Tag: Georgia

Assessing a Model for the Implementation of an Equal Pay Review and Reporting (EPRR) Methodology in Georgia

Posted on November 3, 2021 | Policy Brief

20211102 Assessing a Model of Equal Pay

Georgia’s gender pay gap has started to attract the attention of the population and policymakers alike. The gap persists despite working women generally reporting better labor-market skills and personal characteristics. It has been argued that this could be the result of systematic gender-based workplace wage discrimination, resulting in unequal pay for equal work. The discussion that ensued highlights how the fight to guarantee equal pay for equal work could benefit from establishing an Equal Pay Review and Reporting Mechanism. In response, the ISET-PI team – after reviewing the best international practices – devised and tested an excel based tool that could help companies and governmental agencies identify, monitor, and fight gender discrimination in Georgia. The main quantitative result of the exercise identified that, should reporting be made mandatory, extending the obligation to companies that employ up to 50 people would make the administrative costs for companies and public administration up to twenty times higher; thus, the usefulness of the tool was found to be substantially limited when applied to smaller companies. Finally, the exercise emphasized the reluctance of companies to provide the data required, leading to the conclusion that the successful implementation of such an initiative would require the enforcing agency to have the legal authority to sanction failures to provide the necessary data.

Introduction

One of the key gender inequality indicators is the gender pay gap – or gender wage gap – calculated as the average difference between the remuneration for men and women in the labor market. Its evolution is monitored worldwide, and closing this gap is considered a key step towards more inclusive and prosperous economies and societies. According to the World Economic Forum, as of 2020, no country (including the top-ranked ones) had yet achieved gender parity in wages.

In Georgia, the unadjusted hourly gender pay gap amounts to 17.7 percent of the average male hourly wage (UN Women, 2020). Moreover, when controlling for personal characteristics of men and women, the adjusted hourly gender pay gap in Georgia is estimated to be 24.8 percent (UN Women, 2020). This implies that women, on average, have better observable labor-market characteristics but are still paid less than men.

These findings prompted a core discussion within the Georgian society on the presence of unequal pay for equal work in Georgia as one of the possible reasons for the gap and how to tackle the problem. The idea of equal pay for equal work entails that individuals in the same workplace are given equal pay if they perform the same type of work. Consequently, this potential source of the pay gap can only be verified at the individual employer level. This is accomplished by calculating the unexplained gender pay gap at the organizational/employer level and validating whether, and why, these differences exist.

Given the attention the topic holds in the national discourse, ISET Policy Institute created and tested an excel tool, built in line with the international best practices and adapted to the Georgian context, to help employers and government offices identify and measure the differences in wages between men and women performing equal work. During this process, the team learned several noteworthy lessons, as summarized in this policy brief.

International Experience

There is growing consensus that transparency is critical when dealing with pay inequality and, therefore, gender pay reporting should become the norm. Since 2010, several (mostly developed) countries have introduced reporting schemes to monitor gender pay gaps, promote awareness about gender equality issues throughout society (particularly among employees), and increase organizations’ accountability to address gender inequalities (Equileap, 2021).

However, the gender pay gap is a key issue for which the disclosure of information remains particularly low. Equileap’s 2021 report revealed that 85 percent of organizations worldwide did not publish information on remuneration differences between female and male workers in 2020.

Three countries, according to Equileap, lead the way in gender pay gap reporting: Spain, the UK, and Italy (Figure 1). In each of these top three countries, reporting is mandatory.

Figure 1. Percentage of organizations publishing gender pay information, per country

Source: Equileap, 2021. The figure only includes countries for which more than 49 surveyed organizations were included in the Equileap dataset.

However, even in these countries, and, more generally, in all countries scrutinized by Equileap but Iceland, firms with 50 or fewer employees are not required to report on gender pay gaps.

The Case of Georgia

Georgian legislation clearly establishes the principle of equal pay for equal work for all employees. The requirement applies to both public and private organizations. Nevertheless, enforcement of the law remains a significant challenge.

At present, Georgia has no reporting requirements regarding employee salaries for private organizations. It has not yet designed a reporting scheme for equal pay for equal work, nor has it assigned the task of collecting this information to any governmental body.

Moreover, Labour Inspectorate representatives state that few wage discrimination cases are currently being filed in the country. The main reason behind this is that norms regarding equal pay for equal work have never been properly specified. In addition, there are no explicit criteria defining the concept of ‘equal work’. Thus, employers and employees alike do not seem to fully understand the phrase – equal pay for equal work.

The Excel Tool

After a careful review of the three tools presently utilized to calculate gender pay inequality (the Swiss Logib, the German Logib-D, and the Diagnosis of Equal Remuneration (DER) tool developed by UN Women), ISET-PI built a Georgian model as a modified version of the DER tool that is adapted to the Georgian context and includes some variables from the Swiss tool.

The tool itself is an excel file with several worksheets. The two main facets are the inputted data sheet and the results sheet. Companies may input information on their employees in the data sheet, and the findings will then be demonstrated in the results sheet. The tool first identifies people performing the same work, and classifies jobs based on their official titles, alongside managerial responsibilities and skill requirements. After individuals are grouped by job, the tool calculates the average salary within each group separately for men and women. Thereafter, the pay gap is calculated based on the average salary for the two gender groups.

With the support of the Employers’ Association, several companies of all sizes were approached to test the tool. Unfortunately, only a few agreed to participate, and just two completed the trial: one small-sized enterprise (with 50 or fewer employees) and a large-sized enterprise (with 250 or more employees).

While low participation rates have significantly limited our analysis, we still obtained several important insights which are discussed in the next subsection.

Findings

Firstly, it is important to note that companies’ willingness to share anonymized salary data was very low, even among the companies that completed the test.

Secondly, the usefulness of the tool for obtaining a comprehensive view of equal pay for equal work in small companies (with 50 or fewer employees) appeared fairly limited as few people within the same firm perform the same job.

Thirdly, we performed a simple cost assessment exercise to evaluate the compliance costs – to both companies and the government – of collecting and reporting the gender pay gap. We found that extending the data collection requirement to small companies would increase the compliance costs by up to 20 times (high-cost scenario) compared to an example where small companies are exempt. This is because there are many more small companies in Georgia (146,802), than those classified as medium or large ones (2,752 and 609, respectively).

In addition, during the implementation of the exercise, we became aware of the following:

  • Under the existing legal provisions, it would be extremely difficult to introduce the EPRR in a mandatory format – no governmental agency could sanction companies for failing to comply.
  • Opting for the mandatory option and sanctioning the emergence of unequal pay in certain job categories could incentivize companies to manipulate the data input. In this case, therefore, it would be ill-advised to provide the full tool to companies, as they could more easily adjust data inputting to obtain more favorable indicators through successive iterations.

Conclusion

Setting up an EPRR system is one way to contribute to the implementation of the equal pay for equal work principle.

Designing the Georgian Model for the Implementation of an Equal Pay Review and Reporting Methodology generated several useful insights that might prove valuable for policymakers in Georgia and other developing countries:

1) The EPRR instrument can be utilized for the analysis of gender pay gaps within companies with more than 50 employees. Within smaller companies, evaluating the gender pay gap significantly increases the costs to society, while providing rather limited additional information.

2) The decisions about whether to provide the analytical part of the tool to companies, and whether reporting should be voluntary or mandatory should be taken jointly. If the goal is to provide an instrument to the agency enforcing the equal pay for equal work principle and to facilitate appeals from workers, the tool should be made mandatory. However, in this case, companies should only provide the input data, without having access to the part of the tool that assesses pay gaps at the job level. On the other hand, if the goal of the reform is to support willing companies in their efforts to eliminate unequal pay for equal work conditions, a non-mandatory form may be preferable. In this instance, companies should have access to the full version of the tool. This would allow them to better understand the dynamics that lead to unequal pay and thus put in place internal remedial actions.

3) If the goal is to provide a tool to the agency enforcing the equal pay for equal work principle, it is crucial that any gaps in the associated legislation are closed. As such, the enforcing agency should be capable of sanctioning failures to provide the required data, and prosecuting violations of the equal pay for equal work principle.

Finally, it is important to note that testing the application of the equal pay for equal work principle at the company level through an EPRR system, while useful for identifying potential causes of the gender pay gap and the existence of gender disparities within companies, is just a first step in a longer and more complex process. Once disparities are identified, both companies and enforcing agencies should follow up with additional research and analysis to determine whether these disparities are linked to discriminatory practices, and what type of remedial options could be adopted.

References

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

Food Security in Times of Pandemic in Georgia

Posted on November 16, 2020 | Policy Brief

An image of the wheat field with with grain harvester representing food security

The lockdowns and trade restrictions related to the COVID-19 pandemic resulted in shortages of some major food commodities on international and local markets. In this policy brief, we discuss and analyze Georgia’s response to the crisis in terms of food security and agricultural policy. Furthermore, we provide recommendations to ensure fewer disruptions in food supply chains and low volatility in food prices.

Background

COVID-19 has posed significant risks to the food security of many countries including Georgia. Lockdowns and pandemic-related trade restrictions across the world have resulted in shortages of some major food commodities on international and local markets (e.g. sunflower oil shortage in Russia). As of October 16, 2020, according to a World Bank report, 62 jurisdictions have executed a total of 62 export controls in food commodities since the beginning of 2020 (Table 1).

Table 1. Total number of new export controls and import reforms in the food sector globally since January 2020, by month.

Source: World Bank Group, Global Alert Team, 2020

Most of the interventions have involved import reforms with the largest number of new regulations imposed in March-April.  On August 18, 2020, the Eurasian Economic Commission announced an EAEU import tariff quota on certain agricultural goods, valid for 2021. Turkey has also conducted a price stabilization policy by announcing purchasing prices for apricots, paddy, and dried raisin. On August 5, 2020, the government of Turkey introduced additional customs duties on certain agricultural products including chocolate, pasta, and some food preparations. It also eliminated import duties on wheat and barley in October.

Given that Georgia is a net importer of food, and in light of the trade restrictions imposed by its major trade partners, food security moved up on Georgia’s agricultural policy agenda. In order to weaken the adverse impact of the pandemic, keep food prices stable, and reduce input prices for farmers, the state designed the following set of measures:

  • 10M Georgian lari (GEL) from the Ministry of Environmental Protection and Agriculture (MEPA) budget were allocated to subsidize imports of 9 food products: pasta, buckwheat, vegetable oil, sugar, wheat, wheat flour, milk powder, and beans (Legislative Herald of Georgia, 2020). The program subsidized importers’ additional costs resulting from exchange rate fluctuations and was implemented between March 15-May 15;
  • Additional 16M GEL were allocated for purchasing sugar (5,000 tons), vegetable oil (1,500 thousand liters), and pasta (500 tons) stocks from private companies;
  • An anti-crisis plan, “Caring for Farmers and Agriculture”, was presented by the state on March 12. The plan entailed two forms of aid: direct assistance to farmers and sectoral support. Some of the support measures included the distribution of so-called “agricultural cards”– subsidies for cattle-breeding and land cultivation services for smallholder farmers (registered farms with plots in the range of 0.25-10 ha); provision of cheap diesel fuel for farmers; nullification of costs of land reclamation services; provision of agricultural loans and insurance; grants for machinery, equipment, and cooperatives.

Results of Government Interventions

As of October 9, 2020, state support schemes had the following results:

  • Up to 165,000 farmers had been granted agricultural cards. The size of the subsidy exceeded 28.9M GEL;
  • Under the agro-diesel program (which subsidized fuel prices for agro-producers) 122,000 beneficiaries received discount cards on 32,000 tons of agro-diesel;
  • More than 17,000 policies had been issued and 18,000 hectares (around 2% of agricultural land) had been insured under the agro-insurance program. The value of the insured crop exceeded 160M GEL;
  • Across different regions of Georgia, 255 applications for modernization of the dairy sector were approved. In total, 12.4M GEL were spent on this program;
  • 2,215 agro-loans had been issued with a 6-month interest rate covered by the state. The total amount of loans exceeded 40M GEL, including the co-financing of interest rates, which exceeded 3.3M GEL.

While many farmers have benefited from state support programs, these programs were not directly focused on the main consequences of the pandemic. The major threats posed by the pandemic – disruptions in food supply chains leading to decreased sales of agricultural products and price volatility – were not sufficiently addressed by the state support programs. According to the Georgian Farmers’ Association (GFA), 55% of surveyed farmers and agricultural business representatives encountered complications with product realization due to pandemic-related restrictions. Most farmers depend on the HoReCa (hotels, restaurants, and cafés) and hospitality sector, and their products are largely procured for accommodation and food facilities. 60% of those surveyed claimed that they were simply unable to sell their products due to the closure of hotels, restaurants, and cafés.

Food Price Dynamics

During March-May 2020 – the first months of the pandemic – food prices in Georgia showed upward trends on both a month-on-month and year-on-year basis (Figure 1).

Figure 1. Month-on-month and year-on-year changes in food prices

Source: GeoStat, 2020

The main explanation is likely the depreciation of the GEL against the US dollar: during March-May 2020, the GEL depreciated against the USD by 15.8% from 2.71 to 3.14 compared to March-May 2019 (National Bank of Georgia, 2020). As Georgia is a net importer of food commodities, the depreciation of the GEL put upward pressure on food prices. To limit the GEL depreciation and its impact on food prices, the Government of Georgia subsidized additional costs of importers of major food commodities arising from exchange rate fluctuations. The price restraint mechanism involved negotiating with food importers to not increase prices of their commodities and setting the exchange rate of the GEL against the USD at 3, while the Government of Georgia subsidized the corresponding difference between the actual and fixed exchange rates. Despite minimizing the effects of GEL depreciation, food prices in Georgia experienced a significant increase during the observed period: disruptions in supply chains associated with the COVID-19 pandemic led to food shortages that further increased food prices.

In April, annual food price inflation marked its highest level at 16.1% during March-August 2020.  Since then, annual food price inflation has been decreasing as farming activities resumed after COVID-19-related restrictions were relaxed and seasonal (locally produced) agricultural products appeared on the market. Accordingly, food prices started to decrease on a monthly basis.

However, with very few exceptions, prices for major food commodities that were subsidized by the state during March-May increased for both month-over-month and year-on-year comparison (Table 2). On a monthly basis, the biggest price changes were observed for sugar; while on annual basis prices for buckwheat increased the most.

Table 2. Year-on-year changes in prices of major food commodities, March-September 2020

Source: GeoStat, 2020

While food prices could have increased even more in the absence of subsidies, it appears that the state measures did not fully reach their objectives and could not fully overshadow the adverse impact of the pandemic and GEL depreciation.

Recommendations

The pandemic has shown the need for increasing the level of food security in Georgia. Given the multidimensional nature of food security, a longer-term policy should consider not only an increase in domestic production of key food commodities but also a diversification of import markets to ensure low volatility in food supply and prices. As an immediate response to the pandemic, it is recommended to:

  • further subsidize farm inputs in order to reduce the current costs of production;
  • support farmers in selling their produce;
  • develop state programs that strengthen local producers;
  • focus on diversification of import markets for food commodities which constitute a high share of households’ consumption basket.

References

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

COVID-19 | The Case of Georgia

Posted on May 21, 2020 | Policy Brief

An image of COVID-19 virus representing the COVID-19 outbreak in Sweden

Introduction

Georgia has close to 4 million inhabitants. It borders Russia, Azerbaijan, Armenia and Turkey, which are also its main trading partners. The capital and largest city is Tbilisi with about 1,5 million inhabitants. Agriculture and the tourism sector dominate the local economy.

Georgia reported its first case of Covid-19 on February 27, 2020 and its first deaths on April 6, 2020. The government reacted quickly, banning direct flights from China in late January 2020 and imposing severe travel restrictions even within the country in March 2020. Schools and universities were closed on March 11, 2020. The government banned all larger public gatherings on March 21, 2020, the same day when the country declared the state of emergency. The four major cities of Georgia – Tbilisi, Batumi, Kutaisi and Rustavi – were put under lockdown on April 15, 2020.

As of May 8, 2020, Georgia reported a total of 9 fatalities, suggesting that the virus has quite successfully been contained so far. A breakdown of the healthcare system seems unlikely at the moment. Economically, the situation is more heterogenous. Georgia’s public finances are in a tolerable enough shape to handle a crisis. The public debt to GDP ratio is not very high (44.9% in 2018), and the government budget deficit is also below 3% of GDP. Georgia’s financial system has been praised as one of the strongest among in the ECA region. However, annual inflation in January-February was 6.4%, which is significantly higher than the target level of 3%. Georgia is facing uncertainties in terms of inflationary expectations, and this limits the National Bank of Georgia’s (NBG) ability to stimulate the economy under the current circumstances. Most probably, NBG will not cut the policy rate to avoid provoking further currency depreciation and stoking inflationary expectation even further. Moreover, a major weakness in the Georgian economic system lies in its lack of a broad social safety net infrastructure, which could help support afflicted groups during downturns. Finally, another risk is the substantial informal sector: workers in these sectors are hard to reach via conventional policy measures.

Below, we outline how the Georgian economy has been affected by Covid-19 and what the policy responses have been so far. We will also discuss several economic scenarios and explain which further policy options are thinkable.

How Does the Covid-19 Crisis Affect the Georgian Economy?

Demand Side Effects

  1. A decline in domestic consumption resulting from behavioural and policy changes is to be expected on the demand side – i.e. people staying home as a precaution or because they are required to. In addition, currency depreciation and possible price spikes (due to herding behaviours and potential disruptions in supply chains) are also expected to have a negative effect on consumption and investment.

Household consumption accounts for 66.7% of the Georgian GDP (Geostat, 2018). A significant reduction in household consumption (e.g. spending on transportation, clothing, electronics, and domestic services) would therefore result in an overall slowdown of GDP growth. A slowing of internal demand would hit people working in the informal sector particularly hard; namely, those without a regular salary (e.g. temporary workers, taxi drivers, and other self-employed service sector workers) and small and micro business-owners. Their situation is worsened still because the government’s fiscal stimulus and assistance is unlikely to reach them directly. They are also not expected to benefit from the extra liquidity injected into the financial system, as they will not qualify for bank loans to cover temporary income losses. Another vulnerable group are the formal sector workers employed in companies that face a dramatic decline in their usual economic activities (restaurants, hotels, the entertainment industry, transport, etc.). These companies are likely to put their workers on unpaid leave or simply fire them. Moreover, the slump in household demand will also be made worse by the fact that most families are likely to have limited savings and, therefore, their capacity to smooth consumption is limited. Hence, the crisis may cause a significant drop in well-being and, possibly, further deterioration in individuals’ physical and mental health, alongside the direct impacts of Covid-19

  1. A decline in domestic investment because uncertainty and deteriorating business sentiments will stall business investment decisions. Expectations of a global recession could become self-fulfilling if ‘business-as-usual’ does not resume in the next few months. If companies expect a slowdown in demand, they will also delay investment, and GDP will decline further. Investment (gross fixed capital formation) accounts for approximately 28% of Georgia’s GDP. Thus, the Georgian government has announced capital spending to combat the expected drop in private investment.
  2. A decline in tourism and related business seems inevitable as tourism arrivals and receipts are expected to decrease sharply as a result of the numerous travel bans, and due to precautionary behavior. According to our preliminary calculations, the Georgian economy lost between 3-9% of potential tourism revenue in February. Since the tourism sector accounts for 6% of Georgia’s GDP (GNTA 2018), a direct hit to the industry will substantially impact GDP. In table 1, we work out GDP losses associated with the following scenarios:

Table 1: Net effect of the coronavirus crisis on tourism in Georgia

Note: after each period indicated in the scenarios, tourism is assumed to immediately recover to 2019 levels.
Source: Geostat, NBG, authors’ calculations.

  1. The spillover effect on other sectors: a drop in demand for goods and services in the region, in China, the EU, and the US – will affect the overall economy via trade and production linkages.

While it is difficult to predict how Georgia’s economy will react to a global shock of such magnitude, some preliminary estimations may already be made. Georgia’s growth rate over the last 20 years correlates notably to several neighboring economies. One of the greatest correlations is, unsurprisingly, with Russian economic growth. Russia’s growth is also highly correlated with other countries, reflecting global economic linkages. These correlations are reported in table 2 below:

Table 2: Correlations of growth rates

Table 2 Georgia Russia Armenia Turkey China Kazakhstan Italy Germany France US Israel Ukraine
Georgia 1.00 0.87 0.88 0.66 0.58 0.81 0.67 0.74 0.85 0.69 0.77 0.73
Russia 1.00 0.90 0.60 0.73 0.83 0.64 0.67 0.82 0.63 0.79 0.91

Source: World Bank, authors’ calculations.

In order to explore how a slowdown across major world economies will affect Georgia, we have followed three economic scenarios relating to major world economies, as reported by Orlik et al. (2020). The numbers reflect growth rate changes relative to the baseline (no virus outbreak).

Table 3: Coronavirus effect on growth rates.

Table 3. Coronavirus effect on growth rates Real GDP annual growth change in 2020 compared to the baseline scenario, pp Real GDP growth, % in 2020, assuming a 5% baseline
Russia Germany US Georgia Georgia
Scenario A: Outbreak causes localized disruption -0.9 -1.2 -0.2 -1.09 3.91
Scenario B: Widespread contagion -3 -2.8 -1.3 -3.09 1.91
Scenario C: Global pandemic -4.8 -3.6 -2.4 -4.55 0.45

Source: Orlik et al. (2020); authors’ calculations.

  1. A decline in trade is likely and it is possible to find certain similarities between the current situation and the economic slowdown in the Eastern Europe and Central (EECA) region in 2014-2017, caused by a drop in oil prices and global appreciation of the US dollar. The latter resulted in a sharp decline of external demand, falling commodity prices and regional currency crises, which equally affected the Georgian economy. The country’s goods exports fell by 23%, while imports contracted by 15% in 2015. Trade was only restored to the 2014 level by 2018. While, the forthcoming crisis is expected to not only have stronger negative impacts on external demand, but also disruptions in the production value chains, affecting Georgia’s trade in more severe ways. Trade of all commodities, except food and medicine, is projected to decline, depending on the duration of the shock.
  2. A decline in Foreign Direct Investment (FDI) is to be expected since foreign investors prefer to invest in safe assets. Additionally, currency depreciation expectations will negatively affect FDI. The FDI in Georgia amounted to 1,267.7 mln. USD in 2019 (7.1% of GDP).
  3. A decline in remittance inflows seems likely: since all countries will suffer economically in the aftermath of the health and oil price crises, we expect significant slowdown in remittance inflows from the rest of the word. The remittances decline will hit Georgia particularly hard as it is among the top receiver countries of foreign transfers. For instance, in 2019, money transfer inflows accounted for 9.8% of GDP. Various scenarios for just how much Georgia is set to lose in monetary inflows is presented in table 4 below:
Table 4. Net change in money transfers inflow in 2020 due to coronavirus (Mln. USD)
Scenario 1: 10% decrease of net money transfers in the remaining months of the year (March-December) Scenario 2: 30% decrease of net money transfers in the remaining months of the year (March-December) Scenario 3: 50% decrease of net money transfers in the remaining months of the year (March-December)
-114 -372 -629
Net change in consumption spending due to money transfers decline*
-570 -1,857 –  3,146
Net change as a share of household total real consumption spending**
+0.3% -2.6% -5.5%

* $1 of transfers is assumed to become $0.8 equivalent of consumption spending.

** USD/GEL exchange rate is assumed to equal to the official exchange rate as for March 20th (3.1818) in the remaining months of the year (March-December). Inflation is assumed to be 6% in 2020.

Source: Geostat, NBG, authors’ calculations.

Supply Side Effects

  1. Production disruptions may occur on the supply side. Domestic production suffers as a result of forced business closures and the inability of workers to get to work, as well as disruptions to trade and business as a result of border closures, travel bans, and other restrictions on the movement of goods, people, and capital (in the PRC as a whole fell to 50%–60% of normal levels but is now normalizing, after the introduction of extremely restrictive measures that – so far – no country in the West has been able/willing to mimic. However, in the absence of such restrictions, the crisis may be prolonged, and production might be hard to restart quickly). The overall impact on production may be mitigated by the fact that in some sectors (particularly in manufacturing) production can be ramped up in later periods to compensate for lower production (providing closures do not last too long).
  2. Long-term economic effects need to be taken into account. Covid-19 will impact health via mortality and morbidity, and through changes in (and the diversion of) healthcare expenditure.

Currency Depreciation

The expected decline of tourist inflows, remittances, and exports as a result of reduced foreign demand from Georgia’s trading partners and low world oil prices have already affected the lari exchange rate (mostly through expectation channels). On the other hand, due to restrictions on air travel, the outflow of currency from Georgia to foreign countries will be reduced (the import of tourism services will be lower), which will have a positive effect on the exchange rate. Another positive factor may be that Georgia’s reliance on remittances from oil-exporting countries (like the Russian Federation) has been significantly reduced in recent years.

What Has Been Done to Address the Covid-19 Crisis?

The Government of Georgia timely started applying measures to address dramatic impacts on various market participants:

Businesses

  1. Restructuring loans for businesses affected by the crisis;
  2. Companies that operate in the tourism industry: hotels and restaurants, travel agencies, passenger transportation companies, site-seeing companies, arts and sports event organizers, etc., will have their property and personal income taxes deferred by the Georgian government for four months;
  3. Doubling the volume of VAT refunds to companies, with the aim of supplying them with working capital;
  4. Designing a state program to co-finance interest payments on bank loans by hotels with 4-50 rooms, throughout the country, for the next six months.

Workers

  1. Loan payment deferrals for three months;
  2. Personal income taxes deferred for employees in the tourism industry.

The Health Care System

  1. No new measures are planned at this point.

The Financial System

  1. Easing lending restrictions for commercial banks;
  2. NBG has not cut policy rates and is unlikely to do so given the risks of inflation.

Other Measures

  1. Boosting capital expenditure (CapEx) projects with the aim of providing additional economic incentives;
  2. Governmental price fixing for specific products (rice, pasta, sunflower oil, flour, sugar, wheat, buckwheat, beans, milk powder and its products) by subsidizing corresponding businesses.

Will the Current Measures Be Sufficient?

Given the rapidly changing scope of the crisis, the short answer is simple – probably not. As the forecast seems pessimistic, it is the role of the fiscal stimulus and, where possible, the monetary policy to help soften the economic shock.

It is evident that the measures adopted by the government as well as private commercial banks in Georgia will not be able to directly reach a sizeable group of the population affected by the shock – i.e. those unemployed due to Covid-19; those working in the informal sector; people with low income; or households that are very reliant on remittances transfers. It is important for the government to connect with these groups quickly, not only for humanitarian reasons, but also in the interest of a broader development agenda. In case of relatively prolonged quarantine sizable part of the population will no longer be able to support themselves and their families in coming months.

What More Can Be Done?

We broadly outline the additional monetary and fiscal policy measures that may be considered:

More Forceful Fiscal Intervention:

As previously mentioned, Georgia’s systemic weakness lies in its lack of a broad social safety net infrastructure, which could help target and support afflicted groups during downturns. An unemployment benefits system, which in other countries acts as an “automatic stabilizer” and reduces and mitigates the effect of economic downturns, simply does not exist in Georgia. Yet even with an unemployment benefits system in place, the sizeable informal economy would prevent such a system from effectively easing labor market tensions. In the current situation, the government should attempt to provide cash relief for workers in the informal sector, for the low-income self-employed, and for independent contractors. These groups of workers are the most vulnerable to income flow reduction during the crisis, furthermore, they are unlikely to have access to sick leave benefits or to take advantage from cheaper bank credit.

Based on the experience of other countries, the government perhaps should consider the following measures in addition to current measures:

  • Providing low interest emergency loan/cash advances to affected adults, or direct cash payments to affected households, in particular households with the elderly and children. These measures are valuable as they can quickly reach afflicted groups. Unfortunately, this solution is not well-targeted and risks wasting government funds on those who are not disadvantaged.
  • Simply providing “helicopter money”, or cash transfers to households below a certain income threshold (similar measures are being considered in the US) may be an option, but this measure is subject to the same concerns as above. However, the advantage is that cash transfers allow households to optimize their expenditure and do not distort consumption choices.
  • Another form of wide-reaching support could be state subsidies to help support utility payments for a limited time. These measures, equally, are not well-targeted, nevertheless there may be methods to direct them towards the households which need them the most.
  • Measures to encourage companies to not cut employment in the months following the crisis: following the example of other countries, Georgia may support salary payments for companies, on the condition that they do not reduce employment or force workers to take unpaid leave.

Naturally, none of the proposed measures are perfect as they cannot specifically target those most affected by the crisis, yet they may act as a short-term second-best solution. As these examples show, Georgia should consider to develop a targeted social safety net system in the future. Such a system can make the country more resilient in the face of future crises and unexpected emergencies.

Monetary Policy

While other countries push for fiscal stimulus and monetary expansion, Georgia is facing uncertainties in terms of inflationary expectations. As discussed, this limits NBG’s ability to stimulate the economy under the current circumstances. Annual inflation in January-February was at 6.4%, significantly higher than the 3% target. Going forward, a sharp decline in aggregate demand would reduce the pressure on inflation, while a depreciating nominal effective exchange rate will exert upward pressure. Therefore, the possibility to reduce the monetary policy rate depends on which effect will dominate in the future. In the meantime, NBG has approached the IMF to increase access to funding under its Extended Fund Facility program (NBG). Alongside the additional funds from other international donors, this will positively affect the economy, strengthen the nominal effective exchange rate and, consequently, curb inflation.

In addition to the measures already announced, NBG has the option of decreasing the minimum reserve requirements for deposits attracted in a foreign currency. This will stimulate FX lending and economic activity, without creating depreciation or inflationary expectations.

Overall, the Georgian government responded very timely and efficiently to contain the virus outbreak, earning well-deserved plaudits from the international community and approval from the general public. However, as the scope of the crisis continues to change rapidly, additional measures might soon be needed. As the economic landscape becomes more uncertain, the government needs to ensure that emergency economic stimulus measures directly reach the people most affected by the crisis.

Disclaimer

This policy brief was first published as an ISET policy note on March 25, 2020 under the title “The Economic Response to COVID-19: How is Georgia Handling the Challenge?“. This brief is an adaption of the original note and is published with the consent of the authors.

References

CIA World Fact Book, 2020. “Georgia”.

The Guardian, 2020. “How UK government could support people as coronavirus spreads”.

Imeson, Michael, 2019. “Georgian banks gather rewards for resilience”. The Banker.

IMF, 2019. “Georgia: Fourth Review Under the Extended Fund Facility Arrangement and Request for Modifications of Quantitative Performance Criteria-Press Release; Staff Report; and a Statement by the Executive Director for Georgia.”

Lomsadze, Giorgi, 2020. “Georgia gets rare plaudits for coronavirus response“. Eurasianet.

Migration Policy Institute, 2020. “Global Remittances Guide”.

Orlik, Tom; Jamie Rush; Maeva Cousin and Jinshan Hong, 2020. “Coronavirus Could Cost the Global Economy $2.7 Trillion. Here’s How”. Bloomberg.

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

The Georgian Tax Lottery of 2012 – A Quantitative and Qualitative Evaluation

Posted on November 4, 2019 | Policy Brief

20191104 The Georgian Tax Lottery of 2012 FREE NETWORK Policy Brief Image 01

This policy brief is based on preliminary findings of research that assesses the 2012 Georgian Tax Lottery by Larsen et al. (2019). Tax lotteries are seen as a way to relatively easily augment public revenue while also increasing compliance. Tax lotteries are constructed so that consumers are nudged to ask for a receipt when making a purchase. This receipt contains information which can also be used as a lottery ticket with the possibility of winning prizes. Such tickets also leave traces of transaction records that allow revenue authorities to audit vendors. Given this background, the aim of this paper is to provide a broad, multi-methodological and socio-economic assessment of Georgia’s tax lottery experience in 2012.

Introduction

A well-designed tax system improves economic efficiency, facilitates economic growth and social welfare, (Besley & Persson, 2013). Yet, curbing tax evasion remains one of the key challenges for policy makers, and institutions in charge of revenue administration are experimenting with diverse set of instruments to increase tax compliance and thus revenue.

In addition to the traditional audit-sanctioning mechanism, the taxation literature emphasizes the role of consumers in facilitating tax compliance of businesses. The government can create direct monetary incentives for consumers to request receipts. Turning a receipt into a lottery ticket with a chance of winning a pre-determined prize is an example of such an incentive. The tax lottery motivates and rewards those consumers who become part in the efforts to fight tax evasion by requesting receipts while making purchases. Given that audit-sanctioning mechanisms are very costly for the government, clever usage of a “zero cost policy”, such as tax lotteries, might be advisable (Fabbri & Hemels, 2013).

The aim of this paper is to provide an assessment of the Georgian tax lottery experience in 2012 using both quantitative and qualitative methodologies. The two methodological approaches complement each other and help to investigate the tax lottery from different angles.

The Georgian Tax Lottery

The Georgian Revenue Service (GRS) introduced a tax lottery starting in spring 2012, which was planned to run until January 1, 2013. The aim of the lottery was to popularize the already introduced General Packet Radio Services (GPRS) -based cash registers and make sure that they were used by vendors. Such registers would allow the GRS to gather information about business activities online daily. This, in turn, was due to an effort to fight the shadow economy and be able to audit business revenue, when payments were made by cash. The lottery would thus motivate consumers to ask for receipts. As a communicative resource, the lottery aimed to increase awareness of asking for receipts, as well as to develop a positive attitude in Georgian society towards GRS in the background of harsh fiscal reforms.

In order to participate, customers had to buy goods or services from a vendor who had a GPRS-based cash register. The receipt could be checked for win immediately by mobile phone. The Georgian Tax Lottery was a chance to win money for every customer purchasing anything from groceries, to shoes and hair care. The winning prizes were 10, 20, 50, 100, 10,000 and 50,000 GEL[1]. The 10,000 GEL prizes were awarded once a month while 50,000 GEL prizes were given quarterly.

The lottery ended prematurely on grounds of inefficiency on November 12, 2012 when a new government was elected.

Multi-Method Approach

For the assessment of the tax lottery in Georgia, we employed a multi-method approach combining a qualitative assessment built on an ethnographic approach with quantitative regression-based methods; following the ethnographic approach, we collected opinions, experiences, and views on the tax lottery from the perspective of participating and non-participating businesses, consumers as well as other stakeholders.

The quantitative assessment of the paper investigates whether the existence of the lottery affected businesses’ total revealed turnovers through the facilitation of a receipt-requesting norm. The data for the quantitative analysis conducted in this paper was provided by the GRS. The latter was collected from the daily reports of the GRS system, for two years, 2012 and 2013. The data includes variables, such as the unique cash register identifier, the year and the week of a purchase and address (city and municipality) and the total turnover of the cash register reported through GPRS. GRS also provided the dataset with detailed information on winning tickets. The latter includes daily information on the number of winning tickets and the aggregate daily monetary amount of the prizes.

Three different specifications of linear regression models were run separately on the aggregate country level data. The model-specifications differ in a way that each uses different dependent variables – aggregate weekly sales, average weekly sales per register and number of registers reporting any sales.

Preliminary Results

Table 1: Regression Results of the aggregated analysis on a country level

As may be inferred from the country level regression results reported in Table 1, for all the econometric specifications the ‘lottery’ variable is significant at 1% level. The regression results show that during the weeks of the lottery (weeks 16-46) the aggregate weekly sales are on average 33,363 GEL higher than in the non-lottery weeks (11% more than in non-lottery weeks, based on the log linear model). When looking at the year effect of 2012 in non-lottery weeks, the effects are positive, significant, and, on average, amount to 38,813 GEL. This means that aggregate weekly sales in the non-lottery weeks of 2012, exceed aggregate weekly sales in 2013, on average, by 38,813 GEL. While in this simple model we do not explicitly control for the macroeconomic environment, GDP in 2013 grew by 3.4% while inflation stood close to 0%. These macroeconomic outcomes strengthen predictions of the econometric analysis.

When looking at the average sales per register as the dependent variable instead of aggregate weekly sales, the results are compatible with the results of the first model. There is on average a 282 GEL (7.7%) increase in average turnover during the lottery weeks compared to the non-lottery weeks; and average weekly sales in non-lottery weeks of 2012 exceed average weekly sales in 2013 by 458 GEL, on average. In addition, the positive effect and significance of the year 2012 variable shows that controlling for the non-lottery weeks, something was still driving sales up. This could be the long-term effect of the lottery weeks that continued even after the termination of the lottery; hence some evidence of habit formation.

A similar regression is done with the weekly number of cash registers reporting their income as a dependent variable. The outcome illustrates that during the lottery weeks of 2012, the average number of reported cash registers is 3,199 units (4%) more than those in non-lottery weeks, which is quite compatible with the results reported by the first and second regressions.

Conclusion

Despite seemingly positive results, the lottery was prematurely terminated after parliamentary elections in November 2012. Interviews with stakeholders revealed that the public budget that was allocated for the lottery was deemed insufficient to keep the chances of winning high enough and therefore interest and participation from public had decreased significantly from around 2 mln out of 2.5-2.8 mln receipts checked daily in the first months of the lottery to only 300,000 by the end of the lottery. However, there was a lack of financial resources or interest from the new government to invest additional resources to increase the budget and effectiveness of the lottery.

Regardless of its premature termination lottery itself was thought to have influenced social norms and also started a discussion about tax compliance. The tax lottery also aimed to improve citizens’ attitude towards the GRS. A qualitative analysis, based on multi-ethnographic approach through which we have collected media articles, reports, and other materials expressing views on the Georgian tax lottery, however, showed that strategies of “love and fear” are difficult to make work in combination, and we find it hard to say that citizens’ views of the GRS improved due to the lottery itself. Perhaps even the contrary could be proposed. In terms of an increased trust to the GRS, we conclude with our methodological point that a tax lottery cannot be assessed as an isolated event. Previous and other activities that the revenue services engage in that have an impact on taxpayers and on societal tax, compliance have to be taken into consideration. Fear and unjust treatment especially linger in people’s perceptions.

References

  • Besley, T., & Persson, T. (2013). Taxation and development. In Handbook of public economics (Vol. 5, pp. 51-110). Elsevier.
  • Fabbri, M., & Hemels, S. (2013). ‘Do you want a receipt?’ Combating VAT and RST evasion with lottery tickets. Intertax, 41(8), 430-443.
  • Larsen, L., Arakelyan, R., Gogsadze, T., Katsadze, M., Skhirtladze, S., & Muench, N. (2019). The Georgian Tax Lottery of 2012. A Multi-Methodological Assessment. International School of Economics at TSU, Tbilisi, Republic of Georgia.
  • Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual review of anthropology, 24(1), 95-117.

[1] The exchange rate for a Georgian Lari, GEL, is about 3.0 GEL to 1 EUR.

Agricultural Exports and the DCFTA: A Perspective from Georgia

Posted on February 18, 2019 | Policy Brief

20190218 Agricultural Exports Image 01

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

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.

Georgian Experience of Gender Biased Sex Selection

Posted on October 8, 2018 | Policy Brief

20181008 Georgian Experience Image 01

This policy brief presents the evidence on gender biased sex selection (GBSS) in Georgia, giving an overview of the so-called “sex ratio transition” process, and discussing the determinants of GBSS using a demand and supply-side approach. After its independence from the Soviet Union, Georgia started experiencing a significant rise of the sex ratio at birth (SRB) and in 2004 the country had reached one of the highest SRB rates in the world. A traditionally pronounced son preference was further strengthened by deteriorated economic conditions, decrease in fertility and relatively easy and cheap access to technologies for early sex determination and abortion. However, Georgia has managed to reverse and stabilize a skewed SRB rate. Among the factors that might have contributed are the strengthening of the social security system, improved economic conditions, a rise in fertility rates, economic empowerment of women, and the increased cultural influence of Western values. This trend reversal places Georgia in a unique position and may provide valuable insights for other countries who struggle with the same problem.

It is widely recognized that the Caucasus has traditionally been a “male-dominated region,” with a particularly strong son preference. However, before the dissolution of the Soviet Union in the early 1990s, sex ratios at birth in the Caucasus countries were very close to normal levels.

After independence from the Soviet Union, the SRB started rising immediately in Georgia, reaching 114.1 male births per 100 female births by 1999 (while the biologically normal SRB level is 105 male births per 100 female births). In the early 2000s, SRB peaked and stabilized between 112 and 115 male births per 100 female births for several years.  As Figure 1 shows, after reaching historically high levels in 2004, SRB started to decline and finally returned to a normal level by 2016.

Figure 1. Estimated sex ratio at birth in 1990-2016

Source: UNFPA, 2017.

The sex selection here is not discussed as “an archaic practice” in Georgia, but rather a modern reproductive behavior, a rational strategy responding to the surrounding environment – demand and supply factors. Demand-side factors include socio-economic and cultural factors that make having a boy more beneficial for a family and lower the value of girls – leading to son preference. The fertility rate is also accounted as a demand-side factor since low or decreasing fertility can increase incentives to perform selective abortions. As for the supply-side factors, they cover the ease of access to  technologies for early sex determination and selective abortion and its cost, as well as the content of the legislation regulating abortion.

Demand side factors

Factors increasing demand

Son preference and a patrilineal system. The traditional Georgian family is patrilineal. Patrilineality, also known as the male line, is a common kinship system in which an individual’s family membership derives from and is recorded through his or her father’s lineage. It generally involves the inheritance of property, rights, names, or titles by persons related through male kin. In such systems, women join their husbands’ families after marriage and are expected to care for their in-laws rather than their parents. Sons are expected to stay with their parents and take care of them. Thus, patrilineal systems make daughters less beneficial and desirable to their parents compared to sons. UNFPA (2017) concludes that the practice of post-marital co-residence with parents is still quite widespread in Georgian society, and this pattern is biased towards the male kin line, downplaying the role of women and their kin. The patrilocal residence (the situation in which a married couple resides with or near the husband’s parents) is more common in villages (more than 90%) than in urban areas (75%). The incidence of patrilocal residence is the lowest in Tbilisi (69%). In general, patrilocal residence decreases with improving economic conditions.

Demographic change – changes in fertility rates. Low or decreased fertility rates (when other factors favorable for GBSS are in place) mean that families are no longer able to ensure the birth of a son through repeated pregnancies. In societies characterized by strong son preference, and with increasing availability of sex detection technologies, couples start to opt for sex selection because they want to avoid additional births of girls, something that contraception cannot alone ensure. Therefore, low fertility acts as a “squeeze factor,” forcing parents to make choices ensuring the desired gender composition of their family.

An inverse relationship between fertility and SRB is observed in Georgia. The first decade of transition to market economy was severe for the country. Reducing household size was one strategy chosen by Georgian families to cope with increased rates of unemployment, deterioration of the social security system and deprivation of basic needs such as water and electricity. The decline of fertility during the years 1990-2003 coincided with increased SRB levels. When fertility started to rebound in 2003, the “squeeze factor” began to vanish, removing pressure on the SRB. At the same time, the SRB started to decline.

The low value of women. In Georgia, women are stereotypically perceived as natural caretakers, whose core responsibilities involve child care and household duties. They are also expected be obedient to their husbands and let them have leading positions in various activities (UNDP 2013). The majority of the population in the country thinks that men should be the ones who are the family’s decision-makers and that they should also be the main breadwinners. According to a 2010 study, 83% of respondents think that men should be the main breadwinners in the family, and 63% believe that they should also be the family’s decision-makers (CRRC, 2010). It is evident that such attitudes and values contribute to decrease the perceived value of girls in society, compared to boys, and add additional stimulus to GBSS.

Factors decreasing demand

The strengthening of state institutions and the social security system. Georgia has experienced a deep transformation of its social, economic and political systems in the last fifteen years. Reforms were carried out in all sectors. Most importantly, the country totally restructured its social security system, which was practically non-existent in Georgia at the beginning of the 1990’s. Currently, Georgian citizens are offered: a) universal pension system, above the subsistence minimum, which provides a flat rate benefit to all elderly; b) social assistance, which represents a monthly subsidy to poor families, is well targeted, and has contributed to reducing poverty (Kits et al. 2015), and (c) a universal health insurance system which covers all people who are uninsured by private companies and softens the burden of health care expenditures for households.

These changes, together with the improved general economic situation in the country, have decreased the role of the family as a buffer institution offering protection and stability (notably through sons), and provided more formal alternatives for social security, bank loans, contractual employment, etc. Due to this, the (large) intergenerational family is no longer perceived as the only strategy for coping with social and financial uncertainty.

New cultural influence of Western values. From the early 2000s, Georgia has been increasingly exposed to Western norms and culture through media, migration, increased tourism, and the process of economic integration with the European Union. According to experts, this process was accompanied by “media support and an enthusiastic, quasi-propagandistic hail. The general spirit was to promote an image of Georgia as a country open to the world with West-European views and lifestyles” (UNFPA 2017).

Supply side factors

While the availability of technologies for the early determination of sex and for abortion is not the root cause of GBSS, it constitutes a facilitating supply factor. Without prenatal diagnostics and accessibility of abortion, parents would not be able to resort to selective abortions even if they had a pronounced preference for boys.

Currently, Georgia is among the countries offering high-tech reproductive services. Private clinics, hospitals, and special reproductive medicine centers compete to supply reproductive services, and one can easily see the most recent ultrasound technologies in the great majority of the urban facilities. In addition, the cost of an ultrasound test is extremely low, depending on the service provider. This represents only 1.9%-4.8% of the average monthly incomes per Georgian household. In this context, the GBSS-related demand for prenatal diagnostics can easily be accommodated, when it arises.

Conclusion

Georgia has had a unique experience of “sex ratio transition” in the region, which was an integral part of its overall transformation process. The deteriorated social and economic conditions of households following the beginning of the transition process, coupled with easier and cheaper access to prenatal diagnostics were reflected in a skewed SRB and manifested son preference. Only when socio-economic conditions improved, and the country accelerated its institutional strengthening and modernization process, did the SRB returned to its normal level.

It is too early to conclusively state that Georgia is back to normal SRB levels for good. Birth masculinity still remains at a high level i) for third-order births, as the most of the couples are reluctant to have more than three children, and giving birth to a third child is the last chance for families to have a boy; ii) there is a significant urban-rural divide in the context of birth order. For three or higher order births, SRB is significantly distant from normal levels for almost all regions, reaching beyond 145, while in Tbilisi the bias remains moderate; iii) gender-biased sex selection remains high among poor people and ethnic minorities.

If Georgia is to minimize the incidence of GBSS in the future, it needs to act on several fronts: enhance gender equality through qualitative research and civic activism; increase the perceived value of girls and women in the society through policies and initiatives addressing cultural stereotypes, as well as by publicizing illuminated stories of success of girls and women that provide positive role models; monitor SRB trends; support advocacy actions and awareness-raising campaigns on GBSS and encourage the ethical use of sex detection technologies.

References

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

Conflict, Minorities and Well-Being

Posted on June 17, 2018 | Policy Brief

20180618 Conflict, Minorities and Well-Being Image 01

We assess the effect of the Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. Using the Russian Longitudinal Monitoring Survey (RLMS), we find that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. In comparison, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness. However, the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. We also show that the negative effect of conflict is short-lived with no long-term legacy. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-Slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Militarized conflict affects a myriad of socioeconomic outcomes, such as the level of GDP (Bove et al. 2016), household welfare (Justino 2011), generalized trust and trust in central institutions (Grosjean 2014), social capital (Guriev and Melnikov 2016), and election turnout (Coupe and Obrizan 2016). Importantly, conflict has also been found to directly affect individual well-being (Frey 2012, Welsch 2008).

However, previous research studying individual well-being in transition countries largely abstracts from heightened political instability and conflict proneness, while this has been particularly pertinent in transition countries. Examples of transition countries facing various types of conflicts are abound, such as Yugoslavia, Ukraine, Tajikistan, Russia, Armenia, Azerbaijan, Moldova, and so on. Therefore, it is imperative to explore how conflict shapes well-being in transition countries.

In a new paper (Gokmen and Yakovlev, forthcoming), we add to our understanding of well-being in transition in relation to conflict. We focus on the effect of Russo-Georgian conflict of 2008 and the Ukrainian-Russian conflict of 2014 on the well-being of minorities in Russia. The results suggest that the well-being of Georgians in Russia suffered negatively from the 2008 Russo-Georgian conflict. However, we find no general effect of the Ukrainian-Russian conflict of 2014 on the Ukrainian nationals’ happiness, while the life satisfaction of Ukrainians who reside in the southern regions of Russia in close proximity to Ukraine is negatively affected. Additionally, we analyze the spillover effect of conflict on other minorities in Russia. We find that while the well-being of non-slavic and migrant minorities who have recently moved to Russia is negatively affected, there is no effect on local minorities who have been living in Russia for at least ten years.

Data and Results

We employ the Russian Longitudinal Monitoring Survey (RLMS) which contains data on small neighborhoods where respondents live. Starting from 1992, the RLMS provides nationally-representative annual surveys that cover more than 4000 households with 10000 to 22000 individual respondents. The RLMS surveys comprise a broad set of questions, including a variety of individual demographic characteristics, health status, and well-being. Our study utilizes rounds 9 through 24 of the RLMS from 2000 to 2015.

In this survey, we identify minorities with the question of “What nationality do you consider yourself?” Accordingly, anybody who answers this question with a non-Russian nationality is assigned to that minority group.

We employ three measures of well-being. Our main outcome variable is “life satisfaction.” The life satisfaction question is as follows: “To what extent are you satisfied with your life in general at the present time?”, and evaluated on a 1-5 scale from not at all satisfied to fully satisfied. Additionally, we use “job satisfaction” and “health evaluation” as outcomes of well-being.

Our results suggest that our primary indicator of well-being, life satisfaction, for Georgian nationals has gone down in the Russo-Georgian conflict year of 2008 compared to the Russian majority (see Figure 1). The magnitude of the drop in life satisfaction is about 39 percent of the mean life satisfaction. Our estimates for the other two well-being indicators, job satisfaction and health evaluation, also indicate a dip in the conflict year of 2008. Lastly, our estimates show that the negative impact of the conflict does not last long. Although there is a reduction in the well-being of Georgians both on impact in 2008 and in the immediate aftermath in 2009, the rest of the period until 2015 is no different from the pre-2008 period.

Figure 1. Life Satisfaction of Georgian Nationals in Russia


Source: Authors’ own construction based on RLMS data and diff-in-diff estimates.

Furthermore, when we investigate the effect of the Ukrainian-Russian conflict of 2014, we find no negative effect on the life satisfaction of Ukrainians. One explanation for why the happiness of Ukrainians in Russia does not seem to be negatively affected in 2014 is that the degree of integration of Ukrainians into the Russian society is much stronger than the degree of integration of Georgians. On the other hand, our heterogeneity analysis reveals that in the southern parts of Russia closer to the Ukrainian border, where there are more Ukrainians who have ties to Ukraine, Ukrainian nationals are differentially more negatively affected by the 2014 conflict. The differential reduction in the happiness of Ukrainians is about 19 percent of the mean life satisfaction.

Moreover, we also look into whether there is any spillover effects of the Russo-Georgian and the Ukrainian-Russian conflicts on the well-being of other minorities. We first carry out a simple exercise on non-Slavic minorities of Russia. We pick the sample of non-Slavic ex-USSR nationals that are similar to Georgians in their somatic characteristics, such as hair color and complexion. This group of people include the nationals of Azerbaijan, Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan and Tajikistan. We treat this group as “the countries with predominantly non-Slavic population” as their predominant populations are somatically different from the majority Russians, and thus, might either have been subject to discrimination or might have feared a minority backlash to themselves during the times of conflict. This conjecture finds some support below in Figure 2 in terms of violence against minorities. We observe in Figure 2 that hate crimes and murders based on nationality and race peak in 2008.

Our estimates also support the above hypothesis and propose that there is some negative effect of the 2008 conflict on non-slavic minorities’ happiness as well as their job satisfaction, whereas 2014 conflict has no effect.

Figure 2. Hate Murders in Russia over Time

Source: Sova Center

Next, we investigate the spillover effects of conflict on Migrant Minorities. Migrant minorities are minorities who have been living in their residents in Russia for less than 10 years. We conjecture that these minorities, as opposed to the minorities who have been in place for a long time, could be more susceptible to any internal or external conflict between Russia and some other minority group for fear that they themselves could also be affected. Whereas other types of longer-term resident minorities, which we call Local Minorities, are probably less vulnerable since they have had more time to establish their networks, job security, and most likely also have Russian citizenship. Our estimates back up the above conjecture and demonstrate that migrant minorities suffer negatively from the spillover effects of the 2008 conflict onto their well-being captured by any of the three measures, and not from the 2014 conflict, whereas there is no negative impact on local minorities.

Conclusion

In this paper, instead of focusing on the direct impact of conflict on happiness in war-torn areas, we contribute to the discussion on conflict and well-being by scrutinizing the well-being of people whose country of origin experiences conflict, but they themselves are not in the war zone. Additionally, we show that some other minority groups also suffer from such negative spillovers of conflict. Being aware of such negative indirect effects of conflict on well-being is essential for policy makers, politicians and researchers. Most policy analyses ignore such indirect costs of conflict, and this study highlights the bleak fact that the cost of conflict on well-being is probably larger than it has been previously estimated.

References

  • Bove, V.; L. Elia; and R. P. Smith, 2016. “On the heterogeneous consequences of civil war,” Oxford Economic Papers.
  • Coupe, T.; and M. Obrizan, 2016. “Violence and political outcomes in Ukraine: Evidence from Sloviansk and Kramatorsk”, Journal of Comparative Economics, 44, 201-212.
  • Frey, B. S., 2012. “Well-being and war”, International Review of Economics, 59, 363-375.
  • Gokmen, Gunes; and Evgeny Yakovlev, forthcoming. “War and Well-Being in Transition: Evidence from Two Natural Experiments”, Journal of Comparative Economics.
  • Grosjean, P., 2014. “Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries” Comparative Economic Studies, 56, 424-451.
  • Guriev, S.; and N. Melnikov, 2016. “War, inflation, and social capital,” American Economic Review: Papers & Proceedings, 106, 230-35.
  • Justino, P., 2011. “The impact of armed civil conflict on household welfare and policy,” IDS Working Papers.
  • Welsch, H., 2008. “The social costs of civil conflict: Evidence from surveys of happiness” Kyklos, 61, 320-340.

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.

Education for the Poor

Posted on March 9, 2015 | Policy Brief

20150309-Education-for-the-Poor-1

Authors: Lasha Lanchava and Zurab Abramishvili, ISET and CERGE-EI.

This brief summarizes the results of a study by Lanchava and Abramishvili (2015), which investigates the impact on university enrollment of an unconditional cash transfer in Georgia, designed to help households living below the subsistence level. The program, introduced in 2005, selects recipients based on a quantitative poverty threshold, which gives us the opportunity to measure the influence on university enrollment with an econometric regression discontinuity design. We use data on program recipients from the Social Service Agency of Georgia (SSA) and university admissions from the National Examination Center (NAEC) to create a single dataset and compare the enrollment rates of applicants who are just above and below the threshold. We find that being a program recipient significantly increases a student’s likelihood of university enrollment by as much as 1.4 percentage points (while the sample mean of university enrollment is 12.7%). We also find that the impact is stronger for males and the firstborn children in a family. Our analysis also shows that the effect is equally strong across different locations in the country. Our straightforward policy recommendation is that if a government is trying to increase enrollment in tertiary education, need-based university scholarships may prove to be an appropriate instrument.

Meeting Qualification Mismatch with Vocational Training

Posted on October 20, 2014 | Policy Brief

20141020 Meeting Qualification Mismatch with Vocational Training Image 01

While in an ideal world the qualification preferences of job seekers and employers would coincide, in reality this is often not the case. Besides informational asymmetries (job seekers not knowing which qualifications are demanded by employers) the reason is that employers may be in need of qualifications that are not considered attractive by the job seekers. In the country of Georgia, we want to address this problem through a “recommendation system” which will suggest vocational training to job seekers. There are two main problems to be tackled in this project: (1) How can we decide what would be the most useful qualification for a given job seeker, and (2) how can we incentivize the job seekers to follow our recommendations? This policy brief discusses our approach to this problem.

Introduction

Qualification mismatches are common in many labor markets around the world (see for example, Ghignoni and Verashchagina (2014) for Europe, McGuinness and Sloane (2011) for the UK, and Béduwé and Giret (2011) for France). It is well known that qualification mismatch is a relevant problem also in the country of Georgia, as was shown in various studies (see ISET (2012) and The World Bank (2013)).

The ISET Policy Institute (ISET-PI) was commissioned by the World Bank to assist the Social Service Agency (SSA) of Georgia, an agency of the Ministry of Labor, Health, and Social Affairs, in developing a system which will recommend vocational training to job seekers with the aim to reduce the qualification mismatch in Georgia.

Job Seekers’ Preferences Matter

Vocational training addresses the needs of two different groups. It is demanded by job seekers, who want to improve their human capital in a way that matches their preferences and, in the optimal case, maximizes their chances to get back into employment. At the same time, vocational training also addresses the needs of employers, whose businesses may face shortages in qualified personnel.

It is not enough to only include employers in the analysis if one wants to effectively fight the qualification mismatch. If one does not consider job seeker’s preferences, it may happen that people prefer to not participate in the vocational training system at all. Even if one can effectively incentivize job seekers to attend training programs, as is the case in Germany for example, where the refusal to participate in training is sanctioned by a reduction of unemployment benefits (cf. Neubäumer (2012)), it is likely that involuntary training will be less effective. Therefore, it is problematic that most studies which analyze the demand for qualifications in the job market, for example for the European Union (Lettmayr and Nehls (2012)), New Zealand (Earle (2008)), and Australia (Shah (2010)), exclusively focus on employers and neglect the preferences of the people who are to be trained. In Georgia, we will do it differently.

Why Would Job Seekers Follow Our Recommendations?

The objective of the recommendation system we develop is to maximize the impact the training has on the employment chances of the job seeker. Arguably, this is also the primary goal for most job seekers, as they often state that they want to receive training in an “employable” profession. Therefore, if the purpose of the recommendation system is communicated properly, and if it is transparent and trustworthy, the job seekers may want to voluntarily follow its advice.

Recommendation System vs. Matching Algorithm

One can think of two different ways of advising job seekers in their training choices: recommendation systems and matching algorithms.

Recommendation systems make suggestions to job seekers separately. These kinds of systems are ubiquitous on the Internet. For example, Amazon.com proposes books to its customers based on their purchasing history. In a similar way, a recommendation system for vocational training would suggest vocational training programs to job seekers based on relevant data about their characteristics and the job market situation. Yet its major shortcoming is that a recommendation system will not take into account what other job seekers do and what recommendations were given to them.

For that reason, in a recommendation system, it can happen that the number of people recommended to choose a certain program is larger than that program’s capacity (because the advice comes as a ranking, this does not cause the system to be useless, as the job seeker may then choose the program which is highest in the ranking and which has free places).

Likewise, if many job seekers follow the advice of the recommendation system, oversupply and undersupply of certain qualifications in the job market is not ruled out. This is again due to the fact that recommendations are made separately. If there is a huge demand for, say, plumbers, and many people receive the advice to receive training in plumbing, this may subsequently cause an oversupply of plumbers.

In contrast, a matching algorithm aims at an overall optimum for the whole group of job seekers. Genuine matching algorithms do not make separate recommendations, but propose a globally optimal assignment. In Western countries they are used, for example, to match interns to hospitals, students to universities, and kidneys to dialysis patients. Matching theory is one of the most successfully applied subfields of game theory, acknowledged through the award of the Economics Nobel Prize of 2012 to matching theorist Alvin E. Roth. The standard survey of matching theory is Roth and Sotomayor (1990).

In a matching algorithm, the abovementioned problems of a recommendation system would not occur (up to statistical uncertainty), because the matching algorithm would take into account how the suggestions made by the system affect the demand for a program. It would aim to keep the number of people, likely to choose a program, to remain below its capacity.

While a matching algorithm is more ambitious, it also has disadvantages compared to a simple recommendation system. First of all, the data requirements are higher, as the capacities of programs have to be taken into account. More importantly, in a matching algorithm the recommendations will be generated in a way that is not transparent to the job seeker (though it is possible to give some general explanations). This may reduce acceptance and willingness to participate. The recommendation system, on the other hand, can work in a relatively transparent way. Finally, a recommendation system can be adjusted and changed on an ongoing basis by Social Service Agency personnel without the help of external experts. Given its complexity, this is hardly possible with a matching algorithm.

Therefore, it was decided that the simpler option of a recommendation system is to be pursued. Later, the system may be upgraded to a full-blown matching algorithm.

The Technical Aspects of How Recommendations are made

Consider the situation of a job seeker looking for vocational training. Through the envisioned system, they will receive a recommendation of which qualification to pick in the vocational training system of the SSA.

The pieces of information used for making this recommendation are personal characteristics of the job seeker (like age, gender, preferences, skills, and other information obtained through the website worknet.ge which is operated by the SSA) and the current and future economic situation in different sectors. To this end, we will use value added tax data that can be decomposed into 45 sectors and updated on a monthly basis. For forecasts, we will draw on the Business Confidence Index of ISET, which allows decomposition into 5 sectors.

Given the information about the job seeker and the economic environment in different sectors, we will answer the question: “How many months do we expect the job seeker to be unemployed in the year after the training if the training was in qualification X?” Here, X can be whatever is offered in the vocational training system at the location of the job seeker, for example welder, mechanic, accountant, or IT expert. Alternatively, we could answer the question: “What is the salary we expect the job seeker to have in the year after the training if the training was in qualification X?”

The recommendation made to the job seeker will be: “Choose the training in field X if somebody with your personal characteristics, given the economic situation and outlook, has the lowest expected number of unemployed months (or the highest salary) in X in the year after training in X was received.” This recommendation is likely to be accepted by the job seeker if also the job seeker wants to maximize their employment chances (or maximize salary).

The forecast can be made using econometric regression analysis. Let i be a job seeker and xi be the number of months unemployed in the year after training was received. Then we have for each qualification one estimation equation

FPB_Oct20_fig1where alpha is the intercept and the betas are the coefficients for different personal and economic characteristics. When the alpha and beta coefficients are known, then one can enter the specific data for a job seeker and forecast how long it would take him to find a job if training would be received in a particular field.

For estimating the coefficients, no recommendations will be made for some time (like 3 months) after the system is launched and only information will be collected. The SSA or a specialized survey agency will call the job seekers every month after they received training and ask whether they found employment. Job seekers who received training through the SSA will be obliged to answer this question truthfully. Information about the characteristics of the job seeker is known through their participation in the worknet.ge system, which is a requirement for anybody who wants to receive vocational training through the SSA.

When the recommendation phase starts, further data will be collected. Errors in the estimation of the coefficients will be corrected “automatically” through the feedback (in terms of job market performance of the trainees) that the system gets on an ongoing basis. To increase this effect, the database used for the estimation of the coefficients will be “rolling”, i.e. people who recently received training will be added while those who received training a longer time ago (e.g. one year or more) will be removed from the database.

Conclusion

In Georgia, ISET will design and implement a recommendation system for vocational training, addressing the qualification mismatch in the labor market. As in many other areas, Georgia is willing to go for innovative policy solutions making use of advanced economic methods, very much in line with the country’s reputation as one of the top reformers in the world.

▪

References

  • Béduwé, Catherine and Giret, Jean-Francois (2011): “Mismatch of vocational graduates: What penalty on French labour market?”, Journal of Vocational Behavior 78, pp. 68-79
  • Earle, David (2008): “Advanced trade, technical and professional qualifications: Matching supply to demand”, New Zealand Government Ministry of Education, Auckland.
  • Ghignoni, Emanuela and Verashchagina, Alina (2014): “Educational qualifications mismatch in Europe. Is it demand or supply driven?”, Journal of Comparative Economics, in press
  • ISET (2012): “National Competitiveness Report for Georgia”, Tbilisi.
  • Lettmayr, Christian F. and Nehls, Hermann (2012): “Skills supply and demand in Europe: Methodological framework”, CEDEFOP Working Paper No. 25
  • McGuinnes, Seamus and Sloane, Peter J. (2011): “Labour market mismatch among UK graduates: An analysis using REFLEX data”, Economics of Education Review 30, pp. 139-145
  • Neubäumer, Renate (2012): “Bringing the unemployed back to work in Germany: training programs or wage subsidies?”, International Journal of Manpower 33, pp. 159 – 177
  • Roth, Alvin E. and Sotomayor, Marilda (1990): “Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis”, Econometric Society
  • Shah, Chandra (2010): “Demand for qualifications and the future labour market in Australia 2010 to 2025”, Center for the Economics of Education and Training Working Paper, Monash University
  • The World Bank (2013): “Georgia: Skills Mismatch and Unemployment Labor Market Challenges”, World Bank Report No. 72824-GE

The crisis in Ukraine and the Georgian economy

Posted on March 17, 2014 | Policy Brief

High office buildings facing sky representing Institutions and Services Trade

We analyze how the crisis in Ukraine will likely impact the Georgian economy and distinguish between short-run and long-run effects. We argue that the short-run effects are transmitted through trade and capital flows and that they are rather negative for Georgia and can hardly be bolstered. In the long-run, however, the crisis could improve the competitiveness of the Caucasus Transit Corridor, an important trading route between Europe and Central Asia Georgia participates in. We give recommendations how political decision makers could support such a development in the wake of an impairment of the northern Ukrainian transit routes.

Introduction

When Ukrainian President Victor Yanukovich decided not to sign the association agreement with the European Union and instead opted for a Russian package of long-term economic support, many Ukrainians perceived this not to be a purely economic decision.  Rather, they feared this to be a renunciation of Western cultural and political values, and – to put it mildly – were not happy about this development.

The Russian political system, characterized by a prepotent president, constrained civil rights, and a government controlling important parts of the economy through its secret service, is not exactly the dream of young Ukrainians. Russia can offer economic carrots, but these do not count much against the soft power of Europe that comes in the form of political freedom, good governance, and economic development to the benefit of not just a small group of oligarchs.

Hence, it was all but surprising when many young Ukrainians took their anger about Yanukovich to the streets. After protests that lasted for nearly three months, President Yanukovich fled the country, a temporary government took over, and chaos broke out on the Crimean peninsula.

The dispute about the Crimea has the potential to impede the relations between Russia and the West for a long time to come, in particular if Russia enforces an annexation of the territory. Moreover, the tensions could quickly turn into a military conflict. The aircraft carrier USS George H.W. Bush was moved into an operational distance to the Crimea, accompanied by 20 smaller U.S. warships, and 12 additional fighter planes will be stationed in Poland. Yet even if there will be no direct confrontation between official Russian and U.S. forces, Ukraine could become the battleground of a proxy war, a kind of conflict that was common in the Cold War era. In this respect, one can already read the writing on the wall: the new Ukrainian government begs the U.S. for supplying arms and ammunition, and while the Obama administration is still reluctant to give in to such requests, the call is supported by hawkish U.S. congressmen who might finally prevail.

Ukraine is a country that is geographically close to Georgia and, like Georgia, has vital economic stakes in the Black Sea area. Georgia will not be unaffected by whatever happens in Kiev and Simferopol. In this policy brief, we will inform policy makers about the likely short-run and long-run economic consequences of the turmoil in Ukraine, discuss the challenges and opportunities that may arise, and derive some policy recommendations.

Short-run economic consequences

The crisis in Ukraine will almost instantaneously affect trade and capital flows between Georgia, Ukraine, and Russia. The effects will likely be negative and hit Georgia in a situation of economic recovery.

The Georgian real GDP growth rates were 6.3% in 2010, 7.2% in 2011, and 6.2% in 2012, and the real GDP per capita evolved from about 2,600 USD to about 3,500 USD in this time, but the upsurge discontinued in 2013 (if no other source is mentioned, figures presented in this policy brief (including those in the graphs) come from the Georgian statistical office GeoStat). ISET-PI, in its February 2014 report on the leading GDP indicators for Georgia, estimates the GDP in 2013 to be 2.6%, while GeoStat, the statistical office of Georgia, believes it to be 3.1%.

The unsatisfactory performance of the Georgian economy in 2013 was arguably caused by political uncertainties resulting from the government change that took place in late 2012, and as these uncertainties are largely overcome, most economists believe that Georgia will get back to its remarkable growth trajectory in 2014. The IMF, in its Economic Outlook, predicts a real GDP Growth of 6% in 2014, and the government of Georgia expects this number to be 5%. With an escalating crisis in Ukraine, it is questionable whether these rosy forecasts are still realistic.

Effects on imports

In 2013, Ukraine and Russia were the 3rd and the 4th largest importers to Georgia, respectively. Graph 1 shows the top five importers to Georgia, which together make up about 50% of total imports. The imports from Ukraine and Russia are mainly comprised of consumption goods: of all goods that were imported between 2009 and 2013 from Ukraine and Russia, about 30% were foodstuff. The ten main import goods in this time (in order of monetary volume) were cigarettes, sunflower oil, chocolate, bread, cakes, meat other than poultry, poultry, and sugar.

If the supply of these goods would be reduced through a breakdown of production and logistics, roadblocks, damaged infrastructure etc., the consequences for Georgia would not be utterly severe. From Ukraine and Russia, Georgia receives few goods that are (1) needed for investment projects and (2) cannot be produced domestically (an example of sophisticated investment goods that need to be imported would be ski lifts for tourism projects). Moreover, as Ukraine and Russia supply primarily standard goods that are produced almost everywhere, it is unlikely that a cutback in their imports would lead to sharp price rises in Georgia. Very quickly, increased imports from other countries would close any supply gaps. In addition, many imported consumption goods, like Ukrainian orange juice, are but luxury for ordinary Georgians, who buy their food in cheap domestic markets that sell almost exclusively local products.

Graph01

Effects on exports

A small anecdote may illustrate the status of Georgian products in the Russian market. In the late 1940s and early 1950s, Stalin used to invite his comrades to his Kuntsevo dacha almost every night. At these occasions, he drank only semi-sweet Georgian red wine. His clique, usually preferring Russian vodka, adopted this habit out of fear to displease the dictator. Yet the real highlight of these nightly gatherings took place after midnight, when an opulent feast began, featuring all the delicacies of the Georgian cuisine. Through Stalin (and the fact that Georgia was a preferred destination of Soviet tourism), Georgian food obtained an excellent reputation in most countries of the former Soviet Union, and, to the dismay of Georgians, some younger Russians even do not know that Khinkali is not an originally Russian dish.

As can be seen in Graph 2, Russia and Ukraine are among the top 5 destinations for Georgian produce, together absorbing about 14% of total Georgian exports in 2013. In 2006, two Georgian products that are traditionally highly popular in Russia, namely wine and mineral water (the famous “Borjomi” brand), were banned from the Russian market. Yet in the wake of the diplomatic thaw that set in after the new government assumed power last year, this ban was lifted, and in 2013, the export of these goods regained momentum. In 2013, 68% of all wine exported from Georgia was sold in Russia and Ukraine (44 and 24 percentage points, respectively). In both countries, Georgian wines are sold at the higher end of the price range and are typically consumed by people with middle and high income. It is likely that these exports, in particular those to Ukraine, will be affected considerably by the crisis. This may happen through decreased demand for luxury foods and through a possible depreciation of the Ukrainian hryvna and the ruble vis-à-vis the Georgian lari.

Another sector that may be affected by the situation in Ukraine is the car re-export business. Georgia imports huge numbers of used cars from the U.S., Europe, and Japan, and passes them on to countries in the region. While this business hardly yields potential for real economic progress, it accounts for roughly 25% of Georgian exports! Of these 25%, about 7 percentage points go to Russia and Ukraine. Moreover, many cars are imported to Georgia on the land route from Europe through Ukraine and Russia (often driven by private, small-scale importers). If it will become more difficult to cross the border between Russia and Ukraine, this business, providing income to many low-skilled Georgians, may be at risk.

It should also be noted that Ukrainians and Russians make up an ever-increasing share of the tourists coming to Georgia (though the biggest group of tourists are Israelis). Also through this channel, an economic downturn in Ukraine and Russia will have unpleasant consequences for Georgia.

Graph02

Effects on capital flows

According to the National Bank of Georgia, in 2013 a total of 801 mln USD was flowing in from Russia (see Graph 3). Ukraine contributed 45 mln USD to the money inflows, still significant for an economy as small as Georgia’s. An economic downturn in Russia and Ukraine would hit many Georgian citizens, often pensioners and elderly people, who depend on remittances of their children and other family members sent from these countries. This may aggravate a trend that already exists: in January 2014, money inflows decreased by 4% from Russia and by 5% from Ukraine (compared to January 2013).

Graph03

Long-run economic consequences

Most of the economic dynamics Georgia experienced since 2003 was “catch up growth”. A country permeated by corruption, with a dysfunctional police and judicial system, without protection of property rights and contract enforcement, will grow almost automatically when the government restarts to fulfill its basic functions. Yet once this phase of returning to normal economic circumstances is over (Georgia probably is already in this situation), high growth rates can hardly be achieved without a strong export orientation of the economy, in particular when an economy is as small as Georgia’s. Most economists concerned with Georgia are therefore struggling to identify economic sectors where Georgia is in a good position to develop export potential. The National Competitiveness Report for Georgia, written in 2013 by the ISET Policy Institute on behalf of USAID, therefore extensively discusses the question what Georgia can deliver to the world. Though not related to export in a classical sense, the report points out that one of the advantages Georgia has is its geographical location, providing for possibilities to transform Georgia into a logistics hub.

There are three main routes to transport goods from Europe to the Central Asian countries (e.g. from Hamburg to Taraz in Kazakhstan). One route goes via the Baltic ports of Klaipeda or Riga, and then through Ukraine and Russia, and another route goes overland through Ukraine. A third one, the so called Caucasian Transit Corridor, has the Georgian port city of Poti and Turkey as its Western connection points, then goes through Georgia, Azerbaijan, and the Caspian Sea, and further east it splits up into a Kazakhstan and a Turkmenistan branch.

According to the Almaty based company Comprehensive Logistics Solutions, the fastest and cheapest route is the one through the Baltic ports. The transport from Hamburg to Taraz takes around 33 days and costs 6,220 USD per standard container. The overland transport via Ukraine takes around 34 days and costs 7,474 USD. Finally, transport through the CTC currently takes the longest time, namely around 40 days, and costs 6,896 USD.

Unlike many other economic activities, competition for transportation is more or less a zero-sum game played by nations. If transport through Ukraine and Russia will be restrained due to closed borders and political and economic instability, the total transport volume will not change substantially. Rather, instead of going through the northern routes, the goods will flow through the CTC. A similar development could be observed when the embargo against Iran was tightened and shipping goods through Iranian ports became increasingly difficult for Armenia and Azerbaijan. As a result, Azerbaijan, traditionally importing through Iran and exporting through Poti, now facilitates both its imports and exports through Poti.

This is a great chance for Georgia if it wants to become serious about transforming into a logistics hub. In our policy recommendations, we will speak about how to utilize on this opportunity.

Policy recommendations

Georgia can do little to bolster the short-run effects that are transmitted through the trade and capital flow channels. Political decision makers should be aware of problems that might arise for particularly vulnerable groups in the population, like pensioners who lose income in case remittances from Russia and Ukraine run dry, and help out with social support if necessary.

Regarding the long-run impact, Georgia should use this opportunity for gaining ground in the competition with northern transit routes. The Caucasus Transit Corridor can become much faster and cheaper if (a) a deepwater port and modern port facilities with warehouses will be built in Poti, (b) the road and train infrastructure will be improved, and (c) it will be easier to bring cargo over the Caspian Sea. Regarding the latter point, it would be important to assist Azerbaijan in improving the port management at Baku (in particular reducing corruption), and in reforming the monopolistic Azerbaijani State Caspian Sea Shipping Company.

Azerbaijan invests 775 mln USD into the Georgian part of the Baku-Tbilisi-Kars railway, proving their serious interest to upgrade CTC. Given this impressive commitment of Azerbaijan, Georgia should not stand back.

Conclusion

The crisis in Ukraine yields short-run risks and long-run opportunities for the Georgian economy. While there is little that can be done about the risks, the opportunities call for courageous steps to improve the Caucasus Transit Corridor. If the countries that hold stakes in the CTC are now further reducing the cost of transportation and make the route faster and more customer-friendly, the CTC may establish itself as the main trading route connecting Europe and Central Asia. Once critical investments have taken place, CTC’s advantage could be sustained beyond the current crisis. It is a competitive route that simply needs upgrading, which can happen now as a fallout of the conflict between Ukraine and Russia.

References