Author: Admin
Decomposition of Economic Growth in Belarus
During the last decade Belarus was one of the leaders of growth in the CEE region. Kruk and Bornukova (2013) have analyzed the sources of growth and found that capital accumulation was the main contributor to growth. The contribution of total factor productivity (TFP) to growth was, on the contrary, quite modest. On the sectoral level, capital accumulation was not always accompanied by the increases in TFP. Hence, the new growth policy, modernization, with the bottom line “more capital” may not be the best option for enhancing productivity-based growth. The competitive advantages of Belarus lie in the resource-based and non-tradable sectors, while the majority of the manufacturing sectors are lagging behind in productivity. Belarus has symptoms of a Dutch disease without the trade surplus, and the devaluation of 2011 did not cure it.
During 2003-2012, Belarus had an average growth rate of 7.1%, and during the ‘fat years’, i.e. 2003-2008, it was even higher – 9.5%. Intuitively, this prominent growth is questionable, as it was achieved in the context of dominating state ownership, centralized allocation of resources, government’s control at the factor and goods markets, as well as poor infrastructural reforms (for instance, according to the indices of the EBRD). The Belarusian case challenges the mainstream paradigm of growth in transitional countries, which assumes that the progress in market reforms is the key factor for high and sustainable growth.
The simplest and most widespread explanation of the Belarusian phenomena is based on ‘non-standard’ gains in productivity. This approach assumes that productivity is the engine of growth (World Bank (2012); Demidenko and Kuznetsov (2012)). To a large extent, these gains in productivity are seen as “artificial”, resulting from Russian injections into the Belarusian economy: cheap gas, specific schemes of oil trade, and preferences in access to the Russian markets (Kruk (2010)). However, under this approach, decomposing the growth in productivity by ‘natural’ and ‘artificial’ parts is hardly possible, as the impact of these factors is already hidden in the available data.
The IMF (2010) gave a substantially different explanation of Belarusian growth. They claimed that the average growth of 8.3% over the period of 2001-2008 was mainly capital-based with a contribution of 4.8 percentage points, while the contribution of productivity growth was only 3.0 percentage points (the rest of growth was explained by labor and cyclical factors).
The main reason behind the substantial difference in the explanation of growth factors is the statistical data on capital used during the growth accounting exercise. Belarusian official statistics reports the data on capital stock based on a direct survey of capital assets according to both gross and net (wealth) capital concept. However, the growth rates of capital are reported only for the gross stock of capital. These growth rates are questionable as they demonstrate ‘unnatural stability’ – they fluctuate around 2% for the last 20 years, despite the fact that investments during this period has displayed huge and volatile growth. Statistical offices in other CIS countries have reported similar dynamics of the capital stock. Voskoboynikov (2012), and Bessonov and Voskoboynikov (2008) show that this trend is a consequence of the statistical methodology used in Russia (which the Belarusian methodology is very similar to). In particular, the trend is driven by biased capital investments deflators (which are overestimated) from the periods of high inflation (1990-s and early 2000-s).
If official data is used as the capital input for the growth accounting exercise, the contribution of TFP to growth will be overestimated. Hence, in the studies of the World Bank (2012) and Demidenko and Kuznetsov (2012), the leading role of TFP may be due to the use of the official data on the capital stock.
Motivated by this concern, we use two different methods to evaluate the value of capital inputs (see Kruk and Bornukova (2013) for more details). The first alternative to using the data from direct capital survey is to exploit a perpetual inventory method (PIM): the historical assessment of initial capital stock is further adjusted by the flow of investments and depreciation. However, if there is a bias in deflators within the sample, the series will also be distorted. This problem may be eliminated if the initial stock will be selected at the moment when there is no bias in investment deflator, in the period of moderate inflation. We call this approach PIM-backward.
The second approach to constructing capital series exploits the concept of productive capital and the data on the flow of capital. It assumes that the productive capacity of a capital good depends on its age. The productive stock of a capital good (i.e. the gross stock adjusted by the age-efficiency profile) generates a flow – capital services. The latter is the productive stock adjusted by the user cost of the individual capital good. For the total output of an industry (or economy) one should aggregate the inputs by different capital goods, which in contrast to the net (wealth) concept depends not only on the value of capital goods, but also on their user costs. This approach has solid theoretical foundations, which is the reason it is prioritized in productivity studies.
From the view of available data in the case of Belarus, this approach has a number of powerful advantages. First, we use individual deflators for individual capital goods, which are expected to be less biased than total deflators for the industry. Second, we use heterogeneous depreciation rates for each capital good in each industry based on actual data of ‘accounting depreciation’, while we would have to use homogenous assumptions for each industry in the case of net (wealth) concept. Third, we can exclude residential housing from our measure of capital input.
There are, however, also disadvantages. First, data of newly employed capital goods (in direct surveys of capital assets) and data on capital investments differ rather substantially. Traditionally, the data on capital investments is treated as more reliable, but based on the direct surveys of capital assets we have to use the series of newly employed capital goods as a flow variable when running PIM. Second, we use exogenous real interest rate for computing unit user costs, but the results are very sensitive to our assumptions on the real interest rates across industries. Third, the necessity to exclude residential housing from the data (because of ‘mixed historical prices’) may be interpreted as a loss of information. Given the strengths and weaknesses of the approach, we prioritize it on the industrial level, but prefer the PIM-backward approach for an aggregate economy analysis.
Based on the PIM-backward measure for the total economy (see Figure 1), we may argue that the contribution of TFP to growth was more modest during the last decade than what was reported in the majority of previous studies on Belarusian growth. This finding is of fundamental importance for the growth agenda: only productivity-based growth may be treated as sustainable, since capital growth will slow down as the capital approaches its stationary value. We argue that only the policy directed to promotion of productivity is vital for growth prospects.
Figure 1. Contribution of Production Factors and TFP to the Growth of Gross Value Added (PIM-Backward Approach) 
The dynamics of productivity divided according to industries (see Table 1) display that the leaders in productivity growth are either industries that produce non-tradable goods (communications, finance, construction) or those that have a chance of ‘artificial productivity gains’ (chemical and petrochemical manufacturing, and fuel).
Table 1. Initial Level and Growth Rates of Productivity in Major Industries 
However, the theory suggests that the leaders in productivity growth should be the industries producing tradable goods. . This contradiction may be interpreted in two ways. First, one may argue that a more competitive environment and larger share of private ownership (which are seen in the financial industry, trade and catering) are the core reasons for high productivity level and growth rates in ‘domestic industries’. Second, an attractive position of ‘domestic industries’ may reflect a high level of domestic prices rather than ‘natural’ productivity. The base year for our computations is 2009, in which both the real effective exchange rate of the national currency and income were relatively high. The devaluation of 2011 fixed the problem only temporarily, since the inflation in 2011-2013 quickly eroded the benefits of the devaluation. Therefore, the indicators, in terms of 2009 prices, may capture the changes in nominal values as the main component of the productivity gains, while from a longer-term perspective it would be seen as mainly price movements without substantial progress in productivity. In our view, the second explanation is the main reason for the non-standard disposition of productivity levels and growth rates among industries.
If that is the case, the bigger picture looks as follows. Industries producing tradable goods suffer from the lack of progress in productivity, i.e. lose their competitive advantage; enhancements in total productivity are mainly due to industries with ‘artificial productivity gains’. The latter allows domestic prices to grow, making a productivity illusion of domestic industries. All together these symptoms are quite similar to the Dutch disease.
One more finding from the productivity analysis at the national level is the lack of productivity gains from reallocation of resources from less productive industries to more productive ones. A scatter-plot between capital accumulation growth rates and TFP growth rates (see Figure 2) demonstrates no clear relationship between them.
Figure 2. Growth Rates of Capital Input vs. TFP Growth Rates in Manufacturing Branches, 2006-2010. 
Notes: The sizes of the circles correspond to industry shares in value added.
However, if there was a free allocation of resources, more productive industries would accumulate more capital. Moreover, the same indicators under the PIM-backward approach demonstrate clear negative relationship. A ‘soft’ interpretation of this phenomenon assumes that the lack of reallocation of capital restrains the development of total productivity. A ‘tighter’ interpretation assumes that at least in some industries there is a trade-off between capital accumulation and productivity gains. For instance, in Kruk and Haiduk (2013) it is shown that spurring capital accumulation through the practice of directed lending leads to losses in efficiency through a number of channels. Hence, the simplest way to increase aggregate productivity is to depart from the centralized allocation of capital and unblock capital inflows to more productive industries and vice versa.
Figure 3 documents the mobility of labor markets across the manufacturing industries in Belarus. While one can expect that labor flow into more productive industries, it is not completely true for the Belarusian manufacturing sector.
Figure 3: Labor growth and TFP growth in industries of Belarusian manufacturing, (capital services approach). 
Notes: The sizes of the circles correspond to industry shares in value added.
Two distinct trends emerge in the labor market. On the one hand, some industries exhibit textbook behavior: increases in TFP are associated with increases in the number of people employed. The best example here is the fuel industry, which experiences TFP increases due to preferential oil prices. However, there are industries that gain TFP and lose labor at the same time. The chemical industry, machinery manufacturing and woodworking are examples of this pattern. These industries have experienced rapid capital accumulation, which, coupled with high gains in TFP, should have contributed to the increases in labor productivity. Surprisingly, though, these industries did not attract more labor. A possible explanation for this counterintuitive pattern is the excessive employment at the beginning of the period in question. In this case, a decrease in the number of people employed may have contributed to the increases of TFP.
Indeed, Figure 4 confirms our hypothesis: labor was flowing from the industries with lower labor productivity to the industries with higher labor productivity in general. Industries in which TFP increased and which were accompanied by a labor decrease, featured low labor productivity in the beginning of the period in consideration, more precisely in 2005. Only the chemical industry exhibited the unexpected behavior: it lost labor despite high initial productivity. By getting rid of excessive employment they were contributing to an increase in TFP.
Figure 4: Labor shifts into the sectors with higher labor productivity. 
Notes: The sizes of the circles correspond to industry shares in value added.
How is Belarus doing relative to other countries? We have compared Belarusian TFP to the TFP of the leader of transition, the Czech Republic, and to the regional leader, Sweden. The Czech Republic is more developed than Belarus (in 2010 Czech GDP per capita (PPP-corrected) was 1.73 times higher than in Belarus), and, theoretically, it should be much more difficult and costly for it to continue approaching the technological frontier. However, our findings suggest that the Czech Republic is catching up with Sweden in terms of TFP, and doing it faster than Belarus (see Figure 5).
Figure 5: TFP of Belarus and the Czech Republic relative to TFP of Sweden, (PIM-backward approach).
Over the last 10 years, Belarus has closed only 5 percentage points of the gap with Sweden. The Czech Republic, where the contribution of TFP to growth was more substantial, has managed to close 8 percentage points of the gap.
In absolute numbers (in ‘international’ dollars of 2010), aggregate TFP in Belarus in 2010 was 2.92 versus 4.66 in the Czech Republic and 9.38 in Sweden (according to the PIM-backwards method). However, the aggregate picture does not reflect the situation in the sectors of the economy and industries of manufacturing.
Table 2: Comparative advantage of Belarusian industries: winners and losers (capital services approach) 
Table 2 documents the comparative advantages and disadvantages of the Belarusian economy in 2010 according to the capital services approach. Both the capital services approach and the PIM-backwards approach produce the same winners and losers list with the only difference being that the PIM-backwards method has the construction sector among winners. It is not surprising to see resource-based industries among the winners (mining and quarrying mainly reflects the extraction of potash, while the chemical industry benefits both from potash and from preferential process for Russian oil). Food manufacturing is among the winners mostly due to the price scissors in agriculture: food producers buy their inputs at very low prices. The non-tradable sectors are among winners, and the majority of the manufacturing sectors are among the losers. Again, this is similar to the symptoms of the Dutch disease. It is ironic that Belarus has symptoms of a Dutch disease without the trade surplus. Instead, the desire of the government to inflate wages combined with the preferences for Russia led to the development of the same diagnosis.
Belarusian economic growth is less TFP-led than is commonly believed. While the labor market proves to be relatively successful in its reallocation of employees and its contribution to aggregate increases in efficiency, the capital market is distorted by government interventions. Capital accumulation does not necessarily lead to increases in TFP, and the new modernization policy with the bottom line of “more capital” may not be the best option for enhancing growth. Our conclusion is that Belarus should find new sources for TFP-led growth.
▪
References
- Bessonov, V., Voskoboynikov.I. (2008). “Fixed Capital and Investment Trends in the Russian Economy in Transition.”, Problems of Economic Transition, 51(4), pp. 6-48.
- Demidenko, M., Kuznetsov, A. (2012). “Ekonomicheskiy rost v Respublike Belarus: factory i otsenka ravnovesiya” (Economic Growth in Belarus: Factors and Equilibrium Assessments), National Bank of the Republic of Belarus, Working Paper No.3.
- IMF (2010). “Sources of Recent Growth and Prospects for Future Growth”, IMF, Country Report No.10/16.
- Kruk, D., Bornukova, K. (2013). “Belarusian Economic Growth Decomposition”, unpublished manuscript.
- Kruk, D., Haiduk, K. (2013). “The Outcome of Directed Lending in Belarus: Mitigating Recession or Dampening Long-Run Growth?”, BEROC Working Paper Series, WP No.22
- Kruk, D. (2010). “Vliyanie krizisa na perspectivy dolgosrochnogo ekonomisheskogo rosta v Belarusi” (The Impact of Crisis on the Perspectives of Long-term Growth in Belarus), IPM Research Center Working Paper Seies, WP/10/07.
- World Bank (2012). “Belarus Country Economic Memorandum: Economic Transformation for Growth”, Country Economic Memorandum, Report No. 66614
- Voskoboynikov, I. (2012). “New Measures of Output, Labour and Capital in Industries of the Russian Economy”, Groningen Growth and Development Centre, Research Memorandum GD
Some More Reflections on RCTs
In preparation of next year’s elections, the Swedish government chose recently to replace the Minister for International Development Cooperation. During her long mandate, former Minister Gunilla Carlsson championed the importance of aid evaluation and result focus, and managed to move aid from a quiet consensus to become a hotly debated topic. She also closed down the aid evaluation agency SADEV, following the publication of critical reviews about the work of the agency. Now, an expert group is in charge of rethinking and redesigning development policy evaluation and planning. One of the tools under consideration is randomized control trials (RCTs). This is an area in which Swedish development cooperation has no previous experience. Here are some reflections on RCTs.
In recent years, the methods of development economics have been crucially altered by the introduction of randomized control trials (RCTs). The idea behind RCTs is that development policies can be evaluated similarly to clinical trials in medicine, where subjects are randomly assigned to receive a treatment or to function as a reference or control group. The main benefit of this approach is that the random assignment allows for an estimation of the effect of the treatment (that is, the policy in question), while avoiding unobservable confounding factors or selection issues (see more about the advantages of the method in Banerjee et al. (2008)).
The diffusion of experimental methods in development economics has undoubtedly been a revolution in the academic and, if not yet fully, in the policy world. In the blogosphere there has even been talk of awarding Sveriges Riksbank’s Prize in Economic Sciences in Memory of Alfred Nobel, informally called the Nobel Prize of Economics, to the MIT couple Banerjee – Duflo. Due to their young age and the closeness in time of their contribution, this would be a ”shock” prize meant to give a strong signal. Their creation, the Abdul Latif Jameel Poverty Action Lab (J-PAL), stands for a new approach to both scientific and policy work in development that is a fantastic contribution, and definitely has the connotation of seminal.
However, it might be too early for the profession to sanction a method that has much good to show for, but also potentially undesired consequences. In the camp of critics there are heavy weights such as Angus Deaton and Dani Rodrik of Princeton, and the World Bank’s Philip Keefer and Martin Ravallion. The core of their position is of course not to deny the merits of RCTs, but to advocate their use in the right way and, in particular, as one tool among many others, with important complementarities to the others.
Some points in this context are often made, well understood and widely accepted: the limits of the approach per se, in particular the problem of external validity (the question of how generally applicable are the findings from such studies); the conflict between short-run and long-run implications, especially with respect to some policy areas (support to institution-building among others), and the incentives of policy actors. Another brief in this series by Anders Olofsgård spells out these points very clearly and references to further readings for those interested.
One aspect I find to be missing in the debate is a reflection on what impact this new method has on the three main actors involved, namely the researchers and practitioners in development and their way of working, and the people living in the countries and regions where these studies take place. This will therefore be the focus of this brief.
The Impact on the Scholarly Profession
The creation of experimental infrastructures and the popularity of the RCT methodology have rubbed off on the rest of the empirical practice in development economics and beyond, with ever-increasing demands and expectations on the econometric identification of new studies. However, when it comes to what is possibly the main weakness of RCTs as compared to most observational studies, namely external validity, the corresponding demands and expectations on how this is dealt with seem to fall behind. As pointed out in Rodrik (2008), it is enough to compare the number of pages spent on describing the identification in an average observational study to that on external validity in an average RCT-based paper. If the purpose is to learn “what works in development”, as opposed to “what worked once for a set of 25 primary schools in Uttar Pradesh faced with high drop out rates” [1], it is natural to expect the researcher that really wants to serve this purpose to provide for a desired generality of her findings. With no generality, the findings may be of limited practical use to politicians and practitioners who need to choose a policy tool or make a decision in conditions, which are likely to differ from the exact setting of the study.
During a recent presentation by one of the most active and prominent RCT researchers, the researcher clearly stated at some point that: “[t]his intervention was never thought for scaling up as a policy.” That made me pause. But what is the purpose, then? In my meaning, these studies should fit into a “bigger-picture” understanding, or at least hypothesizing on how development works, what the binding constraints and open challenges are, what might contribute to overcoming them, and how do we proceed from there. Once some candidates are identified, RCTs might, depending on the setting, be used to evaluate and compare before and after the preferred policy is implemented. Unfortunately, this attitude is far from common, beyond what has become the standard of the ‘Introduction paragraphs’.
Quite often RCT studies are extremely precise and accurate on “the impact of X on Y”, even in cases of very small effects, and can be perhaps a bit vague or face bigger uncertainties on the ‘bigger’ question. This means that many, more general (and very relevant) questions are not addressed by development economists just because a RCT is not feasible. An example mentioned in a recent keynote lecture by David Laitin is the BetterBirth Project. This is a WHO program that seems to be making a big difference for infant and maternal health in India’s poorest states through a list of 29 easy, low-cost, low-technology and well-known practices. The main lesson drawn by observers at the Harvard School of Public Health is that people follow the list more accordingly when it is spread through ”human contact”. No mass media advertisement campaign, no punishment or incentive schemes, just ”nice” people visiting, explaining, and demonstrating the list, while – in the words of an interviewed nurse – ”smiling a lot”. At first sight, this seems like something that could be randomized. However, the treatment is so diffuse and fuzzy that the practical implementation would be very challenging. If it is the case that the person meeting the clinics’ personnel and spreading the information has to be somewhat of a mentor in order for the transition to happen, to be kind and pedagogic, repeat the visits indefinitely to make sure that the practices have been adopted, and do whatever else it takes to make them learn, this is very hard to observe with precision. To simply define X as ”presentation of the list in person”, to be compared to, for example, the ”diffusion of the list through an information campaign” would probably run the risk of severely underestimating the impact. This would be because it would bundle together different types of informers and different levels of human interaction. This means that there would be a high risk of zero or insignificant results from such a study. A RCT would need to be complemented by other investigations, for example surveys, in order to find out if there really was an effect and how it came about. All of the above is likely to undermine the publication chances for an academic paper on the issue, thereby discouraging development scholars to study this program.
There are two main ways of augmenting the RCT methodology in the direction of generalizability and external validity: the elbow-grease approach of replication and the resuscitation of the concern for theoretical mechanisms. Replication studies are not very appealing in the perspective of a scholar that aspires academic publications. Besides completely new clever designs that establish a link of causation in a specific case – and possibly for each of these corresponding studies that establishes the absence of such a link in different settings – journals have little interest in publishing more variations on the same theme. Replications with small variations should instead be highly attractive for development institutions and practitioners, precisely for the reason, mentioned above, that they want to learn about effectiveness of alternative strategies in as many different specific contexts as possible. [2] In an ideal world, development institutions and aid bureaucracies would work in close cooperation with universities and academic institutions, involving young researchers before their career-concern-stress phase (perhaps Ph. D. students?) in the design and evaluation of as many of their planned interventions as possible. Moreover, in an ideal world this would be enough reward for the young researchers. This wealth of replications would then favor the possibility of “taking stock” and really learning about some general truth. I do not, however, have a good recipe for making this happen.
Luckily, some scholars are in the meanwhile working on making the pendulum swing back from the purest empiricism to the involvement with theory. Here is a list of possibilities that are important to reflect about, starting from a given RCT:
– The macro problem. How does the found effect compare to the “bigger issue”, the one that most likely set the scene in the ‘Introduction paragraph’ of the study? Few studies go back to this point, after presenting their results. Numerical simulations or structural estimation of theoretical models might help answering this question. (See some examples in Buera et al. (2011) and Kaboski et al. (2011)).
– The alternative hypothesis. What is the particular intervention compared against? If the set of circumstances or policy-relevant parameters that might be varied are too big or too dense for replications, maybe a theoretical model can help to vary them in a smooth and continuous way?
– The strategic reaction. How are the involved economic agents likely to respond in case of an expansion in space, time or both, of the intervention? How would they have responded in the absence of the intervention?
The Impact on Development Practices
As stated above, RCTs may be a powerful tool for the learning and decision-making in development institutions, public or private. However, this assumes a seldom-questioned willingness to learn and change practices on their part. Brigham et al. (2013) show, through a RCT, that these organizations might be subject to confirmation bias. Brigham et al. sent out an invitation to microfinance institutions, offering partnership to evaluate their programs, randomly accompanying it with a survey of previous studies finding positive impact of microcredit, or a survey of studies finding no impact. The second treatment elicited barely half as many responses as the first one, which suggests that at least this type of organizations might not be so interested in learning whether what they do is effective or can be improved. Coupled with the mentioned publication bias, this might skew the distribution of reported, published and established findings even further.
The Impact on the Local Context
Individual studies can of course be affected by the so-called Hawthorne effect or experimenter effect. The phenomenon, by which the act of being experimented upon changes a subject’s behavior, was first observed and got its name in the 1920s in industrial psychology. Although it is clearly hard to establish, it has for decades been a central criticism of the ”participant observation” methodology in anthropology and ethnography. Also behavioral economists, that more recently started using experiments both in labs and in the field, are explicitly careful about it.
Depending on the definition of causality that the researcher has in mind, the fact that having knowledge about being treated impacts outcomes, might not be an issue at all for the measurement of the overall effect of an intervention. The overall effect should include also the (optimal) reaction of the agents (for example a change in behavior, the adoption of other complementary inputs, etc.) and this is actually considered one of the advantages of the method. However, this raises problems for the interpretation of the size of the effect and the analysis of the channels that bring it about. This point is made very clearly by Bulte et al. (2012), who compare a double-blind RCT with a regular one. If all or most of the effect simply comes from the participants knowing to be ”treated” and reacting to it, is the effect still going to be there when the intervention becomes a regular policy? The majority of both authors and critics mostly ignore this important question.
Beyond the perspective of a single study, a different concern comes to mind when considering how a substantial number of RCT studies are clustered geographically. The map below shows a snapshot of the J-PAL interventions in Africa and Asia, which are only a fraction, albeit substantial, of the total.
Figure 1. J-PAL Interventions in Africa and Asia
Reading study after study set in Kenya, or some Indian state, I wonder if people there are starting to get used to private organizations going around giving away assets, or used to temporary local government programs with funky benefit schemes. To my knowledge, no study has yet reflected upon the aggregate impact of experiments and randomized interventions in an area that has many. Might it be the case that exposure to many conditions eventually results in ”experimental fatigue”, or practice effects, which may influence the results of the studies and make the interpretation of the findings difficult?
Even more worrisome, given the frequency of and the resources involved in these interventions, perhaps we should expect an impact on the local political economy. As a parallel, I think about the agrarian reform and the later establishment of the welfare state in post-war Italy, and how they gave major local actors the ability to uphold their clientelistic systems. The newly established rights and entitlements, the various benefits and redistribution programs, were ”filtered” by the local elites and channeled through the traditional ties of family, kinship, friendship and neighborhood. According to comparative analyses of European welfare regimes, clientelism exists, in different forms and intensities, in all Mediterranean welfare states, and it appears to be linked to the process of political mobilization and the establishment of welfare state institutions in these nations.
A recent study by Ravallion et al. (2013) finds that unemployed fail to act on information about the National Employment Guarantee Scheme (NEGS) in India. They hypothesizes that the bottleneck lies with the local government institutions (Gram Panchayats). The GP are supposed to receive the applications and apply for central government resources for planning and implementation of projects, so as to guarantee 100 days of work per year to all adults from rural households who are willing to do unskilled manual labor at the statutory minimum wage. But perhaps – argue the authors – given the strict controls on corruption, the GP officials do not find anything in it for themselves, and hence do not proceed. Of course this is just one of the possible explanations, and moreover the NEGS is not a RCT. But in general the involvement of local official or unofficial power structures in contexts where this type of interventions are increasingly common could be interestingly related to the hypothesis on the ”Mediterranean welfare state” outlined above. The idea definitely deserves investigation.
Conclusions
The popularity of RCTs among development scholars is finally spreading to practitioners. This is mostly good news, there is much to gain and learn from this approach, especially in contexts where it is grossly underexploited, as has been the case in Sweden. However, a near-monopoly of this approach is though not granted, given its non-negligible limitations, often belittled in light of its numerous strengths. Spurring development “one experiment at a time” might take unnecessary extra time and efforts, and bring about other undesirable consequences. Both development scholars and practitioners should not forget the other arrows in their quiver.
References
- Bannerjee, A. and E. Duflo (2008), “The Experimental Approach to Development Economics”, NBER Working Paper 14467.
- Brigham, Matthew, Michael Findley, William Matthias, Chase Petrey, and Daniel Nelson. ”Aversion to Learning in Development? A Global Field Experiment on Microfinance Institutions”. Technical Report, Brigham Young University March 2013.
- Buera, F. J., J. P. Kaboski, and Y. Shin (2011). ”The macroeconomics of microfinance.”
- BREAD working paper.
- Bulte, E., Pan, L., Hella, J., Beekman, G. and S. di Falco (2012). ”Pseudo-Placebo Effects in Randomized Controlled Trials for Development: Evidence from a Double-Blind Field Experiment in Tanzania.” Working Paper.
- Kaboski, J. P. and R. M. Townsend (2011, July). ”A structural evaluation of a large-scale quasi-experimental microfinance initiative.” Econometrica 79, 1357–1406.
- Olofsgård, A. ”What Do Recent Insights From Development Economics Tell Us About Foreign Aid Policy?” FREE Policy Brief Series, October 3, 2011.
- Ravallion, M., et al. ”Try Telling People their Rights? On Making India’s Largest Antipoverty Program work in India’s Poorest State.” Department of Economics, Georgetown University, Washington DC (2013).
- Rodrik, D. (2008). ‘The New Development Economics: We Shall Experiment, but How Shall We Learn?’. Harvard Kennedy School Working Paper No. RWP08-055.▪
[1] The example is fictitious. Any resemblance to real studies is unintended and purely coincidental.
[2] At least in theory – this point is discussed more in the next section.
The Customs Union Between Russia, Belarus and Kazakhstan: Some Evidence from the New Tariff Rates and Trade Flows
Author: Arevik Mkrtchyan, European University Institute.
This brief addresses the Customs Union between Russia, Belarus and Kazakhstan that was established in 2010. It argues that the external tariff schedule reflects a compromise between the interests of its members rather than simple expansion of Russian influence on the CU partners, and that the reduction in trade costs due to elimination of internal borders, benefits both the members of the CU and their external trade partners. Moreover, the impact of alleviated non-tariff trade costs on trade flows is strong and significant, while the tariff impact is insignificant for all members.
Old-Age Poverty and Health – How Much Does Income Matter?
The question concerning the material situation of older people and its consequences for their wellbeing seems to be more important than ever. This is especially true given rapid demographic changes in the Western World and economic pressures on governments to reduce public spending. We use data from the Survey of Health, Ageing and Retirement in Europe (SHARE) to examine different aspects of old-age poverty and its possible effects on deterioration in health. The data contains information on representative samples from 12 European countries including the Czech Republic and Poland. We use the longitudinal dimension of the data to go beyond cross sectional associations and analyze transitions in health status controlling for health in the initial period and material conditions. We find that poverty matters for health outcomes in later life. Wealth-defined and subjective poverty correlates much more strongly with health outcomes than income-defined measure. Importantly subjective poverty significantly increases mortality by 58.3% for those aged 50–64 (for details see Adena and Myck, 2013a and 2013b).
Measuring Poverty
When measuring poverty, the standard approach is to define the poverty threshold at 60% of median equalized income. This standardized measure offers some advantages, such as simplicity and comparability with already existing studies. However, there are valid arguments against its use when analyzing old-age poverty. The permanent-income theory provides arguments against current income as a major determinant of quality of life of older people. Moreover, poverty defined with respect to current income while taking account of household size through equalization, ignores other important aspects of living costs such as disability or health expenditures. Additionally, most analysis using income-poverty measures ignore such aspects as housing ownership and housing costs.
Our analysis examines different aspects of poor material conditions of the elderly. The first poverty definition refers to respondents’ wealth as an alternative to income-defined poverty. Poor households, defined with reference to wealth (“wealth poverty” – WEALTH), are those that belong to the bottom third of the wealth distribution of the sample in each country. For this purpose, household wealth is the sum of household real assets (net of any debts) and household gross financial assets. Secondly, we compare the above poverty measures to a subjective measure of material well-being. This measure is based on subjective declarations by respondents, in which case (“subjective poverty” – SUB) individuals are identified as poor on the basis of a question of how easily they can make ends meet. If the answer is “with some” or “with great” difficulty, individuals in the household are classified as “poor”.
One reflection of potential problems with the standard income poverty measure becomes visible when it is compared with the subjective measure. The graph below shows the differences in country rankings when using one or the other poverty measure. The country with the greatest disproportion is Czech Republic. While being ranked as second according to the income measure, it is ninth according to the subjective measure.
Figure 1. Country Ranks in Old-Age Poverty According to an Income versus a Subjective Measure
Even more striking is the fact that the differences between ranks are not because of over or under classification of individuals as poor, but rather because of misclassification. Figure 2 shows that there is little overlap between different poverty measures. The share of individuals classified as poor according to all three measures is only 7.95%, whereas it is 60% according to at least one of the measures.
Figure 2. Poverty Measure Overlap
Measuring Well-Being
We examine three binary outcomes measuring the well-being of the respondents – two reflecting physical health, and one measuring individuals’ subjective health. The two measures of physical health are generated with reference to the list of twelve symptoms of bad health and the list of twenty-three limitations in activities of daily living (ADLs). In both cases, we define someone to be in a bad state if they have three or more symptoms or limitations. The two definitions are labelled as: “3+SMT” (three or more symptoms) and “3+ADL” (three or more limitations in ADLs). Subjective health “SUBJ” is defined to be bad if the subjective health assessment is “fair” or “poor”. Finally, we also analyze mortality as an “objective” health outcome.
Poverty and Transitions in Well-Being and Health
There is some established evidence in the literature that poverty negatively affects health and other outcomes at different stages of life.[1] At the same time, there is little evidence on how the choice of the poverty measure might result in under- or over-estimation of the effects of poverty. We address this question by examining different poverty measures as potential determinants of transitions from good to bad states of health.
The results confirm that living in poverty increases an individual’s probability of deterioration of health. In a compact form, Figure 3 presents our results from 12 separate regressions (4 outcomes, three poverty measures). Here we report the odds ratios related to the respective estimated poverty dummies. Individuals classified as poor according to the income measure are 37.7% more likely to report bad subjective health in a later wave of the survey than their richer counterparts; they are 4.5% more likely to suffer from 3 or more symptoms; 18.7% more likely to suffer from 3 or more limitations; and 5% more likely to die. The last three effects, however, are not statistically significant.
In contrast, the effects of wealth-defined poverty and subjectively assessed poverty are 2-8 times stronger than those of income poverty, and they are also significant for all outcomes but death. Overall, wealth-defined poverty and subjective assessment of material well-being strongly correlate with deterioration in physical health (exactly the same goes for improvements in health, see Adena and Myck 2013b).
Figure 3. Poverty and Transitions from Good to Bad States Overlap
Poverty and Mortality in the Age Group 50-64
Our analysis reveals differences between age groups and confirms the decreasing importance of income (and thus income defined poverty) with age. As compared to the average effects presented in Figure 3, for the younger age group 50–64 income poverty proves more important as a determinant of bad outcomes, with transition probabilities between 20 and 40% for all outcomes (see Figure 4). The magnitudes are closer to those of other poverty measures, but still lower in all cases. Importantly, we find that wealth-defined and subjective poverty is an important determinant of death in the age group 50–64.
Figure 4. Poverty and Transitions from Good to Bad States 50-64
Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0, Wave 3, release 1, Wave 4, release 1).
Conclusions
The role of financial conditions for the development of health of older people significantly depends on the measure of material well-being used. In this policy brief, we defined poverty with respect to income, subjective assessment, and relative wealth. Of these three, wealth-defined poverty and subjective assessment of material well-being strongly and consistently correlate with deterioration and improvements in physical and subjective health. We found little evidence that relative income poverty plays a role in changes in physical health of older people. This suggests that the traditional income measure of household material situation may not be appropriate as a proxy for the welfare of older populations, and may perform badly as a measure of improvements in their quality of life or as a target for old-age policies. To be valid, such measures should cover broader aspects of financial well-being than income poverty. They could incorporate aspects of wealth and the subjective assessment of material situations as well as indicators more specifically focused on the consumption baskets of the older population.
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References
- Adena, Maja and Michal Myck (2013a): “Poverty and transitions in key areas of quality of life”, in: Börsch-Supan, Axel, Brandt, Martina , Litwin, Howard and Guglielmo Weber (eds.) “Active Ageing and Solidarity between Generations in Europe – First Results from SHARE after the Economic Crisis.”
- Adena, Maja and Michal Myck (2013b) Poverty and Transitions in Health, IZA Discussion Paper 7532, IZA-Bonn.
Development Policy After the Millennium Development Goals: Where Do We Go From Here?
This policy brief reports on a discussion of the Post-2015 Development Agenda held during a full day conference at the Stockholm School of Economics on August 23, 2013. The event was organized jointly by the Stockholm Institute of Transition Economics (SITE) and the Swedish Ministry for Foreign Affairs and was the third installment of Development Day, a yearly development policy conference. The Millennium Development Goals established in year 2000 has been an essential concept for global and national efforts to promote economic, social and human development. Highlighting income poverty, health, education, gender equality and environmental sustainability, the targets have focused global efforts on a set of quantifiable and comparable measures of progress. The question for the development community as these goals reach their endpoint is how to build a successful agenda for the future beyond year 2015. To discuss this challenging question, the conference brought together a distinguished and experienced group of policy oriented scholars and practitioners from governments, International Financial Institutions, the business community as well as NGOs.
In September 2000, world leaders adopted the United Nations Millennium Declaration, committing their nations to a global partnership to reduce extreme poverty. The declaration defined eight time-bound targets expiring in 2015, the so-called Millennium Development Goals (MDGs). These goals specify areas of focus; eradicate extreme poverty and hunger, achieve universal primary education, promote gender equality and empower women, reduce child mortality rates, improve maternal health, combat HIV/AIDS, malaria and other diseases, ensure environmental sustainability, and develop a global partnership for development. They also set explicit targets such as halving the number of people living on less than US$ 1.25 a day and reducing maternal mortality by three quarters from 1990 to 2015. Some commendable success has indeed been realized; already in 2010 the worldwide goal to reduce by half the proportion of people living on less than US$ 1.25 a day was achieved. However, much less progress has been seen in some other areas, including maternal health, and there are countries for which none of the goals are expected to be achieved by 2015. Nevertheless, the use of quantifiable, comparable and time-bound targets to create awareness and direct political resources is generally regarded as a success. The question for the development community as 2015 quickly approaches is thus how to build a successful post-2015 development agenda that builds on what has worked but also incorporates areas identified as missing.
The process to establish a new agenda of course raises many questions and reveals some of the trade-offs involved. There seems to be a consensus that the Millennium Declaration and the MDG framework should serve as a starting point, but there are many details to pin down. For instance, there are important challenges not directly mentioned in the original eight goals such as political conflict, rising inequality and youth unemployment. Many also argue that environmental sustainability, though included, may deserve a more prominent role in the future agenda. On the other hand, loading the Agenda with more and more goals may also dilute the global effort across too many areas, and some scholars argue that the whole idea with specific goals is counterproductive based on an organic view of development ill-suited for social engineering from above. To protect credibility, it is also important to get a sense of what is realistic to aim for, and what responsibility to ascribe to the already developed world. Moreover, even if a consensus can be reached with regards to the goals, opinions on how to best reach those goals will most definitely vary widely.
To get the process towards a new agenda started, the UN Secretary General has launched several initiatives including task teams, special advisors and consultations, but also a High-level Panel of Eminent Persons co-chaired by the Presidents of Indonesia and Liberia, and the Prime Minister of the United Kingdom; also including as its member Gunilla Carlsson, Swedish Minister for Development Cooperation. The panel, led by executive secretary and lead author Homi Kharas, submitted a report to the Secretary General on May 31. The program of Development Day 2013 started with a presentation of the report by Dr. Kharas, and remarks from Minister Carlsson. This was followed by an academic session corroborating projections of the report and outlining its limitations, and two panel discussions on sustainable development and Sweden’s potential as a leader in this process. Below follows a short representation of the main arguments and debates of the day.
A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development
Homi Kharas, Senior Fellow and Deputy Director at the Brookings Institution, presented the main messages contained in the report in the first session. An analysis of the situation since year 2000 shows many positive signs such as high global economic growth; increased international connectedness; a reduction in global inequality; and a substantial drop in absolute poverty rates. However, there are also many challenges ahead; rapid population growth, political conflicts, and the fact that the majority of the extremely poor live in conflict zones, increasing urbanization, a deteriorating environment and dwindling aid flows. This, in turn, leads Dr. Kharas to conclude that ‘business as usual’ is no longer feasible, and a new framework replacing the MDGs is needed.
The report seeks to address these issues and is conceived to serve as a set of guidelines, new goals and targets for the UN Secretary General and for the UN member states for the post-2015 period. At the core of the report is a bold aspiration to eradicate absolute poverty by 2030 through a unified framework of sustainable economic growth, increased social equality and environmental sustainability, and a new global partnership paradigm. This universal agenda, in turn, is proposed to be reached via five paradigm shifts to the status quo, (i) universal inclusion and equality, (ii) environmentally sustainable development, (iii) a transformation of national economies for sustainable growth, (iv) peace and effective, transparent public institutions, and (v) a new and more inclusive global partnership. In the report these broad and major shifts are further delineated across 12 illustrative targets, which, if met, will directly affect more than two billion people across the world and would require about $30 trillion spent by the governments worldwide.
Dr, Kharas emphasized that the report was prepared in cooperation with 5000 civil organizations, 250 large international corporations, and thematic, regional and country consultations all over the world, with another one million people taking part in an online questionnaire. He stressed that this kind of broad cooperation and consultation is needed to implement the goals set by the report and especially to operationalize these goals at the level of each of the member states.
Gunilla Carlsson, Swedish Minister for International Development Cooperation and a member of the UN High-Level Panel, continued the discussion and commended the members of the Panel on the impressive amount of work put in the report. She also emphasized the universal character of the agenda presented in the report, largely applicable both to developing and developed countries.
Carlsson stressed what she identified as the core values of the report; eradication of extreme poverty, prevention of violence and conflict, and inclusive peace. She further underlined the importance of local and global partnerships across governments, business communities and civil society. Broader public-private partnerships are essential both for fostering innovation in development work and to guarantee sufficient amounts of financing. The exact design of such a framework, however, is still an open question, but she hopes Sweden can serve as a leading example.
Both Homi Kharas and Gunilla Carlsson also showed great optimism when asked about the potential to implement the substantive initiatives by 2030. They stressed that not only does the world at present have more resources and more aid flows than it ever have, but the international community, including both public and private actors, is also showing more willingness to help the developing countries integrate successful development models than ever before.
Comments and Reflections
Martin Ravallion, Edmond D. Villani Professor of Economics at Georgetown University, started the commentary and reflection session. He showed how there is a strong current trend of between-country convergence of inequality rates (more equal countries becoming more unequal, while more unequal countries are becoming more equal) and declining poverty rate. The latter decline is to a considerable extent driven by Chinese economic growth, but this is far from the only source. He also underlined that the rate of poverty reduction has increased since the adoption of the MDGs in the 2000s, but said it was too early to judge the success or failure of the MDGs on these grounds.
Based on current trends, Ravallion also presented some estimates of the possibility to achieve the core objective of the report, eradication of absolute poverty by 2030. From a broad range of alternatives, the best case scenario, based on 3% annual growth rates of the world economy, absence of major economic crises and at least not decreasing participation of the poor in the benefits of growth, estimated a fall in absolute poverty rates from about 19% at present to 3% by 2030. In a less optimistic scenario, but historically not unlikely, levels of inequality and poverty would fall at a much slower rate, causing 12% to 14% of the world population to live below the absolute poverty line by 2030. Thus, the conclusion is that total eradication of absolute poverty by 2030 is hardly achievable, but substantial progress can be made, and it depends critically on continued high levels of world economic growth.
Professor Ravallion also stressed that these projections were made possible through a recent revolution in data availability, something the High Level Panel was asking for. To a large extent, this is attributed to a massive data collection effort by the World Bank, which not only provided better coverage of countries around the world, but also allowed for deeper insights into the nature of extreme poverty, including re-calculations and harmonization of cross-country comparable Purchasing Power Parity consumption baskets. This revolution provided more reliable inputs for his prediction models and improved the precision of estimates considerably.
Owen Barder, Senior Fellow and Director for Europe at the Center for Global Development, further emphasized this importance of credible statistics. Barder was somewhat skeptical to the report’s claim to be bold and offering a new approach, arguing that it largely reiterated the goals (jobs for young people, partnership with the private sector, reform of the financial system, etc.) already in the Millennium Declaration from year 2000. He also argued that the claim of success for the MDGs is almost entirely made on the basis of paragraph 19 of the Declaration; the objective to reduce by half the number of people living in absolute poverty. Much less progress has been made on the other explicit objectives, and all other aspects emphasized in the Millennium Declaration but which were not necessarily a part of the MDGs.
Barder suggested that there is too little effort to consistently measure whether rich countries are playing their part in the global partnership. Against that background he presented some preliminary results on the last round of the Center for Global Development’s Commitment to Development Index, calculated on the basis of OECD counties’ participation in aid, trade, investments, migration, environment, security and technology transfers. Over the last 10 years, OECD countries demonstrated on average a modest increase from four to five points on a ten-point scale, with Sweden ranked third from the top with a score of 7.2 for 2011 and 6.8 for 2012. Interestingly enough, this deterioration in the index for Sweden is mainly due to deterioration in the security component of the index, in turn resulting from larger sales of arms to undemocratic regimes, and from decreasing aid and immigration. There is obviously variation across countries, but on average there is scant improvements during the 13 years since the Millennium Declaration. This led Barder to question whether the developed countries have contributed their share to the objective of ending poverty, or if too much of the heavy lifting is left for the developing countries.
Barder concluded the presentation by pointing out the difference in language used in the report, namely the imperative used in the parts of the report describing recommendations for the developing countries, and the subjunctive used for recommendations for the developed countries. Again, to him this difference signaled the need to re-emphasize the importance of political commitment and operational goals also for the already developed countries in the Post-2015 Agenda.
Johan Rockström, Executive Director at the Stockholm Resilience Centre, started out noting that the population of the world is estimated to increase to eight billion people by 2030 and to nine billion by 2050. This, in combination with the currently prevailing development paradigm that emphasizes short-term economic growth over long run sustainability, causing degradation of biodiversity and climate change, means that we are hitting the planetary ceiling of eco-capacity. This suggests that ‘business as usual’ is no longer an option, and a new development paradigm is needed.
To address this issue, Rockström formulated a set of goals for human development balancing the needs of the environment, the needs of society and the needs of the people, all within the Earth’s life-support system. He proposed a broader framework for thinking about these issues, the so-called Sustainable Development Goals (SDGs rather than MDGs), which rebalances the relative weight on environmental, human and economic development with relatively more emphasis on the first two. This approach unifies the MDGs with planetary necessities (material use, clean air, nutrient and hydrological cycles, biodiversity, and climate stability), and sustainable development goals (sustainable food and water security, universal clean energy, governance for sustainable societies, etc.).
Discussion Panels
The first panel of the day focused on issues of sustainable development and was started by Klas Waldenström, Senior Advisor on the Post-2015 Development Agenda at Sida. He argued that the main challenge to the new partnership paradigm discussed earlier, will be the creation of trust both across nations and across the private and public sectors. Referring to the experience of Sida, he cited the successful creation of a network of 25 private Swedish companies focusing on models of sustainable development. An important role of official foreign aid in these partnerships, he argued, was to blend direct financial transfers with a combination of political support and business sector outreach, thereby potentially leveraging the financial flows with alternative sources of capital.
David Fergusson, Deputy Director at the Office of Science and Technology at USAID, called for more and better data in order to be able to operationalize and evaluate the new strategies that hopefully will come out of the report. He also reiterated the importance of transformative solutions for sustainable development and the need to understand that ‘business as usual’ is no longer an option. He also referred to the successful cooperation between Sida and USAID as an example of international collaboration of a new kind, more of which will be needed in the future to overcome the status quo and achieve the goals put forward by the report.
Garry Conille, Special Advisor to President Ellen Johnson Sirleaf of Liberia and UNDP, discussed his experience of working with the MDGs and stressed that possibly the most challenging part was the negotiation between different stakeholders to reach a set of issues well-defined and contained enough to be operational. From his point of view, the major challenge is the operationalization of the rather opaque and broadly defined MDGs and how to find a proper allocation of resources across the many commendable ambitions. He therefore called for an effort to make the post-2015 agenda more practical.
The issue of operationalization was discussed further by Stefano Prato, Managing Director at the Society for International Development. He argued that with such large shifts proposed by the post-2015 agenda, it is perhaps difficult to understand how to work with the vision put forward by the panel. His suggestion for the Panel was to dig deeper into the challenging areas of the report but also to develop more applied recommendations for the member states and especially so for the private institutions desired as part of the new partnerships.
This need for operationalization was supported by Jakob Granit, Centre Director at the Stockholm Environment Institute. In his opinion, the broad vision as presented in the report is indeed difficult to work with, but he also suggested that progress on parts of the agenda can be instructive for how to go further also with the more challenging parts. He also emphasized the importance of a regional approach, building on existing networks of regional partnerships, and again stressed the importance of public-private partnerships to solve common international issues.
The second panel was devoted to the role Sweden can play in global sustainable development and the post-2015 agenda. The discussion was started by Ulla Holm, Global Director at Tetra Laval Food for Development Office. She presented some of Tetra Laval’s experiences of sustainable development work in Bangladesh, an example of a successful public-private partnership. In her view, one of the main pillars of sustainability is to prevent unnecessary food loss, and this can be achieved by building an integrated value chain that supports rural development in the long run. The crucial challenge on this path is the need for concurrent public and private investments, and how to overcome coordination problems and lacking trust across stakeholders. She therefore stressed the need to construct successful public-private partnerships on a large scale and in different areas, but also to make sure to document and scale up the existing models in order to replicate success in the most cost efficient way.
Erik Lysen, Director for International Affairs at the Church of Sweden, stressed the challenges in changing existing institutions and briefly discussed the main motives that could make such changes to occur. He also argued that some of the strongest motives that would actually provide the necessary motivation for change, namely fear, could not be desirable in the long run, but still viable in a context of post-2015 agenda if complemented with better social protection, institutes of civil society and a broader public discussion. Here, NGOs could act as watchdogs and catalysts, strengthening the desire for building new institutions and providing material and human support for their construction at the same time.
Stefan Isaksson, Head of Policy Analysis at the Department for Aid Management at the Ministry for Foreign Affairs, continued the discussion on the challenges of changing existing institutions. He described current efforts to remodel the Swedish aid management system in order to become a more effective bureaucracy. In his view, the major shift in thinking is that of understanding aid less as simply giving money away and more as an investment for a common future. This is needed to improve the selection process of aid projects and also to motivate better the need to make projects and their results measurable and accountable. To achieve this, broader collaboration and consultations across stakeholders is needed. He also mentioned that perhaps at present many aid projects are too conservative, that the failure rate is too low because it reflects an aversion to risk that partly defeats the purpose of official foreign aid. The private sector will always be reluctant to venture into areas with high risk even if the potential social rate of return is high, so for official aid to serve as a more effective complement to private flows, more risk tolerance may be needed.
The issue of understanding aid as investment was discussed in detail by Jonas Ahlen, Investment Manager at the Storebrand Kapitalförvaltning. He described current efforts in the area of sustainable investments, mainly centered in microfinance and agricultural loans. In his opinion, broader involvement in such practices from the private sector would facilitate a transition to sustainable practices, but would at the same time require changing existing regulations in home countries to incentivize and alleviate the risks. He also stressed the need for broader public-private partnerships in these areas and briefly described the new consultative practices established by the Ministry of Finance in Sweden to catalyze private capital participation in for instance infrastructure projects in Sub-Saharan Africa.
Finally, Homi Kharas added to the Sweden-centered discussion by stressing that there exists no systematic assessment of what public-private partnerships can do. In his opinion, possibly the most important role for Sweden is to create conditions that would facilitate public-private partnerships in development and aid. By developing and experimenting with forms of public-private partnerships, as well as with new ways of measuring and monitoring of performance of such partnerships, Sweden could create a case for broader involvement of private funding and thus accomplish perhaps the most difficult part of bridging the post-2015 with the experience and skills of the private sector.
Conclusions
In sum, the discussion at the Development Day 2013 clearly highlighted the importance of sustaining some of the positive trends seen lately for economic and human development but also highlighted how crucial it is to take environmental sustainability into account. There is a growing consensus that long run human development necessitates an understanding of the planetary boundaries, even though exactly what trade-offs this involves and where to put the relative weight on more short run economic development is still debatable. There was also a wide consensus around the importance to get all different parts of society involved and working in tandem. Foreign aid cannot be expected to pull the heavy load by itself. The challenges are far too wide and important. Instead, much hope was attributed to public-private partnerships, but there is a lot of work that remains to make sure these vehicles generate the hoped for solutions. The capital, experience and skills of the private sector are needed. On the other hand, getting the incentives right is not a trivial challenge. Finding models of partnerships that work and can be scaled up may be an area in which Sweden can set an example and lead the way for other nations striving to contribute to long run sustainable development.
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.