Tag: Economic Growth

What Drives Belarus to Be One of the Most Optimistic Nations in Europe?

Posted on March 6, 2023 | Policy Brief

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War in Ukraine, imposed sanctions on Belarus and the worst yearly GDP drop since the 1990s. Despite these challenges, Belarusian households were the third most optimistic in Europe in late 2022, following Lithuania and Montenegro. The Belarusian Consumer Confidence Index, calculated on the basis of four household surveys conducted in Belarus by BEROC’s Belarus Monitoring Project in 2021 and 2022, shows surprising resilience among Belarusians – especially in Q3 and Q4, 2022.  This brief shortly describes the components of the index and their evolution and discusses what factors might have been driving this high index. The brief argues the found optimism among Belarusians could have been driven by a state-owned media influence and by the Belarusian economy performing better than expected.

Optimism Without Grounds?

In 2022, Belarus experienced a 4.7 percent yearly GDP drop, the worst since the 1990s. The main reasons behind the decline is the Russian war on Ukraine and Belarus’ involvement in it, and, consequently, the severe sanctions imposed on Belarus and its main trade and economic partner: Russia. A surge of exports to Russia to counter the sanctions helped prevent the severity of the drop, although it still remains large. Forecasts for 2023 are also not encouraging. The World Bank expects the Belarusian economy to shrink by 2.3 percent. The European Bank for Reconstruction and Development’s forecast is -1 percent, the International Monetary Fund (IMF)’s is +0.2 percent, and the Eurasian Development Bank’s +0.3 percent, whereas the announced official target is +3.8 percent. In total, the GDP decrease could be as large as -6.9 percent in the two coming years, following the World Bank’s worst prognosis. The question is; Is this a lot?

The last GDP decline occurred in 2020 and amounted to a moderate -0.7 percent, despite the apex of Covid-19 related shutdowns, the decrease in the world economy, and the political crisis following the rigged elections in August. The most recent severe GDP drop happened between 2015 and 2016 with a decline of 3.8 percent in 2015 and 2.5 percent in 2016.

Figure 1. A comparison of GDP changes and the CCI values in Belarus in 2021 and 2022.

Note: Based on Eurostat methodology. Source: Belstat, BEROC.

Surprisingly, the lower the GDP, the higher the consumer confidence, as measured by the Consumer Confidence Index (CCI). For example, the CCI was -18.7 percent in Q4 2021, while the GDP increased by 2.3 percent in 2021. On the contrary, the CCI in Q4 2022 was -15.0 percent, while the GDP dropped by massive -4.7 percent (see Figure 1).

The experience from numerous financial crises in the 2010s may play an important role here by moving the expectation baseline and conclusively undermining confidence in the country’s economic institutions. However, even if this is the case, it would not explain the dynamics of consumer confidence in Belarus in relation to the country’s economic performance. In this brief we dig deeper into the determinants of this seemingly ungrounded consumer optimism.

The Consumer Confidence Index

The Consumer Confidence Index (CCI) used for this brief is based on four household surveys conducted in Belarus by the Belarusian Economic and Outreach Center (BEROC)’s Belarus Monitoring Project. The online surveys were conducted in December 2021, and in April, August and November in 2022. The surveys are representative for the urban population aged 18-64 (approximately 5 million people). They have also been weighted by region, sex and age.

The index is designed to measure consumer confidence from -100 percent to + 100 percent (0 being neutral). Consumer confidence is defined as the degree of optimism regarding the state of the economy which consumers express through their saving and spending patterns.

A few approaches for calculating the index can be used. One of them is the Eurostat methodology, which includes answers to four questions about households previous and expected financial position, the expected economic situation in the country, as well as the propensity to buy durable goods. Another approach is the Rosstat methodology, which, in addition to the Eurostat approach, includes one extra question on the previous economic situation in the country. We considered both methodologies to allow for a comparison of Belarus to countries in Europe as well as to Russia.

Belarus Compared to Russia

The CCI value, applying the Rosstat methodology, was -19.4 percent in Belarus in November 2022 (a 3.6 percentage point growth as compared to August 2022), while the index value in Russia was -22.7 percent (a 0.3 percentage point growth).

It is worth mentioning that there was a sharp drop in Q2 2022 in both countries. However, the index values recovered in Q3 2022 to Q4 2021 values, i.e., to the index values prior to the introduction of large-scale economic sanctions and prior to the war.

The pattern is somewhat similar to that during Covid-19-related restrictions, displaying a sharp drop and then a strong recovery. The magnitude of the drop was however much higher in 2020: 20.3 percent in 2020 compared to 10.3 percent in 2022 for Russia. No data is available for Belarus prior to Q4 2021 but the trajectory was likely similar. Apparently, households in neither country appear be desperate (see Graph 1).

Graph 1. The CCI in Belarus and Russia.

Note: Q1 2022 data not available for Belarus. Source: BEROC, Rosstat.

Belarus Compared to Europe

The Belarusian CCI, when excluding the component of the past state of the economy (i.e. applying the Eurostat methodology), was -15.0 percent in November 2022. This was 3.4 percentage points higher than the value in December 2021 and the third highest value in Europe, following Lithuania (-9.2 percent) and Montenegro (-8.6 percent). Moreover, the index was the highest observed for the entire period of observations by BEROC (from December 2021), as depicted in Graph 2.

Graph 2. The CCI in Belarus and the EU.

Source: BEROC, Eurostat.

The index values of the European Union and the Eurozone have not changed significantly from Q2 2022 and currently stand at -26.3 and -24.9 percent, respectively. Naturally, some countries have faced slight reductions, while others have seen slight increases, for instance, the indices for Italy, Croatia and Cyprus had all increased by more than 4 percentage points in Q4, 2022.

As evident from Graph 2, Belarus has since Q4 2021, moved from a below average position to become a leader in optimism on the European continent.

The Past and the Future

Throughout all four surveys, evaluations of the current state of the country and of personal wellbeing contrasted the projections for the future (see Graph 3). The projections for the future are much more positive, which is evident if we compare question 4 and 2 to question 3 and 1. At the same time, the share of negative answers is higher than the share of positive answers for all questions, and the term “optimism” should therefore be taken as the lack of strong negative views on the past and future.

A higher share of “difficult to say or do not know” answers is unsurprisingly found for questions regarding the future.

Graph 3. The composition of the CCI in Belarus for Q4 2022.

Note: All answers to the questions are distributed along a Likert scale from “will improve (has improved)” or “very good” to “will decline (has declined)” or “very bad”. For question 1 (Q1) and question 2 (Q2), the answer options range from “has improved” and “has declined”; and for question 5 (Q5), the answer options range from ”very good” to “very bad”. Source: BEROC.

The largest negative contribution to the index was the question on the current assessment of the country’s economic situation in relation to the previous year (question 1). The share of negative answers was 72 percent in December 2021, and it decreased only to 63 percent in November 2022, even though the economic performance prior to those periods was a 2.3 percent GDP growth and 4.7 percent GDP decline, respectively. Apparently, the worse the economy performed, the better was the perception of the past.

This is however not the case regarding the state of the household’s financial position. The share of negative answers was 48 and 47 percent, and the share of positive answers was 13 and 14 percent in December 2021 and November 2022, respectively.

The answers concerning the future standing of the economy and one’s personal financial position follow the same logic, with large disparities between the evaluation of the country’s economy – which one is negative about – and personal finances – where respondents are more optimistic.

What could influence the changes? We hypothesize that there are at least four possible explanations for the improvement in the CCI from Q1 to Q4, 2022:

a) a stabilization of the situation on the foreign exchange market
b) a slowing GDP decline, reaching a “local minimum”
c) an influence from Belarusian and Russian state-owned mass media outlets
d) failed negative expectations in previous periods

As discussed in a previous FREE Network Policy Brief by Luzgina (2022), the Belarusian currency market has stabilized since April, 2022. The Belarusian exchange rate is somewhat of a “Holy Grail” and a crucial factor for Belarusians after numerous financial crises in 2010s, so its stabilization could act as a positive signal for households. Indeed, when asking respondents about the factors influencing their income, the share of those who attributed this to the exchange rate had in August 2022 decreased by 25 percentage points, as compared to April the same year (from 45 to 20 percent, respectively).

The GDP decline slowed in the second half of 2022, from -4.9 percent in August to -4,7 percent in November. An additional positive development for Belarusians was that the inflation declined in November.

Media consumption is another essential factor in understanding consumer confidence. State-owned and independent media consumers showed significant differences in their assessments of the economy. Only 22 percent of state-owned media consumers rated the economy as “bad” or “very bad” compared to 68 percent of independent media consumers.

In April 2022, the World Bank estimated a possible Belarusian GDP change at -6.5 percent, the IMF
-6.4 percent and S&P -15 percent. The CCI in April was also at the lowest throughout BEROC’s observations at -23.0 percent. Despite these extremely negative forecasts for Belarus’ GDP, the actual outcomes were less catastrophic than expected. This might have improved respondents’ assessment of the future economic situation.

Conclusion

Data from the online household surveys show that imposed sanctions, the Russian war on Ukraine, and a declining economic growth in 2022 have not yet significantly affected the sentiments of Belarusians on a large scale. Rather, Belarusians’ expectations have improved despite serious current and future challenges to the Belarusian economy. In fact, Belarus is among the most optimistic nations in Europe, according to the surveys.

This is arguably due to a financial stabilization and an economic performance above expected, as well as exposure to state-owned media.

With this in mind, we may see an increase in households’ consumption in the following months, which will contribute to a slowdown in the GDP decline or even a slight economic recovery in 2023 – pending no new shocks occur.

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.

Vaccination Progress and the Opening Up of Economies

Posted on July 5, 2021 | Policy Brief

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In this brief, we report on the FREE network webinar on the state of vaccinations and the challenges ahead for opening up economies while containing the pandemic, held on June 22, 2021. The current state of the pandemic in each respective country was presented, suggesting that infection rates have gone down quite substantially recently in all countries of the network, except in Russia which is currently facing a surge in infections driven by the delta-version of the virus. Vaccination progress is very uneven, limited by lacking access to vaccines (primarily Ukraine and Georgia) and vaccine scepticism among the population (primarily in Russia and Belarus but for certain groups also in Latvia, Poland and to some extent Sweden). This also creates challenges for governments eager to open their societies to benefit their economies and ease the social consequences of the restrictions on mobility and social gatherings. Finally, the medium to long term consequences for labour markets reveal challenges but also potential opportunities through wider availability of work–from-home policies. 

Background

In many countries in Europe, citizens and governments are starting to see an end to the most intense impact of the Covid-19 pandemic on their societies. Infection and death rates are coming down and governments are starting to put in place policies for a gradual opening up of societies, as reflected in the Covid-19 stringency index developed by Oxford University. These developments are partially seasonal, but also largely a function of the progress of vaccination programs reaching an increasing share of the adult population. These developments, though, are taking place to different degrees and at different pace across countries.  This is very evident at a global level, but also within Europe and among the countries represented in the FREE network. This has implications for the development within Europe as a whole, but also for the persistent inequalities we see across countries.   

Short overview of the current situation

The current epidemiological situation in Latvia, Sweden, Ukraine, and Georgia looks pretty similar in terms of Covid-19 cases and deaths but when it comes to the vaccination status there is substantial variation.

Latvia experienced a somewhat weaker third wave in the spring of 2021 after being hit badly in the second wave during the fall and winter of 2020 (see Figure 1). The Latvian government started vaccinating at the beginning of 2021, and by early June, 26% of the Latvian population had been fully vaccinated.

Sweden, that chose a somewhat controversial strategy to the pandemic built on individual responsibility, had reached almost 15 thousand Covid-19 deaths by the end of June of 2021, the second highest among the FREE network member countries relative to population size. The spread of the pandemic has slowed down substantially, though, during the early summer, and the percentage of fully vaccinated is about to reach 30% of the population.

Figure 1. Cumulative Covid-19 deaths 

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

Following a severe second wave, the number of infected in Ukraine started to go down in the winter of 2020, with the total deaths settling at about 27 thousand in the month of February. Then the third wave hit in the spring, but the number of new daily cases has decreased again and is currently three times lower than at the beginning of the lastwave. However, a large part of the reduction is likely not thanks to successful epidemiological policies but rather due to low detection rates and seasonal variation. 

In June 2021, Georgia faces a similar situation as Ukraine and Latvia, with the number of cumulative Covid-19 deaths per million inhabitants reaching around 1300 (in total 2500 people) following a rather detrimental spring 2021 wave. At the moment, both Georgia and Ukraine have very low vaccination coverage relative to other countries in the region(see Figure 5).

In contrast to the above countries, Russia started vaccinating early. Unfortunately, the country is now experiencing an increase in the number of cases (as can be seen in Figure 2), contrary to most other countries in the region. This negative development is likely due to the fact that the new Covid-19 delta variant is spreading in the country, particularly in Moscow and St. Petersburg. Despite the early start to vaccinations, though, the total number of vaccinated people remains low, only reaching 10.5% of the population.

Figure 2. New Covid-19 cases

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

In some ways similar to Sweden, the government of Belarus did not impose any formal restrictions on individuals’ mobility. According to the official statistics, in the month of June, the rise in the cumulative number of covid-19 deaths and new daily infections has declined rapidly and reached about 400 deceased and 800 infections per one million inhabitants, respectively. Vaccination goes slowly, and by now, around 8% of the population has gotten the first dose and 5% have received the second.

There were two major waves in Poland during the autumn 2020 and spring 2021. In the latter period, the country experienced a vast number of deaths.  As can be seen in Figure 3, the excess mortality P-score – the percentage difference between the weekly number of deaths in 2020-2021 and the average number of deaths over the years 2015-2019 – peaked in November 2020, reaching approximately 115%. The excess deaths numbers in Poland were also the highest among the FREE Network countries in the Spring of 2021, culminating at about 70% higher compared to the baseline. By mid-June, the number of deaths and cases have steeply declined and 36% of the country’s population is fully vaccinated.

Figure 3. Excess deaths

Turning to the economy, after a devastating year, almost all countries are expected to bounce back by the end of 2021 according to the IMF (see Figure 4). Much of these predictions build on the expectations that governments across the region will lift Covid-19 restrictions. These forecasts may not be unrealistic for the countries where vaccinations have come relatively far and restrictions have started to ease. However, for countries where vaccination rates remain low and new variations of the virus is spreading, the downside risk is still very present, and forecasts contain much uncertainty.

 Figure 4. GDP-growth

Vaccination challenges

Since immunization plays such a central role in re-opening the economy and society going back to normal, issues related to vaccinations were an important and recurring topic at the event. The variation in progress and speed is substantial across the countries, though.

Ukraine and Georgia are still facing big challenges with vaccine availability and have fully vaccinated only 1.3% and 2.3% of the population by the end of June, respectively. Vaccination rates have in the recent month started to pick up, but both countries face an uphill battle before reaching levels close to the more successful countries.

Figure 5. Percent fully vaccinated

Other countries a bit further ahead in the vaccine race are still facing difficulties in increasing the vaccination coverage, though not so much due to lack of availability but instead because of vaccine skepticism. In Belarus, a country that initially had bottleneck issues similar to Ukraine and Georgia, all citizens have the opportunity to get vaccinated. However, Lev Lvovskiy, Senior Research Fellow at BEROC in Belarus, argued that vaccination rates are still low largely because many Belarusians feel reluctant towards the vaccine at offer (Sputnik V).

This vaccination scepticism turns out to be a common theme in many countries. According to different survey results presented by the participants at the webinar, the percentage of people willing or planning to get vaccinated is 30% in Belarus and 44% in Russia. In Latvia, this number also varies significantly across different groups as vaccination rates are significantly lower among older age cohorts and in regions with a higher share of Russian-speaking residents, according to Sergejs Gubins, Research Fellow at BICEPS in Latvia.

Webinar participants discussed potential solutions to these issues. First, there seemed to be consensus that offering people the opportunity to choose which vaccine they get will likely be effective in increasing the uptake rate. Second, governments need to improve their communication regarding the benefits of vaccinations to the public. Several countries in the region, such as Poland and Belarus, have had statements made by officials that deviate from one another, potentially harming the government’s credibility with regards to vaccine recommendations. In Belarus, there have even been government sponsored disinformation campaigns against particular vaccines. In Latvia, the main problem is rather the need to reach and convince groups who are generally more reluctant to get vaccinated. Iurii Ganychenko, Senior Researcher at KSE in Ukraine, exemplified how Ukraine has attempted to overcome this problem by launching campaigns specifically designed to persuade certain age cohorts to get vaccinated. Natalya Volchkova, Director of CEFIR at NES in Russia, argued that new, more modern channels of information, such as professional influencers, need to be explored and that the current model of information delivery is not working.

Giorgi Papava, Lead Economist at ISET PI in Georgia, suggested that researchers can contribute to solving vaccine uptake issues by studying incentive mechanisms such as monetary rewards for those taking the vaccine, for instance in the form of lottery tickets. 

Labour markets looking forward

Participants at the webinar also discussed how the pandemic has affected labour markets and whether its consequences will bring about any long-term changes.

Regarding unemployment statistics, Michal Myck, the Director of CenEA in Poland, made the important point that some of the relatively low unemployment numbers that we have seen in the region during this pandemic are misleading. This is because the traditional definition of being unemployed implies that an individual is actively searching for work, and lockdowns and other mobility restrictions have limited this possibility. Official data on unemployment thus underestimates the drop in employment that has happened, as those losing their jobs in many cases have left the labour market altogether. We thus need to see how labor markets will develop in the next couple of months as economies open up to give a more precise verdict.

Jesper Roine, Professor at SITE in Sweden, stressed that unemployment will be the biggest challenge for Sweden since its economy depends on high labor force participation and high employment rates. He explained that the pandemic and economic crisis has disproportionately affected the labor market status of certain groups. Foreign-born and young people, two groups with relatively high unemployment rates already prior to the pandemic, have become unemployed to an even greater extent. Many are worried that these groups will face issues with re-entering the labour market as in particular long-term unemployment has increased. At the same time, there have been more positive discussions about structural changes to the labour market following the pandemic. Particularly how more employers will allow for distance work, a step already confirmed by several large Swedish firms for instance.

In Russia, a country with a labour market that allowed for very little distance work before the pandemic, similar discussions are now taking place. Natalya Volchkova reported that, in Russia, the number of vacancies which assumed distance-work increased by 10% each month starting from last year, according to one of Russia’s leading job-search platforms HeadHunter. These developments could be particularly beneficial for the regional development in Russia, as firms in more remote regions can hire workers living in other parts of the country.

Concluding Remarks

It has been over a year since the Covid-19 virus was declared a pandemic by the World Health Organization. This webinar highlighted that, though vaccination campaigns in principle have been rolled out across the region, their reach varies greatly, and countries are facing different challenges of re-opening and recovering from the pandemic recession. Ukraine and Georgia have gotten a very slow start to their vaccination effort due to a combination of lack of access to vaccines and vaccine skepticism. Countries like Belarus and Latvia have had better access to vaccines but are suffering from widespread vaccine skepticism, in particular in some segments of the population and to certain vaccines. Russia, which is also dealing with a broad reluctance towards vaccines, is on top of that dealing with a surge in infections caused by the delta-version of the virus.

IMF Economic Outlook suggests that most economies in the region are expected to bounce back in their GDP growth in 2021. While this positive prognosis is encouraging, the webinar reminded us that there is a great deal of uncertainty remaining not only from an epidemiological perspective but also in terms of the medium to long-term economic consequences of the pandemic.

Participants

  • Iurii Ganychenko, Senior Researcher at Kyiv School of Economics (KSE/Ukraine)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
  • Natalya Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR at NES/ Russia)
  • Giorgi Papava, Lead Economist at the ISET Policy Institute (ISET PI/ Georgia)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Anders Olofsgård, Deputy Director of SITE and Associate Professor at the Stockholm School of Economics (SITE / Sweden)

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

Regional Economic Development Along the Polish-German Border: 1992-2012

Posted on May 3, 2021 | Policy Brief

Image of Europe at night from sky via NASA representing regional economic development

In this brief, we summarize the results of a recent analysis focused on the regional economic development in Poland and Germany along the Oder-Neisse border (Freier, Myck and Najsztub 2021a). Economic activity is approximated by satellite night-time light intensity, a comparable proxy available for regions on both sides of the frontier consistently between 1992 and 2012. This period covers the time of economic transformation and the first eight years of Poland’s membership in the European Union. We find that convergence in overall activity across the border has been complete: Polish municipalities that used to be economically much weaker have caught up with those on the German side of the Oder and the Neisse rivers.

Introduction

The question of the harmonious development of economic activity is at the heart of the European integration project (Art. 2, Treaty of Rome, 1957), and the Maastricht Treaty (1992) made economic convergence between member states an explicit objective. In a forthcoming paper (Freier et al. 2021), we take a new approach to the question of regional European integration.

This brief derives from a recent publication in Applied Economics (Freier et al. 2021a), in which we examine the degree of regional economic convergence along the German-Polish border by taking advantage of satellite night-time illumination data covering the period between 1992 and 2012. The data allows us to study detailed regional patterns of economic development along the river-delimited part of the frontier and further inland.

The seminal work by Henderson et al. (2012) was the first to use night-time light intensity data which covers the entire globe to measure economic activity. Unlike traditional regional economic indicators, light intensity data is independent of administrative border reforms and has been collected in a consistent format over the studied two decades.

Our analysis suggests that, over the analysed period from 1992-2012, there has been essentially full convergence in economic activity between municipalities on both sides of the Polish-German border. While the average value of night-time illumination in our selected group of municipalities in 1992 was 3.7 (on a scale between 0 and 63) in Poland and 7.7 in Germany, the respective values were 9.0 and 9.7 by 2012, and the latter difference is not statistically significant. This convergence suggests a much stronger rate of growth in economic activity on the Polish side of the border. Additionally, we show that within Germany, the distance to the border has much less relevance for economic activity compared to Poland, where it reflects interesting trends. In 1992, Polish towns farther from the border showed significantly higher economic performance. Within Poland, this gap has been greatly reduced over the 20 years we analyse, with regions closer to the border growing much faster compared to those farther away.

Night Lights Along the Polish-German Border

In our dataset, we include municipalities that are located within 100 km from the river delimited part of the PL-DE border. To avoid the sensitivity of the analysis to top censoring of the night-time light intensity data, we removed regional capital cities: Berlin (with surrounding municipalities), Dresden, Gorzów Wielkopolski, and Zielona Góra. This leaves us with 488 municipalities on the German side of the border and 193 municipalities on the Polish side.

The night lights data series, provided by the National Oceanic and Atmospheric Association (NOAA), starts as early as 1992 and continues in a consistent, comparable format to 2012. The data is independent of the administrative structures of local governments, which over time have changed on both sides of the border. This allows us to aggregate the night-time lights information for municipalities using the most recent available administrative borders. This data is essentially the only source of information on economic activity that is consistently available and comparable on both sides of the border over such a long period of time.

The night-time lights data has been applied widely as a proxy of economic development on the country and regional level (Henderson et al., 2012; Bickenbach et al., 2016). Clearly, the intensity of night-time lights does not capture the entire spectrum of economic activity. It has been pointed out that the relationship between night-time light intensity and conventional measures of economic development, such as GDP, is likely to differ depending on a region’s stage of economic development (Hu and Yao, 2019). However, we focus on mostly rural and sparsely populated areas (where there is little risk of top censoring of the data), and compare dynamics between regions that are similar in terms of their stage of economic development, geography, and weather. All these factors support the use of night lights as a proxy for regional development in our application (a number of technical steps are necessary to validate and calibrate the data for use in our analysis, see: Freier et al. 2021).

Economic Convergence Along the PL-DE Border

To understand the overall development of economic activity over the period of interest, we map the changes in the night-time light intensity in Figure 1. The colour scale on the map represents differences in light emissions between 1992 and 2012, with the range going from -40 to 40. A negative value indicates a reduction, and a positive value highlights an increase in light intensity. The negative values have been coloured in a blue-green scale (-40 to 0), while positive values in a red scale (0 to +40).

Figure 1. Night lights: changes in light intensity between 1992 – 2012 along the Polish-German border

Notes: municipalities along the PL-DE river border up to 100 km to the border; municipalities marked in grey treated as outliers and excluded from analysis due to high proportion of top-coded lights pixels in 1992; municipality borders as of 2013 (DE) and 2012 (PL). Source: GeoBasis-DE / BKG 2013, PRG 2012, DMSP OLS v4, OpenStreetMap, own calculations. For details see Freier et al. (2021).

As notable in Figure 1, the red areas are predominant. This exemplifies that between 1992 and 2012, nearly all municipalities in this area witnessed positive economic development as manifested in the intensity of night-time lights. We have a few areas that reflect negative dynamics on the German side of the border. This is mainly due to the regional implications of shutting down activity in agriculture and traditional industries as they were unable to compete with West-German technology and productivity. In Poland, green-blue areas are essentially non-existent, illustrating a universally positive economic development over the studied period. This difference in the pace of changes in light intensity between the German and the Polish side reflects a process of rapid convergence of economic development between municipalities on both sides of the border. These developments are represented in Figure 2 which shows the difference between the night-time light intensity in Germany and Poland by year and provides a test for its statistical significance. The estimation is done on mean log pixel values per municipality and clearly highlights the steep path of convergence. In the early nineties, the difference in mean light intensity was around 100 percent – i.e., the mean difference was as high as the mean level of lights on the Polish side of the border.  Already ten years later it reduced to around 50 percent and disappeared by the end of the analysed period. It is notable that, after an initial steep convergence, the difference in light intensity had a period of stagnation between 2002 and 2008. Interestingly, the full convergence which followed coincides with Poland’s entry into the Schengen agreement in December 2007. As seen in Figure 2, the difference in the average night-time light intensity between Poland and Germany was statistically insignificant and essentially zero since 2009.

Figure 2. Difference in mean night-time lights between Germany and Poland over time

Notes: Difference in log of average pixel values per municipality; year fixed effects included, weighted by municipality area; 95% CI. Source: see Figure 1.

Regional Development and Distance from the Border

Thanks to its high degree of geographical precision, the night-time lights data allows us to study the detailed spatial patterns within each country and, in particular, the relationship between distance to the border and economic activity. This is done by looking across the years 1992 to 2012 and examining three-year windows at each end of the analysed period. Our results, which are reported in Table 1, confirm a strong positive relationship between economic activity and distance to the border on the Polish side of the Oder-Neisse rivers. Overall, Polish regions farther from the border show a greater degree of economic activity, but this relationship has substantially diminished over time. While in Germany, economic activity was higher in regions farther from the border and increasing at the average rate of about 0.3% per km, this rate was about three times higher in Poland, falling from about 1.2% per km in 1992-94 to 0.6% in 2010-2012.

 Table 1. Total night-time lights along the Polish-German border, 1992-2012

Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.

Table 2 reports changes in light intensity between the beginning and the end of a specific period. Here, we find some interesting and perhaps disconcerting results on the relationship between the distance to the border and changes in light intensity. While the distance-to-border coefficient in the Polish case for the full period is negative, suggesting that regions closer to the border were catching up to the more developed regions farther away, the corresponding coefficient for the final three years is positive. This means that, in the years 2010-2012, economic development was faster in municipalities farther away from the border. Although the relationship is not very strong (the change in light intensity grows by about 0.1% per kilometre of distance to the border), it still suggests a reversal in the fortunes of municipalities close to the border on the Polish side. This result points towards the fact that homogeneity of development cannot be taken for granted and that physical distance might continue to play a role in determining the regional rate of growth in the future.

Table 2. Changes in night-time lights along the Polish-German border: 1992-2012

Notes: Notes: municipalities along the PL-DE river border up to 100 km to the border; municipality borders as of 2013 (DE) and 2012 (PL); mean municipal total lights calculated using average pixel values per municipality and weighted by municipality area. Standard errors in parentheses, statistical significance: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001. Source: see Figure 1.

Conclusion

In this brief, we report results from a forthcoming paper (Freier et al. 2021) in which we evaluate regional development in municipalities on the German and Polish side of the Oder-Neisse border between 1992 and 2012, using night lights data as a proxy for economic activity. We find that driven by rapid growth in Polish municipalities and somewhat sluggish growth in German ones, the light intensity levels across the Oder-Neisse border show no significant differences by the end of our observation period. This is despite significant initial differences just 20 years earlier and the fact that municipalities on the German side also experienced increases in economic activity. In as far as economic development can be proxied by the intensity of night-time illumination, it seems that economic convergence between regions on both sides of the border was complete by 2012.

We also show interesting patterns regarding the relationship between economic activity and distance from the border. For Germany, this relationship is weakly positive and remains stable throughout the analysed period. In Poland, distance is strongly and positively correlated with light emissions at the beginning of the period, hence indicating that municipalities farther from the border show higher average economic activity. By 2012, however, the border regions have closed most of the gap and the distance to the border is a substantially weaker predictor of economic activity, suggesting a much more homogenous pattern of activity.

Acknowledgements

This brief draws on results reported in Freier et al. (2021a). The authors gratefully acknowledge the support of the Polish National Science Centre (NCN), project number: 2016/21/B/HS4/01574. For the full list of acknowledgements and references see Freier et al. (2021a).

References

  • Bickenbach F, Bode E, Nunnenkamp P and Söder M (2016) Night Lights and Regional GDP. Review of World Economics 152(2): 425–47.
  • Freier, R., Myck, M., Najsztub, M (2021a) Lights along the frontier: convergence of economic activity in the proximity of the Polish-German border, 1992-2012. Applied Economics, available online: doi: 10.1080/00036846.2021.1898534.
  • Freier, R., Myck, M., Najsztub, M (2021b) Night lights along the PL-DE border 1992-2012. Dataset used in Freier et al. (2021a), Zenodo, DOI: 10.5281/zenodo.4600685.
  • Henderson JV, Storeygard A and Weil DN (2012) Measuring Economic Growth from Outer Space. American Economic Review 102(2): 994–1028.

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

Understanding Russia’s GDP Numbers in the COVID-19 Crisis

Posted on March 9, 2021 | Policy Brief

20210308 Understanding Russia GDP Numbers FREE Network Policy Brief Image 02

Russia’s real GDP fell by a modest 3 percent in 2020. The question addressed here is how a major oil-exporting country can go through the COVID-19 pandemic with a decline of this magnitude when oil prices fell by 35 percent at the same time as the domestic economy suffered from lock-downs. The short answer is that it is mainly a statistical mirage. The aggregate real GDP decline would have been almost three times greater than in the official statistics if changes in exports were computed in a way that better reflects their value. In particular, the real GDP calculation uses changes in volumes rather than values to omit inflation, but for exports, it thus ignores large changes in international oil prices. In the end, what the government, companies, and people in Russia can spend is much more closely related to how much money is earned on its exports than how many barrels of oil the country has sold to the rest of the world. More generally, this means that real GDP growth in Russia is not a very useful statistic in years with large changes in oil prices, as was the case in 2020, since it does not properly reflect changes in real income or spending power. When policymakers, journalists, and scholars now start to compare economic developments across countries in the covid-19 pandemic, this is something to bear in mind.

Introduction

The world is closing the books on 2020 and it is time to take stock of the damage done by the COVID-19 pandemic thus far. A year into the pandemic, over 100 million cases have been confirmed and almost 2.5 million people have died worldwide according to ECDC (2021) statistics. Russia has not been spared and Rosstat reported 4 million infected and over 160 000 dead in 2020.

Human suffering in terms of lost health and lives is certainly the main concern in the pandemic, but on top of that comes the damage done to economies around the world. Falling incomes, lost jobs, closed businesses, and sub-par schooling will create significant health and other problems even in a fully vaccinated world for years to come.

Understanding how real GDP has fared in the crisis does not capture all of these aspects, but some. With the IMF’s latest World Economic Outlook update on economic performance out in January 2021, it is easy to start comparing GDP growth across countries (IMF, 2021). GDP growth is a standard measure of past performances in general, but the numbers for 2020 may also enter various domestic and international policy discussions of what does and does not work in protecting economies in the pandemic. For countries that seem to have fared better than their peers, the growth numbers are likely going to be used by incumbent politicians to boost their ratings or by consumers and business leaders making plans for the future.

In short, real GDP numbers are important to most economic and political actors, domestically and globally, with or without a crisis unfolding. It is therefore important to understand how Russia, a major oil exporter with significant losses of lives and incomes in the pandemic, could report a real GDP decline of only 3 percent in 2020 (Rosstat, 2021). Although this is not far from the global average reported by the IMF (2021), it is far better than the 7.2 percent drop in the Euro area, 10 percent fall in the UK, or 7.5 to 8 percent declines of its BRICS peers, South Africa and India. This brief provides the details to understand that Russia’s performance is more of a statistical artifact than a fundamental reflection of the health of the Russian economy.

Oil prices, GDP growth, and the ruble

Russia’s dependence on exporting oil and other natural resources is well documented (see for example Becker, 2016a and 2016b) and often discussed by Russian policymakers and pundits. In particular, changing international oil prices is a key determinant of growth in the Russian economy. Even if the level of real GDP disconnected from oil prices somewhere between 2009 and 2014 (Figure 1), the link between real GDP growth and changes in oil prices persists (Figure 2).

Figure 1. Russia real GDP and oil prices

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

The empirical regularity that still holds is that, on average, a 10 percent increase (decline) in oil prices leads to around 1.4 percent real GDP growth (fall), see Becker (2016a). With a 35 percent decline in oil prices in 2020, this alone would lead to a drop in GDP of around 5 percent.

Figure 2. GDP growth and oil price changes

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

One factor that has a fundamental impact on how the relationship between oil prices and different measures of GDP changes over time is the ruble exchange rate. For a long period, Russia had a fixed exchange rate regime with only occasional adjustments of the rate. A stable exchange rate was the nominal anchor that should instill confidence among consumers and investors. However, when changes in the oil prices were too significant, the exchange rate had to be adjusted to avoid a complete loss of foreign exchange reserves. This was evident in the 90’s with the crisis in 1998 and later in the global financial crisis in 2008/09. Eventually, this led to a flexible exchange rate regime and in 2014, Russia introduced a flexible exchange rate regime together with inflation targeting as many other countries had done before it.

As can be seen in Figure 3, this has important implications for how changes in international oil prices in dollars are translated into rubles. Note that the figure shows index values of the series that are set to 100 in the year 2000 so that values indicate changes from this initial level. Starting in 2011, but more prominently since 2014, the oil price in rubles has been at a significantly higher level compared to the oil price measured in dollars, which is of course due to the ruble depreciating. This affects the government’s budget as well as different measures of income in rubles. However, if oil prices in dollars change, this affects the real spending power of Russian entities compared with economic actors in other countries regardless of the exchange rate regime. Moving to a flexible exchange rate regime was inevitable and the right policy to ensure macroeconomic stability in Russia when oil prices went into free fall. Nevertheless, it does not change the fundamental economic fact that falling oil prices affect the real income of an oil-exporting country. It also makes it even more important to understand how real GDP is calculated.

Figure 3. Oil prices and exchange rate indices

Source: Author’s calculations based on U.S. Energy Information Administration and Central Bank of Russia.

The components of real GPD

GDP is an aggregate number that can be calculated from the income or expenditure side. The focus in this brief is on the expenditure side of GDP. The accounting identity at play is then that GDP is equal to private consumption plus government consumption plus investments (that can be divided into fixed capital investments plus change in inventories) plus exports minus imports (where exports minus imports is also called net exports). Being an accounting identity, it should add up perfectly but in the real world, components on both the income and expenditure sides are estimated and things do not always add up as expected. This generates a statistical discrepancy in empirical data.

Another important note on real GDP (rather than nominal GDP measured in current rubles) is that the focus is on how quantities change rather than prices or ruble values. The idea is of course to get rid of inflation and focus on, for example, how many refrigerators are consumed this year compared to last year and not if the price of refrigerators went up or down. This may sound obvious, but it comes with its own problems concerning implementation and interpretations. For Russia, real GDP becomes problematic because its main export is oil (gas and its related products). The price of oil is just one of many drivers of Russia’s inflation but is an extremely important driver of its export revenues and growth as has been discussed above. On top of that, oil prices are volatile and basically impossible to control for Russia or even the OPEC.

So why does this matter for understanding Russia’s real GDP growth in 2020? The answer lies in how the different components of real GDP are computed. To make this clear, the evolution of the components between 2019 and 2020 is shown in Table 1.   

Table 1. Russia’s GDP components from the expenditure side

Source: Author’s calculations based on data from Rosstat

In short, private consumption fell by close to 9 percent in 2020 compared to 2019; government consumption increased by 4 percent; gross fixed capital formation declined by 6 percent while inventories increased by 26 percent; exports lost 5 percent, but imports went down by 14 so that net exports showed an increase of 65 percent! To calculate the impact these changes have on aggregate GDP growth, we need to multiply with the share of GDP for a component to arrive at the impact on GDP growth in the final column of Table 1.

Although there are some issues to resolve with both government consumption and inventory buildup, to understand real GDP growth in 2020, it is crucial to understand what happened to exports and imports in real GDP data. First of all, how does this data compare with the balance of payments data that measures exports and imports in dollar terms or the data that show the value of exports of oil, gas, and related products? Table 2 makes it clear that the numbers do not compare at all! Again, this is due to real GDP numbers being based on changes in volumes rather than values while the trade date reports values in dollars (that can be translated to rubles by using the market exchange rate).

In the real GDP statistics, net exports show growth of 66 percent in 2020, compared to declines of 37 to 44 percent if merchandise trade data is used. Going into more detail, real GDP data has exports declining by 5 percent, while other indicators fall by between 11 and 37 percent. It is similar with imports (that enter the GDP calculation with a negative sign); the import decline recorded in real GDP is 14 percent, while trade data suggest a 6 percent decline in dollar terms but an increase of 7 percent in nominal ruble terms.

Table 2. Trade statistics

Source: Author’s calculations based on Rosstat, Central Bank of Russia and BOFIT

What would it mean if we use some of these alternative growth rates for exports and imports (while keeping other components in line with official statistics) to calculate aggregate GDP growth in 2020? The rationale for keeping other components unchanged is that this provides a first-round effect of changing trade numbers on real GDP growth.

To make this calculation, the GDP shares of exports and imports (or net exports) in 2019 are needed. Table 1 shows that these numbers are 27 and 24 percent (or a net 3 percent) of total GDP. Multiplying the share of a GDP component with its growth rate gives the contribution of the component to overall GDP growth. The calculations based on different trade data are shown in Table 3. The last line of the table is what GDP growth would have been with these alternative trade data. Note that the real GDP growth number is -2.9 percent when we use the individual components of GDP decomposition (rather than the official headline number -3.1 real GDP growth when using aggregate GDP) so this is shown here to make the table consistent with the alternative calculations. In the last column of Table 3, oil and gas exports are assumed to make up for half of exports and this number disregards changes in other exports or imports to isolate the effect of changes in the value of oil and gas exports from other changes.

The summary of this exercise is that with more meaningful trade data used in calculating GDP growth, Russia would have recorded a decline of around 9 percent rather than 3 percent. This is of course a partial analysis focusing on the trade part of real GDP since this effect is very striking. Other components of the calculation may also have issues that need to be adjusted to arrive at a more realistic growth number. Still, even the current estimate is not unrealistic. For example,  household consumption fell by around 9 percent, which would be consistent with a GDP decline of 9 percent that is not recovered in the future in a permanent income model.

Table 3. GDP growth contributions from alternative trade data

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat

Conclusions

Real GDP growth numbers are important to understand economic developments in a country and provide the foundation for many types of economic decisions. The numbers are also used to compare the economic performance of different countries and evaluate policy responses in the COVID-19 pandemic we are currently part of.

The problem with Russia’s reported growth of minus 3 percent is not that the real GDP calculation is wrong per se, but it is clearly the wrong metrics to use for understanding how incomes and purchasing powers of Russian households, companies, and the government changed in 2020. If we instead use trade data that better reflect plummeting oil prices in international markets, alternative estimates of Russia’s real growth show a GDP decline of (at least) 9 percent.  This is a three times larger drop than the official number of minus 3 percent. This is important to keep in mind when Russia’s economic performance in the pandemic is compared with other countries or while discussing the economic realities of people living in Russia.

References

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

The Role of Partnerships in Economic Reforms of Fragile States: Perspectives from Somalia | Summary

Posted on November 30, 2020 | Policy Brief

Image of balancing stones representing Somalia as a fragile state and its reforms

Fragile states are particularly vulnerable to adverse economic shocks and in need of international support. Through constructive collaboration with international partners, however, fragile state governments can successfully pursue ambitious reform agendas for the short and long run. SITE and MISUM (Mistra Center for Sustainable Markets) invited the Minister of Finance of the Federal Republic of Somalia, Dr. Abdirahman Dualeh Beileh, and the Swedish ambassador to Somalia, Staffan Tillander, to discuss the role of international partnership in the recent development of economic reforms in Somalia. This policy brief provides a summary of the key points that were discussed in the webinar.

Introduction

Fragile states, characterized by poverty, weak governance, and conflict, now also have to confront additional challenges from the COVID-19 pandemic. Negative economic shocks arising from climate change, financial crises, conflicts, and pandemics are known to be particularly detrimental for these countries as the countries lack the resources to cushion the negative impact and are vulnerable to anything exacerbating latent socioeconomic challenges and conflicts. 

In these situations, international support becomes essential in reducing the immediate impact on human welfare and help sustain economic reforms that are necessary for long-run development. Somalia is a good case in point, where the recent consolidation of the country and an ambitious reform agenda together with international partners have set the country on a positive trajectory. This progress is challenged, though, by the pandemic, reinforced by drought and locust swarms.

From Independence to Civil War

Despite Somalia’s economically favorable geographical location and abundance of resources, the country has a turbulent history plagued by poverty, conflict, and humanitarian crises. Dr. Beileh provided thoughts on why the country failed to realize these opportunities and what factors led up to the civil war in 1991.

Following Somali independence in 1960, the country was lacking a sufficient level of educated citizens to run a modern government. In addition, tensions with neighboring countries and community demarcations within Somalia led to conflict and a constant struggle over resources. Also, Dr. Beileh argued that the former colonial powers had an interest in keeping the newly independent African states economically reliant in terms of imports of goods and sourcing of raw materials.

Dr. Beileh suggested that the combination of these factors contributed to the fall of the military regime in 1991 whereby Somalia plunged into civil war. With no recognized government over the following 20 years, this power vacuum became a black spot in Somalia’s history, characterized by war and poverty.

Political Consolidation and Debt Relief

After decades of suffering, in 2012 the Provisional Constitution established a federal political structure, with a parliament and the Federal Government of Somalia. Meanwhile, African Union forces liberated the major cities of Somalia from the terror of Al Shabab. In 2013 the government re-engaged with the World Bank and the IMF, and since 2016 the government together with international partners has engaged in numerous structural reforms. The main objective of the reform agenda was to qualify for international debt relief through the Heavily Indebted Poor Country (HIPC) Initiative introduced by the IMF and the World Bank in 1996 to reduce debt levels to sustainable levels in the world’s poorest countries. In Somalia’s case, this required laws and regulations that strengthened rule of law and sustainable economic management as well as poverty reduction strategies.

In March 2020, Somalia became the 37th country to qualify for the first step of debt relief under the HIPC initiative (“the decision point “) which meant that the country’s national debt was significantly reduced. This successful result was commended by the international community and Dr. Beileh stressed that it would not have been achieved without both international partnership and the resilience of the Somali people. Now, with continued successful reforms Somalia is projected to receive further debt reduction in 2023 (“the completion point”).  

Structural Reforms

Besides significantly reducing the national debt, the HIPC program requirements have led to development in many areas and opened new possibilities for international cooperation.

Laws and regulations that institutionalize the rule of governance and strengthen the federal system are essential HIPC prerequisites. Both Dr. Beileh and Ambassador Tillander stressed that strong governance is not only important for a clear division of tasks and competent and honest conduct within government bodies, but also an important cross-cutting issue that influences the ability of the state to achieve other goals.  Dr. Beileh described how far Somalia has come in this regard. When he started at the ministry of finance in 2017, wages, responsibilities, and accountability were up for negotiation. Today, there are rules and regulations in place that guide the responsibilities and accountability of civil servants. For instance, a public procurement authority has been established with the task of scrutinizing all government procurement and disposal of assets. Ambassador Tillander added that the strained regional tensions caused by the civil war and surrounding conflicts have been eased and the improvements in governance have led to a more constructive dialogue between the federal government and the member states.

Drawing on his experience as minister of finance, Dr. Beileh gave insight into the path of economic reform brought about by the HIPC process. The reforms focused on raising domestic revenue to achieve fiscal sustainability, keeping public expenditures at a sustainable level, and promoting various financial sector reforms. Dr. Beileh discussed the challenges related to gaining popular support for some of these reforms implemented in recent years. It is well known that economic reforms that are beneficial in the long-run often entail short-run costs which make them politically difficult to implement. To regain trust of taxpayers is of particular importance for Somalia given the need to increase domestic fiscal revenues.  Efforts have been made to actively inform the public about government activity and spending in order to increase transparency and convince Somalis that they will benefit from the system.

Ambassador Tillander provided examples of how countries like Sweden can help promote democracy and human rights in Somalia. For instance, Sweden has been working closely with the Somali government to help organize elections and increase voting participation, particularly for politically marginalized groups such as women and young adults.

Looking Forward

Despite Somalia’s recent success with debt forgiveness, both speakers acknowledged that much remains to be done.

The value of high-quality educational institutions and long-term investments in human capital is crucial in Dr. Beileh’s view. Having an educated population gives a country not only the skills and knowledge required to run a government but also helps a diverse society to move in the same direction. Although the need for infrastructure and investments in other areas is crucial for economic development, he insisted that it is educated people who in the end bring wealth, build infrastructure, and run governments.

Ambassador Tillander advocated for further promoting inclusion and merit-based selection in politics and business. He argued that progress is not possible if half of the population are excluded based on gender or age. Also, Somalia needs to move away from the clan as a basis for political power and position. As part of the solution, Ambassador Tillander suggested that Somalia should replace its provisional constitution with a new one that more strongly enshrines democratic elections, human rights, media freedom, and freedom of expression.

Although both speakers recognized that the reforms have been necessary, they mentioned that some reforms have also led to unintended negative consequences. For example, regulations to curb money-laundering and anti-terrorism financing have restricted the ability to transfer money to and from Somalia. As a result, many organizations and NGOs have found it hard to access financing, and it has made it hard for the diaspora to send remittances. To solve this issue, Dr. Beileh suggested policies that would improve the transparency of money flows, focusing on creating a personal id system and on strengthening the domestic financial institutions.

Another central topic at the webinar related to how Somalia and its partners should encourage and facilitate investments beyond foreign aid. Ambassador Tillander explained how there is an international misperception of Somalia and that his visit to the Mogadishu tech forum in 2019 was an eye-opener for him in this regard. These types of high-profile events, organized to attract foreign investments and display the opportunities that exist within Somalia, have attracted numerous young entrepreneurs who interact with their foreign counterparts, and showcase a dynamic and growing Somali business sector which is generally ignored in media-depictions of the country. In the context of the Swedish-Somali partnership, Ambassador Tillander suggested that there are enormous unexplored cross-border business opportunities between the countries, where the Somali diaspora in Sweden could play an important role.

Both speakers suggested that the foundations for communication and exchange are already in place. At this stage, the key to increase private investment is to reduce uncertainty for entrepreneurs and improve the predictability of the Somali financial system. People need to have better access to credit and financing, the banking system needs to become more formal, and the rule of law needs to apply more widely than it does today. Thanks to the HIPC process and the Somali government, steps in this direction are already underway but they must continue in order to build faith in the system, so that entrepreneurs, investors, and innovators are willing to take on the risks that new investments typically entail.

Reflecting on the start of the HIPC process, Ambassador Tillander argued that few people had anticipated the extent of progress that Somalia has achieved in only 4 years. Concluding the event, Ambassador Tillander and Dr. Beileh agreed that the cooperation between Somalia and the international community has been instrumental in encouraging and driving a reform process that would have been extremely difficult otherwise.

 

Speakers at the Event

  • Dr. Abdirahman Dualeh Beileh, Minister of Finance of the Federal Republic of Somalia.
  • Dr. Staffan Tillander, Swedish ambassador to Somalia.
  • Dr. Anders Olofsgård, Deputy Director SITE (moderator)

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

Belarus Economic Outlook

Posted on October 19, 2020 | Policy Brief

20201019 Belarus Economic Outlook. FREE Network Policy Brief Image of dark streets in Minsk representing Belarusian economic outlook

The Belarus economy was already struggling to generate growth before both the corona pandemic and the political protests following the August presidential election. The lack of growth was the result of an incomplete transition process to modernize the economy combined with a strong reliance on the Russian economy and its dependence on international commodity prices that have not paid off in recent years. With the added political turmoil and, so far, lack of a new political and economic strategy, the economic outlook for Belarus looks grim. Even if a full-blown crisis may be avoided by restrictive economic policies, stagnation will nevertheless be the most likely outcome without fundamental reforms.

Introduction

The Belarus economy was for many years doing very well under president Lukashenko, but since the global financial crisis in 2008/09, this course has been reversed. The downward growth trend has been exacerbated by both slumps in international oil prices (particularly important because of linkages with Russia, see Becker 2016a, 2016b, 2018, 2020), and the COVID-19 pandemic. This is clearly illustrated in Figure 1, which shows how the average growth rate has fallen all the way to a negative one percent in the years since 2015, while the period before the global financial crisis generated an average growth of 8 percent.

The lack of growth in Belarus and its causes has been analyzed in several papers long before the current developments. Akulava (2015) discusses how the government already five years ago understood that it needs to stimulate the private sector to generate growth; Kruk and Bornukova (2016) in turn describe how growth in the boom years was driven by capital accumulation but not improvements in productivity (TFP) that could have sustained growth in more recent years. As for policies to generate growth, Kruk (2014) argues that Belarus should focus on institutional changes that create the right incentives for firms and lead to a more efficient resource allocation rather than simply spend money on new equipment for existing firms. The need for productivity-enhancing reforms is further stressed in Kruk (2019) who points out that there is limited space to stimulate growth by expansionary macroeconomic policies.

Although the political situation after the election is strongly linked to the lack of democracy and freedom, the citizens’ willingness to protest is most likely enforced by the very poor economic performance of recent years. And while the importance of economic developments is sometimes glossed over in the current reporting and narrative of Belarus it will be an important factor in the popularity of any future government in Belarus as well as the current one.

Figure 1. Real GDP growth

Source: IMF World Economic Outlook April 2020.

 Note: The chart is based on the April 2020 version of the IMF’s World Economic Outlook and in the just-released October edition, the 2020 forecast is less negative due to global economic developments. However, this does not change the general downward growth trend Belarus has experienced.

Background

On the structural side, the economy of Belarus is heavily connected to Russian economic developments, which in turn depends on international oil prices (Becker 2016a, 2016b). In the group of FSU countries, Belarus stands out as the country that has the largest share of its exports going to Russia and the largest share of its FDI coming from Russia. On top of that, Belarus enjoys subsidized prices on oil and gas from Russia that benefits not only its exporting refineries but also other energy-intensive industries that are important for generating export revenues.

Figure 2. Exports and FDI shares with Russia and Rest of the World

Source: IMF directions of trade, World Bank development indicators and Central Bank of Russia data on FDI

As a final background note, the importance of SOEs in terms of employment has gone down in recent years but SOEs are still an important provider of jobs in Belarus and another sign of an unfinished transition agenda.

Figure 3. Importance of SOEs

Source: Belstat

To improve growth prospects, this is clearly a sector in need of reforms, including some privatizations, to make it more competitive and less of a drain on government finances. However, this process will need to deal with sensitive employment issue regardless of who is in charge politically.

Furthermore, Marozau, Aginskaya, and Akulava (2020) discuss how the corona pandemic may threaten the jobs of the over 1 million people that are employed by SMEs. The financial constraints of the government make it hard to offer widespread support to SMEs, and the authors argue that the government should target future winners among SMEs rather than the big losers in the crisis.

The challenge of increased unemployment is further exacerbated by the lack of an unemployment benefit system with extensive coverage (Bornukova, 2017). The lack of a well-targeted social security system could lead to a new increase in poverty rates. Mazol (2019) shows how past crises had a negative impact on poverty with absolute poverty increasing almost twofold in 2015/2016.

Recent Developments

The economy in Belarus was facing challenges (like much of the world) this year due to the COVID-19 pandemic well before the political crisis following the August election triggered additional problems. The IMF growth forecast for the year was well into negative numbers and given the (not always stable) links to Russia and thus to oil prices, the longer-term outlook was cloudy as well. Although the IMF’s October forecast shows less negative growth for 2020 (from minus 6 to minus 3 percent as the world is expected to see less of a contraction due to the COVID-19 pandemic), the longer-term outlook is one of stagnation with annual growth of around 1 percent.

For 2020, the economic and political difficulties can be seen in exchange rate developments as well as in the evolution of foreign exchange reserves (Figures 4 and 5).  In some ways, the 25-30 percent depreciation of the currency viz the dollar and euro is not the full story on the currency, since the exchange rate viz the Russian ruble has been much more stable. Given the close links to the Russian economy, this is quite important to note. Indeed, foreign currency reserves (the more liquid part of international reserves) have gone down by some 40% this year but are still at around 3 billion USD.

Additional pressure on the financial system in the past months came from significant withdrawals and people moving their savings to hard currencies after the August election. Krug and Lvovskiy (2020) discuss how this development is driven by political turmoil and also how the lack of trust that is currently generated in the system will lead to further stagnation of the economy. This line of reasoning is supported by Mazol (2018), who shows how financial stress in the past has contributed to costly economic contractions.

Figure 4. Exchange rate indices

(Jan 2020=100)

Source: National Bank of Belarus

 

Figure 5. Foreign exchange reserves

Source: National Bank of Belarus

Outlook and Policy Conclusions

The current economic policy will not generate growth in the short or long term by itself and the current political situation is clearly affecting growth negatively. The current political leadership could of course once again turn to Russia to ask for economic assistance in various forms, including loans, subsidies, or investments. Given the situation in Belarus, this will clearly come at a high political cost that will not necessarily be immediately transparent to people in Belarus or the outside world. Further, a sufficient level of assistance is not bulletproof either – Russia is itself facing difficult economic times ahead, both because of the COVID-19 pandemic and its impact on oil prices but also because of its own inability to generate sustainable growth that is not based on oil, gas and minerals (Becker, 2018, 2020).

How long the political and economic repression can go on without triggering a full-blown meltdown of the financial system in Belarus is anyone’s guess. Unfortunately, a policy mix of more restrictions on financial and exchange transactions in combination with accepting stagnation has been shown to be a model that has “worked” from Cuba, to Iran, Venezuela and North Korea for very long periods of time, so there are no given deadlines for such regimes.

Regardless of short-term policy changes, Russia will remain an important economic player in Belarus for a long time unless something dramatic changes. If there is a transition of political power in Belarus, any new political leadership will have to make careful choices with regard to its relationship with Russia. Quickly cutting ties to its big eastern neighbor could turn out to be very costly for Belarus from an economic perspective given the structure of trade, subsidies, and investments between the two countries.

If the EU (or the West more generally) wants to provide Belarus with a realistic economic alternative to Russia in the short run, it will need to provide substantial funding and strongly support a wide-ranging economic reform program that will need to address transition issues that most of its neighbors did many years ago. This will involve not only selling state assets to foreign investors but also changing the economic system from the ground up, including institutions and management practices. Another important part of the needed change is modern Western education. The importance of higher education institutions (HEI) to generate growth in Belarus is stressed by Marozau (2019), who discusses the role of HEIs in improving productivity and how the universities in Belarus fail to stimulate innovation and entrepreneurship.

The support package may not be cheap for the EU financially but helping the people in Belarus to finally make the transition to a modern, democratic market economy on the doorstep of the EU would certainly be worth it. The question is if the EU will manage to unite around such a policy in a time of COVID-19 lockdowns and economic hardship within its current boundaries. Patience may be required among those that fight for their freedom and a new economic model in Belarus.

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.

Revisiting Growth Patterns in Emerging Markets

Posted on October 15, 2018 | Policy Brief

20181014 Revisiting Growth Patterns Image 01

Recent studies document that emerging markets are rather similar in their growth patterns despite profound differences in starting conditions and productivity fundamentals. This challenges the common view on productivity as the main growth engine. The crucial role of the external environment for emerging markets emphasized by numerous studies adds to this doubt. I argue that productivity fundamentals still matter and remain the core driver of sustainable growth. However, external factors are crucial for understanding deviations from the trajectory of sustainable growth, i.e. episodes of growth accelerations/decelerations.

Challenges for Understanding Growth in Emerging Markets

As we enter the 4th decade of economic transition in Central and Eastern Europe (CEE), the causes and directions of causality of long-term growth in emerging markets might need to be reconsidered. Some recent studies emphasize that growth trajectories in emerging markets are pretty similar, i.e. average growth rates do not differ too much, while jumps and drops in growth rates are synchronous for the bulk of emerging economies (e.g. Fayad and Perelli, 2014). For instance, a decade ago the level of GDP per capita (in 2011 international $) in Macedonia was roughly 45% of that in the Slovak Republic, which likely reflected the productivity (measured through the Global Competitiveness Index) gap  between them. During the last decade, Macedonia has roughly closed this productivity gap. Growth theory would postulate that this should have transformed into faster output growth in Macedonia vs. Slovak Republic closing well-being gap. However, the two countries’ had throughout the decade roughly equal average output growth and the well-being gap today is still the same as it was ten years ago.

Such observations seem to conflict with existing theoretical views. First, this is a challenge to the well-being convergence concept that results from growth theory. Moreover, if we measure growth in terms of the speed of closing the well-being gap with respect to the frontier (the US economy), one may argue even for divergence. For instance, Figure 1 presents a scatter-plot for a sample of emerging markets relating the initial conditions – well-being level in 1995 (GDP per capita  relative to one of the US economy) – and the average speed of well-being gap (vs. the US economy) closing throughout 1996-2017  (measured in p.p. of corresponding gap ).

Second, the evidence that productivity gains do not automatically trigger output growth challenges a common view that productivity is the major driver for sustainable growth.

Figure 1.Starting Conditions and Well-Being Gains

Source: Own computations based on data from World Development Indicators database (World Bank).

What are possible explanations for the observed similarity in growth rates of emerging markets?

A study by the IMF (2017) suggests a response: growth in emerging markets is similar and synchronous due to the external environment. This study emphasizes the crucial dependence of medium-term growth in developing countries on the following factors: growth of external demand in trade partners, financial conditions, and trade conditions. Moreover, it states that these factors are dominant in explaining the episodes of growth strengthening/weakening.

Does this explanation change the growth nexus for emerging markets? Can one state, that while external factors are crucial for growth and growth in developing countries is rather homogenous, the productivity gains are not so important anymore?

I would say no. First, for better understanding of growth patterns we must clearly compare the relative importance of productivity gains vs. external factors in affecting the growth schedule. Second, we must separate relatively short-term fluctuations in GDP growth from sustainable growth.

Detecting Relative Importance of Growth Drivers

To answer the question about the relative importance of productivity fundamentals and growth factors, I study a panel of 34 emerging market economies (EBRD sample netted from 3 countries for which the data is not available) for 11 years (2007-2017).

To evaluate the relative importance of productivity and external factors, I use a standard approach of running panel growth regressions with fixed effects. At the same time, I make a number of novelties in the research design.

First, for measures of productivity, I engage a unique database – Global Competitiveness Indicators by World Economic Forum (WEF). Although this database provides an insightful perspective on productivity fundamentals at the country level, it is rather seldom a ‘guest’ in economic research. From this database, I extract a number of individual indicators in order to detect which ones among them that have the strongest growth-enhancing effect. For an alternative specification, I use principal components of 9 individual indicators from this database as proxies for productivity gains.

Second, for external factors, I use an approach similar to the IMF (2017) and calculate variables representing external demand growth, trade conditions, and financial conditions (such as a measure of capital inflows) for each country. Moreover, in respect to external demand growth, I use different competing measures (based on either imports of GDP growth of trade partners) and choose the best one in each individual equation. By doing so, I allow this dimension of the external environment to be represented in each model to the largest possible extent.

Third, I depart from using output growth as the only measure of economic growth and response variable in growth regressions. I argue that for international comparison purposes it is worthwhile to consider also the speed of closing the gap towards the frontier (the US economy). On the one hand, this measure is strongly correlated with the traditional output growth rate. On the other hand, this measure, in a sense, nets out the growth rate of a country from global growth, thus capturing something more unique and peculiar just to individual countries’ gains in well-being. Furthermore, I argue that in the discussion about the factors behind growth, one should distinguish between relatively short and long term growth. Annual growth rates, especially at relatively short time horizon, are too dependent on fluctuations, which may be interpreted in terms of growth rate strengthening/weakening. However, to emphasize the property of growth sustainability, we should get rid of ‘unnecessary noise’. For this purpose, I also introduce a trend growth rate measured in a most simple way as the 5 year moving average (following the discussion in Coibion et al. (2017), show that the bulk of measures of ‘potential’ growth are not good enough to get rid of demand shocks and these measures are pretty close to simple moving average measures).

I apply this definition of trend growth both to ‘standard’ GDP growth rate and to the speed of closing the gap towards frontier. So, finally I have 4 response variables: ‘standard’ growth rate, the speed of closing the gap to frontier, and two corresponding measures of trend growth.

Sustainable Growth Mainly Depends on Productivity

Having short-term (annual) growth rate as response variable (either ‘standard’ or the one in terms of closing the gap) provides results close to those in IMF (2017). It may be interpreted in a way that the external environment is more important than productivity factors. If dividing all regressors into two broad groups of factors – external and productivity – the former is responsible for up to 70% of the growth effect, while the latter for about 30%. Among external environment factors, the most important one is financial conditions. Its relative importance is roughly 50% of the group of external factors’ total.

Among productivity fundamentals, an important contributor to short-term growth is the quality of the macroeconomic environment. According to the methodology of WEF (2017), this indicator encompasses the fiscal stance, savings-investment balance, the external position, inflation path, debt issues, etc.

When refocusing from short-term growth to the growth trend as a response variable, the relative importance of the factors behind growth changes. Productivity fundamentals in this case drive up to 80% of growth effect, while external factors are responsible for the remaining 20%. It is worth noting here that the proportion in favor of productivity factors is higher for the concept of closing the gap to frontier rather than for ‘standard’ trend growth rate. This evidence may be interpreted as additional justification for treating this measure of growth as ‘good’ at reflecting individual properties of a country in a global landscape.

Furthermore, the role of individual variables also changes. Among external factors, the most important role in driving sustainable growth belongs to trade conditions and external demand growth, while the role of financial conditions is either miserable or insignificant at most. Among productivity factors as drivers of trend growth, the quality of the macroeconomic environment seems to play a special role, as well as the efficiency of the goods market and the financial system.

Conclusions

The evidence showing rather similar and synchronous growth in emerging markets and recent evidence on the crucial importance of external factors for emerging markets should not lead us to incorrectly believe that productivity fundamentals do not matter anymore. Productivity fundamentals are still the core driver of sustainable growth. At the same time, we should keep in mind the important role of the external environment for emerging markets. However, changes in the external environment are more likely to generate relatively short-term growth rate fluctuations, while having a modest impact on the sustainable growth trajectory. Hence, a country aiming to secure sustainable growth should still first of all think about productivity fundamentals.

References

  • Coibion, O., Gorodnichenko, Y, Ulate, M. (2017). The Cyclical Sensitivity in Estimates of Potential Output, National Bureau of Economic Research, Working Paper No. 23580.
  • EBRD (2017). Transition Report 2017-2018, European Bank for Reconstruction and Development, London, UK.
  • Fayad, G., and Perelli, R. (2014). Growth Surprises and Synchronized Slowdown in Emerging Markets—An Empirical Investigation, IMF Working Paper, WP/14/173.
  • IMF (2017). Roads Less Traveled: Growth in Emerging Markets and Developing Economies in a Complicated External Environment, in IMF World Economic Outlook, April, 2017, pp. 65-120.
  • World Economic Forum (2017). The Global Competitiveness Report 2017-2018, Geneva: World Economic Forum.

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.

Is There a Dutch Disease in Russian Regions?

Posted on March 19, 2018 | Policy Brief

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The low economic diversification in Russia is commonly blamed on the abundance of energy resources. This brief summarizes the results of our research that investigates the presence of Dutch disease effects across Russian regions. We compare manufacturing subsectors with different sensitivity to the availability of natural resources across Russian regions with varying natural resource endowments. We find no evidence of differential deindustrialization across subsectors, thereby offering no support for a Dutch disease. This finding suggests that the impact of energy resources on Russian manufacturing is more likely to go through the “institutional resource curse” channel. Thereby, we argue that more efficient policies to counteract the adverse effect of resources on the Russian economy should focus on improving the institutional environment.

Russian abundance in oil and gas, and the ways it could negatively affect long-term economic performance and institutional development is not a new debate. One of the key concerns is the influence of energy resources on Russian industrial structure. Energy resources are often blamed for the low diversification of the economy, with an extensive resource sector and the dominant oil and gas export share.

In a forthcoming chapter (Le Coq, Paltseva and Volchkova), we contribute to this debate by exploring the channels through which abundance in energy resources influences the industrial structure in Russia. Our main focus is on the deindustrialization due to the expansion of the natural resource sector, the so-called ‘Dutch disease’. Specifically, we explore the impact of energy resources on the growth of manufacturing subsectors in Russian regions. Adopting a regional perspective allows us to separate the Dutch disease mechanism from the main alternative channel of the institutional ‘resource curse’. This brief summarizes our findings.

Dutch disease vs. institutional resource curse

The Dutch disease and the institutional resource curse are, perhaps, the most discussed mechanisms proposed to explain the influence of natural resources on economic performance (see e.g., earlier FREE brief by Roine and Paltseva for a review). In an economy facing a Dutch disease, a resource boom and resulting high resource prices shift production factors from manufacturing industries towards resource and non-tradable sectors. As a result, a country experiencing a resource boom would end up with a slow-growing manufacturing and an under-diversified economic structure. Since the manufacturing sector is often the main driver of economic growth, the economic development may be delayed. If, instead, an economy is suffering from the institutional ‘resource curse’, it is the interplay of weak institutions and adverse incentives created by resource rents that leads to a slow growth of manufacturing and delayed development.

Importantly, offsetting the potential negative impact of these two channels requires different policy interventions. In the case of a Dutch disease, a state can rely on direct industrial policy mechanisms targeted towards increasing the competitiveness of the manufacturing sector and isolating it from the effect of booming resource prices. For example, it can use subsidies or targeted trade policy instruments, or channel money from increased resource prices out of the economy through reserve fund investments abroad.

In the case of an institutional resource curse, on the other hand, resource rents and weak institutions may undermine and disrupt the effect of such policies. In this case, state policies should be targeted, first and foremost, towards promoting good institutions such as securing accountability and the transparency of the state, and protecting property rights. This suggests that properly understanding the channels through which resource wealth impacts the economy is necessary for choosing appropriate remedial measures.

In our analysis, we address the differential impact of energy resources in Russian regions. This regional perspective allows us to single out the Dutch disease effect, and disregard the mechanisms of a political resource curse to the extent that the relevant institutions do not differ much across regions.

Resource reallocation effect vs. spending effect

The mechanism of a Dutch disease implies two channels through which a resource boom negatively affects the manufacturing sector. First, a resource boom implies the reallocation of production factors from other sectors of economy such as manufacturing or services to the resource sector, a so-called ‘resource reallocation effect’. Second, an additional income resulting from a boom in the resource sector leads to an increase in demand for all goods and services in the economy. This increase in demand will be accommodated differently by different sectors, depending on their openness to world markets. Namely, in non-tradable sectors, isolated from international competition, there will be an increase in prices and output. This, in turn, will increase the prices on domestic factor markets. For tradable manufacturing sectors the price is determined internationally and cannot be adjusted domestically. As a result, production factors will also reallocate away from manufacturing to non-tradable sectors, a so-called “spending effect”.

The strength of either effect is likely to be different across different subsectors of manufacturing depending on the sectoral specificities. In particular, subsectors with higher economies of scale are likely to be more affected by the outflow of factors towards the resource sector through the “resource reallocation effect”. Similarly, subsectors that are more open to international trade are likely to be affected by the “spending effect”.

These observations give raise to our empirical strategy: we access differences in growth of regional manufacturing subsectors with different sensitivity to the availability of energy resources, where sensitivity reflects economies of scale, for the first mechanism, and openness to the world market, for the second mechanism. In other words, we test whether manufacturing subsectors with higher economies of scale (or openness) grow slower than subsectors with lower economies of scale (or openness) in regions rich in energy resources, as compared to the regions poor in energy resources. Observing differential deindustrialization, depending on the industry’s exposure to the tested mechanism, would offer support to the presence of a Dutch disease.

Note that the validity of our empirical strategy relies on the fact that there is high variation in resource abundancy and structure of the manufacturing sectors across Russian regions (as illustrated by Figures 1 and 2).

Figure 1. Geographical distribution of fuel extractions relative to gross regional product; 2014, percent.

Source: Authors’ calculation based on Rosstat data. Note: Figures for regions exclude contribution of autonomous okrugs where applicable.

Figure 2. Regional diversity in manufacturing structure, 2014.

Source: Rosstat.

Data and results

Our empirical investigation covers the period 2006—2014. The data on manufacturing subsector growth and regional energy resource abundancy come from Rosstat, the sensitivity measures across different manufacturing sectors are approximated based on data from Diewert and Fox (2008) (economies of scale in US manufacturing), and OECD (sectoral openness to trade).

The results of our estimation show that the differences in growth rates of manufacturing subindustries across Russian regions with varying natural resource endowments cannot be explained by the sensitivity of these subindustries to the availability of energy resources. This can be seen from Table 1, where the coefficient of interest – the one of the interaction term between the measure of sectoral sensitivity if resource abundance and regional energy resource wealth – is not significantly different from zero, no matter how we measure the sensitivity: by the returns to scale or by openness to international trade.

Table 1. Estimation of Dutch disease effect with different sensitivity measures.

Dependent variable: average annual growth index of sectoral output
Sensitivity measure: Economies of scale Sensitivity measure: Openness
Subsector sensitivity * Size of the fuel extraction sector in the region

 

 -0.0353

(0.0873)

 0.0674

(0.0954)
Subsector fixed effect YES YES
Region fixed effect YES YES
Observations 1,185 1,185
R-squared 0.1574 0.1577

Source: Authors’ calculations.

These results hold true if we control for differences in regional taxes, labor market conditions, and other region-specific characteristics by including regional and sectoral dummy variables, if we consider alternative measures of energy resource wealth, and if we use other, non-parametric estimation methods.

In other words, our data robustly offers no support for the presence of a Dutch disease in Russian regions.

Conclusion and policy implications

Diversification is often mentioned by the Russian government, as one of the top economic policy priorities, and the need for ‘diversification’ has been used in the political debate as an argument for an active industrial policy.

However, the policy measures that are necessary to counter the effect of abundant energy resources on diversification and, more generally, on economic development may be highly dependent on the prevailing channel through which resources affect the economy. In particular, while active industrial policy may be justified as a remedy in the case of a Dutch disease, industrial policy may well be ineffective, or even harmful, in the presence of an institutional resource curse mechanism.

In our study, we find no support for the Dutch disease effect when looking at the impact of energy resources on the growth of regional manufacturing sectors. Thereby, to counterbalance the resource curse effect on the Russian economy, we argue that it may be more efficient to improve the institutional environment than to use active government policies affecting industrial structures.

References

  • Diewert, W. E and Fox, K. J. (2008) ‘On the estimation of returns to scale, technical progress and monopolistic markups’, Journal of Econometrics, 145(1-2): 174-93.
  • Le Coq, C., Paltseva E., and Volchkova N., forthcoming. “Regional impacts of the Russian energy sector”, in Perspectives on the Russian economy under Putin, eds. Becker and Oxenstierna, London, Routledge.

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 Russian economy under Putin (so far)

Posted on March 15, 2018 | Policy Brief

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Russians are heading to the polling booths on March 18, but where will the economy head after Putin has been elected president again? This brief provides an overview of the economic progress Russia has made since 2000 as well as an economic scorecard of Putin’s first three tenures in the Kremlin and uses this to discuss what can be expected for the coming six years. Although significant growth has been achieved since 2000, all of this came in the first two tenures of Putin in the Kremlin on the back of increasing oil prices. In order to generate growth in his upcoming presidential term, Putin and his team will need to address the significant needs for reforms in the institutions that form the basis for modern market economies. Otherwise, Russia will continue to be hostage to the whims of the international oil market and eventually lose most of its exports and government revenues as the world moves towards a carbon free future. Perhaps this is beyond the scope of Putin as president, but not beyond the horizon of young Russians that will be casting their votes on Sunday and in future elections.

Let’s assume that Putin will be elected president again on March 18 (for once a very realistic assumption made by an economist). What will this mean for the Russian economy in the coming six years given what happened during his previous and current tenures in the Kremlin? To assess the future as well as to understand Putin’s power and popularity, this brief starts by looking back at the economic developments in Russia since Putin first became president.

Although many different factors enter the power and popularity function of Putin, economic developments have a special role in providing the budget constrain within which the president can operate. A higher income level means more resources to devote to any particular sector, project, voting group or power base. This is not unique to Russia, but sometimes forgotten in discussions about Russia, that often instead only focus on military power or control of the security apparatus and media. These are of course highly relevant dimensions to understand power and popularity in Russia, but so is economic development, particularly in the longer run.

Russia’s economy in the world

The economic greatness and progress of a country is usually assessed in terms of the size of the economy, how much growth that has been generated, and how well off the citizens are relative to the citizens of other countries. So, by our common indicator gross domestic product (GDP), has Russia become a greater and more powerful country since Putin first became president? Table 1 shows two things, the absolute level of GDP measured in USD at market exchange rates and the rank this gives a country in a sample of 192 countries in the world that the IMF collects data on (this brief is too short for a long discussion of the most relevant GDP measure, but GDP at market exchange rates makes sense when comparing the economic strength of countries in a global context, Becker 2017 provides a discussion of alternative measures as well). When Putin become president for the first time in 2000, the value of domestic production was estimated at $279 billion, which implied a 19th place in the world rankings of countries’ GDP. In 2016, almost three presidential terms of Putin later, Russia’s GDP had increased by 4½ times to $1281 billion and its ranking improved to 12th place in the world. This clearly is an impressive record by most standards. However, the Russian economy is still the smallest economy of the BRIC countries and corresponds to only 7 percent of the US economy in 2016. In other words, impressive progress by Russia but the country is (still) not a global superpower in the economic arena.

Table 1. Russia in the world (GDP in USD bn)

Source: IMF (2017)

For the average Russian, income per capita is a measure more closely connected to consumption and investment opportunities or ‘welfare’. Progress in this area is also more likely to affect how individuals assess the performance of its political leaders. Of course, progress in terms of overall GDP and GDP per capita is closely linked unless something unusual is happening to population growth. Therefore, it is not surprising that GDP per capita also increased by around 4½ times between 2000 and 2016 (Table 2). This is the first order effect of the economic development in Russia, but in addition, citizens of Russia moved up from a world income rank of 92nd to 71st. This has implications when Russian’s compare themselves with other countries and can in itself provide a boost of national pride.

It also directly affects opportunities and status for Russians visiting other countries. Being at place 71 may not be fully satisfactory to many, but we should remember that due to the rather uneven income distribution in Russia, many of the people that travel abroad are far higher up on the global income ranking than what this table indicate. Nevertheless, Russia is far behind the Western and Asian high-income countries in terms of GDP per capita. And although the picture would look less severe if purchasing power parity measures are used, the basic message is the same; Russia has still a lot of catching up to do before its (average) citizens enjoy the economic standards of high-income countries.

Table 2. Russian’s in the world (GDP/capita)

Source: IMF (2017)

The macro scorecard of Putin

So what generated the impressive 4½ times increase in income in USD terms from 2000 to 2016 and can we expect high growth during Putin’s next six years in office? The short answer to the first question is the rise in international oil prices and to the second question, we don’t know. Table 3 provides a comparison of different economic indicators for Putin’s two first terms in office compared with his current term (where GDP data ends in 2016 so the sample is cut short by a year). It is evident that the impressive growth over the full period is entirely due to the strong growth performance in the first two presidential tenures. Rather than generating growth in the most recent period, the economy has shrunk. This is explained by the evolution of international oil prices, which quadrupled in the first eight years and instead halved in the more recent period. These swings in oil prices have also been accompanied by significant shifts in foreign exchange reserves, the exchange rate, and the value of the stock market.

In Becker (2017) I discuss in more detail the importance of international oil prices in understanding the macro economic development in Russia. In particular, it is important to note that it is changes in oil prices that correlate with GDP growth and other macro variables and that the problems with predicting oil prices makes it very hard to make good predictions of Russian growth.

Table 3. A macro scorecard of Putin in office

Source: Becker (forthcoming)

Policy conclusions

To break the oil dependence and take control of the economic future of Russia, the president will need to implement serious institutional reforms that constitute the basis for a modern, well-functioning market economy in his next term. Otherwise, Russia will continue to be hostage to unpredictable swing in international oil prices and nobody—including the president, the central bank, the IMF and financial markets—will be able to predict where the Russian economy is heading in the next couple of years.

Figure 1. Reforms (still) needed

Source: World Bank (2017)

In the longer run, the prediction is much easier. With the world moving towards a green economy, the price of oil will see a structural decline that will rob Russia (and other oil exporters) of most of its export and government revenues. The reforms which basically every economist agree are needed are related to market institutions and Figure 1 provides a clear illustration of key reform areas. The progress during Putin’s years in office has been modest at best. Swedish institutions in 2016 have been added to the figure as a comparison and it is clear that the institutional gap between Russia and Sweden is significant. Of course, all countries are different, but Russian policy makers that are interested in reforming its economy are most welcome to Sweden for a discussion of what we have done to build our institutions.

References

  • Becker, T. (2017). ‘Macroeconomic Challenges’, in Rosefielde, S., Kuboniwa, M., Mizobata, S. and Haba K. (eds.) The Unwinding of the Globalist Dream: EU, Russia and China, Singapore: World Scientific Publishing.
  • Becker, T. (forthcoming), ‘Russia’s economy under Putin and its impact on the CIS region’, Chapter 2 in T. Becker and S. Oxenstierna (eds.) Perspectives on the Russian Economy under Putin, London: Routledge.
  • IMF (2017), World Economic Outlook database, April 2017 edition available at http://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx
  • World Bank (2017), Worldwide Governance Indicators (WGI), 2017 update available at http://info.worldbank.org/governance/wgi/index.aspx#home

Stylized Facts from 25 Years of Growth in Transition

Posted on February 26, 2018 | Policy Brief

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This brief summarizes the growth experience of transition countries 25 years after the dissolution of the Soviet Union. We divide our sample into two main groups: the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries (ex Baltics) that emerge from the Soviet Union (FSU12). The growth experiences of these two groups have been distinctly different. The magnitude of the initial transition decline in output was much more severe in the FSU12 group. Despite growing almost 2 percentage points faster than the average EU10 for the following fifteen years, the FSU12 group is still further behind the EU10 group than they were at the beginning of transition. This illustrates how hard it is for countries to recover from large negative income shocks and thus the importance for countries to avoid such negative events. However, there are no signs of transition countries being stuck in a low or middle-income trap or that natural resource wealth leads to lower growth during this period.

2017 marked the 25-years anniversary after the dissolution of the Soviet Union and the beginning of the transition for the economies in the region. In a recent paper, we explore the growth experience of transition countries over these 25 years (Becker and Olofsgård, 2017). The paper has four main parts: an overview of the transition literature focusing on growth; a part that provides a detailed description of growth in transition; an analytical section that investigate if we can explain growth in transition countries with a standard growth model; and finally an exploration of whether institutional and other variables that have been highlighted in the transition literature (but are excluded from the basic growth model) are correlated with growth in transition countries. This brief summarizes the descriptive part of the paper, while the more analytical sections will be the topic of future briefs.

For most of the paper, we divide our sample into two main groups; the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries that emerged from the Soviet Union (FSU12). In addition, we include three transition countries that are not part of either group (Croatia, Albania and Macedonia – Other3) and we also divide the FSU12 group into the four countries that export significant amounts of fuel (FSUF) and the eight countries that do not (FSUNF). There are of course remaining differences within these groups, but this aggregate analysis allows us to see certain patterns in the transition process more clearly.

Initial output collapses

The focus in economics is often on how to generate higher growth and not about protecting against significant drops in output. There are some exceptions, including Becker and Mauro (2006) and Cerra and Saxena (2007), where the focus is on output losses and how countries recover after crises. For transition countries, a very important feature of the economic development process is exactly the initial drop in income and the time it has taken countries to recover from the initial phase of transition. Table 1 shows how much income fell in the different country groups and the time it took to get back to the pre-transition income level.

Table 1. Output drops and recoveries

Source: Becker and Olofsgård (2017)

The initial collapse in the FSU12 group was enormous, with income cut in half. The EU10 countries also had massive output losses, but “only” lost a quarter of their income on average. This took over a decade to recover from, while the path back to pre-transition income levels in the average FSU12 country was almost twice as long. There have been many papers written on the economic chaos that was part of the initial transition process, and explanations for this decline has been attributed to, e.g., misleading data, lack of functioning markets, shock therapy and poor economic and legal institutions in general. All of these factors have likely played important roles in the process, but regardless of the explanation, this was a very unfavorable time in terms of economic outcomes for hundreds of millions of people in these countries. Avoiding such costly drops in output should be a top priority for economic policy makers in any country at all times, not just in transition.

From collapse to growth

In most transition countries, the initial phase of decline in transition lasted several years, but eventually the negative growth rates turned positive (Figure 1). Again, we can see that the EU10 group had fewer years of declining incomes with growth resuming in 1993, while for the FSU12 group, growth in transition only started in 1996/7.

Figure 1. Bust-Boom countries

Source: Becker and Olofsgård (2017)

What is less visible in Figure 1 due to the wide scale needed to capture the initial output drops is that the FSU12 groups has shown significantly higher growth than the EU10 group in the last 15 years. Over the more recent period, the average FSU12 country has grown by close to 6 percent, while growth for the EU10 has been around 4 percent per annum (Table 2).

Table 2. Real GDP/cap growth

Source: Becker and Olofsgård (2017)

The faster growth in FSU12 countries is particularly pronounced among the fuel exporters, which were growing by one and a half percentage point faster than the non-fuel exporters between 2000 and 2015. But the table also shows that the very negative growth experience during the first ten years of transition is hard to erase and the EU10 countries have grown faster over the full 25-year period compared to the FSU12 countries. In terms of understanding the growth experience of the different country groups and time periods, it is clear that the sharp increase in international oil prices during the last 15 years of the period generated high growth in the fuel exporting countries in the FSU12 group. Interestingly though, also the non-fuel exporters grew faster than the EU10 in this time period. This is likely linked to spillovers from Russia to the other countries in the region, but could also be related to some recovering after the massive initial declines in output. Such macro and external factors are not always stressed in discussions of growth in transition countries, which more often focus on the pace of reforms or strength of institutions, but seem to be relevant at this aggregate level when comparing the initial and later phases of transition.

Relative incomes in transition countries

Growth or the lack thereof is of importance in determining income levels, which is what we generally think is what influences welfare. The question is then what the growth processes we have analyzed imply for income levels in transition countries, and in particular, how the income levels in these countries compare with other countries.

Figure 2. Income relative to 15 old EU countries

Source: Becker and Olofsgård (2017)

The short story here is that the relative ranking of the different groups is largely unchanged from the start of transition until the end of 2015. The group of countries that eventually joined the EU has the highest income level while the non-fuel exporting FSU countries have the lowest. However, the leading group still only has around 60 percent of the income of the average “old” EU country while the average FSU12 country has half of that or around 30 percent of the income of the old EU countries. This puts the relatively high growth rates of the FSU12 group over the last 15 years in perspective; the road to reach old EU level incomes is long indeed. Also, within the FSU group, it is clear that there is a sharp dividing line between the fuel exporters and the rest. This is in stark contrast to the notion of a “natural resource curse” that is often blamed for poor growth in oil and mineral rich countries.

Growth traps in transition?

One issue that comes up with regards to both low and middle-income countries is if they are stuck at a certain level in the relative income rankings of the world. This is referred to as the low or middle-income trap and the question is if there are signs of transition countries being stuck in such traps.

Figure 3. Moving up the income ladder

Source: Becker and Olofsgård (2017)

Figure 3 shows how transition countries are classified into the World Banks income groups low income (1 in the Figures scale), lower middle income (2), higher middle income (3) and high income (4) groups.

It is clear that the FUS 12 group of countries was sliding down the scale initially, but since the beginning of the 2000’s, all of the transition countries have been climbing up the World Bank income ranking scale without any apparent signs of a low or middle-income trap.

Policy conclusions

There are of course country differences along all the dimensions discussed in this brief but grouping the transition countries together provides some interesting general observations of growth in transition. First of all, it is clear that it is very hard to fully recover from large drops in income. Even with the help of some extra growth following a crisis, it seems to take a long time for most countries to make up for lost ground. This suggests that policy makers in transition as well as other countries need to take measures to hedge the really bad outcomes and not only focus on how to generate an extra one percent of growth.

The other observation is that at the aggregate level, external factors and more mechanical macro boom-bust-boom type of growth factors may dominate what we generally think of as the long-run determinants of growth (such as institutions, education, and micro level reforms to make markets work better) over very long time spans. This does not mean that the focus on the more fundamental growth drivers should diminish, but it is important that reforms in these areas are complemented with a macroeconomic framework that reduces the risks of costly output collapses.

Finally, it is clear that the incomes generated by natural resources can produce growth at the macro level and that there is little evidence that transition countries should be stuck at any particular level in the global income rankings. Go transition countries!

References

  • Becker, T, and A. Olofsgård (2017), “From abnormal to normal—Two tales of growth from 25 years of transition”, SITE Working paper 43, September.
  • Becker, T., and P. Mauro, (2006). “Output Drops and the Shocks That Matter”. IMF Working Papers 06/172.
  • Cerra, V., and S.C. Saxena (2008). ”Growth Dynamics: The Myth of Economic Recovery”. American Economic Review, 98(1), 439–457.