Author: Admin
Decentralization, E-Procurement and Efficiency of Public Procurement in Ukraine
This brief is based on a research that investigates if there’s a synergy effect of procurement and decentralization reforms in Ukraine on procurement efficiency. The analysis shows the similarity between new and old administrative units procurement performance. Although the analysis does not provide evidence of a significant synergy effect, such a similarity could be considered as something positive (due to the lower market power and capacity of the newly created administrative units) that should be analyzed further.
Decentralization in Ukraine
In April 2014, the Ukrainian government launched a systemic decentralization reform – a delegation of a significant part of resources and responsibilities from oblast and raion-level executive branches of the government to the local self-government level. The key issue of the reform was the creation of a strong basic level of local self-government in line with the European Charter of Local Self-Government. This is done through the creation of amalgamated hromadas (AH), the merger of several settlements with a single administrative centre.
An AH is governed by a council of the amalgamated hromada (CAH), a representative body of a local self-government. It is elected by residents of territorial communities and is responsible to independently resolve local issues, develop and approve AH budgets. Particularly, the government redistributed tax revenues and expanded the system of state subsidies (medical and educational subvention, subvention on development of amalgamated hromadas etc.) that could be used according to AH decisions.
In 2015, 159 AH were established. As of September 2018, 831 AH made up by 3,796 hromadas with 7 million residents (decentralization.gov.ua).
Public E-Procurement
Contemporaneously, a reform of public procurement has been implemented. According to the Law on Public Procurement of Ukraine, since August 2016, public procurement must be announced and executed through ProZorro, a public procurement web portal administered by the state enterprise. The e-procurement system consists of a central database, an auction module and commercial marketplaces (Figure 1).
In order to participate in public tenders, bidders can choose one of 22 commercial marketplaces (7 companies that provided initial investment for the project became the first marketplaces). The commercial marketplaces are web resources managed by private companies that provide access to the electronic procurement system.
Figure 1. ProZorro Architecture
The electronic procurement system does not completely cover the procurement cycle. Actually, it only covers the tendering process while planning and contract execution are mostly out of the system (plans are published online). Moreover, the existing legislation provides opportunities to manipulate tendering process by switching between different procedures. Within the ProZorro system there are 6 main procurement procedures that can be used by procuring entities depending on the volume and specifications of their needs.
Selection of procedures is based on a Threshold principle. There are three thresholds (Figure 2):
- Lower Threshold (LT). Contracting authorities are not obliged to report procurements in the electronic system if the total value of procurement is lower than UAH 50,000.
- Higher Threshold (HT). Contracting authorities are not obliged to use open competitive procedures if the total value of tender is lower than a defined level. This level is equal to UAH 200,000 for goods and services and UAH 1.5 million for construction.
- Euro Threshold (ET). The value of tender that requires applying the strictest competitive auction procedure. The euro threshold corresponds to thresholds used in EU public procurement legislation and is different for goods and services and construction works.
Transparency and efficiency indicators
Typically, a procurement process is divided into three stages: pre-tender stage, tender stage and post-tender stage. To measure efficiency and transparency of AH procurement, we constructed a system of eleven indicators that evaluate each stage of the procurement process (Table 1).
Table 1. Transparency and efficiency indicators used in the report
- Avoiding ProZorro. Both this and the following indicator would be associated with a decreased transparency, and, while not necessarily evidential, raise suspicions about procurement done in a less efficient and more collusive/corrupt way. AH are not inclined to avoid the ProZorro system. The share of procurements outside of ProZorro per AH is smaller than the corresponding indicators for other administrative units (Raion State Administrations, RSA, and unamalgamated hromadas, UH).
- Avoiding higher and euro thresholds. An analysis of ProZorro data shows that for AH, 84% and 11% of AH have at least one “suspicious” case for each corresponding threshold. For, RSA the indicators are 73% and 31% respectively.
- Unanswered questions. Having a productive dialogue with suppliers is of crucial importance for the success of the procurement. It helps to adjust the tender documentation so that it does not include discriminatory demands. Analysis shows that this practice is not dominant: relatively small proportion of AH and UH uses it.
- Level of competition. Higher competition is normally assumed to imply more efficient procurement deals. There is no difference in competition across administrative units and products (measured by the number of bidders per tender).
- High disqualification rates can be a consequence of ill prepared tender documentation with unclear technical specification or it can be a consequence of suppliers’ inexperience. It can also be a sign of corruption, when a tender committee is trying to find any reason to disqualify ‘unwanted’ suppliers. The analysis shows that disqualification is not a significant problem and, in fact, there are no significant differences across administrative units and products.
- Success rates. To successfully complete competitive procurement, the contracting authority has to determine the technical description of a good and its expected value based on their budget and market analysis. It also has to prepare and publish tender documentation and answer questions of potential suppliers. Finally, after the auction, the contracting authority has to evaluate documents of the auction winner and sign a contract. Failure in each of these steps will lead to unsuccessful or cancelled procurement. There is no significant difference across administrative unit groups in terms of procurement success rate
- Abnormal saving rate. Generally, a high saving rate (the difference between tender expected and contracted values) is regarded as a positive indicator, however, a too high rate is suspicious. It can be a sign of an inadequate expected value or an abnormally low price (suspicious behavior on behalf of the supplier). For the purpose of this study, we consider a saving rate abnormal if it is greater or equal to 30%. The analysis shows that AH had a significantly lower share of tenders with abnormally high saving rate than RSA. On average, 0.6% (in terms of value) of AH tenders are suspicious, for RSA this indicator equals 6.1%
- Contract termination. Frequent contract termination is a sign of significant inefficiencies in the procurement function of contracting authorities. The share of terminated contracts (as a percentage of the total contract value) is approximately similar for AH and RSA. On average, one AH has 5% of contract value terminated, while RSA indicator equals to 6%.
- Fixing the price with additional agreements. Although the Ukrainian Law on Public Procurement gives the right to amend the price per unit indicated, this right can be misused. It could lead to significantly higher costs. RSA are strikingly different from the other two groups – on average 20% of the RSA contract value stems from contracts with amended prices. This difference is the consequence of the different structure of goods and services procured by AH and UH on the one hand and RSA on the other.
- Share of largest supplier. Generally, it is considered to be a good practice, when contracting authorities are not overly dependent on one supplier. Approximately 30% of contract value of average AH and RSA belongs to one supplier. For UH this indicator is even higher (on average 48%) but it could be the consequence of the smaller number of contracts signed by UH.
Effect on prices
If contracts are successfully executed, the price of a good usually summarizes the efficiency of the procurement process.
There are many factors that affect the prices of goods in public procurements. On the one hand, AH (a) “realized” that they spent their own money and thus, they have more incentives to save and (b) have more power to choose where to spend. On the other hand, there are some factors that have the opposite effect: (a) because of low quantity demanded, the tenders announced are not interesting for large companies that could potentially provide lower price, and (b) the procurement officers could have insufficient capacity to negotiate lower price. Although, it is impossible to evaluate all these factors, we can assess their outcome – the contract price of a good.
For this analysis we looked at the prices on homogeneous goods such as food (potato, butter, eggs) and fuel (petrol A95, petrol A-92, diesel.
Table 2 summarizes the prices on the goods received by hromadas and compares it to the prices received by other types of entities (UHs and RSAs).
The data shows that for food products, AH average prices are lower or not different from UH, and slightly higher or not different from prices received by RSAs.
Even though there are some differences in prices of Petrol A-95 (partially due to inefficient planning and contracting at periods of higher prices), in general, the price level is very similar between all the entities.
In most cases, despite some warnings, there were no significant gaps between AH’s prices and UH or RSA. Moreover, the more competitive is the market of goods procured, the closer are prices received by different administrative units.
Table 2. Prices of goods by administrative units in 2017
Conclusion
The analysis shows the similarity between AH and RSA in terms of number of procurements, success and disqualification rates as well as competition level and share of terminated contracts. However, in cases when it is allowed by the Procurement Law, AH are more likely than RSA to choose direct selection of a supplier than a competitive procedure. Such behavior can be caused by a lack of professionalism (or even corruption), a desire to select a local trustworthy company or just because it is easier and faster to conduct uncompetitive procedure below the threshold.
On the other hand, AH are less inclined (in comparison to RSA) to avoid the ProZorro system (by using procurements below UAH 50 K) and to sign additional agreements that increase the price. Such behavior is potentially punishable by law. It can be suggested that procurement officers of AH only recently started to work with tenders above HT and are therefore more conscious of possible negative consequences of such actions.
The price per unit is the key indicator that summarizes information on procurement efficiency. Although AH show varying price efficiency, their prices of procured goods, in general, are not worse than in other administrative units’ groups. The more competitive the market, the closer are prices (especially in the case of fuels). Even if some gaps were observed, these differences are decreasing over time. Better planning can help to receive lower prices (better estimation of needs and choosing appropriate periods for procurement).
Currently, the analysis does not provide an evidence on a significant synergy effect of decentralization and procurement reforms. There are no significant differences between old and new administrative units. However, usually new communities have lower market power and capacity, and “no difference” could be considered as a positive sign that should be analyzed further.
References
- Shapoval, Natalia; Iavorskyi, Pavlo; Stepaniuk, Oleksa; Kovalchuk, Arthur. 2018. “An evaluation of decentralization impact on transparency and efficiency of public expenditures”. SKL International AB.
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.
Poverty Dynamics in Belarus from 2009 to 2016
This brief is based on research that studies the incidence and determinants of poverty in Belarus using data from the yearly Household Budget Surveys for 2009-2016. Poverty is evaluated from a consumption perspective applying the cost of basic needs approach. According to the results, in 2015-2016, absolute poverty in Belarus increased twofold and reached 29% of the population. Large household size, high number of children, single mothers and unemployment negatively affect household welfare and increase poverty risk. Moreover, living in rural areas increases the likelihood of being poor and correlates negatively with welfare.
Introduction
Sizeable and increasing poverty poses a threat to social stability and long-term sustainability for every country. Before 2009, Belarus registered over a decade of high and sustainable economic growth that enhanced the average standard of living and raised a substantial number of Belarusians out of poverty. According to the National Statistical Committee of the Republic of Belarus (Belstat), the poverty rate in Belarus (by official definition) has decreased from 41.9% of the population in 2000 down to 6.1% in 2008. The largest reported decline in poverty was in 2001 – from 41.9% to 28.9%.
Since then, Belarus experienced several episodes of economic crises – in 2009, 2011 and 2015-2016 (Kruk and Bornukova, 2014; Mazol, 2017a). Such economic downturns typically introduce considerable survival problems for many households. For example, according to the World Bank, in some countries the poverty rate may reach 50% (World Bank, 2000). In light of this, the small increase (0.3%) in the official poverty measure during these periods casts doubt on the official methodology used for poverty calculations. This brief describes an alternative measure of absolute poverty based on the widely recognized cost of basic needs approach; and summarizes the results of the study of how economic downturns in Belarus influenced welfare and poverty at the household level.
Data and methodology
The data used in this research are pooled cross-sections from 2009 to 2016 of the yearly Belarusian Household Budget Surveys with on average 5000 households in each sample obtained from Belstat. These surveys consist of household and individual questionnaires that contain important data about households including decomposition of expenditures and income by categories, detailed data on consumption of food items, the size, age and gender composition of households, living conditions, etc.
The analysis applies the cost of basic needs approach (Kakwani, 2003). It first estimates the cost of acquiring enough food for adequate nutrition (nutrition requirements for households of different size and demographic composition) per person (food poverty line) and then adds the cost of non-food essentials (absolute poverty line). The calculated poverty lines for each sampled household are compared with the household consumption per person. All measures are preliminary deflated to take into account differences in purchasing power over time and regions of residence.
In contrast, the official poverty measurement compares per capita disposable income of a household with national (official) poverty line, which is the average per capita subsistence minimum budget of a family with two adults and two children (see Table 1).
Table 1. Consumer budgets and absolute poverty line by year in Belarus, in constant BYN
| Year | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
| Subsistence minimum budget1 | 247 | 258 | 293 | 317 | 332 | 362 | 369 | 373 |
| Minimum consumer budget2 | 372 | 396 | 367 | 448 | 491 | 517 | 554 | 620 |
| Absolute poverty line3 | 383 | 395 | 437 | 448 | 468 | 475 | 499 | 520 |
Source: 1 Belstat; 2 Ministry of Labour and Social Protection Republic of Belarus; 3 author’s own calculations.
The empirical strategy of the analysis assumes setting the food, non-food and absolute poverty lines using the cost of basic needs approach, estimating poverty measures at the level of entire Belarus and its regions based on Foster-Greer-Thorbecke’s poverty indices (Foster et al., 1984), and analyzing the determinants of welfare and poverty using OLS and probit regressions.
Poverty incidence
The timeline of poverty analysis for Belarus can be subdivided into three periods: crisis of 2009-2011, recovery of 2012-2014, and a crisis of 2015-2016 (see Figure 1).
The results show that during the first period (from 2009 to 2011), absolute poverty at the national level increased from 30.9% to 32.6%. Incidence of absolute poverty for rural and urban areas in 2011 reached 45% and 28% of the population, correspondingly.
Figure 1. Incidence of absolute poverty and GDP per capita growth in Belarus
Source: Author’s own calculations.
Note: Estimates reflect weighted household data.
The second period (from 2012 to 2014) was characterized by a sharp poverty reduction. For example, the absolute national poverty headcount ratio has plummeted from 32.6% in 2011 to 14.9% in 2014, rural poverty dropped by 22.1 percentage points or almost by half and urban poverty decreased by 16.2 percentage points.
In turn, the third period saw a sharp rise in the incidence of poverty. From 2015 to 2016, the headcount ratio for absolute poverty increased by 14.4 percentage points. As a result, in 2016 absolute poverty in Belarus reached 29.3% or almost the same as in 2009 and 2011 (Mazol, 2017b).
Causes and determinants of poverty
The significant increase in poverty in 2015-2016 was due to a combination of several factors, including the household income decline in comparison with its growth in previous years, the increasing need to spend more on food necessities and the growth in food and especially non-food price levels.
As the Figure 2 shows, starting from 2015 there has been a rapid increase in the real cost of non-food budget for Belarusian households, while the food budget has remained almost the same in real terms. Correspondingly, in 2016 the non-food poverty line increased by 14.6%, while the food poverty line went up only by 2.9%.
Figure 2. Real monthly average per capita household expenditure on food and non-food items and real monthly standardized food and non-food poverty lines, 2009-2016, in BYN
Source: Author’s own calculations.
Note: Estimates reflect weighted household data.
Furthermore, as income fell (by 7.2% in 2015-2016), the share of food items in total expenditure increased and real non-food expenditure decreased. This is because household income was not enough to cover both expenditures on food and non-food items. Due to the 2015-2016 economic crisis the cost of meeting the food essentials increased so fast that it has squeezed the non-food budget, leaving insufficient purchasing power for non-food items.
The study also shows that among factors that substantially influence household welfare and poverty at the household level in Belarus are family size, the number of children in a household, presence in the household of economically inactive members. Moreover, single mothers in Belarus appear to be noticeably more vulnerable to macroeconomic shocks than full families both from welfare and poverty perspectives.
Additionally, one of the most important determinants of welfare and poverty in Belarus is spatial location of a household. In particular, poverty highly discriminates against living in rural areas. The poverty incidence for rural areas over 2009-2016 is approximately 10.5 percentage points (or 44%) higher than the national average, while that of the urban areas is nearly 4 percentage points (or 16%) below national average. Moreover, in 2015-2016 urban and rural disparity for poverty widened even more and reached 25.3% for urban vs 40.6% for rural areas.
Finally, two more factors, savings and access to a plot of land, have on average a large positive influence on consumption expenditure and aa negative one on the chance of getting poor.
Conclusion
Poverty alleviation and development reflect economic and social progress in any country. While Belarus initially achieved noticeable progress in this dimension, the economic and social development in recent years seems to increase problem of poverty in Belarus. The estimates show that in 2015-2016, absolute poverty in Belarus increased almost twofold. Household size, large numbers of children in a household, the presence in the household of economically inactive members are all factors that decrease household welfare and increase poverty. Single mothers also appear to be substantially more vulnerable to macroeconomic shocks. Finally, one of the most important determinants of welfare and poverty in Belarus is if a household is rural. These findings are important warning signals for the design of pro-poor policies in Belarus.
References
- Foster, J., J. Greer, and E. Thorbecke. (1984). A Class of Decomposable Poverty Measures. Econometrica, 52: 761-766.
- Kakwani, N. (2003). Issues in Setting Absolute Poverty Lines, Poverty and Social Development Papers No. 3, June 2003. Asian Development Bank.
- Kruk, D., Bornukova, K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper Series, WP no. 24.
- Mazol, A. 2017a. The Influence of Financial Stress on Economic Activity and Monetary Policy in Belarus, BEROC Working Paper Series, WP no. 40.
- Mazol, A. 2017b. Determinants of Poverty With and Without Economic Growth. Explaining Belarus’s Poverty Dynamics during 2009-2016, BEROC Working Paper Series, WP no. 47.
- World Bank (2000). Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia. Washington DC, The World Bank.
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.
Economic Growth and Putin’s Approval Ratings —The Return of the Fridge
This brief discusses how the approval ratings of president Putin covaries with economic growth. In most years the relationship between approval ratings for Putin and growth looks like approval ratings for politicians in most countries so that when growth is higher, the president is more popular. Or to use an American expression “it’s the economy stupid”. The caveat in Russia is that external events at times overshadow the importance of growth to the extent that the president’s ratings stay high and can even go up despite a faltering economy. In a time of low Russian growth, this is not good news for geopolitics unless Putin can be convinced to focus on policies that generate high, sustainable growth instead of international turbulence. That said, it is clear that poor economic growth carries a political cost also in Russia. The only sustainable way of maintaining high approval ratings for the president is by fostering economic growth since in the contest between “the TV and the fridge”, the fridge will eventually win.
Russia is a complex country and culture. Instead of the simple American saying “it’s the economy stupid”, Russians talk about “the TV vs the fridge”. This translates into that concerns about the economic situation can be made irrelevant by propaganda so that voters turn their eyes away from the half-empty fridge to follow how Russia’s armed forces fight the enemy in foreign countries.
The propaganda messages have of course varied over the years, but it seems that external enemies that threaten the nation are at the heart of many of the messages. This theme in propaganda is of course not unique to Russia, but it seems to carry more weight in Russia than in other countries.
The observation that propaganda is used and that it seems to work to a relatively large extent at times can lead to the conclusion that “it’s not the economy stupid” when it comes to approval ratings of the Russian leadership.
This observation is tempting as another piece of evidence on how Russia is different and unique, but this brief will show that in most times, it is indeed “the economy stupid” also in Russia.
Putin’s ratings and growth
The idea that Russia is different in that growth would not be important for the president’s approval rating can be justified empirically when we look at the full series of approval ratings of Putin as measured by the Levada center and corresponding quarterly growth rates going back to 1999 (Figure 1).
Instead of showing a strong positive correlation as we would expect, the correlation is negative 0.3. However, a more careful look at the observations in the scatter plot suggests that there are a few clusters of observations that create this negative correlation. In the figure, three distinct clusters are marked; first there is the period when Russia rebounded strongly from the 1998 crisis in 1999-2000, with growth rates that have not been seen before or after that time in Russia. The growth was an artefact of the previous massive decline in income in combination with a large devaluation, and later followed by oil price increases. This happened in Putin’s initial time in the highest offices when he was prime minister, interim president and then elected president. Although Putin enjoyed high ratings as a consequence, it was not in line with the extreme growth rates that were the result of events preceding his tenure and can thus be regarded as outliers.
The second cluster is related to the global financial crisis in 2008/09 when Russian growth took a major hit as oil prices collapsed and the exchange rate was not allowed to appreciate correspondingly. However, this crisis was blamed (as in many other countries) on the US and the West and did not cost Putin in terms of approval ratings.
The final cluster is related to the annexation of Crimea and ongoing involvement in the conflict in Eastern Ukraine. This period also coincides with a sharp drop in oil prices that taken together led to negative growth that then remained low for a prolonged period. We should note that before the annexation of Crimea, growth rates in 2013 were very low at 1-2 percent with approval ratings going down to 63 percent, which was an all-time low since Putin’s first year in office.
Figure 1. Ratings and growth
Source: Becker (2019)
If we purge the data from the three exceptional episodes that we have identified above, we get Figure 2. Note that the scale has not been changed from Figure 1. Now there are no observations of negative growth rates, but the distribution of growth rates is still rather spread out, going from around 1 to 9 percent growth. The spread of the growth distribution is important since it allows us to identify the relationship between growth and approval ratings more clearly.
The relationship between approval ratings and growth in Figure 2 is strongly positive with a correlation coefficient of 0.7, and in line with what we would expect in other countries. This is a quite remarkable shift from the negative correlation in Figure 1. Note that if approval ratings in 2014 had been behaving as in “normal” years, the regression line would have put them around 60 percent instead of the actual approval rating that peaked at 86 percent after the annexation of Crimea. Such is the strength of the TV.
Figure 2. Ratings and growth
Source: Becker (2019)
This is very clear evidence that Russia is a “normal” country in “normal” times, but that there are also times when other forces overshadow this normalcy.
Policy conclusions
Are there any policy conclusions that can be drawn from the stark contrast between figures 1 and 2? The answer is a very clear “yes”, both for the Russian leadership but also for the rest of the world that has economic interests and security concerns with Russia.
For the Russian president, the message is that it pays in terms of high approval ratings to generate growth and “keeping the fridge well stocked”. It is also clear that the high popularity rating that was seen after the annexation of Crimea has been followed by several years of poor growth. A forthcoming brief discusses how the increased uncertainty created by this event has led to lower capital inflows, lower domestic investments and lower growth.
Not surprisingly, the sustained low growth has started to show in terms of falling approval ratings. The polls at the end of 2018 and early 2019 (for when there is not yet data on growth rates) indicate a significant decline in approval ratings, down to 64 percent from over 80 percent at the end of 2017. This is linked to protests over pension reforms, but they in turn are a result of lower government revenues in an economy that lacks growth.
In other words, if growth does not return before the propaganda loses its appeal, this will eventually result in falling approval ratings for the president, which is what we are seeing now.
There are potentially also some policy conclusions for Russia’s foreign investors, trading partners and neighbors. When growth in Russia is low and no credible reform programs are on the horizon, expect external actions that take the attention away from poor economic performance while increasing the level of uncertainty both in Russia and abroad.
For the more pro-active external actors, finding ways to support Russia’s return to growth through dialogue on real economic reforms could perhaps be both politically feasible and of mutual interest to Russia and the West. There are clearly some geopolitical issues that may interfere with this process, but it should still remain high on the wish list of regular people in Russia and elsewhere. Let the fridge rule!
References
- Becker, T., 2019. “Russia’s macroeconomy—a closer look at growth, investment, and uncertainty”, forthcoming SITE Working Paper.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Agricultural Exports and the DCFTA: A Perspective from Georgia
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part – the Deep and Comprehensive Free Trade Area (DCFTA). In this policy brief, we discuss the changes and analyze the agricultural exports statistics of Georgia since 2014. Furthermore, we will provide the recommendations to capitalize on the opportunities that the DCFTA offers to Georgia.
Georgia is a traditional agrarian country, where agriculture constitutes an important part of the economy. 36.6% of the country’s territory are agricultural lands and 48.2% of the Georgian population live in villages. Although 55% of population are employed in agriculture, Georgia’s agriculture accounts for only 15.8% of its GDP (Geostat, 2019). Agricultural exports constitute an important part of Georgia’s economy, accounting for about 25-30% of total exports.
On June 27, 2014, Georgia and the EU signed an Association Agreement (AA) and its integral part, the Deep and Comprehensive Free Trade Area (DCFTA). On July 1st, 2016, the DCFTA fully entered into force. The DCFTA aims to create a stable and growth-oriented policy framework that will enhance competitiveness and facilitate new opportunities for trade. The DCFTA widens the list of products covered by the Generalized System of Preferences+ (GSP+) and sets zero tariffs on all food categories (only garlic is under quota), including potentially interesting products for Georgian exports – wine, cheese, berries, hazelnuts, etc. (Economic Policy Research Center, 2014).
As July 2018 marked only two years since the implementation of the DCFTA between Georgia and EU, valuable conclusions on its impact cannot be formulated yet. In this policy brief, we will give an overview of Georgia’s agricultural trade statistics, particularly, we will focus on agricultural exports and provide recommendations for capitalizing on opportunities offered by the DCFTA.
Georgia’s agricultural trade
Despite its potential and natural resources, Georgia is a net importer of agricultural products. In 2018, Georgia’s agricultural exports increased by 23.2% (181 million USD), while the respective imports grew by only 15.5% (179 million USD) compared to 2017. Therefore, the trade balance (the difference between exports and imports) remained almost unchanged at (-394) million USD (Figure 1).
Figure 1: Georgia’s Agricultural Trade (2014-2018)
Source: Geostat, 2019
Out of the sharp increase in agricultural exports, 100 million USD are attributed to tobacco and cigars. Since Georgia cultivates very little tobacco, the growth was instigated mostly from the import, slight processing and re-export of tobacco products. Consequently, the export of tobacco and cigars increased by 240% in 2018, and it currently holds second place (after wine) in Georgia’s total food and agricultural exports. It should be mentioned that wine exports contributed to 26 million USD in export growth.
Over the last five-year period, the top export countries for Georgia were mainly neighboring counties (Azerbaijan, Russia, Armenia, Turkey); for imports, we see the same neighboring countries as well as China and Ukraine. Observing the trade statistics over the years, 45% of Georgia’s agricultural exports were destined for markets in countries of the former Soviet Union, so-called Commonwealth of Independent States (CIS), while the EU’s share in Georgia’s total agricultural exports was 24%.
Trade relationships between Georgia and the EU
The EU is one of Georgia’s largest trade partners. The EU’s share of total Georgian imports was 28% in 2018, and for exports, 24%. Total exports have been more or less stable since 2014, except for 2016, when an 11% decrease was observed (Figure 2). Specifically, for agriculture, in 2017, the EU’s share of Georgian imports was 22%, and its share of exports was 19%. During the same period, the top export products were hazelnuts (shelled), spirits obtained by distilling grape wine or grape marc, wine, mineral and aerated waters and jams, jellies, marmalades, purées or pastes of fruit.
Figure 2: Total and Agricultural Exports to the EU (2014-2018)
Source: Geostat, MoF, 2019
In 2015 (before the full enforcement of the DCFTA), Georgia’s agricultural exports to EU countries (including the United Kingdom) increased by 20% compared to the previous year. This positive trend remained in 2016, when the same indicator increased by 5%. In 2017, which was quite a bad year in terms of harvest in Georgia, we observed a 38% decrease in the country’s agricultural export to the EU (Figure 2). This decrease was mainly caused by a significant decrease (64%) in hazelnut exports during the same period. The reason for such a large decrease is that hazelnut production suffered from various fungal diseases due to unfavorable weather conditions in 2017. The Asian Stink Bug invasion worsened the situation, and in the end, hazelnut exports dropped dramatically in both value and quantity. In 2018, Georgia’s agricultural export in EU slightly increased by 6% compared to 2017.
Trade relationships between Georgia and CIS countries
It is interesting to observe agricultural trade within the same time period with CIS countries. In 2018, the CIS’ share of Georgian imports was 51%, and its share of exports was 60%. The top export products to CIS countries were wine, mineral and aerated waters, spirits obtained by distilling grape wine or grape marc, hazelnuts (shelled), and waters, including mineral and aerated, with added sugar, sweetener or flavor, for direct consumption as a beverage. As we can see in both EU and CIS countries, the top export products are more or less the same. However, the main export destination market for Georgian hazelnuts are EU countries, but wine is mostly exported to the CIS countries.
Figure 3: Agricultural Exports to CIS Countries (2014-2018)
Source: Geostat, MoF, 2019
Due to the worsened economic situation in CIS countries, Georgia’s agricultural exports to these countries decreased by 37% in 2015. Such a sharp decrease was mainly driven by a significant decrease in the export of alcoholic and non-alcoholic beverages, hazelnut, and live cattle. However, since 2015, Georgia’s agricultural exports to CIS countries have been increasing; we observed a slight 2% increase in the value of agricultural exports in 2016, while the same indicator was 37% in 2017 (Figure 3). That was mainly caused by the increased exports of alcoholic and non-alcoholic beverages (wine by 61%, spirits by 28%, mineral and aerated waters by 22%). In 2018, Georgia’s agricultural export in CIS countries increased by 12% compared to 2017.
Conclusion
Despite its potential and comparative advantage in agriculture, Georgia is still a net importer of agricultural products and has negative trade balance (-394 mn USD). Two years after the DCFTA came into force, it is challenging to know its impact on Georgia’s agricultural trade due to the insufficient passage of time since. Notwithstanding, we can formulate some conclusions from trade statistics. The diversity of the destinations for Georgia’s agricultural exports has not changed through the years. Georgia’s agricultural exports has increased to the EU, but at a quicker pace to CIS too. Furthermore, Georgia’s share of agricultural exports to CIS countries is still significant (60%).
While it is obvious that Georgia needs to diversify its agricultural export destination markets, there are several challenges facing small and medium size farmers and agricultural cooperatives in Georgia that are not specific to implementation of the DCFTA. As the previous regime (GSP+) with the EU already covered most products, the DCFTA did not represent a significant breakthrough. On the path to European integration, the biggest challenge for Georgia is to comply to non-tariff requirements such as food safety standards and SPS measures. The attention should be paid on providing consultations to farmers regarding certification processes and standards and better information sharing (e.g. developing online platforms).
In Georgia, agri-food value chains are not well-developed and lack coordination among different actors. In order to capitalize on opportunities offered by the DCFTA, government and private sector should work together to improve logistics infrastructure. There is a need for upgrading at every stage of export logistics: warehousing, processing, labeling, regional consolidation, final customer services. In this regard, there are high approximation costs for business that should be considered as long-term investment to modernize agriculture and improve food the safety system in the country. This would boost the export potential not only to the EU, but to other countries with similar requirements as well.
References
- ISET Policy Institute, 2016. “DCFTA Risks and Opportunities for Georgia”
- Economic Policy Research Center, 2014. “Agreement on the Deep and Comprehensive Free Trade Area and Georgia”. Available only in Georgian
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.
Intergenerational Mobility in Africa
Recent economic research suggests that childhood environments in part determine success in life. So far, most of this evidence comes from rich countries. In a new paper, we use education data to measure intergenerational mobility across 26 African countries and find large differences across space. Results using data on migrants suggest that regions have causal effects on social mobility of Africans.
Why do people “make it” in life? Few of us can claim, as Robert Strauss, former U.S. ambassador to the Soviet Union and Russia, once quipped, that we were born in a log cabin we built ourselves. One chunk of individual success in climbing the social ladder is determined by our parents – be it through their genes (Sacerdote 2002, 2004), their parenting style (Doepke and Zilibotti 2017), or their income and connections. Another chunk is individual effort. Companies like Apple or Google were started in garages. That leaves our surroundings. Can the places we grow up in raise us up or pull us down? A growing body of research suggests that they can.
Growing evidence that “places matter” for individual mobility
At the forefront of these efforts, Chetty and Hendren (2018a, 2018b) have compared the incomes of American children to those of their parents. They link parents to kids through social security numbers in tax returns. Among families that moved, they find that children exposed to places with higher average social mobility for longer during childhood do better than children exposed to places with lower average mobility. Importantly, this holds when comparing the kids of parents with the same income and other observable characteristics, i.e. holding the “starting line” constant for everyone. Their findings have been reproduced for the U.S. (Chetty, Hendren, and Katz 2016), (Chyn 2018), Canada (Laliberte 2018), Australia (Deutscher 2018), and Denmark (Eriksen 2018). By contrast, studies identifying the causal effects of places on individual mobility in developing countries are still rare (a recent contribution by Asher, Novosad, and Rafkin (2018) on India is a notable exception).
New evidence from Africa
In a new paper (Alesina et al. 2018) we fill part of this gap by examining intergenerational mobility in Africa. After decades of stagnation, there is optimism about Africa’s future. Growth has returned (Young 2012), and some now see Africa as a continent of “1.2 billion opportunities” (Economist 2016). At the same time, anecdotal evidence suggests large inequalities, indicating that the recent aggregate gains may not be broadly shared, and that social mobility remains limited.
Measuring intergenerational mobility using education data
Measuring intergenerational mobility in Africa is difficult because of patchy data. Economists typically think of mobility in terms of income or wealth. In Africa, we lack tax records as well as administrative information linking children to parents. Instead we rely on censuses from 26 African countries and measure mobility using education data on children that share a household with their parents. [Card, Domnisoru, and Taylor 2018; Azam and Bhatt 2015; Narayan et al. 2018; Black and Devereux 2011 also study intergenerational mobility using education data.]
We measure upward mobility as the likelihood that kids of parents with less than primary education complete at least primary school. Similarly, we call an individual downwardly mobile if her parents have completed at least primary education and she has failed to do so. We compute these measures among children aged 14-18. This gives them enough time to complete primary school if they were ever going to do so. At the same time, most children at that age still live with their parents, which limits potential bias from co-residence selection.
Using education to measure social mobility has five advantages. First, education is a broad measure of living standards, reflecting not just income, but also aspirations and capabilities. Second, unlike income, much of which is informal and therefore under-reported in poor countries, schooling can be easily measured. Third, education, once completed, remains fixed and so intergenerational mobility can be assessed early in life. Fourth, “Mincerian returns” – how much extra income one more year of schooling commands in the labor market – seem to be especially high in Africa (Young 2012; Psacharopoulos 1994; Caselli, Ponticelli, and Rossi 2014), suggesting that education is a meaningful proxy of income. Finally, more schooling is correlated with many positive outcomes: household asset ownership, lower fertility, and even support for democracy. These correlations hold strongly comparing two individuals living in the same place, which means that education “quantity” is a useful stand-in-measure of living standards, even if the quality of schooling differs from place to place.
Main data patterns
The census data give us millions of individual observations to accurately measure intergenerational mobility over time (birth-cohorts) and in small geographic areas. First and most prominently, the descriptive analysis reveals differences in mobility both across and within countries. Figure 1 shows the geography of upward mobility across the 26 countries. Darker regions indicate places with lower mobility – children of illiterate parents are less likely to finish primary school.
Figure 1. Upward mobility across Africa
Source: Alesina et al. 2018
Country-differences are clearly important – South Africa is more mobile than Mozambique. Still, even within countries, there are vast differences as figure 2, which zooms in on Ghana, illustrates.
Figure 2. Upward mobility in Ghana
Source: (Alesina et al. 2018)
In some regions in Northern Ghana, average mobility is below .2 while it exceeds .8 in Accra, the capital. Second, while mobility does increase over time, these increases are modest and most pronounced in the most recent decades. This is still consistent with overall rising education, since average schooling in Africa was low until recently. Taking patterns one and two together, the persistent variation in mobility between places is more important than changes in mobility over time.
What accounts for differences in mobility across space? By far the strongest correlate of intergenerational mobility is the average literacy in the same place in the generation of the parents. This means that, comparing two individuals that grew up as children of illiterate parents in different regions, the individual that grew up in the region that has higher literacy in her parents’ generation has a greater chance of completing at least primary school. Several explanations might account for this pattern. Most simply, some regions have more schools than others, and can educate more individuals “per period”. One alternative story are peer effects: even though my parents are uneducated, I learn by example from the people around me that going to school is possible and desirable.
Beyond the correlation with initial education, we find that geography, colonial history, and at-independence development matter for intergenerational mobility. There are two important caveats to these results. First, pinning down the mechanism of why initial literacy and mobility are related remains a challenge. Second, these results represent correlations and not causally identified effects.
Causal effects of regions
To make causal inferences, we use data on families that have moved between two regions within a country in two ways. First, we compare siblings from migrant households, one child born in the origin of migration, the other in the destination. Figure 3 shows a (binned) scatter plot of the association between average birth-region upward mobility (computed among non-migrants) on the horizontal and individual likelihood of upward mobility on the vertical axis, conditional on household as well as birth-cohort effects. The slope indicates that kids born in a region with a ten percent higher mobility are 2.65 percent more likely to complete primary school compared to their siblings born in a different region with lower mobility.
Figure 3. Migrant vs non-migrant siblings
Source: (Alesina et al. 2018)
Second, we compare migrants that moved at different ages during childhood. Figure 4 plots the effects on individual outcomes of moving from a place of on average zero mobility to a place where all children of uneducated parents become educated against the age of the child at which the move occurred, once again comparing individuals within the same household. As intuition would suggest, earlier moves to better regions have larger positive effects than later moves, and effects turn insignificant towards the end of the period relevant for primary school.
For both empirical strategies, the sibling comparisons (enabled by household fixed effects) are crucial to separate treatment effects of regions from sorting whereby illiterate parents that may be more motivated/capable in educating their children move to regions with greater average opportunities.
Figure 4. Migration exposure effects
Source: (Alesina et al. 2018)
Conclusion
New research points to the importance of “places” in shaping individual social mobility. Complementing several recent works on developed economies, we document that opportunities for educational advancement vary widely within and across African countries. The strongest correlate of differences in mobility between places are differences in the initial education level in the generation of the parents, with more educated places showing higher mobility. Using information on migrants, we find that regions have a causal impact on individual outcomes. Taken together, our results suggest that initial conditions have persistent effects on the transmission of human capital between generations and that overall regional differences in human capital transmission in turn matter for who “makes it” in Africa.
References
- Alesina, Alberto, Sebastian Hohmann, Stelios Michalopoulos, and Elias Papaioannou. 2018. “Intergenerational Mobility in Africa.” Centre for Economic Policy Research Discussion Paper 13497 https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13497
- Asher, Sam, Paul Novosad, and Charlie Rafkin. 2018. “Intergenerational Mobility in India: Estimates from New Methods and Administrative Data.” Mimeo, Dartmouth College.
- Azam, Mehtabul, and Vipul Bhatt. 2015. “Like Father, Like Son? Intergenerational Educational Mobility in India.” Demography 52 (6): 1929–59. https://doi.org/10.1007/s13524-015-0428-8.
- Black, Sandra E., and Paul J. Devereux. 2011. “Recent Developments in Intergenerational Mobility.” In Handbook of Labor Economics, 4B:1487–1541. Elsevier.
- Card, David, Ciprian Domnisoru, and Lowell Taylor. 2018. “The Intergenerational Transmission of Human Capital: Evidence from the Golden Age of Upward Mobility,” 102.
- Caselli, Francesco, Jacopo Ponticelli, and Federico Rossi. 2014. “A New Dataset on Mincerian Returns.” Unpublished.
- Chetty, Raj, and Nathaniel Hendren. 2018a. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.” The Quarterly Journal of Economics 133 (3): 1107–62. https://doi.org/10.1093/qje/qjy007.
- ———. 2018b. “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates.” The Quarterly Journal of Economics 133 (3): 1163–1228. https://doi.org/10.1093/qje/qjy006.
- Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” American Economic Review 106 (4): 855–902. https://doi.org/10.1257/aer.20150572.
- Chyn, Eric. 2018. “Moved to Opportunity: The Long-Run Effects of Public Housing Demolition on Children.” American Economic Review 108 (10): 3028–56. https://doi.org/10.1257/aer.20161352.
- Deutscher, Nathan. 2018. “Place, Jobs, Peers and the Teenage Years: Exposure Effects and Intergenerational Mobility.” Mimeo.
- Doepke, Matthias, and Fabrizio Zilibotti. 2017. “Parenting With Style: Altruism and Paternalism in Intergenerational Preference Transmission.” Econometrica 85 (5): 1331–71. https://doi.org/10.3982/ECTA14634.
- Economist, The. 2016. “1.2 Billion Opportunities.” The Economist.
- Eriksen, Jesper. 2018. “Finding the Land of Opportunity Intergenerational Mobility in Denmark.” Mimeo.
- Laliberte, Jean-William. 2018. “Long-Term Contextual Effects in Education: Schools and Neighborhoods.” Mimeo.
- Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Silvia Redaelli, Christoph Lakner, Daniel Gerszon Mahler, Rakesh Ramasubbaiah, and Stefan Thewissen. 2018. Fair Progress?: Economic Mobility Across Generations Around the World. World Bank Publications.
- Psacharopoulos, George. 1994. “Returns to Investment in Education: A Global Update.” World Development 22 (9): 1325–43. https://doi.org/10.1016/0305-750X(94)90007-8.
- Sacerdote, Bruce. 2002. “The Nature and Nurture of Economic Outcomes.” American Economic Review 92 (2): 344–48. https://doi.org/10.1257/000282802320191589.
- ———. 2004. “What Happens When We Randomly Assign Children to Families?” NBER Working Paper 10894. https://www.nber.org/papers/w10894.
- Young, Alwyn. 2012. “The African Growth Miracle.” Journal of Political Economy 120 (4): 696–739. https://doi.org/10.1086/668501.
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.
Development of Belarusian Higher Education Institutions Based on the Entrepreneurial University Framework
In contrast to developed Western countries, higher education institutions (HEIs) in transition economies such as Belarus do not have the pretension to being key actors in cutting-edge innovation and in creating entrepreneurship capital. Rather, they tend to educate job seekers or knowledge workers, as well as to adapt, redevelop and disseminate existing knowledge and technologies. At the same time, policy makers in Belarus have realized that transformation of HEIs is needed to respond to the global challenges. In this regard, this policy brief discusses prerequisites and factors conditioning the development of entrepreneurial HEIs in Belarus.
Capitalizing on state-of-the-art academic research, as well as on the custom-made survey of Belarusian faculty members, the brief concludes that Belarusian policy makers need to create a supportive institutional environment before requiring from HEIs outcomes of the entrepreneurial mission. First-priority measures for the current stance are delineated.
Entrepreneurial University and University 3.0
As a productivity factor, entrepreneurial activities started appearing in economic growth models at the beginning of the twenty-first century (Wennekers & Thurik, 1999; Wong et al., 2005). Consequently, the role of HEIs broadened from educating labor force and knowledge creation to development of “entrepreneurial thinking, action and institutions” (Audretsch, 2014) – HEIs took on the third “entrepreneurial” mission.
Well-studied outcomes of this mission are new firms (academic spin-offs, spin-outs, student-led start-ups), patenting, licensing and the development of entrepreneurial culture and attitudes among graduates and academics.
The concept of an entrepreneurial HEI is multifaceted and is explored within different research streams: from knowledge transfer to entrepreneurship education and HEI management. Consequently, there is no consensus in the understanding of the term “entrepreneurial university” that can, for this policy brief, be broadly defined as a HEI that acts entrepreneurially and is a natural incubator, creating a supportive environment for the startup of businesses by faculty and students, promoting an entrepreneurial culture and attitude for the purpose of responding to challenges of the knowledge-based economy, and facilitating economic and social development.
Figure 1. Evolution of the HEIs’ missions
Source: Adapted from Guerrero & Urbano (2012)
Meanwhile, the concept of “University 3.0” –mostly corresponding to the concept of “Entrepreneurial university” and adopted from J.G. Wissema – started appearing in Russian publications, where the number ‘3’ corresponds to the three HEI missions or to the third generation of HEIs. A possible explanation of this renaming is that, on the one hand, in the post-Soviet context entrepreneurship per se still does not have a positive meaning in a broader society and it is not associated to HEIs. On the other hand, it was expected that such numbering makes the evolution visible. However, this led to speculation on this numbering and gave rise to publications on University 4.0 that should correspond somehow to Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies.
Admittedly, the entrepreneurial mission of HEIs is not associated or equaled to start-ups and knowledge transfer any more, but is increasingly considered as a procedural framework for HEI’s and individual’s behavior.
Belarusian Context
Political, economic, social, technological and legal conditions determine the path and the speed of the evolution of HEIs as well as their contribution to national economies in different stages of economic development. Thus, in Belarus – an efficiency-driven economy, i.e., a country growing due to more efficient production processes and increased product quality (World Economic Forum, 2017), – HEIs are considered to contribute to economic development if they successfully fulfill teaching and research missions. While the outcomes of the third mission are supposed not to be relevant at this stage (Marozau et al., 2016).
However, trying to replicate the success of Western HEIs in the development of the entrepreneurial mission, the Ministry of Education of Belarus initiated the Experimental project on implementation of the “University 3.0” model aimed at the development of research, innovation and entrepreneurial infrastructure of HEIs for the creation of innovative products and commercialization of intellectual activities.
In general, Belarus has a state-dominated well-developed, by some estimates, oversaturated higher education sector that remains mostly rigid and unreformed since the Soviet times. Belarus outperformed all CIS and EU countries except Finland in terms of the number of students per 10,000 population in 2014 (Belstat, 2017) and according to the World Bank has one of highest enrollment rates in tertiary education of about 90%.
Belarusian students have quite high entrepreneurial potential in comparison to other countries participating in the Global University Entrepreneurial Spirit Students‘ Survey (GUESSS). Thus, in five years after graduation, 56.8% intend to be entrepreneurs, while the global average level is 38,2% (Marozau and Apanasovich, 2016). However, curricula of most specialties majors provided by Belarusian HEIs are not supplemented with formal and experiential entrepreneurship education to equip students with entrepreneurial competencies. Innovative methodologies and entrepreneurial approaches to teaching as well as faculty entrepreneurial role models are rare. Moreover, all changes in degree syllabuses need state approval that makes HEIs less flexible and nimble. The situation is further complicated by the fact that supporting entrepreneurial activity has not been an important part of the HEI culture.
Methodological Approach
We conducted online and face-to-face surveys of 48 Belarusian HEI authorities and faculty members that were based on HEInnovate self-assessment tool widely used by policy makers and HEI authorities (see Marozau, 2018).
Overall, emails were sent out to a population of 284 pro-active and advanced representatives of the Belarusian academic community whose email addresses were available in the databases of BEROC and the Association of Business Education. We benefitted from open-ended questions included in the questionnaire to study how representatives of Belarusian HEIs perceived the Entrepreneurial university (University 3.0) concept as well as its conditioning factors and potential outcomes.
Main Findings
First of all, we revealed that the Belarusian academic community is not unanimous in understanding the concept “Entrepreneurial university”. According to the main emphasis provided by respondents, we got the following distribution of answers about what an entrepreneurial is: 12 respondents associated the concept with knowledge transfer and commercialization; 7 respondents stressed the interrelation of teaching, research and innovations; 5 respondents believed that the concept is about earning money; 1 respondent indicated that an entrepreneurial university means developing entrepreneurial competences.
These findings demonstrate the general misunderstanding or fragmented understanding of the phenomenon that may lead to a negative attitude from both HEI staff and policy makers and stress the importance of raising awareness and providing training at least for decision makers and spokesmen.
Figure 2 demonstrates the results of the assessment of Belarusian HEIs against the categories proposed by HEInnovate (1 – very low; 5 – very high).
Figure 2. Assessment of HEIs
Source: Author’s own elaborations
We distinguished pairwise between (i) HEIs that participated in the Experimental project and those that did not: (ii) estimates of faculty members that were aware of the concept and those who were not.
Surprisingly, the representatives of HEIs that were left beyond the scope of the Experimental project and those who were aware of the concept perceived their HEIs more advanced in all the areas.
To understand this paradox, we used the chi-square test for independence to discover if there was a relationship between two categorical variables – awareness of the concept and employment at a HEI participating in the Experimental project. Surprisingly, no statistically significant relationship was identified evidencing that implementation of the Experimental project went without raising awareness and wider involvement of faculty.
The analyses of answers to open-ended questions showed that many environmental factors are not only unsupportive to the HEI entrepreneurial development but jeopardize the sustainability of the higher education system in general.
Conclusions
The main conclusions from the study are as follows:
- Belarus has not reached the stage of institutional development to foster entrepreneurial HEIs and to expect outcomes of the entrepreneurial mission. To some extent, this explains the skepticism and misunderstanding of the concept of “Entrepreneurial university” (University 3.0).
- The main omission of the Experimental project is that the education and training of HEI authorities and faculty are not defined as first-priority measures. Such policy initiatives need to be clear in their objectives, tools, benefits and outcomes as well as evidence-based and open for discussion.
- Comprehensive initiatives in this sphere should be developed and implemented in close collaboration with the Ministry of Economy that is responsible for entrepreneurship, the business environment, entrepreneurial infrastructure as well as the State Committee for Science and Technology that is subordinated to the Council of Ministers and deals with the state policy in its sphere.
An important concern here is whether it is currently feasible to have the measures that are relevant and not-for-show rather than half-way initiatives and sticking plaster solutions despite the lack of funding, and absence of elaborate study in the field.
- Since the weakest area of Belarusian HEIs according to the HEInnovate tool is the problem of ‘Measuring impact’, the state should reconsider short-term target indicators for HEIs such as export growth rate and workforce productivity growth rate to stimulate investments the entrepreneurial transformation. It is worth monitoring such indicators as number of start-ups/spin-offs founded by graduates/faculty members; number of patents, licenses, trademarks co-owned by a HEI, income from intellectual property; number of R&D projects funded by enterprises etc. Alternatively, the Ministry of Education could adopt the ranking of entrepreneurial and inventive activity of universities used in Russia.
- Development of entrepreneurship centers as organizational units at HEIs – ‘one-stop shops’ or ‘single front doors’ for students, faculty, businesses – could be an initial step towards both raising awareness and the integration and coordination of entrepreneurship-related activities within a HEI in order to increase their impact and visibility of these activities.
References
- Audretsch, David B., 2014. “From the entrepreneurial university to the university for the entrepreneurial society.” The Journal of Technology Transfer 39(3), 313-321.
- Belstat (2017). Education in the Republic of Belarus. Statistical book.
- Guerrero, Maribel, and David Urbano, 2012. “The development of an entrepreneurial university.” The journal of technology transfer 37(1), 43-74.
- Marozau, Radzivon, Maribel Guerrero, and David Urbano, 2016 “Impacts of universities in different stages of economic development.” Journal of the Knowledge Economy, 1-21.
- Marozau, Radzivon and Vladimir Apanasovich, 2016. National GUESSS Report of the Republic of Belarus. http://www.guesssurvey.org/resources/nat_2016/GUESSS_Report_2016_Belarus.pdf
- Radzivon Marozau, 2018. Modernization and development of Belarusian higher education institutions based on the entrepreneurial university framework. BEROC Policy Paper Series, PP no.63.
- Wennekers, Sander, and Roy Thurik, 1999. “Linking entrepreneurship and economic growth.” Small business economics 13(1), 27-56.
- World Economic Forum, 2017. “Global Competitiveness Report 2017-2018”, edited by Klaus Schwab.
- Wong, Poh Kam, Yuen Ping Ho, and Erkko Autio, 2005. “Entrepreneurship, innovation and economic growth: Evidence from GEM data.” Small business economics 24(3) 335-350.
Acknowledgments: The author expresses gratitude to Prof. Maribel Guerrero from Newcastle Business School, Northumbria University for her valuable comments and reviews as well as to Yaraslau Kryvoi and Volha Hryniuk from the Ostrogorski Centre (Great Britain) for coordinating the research project that has resulted in this policy brief.
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.
Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed Through Cryptocurrencies?
Using novel approaches that exploit the blockchain to identify illegal activity, we estimate that around $76 billion of illegal activity per year is financed through payments in bitcoin (46% of bitcoin transactions). This staggering number is close to the scale of the US and European markets for illegal drugs and suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.”
Cryptocurrencies have grown rapidly in price, popularity, and mainstream adoption. The total market capitalization of bitcoin alone exceeds $150 billion as of July 2018, with a further $150 billion in over 1,800 other cryptocurrencies. The numerous online cryptocurrency exchanges and markets have a daily dollar volume of around $50 billion. Over 170 ‘cryptofunds’ have emerged (hedge funds that invest solely in cryptocurrencies), attracting around $2.3 billion in assets under management. Recently, bitcoin futures contracts have commenced trading on the major US derivatives exchanges (CME and CBOE), catering to institutional demand for trading and hedging bitcoin. What was once a fringe asset is quickly maturing.
The rapid growth in cryptocurrencies and the anonymity that they provide users has created considerable regulatory challenges, including the use of cryptocurrencies in illegal trade (drugs, hacks and thefts, illegal pornography, even murder-for-hire), potential to fund terrorism, launder money, and avoid capital controls. There is little doubt that by providing a digital and anonymous payment mechanism, cryptocurrencies such as bitcoin have facilitated the growth of ‘darknet’ online marketplaces in which illegal goods and services are traded. The recent FBI seizure of over $4 million of bitcoin from one such marketplace, the ‘Silk Road’, provides some idea of the scale of the problem faced by regulators.
In a recent research paper (Foley, Karlsen, and Putnins, 2018), which is forthcoming in the Review of Financial Studies, we quantify the amount of illegal activity that involves the largest cryptocurrency, bitcoin. As a starting point, we exploit several recent seizures of bitcoin by law enforcement agencies (including the US FBI’s seizure of the Silk Road marketplace) to construct a sample of known illegal activity. We also identify the bitcoin addresses of major illegal darknet marketplaces. The public nature of the blockchain allows us to work backwards from the law enforcement agency bitcoin seizures and the darknet marketplaces through the network of transactions to identify those bitcoin users that were involved in buying and selling illegal goods and services online. We then apply two econometric methods to the sample of known illegal activity to estimate the full scale of illegal activity. The first exploits the trade networks of users to identify two distinct ‘communities’ in the data—the legal and illegal communities. The second exploits certain characteristics that distinguish between legal and illegal bitcoin users, for example, the extent to which individual bitcoin users take actions to conceal their identity and trading records, which is a predictor of involvement in illegal activity.
We find that illegal activity accounts for a substantial proportion of the users and trading activity in bitcoin. For example, approximately one-quarter of all users (26%) and close to one-half of bitcoin transactions (46%) are associated with illegal activity. The estimated 27 million bitcoin market participants that use bitcoin primarily for illegal purposes (as at April 2017) annually conduct around 37 million transactions, with a value of around $76 billion, and collectively hold around $7 billion worth of bitcoin.
To give these numbers some context, the total market for illegal drugs in the US (Kilmer et al, 2014) and Europe (EMCDDA, 2013) is estimated to be around $100 billion and €24 billion annually. Such comparisons provide a sense that the scale of the illegal activity involving bitcoin is not only meaningful as a proportion of bitcoin activity, but also in absolute dollar terms. The scale of illegal activity suggests that cryptocurrencies are transforming the way black markets operate by enabling ‘black market e-commerce’. In effect, cryptocurrencies are facilitating a transformation of the black market much like PayPal and other online payment mechanisms revolutionized the retail industry through online shopping.
In recent years (since 2015), the proportion of bitcoin activity associated with illegal trade has declined. There are two reasons for this trend. The first is an increase in mainstream and speculative interest in bitcoin (rapid growth in the number of legal users), causing the proportion of illegal bitcoin activity to decline, despite the fact that the absolute amount of such activity has continued to increase. The second factor is the emergence of alternative cryptocurrencies that are more opaque and better at concealing a user’s activity (e.g., Dash, Monero, and ZCash). Despite these two factors affecting the use of bitcoin in illegal activity, as well as numerous darknet marketplace seizures by law enforcement agencies, the amount of illegal activity involving bitcoin at the end of our sample in April 2017 remains close to its all-time high.
In shedding light on the dark side of cryptocurrencies, we hope this research will reduce some of the regulatory uncertainty about the negative consequences and risks of this innovation, facilitating more informed policy decisions that assess both the costs and benefits. In turn, we hope this contributes to these technologies reaching their potential. Our work also contributes to understanding the intrinsic value of bitcoin, highlighting that a significant component of its value as a payment system derives from its use in facilitating illegal trade. This has ethical implications for bitcoin as an investment. Third, the techniques developed in the paper this brief is based on can be used in cryptocurrency surveillance in a number of ways, including monitoring trends in illegal activity, its response to regulatory interventions, and how its characteristics change through time. The methods can also be used to identify key bitcoin users (e.g., ‘hubs’ in the illegal trade network) which, when combined with other sources of information, can be linked to specific individuals.
References
- EMCDDA, 2013. “EU drug markets report: a strategic analysis.” Lisbon, January 2013.
- Foley, Sean; Jonathan R. Karlsen; and Talis J. Putnins, 2018. “Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed Through Cryptocurrencies?” (October 21, 2018), forthcoming in the Review of Financial Studies.
- Kilmer, Beau; Susan S. Sohler Everingham; Jonathan P. Caulkins; Greg Midgette; Rosalie Liccardo Pacula; Peter H. Reuter; Rachel M. Burns; Bing Han; and Russell Lundberg, 2014. “What America’s Users Spend on Illegal Drugs: 2000–2010.” Santa Monica, CA: RAND Corporation, 2014.
Acknowledgment: This Policy Brief is based on a recent research paper (Foley, Karlsen, and Putnins, 2018), which is forthcoming in the Review of Financial Studies, published by Oxford University Press and the Society for Financial Studies.
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.
How Are Gender-role Attitudes and Attitudes Toward Work Formed? Lesson from the Rise and Fall of the Iron Curtain
Gender differences in attitudes toward work and gender-role attitudes are important determinants of gender inequality in the labor market. In this brief we show that these attitudes vary considerably across countries and can also change within the same country over a relatively short time period. We then present evidence that politico-economic regimes that make substantial effort to bring women into the labor market can shape these attitudes: gender differences in attitudes toward work decrease, and gender-role attitudes become less traditional. Cultural norms with long historical roots are not necessarily invariant to large shocks, and policies aimed at raising women’s presence in the labor market can activate virtuous cycles of increasing female employment.
Gender inequality and cultural attitudes
Levels of gender inequality in the labor market differ considerably worldwide, even among countries at similar levels of economic development. Policies, technology, and economic conditions have long been shown to play an important role in explaining cross-country and regional differences in gender inequality. More recently, researchers have emphasized the role of cultural attitudes, such as women’s attitudes toward work and gender role attitudes (i.e. the beliefs that individuals hold regarding the appropriate roles of men and women in societies). Fortin (2008), for instance, finds that gender differences in attitudes towards work account for part of the existing gender wage gap in the US. Further, Fernández et al. (2004) show that differences in gender-role attitudes partly explain existing variation in female labor force participation. Given that gender differences in attitudes toward work and gender-role attitudes contribute to explain gender inequality in the labor market, economists have recently started studying the origins of these attitudes and their sources of variation over time.
In this policy brief we first document variation across space and over time in gender differences in attitudes toward work and gender-role attitudes; then, we present evidence that politico-economic regimes that put emphasis on women’s inclusion in the labor market can shape these attitudes.
Gender-role attitudes and attitudes toward work across space and over time
The World Values Survey (Inglehart et al., 2014) asks questions, among others, about the importance of work in one’s life, and about one’s beliefs on the appropriate roles for women and men in society.
Based on these questions, we measure gender differences in the importance given to work, and levels of agreement with statements regarding gender roles. Below we show that such measures vary considerably among a sample of countries in Europe and Central-Asia, as well as within countries over time.
Figure 1 shows gender differences in the percentage of survey respondents who reported that work was very important or rather important to them in the survey wave of 1995-1998. There is substantial cross-country variation in whether men or women give more importance to work, and in the magnitude of the gender difference. Moreover, the underlying variation across women is larger than across men (data not shown): the minimum and maximum values among men are 84% (in Georgia) and 97.5% (in Bosnia), whereas the respective values for women are 77% (in Georgia) and 96.6% (in Macedonia).
Figure 1. Gender differences in attitudes toward work
Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: Please say, for each of the following, how important is work in your life, and the options given are Very important, Rather important, Not very important, Not at all important. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.
Figures 2 and 3 show variation across countries in gender role attitudes. The share of respondents who agree with the statement “A working mother can establish just as warm and secure a relationship with her children as a mother who does not work “varies from a minimum of 47% in Poland to a maximum of 93% in Finland. The share of respondents who agree with the statement “Both the husband and wife should contribute to household income” varies from a minimum of 78% in Armenia and Finland to a maximum of 98% in Albania.
Figure 2. Working mother: warm relationship with her children.
Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each?. Do you agree strongly, agree, disagree, or disagree strongly? A working mother can establish just as warm and secure a relationship with her children as a mother who does not work. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.
Figure 3. Husband and wife should both contribute to income.
Source: Data are from the 1995-1998 wave of the World Values Survey. Individuals are asked the following question: People talk about the changing roles of men and women today. For each of the following statements I read out, can you tell me how much you agree with each. Do you agree strongly, agree, disagree, or disagree strongly? Both the husband and wife should contribute to household income. Countries selected are those in Europe and Central Asia where the question was asked in the 1995-1998 wave.
A recent strand of the economics literature analyzes the long-term determinants of attitudes and finds that they have very deep historical roots (see Giuliano, 2018). However, attitudes also evolve over time. Figures 4 and 5 show that while in some countries attitudes remain rather stable after 1998, in other countries they change substantially. In Russia, for instance, the gender difference in attitudes toward work has doubled over a period of ten years, with men becoming from 5 to 10 percentage points more likely than women to report that work is important to them. Turning to gender-role attitudes, the percent of respondents who think that a working mother can have a warm relationship with her children has increased the most in countries as different as Macedonia and Spain. The percent of individuals who think that both husband and wife should contribute to income has increased relatively sharply in Moldova, while declining rather substantially in Montenegro and especially in Serbia.
Figure 4. Gender differences in attitudes toward work over time.
Source: See Note to Figure 1.
Figure 5. Gender role attitudes over time.
Source: See Notes to Figures 2 and 3.
The graphs thus suggest that the attitudes considered here vary not only cross-sectionally but can also change over a relatively short time period. A natural question to ask is then: what type of shocks cause a change in gender differences in attitudes toward work and in gender role attitudes?
The role of politico-economic regimes in shaping attitudes
In recent work (Campa and Serafinelli, 2018), we show that politico-economic regimes that focus on women’s inclusion in the labor market can reduce gender differences in attitudes toward work and make gender-role attitudes less traditional. Studying the question of whether politico-economic regimes can change attitudes is difficult, because countries or regions exposed to different regimes are likely very different along many other dimensions, including their history, which is known to shape attitudes. To circumvent this problem, we exploit the imposition of state-socialist regimes across Central and Eastern Europe and their efforts to promote women’s economic inclusion (see Campa and Serafinelli, 2018). First we focus on the socialist regime that emerged in East-Germany in 1949. This regime favored women’s access to tertiary education and to qualified employment through massive childcare provision and other policies that were popular throughout the entire Central and Eastern European region. Conversely, in West-Germany, women were encouraged to either stay home after they had children or take part-time jobs after extended breaks (Trappe, 1996; Shaffer, 1961). Since East and West-Germany before 1949 were part of the same country and as such had a common history and shared institutions, we can compare attitudes in East- and West-Germany after the separation to isolate the impact of different politico-economic regimes on attitudes. In other words, the underlying hypothesis is that attitudes toward work and gender role attitudes in East- and West-Germany were the same before the separation. Such a hypothesis is arguably valid especially because we compare only individuals who, during the separated years, lived relatively close to the East-West border (e.g. within 50 km from the border), and are, thus, expected to have close enough (geography, culture and social norm-driven) preferences and attitudes before the separation.
The results of the comparison can be summarized as follows: (a) due to exposure to a different politico-economic regime, East-German women participated more in the labor market and became more educated than their West-German counterparts; (b) the importance given to work by East-German women increased, which led to a lower gender gap in attitudes toward work with respect to West-Germany; (c) both women and men in East-Germany developed less traditional attitudes than West Germans regarding the relationship of working mothers with their children and the gender division of roles in the household.
In the second part of the paper, we also extend the analysis to a number of transition countries in the Central and Eastern European region. We show that in Central and Eastern Europe between 1945 and 1990 gender-role attitudes became less traditional than in Western Europe.
Conclusion
In this brief we have documented that gender differences in attitudes toward work and gender role attitudes vary substantially across space and can change over a relatively short time period. Since these attitudes affect the level of gender inequality in the labor market, understanding their determinants is important and policy-relevant. In recent work (Campa and Serafinelli, 2018), we exploit the imposition of state-socialist regimes in Central and Eastern Europe and show that individuals exposed to different regimes develop different attitudes toward work and different gender-role attitudes.
Such a finding suggests that policies aimed at increasing women’s participation in the labor market can activate virtuous cycles; namely, such policies might improve the cultural acceptance of female work, thus potentially further raising women’s labor force participation. The evidence from the Central and Eastern European region also suggests that history is not necessarily an excuse for inaction regarding women’s participation in the labor market. While deeply rooted cultural norms can be an obstacle to women’s economic empowerment, these norms are not necessarily absolutely time-invariant, and can respond to important economic and policy shocks.
A caveat to such conclusions is that the evidence presented here is specific to women’s attitudes toward work and attitudes regarding the acceptability of female work. Other attitudes and norms are also important in defining the level of gender equality in a society, such as those involving the division of roles in a couple when both couple members work outside of the home, the acceptability of violence against women, the suitability of women and men to different fields of education. Little is known about these attitudes and more research is needed to understand which policies, if any, can change them.
References
- Campa, P. and M. Serafinelli (2018), Politico-economic regimes and attitudes: Female workers under state-socialism, Review of Economics and Statistics, Forthcoming
- Fernández, R., A. Fogli and C. Olivetti (2004), Mothers and sons: Preference formation and female labor force dynamics, Quarterly Journal of Economics 119(4): 1249–1299.
- Giuliano (2018). Gender: A Historical Perspective, in Oxford Handbook on the Economics of Women, ed. Susan L. Averett, Laura M. Argys, and Saul D. Hoffman, New York: Oxford University Press, forthcoming.
- Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (eds.). 2014. World Values Survey: Round Three – Country Pooled Datafile Version: www.worldvaluessurvey.org/WVSDocumentationWV3.jsp.
- Shaffer, H (1981), “Women in the two Germanies: A comparison of a socialist and a non-socialist society.”
- Trappe, H (1996), “Work and family in women’s lives in the German Democratic Republic”, Work and Occupations 23(4): 354–377.
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.
Gender in Economics: From Survival to Career Opportunities
Gender inequality goes beyond discrimination and sexism. It is also a matter of efficiency and development, and therefore, the socioeconomic losses that result from such inequality must be acknowledged and tackled. This policy brief summarizes the presentations held during the 6th SITE Academic Conference at the Stockholm School of Economics on December 17-18 2018. The event brought together scholars from around the world to examine existing forms of gender inequality, its causes, consequences, and policy interventions through a series of keynote speeches, research presentations and panel discussions.
Gender and survival
The reality of gender inequality is diverse throughout the world. The extent to which women and men face different opportunities and reach different outcomes vary substantially across countries and regions, and the forms of inequality that women face also vary geographically.
While richer countries have mostly closed their gender gaps in health and education, in other parts of the globe women are still struggling to survive, to make their marriage and reproductive choices freely, and to achieve the same educational opportunities as men. This is exactly where modern economic research can facilitate the understanding of the roots of such inequalities in each society, as well as the most likely drivers of change.
Corno, Hildebrandt and Voena (2017) show that in Sub-Saharan Africa and India, the age of marriage is a result of short-term changes in economic conditions (such as a reduction in crop yields due to droughts). Therefore, through for instance insurance mechanisms and temporary transfers, economic policy can influence marriage markets and the age of marriage. Relatedly, according to Ashraf, Bau, Nunn and Voena (2018), a girl in Indonesia or Zambia has a higher probability of being educated if she belongs to a group practicing bride price, defined as the “price” paid by a groom or his family to the bride’s family. This means that marriage markets could be a driver of educational investment. Cousin marriage is another issue within this context. Edlund (2018) suggests that this system serves as a barrier for economic growth by favoring men over women, the old over the young, and the collective over the individual. In general, challenging these marriage systems and improving female economic opportunities require a deeper understanding of the economic role of traditional cultural norms and institutions.
Some groups of women struggle for survival even in the so called “developed world”, being victims of gender violence. Sex workers in the United States are a particularly vulnerable population in this matter. Cunningham, DeAngelo and Tripp (2017) point out that, given that prostitution in most cities of the US isn’t only illegal, but also very dangerous (recording the highest homicide rate of any female occupation), it is critical to improve sex workers’ safety. Craigslist Erotic Services (CES) seemed to have contributed to it, by reducing female homicide rates by 17.4%. Apparently, this was a result of street prostitutes moving indoors and being able to filter clients to be safer. It is, therefore, suggested that the closure of such a platform put sex workers in an even more vulnerable position. Similarly, when it comes to adult entertainment establishments and its relation to sex crimes, Ciacci and Sviatschi (2018) argue that this type of businesses helps decrease daily sex crimes between 7-13% in the precinct where they are located.
When discussing approaches to prostitution, the “Nordic Model” has been highly praised and adopted by several countries. The term refers to a reform initiated in Sweden that considers buying sex a criminal offense, while decriminalizing those who are prostituted. However, preliminary results from Perrotta Berlin, Spagnolo, Immordino and Russo (2018) suggest that intimate partner violence and violence against women might have increased because of its enactment in Sweden.
Gender violence, however, isn’t only domestic or affecting sex workers. Borker (2018) claims that, in India, female college students are willing to choose less prestigious universities, to make additional expenses and to spend more time on transportation than their male counterparts only to avoid harassment on the street or public transportation. Street harassment, therefore, perpetuates gender inequality in both education and potentially the labor market.
Challenging social norms
As already seen, even the most gender-equal countries still suffer from persistent forms of inequality that need to be acknowledged and tackled. Doing so will result both in fairer societies and in more efficient economies, because it will make full use of both halves of the world’s skills and knowledge.
Friebel, Auriol and Wilhelm (2018) state that, in Europe, it is harder for women to make a career in economics. The representation of women in academics is low, and the higher ranked the university, the lower is the representation. This could be a consequence of several issues, one of them being the “glass ceiling”.
The glass ceiling, according to Bertrand (2017), is the phenomenon by which women remain underrepresented in high-level occupations, and earn less. Even in countries such as Denmark and Sweden, women still receive less for the same jobs. There are many potential explanations for this. One of them refers to the gender differences in psychological attributes in work, such as the idea of women performing worse under pressure or being unwilling to compete. This interpretation ultimately falls under the nature vs nurture discussion and only accounts for up to 10% of the pay gap. Another reason states that women suffer the penalties associated with demanding more flexibility. Such demand comes from the need to perform non-market work, like domestic work and, especially, caring for children. This means that women, especially the more educated ones, are paying a disproportionate price in the labor market for raising a couple’s children. Giving women more flexibility won’t crack the glass ceiling, au contraire, it will backfire because flexibility is negatively priced in the market. Besides, it doesn’t address the earning gaps. A more compelling proposal is to shift the focus from increasing flexibility to changing social norms and gender role attitudes. Normalizing and encouraging paternal child care in workplaces, for example, could be a way to do so.
Social norms based on traditional gender stereotypes also seem to be the reason why in Sweden, promotions to top jobs dramatically increase women’s probability of divorce but do not affect men’s marriages, as reported by Folke and Rickne (2018). In this case, promoting norms and policies with a more gender-equal approach to couple formation could increase the share of women in top jobs.
Given the importance of social norms, understanding how they can change is crucial. In Saudi Arabia, two studies were conducted on the influence of misperceived social norms. Both showed that the low-cost intervention of simply providing information could make a big difference. In one case, Bursztyn, González and Yanagizawa-Drott (2018) have evidenced that most young married men privately support female labor force participation (FLFP) outside of home. Nevertheless, they tend to underestimate the level of support for FLFP by other men. When correcting those misperceptions, the men’s willingness to let their wives join the labor force increases. Comparably, Ganguli and Zafar (2018) have shown that there is an increased likelihood of working full-time for female students when they, along with their close circles, receive information about the labor market and the aspirations of other women peers.
Challenging social norms isn’t only beneficial when discussing the glass ceiling and FLFP, it also has the potential to improve public health. In fact, Milazzo (2018) argues that women’s increased mortality rate in India can be an unintended consequence of son preference. Son preference induces women with a first-born daughter to adopt behaviors that increase the risk of maternal morbidity and mortality. Therefore, interventions to change deeply rooted social norms such as the boy preference could significantly reduce maternal mortality risk.
Bridging research and policy
In Malawi, research by Perrotta Berlin, Bonnier and Olofsgård (2017) on aid project location suggests that proximity to aid has a positive effect on the lives of women and children. Likewise, Goldstein (2018) reports that the World Bank’s Empowerment and Livelihoods for Adolescents (ELA) program in Uganda has also led to positive reproductive outcomes and income effects. These results illustrate the importance of reducing the divide between research and policy. Research has the potential of serving as an instrument for informed policy-making and aid intervention.
The Organization for Economic Cooperation and Development (OECD), for instance, applies research to create tools that help improve economic and social well-being. Two of those tools are the Social Institutions and Gender Index (SIGI) and the Development Assistance Committee (DAC) Gender Equality Policy Markers. On one hand, Missika (2018) explains that the SIGI is a cross-country measure of discriminatory social institutions against women and girls. Though the progress is slow (it might take around 200 years to close the gender gaps), its use gradually promotes the creation of locally designed solutions that, combined with adequate legislation, could enhance gender equality. On the other hand, Williams (2018) states that the DAC Gender Equality Policy Markers are meant to ensure that women have access to and benefit from finance.
Consistently , for the Swedish International Development Agency (SIDA), which works on behalf of the Swedish government, gender equality is a priority that permeates its interventions. In this context, the Feminist Foreign Policy has strengthened Sweden’s commitment in the topic.
Prior to finalizing the conference, representatives of the FROGEE Network (Forum for Research on Gender Economics in Eastern Europe and Emerging Economies) made a short presentation about the key challenges for achieving gender equality in their countries and the research opportunities available.
Conference material, including presentations, can be found here.
Speakers at the conference
Marianne Bertrand, University of Chicago
Alessandra Voena, University of Chicago
Alessandra González, University of Chicago
Anders Olofsgård, SITE
Annamaria Milazzo, World Bank
Bathylle Missika, OECD Development Centre
Eva Johansson, SIDA
Girija Borker, World Bank
Guido Friebel, Goethe University Frankfurt
Ina Ganguli, University of Massachusetts
Amherst Johanna Rickne, Stockholm University
Lena Edlund, Columbia University
Lisa Williams-Katz, OECD
Maria Perrotta Berlin, SITE
Markus Goldstein, World Bank
Michal Myck, CenEA
Riccardo Ciacci, The University Loyola Andalucía
Scott Cunningham, Baylor University
References
- Ashraf, Nava; Natalie Bau, Nathan Nunn, and Alessandra Voena. 2018. “Bride Price and Female Education”. The National Bureau of Economic Research Working Paper No. 22417.
- Bertrand, Marianne. 2017. “The Glass Ceiling”. Becker Friedman Institute for Research in Economics Working Paper No. 2018-38.
- Borker, Girija. 2018. “Safety First: Perceived Risk of Street Harassment and Educational Choices of Women”. Job market paper.
- Bursztyn, Leonardo; Alessandra González, and David Yanagizawa-Drott. 2018. “Misperceived Social Norms: Female Labor Force Participation in Saudi Arabia”.
- Ciacci, Riccardo; and Maria Micaela Sviatschi. 2018. “The Effect of Adult Entertainment Establishments on Sex Crime: Evidence from New York City”.
- Corno, Lucia; Nicole Hildebrandt, and Alessandra Voena. 2017. “Age of Marriage, Weather Shocks, and the Direction of Marriage Payments”. The National Bureau of Economic Research Working Paper No. 23604.
- Cunningham, Scott; Gregory DeAngelo, and John Tripp. 2017. “Craigslist’s Effect on Violence Against Women”.
- Edlund, Lena. 2018. “Cousin Marriage Is Not Choice: Muslim Marriage and Underdevelopment”. American Economic Association Papers and Proceedings, Volume 108, pages 353- 57.
- Folke, Olle; and Johanna Rickne. 2018. “All the Single Ladies: Job Promotions and the Durability of Marriage”.
- Friebel, Guido; Emmanuelle Auriol, and Sascha Wilhelm. 2018. “Women in Europen Economics”. [Mimeo]
- Ganguli, Ina; and Basit Zafar. 2018. “Information and Social Norms: Experimental Evidence on Labor Market Aspirations of Saudi Women”. [Mimeo]
- Goldstein, Markus. 2018. “Evidence on adolescent empowerment programs from four countries”. [Mimeo]
- Milazzo, Annamaria. 2018. “Why are adult women missing? Son preference and maternal survival in India”. Journal of Development Economics, Volume 134, pages 467-484.
- Missika, Bathylle. 2018. “Are laws and social norms still an obstacle to gender equality? Lessons from the SIGI 2019”. [Mimeo]
- Perrotta Berlin, Maria; Evelina Bonnier, and Anders Olofsgård. 2017. “The donor footprint and gender gaps”. WIDER Working Paper 2017/130, United Nations University World Institute for Development Economics Research.
- Perrotta Berlin, Maria; Giancarlo Spagnolo, Giovanni Immordino, and Francesco Flaviano Russo. 2018. “Prostitution and Violence: Empirical Evidence from Sweden”. [Mimeo]
- Williams, Lisa E. 2018. “Financing for gender equality beyong ODA”. [Mimeo]
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.
US-China Trade War of 2018 and Its Consequences
The trade war between the United States and China became one of the most significant events in the global economy in 2018. This policy brief explores the main drivers of the US-China Trade War, including trade imbalances and intellectual property concerns, and examines the potential consequences for both countries as well as the broader impact on other economies, such as Russia.
Chronology of the Trade War
Donald Trump started the war, raising import tariffs on solar panels in January 2018, of which the main supplier is China. In response, on April 2nd, China raised import duties on 128 commodities originating from the United States. On July 6th, the US increased tariffs on Chinese goods by 25 pp., imports worth $34 billion. China responded symmetrically. In August, the United States increased the tariffs on another $16 billion of imported goods from China, to which a symmetrical response again followed. In September, the United States again applied higher tariffs for $200 billion of Chinese exports, and China for $60 billion of US exports. At each stage of the conflict escalation, China appealed to the WTO with complaints about the actions of the United States, pointing to the inconsistency of their actions with the obligations and principles of the WTO. There were several meetings of official representatives from the United States and China – without any significant results.
What are the main reasons for this unprecedented escalation?
Imbalance and Intellectual Property
The economies of the US and China today are by far the largest in the world, and the trade turnover between the two countries is one of the most important. A remarkable feature of these trade flows over last decades is their imbalance. In 2017, the United States imported $526 billion worth of goods from China, while China’s imports from the United States amounted to $154 billion. Part of this imbalance is offset by trade in services, but it is not enough to even it out: in the same the year the United States delivered $57 billion worth of services to China while importing services of $17 billion from China.
Experts have different views on this imbalance. On the one hand, there is a perception that it is a source of world economy vulnerability, a source of potential crisis. Therefore, it is necessary to reduce the trade deficit. Another point of view is that this imbalance merely reflects the fact that the US economy and its assets are very attractive to investors from all over the world, including Chinese – and that, in turn, requires that the surplus of capital flows biased to US side, was compensated by the corresponding deficit of trade in goods and services. One such investor is the Chinese state itself, which for many years has been pursuing a policy of exchange rate undervaluation in order to promote foreign trade. It led to an enormous accumulation of foreign exchange reserves and as of January 2018, China held $1.17 trillion of US bonds and was the largest creditor of US government.
US President Donald Trump referred to this trade imbalance as one of the reasons for the outbreak of this trade war against China. Trump aims at reducing the deficit by $100 billion from the current $375 billion. The unilateral increase in import tariffs applied to Chinese goods was the first action of the US administration in this direction.
The second, no less important, formal reason for the trade war is the inadequate protection of intellectual property rights in China. China’s production of counterfeit products, the lack of adequate practices and laws to protect foreign technologies from illegal dissemination in the country, is not news to anyone. And although the almost two decades since China’s WTO accession have meant a largely modernized legal framework in this regard, a number of important provisions are still inconsistent with international practices, and the implementation of existing intellectual property rights leaves much to be desired. Established in 2012, The Commission on the Theft of American Intellectual Property identifies China as the most malicious violator of US rights. The exact damage is not known, but the commission assessment of the losses to the American economy due to the forced transfer of technology to Chinese partners – which is an unspoken condition of foreign manufacturers access to the Chinese market – industrial espionage, contradictions in legislation, requirements for the storage of sensitive data in China are in the range from $225 to $600 billion per year (Office of US Trade Representative, 2018).
While both the trade deficit and the intellectual property rights issue were recognized for many years, it was in 2018 that Trump started acting on them. Therefore, in order to discuss the potential impact of the conflict between the world’s largest economies on themselves and other economies, such as Russia, it is important to understand what drives the actions undertaken by Trump’s administration.
Populism
Trump won the elections in 2016 with a minimum margin against the Democratic rival. To provide support for his decisions and to increase the chances of being reelected for the next term in 2020, it is crucial to maximize the pool of his supporters. Trade policy measures aimed at import substitution are very effective populist policies in any country. One of the first steps made by the US toward trade war was the increase in import tariffs on steel and aluminum – for all countries. Metallurgy and coal industries are among the most organized and strong lobbyists in any country. The European Union as an economic organization started with the European Coal and Steel Association. By aligning interests with these sectors much can be achieved in relation to trade liberalization, and vice versa – by increasing the level of protectionism, a significant popularity increase can be among voters whose incomes depend on the success of companies in these industries.
Deterrence
China works hard raising the technological level of its economy. In recent years the Chinese government and Communist party launched a number of ambitious programs aimed at achieving a technological breakthrough, lessening the dependence on imported technologies by substituting them with ones produced by domestic innovation centers. These programs specify the priority sectors, in which state subsidies are provided for the acquisition of foreign technologies by Chinese companies and their adaptation. One of the common arguments was that the United States believes that powerful state support for technology sectors in China, along with the existing problems in protecting intellectual property rights, increases the risks and potential losses of American companies.
However, while these concerns seem reasonable at first, they should not be taken at the face value.
China’s ability to push out American companies in the high-tech sector on the world market seems rather limited. So far, China has only succeeded in increasing its share in the middle and low technology segments. Instead, in recent years, China is rapidly increasing its defense spending, which in 2017, for the first time, reached a level of 1 trillion yuan (about $150 billion). China’s defense spending is the second highest in the world after the United States. Moreover, it’s growing very fast. While in 2005 the Chinese nominal defense expenses were only 10% of American expenses, in 2018 they are already around 40%. The dominance of state enterprises in the defense industry in China implies that the real purchasing value of these expenditures is quite comparable. New and existing Chinese industrial policy programs target military and dual-use industries among others. Therefore whilst addressing the intellectual property rights problem in China now, Trump’s administration also aims at preserving US leadership position in the military sector, which finds widespread support in Trump’s main voter groups among Republicans.
Obsolete Weapon
Historically, trade wars implied tariff escalations to protect domestic industries from foreign competition. Today, the Trump administration behaves in a similar manner. However, the circumstances now are fundamentally different from those in the first half of 20th century and earlier. Firms not only trade in final goods, but more and more they trade in intermediate products and within firms themselves (Baldwin, 2012). The distribution of the production process to many companies across different countries of the world leads to two important effects, which were not observed in previous trade wars.
First, it is the effect of the escalation of tariff protection in the framework of the value chains. The import tariff is applied to the gross value of the product crossing the customs border. However, the exporting firm’s contribution to the gross value might be quite small. So the effective level of the tariff will be higher than the nominal level of the tariff, known as a so called amplification effect (World Bank, 2017, page 98). It means that the effective growth of the tariff by 25 percentage points in relation to Chinese imports will significantly exceed 25 % and in some cases can even become prohibitive. So, the tariff warfare will result in significantly greater losses for the sectors involved in the value chains, compared to the sectors less exposed to them. It means that foreign investors and multinational companies in China will suffer bigger losses compared to purely domestic Chinese companies. The Peterson Institute for International Economics made an assessment and confirmed these observations (Lovely and Yang, 2018).
Second, China’s participation in international multinational companies most often occurs in the assembly segments, while developed countries’ companies contribute at other stages, such as with innovation, design, financial and consulting services, marketing, and after-sales services. Then, the protectionist measures against goods produced in China by multinational companies will hit an American economy, generating losses in the service segments. A similar episode happened, for example, in 2006, when the European Union introduced anti-dumping duties on imported footwear from China and Vietnam, which in turn lead to a decline in the services sector in Europe – imported footwear contained a significant share of the value added created by European designers and distributors (World Bank, 2017). Obviously, we will observe the same consequences in the United States now, since the role of the American services sector in creating and promoting Chinese goods on the American market is significant and according to World Bank estimates in 2011, the contribution of value added generated by foreign services in China’s gross exports amounted to about 15% (World Bank, 2017).
Thus, not only the economy of China, but also the US economy itself will suffer from the growth of import tariffs in the USA. The USA is not an exception here – the governments of most countries continue to live in the paradigm of trade policy, which suits the structure of the world trade as at the beginning of the 20th century, while trade has gone far ahead and requires much more elaborate effective regulatory tools than tariffs on imported goods.
Consequences for Russia
The consequences of the US trade war with China for the Russian economy depend on what the main goals of the war are. If the motive is primarily electoral – to secure enough support in 2020, one can expect that the protective measures will be short-lived, and the geographical distribution of investment flows will remain almost intact and that China will remain an important location for global value chains transactions. The trade war will in this case lead to some economic slowdown in the short term. The main effects will be related to the redistribution of income within economies, where protected sectors will benefit on the expense of all other sectors. In these circumstances, Russia would suffer direct losses from the growth of tariffs on their exports to US (now it is predominantly steel and aluminum), but for the economy as a whole, the losses will not be significant, especially relative to the losses Russia bears because of sanctions.
However, if the main reason for the trade war has a long-term perspective, the investors will be forced to adjust the geography of their investment plans and China will face a significant outflow of foreign investments, which will significantly affect Chinese – and global – economic growth. In this case, both for Russia and for the whole world, the indirect effect of the US-Chinese trade conflict will be quite noticeable and it will take years to create new trade links and restore world trade and global value chains.
References
- Baldwin, Richard, 2012. “Global supply chains: why they emerged, why they matter, and where they are going”, CTEI Working papers 2012-13, The Graduate Institute, Geneve
- Lovely, Mary E., and Liang Yang, 2018. “Revised Tariffs Against China Hit Chinese Non-Supply Chains Even Harder.” PIIE Policy brief, Peterson Institute
- Office of the US Trade Representative. March 22, 2018. “Executive office of the President findings of the investigation into China’s acts, policies, and practices related to technology transfer, intellectual property, and innovation under section 301 of the trade act of 1974.” https://ustr.gov/sites/default/files/Section%20301%20FINAL.PDF
- World Bank, 2017. “Measuring and analyzing the impact of GVCs on economic development”. World Bank, Washington DC.
Note
A longer version of this brief has been published in Russian by Republic: https://republic.ru/posts/92217
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.