequency of related hashtags among the top-five most tweeted topics.

Figure 2. Prevalence of war-related hashtags.

Note: The map shows the share of days on which war-related hashtags (in our 11 categories) were among the top five highest volume topics on X/Twitter between 24/02/2022 and 18/12/2023. Countries in white are not among the 62 countries in the dataset. The source are data scraped from archive.twitter-trending.com

To some extent, this heterogeneity is explained by distance. Figure 3 plots the frequency of war-related trends against geographical proximity to the conflict zone (represented by the distance from each country’s capital to the city of Kharkiv in eastern Ukraine, a major point of focus during the ongoing war). The relationship is clearly negative, suggesting that physical distance from the crisis reduces the intensity of online discourse and public interest. Unsurprisingly, the number of related trends is highest in countries directly or indirectly involved in the conflict – Ukraine, Russia, and Belarus – as well as in Latvia which borders both Russia and Belarus.

Figure 3. Frequency of war-related hashtags and distance from Kharkiv.

Note: The chart shows the log of the distance from each country’s capital city to the city of Kharkiv in km on the x-axis and the logged frequency of war-related topics among the top five highest volume topics in that country between 24/02/2022 and 18/12/2023 on the y-axis. The source are data scraped from archive.twitter-trending.com

Variation in Attention Over Time

Over the past two years, the war has sustained a relatively high intensity. By contrast, global attention on X/Twitter has been more sporadic, spiking around specific events. This is shown in Figure 4, which plots the day-to-day variation in the number of battle events as recorded by the Armed Conflict Location & Event Data Project (ACLED) (in blue) as well as the share of countries where war-related tweets are trending (in orange). Attention was highest at the time of the invasion in February 2022 and the days of the Wagner Group rebellion in June 2023. Overall, the correlation between twitter trends and conflict intensity is positive but relatively weak.

Figure 4. Frequency of war-related hashtags and intensity of conflict.

Note: The chart shows the number of daily battle events in Ukraine as classified by ACLED on the left axis (in blue) and the share of countries where war-related topics were trending on the respective day on the right axis (in orange). The sources are ACLED’s Ukraine conflict monitor and data scraped from archive.twitter-trending.com

Attention also reacts to other major global events. Figure 5 compares the number of top-five trending hashtags related to the categories of interest in each country on two specific dates: February 24, 2022, the day of Russia’s full-scale invasion of Ukraine, and October 7, 2023, the day of a Hamas terror attack on Israel. On the day of the Russian invasion, the majority of countries in our sample exhibited the highest value. In contrast, on the day of the Hamas attack, related hashtags were trending almost nowhere outside Ukraine and Russia, indicating that global attention and engagement with this new ongoing crisis significantly overshadowed the focus on the situation in Ukraine. This shift in attention demonstrates how breaking news can capture the public’s interest and divert focus from ongoing crises, affecting the level of engagement on social media and potentially influencing the global response and discourse surrounding these events.

Figure 5. Map of prevalence of war-related hashtags on two different dates.

Note: The maps show the share of the top five highest volume topics on twitter related to Russia’s war on Ukraine. The map on the left shows 24/02/2022 – the day of Russia’s invasion. The map on the right shows 07/10/2023 – the day of a Hamas terror attack on Israel. Countries in white are not among the 62 countries in the dataset. The source are data scraped from archive.twitter-trending.com

While some events impact attention globally, others affect the salience of the conflict for a specific country. Figure 6 shows that people pay more attention to the war when there is a tangible connection to their own country. The panel on the left shows that war-related topics were more likely to trend in a country around the days where the country announced an aid package for Ukraine (military, financial or humanitarian). It shows an increasing trend in the preceding days and a peak on the day of the announcement. The panel on the right shows that war-related topics were more likely to trend in a country around the days of a visit from President Zelenskyj. This effect is large in magnitude but only lasts for around three days.

Figure 6. Likelihood of hashtags trending in relation to country-specific event.

Note: The charts show variation in the share of countries where at least one war-related topic was among the top five highest volume topics on days relative to a specific event. In the left chart, day 0 represents the day on which a country’s government announces an aid package for Ukraine. In the right chart, day 0 represents the day on which President Zelenskyj arrived in a country for an official visit. The source for these charts are: (i) the Kiel Institute’s Ukraine Support Tracker (Trebesch et al., 2023), (ii) Wikipedia’s list of official visits by President Zelenskyj and (iii) data scraped from archive.twitter-trending.com

While the events above act as drivers of attention, it is also interesting to consider what causes war-related topics to drop out of the top five trending topics. We distinguish between two reasons why war-related hashtags could stop trending: (i) a loss of interest that results in a reduction in the absolute number of related tweets (ii) the rise of other topics that displace war-related tweets from the top five. Figure 7 focuses on days where war-related topics dropped out and compares the volume of tweets on the last day where they were in the top five, to the threshold they would have had to surpass in order to make the top five on the subsequent day. In cases where the threshold is lower than the previously observed volume of tweets (a ratio of less than 1), the topic would have kept trending had it sustained its volumes, and one can conclude there was an absolute loss of interest. In cases where the ratio is greater than one, it is possible that the topic sustained its previous volume of tweets but was crowded-out by the rise of a new trending topic. Figure 7plots the histogram of this ratio. 73 percent of the cases are in the first category (loss of attention) and 27 percent are in the possible crowding out category. This provides further evidence to suggest that attention to the war on social media is typically fleeting.

Figure 7. Loss of attention vs crowding out.

Note: The sample are country-days where war-related topics were among the top five highest-volume topics but then dropped out of the top five the next day. The chart provides a histogram of the ratio of the threshold for making the top five on the subsequent day to the highest volume of tweets of a war-related topic. Values below 1 (in blue) indicate that the volume was above the next day’s threshold and the topic declined in absolute terms. Values above 1 (in orange) indicate that the volume was below the next day’s threshold. The source are data scraped from archive.twitter-trending.com

We also examine the content of discussions on the first day after war-related hashtags drop out of the top five. The word cloud in Figure 8 suggests that on such days, people primarily discuss entertainment topics like music and football.

Figure 8. Word cloud of hashtags trending on days war-related categories drop out.

Note: The figure provides a word cloud of trending topics on country-days where no war-related topic was among the top five highest volume topics, but at least one war-related topic had been in the top five on the previous day. The source are data scraped from archive.twitter-trending.com

Content and Context of War-Related Discourse

In addition to providing insight into the level of engagement, hashtag analysis can also reveal the content and context of popular discourse surrounding the war. By examining words trending on the same days as those from our 11 categories, we can gain a better understanding of the topics people are discussing and how the conversation varies across different regions. Figure 9 illustrates this through word clouds, showing the language used in NATO countries on the left and in countries that abstained or voted against the United Nations General Assembly Resolution ES-11/1 on the right. This resolution, dated March 2, 2022, condemned the brutal invasion of Ukraine and demanded that Russia immediately withdraw its forces and comply with international law.

This exercise allows us to compare the dominant themes and narratives in these two groups of countries and observe any differences in public perception and discourse regarding the conflict. The prevalence of cryptocurrency and NFT (non-fungible tokens) references in the word cloud on the right is suggestive of how economic interests and alternative financial systems could be relevant for the positions of countries that abstained or voted against the resolution, and how this might affect their involvement or response to the conflict. On the left, words like “NATO”, ”Biden”,  and ”Trump” clearly stand out, suggesting that these topics are central to the discourse on the war in NATO countries. This could indicate a focus on geopolitical alliances, international cooperation, and the role of key political figures in shaping the response to the conflict. Interestingly, “Putin” is very prominent in the left word cloud while “Russia” and “Russian” are more prominent on the right. This could indicate that Putin is seen and discussed as the primary antagonist in NATO countries.

Figure 9. Word cloud of hashtags in NATO countries vs Russia-friendly countries.

Note: These word clouds represent topics that trend on days where at least one war-related topic is trending in the respective country. The cloud on the left shows NATO countries. The cloud on the right shows countries that either abstained or voted against United Nations General Assembly Resolution ES-11/1. The source are data scraped from archive.twitter-trending.com

Conclusion

This brief uses X/Twitter trends as a barometer of public interest in Russia’s war in Ukraine. We show how attention fluctuates over time in response to developments in the conflict, to other breaking news, and to local events that make the conflict salient for a domestic audience. We also provide descriptive evidence on the variation across geographical regions and among different groups of countries. Additionally, we analyse the instance where Ukraine-related topics stop trending and find suggestive evidence that this is typically due to a gradual loss of interest rather than crowding out by new distracting trends.

Public attention and engagement drive policy in democratic countries, and the sustained support of its democratic allies is vital for Ukraine during this critical time. Understanding the patterns and influences of public attention is crucial for developing effective strategies to sustain engagement and support. This can be achieved for example by regularly highlighting the ongoing significance and bearing of Russia’s war against Ukraine, even as other events dominate the headlines. Emphasizing the impact of the conflict on individuals and communities, as well as its broader implications for international relations and global security, can help sustain public interest and engagement.

References

  • ACLED. Ukraine Conflict Monitor. https://acleddata.com/ukraine-conflict-monitor/
  • Trebesch, C., Antezza, A., Bushnell, K., Bomprezzi, P., Dyussimbinov, Y., Frank, A., Frank, P., Franz, L., Kharitonov, I., Kumar, B., Rebinskaya, E., Schade, C., Schramm, S., and Weiser, L. (2023). The Ukraine Support Tracker: Which countries help Ukraine and how? Kiel Working Paper, 2218, 1-75.
  • Twitter Trending Archive. Scraped on ##/12/2023. https://archive.twitter-trending.com/

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.

Monetary Policy in Belarus Since Mid-2020: From Rules to Discretion

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The most important “safeguard” against negative consequences from government’s economic policy mistakes is an independent monetary policy aimed at maintaining inflation near a pre-announced target and smoothing out short-term fluctuations. In Belarus, various monetary policy regimes have been employed and, for most of history, the ability of the National Bank of Belarus to set goals and deploy monetary policy instruments without government intervention has been limited. As a result, monetary policy in Belarus tend to exacerbate negative shocks to the Belarusian economy rather than play a stabilizing role. Since mid-2020, the National Bank has de facto lost operational and institutional independence, and monetary policy has become discretionary – focused on stimulating economic activity. As of 2024 this discretionary and expansionary monetary policy has increasingly come into conflict with the need to ensure macroeconomic stability.

Monetary Policy Design in Belarus: Developments in the Last Decade

Since 2015, the National Bank of Belarus (henceforth the National Bank) has declared its monetary policy regime to be monetary targeting. The primary goal of such policy is price stability, while the intermediate target is broad money supply growth. However, research results show that monetary targeting was employed only until mid-2016. From mid-2016 to mid-2020, the National Bank implicitly employed flexible inflation targeting (Kharitonchik, 2023b).

In mid-2020 the National Bank de facto lost its operational independence as the bank was no longer in control of the rules concerning monetary policy (Kharitonchik, 2023a). In 2022-2024, among other things, targets were set for inflation, the growth of the ruble monetary base, broad money supply, the banks’ claims on the economy, and the refinancing rate level. Thus, the National Bank seeks to simultaneously control both the volume of money in the economy and the prices. This is, in practice, expressed in the implementation of discretionary and situational monetary policy.

Under pressure from the government, the National Bank’s monetary policy has since mid-2020 focused on stimulating economic activity, with a high degree of tolerance to inflationary risks. After the US, EU, UK, and several other countries imposed strict sanctions on Belarus in the beginning of 2022, the government’s pressure on the National bank to support economic activity increased even further.

Since October 2022, the only inflation regulator has been strict price controls, exercised by the government in the form of a system of price regulations for approximately 85 percent of the items in the consumer basket. According to the system, manufacturers are obliged to coordinate wholesale prices with government authorities and retailers were in Q4 2022 forced to adjust prices. The system has been modified several times, but as of 2024, it continues to operate in an extremely rigorous version.

Besides the erosion of operational independence, the recent years have been characterized by a marked decline in the institutional framework for executing monetary policy. Aspirations to enhance transparency and accountability of the National Bank to the public seem to be history, at least for the time being. The frequency of the bank’s communication has decreased significantly and its content, as well as the bank’s published data and analytical materials have deteriorated. There are no longer any National Bank briefings on the outcomes of its board meetings, nor are there clear explanations of the decisions made or meetings with the expert community.

The National Bank also introduces uncertainty and undermines confidence in its policies with its strange approach to setting and announcing inflation targets. The increased inflation target, from the previous 5, to 7-8 percent for 2023 is unexplained, the explicit inflation target for 2024 was not presented until the end of August 2023, and the medium-term inflation target is nonexistent. Under such conditions, investment planning and forecasting becomes challenging, necessitating substantial efforts to rebuild trust in monetary authorities for the future.

Figure 1. Inflation and inflation targets in Belarus, 2015–2023.

Source: Author’s estimates based on data from Belstat and the National Bank of Belarus.

Between 2020 and 2023, the National Bank was unable to effectively implement monetary policy in a coordinated manner, falling short in achieving de jure primary and intermediate targets. Thus, inflation in 2020–2022 was significantly higher than targeted levels, while the money supply growth was close to the lower bound of its target range (see Figure 1 and 2).

Figure 2. Broad money growth and its target in Belarus, 2015–2023.

Source: Author’s estimates based on data from the National Bank of Belarus.

In 2023, the inflation was below its target due to total price controls, while money supply growth was twice its target (see Figure 2). Such targeted monetary policy dynamics indicate the instability of the economy’s demand for money and the money multiplier, the instability and poor predictability of money velocity in the face of shocks to the Belarusian economy, as well as the lack (or inability) of a strict commitment by the National Bank to achieve the primary goal of monetary policy.

Monetary Conditions Between 2020 and 2023

Monetary conditions are calculated as a weighted combination of deviations of real interest rates on assets in Belarusian rubles and the real effective exchange rate of the Belarusian ruble from their equilibrium levels. As detailed in Figure 3, the monetary conditions for 2020–2023 are considered stimulative for economic activity and pro-inflationary.

Figure 3. Monetary conditions in Belarus,2015–2023.

Source: Author’s estimates based on QPM BEROC (Kharitonchik, 2023b).
Note: Monetary conditions are estimated as a combination of deviations of real interest rates on the Belarusian ruble assets and of the real effective Belarusian ruble exchange rate from their equilibrium (or inflation-neutral) levels (assessed within the model). Positive monetary condition values indicate their restraining-economic-activity and disinflationary stance, and negative monetary condition values indicate their stimulating and pro-inflationary stance.

In 2020, the soft monetary conditions (the combined effect of interest rates and the exchange rate on the economy) were determined by the behavior of the exchange rate. The Belarusian ruble weakened significantly and became undervalued in 2020 due to a sharp increase in demand for foreign currency at the onset of the Covid-19 pandemic in Belarus and following the presidential elections in August 2020.

As a result of the National Bank’s discretionary monetary policy, interest rates’ volatility significantly increased. A deterioration of the liquidity situation in the banking system and increased risks to the economy during the acute phase of the socio-political crisis in 2020 resulted in interest rates restraining economic activity in September-December 2020.

In 2021, there was a notable improvement in the economic situation in Belarus compared to the crisis experienced in 2020. External demand for Belarusian goods and services rose, and export prices increased significantly which contributed to an increase in foreign currency earnings. As a result, the undervaluation of the Belarusian ruble neutralized during 2021, the banking system moved to a liquidity surplus, and interest rates decreased noticeably, creating soft monetary conditions (see Figure 3).

In 2022–2023, monetary conditions became even softer against the backdrop of increasing priority for the National Bank to support economic activity over inflation containment. The Belarusian ruble again became undervalued which increased foreign trade and allowed for the banking system’s liquidity surplus to expand significantly (see Figure 4).

The realization of a substantial liquidity surplus in 2022 resulted from the National Bank’s active emission policy, likely associated with considerable government pressure. The National Bank injected at least 1.7 billion Belarusian rubles (0.9 percent of GDP) into the financial system through lending to non-deposit financial organizations in 2022, and more than 1.9 billion Belarusian rubles (1 percent of GDP) in 2022 and 1.1 billion Belarusian rubles (0.5 percent of GDP) in 2023, through the purchase of government bonds on the secondary market.

Figure 4. Banking system liquidity in Belarus, 2017–2023.

Source: Author’s estimates based on data from Belstat and the National Bank of Belarus.

Under a colossal and stable liquidity surplus – not withdrawn by the National Bank – interest rates in the money and credit-deposit markets, in 2022-2023, repeatedly reached historically low levels in nominal terms, and in real terms remained  significantly below their equilibrium levels (see Figure 3).

The Monetary Conditions’ Impact on Economic Activity and Inflation in Belarus, 2022–2024

Under loose monetary conditions there was a significant strengthening of the credit impulse (share of new loans in GDP) from Q3 2022 and onwards (BEROC, 2023). In this environment of increased credit activity, the money supply grew at a rapid pace in the second half of 2022–2023 (see Figure 2). The money supply growth significantly exceeded an inflation-neutral pace and by the end of 2023, the volume of real money supply exceeded the inflation-neutral level by almost 10 percent.

Expansionary monetary policy was one of the drivers of the rapid economic recovery in the second half of 2022–2023. The negative output gap, which widened in Q2 2022, following increased sanctions against Belarus, was offset in Q1 2023. Moreover, in Q2–Q4 2023, GDP surpassed its equilibrium level (see Figure 5).

Figure 5. Output gap decomposition in Belarus, 2015–2023.

Source: Author’s estimates based on QPM BEROC (Kharitonchik, 2023b).
Note: The output gap is the deviation of real GDP from its potential (or equilibrium) level, where potential is understood as such a volume of GDP that does not exert any additional pro-inflationary or disinflationary pressure.

By 2024, the Belarusian economy reached a state of moderate overheating (see Figure 5). Currently, loose monetary policy fuels demand but the ability to adjust supply to increased demand levels is limited under sanctions and labor shortages. This mismatch between supply and demand would normally lead to a significant acceleration of inflation. However, due to the strict price controls, this is yet to realize. In fact, inflation reached a historically low value of 2.0 percent Year over Year (YoY), in September 2023. Nonetheless, inflation in Belarus began to accelerate in Q4 in 2023 and amounted to 5.8 percent YoY at the end of the year. In an environment of excess demand and a shortage of workers, firms’ costs rise and translate into higher selling prices, albeit on a limited scale and with a time lag due to the price controls (see Figure 6).

Figure 6. CPI inflation in Belarus, 2015–2023.

Source: Author’s estimates based on data from Belstat. Calculations based on QPM BEROC (Kharitonchik, 2023b).

A prolonged combination of total price controls and loose monetary policy leads to an inflationary overhang – the potential for delayed accelerated price growth. Inflation overhang is a highly undesirable phenomenon since it increases the risk of a price surge in the future and the need for a sharp and aggressive tightening of monetary policy. The inflationary overhang in Belarus is estimated at 5–9 percent (for the end of 2023).  This means that there is a risk of a sharp increase in prices by 5-9 percent if price controls are removed or significantly relaxed.

Conclusion

Since mid-2020, the National Bank has de facto lost its operational independence, and monetary policy has become discretionary, focused on stimulating economic activity. By the beginning of 2024, this discretionary and overly loose monetary policy has increasing come into conflict with the task of ensuring macroeconomic stability.

The Belarusian economy enters 2024 in a state of low economic growth potential (about 1 percent per year) and an imbalance of supply and demand, which creates threats of intensified inflation and a decline in foreign trade.

Attempts by the authorities to artificially maintain high rates of GDP growth and low inflation through excessively stimulating economic policies and archaic price controls may lead to an economic overheating by the end of 2024 similar to the situations leading up to the currency crises in 2009, 2011 and 2015. Under such developments, the fragility of the economy and the likelihood of an economic crisis in Belarus will increase.

To prevent such negative development, it is critical to gradually normalize the monetary policy design in coordination with fiscal policy. Key recommendations from experts for a strengthening of the stabilizing role of monetary policy include ensuring the National Bank’s independence, eliminating discretionary and subjective policymaking, and outlining a clear hierarchy of monetary policy goals (Kruk, 2023).

Simulation results indicate that the use of flexible inflation targeting is the most preferable monetary policy strategy for Belarus under existing sanctions and internal and external capital control measures (as discussed in Kharitonchik, 2023a).

Lastly, as monetary policy is about managing expectations for which trust (i.e. credibility) plays a key role, restoring the public’s trust in the National Bank is essential. To achieve this, the National Bank needs to reestablish communication with the public and resume the publication of analytical and statistical reports, at a minimum matching the extent seen in early 2021.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.