Author: Admin
Trade Policy Uncertainty and External Trade: Potential Gains of Ukraine Joining the CU vs. the Signing Free Trade Agreement with the EU
This policy brief summarizes the results of recent research which predicts gains in Ukrainian exports from signing a deep and comprehensive free trade agreement with EU, and compares these gains with predicted gains from joining the Customs Union of Belarus, Kazakhstan, and Russia. We argue that the gains would be mostly due to elimination of uncertainty in trade policy of Ukraine with the CU and the EU countries. We find that European integration brings higher potential for export growth, and that it also shifts the structure of Ukrainian exports towards capital goods, reducing the share of raw materials in total export.
Trade Policy Uncertainty and Export
Trade policy uncertainty (TPU) is a powerful negative factor that prevents economy from the realization of its export potential. In a recent paper, Handley and Limao (2012) argue that since the exporting decision involves substantial fixed costs, TPU significantly affects investment and entry decisions in international trade. In particular, they show that preferential trade agreements (PTAs) are important even when the pre-PTA tariff barriers are low. Comparing pre- and post-EU accession patterns of Portuguese exports, they find that Portuguese trade increased dramatically after 1985. The increase was the largest towards the EU partners, suggesting that it was caused by the accession. Export expanded through considerable entry of Portuguese firms into EU markets, even in industries where applied tariffs did not change. Handley and Limao estimated that the tariff reduction, which averaged 0.66 percentage points, has been responsible for only 20 percent of the increase in exports to EU10 after the EU accession, while 80 percent of the increase was due to resolving TPU.
Handley and Limao further argue that the Portuguese example should be highly relevant for any small open economy, facing important trade policy choices. In this regard, Ukraine is facing a very hard choice of selecting its regional integration strategy – towards the EU or the Customs Union (CU) with Belarus, Kazakhstan and Russia, resulting in severe TPU. The options are mutually exclusive since the CU trade policy is not compatible with neither the WTO commitments of Ukraine, or with the parameters of the deep and comprehensive free trade agreement (FTA) between Ukraine and the EU, finalized in 2012. Average tariff protection within the CU in 2012 was 10 percent (Shepotylo and Tarr, 2012), while the average WTO binding tariff rates in Ukraine were only 5 percent; the parameters of the FTA with the EU are even less protective, which would cause even stronger disagreements regarding the tariff schedules. Moreover, technical and phyto-sanitary standards in the EU and the CU are different; therefore, it would be extremely hard to harmonize the Ukrainian standards with both of them.
Despite low tariff protection, uncertainty on the parameters of the long run trade policy of Ukraine with the CU and EU countries is extremely high. It is crucial for both foreign and domestic investors to understand in what direction the regional integration will proceed before making decisions on investing or exporting, since these decisions can incur substantial sunk costs. Suppose that a large European multinational firm were interested in including Ukrainian companies in its production chains only if Ukraine signs the FTA with the EU (integrate vertically). If Ukraine instead joined the CU, this presumed European company would rather be interested in horizontal integration and invest by building a plant for final assembly of products to serve the Ukrainian and CIS markets. For Russian companies the situation would be the reversed. They would be interested to integrate vertically if Ukraine is a member of the CU and integrate horizontally if Ukraine signed FTA with EU. However, since vertical and horizontal integration are quite different strategies, neither European nor Russian companies invest in Ukraine before the uncertainty is resolved. The same holds true for domestic companies which would like to extend their export activities to new markets. Since entrance to new markets is costly and requires some irreversible investment, it is optimal to wait until the policy uncertainty is resolved.
Modeling Trade Policy Options of Ukraine
In Shepotylo (2013), we investigate which integration scenario is more preferable for Ukraine under the assumption that TPU is fully resolved and Ukraine trades up to its potential. Based on export data in 2001-2011, we estimate the gravity model by Helpman, Melitz, and Rubinstein (2008) method, adjusted for panel data case and endogeneity of a decision to sign a PTA. Using this model, we predict bilateral exports of Ukraine under three counterfactual scenarios: a) Ukraine joined the Customs Union in 2009 (CU); b) Ukraine signed the FTA with the EU in 2009 (EU FTA); c) Ukraine joined the EU in 2009 (EU). The model predictions take into account the level of economic development, geographical location, industrial structure, and quality of government and regulatory agencies. It also accounts for macro trends, including the global trade collapse of 2008-2009.
The results are not intended for a short-term forecast, but should be rather used as indicators of the long-run effects. Their interpretation is as follows. Suppose that Ukraine has signed the FTA with the EU in 2009. Taking into account all observable characteristics of Ukraine, what would be the level of Ukrainian export of product k to country j, if Ukraine, in all other respects, would behave as a typical country-member of the FTA EU? That would involve removal of the trade policy uncertainty, stronger integration of domestic companies into the global supply chains, and increase in foreign direct investments from the EU countries.
Unlike the studies based on the Computable General Equilibrium (CGE) method, which assumes that the policy choice affects the economy only marginally through reduced tariff barriers, and that the underlying economic structure and expectations of the economic agents remain intact, the gravity model captures all changes that occur in the economy over the investigated period and extract the differences in export flows between any two counterfactual scenarios, given all background economic changes.
Results
Our main results are as follows. First, the actual exports of Ukraine are far below their potential, compared with performance of both the CU countries and the FTA EU countries. The expected long run gains in Ukrainian exports to all countries under the CU scenario are equal to 17.9 percent above the export level in 2009-2011. The corresponding number for the FTA EU scenario is 36 percent, and for the full EU scenario, 46.1 percent. Based on 2011, the export of Ukraine would have been 98 billion US dollars under the EU scenario, 91 billion US dollars under the FTA EU scenario, and 72 billion US dollars under the CU scenario. All these numbers should be compared with the actual 68 billion US dollars of Ukrainian export in 2011.
Figure 1. Ukrainian Export under the Different Scenarios
Second, any scenario predicts that Ukraine severely underperforms in its trade with both CIS and EU countries, while its export to the rest of the world is in line with the predictions of the model. These results are consistent with the theory that unresolved TPU in relationships with the CIS and EU countries severely hurts the Ukrainian export potential to these countries.
Table 1. Ukrainian Export under the Different Scenarios
Note: CIS – Commonwealth of Independent States; EU12 – countries that joined EU after 2003; EU15 – countries that joined EU before 2004; RoW – rest of the World
Third, CU integration would be more beneficial for the Ukrainian agriculture and food industry, while FTA EU or full EU integration would be more beneficial for textiles, metals, machinery and electrical goods, and transportation. Conditional on not worsening its market access to Russia, Ukraine would expand its trade in these sectors to all countries, including Russia and other members of CU.
Figure 2. Expected Increase of Ukrainian Export under the Different Scenarios 
Finally, the CU integration would lead to a small increase in the share of capital goods from 17 percent to 20 percent of total exports. FTA EU would increase the share of capital goods to 28 percent, while full EU integration would increase it to 29 percent. In all scenarios, the share of raw materials would decline from 16 percent to 10-12 percent. The share of intermediate goods would decline from 48 percent to around 40 percent under the two EU scenarios and would only marginally decrease under the CU scenario. The share of consumer goods would remain stable around 20 percent.
Conclusions
Ukraine would be better off by signing a deep and comprehensive trade agreement with the EU and integrate into its production chains than joining the CU. Right now, Ukraine severely underperforms by exporting far below its potential. Evidence shows that high trade policy uncertainty plays a large role in Ukraine’s poor performance, since the gap between actual and potential exports are mainly due to low levels of export to the EU and CIS countries. Moreover, Ukraine should be interested in moving the integration process even further, because EU accession would bring even better results.
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References
- Handley, K., & Limão, N. (2012). Trade and investment under policy uncertainty: theory and firm evidence (No. w17790). National Bureau of Economic Research.
- Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. The Quarterly Journal of Economics,123(2), 441-487.
- Shepotylo, O., & Tarr, D. (2012). Impact of WTO accession and the customs union on the bound and applied tariff rates of the Russian federation. World Bank Policy Research Working Paper, (6161).
The European Commission against Gazprom: Should Gas Contracting Arrangement Be Changed?
This policy brief discusses EC’s claim that Gazprom abuses its dominant position. I argue that parts of the claim, like denying Third Party Access, are warranted but others related to the contracts offered by Gazprom to different Member States need not be. In fact, major market players in Europe offer similar contracting forms. In this case, the literature on the competitive effect of long-term supply contracts have stressed that such effect depends on the exact contract arrangement. For example, offering multi-years contract may indeed increase the competition on one part of the market. Having a gas supply contract with a price fully linked to the price of a gas hub may on the other hand reduce the competition among big gas suppliers. Hence, the assessment of Gazprom’s abuse of dominant position should be based on a careful analysis of the many contracting forms that have been agreed between Gazprom and customers in the Member States.
On the 4th of September 2012, the European Commission (EC) opened a proceeding against Gazprom, investigating whether Gazprom has abused its dominant market position in Central and Eastern Europe’s gas supply (see http://europa.eu/rapid/press-release_IP-12-937_en.htm?locale=en). The allegation relies on two different points. First, Gazprom has been accused of denying access to its network pipeline when requested by competing gas supplier. Second, the contractual arrangement offered by Gazprom itself has been under scrutiny. A Gazprom contract usually includes a “destination clause”, that forbids any gas reselling by the buyer. Moreover, the typical Gazprom contract usually specifies a fixed quantity (with a take or pay clause) at a price indexed to the oil price (see Sartori, 2013 for a more extensive description of the EC’s proceeding.)
The objective of this policy brief is to discuss the EC’s claim of Gazprom’s abuse of dominant position. I argue that while the denial of Third Party Access appears as an obvious case of abuse of dominant position, the contractual arrangements offered by Gazprom need not be.
Characterization of Gazprom’s Abuse of Dominant Position
Denying access to Gazprom’s pipelines limits competition and thereby benefits Gazprom as controlling a pipeline constitutes a natural monopoly. This fact has been recognized for a long time with the requirement for a third party access to gas networks in the EU Gas Directive (Directive 2009/73/EC). The first part of the proceeding thus seems to be justified.
The EC proceeding also found that the contractual arrangements offered by Gazprom reflected an abuse of dominant position. The claim is that Gazprom locked in its customers. When signing a contract with Gazprom, buyers agreed on a fixed quantity irrespective of their “real” consumption (“take or pay” clause) and are not allowed to resale ex post excess quantity on the market (“destination clause”). Given that gas contracts usually are signed for many years, the lock-in period can be long. Moreover, the price of the gas contract is usually pegged to the oil price so that it reflects current supply and demand conditions for oil rather than for gas. One implication is that the contracted gas prices did not reflect the severe drop in the gas market price in 2008 (BP report, 2012).
The EC’s allegation that Gazprom has abused its dominant position is thus based not only on the fact that Gazprom is denying third party access to its pipelines but also on the long term contracts with a fixed quantity and an oil indexed price.
Next, I argue that the second part of the claim is questionable. Forcing Gazprom to propose contracts with flexible quantities, shorter contract lengths and no indexation to the oil price may not limit the abuse of Gazprom’s dominance. Depending on the exact contract arrangement (quantity, duration, and indexation), the abuse of dominant position could be more or less severe.
Contract Arrangement and Market Competition
It is important to stress that the major gas suppliers of Europe, like Sonatrach or Statoil, offer similar contract arrangements. So, are long-term supply contract arrangements pro or anticompetitive given that all major competitors use such contracts? The answer to this question typically depends on the contractual details. In what follows, I discuss briefly when contracts provided by major market players could alleviate the abuse of dominant position.
It has been shown that firms may have less incentive to exercise market power, if they have large contract positions (e.g. Allaz and Vila, 1993). Intuitively, a firm obtains a leadership position by selling contracts before going on the spot market. Motivated by this opportunity, all players participate in the contract market and as a consequence compete more aggressively overall. Offering long-term supply contracts may therefore enhance competition among gas suppliers.
The competitive effect of long-term supply contract may not always be present when suppliers and buyers repeatedly sign contracts. In a dynamic setup, it has been shown that allowing contracting for major players may reduce competition. Contracting could be used to reduce demand elasticity by increasing spot market exposure (e.g. Mahenc and Salanié, 2004). Contracting could also increase the likelihood and severity of collusion (Ferreira, 2003; Le Coq, 2004; Liski and Montero, 2006). The reason is that a collusive agreement is easier to sustain in a dynamic setup if firms offer contracts. A collusive strategy is sustainable provided that firms have no incentives to cheat, i.e. the repeated collusive profits exceed the immediate profit from the deviation and the price war following defection. The short run gains from cheating are reduced if all firms have signed contracts as the defecting firm will not capture the demand already covered by competitors’ contract sales. Compared to the case with no contracts, this reduces the gains from defection without changing the punishment path, and therefore makes collusion easier to sustain. In a dynamic setup, offering contracts may therefore increase the likelihood of collusion.
Green and Le Coq (2010) have shown, however, that the anti-competitive effect of contracts depends on their duration. The longer the contracts last, the more difficult it is to sustain collusion. Intuitively, a deviation from the collusive agreement will trigger punishments, which depend on the contract duration. The longer the contract lasts, the smaller would be the punishment profit, which would increase the incentive to deviate.
The contract price’s format also matters when estimating the anti-competitive effect of any contract arrangement. The stronger the degree of indexation to the spot price the easier it is to sustain collusion (Le Coq, 2013). In particular, if a contract price would be fully indexed on a gas spot (hub) price, irrespective of the contract’s duration, it is always easier to collude. The intuition underlying this result is two-fold.
First, given that the contracted quantities are not traded in the spot market, contracts reduce the size of the market that a deviator can serve when undercutting the rival’s price. Second, given that the contract’s price equals the spot price, the contract does not affect profit levels in the punishment phase. Consequently, profits in the punishment phase can be driven down to zero just as in the case when there is no contract market. Moreover, contracts with others forms of indexation have the same qualitative effects, provided that the indexation to the spot price is sufficiently strong. Interestingly, with full indexation, the anti-competitive effect of supply contract holds even if contracted quantities are flexible (can be renegotiated).
To conclude, changing the contract arrangement between Gazprom and European customers may not alleviate the abuse of Gazprom’s dominant position. A detailed analysis of the (many) contract arrangements offered by Gazprom needs to be conduct first to be able to make such claim.
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References
- Allaz, B., Vila, J.-L., 1993. Cournot competition, forward markets and efficiency. Journal of Economic Theory 59 (1), 1–16.
- BP Statistical Review of World Energy June 2012
- Directive 2009/73/EC of the European Parliament and of the Council concerning common rules for the internal market in natural gas and repealing Directive 2003/55/EC, OJ L 211.
- Ferreira, J.L., 2003. Strategic interaction between futures and spot markets. Journal of Economic Theory 108 (1), 141–151.
- Liski, M., Montero, J.-P., 2006. Forward trading and collusion in oligopoly. Journal of Economic Theory 131 (1), 212–230.
- Le Coq, C., 2004. Long-term supply contracts and collusion in the electricity market. Stockholm, SSE/EFI Working Paper Series in Economics and Finance 552.
- Le Coq, C., 2013 Supply Contracts and Competition on the Spot: How indexation and duration matter? Mimeo.
- Le Coq, C., R. Green, 2010 The Length of Contracts and Collusion International Journal of Industrial Organization 28(1), 21-29, 2010.
- Mahenc, P., Salanié, F., 2004. Softening competition through forward trading. Journal of Economic Theory 116 (2), 282–293.
- Sartori N., 2013. The European Commission vs. Gazprom: An Issue of Fair Competition or a Foreign Policy Quarrel? IAI working paper 13103
Alcohol Consumption and Mortality
Many studies have shown that alcohol consumption is the main cause of death among working age Russian males and, in particular, among those younger than 40 (see Bhattacharya et al., 2013, Brainerd and Cutler, 2005, Denisova, 2010, Leon et al., 2007, Triesman, 2010, Yakovlev, 2013a, 2013b). A noteworthy example that illustrates this point is the decrease in male mortality rates during the Gorbachev anti-alcohol campaign. During five years of this campaign, which restricted sales and increased the price of alcohol, alcohol consumption fell by 40%. During the same period, male mortality rates fell by 25%. Furthermore, this trend reversed at end of the Gorbachev anti-alcohol campaign with the liberalization of the alcohol market and surge in mortality by the end of 1990s and beginning of 2000s (see Triesman, 2010 and Bhattacharya et al., 2013). These trends appear to be consistent with the idea that access to more alcohol is related to higher rates of male mortality.
Despite recent regulatory measures imposed by the Russian government to end this trend, male live expectancy remains low: it is 4 years below world average and below poor countries, such as North Korea or Yemen.
The economic literature emphasizes several features of alcohol consumption that are important for policy makers. First, alcohol, and especially hard alcohol, is a relatively elastic good. This implies that an increase in the price of alcohol as well as other costs (such as time costs) will result in an even larger drop in alcohol consumption relative to the price drop. If they are linked, this should also be associated with a fall in mortality rates (see Cook and Moore, 2000, Leung and Phelps, 1993).
Second, alcohol is a “social” good (Kremer and Levy, 2008, Krauth, 2005, Yakovlev, 2013a). People like to drink with others. Drinking often takes place in groups of peers, and peer decisions on whether to drink or not affect personal decisions related to drinking. Peer effects are especially strong among younger generations. The presence of peer effects implies the presence of a so-called social multiplier: the effect of government policy (for example, alcohol taxation) will be higher in the presence of peer effects. A policy such as a rise in taxation will not only affect an individual by encouraging them to consume less, but also have a spillover effect on his or her peers resulting in them drinking less as well. This should, overall, generate a larger decrease in alcohol consumption than would be the case through the effect on individuals alone (i.e. if people choose to drink based purely on their own preferences without paying attention to their peers or social groups). As it was shown by Yakovlev (2013a), for males below age 30, the peer effect increases the price elasticity of alcohol consumption by 50%. This means that a government policy, such as an increased alcohol tax, should generate a 50% higher decrease in alcohol consumption for the younger generation. Furthermore, this should also lead to an even larger reduction of mortality rates.
A third aspect of alcohol consumption is that alcohol is a habit-forming good (see Cook and Moore, 2000). The consumption of alcohol, as well as consumption of certain types of alcoholic beverages, tends to form habits related to these goods. These habits are strong and they potentially affect personal consumption even decades later. If a person starts to consume alcohol in their youth, this means that they are likely to continue and be more likely to consume alcohol in later years simply because they have a past history of consuming this product.
These three aspects have several policy implications. First, due to habits and peer effects, government policies aiming to reduce mortality rates by decreasing alcohol consumption will potentially have greater impact on younger generations than on older. This is simply because peer effects tend to be stronger among youths, but also because decreased consumption earlier in life will reduce the chances of consuming alcohol later in life and have, as a consequence, even longer term effects on society’s level of alcohol consumption. Thus, policy makers should pay special attention on younger groups of the population, in particular, policy tools such as the restriction of alcohol sales near schools and other educational facilities if the goal is to reduce the negative impact of alcohol on life expectancy. Second, the effect of this policy could be long lasting: once habits form, patterns of consumption could be affected for many years afterwards. In other words, the full effects of a policy aiming to curb alcohol consumption to improve mortality rates will not be immediately observed. Instead, part of the change in the future would be attributed to past changes in alcohol consumption.
Another aspect of alcohol consumption of importance for mortality rates concerns the habits individuals form regarding what types of alcoholic beverages, such as beer or vodka (see Yakovlev, 2013b), they drink. This has policy implications since not all beverages have the same degree of harm. If an individual consumes beer during his or her teens, she or he would likely prefer beer ten (or even more) years later. If she or he starts with vodka, she or he will likely prefer vodka. Moreover, Yakovlev (2013b) shows that beer and vodka are substitutes: an increase in the price of beer will decrease the consumption of beer and increase the consumption of vodka, or vice versa. Because beer is a less harmful alcoholic beverage than vodka, an increase in the relative price of vodka with respect to beer should improve public health to the extent that people switch to consuming a less harmful form. In addition, this effect should be stronger in the long run with individuals forming habits toward beer consumption at the expense of the more harmful vodka and, overall, we should expect morality rates to be improved as a result, although not by as much as in the case when people stop or do not consume alcohol.
There are several other features of alcohol consumption worth mentioning but which will not be addressed in detail in this brief. Alcohol consumption is correlated with not only personal health and well-being, but also with the well-being of others: it is associated with negative externalities such as crime, violence, and traffic accidents etc. Alcohol consumption also exhibits several “non-fully-rational” features such as time inconsistency or myopia (Gruber and Koszegi, 2001). In this case, a restriction on the times when alcohol sales are permitted could be a possible effective policy tool to reduce heavy drinking. This happens because people tend to underestimate how much they would like to drink in the future or want to drink less in the future than they expect, and thus prefer not to store alcohol at home. Finally, alcohol consumption is a substitute for other activities, such as sports (Tsai, 2013). Promoting these activities could encourage people to switch from alcohol consumption to healthier behavior, and, conversely, reducing alcohol consumption could foster greater levels of participation in sports activities.
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Literature
- Bhattacharya, Jay, Christina Gathmann, and Grant Miller. 2013. “The Gorbachev Anti-Alcohol Campaign and Russia’s Mortality Crisis” AEJ: Economic Policy 2012
- Cook, Philip J. and Moore, Michael J. 2000. “Alcohol”, Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed. ), Handbook of Health Economics, edition 1, volume 1, chapter 3.
- Brainerd, Elizabeth and David Cutler, 2005, “Autopsy on an Empire: Understanding Mortality in Russia and the Former Soviet Union.” Journal of Economic Perspectives, American Economic Association, vol. 19(1), pages 107-130,Winter.
- Denisova, Irina. 2010. “Adult mortality in Russia: a microanalysis”, Economics of Transition, Vol. 18(2), 2010, 333-363.
- Gruber, Jonathan and Botond K˝oszegi. 2001. “Is Addiction ‘Rational?’ Theory and Evidence.” Quarterly Journal of Economics (2001), 116(4), pp. 1261-1305.
- Kremer, Michael, and Dan Levy. 2008. “Peer Effects and Alcohol Use among College Students.” Journal of Economic Perspectives, 22(3): 189–206.
- Krauth, Brian. 2005. “Peer effects and selection effects on smoking among Canadian youth.” Canadian Journal of Economics/Revue canadienne d’économique, Volume 38, Issue 3, pages 735–757, August 2005.
- Leon, David, Lyudmila Saburova, Susannah Tomkins, Evgueny Andreev, Nikolay Kiryanov, Martin McKee, and Vladimir M Shkolnikov. 2007. “Hazardous alcohol drinking and premature mortality”
- Leung S. F., and Phelps, C. E. “My kingdom for a drink…?” A review of estimates of the price sensitivity of demand for alcoholic beverages. In: Hilton, M. E. and Bloss, G., eds. Economics and the Prevention of Alcohol-Related Problems. NIAAA Research Monograph No. 25, NIH Pub. No. 93–3513. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism, 1993. pp. 1–32.
- Tsai, 2013, “Peer effects in physical training.” NES, mimeo
- Yakovlev, Evgeny 2013, “Peers and Alcohol: Evidence from Russia”, NES/CEFIR working paper
- Yakovlev, Evgeny 2013, “USSR Babies: Who drinks vodka in Russia”, NES/CEFIR working paper
Can Anti-Smoking Campaigns Increase Obesity? Evidence from Belarus
Authors: Aliaksandr Amialchuk, University of Toledo, and Kateryna Bornukova, BEROC.
In this brief, we discuss the possible effects of an anti-tobacco campaign on obesity levels in Belarus based on results of Amialchuk et al (2012). Both smoking and obesity are among the main health concerns in Belarus. Negative correlation between smoking and body weight is well documented, but can anti-tobacco campaign cause an increase in obesity rates? Results of studies from developed countries provide mixed evidence. In Amialchuk et al (2012), we use household survey data from Belarus to establish the link between smoking and body mass index (BMI). We use cigarette prices and regional smoking prevalence as instruments for smoking, and find a negative effect of smoking on BMI. Moreover, using the quantile regression approach, we find that smoking has different effects on body weight for different BMI quantiles, with the largest negative effect in the upper part of the conditional BMI distribution. These findings suggest that anti-tobacco campaigns may slightly increase obesity rates, and campaigns should therefore ideally also include measures to promote a healthy lifestyle. On the other hand, the potentially modest weight gain from an anti-tobacco campaign is likely to be more than offset by the general improvements in health.
Smoking and Obesity in Belarus
Smoking prevalence in Belarus, like in many other transitional countries, is quite high. According to the Belarusian Household Survey of Income and Expenditure from 2010, the smoking rate was 26%, with a much higher prevalence of among men (49.3%) compared to women (9.5%).[1]
Despite the troubling levels of smoking prevalence, little has been done to combat smoking in Belarus. While most of the post-Soviet economies liberalized the tobacco industry, it remains under government control in Belarus. The profits of the state-owned cigarette producers, along with tobacco taxes, constitute an important part of Belarusian budget revenues. This might explain why the Belarusian government has not engaged in anti-tobacco campaigns in the past. However, Belarus is currently implementing Anti-Tobacco Plan for 2011-2015 in cooperation with the World Health Organization.
The Anti-Tobacco Plan includes a variety of anti-tobacco actions and measures. In particular, the government has plans to gradually increase tobacco taxes, introduce smoking-free zones and restrict smoking in public places, along with a massive informational campaign about the dangers of smoking and ways to quit. These measures have the potential to lead to a significant decrease in smoking prevalence. However, an unintended consequence of these policies might be an increase in overweight and obesity rates.
In fact, obesity is another important health problem of Belarus. In 1996-2008, (the period of analysis in Amialchuk et al (2012)), the mean BMI among adults was 26, which suggests that an average Belarusian adult is just on the borderline between healthy weight and overweight. In particular, 34% of adults are overweight, while approximately 15% of adults are obese. Moreover, the distribution of weight status has undergone substantial changes over time: the percentage of individuals in the right tail of the BMI distribution has increased over time, with the percentage of obese increasing faster than the percentage of overweight individuals.
The Link between Smoking and Obesity
The negative relationship between smoking and body weight is well-documented in the medical literature. This inverse relationship is mostly attributed to how smoking affects body weight by boosting metabolism and suppressing appetite. However, causality is usually difficult to establish: for example, a smoking person may also be more likely to eat unhealthy foods and care less about their health in general. Nevertheless, most of the previous studies have found a significant negative effect of smoking on body weight.
Since in many developed countries, the decrease in smoking prevalence coincided in time with the surge in both overweight and obesity rates, the question arises whether anti-smoking campaigns are in part responsible for the increase in obesity rates. However, the evidence on the effects of anti-tobacco campaigns on overweight/obesity rates in developed countries is mixed. Some studies do not find any significant effect on obesity (Nonnemaker et al, 2009).
Evidence from Belarus
As mentioned above, smoking behavior and BMI may be jointly determined, and to deal with the challenge of establishing causality, we utilize the method of instrumental variables analysis. We employ two instrumental variables in our estimation: (i) the mean number of cigarettes smoked per day in the same year-region-gender- and education group as the respondent, and (ii) the average yearly price per pack of cigarettes in the region where the respondent lives. Gilmore et al. (2001) identify important demographic and socio-economic differences in smoking rates, which dictates our use of gender and education categories (below secondary, secondary, university degree) to construct groups of observations that will be followed over time. The use of region as a grouping variable allows us to capture the social norm associated with smoking at the regional level. We exclude the individual’s own cigarette smoking when we create group-level means. Group-specific smoking prevalence is likely to be predictive of the individual’s own smoking preferences, but is unlikely to have a direct effect on individual’s weight status other than through the effect on individual’s smoking. After accounting for the fixed differences in average smoking among regions, gender, and education groups within each year, the source of variation that is available to identify the effect of the instrument on individual’s smoking is the differences in smoking prevalence among various interactions of year, region, gender and education categories.
We use lagged prices as instrument for current year cigarette consumption of the individuals in order to account for the addictive and inelastic nature of demand for smoking and the inability to quickly change smoking behavior after a price change. Furthermore, we use natural log of cigarette prices in order to account for the potentially non-linear effect on the number of cigarettes smoked. Cigarette prices are likely to influence an individual’s BMI only through its effect on smoking.
Other controls in our regressions include total personal income; household size; age; gender; single vs. married indicator; indicators of self-reported health status (good health, fair health, and poor health indicators); number of medical visits in the last 3 months; indicator for having been hospitalized in the last 12 months; indicator for whether health affects ability to work; sports practicing indicator; indicators for the educational attainment (university diploma, secondary education); and indicators for being currently employed, having ever worked, and being a student.
Our endogeneity-corrected estimates suggest that one additional cigarette per day would decrease BMI by roughly 0.23 units, and would reduce the probability of being overweight by approximately 2.5%. Furthermore, there is a small but significant effect on the likelihood of being obese: an additional cigarette smoked per day decreases the probability of being obese by 1.3%. Our results suggest an important implication that smoking is inversely related to body weight, and has some effect on obesity rates.
We also explore the difference in the effect of smoking on body weight across different quantiles of conditional BMI distribution. The largest effect is obtained for the 75th and 90th percentiles, and the smallest effects for the 10th and 25th percentiles. Smoking has a large effect on the body weight of individuals who are at the upper tail of the BMI distribution. These findings suggest that a reduction in smoking rate may lead to an increase in obesity rates by inducing weight gain among the population near the top end of the conditional BMI distribution.
While we found evidence of a possible increase in obesity rates resulting from the anti-tobacco campaign, it is important to remember that adverse health effects of smoking are numerous and the health benefits of smoking cessation are far in excess of the risk of weight gain. The current high prevalence of smoking and number of overweight individuals in Belarus constitute a major public health concern. Our results suggest that the prevalence of overweight and obesity might be exacerbated by the anti-tobacco campaign. From a policy perspective, an increase in obesity rates among the general population may be a reasonable concern for policy instruments targeted at reducing the overall smoking rates. It would therefore be wise to promote healthy eating habits and sports together with the anti-smoking campaign. However, the potentially modest weight gain from anti-tobacco campaign only is likely to be more than offset by the general health improvements associated with a decline in smoking rates.
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References
- Amialchuk, A., K. Bornukova, M. Ali, 2012. Smoking and Obesity Revisited: Evidence from Belarus. BEROC Working Paper Series, WP no. 19
- Gilmore, A.B., McKee, M., Rose, R., 2001. Prevalence and determinants of smoking in Belarus: A national household survey, 2000. European Journal of Epidemiology 17: 245-253
- Nonnemaker, J., Finkelstein, E., Engelen, M., Hoerger, T., Farrelly, M., 2009. Have efforts to reduce smoking really contributed to the obesity epidemic? Economic Inquiry 47, 366–376
[1] The social norms explain difference in smoking rates of men and women. In younger population, however, gender differences in smoking rates are less pronounced.
Preferences for Redistribution in Post-Communist Countries
Public attitudes toward inequality and the demand for redistribution can often play an import role in terms of shaping social policy. The literature on determinants of the demand for redistribution, both theoretical and empirical, is extensive (e.g., Meltzer and Richard 1981, Alesina and Angelotos 2005). Usually, due to data limitations, transition countries are usually considered to be a homogeneous group in empirical papers on the demand for redistribution. However, new data on transition countries allow us to look more deeply into the variation within this group, and to look at which factors are likely to play a significant role in shaping a society’s preferences over redistribution.
The data we use are from the second round of the EBRD and WB Life in Transition Survey (LiTS) (EBRD Transition Report 2011). This is a survey of nationally representative samples consisting of at least 1000 individuals in each of the 29 transition countries.[1] In addition, and for comparison purposes, this survey also covers Turkey, France, Germany, Italy, Sweden and UK. Furthermore, in six of the countries surveyed – Poland, Russia, Serbia, Ukraine, Uzbekistan and UK – the sample consists of 1500 individuals.
Redistribution is, in general, a complex issue, which can take various forms and rely on different mechanisms. In this policy brief, we will only focus on two forms of public attitudes towards redistribution. The first is direct income redistribution from the rich to the poor and public preferences for or against this form of redistribution. The second is indirect redistribution through the provision of public goods, some of which favor certain groups of population over others. In particular, we will consider preferences over extra government spending allocations in the areas of education, healthcare, pensions, housing, environment and public infrastructure. Generally, we would like to explore in greater detail to what extent there are differences across countries in terms of public preferences over redistribution and what might explain differences both within and across societies.
Both survey rounds include questions regarding public preferences towards income redistribution, direct (from the rich to the poor) and indirect (through government spending towards certain public goods). Data for exploring public preferences for direct redistribution can be obtained from a question in the survey that asks respondents to score from 1 to 10 whether they prefer more income inequality or less. More specifically, in the LiTS 2010, the question is the following:
Q 3.16a “How would you place your views on this scale: 1 means that you agree completely with the statement on the left “Incomes should be made more equal”; 10 means that you agree with the statement on the right “We need larger income differences as incentives for individual effort”; and if your views fall somewhere in between, you can choose any number in between?
Note, however, that we use the reverse of this so that 10 represents greater equality and 1 represents wider differences. Bearing this in mind, figure 1 shows the average scores for redistribution preferences for a selection of the countries for 2010 and shows a sizeable variation ranging from 4.4 (more inequality) in Bulgaria to 7.87 (greater equality) in Slovenia. The mean for Russia is 6.92.
The data also allows for a comparison to be made between these preferences in transition countries and in the developed economies covered in the survey. For instance, Russians are on average close to Germans in their preferences for redistribution, while Estonians and Belarusians prefer less redistribution and are closer to the British, on average.
Figure 1. Preferences for Direct Redistribution
Indirect measures of attitudes towards redistribution can add further depth to these societies’ preferences. In particular, the indirect measures in the 2010 survey are derived from a question that asks respondents to rate from 1 to 7 their first priorities for extra government spending.
Q 3.05a “In your opinion, which of these fields should be the first priority for extra government spending: Education; Healthcare; Housing; Pensions; Assisting the poor; Environment (including water quality); Public infrastructures (public transport, roads, etc.); Other (specify)”?
The country averages for these indirect measures for 2010 are presented in Figure 2. The graph reveals a sizeable cross-country variation. For instance, 43.5% of respondents in Mongolia preferred channeling extra government money to education, while 48.7% of respondents in Armenia selected higher healthcare spending. Almost 39% of respondents in Azerbaijan chose assistance to the poor as the first priority for government spending, while the corresponding figure was only 8.3% in Bulgaria and 4% in the Czech Republic. More than 34% of the Russians choose healthcare as their first priority, another 20% choose education, 15% would like the money to be channeled to housing, 14.5% to pensions, 11% to support the poor, 3% to support environment, and only 2% to public infrastructure (2010).
These numbers highlight that there are sizeable differences across the transition countries regarding preferences for redistribution. Also, regarding the form of indirect redistribution in terms of preferences over how government budgets should be prioritized and allocated. Several groups of factors or determinants are typically listed in academic literature to help explain what drives public preferences over the degree and form of redistribution. In the first group of factors, there are various determinants at the individual level. Within the group of individual determinants, self-interest or rational choice of a degree of redistribution favorable to the individual with usual (individual) preferences are stressed. Alternatively, motives behind a preference for redistribution can be related to social preferences (preferences for justice or equity) and reciprocity. Within this general group of self-interest, attitudes towards risks can be stressed as a crucial factor behind demands for social insurance and hence for indirect forms of redistribution. Individuals’ prospects of upward mobility, expectations about their future welfare or ‘tunnel effect’ in shaping their views and preferences over redistribution are also underlined. Also, the commonly held beliefs about the causes of prosperity and poverty are considered to be important in shaping the public’s attitudes under the umbrella of social preferences.
The literature covers possible institutional determinants for preferences towards redistribution and emphasizes the role of the level of inequality in a society and typically relates to the median voter hypothesis in democracies. It is also stressed that welfare regimes (liberal, conservative) can play a role in shaping the level of public support for redistribution.
Figure 2. Preferences for Indirect Redistribution
A closer examination of the data and estimates of the factors shaping individuals preferences over redistribution in the 2010 survey, are consistent with motives involving strong self-interests of the respondents.[2] Those from richer households have less support for redistribution, with the result being robust to the measure of household income used. The past trend in household income positions is insignificant, while the higher the expected income position of household in the coming four years, the less supportive the respondents are of income redistribution (elasticity -0.1). Those who experienced severe hardships with the recent crisis tend to support redistribution more than those who had little problems or not at all (elasticity 0.13).
Furthermore, the role of preferences towards uncertainty is confirmed: the higher the (self-reported) willingness to take risks, the less likely the individual is to support or favor redistribution. Respondents with tertiary education are less inclined to support redistribution of income from the rich to the poor, compared to those with secondary education (elasticity is -0.4). Having a successful experience with business start-ups also decreases demand for income redistribution from the rich to the poor (elasticity -0.3). Those living in rural areas are more in favor of redistribution compared to metropolitan areas, while living in urban areas shows the same level of support for redistribution as those living in metropolitan areas. In each of these cases, it appears that those who would benefit the most from redistribution favor it more than those who view it as coming at their expense, or possible expense in the future.
Beliefs regarding the origins of success and poverty are also shown to be statistically significant and negative, as predicted: those who believe effort and hard work or intelligence and skills are the major factors for success are less supportive of income redistribution (elasticity -0.16). Those who consider laziness and lack of will power the major factors for people’s lack of success are also, consistently, less supportive of redistribution (elasticity -0.2).
It also turns out that better democratic institutions are correlated with a higher demand for redistribution. The result is robust across the measures used, i.e. it does not seem to depend on the particular measure used. The size of the effect is quite pronounced: a one standard deviation increase in the democracy measure increases demand for redistribution from 16 percentage points, when the voice and accountability measure is used, to 33 and 36 percentage points when controls of the executives and democracy index are used.
Furthermore, the better the governance institutions, as measured by the rule of law and control of corruption indexes, the higher is the demand for redistribution. However, the result is not robust to the various measures used. Government effectiveness appears to be insignificant (though with a positive direction), and the regulatory quality measure is insignificant but with a negative direction. The size of the effects is again quite pronounced. A one standard deviation increase in the rule of law measure increases demand for redistribution by 17 percentage points, and a one standard deviation increase in the control of corruption measure increases demand for redistribution by 27 percentage points.
The higher the level of inequality, the larger is the demand for redistribution as might be expected. This result is robust across all measures used. The size of the effect varies from 16 to 18 percentage points in response to a one standard deviation increase.
A regression analysis of preferences towards indirect redistribution also shows that self-interest motives are very pronounced, but there are traces of social preferences as well. In particular, younger people (age 18-24) would like to have more subsidized education and housing at the expense of healthcare and pensions in comparison with the age 35-44 reference group. Those in the age 25-34 group would like to redistribute public spending to housing and environment at the expense of education, pensions and public infrastructure. Respondents in the age 45-54 group would also like to redistribute additional spending from education but to pensions. The two groups of older people (age 55-64 and 65+) would like to shift extra spending from education and housing to healthcare and pensions. The group of age 65+ would also like to shift money from assistance to the poor.
Respondents with tertiary education (in comparison with holders of a secondary degree) favor extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assisting to the poor, thus revealing additional elements of social motivations. Respondents with primary education, when compared to holders of secondary degree, would like to redistribute public money from education to pensions and assistance to the poor. Respondents with poor health favor additional spending on healthcare and pensions at the expense of education.
High skilled (in terms of occupational groups) respondents would like to redistribute public money from pensions to education. Those with market relevant experience of being successful in setting up a business tend to support education and public infrastructure at the expense of housing and pensions, though the result lack statistical power.
Respondents from households with higher income support extra spending for education, environment and public infrastructure at the expense of healthcare, pensions and assistance to the poor; again pointing to the other elements of possible social motivations. Those with a self-reported positive past trend in income position tend to support spending extra money on the environment at the expense of assistance to the poor (the latter lacks statistical power). If the respondent lives in its own house or apartment, s/he tends to support redistribution from housing and assistance to the poor, to healthcare and pensions.
Respondents whose households were strongly affected by the crisis would like expenditure on environment and public infrastructure to be reduced. Those with higher self-reported willingness to take risks would redistribute extra public money to education at the expense of healthcare and housing.
Respondents who believe that success in life is mainly due to effort and hard work, intelligence and skills favor education at the expense of assistance to the poor and public infrastructure, suggesting they might view education as the key to escape poverty. Those who think that laziness and lack of willpower are the main factors behind poverty would, unsurprisingly, redistribute extra public money from assistance to the poor to healthcare.
Males (as compared to females) favor extra spending on education, housing, environment and public infrastructure at the expense of healthcare. The self-employed favor extra spending of public money to pensions at the expense of housing. There is no difference across respondents living in metropolitan, rural or urban locations.
A regression analysis shows that better democratic institutions are correlated with higher support for allocation of additional public spending to education and healthcare, environment and public infrastructure. The effects are larger for education and healthcare: one standard deviation in the democracy index increases the support for spending money on education by 3 percentage points, for healthcare by 3.1 percentage points, and only by 0.4 and 0.6 percentage points for environment and public infrastructure, respectively. This reallocation is at the expense of assistance to the poor (3.5 percentage points), housing (2.6 percentage points) and pensions (1.1 percentage points). The pattern is robust to the measure of democratic institutions used, though the marginal effects vary slightly depending on the measure.
The influence of governance institutions is similar. Respondents in countries with better governance institutions favor allocation of extra public money to education (3.2 percentage points in response to one standard deviation in government effectiveness), health care (2.9 percentage points), environment (0.9 percentage points) and public infrastructure (0.6 percentage points). The reallocation is at the expense of assistance to the poor (4.2 percentage points), housing (3.3 percentage points) and pensions (0.2 percentage points). The pattern is also robust to the measure of governance institutions with the marginal effects varying slightly depending on the measure.
The higher the level of inequality in a country, the higher the demand for spending extra public money for education at the expense of assistance to the poor, pensions and public infrastructure. A one standard deviation increase in the index, increases demand for spending extra public money on education by 3.8 percentage points, and decreases spending on assistance to the poor by 2 percentage points, pensions by 1.9 percentage points, and public infrastructure by 0.06 percentage points. The results are robust to the inequality measure used.
Overall, the analysis provides empirical evidence that transitional countries are not homogeneous with respect to preferences for redistribution, with sizeable variations in country averages and in public preferences. The study of individual determinants of preferences for redistribution confirms a dominant role of self-interest, with some indications of social sentiments as well. In addition to the usual measures used in individual level analysis, these data allow better control for both positive and negative personal and household experience. The study of institutional determinants also confirms the role of income inequality in shaping public attitudes. In particular, higher inequality is confirmed to increase the demand for direct income redistribution. A novel motive of the paper is the influence of democracy and governance institutions on demand for redistribution. Better democracy and governance institutions are likely to stimulate demand for income redistribution, revealing both higher societal demand for redistribution and appreciation of the potential capability of the government to implement redistribution effectively.
The study of individual determinants of indirect demand for redistribution adds to the overall picture and confirms not only the self-interest motives but also social preferences especially pronounced among people with tertiary education and in high income groups. Better democratic and governance institutions stimulate redistribution of public money towards education, healthcare, environment and public infrastructure, while weaker democratic and governance institutions increases demand for allocation of public money to assistance to the poor, housing and pensions.
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References
Meltzer, A., Richards, S., 1981. “A Rational Theory of the Size of Government”. Journal of Political Economy 1989, 914–927.
Alesina, A., Angeletos, G.M., 2005. “Fairness and Redistribution”. The American Economic Review, 95(4), 960-98
[1] The countries covered were: Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Croatia, Czech Republic, Estonia, FYROM, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyzstan, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine and Uzbekistan.
[2] The basic empirical equation to study individual determinants of public preferences towards income redistribution is the OLS with country fixed effects (for direct redistribution) and multinomial regression with country fixed effects (for indirect measures). When studying the influence of institutions, the equations are transformed to replace country fixed effects with an institutional measure (one at a time). To control for the basic economic differences, average GDP per capita was included.
Political Islam and Women’s Rights – Evidence from Turkey
In this policy brief, I discuss how state-of-the-art econometric techniques can be used to shed light on the causal effects of Islamic rule on women’s rights. A central empirical challenge is that the identity of a politician is endogenous to voter characteristics, which in the case of Islamic political participation is particularly important due to the prevalence of banning such parties in many Muslim countries. Using a research design called Regression Discontinuity, I show that despite a negative association between Islamic rule and female participation in education in Turkey, the causal effect of an Islamic party on women’s rights is positive. In the case of Turkey, this represents the Islamic political movement’s advantage over secular alternatives in overcoming barriers to female participation in voluntary education institutions among the poor and pious.
Capital Structure and Employment Flexibility
Author: Olga Kuzmina, NES.
This policy brief focuses on the relationship between employment policies and their potential impact on firms’ decisions and outcomes. In particular, the question dealt with here is whether policies aiming to promote job stability could have an impact on a firm’s capital structure and the ability to respond to negative shocks and survive. The policy implications of this relationship are important since policy makers, while aiming to promote job stability among workers, may in fact inadvertently harm firms by leaving them less able to withstand downturns, and especially those firms that cannot quickly adjust their capital structure.
Directed Lending: Is It An Efficient Tool to Modernize the Economy?
Over the last couple of years, the growth rate of potential Belarus’ GDP declined. The government intends to revive economic growth by the policy of ‘modernization’, in practice pinned down to a drastic increase in the volume of capital investment, including by the means of directed lending. As the pre-crisis macroeconomic imbalances are at least partially cured, the government seems to be eager to apply a familiar policy tool. However, the empirical analysis of the effects of directed lending on total factor productivity and economic growth casts serious doubts on the efficiency of this policy tool.
Over the last couple of years, the growth rate of potential Belarus’ GDP declined. This conclusion is robust as suggested by the application of competing methodologies to assess potential GDP. For instance, the statistical filters, including the HP-filter, the Kalman filter, and the production function approach, produce different levels of potential growth, but generate similar growth rate dynamics, particularly the downward trend. From this perspective, the tendency for high and sustainable GDP growth in Belarus is increasingly compromised.
Economic authorities seem to be aware of that fact. For instance, the Ministry of Economy stresses the need to create a new, ‘highly productive’ sector in the national economy as the new engine of growth. An ambitious plan involves expanding the size of this sector to contribute to about half of the GDP growth rate, aimed at 12 per cent per annum by 2015. The creation of this ‘highly productive sector’ falls into recent policy initiative, called ‘modernization’. Under this banner, the government plans to renovate the capital stocks (primarily machinery, equipment, and transport vehicles) of a large number of state-owned enterprises. In a nutshell, this strategy may be seen as a way to facilitate technical progress embodied in capital.
What is necessary, according to the government, is to make a spurt in capital investments, often on a case-by-case basis. The government has a pool of enterprises to be modernized. The majority of them are unable to modernize themselves – i.e. radically increase capital investments – due to the lack of internal funds and poor access to external finance. Accordingly, directed lending is considered to be a useful policy instrument of modernization. In 2013, the Development Bank plans to considerably increase its credit portfolio (by about USD 0.5 billion) by financing projects at subsidized interest rates under the ‘modernization’ program. Recently, the government compiled a list of 67 agricultural enterprises liable to have an access to cheap loans for modernization purposes from the Development Bank. In addition, state-owned banks will continue the provision of policy loans that can be considered as directed ones.
With directed loans, we mean those loans that are typically granted to selected borrowers at interest rates lower than the market interest rates. In Belarus, directed lending has been an important policy tool over the last decade. Selective credit programs have been applied to prevent underinvestment and to stimulate output growth.
According to the estimations of Fitch Ratings (2010), almost a half of the outstanding loans in the Belarusian economy by the end of 2009, were directed ones. The IMF provided a slightly smaller, but still substantial figure of 46.2 percent (IMF, 2010). According to our own calculations, by 2011, the volume of directed loans amounted to about 40 percent of the total volume of outstanding loans. These loans have been made abundant in agriculture and housing construction sectors and, to a lesser extent, in manufacturing. This massive presence of selective credit in the national economy can be seen as a large factor contributing to the currency crisis of March 2011.
Accordingly, after the crisis, and following the necessity to ‘clear up’ the assets of the national banking system, the share of directed lending was reduced. We estimate that in 2012, the ratio of directed loans in total loans dropped to roughly 30 percent. However, the recent rhetoric of the development of ‘highly productive’ sectors and modernization is indicative of the intention to find new life for this old cloth. Directed lending is expected to revitalize enfeebling growth. In 2012, real GDP growth amounted to 1.5 percent against the background of the initial government plan of 8.5 percent.
Under selective credit programs, banks have been partially deprived of their autonomy to make decisions over the provision of credit. Thus, banks’ intermediation role has been circumscribed by the authorities. In theory, directed loans may spur capital accumulation as beneficiaries of these loans have access to cheap loans and thus invest and – arguably – produce more. In Belarus, there has also been an additional incentive, i.e. the necessity to substitute depreciating and outdated capital stock, inherited from the Soviet past. At the same time, political interference into the process of credit provision suggests that loans may be allocated to lower-yielding projects, and thus dampen growth rates of factor productivity and GDP (Fry, 1995). In addition, non-favored companies – typically from the private sector – face higher interest rates as their state-owned counterparts receive substantial discounts for their use of capital.
So far, these soft budget constraints in the financial system have allowed favored companies to receive loans up to three times cheaper, if judged by the level of real effective interest rates. Although private companies tend to be more efficient than state-owned enterprises in terms of factor returns and profitability, higher interest rates may reduce the volume of outstanding market loans. Furthermore, increases in the volume of cheap residential loans, which do not contribute directly to enhancement of productive capacity of the economy, may dampen the returns on investment further.
Governments have traditionally relied on selective credit programs by stressing positive externalities and spillovers for the economy as a whole (DeLong and Summers, 1991). Commercial banks care about private returns, while governments seek to maximize social returns by financing firms, which are capable of generating positive externalities. Unfettered operations of credit allocation mechanisms minimize allocation inefficiency and induce banks to minimize the costs of financial intermediation, thereby making credit more accessible.
How do these competing forces meet in Belarus and what are the effects of their joint working? In answering those questions, we have conducted an empirical analysis of the effects of directed lending on total factor productivity dynamics. The latter is considered to be a good proxy to observe the impact of selective credit programs on the efficiency of actor use.
The results of our econometric analysis show that over the period concerned, 2000–2012, the expansion of directed lending in Belarus has negatively affected total factor productivity dynamics and, subsequently, negatively contributed to the rates of GDP growth. A positive impact on growth, stemming from additional capital accumulation might nevertheless occur, but with a substantial lag. This likely positive impact is associated with the ability of banks to increase the volume of market loans alongside with the rising volume of directed loans. The option has been made possible only due to massive liquidity injections by the government and mainly the National Bank of Belarus. However, such injections are problematic to maintain over the medium to the long run as they have severe inflationary repercussions for the economy.
The effects of individual components of directed lending are mainly the same. In particular, loans for residential construction, provided to households in need, negatively affect total factor productivity. Moreover, it is through housing loans the adverse effects of directed lending upon factor productivity are mainly realized. The interest rate spread – between preferential interest rate and market interest rate – amplifies these negative relationships. Lower preferential rates result in larger losses in total factor productivity. Loans to agricultural firms have similar impact, although it has to be emphasized that the overall impact on total factor productivity approaches zero (not negative, as in the case of housing loans).
We also find that for Belarus, an increase in the total volume of directed loans leads to an increase in the volume of market loans. Both the National Bank and, to a lesser extent, the government, strive to minimize risks in the national banking system, which provide loans with smaller returns and/or non-performing policy loans. Similar challenges have been observed in China, where the Central Bank has been forced to recapitalize domestic banks to support economic growth after the global financial crisis of 2008. In 2007–2008, Chinese growth of 8–10 percent was driven by new lending averaging 30–40 percent of GDP, of which up to a quarter of the loans might have been non-performing, amounting to losses of 6–10 percent of GDP (Das, 2012).
In Belarus, the recapitalization policy, apart from its inflationary consequences, has other important effects. In particular, it prevents a dangerous trade-off between directed loans and market loans to resurface, whereby the former crowds out the latter as banks are unable to expand their portfolios due to the liquidity constraints.
Therefore, unless the expansion of directed loans would be checked, adverse effects of selective credit programs on productivity and growth would not evaporate, with negative consequences for the whole economy. Regarding policy recommendations, we claim that there is a need to fundamentally revise directed lending policies or to even minimize it to the extremes by allowing standard market mechanism for credit allocation to prevail in the national economy. Furthermore, we argue that directed lending, even after some cosmetic changes in the system design made in 2012, is not an efficient tool for economic growth promotion.
Tentative results of growth accounting made at the level of selected important industries suggest that the downward growth dynamics is associated with weak total factor productivity growth, i.e. disembodied technical progress. Improvement of total factor productivity seems to have the biggest potential for revival of economic growth. Therefore, the use of directed lending, as a policy instrument that hampers total factor productivity dynamics, may undermine prospects for long-term economic growth in Belarus.
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References
- Das, S. (2012). “All Feasts Must Come to an End– China’s Economic Outlook”, Euro Intelligence, 11 March, viewed 12 April 2012.
- DeLong, J.B. and L.H. Summers, (1991). “Equipment Investment and Economic Growth”, Quarterly Journal of Economics 106, 2, pp. 445–502.
- Fitch Ratings, (2010). “Directed Lending: On the Up or on the Way Out?”, Belarusian Banking Sector, May.
- Fry, M.J. (1995). Money, Interest, and Banking in Economic Development (John Hopkins University Press, Baltimore and London).
- IMF (2010), “Republic of Belarus: Fourth Review under the Stand-By Arrangement”, IMF Country Report 10/89, viewed 15 July 2012.
Fact or Fiction? The Reversal of the Gender Education Gap Across the World and the Former Soviet Union
In this policy brief, I discuss the reversal of the gender education gap in many countries around the world – a fact that is still not widely known, although is increasingly gaining attention. I describe recent studies that have documented this fact for both developed and developing countries and have provided evidence on the trend. As there has not been much analysis of the education gap in the former Soviet Union countries, I present some measures of the education gap in the USSR and FSU countries, and compare them to other countries around the world. Finally, I discuss the potential causes of the reversal identified in the literature and how the reversal of the gap is related to other gender disparities.
Empirical Evidence on Natural Resources and Corruption
This policy brief addresses the relationship between resource wealth and a particular institutional outcome – corruption. We overview some recent empirical evidence on this relationship and outline results of an on-going research project addressing a particular aspect of resource-related political corruption: transformation of resource rents into personal wealth hidden at off-shore deposits. The preliminary results from this project suggest that at least 8 percent of oil and gas rents are converted into personal political rents in countries with poor political institutions.