Project: FREE policy brief
Career Women and the Family – A New Perspective on the Role of Minimum Wage
This brief finds that whereas in the 1980s richer women had fewer children than women near the middle of income distribution in the US, it is no longer true today. It argues that the rise in inequality is the main driver for this change. Greater income inequality enables high-income families to outsource household production to lower-income people. Changes to minimum wage laws are thus likely to affect the fertility and career decisions of the rich.
“I have frequently been questioned, especially by women, of how I could reconcile family life with a scientific career. Well, it has not been easy.”
– Marie Curie, 1867-1934
Much has been made of women “leaning in” at work at a cost to their families. Indeed, this discussion has become more prevalent as women have surpassed men in higher education in most developed countries, and have entered prestigious careers en masse, a fact reinforced by public policy. For example, in 2012 the European Commission published a special report on women in decision-making positions, suggesting legislation to achieve balanced representation of women and men on company boards. One natural question to ask is, how high is the cost of a woman’s career to her family? This is a difficult, multifaceted, and even sexist question to ask.
High-income women have historically had fewer kids (Figure 1 for the year 1980). Social scientists’ leading explanations rely on the difficulty of combining children and a career. Under this view of the world, as more women focus on their careers, they have fewer children. On the other hand, the evidence shows that more educated (or wealthier) women produce more educated children. Given these two regularities, the majority of children are born to poorer mothers, and thus receive an inferior education. Moreover, this creates a feedback loop that depresses the average education through time making us question our ability to sustain a satisfactory average level of education.
Figure 1. Fertility rates by income deciles, 1980 and 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married women. Fertility rates are hybrid fertility rates, constructed by age-specific deciles. Deciles are constructed using total household income.
However, the negative relationship between family income and fertility ceases to hold after the 2000s. Figure 1 shows that for the year 2010, the cross-sectional relationship between income and fertility has flattened or even become a U-shape. Today, high-income women have higher fertility rates than those of women near the middle of income distribution. This is a result of a substantial increase in fertility among women in the 9th and 10th decile of family income: they increased their fertility by 0.66 & 0.84 children, respectively. The rise in fertility of high-skilled females was first documented in Hazan and Zoabi (2015), discussed in a previous FREE Policy Brief. The implications are profound; children are more likely to be born to wealthier or more educated mothers than in the past. This has a far-reaching impact on the future composition of the population.
How can we understand the change in fertility patterns over time? We argue that rising wage inequality played an important role. Data for the years 1980 and 2010 show that average real hourly wages, quoted in 2010 $ grew from $28 ($51) to $50 ($64) for women (men) in the 10th decile of the income distribution. This increase was accompanied by stagnant wages for women (men) in the 1st decile, precisely the people who are most likely to provide services that substitute for household chores (Figure 2). Thus, growing wage inequality over the past three decades created both a group of women who can afford to buy services that help them raise their children, and a group who is willing to supply these services cheaply. In a recent paper, we found that the increase in wage inequality from 1980 and 2010 can actually explain the rise in high income fertility (Bar et al. 2017). Moreover, this rise in inequality has resulted in a large increase in college attendance through the changing patterns of fertility. This is because more children are now born to highly educated mothers.
Figure 2. Wives’ Wage by Income Decile 1980 & 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married men. Deciles are constructed age-by-age, using total household income. Representative wages for each decile is the average of these decile-specific wages from ages 25 to 50.
Our new understanding of the interrelation between income inequality, the relative cost of home production substitutes, fertility pattern and educational choice induces us to rethink some typical economic debates. For instance, consider the minimum wage. The typical debate about the minimum wage is focused on how it affects lower wage individuals in terms of income and their ability to find work. However, if people who earn the minimum wage are disproportionately also those who help raise wealthier families’ children, or simply make running a household easier, then a higher minimum wage can make home production substitutes more expensive for high wage women, making it harder for them to afford both a family and a career. While indirect, this effect can be significant. Figure 3 shows the distribution of the real wage, relative to the minimum wage, both for the industries of the economy associated with home production substitutes and other sectors of the economy. The figure clearly shows that workers in industries associated with home production substitutes are concentrated around the minimum wage and thus are much more likely to earn wages that are close to the minimum wage.
Figure 3. The distribution of real wages, relative to the effective real minimum wage in each state and year, by sector of the economy
Notes: Data from Current Population Survey, 1980–2010, using all workers.
Interestingly, we calculate a change in the cost of home production substitutes following an increase of the Federal minimum wage from $7.25 to $15/hour, as suggested by Bernie Sanders during the 2016 presidential election. It turns out that this increase in the minimum wage would increase the cost of market services that substitute for household chores by about 21.1%. Indeed, the minimum wage has a strong impact on the average wages of workers producing home production substitutes. However, how does this increase affect the economy?
According to our theory, higher costs of home production substitutes would affect women’s choice of how to allocate their time between labor force participation and home production, including raising children. The higher cost of these substitutes induces women to buy less of them and spend more of their time producing home production goods. Indeed, we find that the increase in the minimum wage decreases fertility and increases mothers’ time at home, and more so for higher income households. The magnitudes are large. A 10th (5th) decile household decreases fertility by 12.8% (9.4%), while the mother spends 9.7% (2.5%) more time at home. Notice that these numbers are calculated under the assumption that women can adjust fertility. What about those who are “locked in” their fertility choice? We recalculate changes in mother’s time at home for these mothers using the model’s fertility in 2010 with the increased cost of market services that substitute for household chores. A 10th decile mother increases time at home by 25.9%, while a 5th decile mother increases it by 13.1%. These numbers are larger as the family has not had a chance to scale back fertility. The short run effect on labor supply is also very large. The average reduction in labor supply by women in the 9th and 10th deciles is 3.5%.
Whether an increase in the minimum wage is good or bad for the society is a big question. Not only does it lie beyond the scope of our theory, but also beyond the scope of social sciences. However, the one modest contribution we try to make is in observing that an increase in the minimum wage heightens the rivalry between a woman’s career and family. As such, it forces women to forgo one in order to opt for the other.
The sexist nature of our question lay in the implicit assumption that it is the mother’s responsibility to look after the children or home production in general, rather than the father’s. While once this was a nearly universal attitude, it is now increasingly common for fathers to take a more central role in childcare rather than leave everything to the mother. How does this change in gender roles affect our analysis? In modern times, both spouses’ careers are potentially affected by children, as both parents take a role in child care. Fathers are now facing the same tradeoffs as mothers did in the traditional gender role story: children vs. careers. As a result, marketization is more important than ever for career oriented parents.
Talk to a high wage family and no doubt that they’ll readily tell you how important their ability to purchase daycare, prepared food, or other help at home is to their success as parents. Perhaps parents don’t realize that the price of these goods are so intricately linked to inequality or the minimum wage, but the policy maker should bear in mind that these are key factors for career women and the family.
References
- Hazan and Zoabi (2015), “Do Highly Educated Women Have Smaller Families” The Economic Journal
- Bar, Hazan, Leukhina, Weiss, and Zoabi (In progress) “Is the Market Pronatalist? Inequality, Differential Fertility, and Growth Revisited”
Political Responsibility for Economic Crises
This brief summarizes the results of research on the political costs of large-scale economic crises. In a large historic sample of countries, we study the impact of different types of crises, such as sovereign and domestic defaults, banking crises and economic recessions, on political turnover of top politicians: heads of the state and central bank governors. According to the findings, only default on domestic debt increases the probability of politicians’ turnover but not the default on external debt. As argued, this is due to the fact that the latter is not directly felt by the voters. In addition, we find that although currency crises increase chances of head of central bank turnover, it does not affect tenures of heads of state. Presumably, this is the case since currency crises are in the eyes of the public the responsibility of CB governors. These findings are relevant for both developed and transition economies, but are especially important for the latter as political turmoil and economic recessions are more prevalent in developing nations.
Overview and Key Findings
Large-scale economic crises are associated not only with the economic downturns, but also with political turnover. When the national economy is in a critical state, a default declaration often turns the economy back to growth as it is typically viewed as an act of acknowledging a problem and showing readiness for changes. However, politicians responsible for the economy and leaders of the states are often reluctant to declare default and try to postpone it, which worsens the situation. One of the reasons behind such unwillingness to act is a fear of a political turnover following the open acknowledgement of a problem.
This brief summarizes the findings Lvovskiy and Shakhnov (2018). We investigate the statistical evidence of political costs related to different types of economic crises.
We find that the effects of a crisis depend on the crisis type and on whether it was in the area of responsibility of a given politician. For example, external sovereign defaults have no effect on political turnover, which we interpret as external sovereign default having a small impact on the general public. On the contrary, domestic sovereign defaults have a large impact on the country population and often lead to the replacement of the top executive. In turn, banking crises are followed by the downfall of the government at the level of chief executive as well as the governor of the central bank.
While there is large literature on career concerns of politicians and political turnover, the majority of papers either focus on the regular changes through elections in democratic regimes (Treisman, 2015) or study a particular non-democratic country, like China (Li and Zhou, 2005). However, throughout history, crises have often happened in transition, non-democratic or not fully democratic countries. Furthermore, even in democratic countries many changes of government have been irregular. Since a delay in default declaration usually harms economies it is important to understand the mechanisms behind it in different institutional settings. Our paper contributes to this understanding by analyzing the impact of economic crises on political survival in a wide set of countries and regimes. Better understanding of the political costs that the top executives face while making such decisions is crucial for the prediction of these decisions as well as for international default negotiations and consultations.
Below we describe our finding in some more detail.
Statistical Analysis and Results
Our analysis consists of two main parts. We start with the political turnover for heads of state, who are in charge of the performance of the whole economy, which we measure by the GDP growth. Then, we look at central bank (CB) governors, who are in charge of the monetary policy, price stability, stability of the financial sector and banking supervision.
Table 1. Head of state changes
Table 1 presents the estimated linear probability regression models for the head of state turnover. As expected, elections have a strong impact on the probability of the turnover of the head of state. Further, as Column 1 in Table 1 shows default on external debt has no significant impact on the head of state tenure while default on domestic debt increases the yearly chances of being displaced by 34 %. This supports the idea that voters care more about their own savings than about the general situation with the state’s budget. When we look at the effect of past crises (the predictor variable in this case is whether a crisis took place last year), Column 2 coefficients for both external and domestic defaults appear to no longer be statistically significant. Instead, banking crises become significant. This situation could be due to the fact that one of the common consequences of domestic defaults is an ongoing distortion of savings which often leads to deposit runoffs, so the effect of the previous year’s domestic default now acts through a banking crisis.
Table 2. Central bank governor changes
Table 2 presents similar results but this time the left hand side variable is CB governor turnover. Similarly to the case with the head of state turnover, only default on domestic debt has a significant effect on the CB’s governor tenure and not the one on external debt. The main differences with Table 1 are that elections do not statistically predict turnover of CB heads while currency crises do. The former result is expected since in most countries there are no direct elections of central bank governors and central banks often have some degree of independence from the government. The latter result, that currency crises have a significant impact on CB governors’ tenures, implies that since currency control is one of the roles of a CB, its head is held accountable for currency crises and not the head of a state.
Conclusion
We examine the political cost of different types of economic crises, and find non-uniform effects of different types of crises on the political survival of various key officials. Domestic defaults, and recent banking crises are shown to be costly both for heads of states and central bank governors, while currency crises only have an impact on the political survival of the latter.
Interestingly and importantly, we find no evidence of the impact of (external) sovereign default on political turnover of the head of state or central bank governors. In other words, contrary to Yeyati and Panizza’s (2011) suggestion, it seems that there is no immediate political cost at the top associated with (external) sovereign default. One possible explanation is that the public does not punish a politician for defaults because by defaulting, the politician makes the optimal decision. In a modern world, many developing nations experience rapid growth of their sovereign debt. The presented evidence brings partial optimism that even if economic mistakes have already been made, top politicians would understand that acknowledging a problem and making steps toward its solution may not always be as costly for them as has previously been thought.
References
- Li, Hongbin; Li-An Zhou, 2005. “Political turnover and economic performance: the incentive role of personnel control in China,” Journal of Public Economics, 89 (9), 1743 – 1762.
- Lvovskiy, Lev; Shakhnov, Kirill, “Political Responsibility for Different Crises”, BEROC working paper #50, 2018
- Treisman, Daniel “Income, Democracy, and Leader Turnover”, American Journal of Political Science, 2015, 59 (4), 927–942.
- Yeyati, Eduardo Levy and Ugo Panizza, “The elusive costs of sovereign defaults,” Journal of Development Economics, January 2011, 94 (1), 95–105.
Focus on Investment: A Brief Look at Regulatory Developments in EU Telecommunications
The European Commission recently proposed a revision to its existing regulatory framework for telecommunications, the details of which have been amply discussed and are currently being negotiated. A pivotal theme of the revision is a stronger emphasis on stimulating investments into broadband networks capable of delivering high-speed (100+ Mbps) internet services. This brief highlights and briefly discusses some key changes in that regard.
Introduction
High-speed broadband networks are the backbone of the fast-growing digital economy. Promoting citizens’ access to such networks has been one of the European Commission’s stated policy priorities at least since 2010, when it launched its “Digital Agenda for Europe” (EC, 2014). Its policy mix of choice involves measures and funds facilitating deployment of so-called next-generation access networks on the one hand (commonly taken to mean access networks capable of delivering speeds exceeding 100 Mbps), while on the other hand regulating access to such networks to the extent perceived necessary to deal with potential problems resulting from incumbent network operators’ degree of market power. As regulation may harm incentives to invest in network infrastructure in the first place, a balance between investment promotion and competitive safeguards needs to be struck.
Motivated by what it considers to be a sub-optimally low speed of network upgrading in at least some of the EU’s member states, the Commission has sought to adjust its policy balance in favor of investments by proposing a revision (EC, 2016) of its regulatory framework for electronic communications, called the European Electronic Communications Code (EECC), which defines a standard approach to regulating fixed broadband network operators deemed to possess significant market power. That revision has been commented upon and discussed by the European Parliament and the European Council as well as various private and public stakeholders (Szczepański, 2017). Several amendments have been proposed and further discussion is ongoing to reach a compromise between the European institutions.
Background
Telecommunications networks were until more recently typically owned by vertically integrated, often formerly state-run, national incumbents who even after their privatization and the elimination of most legal barriers to entry were considered to possess significant market power. The EECC’s key remedy to such market power is so-called network unbundling at the wholesale level: considering the retail market for internet service provision potentially competitive, unbundling means granting competing internet service providers regulated access to the incumbent operator’s physical local-area access network, which is commonly regarded as the key bottleneck in internet service provision. Choosing the intrusiveness of the access obligation is up to the national regulatory authority (NRA), ranging from merely demanding that the incumbent publicly post a reference offer, to stricter measures such as non-discrimination, “fair and reasonable” pricing, and ultimately, full-on access price regulation, typically implemented with price caps derived from regulatory costing models. A recommendation from 2013 (EC, 2013) outlines methodological guidelines to national authorities.
Key changes
The proposed EECC revision makes the abovementioned recommendation binding, which may partly be an attempt to further harmonize regulatory practice between member states, with a view to encouraging cross-border investments by operators and service providers. It also encourages NRAs to, where possible, abandon more rigid price regulation in favor of margin squeeze tests. Margin squeeze occurs when a vertically integrated firm with market power in the wholesale segment of a production chain “squeezes” retail competitors by setting high wholesale and low retail prices, to the extent that even equally efficient, or at least reasonably efficient, retail competitors cannot survive if they are dependent on the dominant firm’s wholesale product. Moreover, and more importantly in terms of boosting deployment, the proposal encourages lighter-touch regulation for operators deploying new network infrastructure (Art. 72), and specifically relaxes regulation for deployment projects open to co-investments between operators (Art. 74). It also extends the market review period, i.e. the frequency at which NRAs are expected to update their market analysis and regulatory policy, from three to five years, giving operators a longer planning horizon, and encourages NRAs to consider any existing commercial wholesale offers in their market analysis, which can be interpreted to mean that anything short of full market foreclosure should be looked upon benevolently (Articles 61 and 65). In line with this latter development, which suggests a focus on wholesale access per se, is Article 77. This article exempts so-called wholesale-only networks – non-integrated networks whose very business model is selling access to interested internet service providers – from strict access price regulation, at least ex-ante. Typically, a presumption of consumer harm absent regulation is sufficient for intervention. Article 77 turns the tables on regulatory authorities by requiring evidence of actual consumer harm.
A counterpoint to these deregulatory elements is Article 59.2, which under certain conditions not only allows but obliges NRAs to impose access obligations on owners of existing physical infrastructure “up to the first concentration point”, in practice affecting mostly in-building wiring and cables, even when these owners have not been identified as dominant in any relevant market. In countries such as Sweden, where in-house wiring is often not owned by any operator but rather by the respective building’s owner(s), implementing such obligations may pose a regulatory challenge.
Finally, Article 22 requires NRAs to chart existing infrastructure as well as deployment plans across the country and enables them to define “digital exclusion areas” where no high-speed broadband infrastructure exists or is planned. In such areas, they may organize calls for interest to deploy networks, also with a view to resolving potential coordination problems between operators resulting from so-called “overbuild risk”: deployment in some lower-density areas may only be profitable if most of the customer base in that area can be captured, leading to a standoff between operators who cannot, do not want to, or are not allowed to communicate and coordinate their deployment strategies. As a result, investment is delayed.
A rather piquant detail here is that the proposed code allows NRAs to take action against operators it suspects of “deliberately” providing “misleading, erroneous or incomplete” information about their deployment plans. Included to prevent gaming, this provision carries the risk of suppressing investors’ appetite for the designated exclusion areas lest they be punished in case they change their mind. A minimum of mutual trust between the national regulator and market participants seems crucial for this mechanism to succeed.
Conclusion
The Commission’s proposed new regulatory framework emphasizes investment in, and take-up of, high-speed (100+ Mbps) broadband networks, explicitly defining such enhanced connectivity as a new regulatory objective on equal footing with the existing ones, most notably the promotion of competition. The present brief points out some key regulatory changes aimed at the fulfilment of these respective objectives. In terms of the revision’s impact on high-speed broadband deployment in the EU’s member states, it is difficult to make a general prediction since Europe is somewhat heterogeneous with respect to high-speed broadband penetration. For example, the 2016 EU overall NGA coverage was 75.9 % of households, but coverage rates of individual countries ranged from 99.95 % and 99.86 % in Malta and Belgium respectively to 47.0 % in France and a mere 44.2 % in Greece (EC, 2017). To the extent that the new code encourages investment relative to the old regime, regions with lower current coverage stand to benefit more. To the extent that the lower pace of deployment in those areas is the result of other factors orthogonal to regulation (one example being demand uncertainty), it will have a limited effect.
References
- European Commission, 2013. “Commission Recommendation on consistent non-discrimination obligations and costing methodologies to promote competition and enhance the broadband investment environment.”
- European Commission, 2014. “The European Union Explained: Digital agenda for Europe.”
- European Commission, 2016. “Proposal for a Directive of the European Parliament and the European Council establishing the European Electronic Communications Code (Recast).”
- European Commission, 2017. “Broadband Coverage in Europe (2016): Mapping progress towards the coverage objectives of the Digital Agenda.”
- Szczepański, M., 2017. “The new European electronic communications code”, EU Legislation in Progress briefing, European Parliamentary Research Service.
When Fair Isn’t Fair: Framing Taxes and Benefits
Taxes and benefits create incentives for people to adopt or avoid certain behaviours. They create premiums for (socially) preferred states. A premium can be determined by either taxing unwanted behaviour or by subsidizing desired behaviour. The resulting economic incentive for changing one’s behaviour is nominally equivalent under both mechanisms. However, the choice of frame for an incentive to be either described in terms of a tax or as a benefit can strongly influence perceptions of what is fair treatment of different, e.g. income, groups. Using a survey-experiment with Flemish local politicians, we show policy-makers to be highly susceptible to such tax and benefit framing effects. As such effects may (even unintendedly) lead to sharply different treatment of the same group under the two mechanisms, important questions arise, particularly for the design of new tax and benefit schemes.
The design and implementation of redistributive policies usually evoke much discussion. Opinions, both in public and often also in political debate, tend to be driven by ethical and fairness considerations. However, such concerns can lead to unintended consequences and – at least in terms of ex-ante intended fairness – to ex-post imbalanced incentive structures for different (income) groups.
An important function of taxes and benefits is the creation of premiums for certain behaviours or actions. Either unwanted behaviour may be taxed and thereby sanctioned, or desired behaviour may be encouraged through benefits. Irrespective of the method chosen, an economic incentive is created for individuals to opt for the desired behaviour.
The way such premiums are defined can usually be thought of as a two-step process. First, a baseline for a given behaviour, action, or state is chosen as a reference-point. For instance, baseline behaviours could be to not have retirement savings, to not use safety-certified equipment or follow accepted standards at work, or to not have children. Arguably, these are cases warranting the creation of incentives to encourage people to adopt the socially desirable behaviours of saving money for their old age, working in a safe environment, and having children. The second step, then, requires a choice of mechanism to create an incentive. The mechanism can be to either punish the unwanted behaviour – such as not adhering to safety standards at work – or to grant (cost-reducing) subsidies and benefits for taking the desired action, such as saving for old age or having children.
Importantly, the combination of the chosen reference point and the mechanism to create the incentive can influence the way people think about the fairness of an incentive when the targets belong to different (income) groups. Schelling (1981) demonstrated this point in an in-class experiment, which, somewhat simplified, runs as follows:
Families typically receive some child benefit: they get a certain sum per child. Imagine there are two families, one poor and one rich, both with their first child. What amounts of child benefit should each family get? Should the poor get more than the rich, should both families get the same, or should the rich family get more for having a child than the poor family? Schelling’s students would tend to voice support for either the poor getting more or both families getting the same. After all the rich family is surely already affluent enough to support their child. At the extreme, the rich family would get nothing for having a child, and the poor family quite a lot.
Now think of a world where the standard is to have a child, and couples who do not have a child have this ‘socially undesirable’ behaviour ‘penalised’ through a fee, for instance in the form of a tax. Should the poor couple pay a higher fee, should both couples pay the same, or should the rich couple pay a higher fee? The students now overwhelmingly supported requiring the rich couple to pay more. After all, they have more disposable income. However, in this case, the rich couple receives a lot for having a child (they no longer need to pay the steep fee), whereas the poor family may get no (additional) economic incentive for having a child. The treatment of the same family thus obviously drastically differs between the two frames. At the extreme, the poor family gets quite a lot for changing from having no children to having one child in the first frame, but nothing in the second frame. For the rich family, the situation is the reverse: there is no premium for having a child in the first frame, but potentially quite a high premium for having a child in the second frame.
Does this thought-experiment matter outside the classroom (see also Traub 1999, McCaffery & Baron 2004), beyond the context of child benefit, and among those actually exposed to the design considerations of tax and benefit systems? In a recent paper (Kuehnhanss & Heyndels 2018), we test the occurrence of such framing effects with elected local politicians in Flanders, Belgium, who are involved in the budgetary decision-making in their municipalities.
Framing experiment
We invited 5,928 local politicians to take part in an online survey on economic and social preferences in spring 2016. Participation was voluntary, not incentivised, and questions were not compulsory, allowing respondents to skip them if they so chose. In total, 869 responses to the survey were registered and (N1=) 608 participants provided usable answers to the questions relevant to the framing effect described above.
Participants were randomly allocated to one of two groups, each receiving a slightly different wording of the following question:
“In Belgium couples receive financial benefits from the state. Suppose that it is not relevant how the transfer is funded, and ignore any other benefits, which might come into play. How much [more / less] should a couple [with their first child / without children] receive per month than a couple [without children / with their first child]?”
One group saw the question in the benefit frame with only the italicised phrases in the brackets displayed; the other group saw the question in the tax frame with only the phrases in boldface displayed. In both groups, participants were then asked to fill in amounts they would consider appropriate for each of three couples with different monthly net incomes: €2,000, €4,000, or €6,000, respectively.
With framing effects – and distinct from classic rational choice models – the expectation is that the three couples would be treated differently depending on the phrasing of the question. In the italicised benefit version the amount granted should be decreasing with the income of the family. In the boldface tax version the stated amount should be increasing with the families’ income.
Figure 1. Results child scenario
Source: Kuehnhanss & Heyndels (2018, p.32)
As Figure 1 shows, the results strongly conform to this pattern. The low-income (€2,000) couple is granted an average of €330 in the benefit frame, but only €178 in the tax frame (recall that the premium in the latter arises from no longer receiving less – or ‘paying a fee’ – once there is a child). For the high-income (€6,000) couple, the amounts granted average €132 in the benefit frame, but a much higher €368 in the tax frame.
Environmental taxes and benefits
Child benefit systems are usually a well-established part of countries’ tax and benefit systems. The design of new instruments is more common in policy areas undergoing, for instance, technological change or being newly regulated. A relevant example is policy on the promotion of environmentally friendly behaviour and technologies, e.g. through ‘green’ taxes and subsidies. To test the validity of the hypothesised framing effect, we also included a second scenario in our survey related to the municipal interests of our respondents, namely car taxes. Flemish municipalities receive income from a surcharge levied on the car taxes paid by motorists. Consequently, we asked our participants (N2 = 525, see the paper for details) to imagine the introduction of a new environmental certificate for cars in Belgium, and to provide amounts they would consider appropriate for the difference in annual tax paid on cars with or without the certificate. Specifically, roughly one half of participants was asked how much less the owner of a certified car should have to pay in annual car tax than the owner of a non-certified car (the subsidy frame). The other half was asked how much more the owner of a non-certified car should pay in annual car tax than the owner of a certified car (the tax frame). The question was again asked for three different levels, proxying wealth via the cost of the cars: €15,000, €30,000, and €45,000, respectively.
Figure 2. Results car scenario
Source: Kuehnhanss & Heyndels (2018, p.32)
Figure 2 shows the results. The effect is less pronounced in this scenario, as the slope for the granted amounts in the subsidy frame remains largely flat or slightly increases. Nonetheless, a substantial framing effect remains. In the tax frame, the amount of the premium (i.e. the amount of taxes no longer owed once a certificate is obtained) strongly increases with the cost of the car. Taking the most expensive car (€45,000) as an example, we thus observe differential treatment across frames also in this scenario. In the subsidy frame, the premium for having a certificate is €778, in the tax frame it is a much higher €1,333.
Conclusion
These results suggest a strong and economically meaningful effect of framing among policy-makers with a stake in tax and benefit systems. While the exact mechanism driving the results invites further research, the strongly divergent premiums, and hence distribution of incentives, across baseline frames raise concerns of unintended effects in the design of taxes and benefits. Especially new schemes – e.g. ‘green’ policy, reform, or regulatory expansion – may benefit from increased scrutiny in the design process. Awareness of susceptibilities to framing and its potential influence on the formulation of individual tax and benefit instruments may help to align intended fairness, incentive structures, and redistributive outcomes.
References
- Kuehnhanss Colin R.; and Bruno Heyndels, 2018. ‘All’s fair in taxation: A framing experiment with local politicians’ Journal of Economic Psychology, 65, 26-40.
- McCaffery, Edward. J.; and Jonathan Baron, 2004. ‘Framing and taxation: Evaluation of tax policies involving household composition’ Journal of Economic Psychology, 25(6), 679–705.
- Schelling, Thomas C., 1981. ‘Economic reasoning and the ethics of policy’ Public Interest, 63, 37–61.
- Traub, Stefan, 1999. Framing Effects in Taxation. Heidelberg: Physica-Verlag
Is There a Dutch Disease in Russian Regions?
The low economic diversification in Russia is commonly blamed on the abundance of energy resources. This brief summarizes the results of our research that investigates the presence of Dutch disease effects across Russian regions. We compare manufacturing subsectors with different sensitivity to the availability of natural resources across Russian regions with varying natural resource endowments. We find no evidence of differential deindustrialization across subsectors, thereby offering no support for a Dutch disease. This finding suggests that the impact of energy resources on Russian manufacturing is more likely to go through the “institutional resource curse” channel. Thereby, we argue that more efficient policies to counteract the adverse effect of resources on the Russian economy should focus on improving the institutional environment.
Russian abundance in oil and gas, and the ways it could negatively affect long-term economic performance and institutional development is not a new debate. One of the key concerns is the influence of energy resources on Russian industrial structure. Energy resources are often blamed for the low diversification of the economy, with an extensive resource sector and the dominant oil and gas export share.
In a forthcoming chapter (Le Coq, Paltseva and Volchkova), we contribute to this debate by exploring the channels through which abundance in energy resources influences the industrial structure in Russia. Our main focus is on the deindustrialization due to the expansion of the natural resource sector, the so-called ‘Dutch disease’. Specifically, we explore the impact of energy resources on the growth of manufacturing subsectors in Russian regions. Adopting a regional perspective allows us to separate the Dutch disease mechanism from the main alternative channel of the institutional ‘resource curse’. This brief summarizes our findings.
Dutch disease vs. institutional resource curse
The Dutch disease and the institutional resource curse are, perhaps, the most discussed mechanisms proposed to explain the influence of natural resources on economic performance (see e.g., earlier FREE brief by Roine and Paltseva for a review). In an economy facing a Dutch disease, a resource boom and resulting high resource prices shift production factors from manufacturing industries towards resource and non-tradable sectors. As a result, a country experiencing a resource boom would end up with a slow-growing manufacturing and an under-diversified economic structure. Since the manufacturing sector is often the main driver of economic growth, the economic development may be delayed. If, instead, an economy is suffering from the institutional ‘resource curse’, it is the interplay of weak institutions and adverse incentives created by resource rents that leads to a slow growth of manufacturing and delayed development.
Importantly, offsetting the potential negative impact of these two channels requires different policy interventions. In the case of a Dutch disease, a state can rely on direct industrial policy mechanisms targeted towards increasing the competitiveness of the manufacturing sector and isolating it from the effect of booming resource prices. For example, it can use subsidies or targeted trade policy instruments, or channel money from increased resource prices out of the economy through reserve fund investments abroad.
In the case of an institutional resource curse, on the other hand, resource rents and weak institutions may undermine and disrupt the effect of such policies. In this case, state policies should be targeted, first and foremost, towards promoting good institutions such as securing accountability and the transparency of the state, and protecting property rights. This suggests that properly understanding the channels through which resource wealth impacts the economy is necessary for choosing appropriate remedial measures.
In our analysis, we address the differential impact of energy resources in Russian regions. This regional perspective allows us to single out the Dutch disease effect, and disregard the mechanisms of a political resource curse to the extent that the relevant institutions do not differ much across regions.
Resource reallocation effect vs. spending effect
The mechanism of a Dutch disease implies two channels through which a resource boom negatively affects the manufacturing sector. First, a resource boom implies the reallocation of production factors from other sectors of economy such as manufacturing or services to the resource sector, a so-called ‘resource reallocation effect’. Second, an additional income resulting from a boom in the resource sector leads to an increase in demand for all goods and services in the economy. This increase in demand will be accommodated differently by different sectors, depending on their openness to world markets. Namely, in non-tradable sectors, isolated from international competition, there will be an increase in prices and output. This, in turn, will increase the prices on domestic factor markets. For tradable manufacturing sectors the price is determined internationally and cannot be adjusted domestically. As a result, production factors will also reallocate away from manufacturing to non-tradable sectors, a so-called “spending effect”.
The strength of either effect is likely to be different across different subsectors of manufacturing depending on the sectoral specificities. In particular, subsectors with higher economies of scale are likely to be more affected by the outflow of factors towards the resource sector through the “resource reallocation effect”. Similarly, subsectors that are more open to international trade are likely to be affected by the “spending effect”.
These observations give raise to our empirical strategy: we access differences in growth of regional manufacturing subsectors with different sensitivity to the availability of energy resources, where sensitivity reflects economies of scale, for the first mechanism, and openness to the world market, for the second mechanism. In other words, we test whether manufacturing subsectors with higher economies of scale (or openness) grow slower than subsectors with lower economies of scale (or openness) in regions rich in energy resources, as compared to the regions poor in energy resources. Observing differential deindustrialization, depending on the industry’s exposure to the tested mechanism, would offer support to the presence of a Dutch disease.
Note that the validity of our empirical strategy relies on the fact that there is high variation in resource abundancy and structure of the manufacturing sectors across Russian regions (as illustrated by Figures 1 and 2).
Figure 1. Geographical distribution of fuel extractions relative to gross regional product; 2014, percent.
Source: Authors’ calculation based on Rosstat data. Note: Figures for regions exclude contribution of autonomous okrugs where applicable.
Figure 2. Regional diversity in manufacturing structure, 2014.
Source: Rosstat.
Data and results
Our empirical investigation covers the period 2006—2014. The data on manufacturing subsector growth and regional energy resource abundancy come from Rosstat, the sensitivity measures across different manufacturing sectors are approximated based on data from Diewert and Fox (2008) (economies of scale in US manufacturing), and OECD (sectoral openness to trade).
The results of our estimation show that the differences in growth rates of manufacturing subindustries across Russian regions with varying natural resource endowments cannot be explained by the sensitivity of these subindustries to the availability of energy resources. This can be seen from Table 1, where the coefficient of interest – the one of the interaction term between the measure of sectoral sensitivity if resource abundance and regional energy resource wealth – is not significantly different from zero, no matter how we measure the sensitivity: by the returns to scale or by openness to international trade.
Table 1. Estimation of Dutch disease effect with different sensitivity measures.
| Dependent variable: average annual growth index of sectoral output | ||
| Sensitivity measure: Economies of scale | Sensitivity measure: Openness | |
| Subsector sensitivity * Size of the fuel extraction sector in the region
|
-0.0353
(0.0873) |
0.0674
(0.0954) |
| Subsector fixed effect | YES | YES |
| Region fixed effect | YES | YES |
| Observations | 1,185 | 1,185 |
| R-squared | 0.1574 | 0.1577 |
Source: Authors’ calculations.
These results hold true if we control for differences in regional taxes, labor market conditions, and other region-specific characteristics by including regional and sectoral dummy variables, if we consider alternative measures of energy resource wealth, and if we use other, non-parametric estimation methods.
In other words, our data robustly offers no support for the presence of a Dutch disease in Russian regions.
Conclusion and policy implications
Diversification is often mentioned by the Russian government, as one of the top economic policy priorities, and the need for ‘diversification’ has been used in the political debate as an argument for an active industrial policy.
However, the policy measures that are necessary to counter the effect of abundant energy resources on diversification and, more generally, on economic development may be highly dependent on the prevailing channel through which resources affect the economy. In particular, while active industrial policy may be justified as a remedy in the case of a Dutch disease, industrial policy may well be ineffective, or even harmful, in the presence of an institutional resource curse mechanism.
In our study, we find no support for the Dutch disease effect when looking at the impact of energy resources on the growth of regional manufacturing sectors. Thereby, to counterbalance the resource curse effect on the Russian economy, we argue that it may be more efficient to improve the institutional environment than to use active government policies affecting industrial structures.
References
- Diewert, W. E and Fox, K. J. (2008) ‘On the estimation of returns to scale, technical progress and monopolistic markups’, Journal of Econometrics, 145(1-2): 174-93.
- Le Coq, C., Paltseva E., and Volchkova N., forthcoming. “Regional impacts of the Russian energy sector”, in Perspectives on the Russian economy under Putin, eds. Becker and Oxenstierna, London, Routledge.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Russian economy under Putin (so far)
Russians are heading to the polling booths on March 18, but where will the economy head after Putin has been elected president again? This brief provides an overview of the economic progress Russia has made since 2000 as well as an economic scorecard of Putin’s first three tenures in the Kremlin and uses this to discuss what can be expected for the coming six years. Although significant growth has been achieved since 2000, all of this came in the first two tenures of Putin in the Kremlin on the back of increasing oil prices. In order to generate growth in his upcoming presidential term, Putin and his team will need to address the significant needs for reforms in the institutions that form the basis for modern market economies. Otherwise, Russia will continue to be hostage to the whims of the international oil market and eventually lose most of its exports and government revenues as the world moves towards a carbon free future. Perhaps this is beyond the scope of Putin as president, but not beyond the horizon of young Russians that will be casting their votes on Sunday and in future elections.
Let’s assume that Putin will be elected president again on March 18 (for once a very realistic assumption made by an economist). What will this mean for the Russian economy in the coming six years given what happened during his previous and current tenures in the Kremlin? To assess the future as well as to understand Putin’s power and popularity, this brief starts by looking back at the economic developments in Russia since Putin first became president.
Although many different factors enter the power and popularity function of Putin, economic developments have a special role in providing the budget constrain within which the president can operate. A higher income level means more resources to devote to any particular sector, project, voting group or power base. This is not unique to Russia, but sometimes forgotten in discussions about Russia, that often instead only focus on military power or control of the security apparatus and media. These are of course highly relevant dimensions to understand power and popularity in Russia, but so is economic development, particularly in the longer run.
Russia’s economy in the world
The economic greatness and progress of a country is usually assessed in terms of the size of the economy, how much growth that has been generated, and how well off the citizens are relative to the citizens of other countries. So, by our common indicator gross domestic product (GDP), has Russia become a greater and more powerful country since Putin first became president? Table 1 shows two things, the absolute level of GDP measured in USD at market exchange rates and the rank this gives a country in a sample of 192 countries in the world that the IMF collects data on (this brief is too short for a long discussion of the most relevant GDP measure, but GDP at market exchange rates makes sense when comparing the economic strength of countries in a global context, Becker 2017 provides a discussion of alternative measures as well). When Putin become president for the first time in 2000, the value of domestic production was estimated at $279 billion, which implied a 19th place in the world rankings of countries’ GDP. In 2016, almost three presidential terms of Putin later, Russia’s GDP had increased by 4½ times to $1281 billion and its ranking improved to 12th place in the world. This clearly is an impressive record by most standards. However, the Russian economy is still the smallest economy of the BRIC countries and corresponds to only 7 percent of the US economy in 2016. In other words, impressive progress by Russia but the country is (still) not a global superpower in the economic arena.
Table 1. Russia in the world (GDP in USD bn)
Source: IMF (2017)
For the average Russian, income per capita is a measure more closely connected to consumption and investment opportunities or ‘welfare’. Progress in this area is also more likely to affect how individuals assess the performance of its political leaders. Of course, progress in terms of overall GDP and GDP per capita is closely linked unless something unusual is happening to population growth. Therefore, it is not surprising that GDP per capita also increased by around 4½ times between 2000 and 2016 (Table 2). This is the first order effect of the economic development in Russia, but in addition, citizens of Russia moved up from a world income rank of 92nd to 71st. This has implications when Russian’s compare themselves with other countries and can in itself provide a boost of national pride.
It also directly affects opportunities and status for Russians visiting other countries. Being at place 71 may not be fully satisfactory to many, but we should remember that due to the rather uneven income distribution in Russia, many of the people that travel abroad are far higher up on the global income ranking than what this table indicate. Nevertheless, Russia is far behind the Western and Asian high-income countries in terms of GDP per capita. And although the picture would look less severe if purchasing power parity measures are used, the basic message is the same; Russia has still a lot of catching up to do before its (average) citizens enjoy the economic standards of high-income countries.
Table 2. Russian’s in the world (GDP/capita)
Source: IMF (2017)
The macro scorecard of Putin
So what generated the impressive 4½ times increase in income in USD terms from 2000 to 2016 and can we expect high growth during Putin’s next six years in office? The short answer to the first question is the rise in international oil prices and to the second question, we don’t know. Table 3 provides a comparison of different economic indicators for Putin’s two first terms in office compared with his current term (where GDP data ends in 2016 so the sample is cut short by a year). It is evident that the impressive growth over the full period is entirely due to the strong growth performance in the first two presidential tenures. Rather than generating growth in the most recent period, the economy has shrunk. This is explained by the evolution of international oil prices, which quadrupled in the first eight years and instead halved in the more recent period. These swings in oil prices have also been accompanied by significant shifts in foreign exchange reserves, the exchange rate, and the value of the stock market.
In Becker (2017) I discuss in more detail the importance of international oil prices in understanding the macro economic development in Russia. In particular, it is important to note that it is changes in oil prices that correlate with GDP growth and other macro variables and that the problems with predicting oil prices makes it very hard to make good predictions of Russian growth.
Table 3. A macro scorecard of Putin in office
Source: Becker (forthcoming)
Policy conclusions
To break the oil dependence and take control of the economic future of Russia, the president will need to implement serious institutional reforms that constitute the basis for a modern, well-functioning market economy in his next term. Otherwise, Russia will continue to be hostage to unpredictable swing in international oil prices and nobody—including the president, the central bank, the IMF and financial markets—will be able to predict where the Russian economy is heading in the next couple of years.
Figure 1. Reforms (still) needed
Source: World Bank (2017)
In the longer run, the prediction is much easier. With the world moving towards a green economy, the price of oil will see a structural decline that will rob Russia (and other oil exporters) of most of its export and government revenues. The reforms which basically every economist agree are needed are related to market institutions and Figure 1 provides a clear illustration of key reform areas. The progress during Putin’s years in office has been modest at best. Swedish institutions in 2016 have been added to the figure as a comparison and it is clear that the institutional gap between Russia and Sweden is significant. Of course, all countries are different, but Russian policy makers that are interested in reforming its economy are most welcome to Sweden for a discussion of what we have done to build our institutions.
References
- Becker, T. (2017). ‘Macroeconomic Challenges’, in Rosefielde, S., Kuboniwa, M., Mizobata, S. and Haba K. (eds.) The Unwinding of the Globalist Dream: EU, Russia and China, Singapore: World Scientific Publishing.
- Becker, T. (forthcoming), ‘Russia’s economy under Putin and its impact on the CIS region’, Chapter 2 in T. Becker and S. Oxenstierna (eds.) Perspectives on the Russian Economy under Putin, London: Routledge.
- IMF (2017), World Economic Outlook database, April 2017 edition available at http://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx
- World Bank (2017), Worldwide Governance Indicators (WGI), 2017 update available at http://info.worldbank.org/governance/wgi/index.aspx#home
Poland’s Road to “High Income Country” Status: Lessons Learnt – Not Only for Other Countries
In this brief we summarize and discuss results presented in a recent World Bank Report focused on Poland’s path from middle to high-income country status. In the period until 2015, Poland’s economic development distinguished itself by its stability and consistency of the implemented reform package, and its inclusive nature. Poland became classified as a high-income country after only 15 years from gaining a middle-income status. At the same time, income inequality remained stable and absolute poverty levels fell significantly. The World Bank Report offers lessons from and insights for Poland, which are discussed from the perspective of the policies implemented by the governments in the last two years.
Poland’s status in the World Bank nomenclature has recently been “upgraded” from being middle to high-income country. While this categorization is only a nominal change, it reflects the country’s economic development over the recent decades and is an important recognition of the success of a wide range of reforms implemented across a broad number of areas. Notably, Poland moved from the middle to high-income status in a period of less than 15 years.
In a book recently published by the World Bank, it is argued that the Polish experiences from the reform process can serve as valuable lessons for countries that are in the process of, or have just embarked upon major socio-economic reforms, as well as for those, who have fallen into the so-called middle-income trap and are looking for solutions to their stagnant economies. At the same time, in comparison to other established high-income countries, there are a number of insights that Poland’s policy makers ought to bear in mind in order to stay on course of the reform process and continued stable growth.
Looking at policies of the recent governments, however, one gets a strong impression that some important insights have been ignored. As rapid population aging looms over the horizon, the lack of necessary adjustments combined with the risks to stability of the political and economic environment might in the medium run have significant implications for Poland’s further development.
The big picture
The key feature of the Polish socio-economic policy approach, over the period covered by the World Bank analysis (i.e. up to 2015), was a unique consistency of a broad direction taken by subsequent administrations. This allowed the reform process to develop without major breaks or U-turns, which ensured the overall stability of the socio-economic environment and provided stable investment prospects. The World Bank highlights the key role of institutions, including rule of law, property rights, and democratic accountability of different levels of government. Basic market institutions, including the respect for rules on price and product regulations, corporate governance and market regulations, as well as foreign trade and investment, have played a crucial role. This framework allowed for continued improvement in the efficiency of resource allocation – including the allocation between sectors of the economy, as well as between and within enterprises.
Crucially, Poland prepared well and took full advantage of the integration with the European Union. The EU accession was first used as a common anchor for stability of the reform process, and after 2004, the European funds became an additional engine of growth. At the macro level, stability of the fiscal framework with limited deficits and public debt were combined with appropriate regulation and supervision of the financial sector, an independent central bank, and close links to global markets.
Shared prosperity
While the above points provided the basis for Poland’s economic development, the Report highlights another unique feature of Poland’s success, namely the degree to which the fruits of the process have been equally shared among different groups of society. The overall income inequality has remained relatively stable, with the Gini coefficient actually falling slightly between 2005 and 2014, from 0.351 to 0.343. Relative income poverty levels remained stable over this period (at about 20%), and the levels of absolute poverty fell significantly. For example, the proportion of the population living on less than $10 per day fell from 51.3% in 2005, to 29.6% in 2014. Growing incomes were primarily driven by increases in labor earnings, but employment growth – in particular among older age groups –also made a contribution. The government’s labor market policy also played a role with a rapid increase in the level of the national minimum wage (NMW), which grew by 65% in real terms between 2005 and 2015, i.e. almost twice as fast as the average wage. While there is evidence that the rapid growth in the NMW had negative effects on employment – in particular among temporary, young, and female workers, these have been relatively modest. Additionally, the tax and benefit policy has contributed to reduced inequality. It has been estimated that nearly half of the reduction in the Gini coefficient, over the period 2005–2014, resulted from reforms of the tax and benefit system (Myck and Najsztub, 2017).
It is clear that human capital was one of the cornerstones of Poland’s success in recent years. Developments on the labor market, such as a rapid productivity growth, were facilitated by a well-educated labor force, which could respond and adjust to the changing conditions and requirements. In this regard, Poland’s advantage in comparison to many other low and middle-income countries has been the relatively high level of spending on public education and healthcare, not only since the start of the economic transformation in the 1990s, but also before that. Indicators, such as the infant mortality rate, were low in Poland already in the 1980s, and have since further improved (see Figure 1). For a long time, public spending on education has been at levels comparable to those in established high-income countries (see Figure 2). Additionally, a series of reforms to the education system since 1990, have resulted in improvements in the quality and coverage of education. This, in turn, has lead to a rapid improvement of scores in language, mathematics, and science in the PISA study (Programme for International Student Assessment), in which Polish students recently outperformed those from many other OECD countries (OECD 2014). Importantly, the improvements in the education results have been found across the socio-economic spectrum, which further stresses the inclusive character of the changes that have taken place.
Figure 1. Infant mortality rate (per 1,000 live births), 1980 and 2014
Notes: Countries grouped in the following manner: red – middle-income countries; blue – new high-income countries; green – established high-income countries. Horizontal lines represent group averages. Source: World Bank (2017), Figure 5.16, based on World Development Indicators.
Figure 2. Government expenditure on education, percent of GDP, 1990
Source: World Bank (2017), Figure 5.11, see notes to Figure 1.
Insights for Poland
“As economies enter the high-income group, weakness in economic institutions such as the rule of law, property rights, and the quality of governance become increasingly important to sustain convergence.”
World Bank (2017)
While the Polish reform experience, over the period examined in the World Bank Report, offers important lessons for other countries aspiring to the high-income status, the authors point out that Poland’s continued development needs to rely on further improvements in a number of key areas. The following policy areas have been highlighted in the Report:
- Working on more inclusive political and economic institutions and enhancing the rule of law with the focus on the judiciary;
- Adjustments to fiscal policy in particular to deal with the consequences of population aging;
- Increasing the domestic level of savings to facilitate large investment needs;
- Supporting innovation through more intense competition and high quality research education;
- Improving social assistance programs and access to high quality health and education for low income groups;
- Increasing the progressivity of the tax system to support inclusive growth;
- Adjusting migration policies to bring in skills and innovative ideas and compensate for the country’s aging workforce.
“Sustaining Poland’s record of high, stable growth will require adjustments to fiscal policy (…). Government will need to create the fiscal space to deal with the increasing pressures coming from aging, the inevitable decline of EC structural funds for investment, and a more uncertain global context.”
World Bank (2017)
Lessons, insights and recent policies
While several of the Law and Justice majority governments’ policies since 2015 have been well in line with the World Bank recommendations, there have also been a number of questionable policy areas. One major concern seems to relate to the broad background of reforms of the judiciary, which have drawn significant criticism of the European Commission and other international institutions. Implications of such major changes for economic growth are uncertain but potentially very damaging.
Another long-term concern arises from the new pension age reform. From the socio-economic perspective, rapid ageing of the population is one of the main challenges facing the country. Between 2015 and 2030, the number of people aged 65+ will grow from 6.1 million to 8.6 million, i.e. by over 40%. This will put significant strains on the country’s public finances due to increasing public-pension expenditures and growing costs of health and long-term care. These pressures will only be exacerbated by the current government’s decision to lower the statutory retirement age to 60 for women and 65 for men, from the target uniform age of 67 legislated in the reform of 2012. Given the contributions-defined nature of the Polish pension system, this will result in significantly lower levels of pensions, especially among women, and a substantial drain on public finances resulting from lower levels of contributions and taxes.
The generous family benefits of the Family 500+ Program – implemented in 2016 and which cost about 1.3% of the GDP – have also been criticized on a number of grounds. They have undoubtedly changed the financial conditions of numerous families and limited the extent of child poverty. At the same time, they contribute to maintaining low levels of female labor-force participation and there is so far little indication that they have significantly changed Poland’s very low fertility rate. It seems that while the program may have positive long-term consequences resulting from reduced poverty, it is unlikely to shift the demographic dynamics.
Uncertainty also surrounds the consequences of a haphazard major education reform, which is another trademark policy of the Law and Justice party. The reform re-introduced the 8+4 system in place of the post-1999 three-level educational arrangement (6+3+3). The new system takes the number of years of general education back from 9 to 8 years, and instead extends by one year the length of secondary schooling. While the potential effects of such a change are difficult to foresee, the 8+4 system may be in particular disadvantageous to children from rural areas, who are most likely to continue their education in their rural primary schools for the two extra years.
A number of steps taken by the government since late 2015, and in particular those related to the redistributive policies implemented in the last two years, seem to be consistent with the World Bank insights. On the other hand, the approach towards the reforms of the judiciary, the general approach to the rule of law, and the reforms of education and pension regulations, quite clearly appear to ignore not only the insights, but also the lessons resulting from Poland’s own experience of the recent decades. Given the challenge of rapid aging in the Polish population, there seems to be much gained from taking them seriously if the current and future administrations want to ensure Poland’s continued inclusive growth and to secure its status as an established high-income country.
********
This policy brief draws heavily on the World Bank (2017) Report: “Lessons from Poland, Insights for Poland: A sustainable and inclusive transition to high-income status” (co-authored by Michal Myck) and the accompanying Working Paper by Myck and Najsztub (2016). Views and opinions expressed in this brief are the sole responsibility of the author and are not endorsed by the World Bank or CenEA.
References
- Myck, M., and M. Najsztub (2016) “Distributional Consequences of Tax and Benefit Policies in Poland: 2005–2014.” CenEA Microsimulation Report 02/16, Centre for Economic Analysis, Szczecin.
- OECD (Organisation for Economic Co-operation and Development) (2014) PISA 2012 Results: What Students Know and Can Do—Student Performance in Mathematics, Reading and Science (Volume I: Revised edition, February 2014). Paris: OECD Publishing.
- World Bank (2017) “Lessons from Poland, Insights for Poland: A sustainable and inclusive transition to high-income status”, The World Bank, Washington.
School Financing, Teacher Wages and Educational Outcomes in Russia
The policy proposal to increase the share of budget spent on public education implies that higher financing leads to better quality of education. This, however, is far from certain. We test and compare the effects that different levels of financial resources available to schools and relative teacher wages have on educational outcomes. Russia provides a good opportunity for testing this relationship due to its high level of regional heterogeneity. We find that increasing school financing per se does not noticeably improve educational outcomes. Only when additional financing leads to an improvement of the position of teachers in the regional wage distribution, we observe higher educational outcomes for students. We provide some tentative evidence on the possible channels of this effect.
School education is a complex and multifaceted process, and measurable educational outcomes are affected by many different factors. These may include students’ innate abilities and family resources as well as various characteristics of the school environment and teaching practices. In the literature, one of the important factors is the level of school financing provided by the government. This is also one of the key issues in the debates about the public policy in education. However, there is no consensus in the academic literature about the degree of influence of financial resources available to schools on educational outcomes.
The effect of school financing should depend on how it is spent. Since education is a human capital-intensive sector, a major part of this money is spent on teacher remuneration. Whether the size and structure of teacher pay affect the effectiveness of their work and ultimately the student outcomes is still an open question. Some studies argue that it is not absolute but that relative teacher wages matter (Loeb and Page, 2000; Britton and Propper, 2016). Hanushek et al. (2017) use cross-country data and show that the relative position of teachers in the wage distribution affects self-selection into the teaching profession in terms of skills, and that teacher skills in turn affect student outcomes.
While there are studies looking at various determinants of the quality of school education in the transition-economy context (e.g. Amini and Commander, 2012), the effect of school financial resources has not yet been studied. In Lazareva and Zakharov (2018), we exploit spatial variation in educational resources in Russia to try to answer this question. We test and compare the effects of school budget financing and relative teacher wages on educational outcomes for the period 2006–2014. We estimate these effects for two different measures of educational outcomes at different levels of school education system.
Institutional Context and Data
In Russia the system of general education covers eleven years: the first nine years are compulsory for all children, after that one can continue to high school for two more years or move into vocational education system. The school system is predominantly financed by the government and the share of private schools is very low.
In the 1990s and early 2000s, the system of general education was heavily underfinanced. Teacher remuneration was quite low compared to the average wage in the economy, and a job as a schoolteacher was not very attractive. In the mid-2000s, with the fast economic growth, the Russian government made an effort to increase school financing and to raise teacher wages. Importantly, schools are financed at the regional level, through the budgets of the regions, which results in significant cross-regional variation.
There are 85 administrative regions currently in Russia and they differ a lot in terms of economic conditions, regional budget income and expenditures. We use data on regional-level budget expenditures on general education from the Russian Treasury statistics (http://www.roskazna.ru/). In order to account for inflation and cross-regional differences in prices, we normalize the per-student amount of school budget financing by the minimum regional cost of living (as estimated by the Russian statistical office) in a particular year.
As our data show, the amount of budget financing of the general education system has been growing in real terms during 2006–2013. The average regional budget financing per student (adjusted for the differences in the cost of living across regions and years) has increased by 40% during this period. A large part of this growth occurred in 2012. In that year a presidential decree was adopted which required that teachers’ wages should be raised to the level of the average regional wage. Regions had to allocate more money for teacher wages during the following years in order to meet this target. Even after adjusting for the regional cost of living, the level of school financing differs a lot across regions throughout the period.
The amount of school financing is also significantly correlated with the gross regional product per capita, i.e. with the level of economic development of the region. We observe the largest gap in school financial resources between the small group of the richest regions (Moscow, Sankt Petersburg and resource extracting regions) and the remaining regions. Such persistent inequality in school resources may lead to unequal access to high quality education across Russian regions. This inequality is exacerbated by the fact that in less economically developed regions families have fewer resources to compensate for the underfinancing of public schools.
The structure of school expenditures in the regional budgets shows that the major part of financing (about 80 percent) is spent on remuneration of teachers and school administration. Hence, the effect of regional school expenditures on student outcomes should go through teacher wages. We use data on average regional teacher wages from Rosstat (Russian Federal State Statistics Service) and the Russian Ministry of Education. As we argued previously, it is important to test the effect of relative teacher salary. Our data show that the average regional school wage relative to the average regional wage has grown during the observation period, in particular in 2008–2009 and, at a higher rate, in 2012–2013 (due to the presidential decree mentioned above). Again, there is a significant variation among regions, which is observed throughout the period.
Empirical Results
In order to test the effect of school resources and teacher wages on educational outcomes, we use two measures of educational outcomes. First, we use the average regional score on Unified State Examination (USE). It was introduced in all Russian regions starting from 2009 and students graduating from grade 11 take the test. This is a high stakes examination as the result of this exam is accepted as entrance exams at universities throughout the country. USE in mathematics and Russian language are compulsory for all graduates of grade 11. Therefore, we will use the scores in these subjects. Note that USE scores measure educational outcomes of those students that stayed in high school after grade 9 – this is about 60 percent of the age cohort.
An alternative measure of educational outcomes is the data from PISA international educational assessment (PISA – Programme for International Student Assessment run by OECD, http://www.oecd.org/pisa/). Russia participates in PISA since 2003. We use data from waves 2006, 2009, 2012, and 2015. Students take this test at the age of 15, which means that the majority of this age cohort is in grade 9.
In our regression analysis on regional data, we additionally control for a number of regional characteristics that may be correlated with school financing or teacher wages, such as population size, share of urban population, regional poverty (share of population below the poverty line), within-region income inequality (decile coefficient), and gross regional income per capita (also adjusted for the cost of living). Since we have panel data, we use a panel fixed effects estimation method, which accounts for all unobserved time-invariant regional heterogeneity.
Our results show that the level of per-student school financing does not significantly affect USE results. At the same time, we find a significant positive effect of relative teacher wages on USE results both in math and Russian language with the lag of one to two years. We find the same results on PISA data: individual student scores in math, reading and science are significantly positively affected by the level of the relative regional teacher wages. Our results hold in instrumental variable estimation, which we conduct in order to account for potential endogeneity problems.
What are the potential channels through which relative teacher wage may affect student results? One possible channel is self-selection of teachers. When teacher wages increase relative to other jobs, being a teacher become more attractive for higher skilled individuals. Higher skilled teachers help students to achieve better educational results. We cannot directly test this channel, as we do not have data on teacher turnover in Russian schools. Besides, we observe a positive effect of relative teacher wages on student scores with a lag of just one-two years. This seems to be a too short time period for teacher turnover to have a significant effect.
Another potential channel of the observed effect is an improvement in teacher motivation or teacher morale. We can only provide some suggestive evidence for this effect. In the early and mid-2000s, when teacher pay was quite low, a significant share of teachers were considering quitting their jobs or switching to another occupation. As teacher survey data show, after the significant increase in teacher pay in 2008–2012 this share declined and teacher motivation and job satisfaction improved. Additional evidence in support of this hypothesis comes from the school-level data in the PISA 2012 survey. We estimate the effect of relative regional school wage on teacher morale (as evaluated by a school head) and find a positive and statistically significant relationship.
Conclusion
We find that increasing school financing from the regional budgets per se does not noticeably improve educational results. Only when additional financing leads to an improvement of the position of teachers in the regional wage distribution, we observe higher educational outcomes for students. The potentially interesting future direction of research is to study how not just the relative size, but also the structure of teacher wages (i.e. elements of incentive pay introduced in Russian schools) affects educational outcomes.
References
- Amini, Chiara & Commander, Simon, 2012.”Educational Scores: How does Russia Fare?” Journal of Comparative Economics, Elsevier, vol. 40(3), pages 508-527.
- Britton, Jack and Carol Propper, 2016, Teacher pay and school productivity: Exploiting wage regulation, Journal of Public Economics 133 (2016) 75–89.
- Hanushek, Eric A., Marc Piopiunik, Simon Wiederhold, 2017, The Value of Smarter Teachers: International Evidence on Teacher Cognitive Skills and Student Performance, NBER Working Paper w20727.
- Lazareva, O. and A. Zakharov, 2018, School Financing, Teacher Wages and Educational Outcomes: Evidence from the Russian School System.
- Loeb, Susanna and Marianne E. Page, 2000, Examining the Link between Teacher Wages and Student Outcomes: The Importance of Alternative Labor Market Opportunities and Non-Pecuniary Variation, the Review of Economics and Statistics 2000 82:3, 393-408.
Stylized Facts from 25 Years of Growth in Transition
This brief summarizes the growth experience of transition countries 25 years after the dissolution of the Soviet Union. We divide our sample into two main groups: the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries (ex Baltics) that emerge from the Soviet Union (FSU12). The growth experiences of these two groups have been distinctly different. The magnitude of the initial transition decline in output was much more severe in the FSU12 group. Despite growing almost 2 percentage points faster than the average EU10 for the following fifteen years, the FSU12 group is still further behind the EU10 group than they were at the beginning of transition. This illustrates how hard it is for countries to recover from large negative income shocks and thus the importance for countries to avoid such negative events. However, there are no signs of transition countries being stuck in a low or middle-income trap or that natural resource wealth leads to lower growth during this period.
2017 marked the 25-years anniversary after the dissolution of the Soviet Union and the beginning of the transition for the economies in the region. In a recent paper, we explore the growth experience of transition countries over these 25 years (Becker and Olofsgård, 2017). The paper has four main parts: an overview of the transition literature focusing on growth; a part that provides a detailed description of growth in transition; an analytical section that investigate if we can explain growth in transition countries with a standard growth model; and finally an exploration of whether institutional and other variables that have been highlighted in the transition literature (but are excluded from the basic growth model) are correlated with growth in transition countries. This brief summarizes the descriptive part of the paper, while the more analytical sections will be the topic of future briefs.
For most of the paper, we divide our sample into two main groups; the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries that emerged from the Soviet Union (FSU12). In addition, we include three transition countries that are not part of either group (Croatia, Albania and Macedonia – Other3) and we also divide the FSU12 group into the four countries that export significant amounts of fuel (FSUF) and the eight countries that do not (FSUNF). There are of course remaining differences within these groups, but this aggregate analysis allows us to see certain patterns in the transition process more clearly.
Initial output collapses
The focus in economics is often on how to generate higher growth and not about protecting against significant drops in output. There are some exceptions, including Becker and Mauro (2006) and Cerra and Saxena (2007), where the focus is on output losses and how countries recover after crises. For transition countries, a very important feature of the economic development process is exactly the initial drop in income and the time it has taken countries to recover from the initial phase of transition. Table 1 shows how much income fell in the different country groups and the time it took to get back to the pre-transition income level.
Table 1. Output drops and recoveries
Source: Becker and Olofsgård (2017)
The initial collapse in the FSU12 group was enormous, with income cut in half. The EU10 countries also had massive output losses, but “only” lost a quarter of their income on average. This took over a decade to recover from, while the path back to pre-transition income levels in the average FSU12 country was almost twice as long. There have been many papers written on the economic chaos that was part of the initial transition process, and explanations for this decline has been attributed to, e.g., misleading data, lack of functioning markets, shock therapy and poor economic and legal institutions in general. All of these factors have likely played important roles in the process, but regardless of the explanation, this was a very unfavorable time in terms of economic outcomes for hundreds of millions of people in these countries. Avoiding such costly drops in output should be a top priority for economic policy makers in any country at all times, not just in transition.
From collapse to growth
In most transition countries, the initial phase of decline in transition lasted several years, but eventually the negative growth rates turned positive (Figure 1). Again, we can see that the EU10 group had fewer years of declining incomes with growth resuming in 1993, while for the FSU12 group, growth in transition only started in 1996/7.
Figure 1. Bust-Boom countries
Source: Becker and Olofsgård (2017)
What is less visible in Figure 1 due to the wide scale needed to capture the initial output drops is that the FSU12 groups has shown significantly higher growth than the EU10 group in the last 15 years. Over the more recent period, the average FSU12 country has grown by close to 6 percent, while growth for the EU10 has been around 4 percent per annum (Table 2).
Table 2. Real GDP/cap growth
Source: Becker and Olofsgård (2017)
The faster growth in FSU12 countries is particularly pronounced among the fuel exporters, which were growing by one and a half percentage point faster than the non-fuel exporters between 2000 and 2015. But the table also shows that the very negative growth experience during the first ten years of transition is hard to erase and the EU10 countries have grown faster over the full 25-year period compared to the FSU12 countries. In terms of understanding the growth experience of the different country groups and time periods, it is clear that the sharp increase in international oil prices during the last 15 years of the period generated high growth in the fuel exporting countries in the FSU12 group. Interestingly though, also the non-fuel exporters grew faster than the EU10 in this time period. This is likely linked to spillovers from Russia to the other countries in the region, but could also be related to some recovering after the massive initial declines in output. Such macro and external factors are not always stressed in discussions of growth in transition countries, which more often focus on the pace of reforms or strength of institutions, but seem to be relevant at this aggregate level when comparing the initial and later phases of transition.
Relative incomes in transition countries
Growth or the lack thereof is of importance in determining income levels, which is what we generally think is what influences welfare. The question is then what the growth processes we have analyzed imply for income levels in transition countries, and in particular, how the income levels in these countries compare with other countries.
Figure 2. Income relative to 15 old EU countries
Source: Becker and Olofsgård (2017)
The short story here is that the relative ranking of the different groups is largely unchanged from the start of transition until the end of 2015. The group of countries that eventually joined the EU has the highest income level while the non-fuel exporting FSU countries have the lowest. However, the leading group still only has around 60 percent of the income of the average “old” EU country while the average FSU12 country has half of that or around 30 percent of the income of the old EU countries. This puts the relatively high growth rates of the FSU12 group over the last 15 years in perspective; the road to reach old EU level incomes is long indeed. Also, within the FSU group, it is clear that there is a sharp dividing line between the fuel exporters and the rest. This is in stark contrast to the notion of a “natural resource curse” that is often blamed for poor growth in oil and mineral rich countries.
Growth traps in transition?
One issue that comes up with regards to both low and middle-income countries is if they are stuck at a certain level in the relative income rankings of the world. This is referred to as the low or middle-income trap and the question is if there are signs of transition countries being stuck in such traps.
Figure 3. Moving up the income ladder
Source: Becker and Olofsgård (2017)
Figure 3 shows how transition countries are classified into the World Banks income groups low income (1 in the Figures scale), lower middle income (2), higher middle income (3) and high income (4) groups.
It is clear that the FUS 12 group of countries was sliding down the scale initially, but since the beginning of the 2000’s, all of the transition countries have been climbing up the World Bank income ranking scale without any apparent signs of a low or middle-income trap.
Policy conclusions
There are of course country differences along all the dimensions discussed in this brief but grouping the transition countries together provides some interesting general observations of growth in transition. First of all, it is clear that it is very hard to fully recover from large drops in income. Even with the help of some extra growth following a crisis, it seems to take a long time for most countries to make up for lost ground. This suggests that policy makers in transition as well as other countries need to take measures to hedge the really bad outcomes and not only focus on how to generate an extra one percent of growth.
The other observation is that at the aggregate level, external factors and more mechanical macro boom-bust-boom type of growth factors may dominate what we generally think of as the long-run determinants of growth (such as institutions, education, and micro level reforms to make markets work better) over very long time spans. This does not mean that the focus on the more fundamental growth drivers should diminish, but it is important that reforms in these areas are complemented with a macroeconomic framework that reduces the risks of costly output collapses.
Finally, it is clear that the incomes generated by natural resources can produce growth at the macro level and that there is little evidence that transition countries should be stuck at any particular level in the global income rankings. Go transition countries!
References
- Becker, T, and A. Olofsgård (2017), “From abnormal to normal—Two tales of growth from 25 years of transition”, SITE Working paper 43, September.
- Becker, T., and P. Mauro, (2006). “Output Drops and the Shocks That Matter”. IMF Working Papers 06/172.
- Cerra, V., and S.C. Saxena (2008). ”Growth Dynamics: The Myth of Economic Recovery”. American Economic Review, 98(1), 439–457.
Financial Stress and Economic Contraction in Belarus
This brief summarizes the results of an analysis of financial stress episodes in the Belarusian economy. Based on a principal component analysis, I construct a financial stress index for Belarus (BFSI) that incorporates distinctive indicators for the banking sector, exchange market and external debt risks covering the period January 2004 to September 2016. Next, I identify episodes of financial turmoil in Belarus using the BFSI and assess the consequences for the real economy. Finally, I investigate the long-run relationship between financial stress and economic activity in Belarus.
It has become conventional wisdom that a well developed and smoothly operating financial system is critically important for economic growth (see Levine, 2005). It helps in overcoming frictions in the real sector, influencing economic agents’ savings and investment behavior, and therefore enabling the real economy to prosper (Beck, 2014).
In contrast, financial stress to financial system can be defined as the force that influences economic agents through uncertainty and changing expectations of loss in financial markets and financial institutions. It arises from financial shocks such as banking or currency crises (Iling & Ying, 2006). Consequently, the current stress level in the financial system can be quantified by combining a number of key individual stress measures into a single composite indicator – the Financial Stress Index (FSI).
In practice, such indices are already widely used, and allow regulators to maintain financial stability and help investors to assess the overall riskiness of investments in financial instruments of the country. The FSI for Belarus (BFSI) has been estimated for the first time and can be used as an early warning signal of systematic risk in the Belarusian financial sector (Mazol, 2017). In the financial context, systematic risk captures the risk of a cascading failure in the financial sector, caused by inter-linkages within the financial system, resulting in a severe economic downturn.
Construction of the FSI for Belarus
Based on a principal component analysis, the calculated index incorporates distinctive indicators for banking-sector risk estimated by the Banking Sector Fragility Index (BSFI), currency risk assessed by the Exchange Market Pressure Index (EMPI), and the external debt risk proxied by the growth of total external debt.
The BFSI reflects the probability of a crisis (episode of financial stress) – the smaller is the indicator, the better. The stability regime ends, when the BFSI exceeds a predetermined threshold. In particular, episodes of financial stress are determined as the periods when the BFSI is more than one standard deviation above its trend, which is captured by the Hodrick–Prescott filter. The identified episodes of financial stress show that one or more of the BFSI’s subcomponents (banking, external debt or foreign exchange) has changed abruptly.
Episodes of financial stress
During 2004—2016, two episodes of financial stress were detected in the economy of Belarus (see Figure 1). In both cases, there were large devaluations of the Belarusian currency, caused by the need to adjust its real exchange rate.
Figure 1. Episodes of financial stress in Belarus 2004—2016
Source: Author’s own calculations.
The first episode began in December 2008 and ended in May 2009. This episode was mainly a consequence of the global economic and financial crisis that caused a deep recession in Russia, reducing Russia’s demand for import of products from Belarus, further loss of competitiveness due to the sharp depreciation of the Russian ruble and deterioration of the current account balance and the depletion of foreign exchange reserves.
The second episode of financial stress began in December 2011 and ended in May 2012. It was caused by the renewed unbalanced macroeconomic policy aimed primarily at boosting aggregate demand by increasing government spending and accelerating economic growth; and monetary policy aimed at targeting the exchange rate. All this has led to problems in the foreign exchange market that eventually encompassed issues in the banking sector and caused a sharp reduction in foreign exchange reserves.
Financial stress and recessions
Figure 2 shows the contribution of each of the sub-indices to the increase in the BFSI.
Figure 2. The dynamics of components of BFSI during 2004-2016
Source: Author’s own calculations.
The main feature of the graph is that the currency stress is the prevailing factor in the two identified stress episodes. However, while the origins of the second episode were in the currency market, by early 2012, the stress had become much more broad based – the banking stress and the external debt stress contributed significantly to BFSI growth at the same time.
In contrast, since the beginning of 2016 until the end of the observation period, an upward movement in the BSF sub-index was detected indicating that the National Bank of Belarus (NBB) had to be worried about instability in the banking sector, which was mostly related to a loans crisis of state-owned enterprises (SOEs). A loans crisis of SOEs in Belarus means the inability of these enterprises to repay their debts and the need for budget coverage of their obligations and investments in fixed capital (see Figure 3). This happened due to a significantly higher cost of capital for SOEs after the second episode of the financial stress had begun.
Figure 3. Sources of investment financing and overdue loans of Belarusian enterprises
Source: Belstat.
Correspondingly, in the late 2016, the above problems have amplified the external debt stress (lack of external financing) in the economy of Belarus (see Figure 2).
Next, the results showed that financial stress negatively influences economic activity proxied by the index of composite leading indicators (CLI). In particular, an increase by one standard deviation (s.d.) in the BFSI leads to the contraction in the CLI index by 0.5 s.d. (see Mazol, 2017).
Moreover, financial stress has caused significant real output losses. The first episode of financial stress has resulted in the contraction of GDP by 5.9%. Second one has pushed Belarusian economy into a severe recession, which lasted 52 months with cumulative output losses about 12.9% of GDP (see Table 1).
Table 1. Descriptive statistics on episodes of financial stress and recessions in Belarus
| Episodes of financial stress | Duration (months) | Output lossa
(% of GDP) |
Number of months after start of financial stress to recession | |
| Financial
stress |
Recessionb | |||
| December 2008 –
May 2009 |
6 | 12 | -5.85 | 0 |
| December 2011 –
May 2012 |
6 | 52 | -12.89 | 6 |
Note: a) output loss is measured as GDP below trend during recession; b) a recession is occurred if there was a serious contraction in the economic activity (CLI) during six month or more. Source: Author’s own calculations.
Finally, a great reliance of Belarusian economy on external financing is associated with longer and sharper downturn in the aftermath of second episode of financial stress (see Figure 2).
Conclusion
The study has three policy implications. First, the BFSI may be considered as a comprehensive indicator that successfully determines the main episodes of financial stress in Belarusian economy and can be used to study their macroeconomic consequences.
Second, the BFSI identifies the most salient stress factors for Belarus, thereby showing which financial sectors need to be monitored carefully by national regulator to avoid a critical buildup of risks in the financial system.
Third, efforts to confine financial stress will support the country’s economic activity in the long run, which may include intervention in the foreign exchange market and build up of investor confidence in the economy.
References
- Beck, Thorsten, 2014. “Finance, growth, and stability: lessons from the crisis”. Journal of Financial Stability, 10, 1-6.
- Illing, Mark; and Ying Liu, 2006. “Measuring financial stress in a developed country: an application to Canada”. Journal of Financial Stability, 2, 243-265.
- Levine, Ross, 2005. “Finance and growth: theory and evidence”. In: Aghion, P., Durlauf,S.N. (Eds.), Handbook of Economic Growth, vol. 1A. Elsevier, Amsterdam, 865-934.
- Mazol, Aleh, 2017. “The influence of financial stress on economic activity and monetary policy in Belarus”. BEROC Working Paper Series, WP no. 40, 33 p.