Author: Admin
Global Inequality – What Do We Mean and What Do We Know?
Concerns about global economic inequality have become central in today’s policy debate. This brief summarizes what is known about the development of inequality globally, emphasizing the difference between the developments within countries and between countries. In the former sense, inequality has risen in most countries in the world since the 1980s, but in the latter sense inequality, has (most probably) dropped. To ensure future progress in terms of continued decreasing global inequality, fighting increasing inequality within countries is likely to be central.
In recent years, the distribution of income and wealth has emerged as one of the most widely discussed issues in societies everywhere. US President Barack Obama has called rising income inequality the “defining challenge of our time”, the topic has been on the agenda at meetings of the World Economic Forum in Davos, and studies by the IMF and the OECD (e.g., OECD, 2014, and IMF, 2014) have associated income inequality with lower economic growth. Thomas Piketty’s best-selling book “Capital in the Twenty-First Century” (2014) has placed the topic center-stage well outside academic and expert circles. At the same time, some have argued that all the talk about increasing inequality is in fact wrong and that it misses what they perceive as the more important story, namely, the decreasing global inequality. So, which is it, and what conclusions can be drawn?
Different Ways of Viewing the Facts About Global Inequality
When people talk about global income inequality there are a number of things that could be referred to. First, one might think of the inequality within countries across the world. From this perspective, the question in need of an answer would be: “How has inequality within individual countries changed globally in recent decades?” The short answer is that it has increased in most places. This is certainly the case in most of the developed world since the 1980s, while in emerging markets and developing countries (EMDCs) there are greater differences across time and regions. Looking at disposable incomes at the household level (the most commonly used measure in international comparisons) most countries in Asia and Eastern Europe have seen marked increases of inequality, while the trend seems to have been the opposite in Latin America and in large parts of Africa. In level terms, the development has been one of convergence since, on average, the countries in Eastern Europe and Asia started at much lower levels than those in Latin America and Africa. The development has resulted in that inequality levels are today on average at similar levels, with a Gini coefficient of between 0.4 and 0.45, in Africa, Asia, and Latin America (see figure 1 below and IMF, 2015) The same is true for the average across OECD countries where inequality has increased the most in percentage terms in countries starting at low levels, with the US being an exception in that inequality has increased even though the level has always been at the higher end among developed economies (e.g., OECD, 2015). The European average is today around 0.3 while the household disposable income Gini in the US is just below 0.4.
Figure 1. Change in the net Gini Index, 1990-2012
Source: IMF, 2015.
Looking at other income inequality measures, such as top income shares, the picture is similar: inequality has increased in most countries for which we have data since the 1980s. While it is important to recognize that top income shares are a very different measure of inequality, it has been shown that there is a close relationship between top income shares and the Gini coefficient in terms of capturing both level differences across countries and trends in the development (e.g., Leigh, 2007 and Morelli, Smeeding and Thompson, 2015). This together with one of the main strengths of the top income measure, namely, the length of the time series, allows us to put the recent developments in a historical perspective.
Figure 2 shows the income share of the top decile group for a number of mainly developed countries over the 20th century, illustrating the surprisingly common trends over the past 100 years (but also important level differences). On average, top shares (driven mainly by what happened in the top 1 percent) dropped from the beginning of the century until about 1980 after which it has risen in a fanning-out fashion. The point of the figure is clearly not to illustrate any individual country but rather to illustrate the overall long-run trend. For details of the historical development of income as well as wealth distribution, see Roine and Waldenström (2015).
Figure 2. Top 10 percent income share over the 20th century
Source: World top income database (WTID).
While the overall picture of rising inequality in most countries over the past decades is pretty clear, the development between countries is less so. There are two main reasons for this. First, it depends on what is considered the unit of observation and how these units are weighted. Second, it depends on what one assumes about the vast gaps in data availability, in particular in EMDCs (see e.g., Lakner and Milanovic, 2013, for more details).
As explained by for example Milanovic (2012) there are essentially three different ways in which one might think about the global distribution of income: 1) Treat every country as one observation and use a country’s GDP per capita as the measure of income; 2) do the same as in 1) but give different weight to each country according to its population; 3) Treat individuals (or households) as the unit of observation regardless of where people live. In all three cases it is possible to line up all observations from the poorest to the richest (and, hence, also to calculate a Gini coefficient). In the first way of looking at the world, we treat everyone in each country as being represented by the country’s average income and we also give the same weight to Luxemburg and India. In the second case, we recognize that more people live in India and weight it accordingly but we still, by construction, force everyone in each country to have the country average, thus ignoring within country inequality. Only in the last approach do we actually take into account both relative population size and differences in development within countries. This clearly seems the most satisfactory way to look at what has happened, but it is also the most demanding in terms of data.
In terms of the first two approaches, inequality in the world has fallen in the past decades. This is especially clear when weighting countries by population size. Rapid growth in China and India has caused average incomes in the world’s most populous and initially poor countries to increase faster than the global average, implying a reduction in global inequality. Some may think that this is not surprising and only to be expected since these countries start at such low levels, but in fact, this development marks the reversal of a 200-year trend toward increasing global inequality. Even “catch-up growth” is certainly not to be taken for granted.
Now the real question is this: What has happened to the global income distribution if we take into account the recent increasing inequality within many countries, including China and India? The answer turns out to complicated and uncertain (see Lakner and Milanovic, 2013 for details) but in the end most of the evidence points to decreasing global inequality in this sense too. As François Bourguignon puts it in a recent article in the Foreign Affairs: “…the increase in national inequality has been too small to cancel out the decline in inequality among countries” (Bourguignon, 2016, p. 14).
To understand both of these counteracting forces it is illustrative to look at real income growth across the global income distribution. Figure 3 below is taken from a presentation by Branko Milanovic, organized by SITE in 2014 (and available online here). It shows the real income growth for different percentile groups in the global distribution over the period 1988-2008. Moving from left to right the figure shows positive but modest growth for the very poorest individuals in the world, and much higher growth for the groups just above, with rates increasing toward the middle of the global distribution. In the range of about 5 dollars/day (in PPP adjusted terms) growth has been the highest. By developed-country standards, these people are still very poor, but globally they are truly the “middle class” in the sense that they make up the middle of the global income distribution. Moving further right we see a sharp drop in real income growth at a level around the 80th percentile. This part of the distribution is mainly populated by the lower middle classes of the developed world, and here income growth has been essentially zero over the past decades. Moving further right we again see a sharp increase in real income growth illustrating the large gains going to individuals in the top of the global income distribution.
Figure 3 summarizes much of what has happened: the left part showing the rapid growth of income among most of the world’s relatively poor, while the right shows the increasing inequality in the developed world, with the top of the distribution gaining the most.
Figure 3. Real income growth at various percentiles of the global income distribution, 1988-2008 (in 2005 PPPs).
Source: Lakner and Milanovic (2013).
Why This Matters and What Should Be Done About Global Inequality?
The forces that explain what has happened are of course complex and differ over time and across countries but one thing seems clear, the growth of real incomes in developing countries as well as the relative decline of incomes in the lower end of the income distribution in developed countries have at least in parts been shaped by the same intertwined processes of globalization and technological development. Overall, these processes are powerful positive developments, but at the same time it is easy to see how those who perceive themselves as losers in these developments may try to resist them using their political voice. It is important to remember that globalization is the result of a combination of technology and political decisions, and consequently not an inevitable process. After all, the globalization backlash in the period 1914-1945 did not happen because the technological feasibility of the process suddenly disappeared.
The appropriate government responses are of course also likely to be different across countries, but here there are also some common factors that stand out. In the developing world, the most challenging aspects will have to do with maintaining state capacity and the ability to tax increasingly mobile tax bases. In many developing countries taxation will also be key, but here the challenge is more about creating a capable and accountable state in the first place. As succinctly and, I think, correctly put by Nancy Birdsall in a review of Thomas Piketty’s “Capital in the Twenty-First Century”: “(I)n the developing world, the challenge is not, at least not yet, the one Piketty outlines — that an inherent tendency of capitalism is to generate dangerous inequality that if left unchecked will undermine the democratic social state itself. The challenge is the other way around: to build a capable state in the first place, on the foundation of effective institutions that are democratically accountable to their citizens.”
References
- Atkinson, Anthony B. 2015. “Inequality – What can be done?” Harvard University Press.
- Birdsall, Nancy. 2014. “Thomas Piketty‘s Capital and the developing world”
- Ethics & International Affairs / Volume 28 / Issue 04 / Winter 2014, pp 523-538.
- Bourguignon, François, and Christian Morrison. 2002. “Inequality among World Citizens: 1820-1992”, The American Economic Review, Vol. 92, No. 4. (Sep., 2002), pp. 727-744.
- Bourguignon, François. 2016. “Inequality and Globalization. How the rich get richer as the poor catch up”, Foreign Affairs, Volume 95, Number 1, pp. 11-16
- Lakner, Christoph, and Branko Milanovic. 2013. “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession.” WB Policy Research Working Paper 6719, World Bank, Washington.
- Leigh, Andrew. 2007. “How closely do top income shares track other measures of inequality?”, The Economic Journal, 117 (November), 589–603.
- OECD (2015), “Growth and income inequality: trends and policy implications”, OECD Economics Department Policy Notes, No. 26 April 2015.
- OECD. 2011. Divided We Stand: Why Inequality Keeps Rising. Paris: OECD Publishing.
- OECD. 2012. “Reducing Income Inequality While Boosting Economic Growth: Can It Be Done?” In Economic Policy Reforms: Going for Growth. Paris: OECD Publishing.
- Ostry, Jonathan David, Andrew Berg, and Charalambos G. Tsangarides. 2014. “Redistribution, Inequality, and Growth”, IMF SDN, February 17, 2014
- Milanovic, B. 2013. “Global Income Inequality by the Numbers: in History and Now.” Global Policy 4 (2): 198–208.
- Morelli, Salvatore, Smeeding, Timothy, and Jeffrey Thompson. 2015. “Post-1970 Trends in Within-Country Inequality and Poverty: Rich and Middle Income Countries”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
- Piketty, Thomas. 2014. “Capital in the Twenty-first Century”. Cambridge, Massachusetts: Harvard University Press.
- Pritchett, Lant. “Divergence, Big Time.” Journal of Economic Perspectives, Summer 1997, 11(3), pp. 3-17.
- Roine, Jesper, and Daniel Waldenström. 2015. “Long-Run Trends in the Distribution of Income and Wealth”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
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.
Is Local Monetary Policy Less Effective When Firms Have Access to Foreign Capital?
Central banks affect growth in part by raising or lowering the cost of investment through their influence over local interest rates. We examine whether the ability of local firms to raise money abroad reduces the influence of local monetary policy authorities. Surprisingly, it does not. In fact, we find that firms that are able to raise equity capital from foreign investors are more responsive, not less, to local monetary policy shocks than those that raise capital only in the domestic market. These findings suggest that foreign investors confer an efficiency effect, improving the sensitivity of stock prices to local monetary policy shocks.
One means by which central banks affect economic growth is by influencing interest rates that impact the cost of financing for firms. For example, when a central bank lowers interest rates, those lower rates make new investment cheaper and more profitable. That encourages companies to invest more. Profits rise, firms hire more and we see growth in the economy as a whole.
When firms are able to raise money abroad, they are no longer as dependent on the local economy for financing. This potentially causes problems for central banks and other local monetary policy authorities who wish to influence the local economy by controlling interest rates.
This brief summarizes the results of Francis, Hunter and Kelly (2016), where we examine the extent to which monetary policy authorities’ influence differs across firms that are able to access foreign capital (also called “investable stocks”) and those that are largely dependent on the local market (also called “non-investable stocks”). Contrary to expectations, the evidence shows that firms that are able to raise foreign capital by being open to foreign equity investment are actually more sensitive to local monetary policy shocks than those that are not.
The perks and perils of financial liberalization
Over the last 30 years, the authorities in several less developed countries liberalized their domestic financial markets by allowing foreign ownership of local stocks. There are tremendous benefits for the local firms that became ‘investable’ as these countries liberalized, relative to firms that remained dependent solely on domestic stock markets. These include, inter alia, (1) being able to raise large tranches of foreign capital at lower rates than available in the domestic market, which reduces their financing constraints and increases their ability to invest, (2) substantial improvement in the liquidity of their stocks, (3) improvements in corporate governance and reporting (see Reese and Weisbach, 2002), and (4) greater efficiency with which their stocks incorporate value-relevant information.
Despite these benefits, there is widespread concern that liberalization comes with several problems. First, foreign capital flow (“hot money”) can cause excess volatility in local stock markets and exchange rates when foreign investors rapidly repatriate their funds. Second, local firms may become sensitive to foreign monetary policy shocks, and those foreign monetary shocks may be contrary to what is needed in the local economy. Third, and perhaps chief among the problems, is that if a large segment of domestic firms is able to raise capital abroad, then local monetary authorities may lose their ability to influence the domestic economy through their control of local policy interest rates. We examine this last concern in this policy brief below.
What does the research tell us?
One of the big challenges when measuring the impact of changes in monetary policy on an economy is the fact that the effects of investment started or stalled by changes in monetary policy may take months, or even years, to play out. The long time frame makes it very difficult to tell whether changes in monetary policy affect the macro economy. To solve this problem we follow in the footsteps of the former Chair of the U.S. Federal Reserve, Ben Bernanke (see Bernanke and Blinder, 1992, and Bernanke and Kuttner, 2005) and examine the impact of monetary policy shocks on stock returns. We do this because stock prices reflect anticipated changes in the economy and they are one of several channels through which monetary policy actions are transmitted to the real economy. That is, if local stock prices respond to monetary policy changes, it is likely the local economy will respond as well.
Because stock prices move in anticipation of future improvements in the economy, it is very important that we measure monetary policy surprises (also referred to as shocks) and not merely observed changes in monetary policy. To do this we model expectations about local monetary policy as a function of changes in oil price, changes in the U.S. Fed-funds rate (a proxy for changes in U.S. monetary policy), local industrial production growth, inflation rate and exchange rate changes. Details are described in the companion paper to this brief, Francis, Hunter and Kelly (2016).
We examine the impact of local monetary policy shocks on local stock returns. We find that for 17 of the 24 developing markets in our sample a one-standard-deviation surprise increase in local monetary policy interest rates results in an immediate and statistically significant 1.06% decline in the country’s overall stock market index. Interestingly, the unresponsiveness of the remaining seven stock markets to local monetary policy is not entirely due to the dominance of foreign (U.S.) monetary policy. In only four of the seven markets is foreign monetary policy simultaneously significant.
As noted above, one possible concern is that local monetary policy influences the investment and financing decisions of only non-investable firms. However, we find that firms that have access to foreign equity capital are at least as sensitive to local monetary policy shocks as are firms that are closed to foreign equity investment. In about 30 percent of our sample, Chile, Mexico, Venezuela, Jordan and Russia, firms that are open to foreign investment are even more sensitive than the ones that are closed. This evidence is consistent with the hypothesis that foreign investor participation in investable stocks improves the informational efficiency of investable firms’ stock prices, making them more sensitive to local monetary policy shocks. We call this an “efficiency” effect. This is counter to the predictions of the “integration” effect, whereby local stocks that are accessible to foreign investors are more responsive to foreign, not local, monetary policy shocks.
As an example, consider the impact of local monetary policy shocks on the returns of Brazilian stocks that are open and closed to foreigners, as depicted in Figure 1. In both panels the stock market is subjected to a one- standard-deviation surprise tightening of monetary policy, which is equivalent to 0.52% higher policy interest rates. In the top panel, the response is a statistically significant decline of 1.9% in the stock prices of firms that are open to foreign investment. In the bottom panel, the same monetary policy surprise translates into a statistically insignificant 0.9% lower prices for stocks that are closed to foreign investment. These findings are typical of the other countries in our companion study.
Figure 1. Impulse Responses of Brazilian Stock Returns to a One-Standard-Deviation Structural Shock in Local Monetary Policy
Investable stocks – open to foreign investment
Non-investable stocks – closed to foreign investment
Source: This figure reports impulse responses (center line) of investable and non-investable stock returns over 24 months in response to a one-standard-deviation structural shock in local Brazilian monetary policy. The impulse responses are obtained from a structural VAR model with eight endogenous variables: oil prices, the U.S. Fed-Funds rate, local industrial production growth, inflation, exchange rates and investable and non-investable Brazilian stocks. The left axis is in percent and the horizontal axis is months after a policy shock. The outer bands are probability bands used to determine statistical significance. The impulse response in a given period is significant, if both outer bands are on the same (lower or upper) side of the horizontal line at zero.
Ruling out alternate interpretations
One concern with the above results is that they might be driven by the simultaneous response of stock prices and monetary policy to emerging market crises that occurred during our sample period. However, the results when controlling for the Mexican and Asian currency crises are materially the same. The Russian default in 1998 is prior to the start of our data for Russia.
Additionally, one might be concerned that when we separate stocks into investable and non-investable, what we are really doing is separating firms on the sensitivity of their product markets to changes in the local economy. To examine this, we determine whether our results hold for stocks that operate in traded-goods markets and for those that operate in non-traded goods markets. We continue to find that investable stocks are more sensitive than non-investable stocks to local monetary policy in both markets.
Summary and policy implications
Our research suggests that firms that are open to foreign investment are at least as sensitive to local monetary policy as are firms that remain closed to foreign investment. These findings assuage a non-trivial concern among monetary policy authorities that while access to foreign capital has many benefits, it may come at the cost of a loss of ability to influence one’s own local economy. The primary policy implication of our work is that foreign investment in local stocks does not result in a loss of monetary policy control. In fact, our results suggest that foreign investment makes local firms more responsive to monetary policy shocks.
On the Timing of Turkey’s Authoritarian Turn
This policy brief examines the timing of Turkey’s authoritarian turn using raw data measuring freedoms from the Freedom House (FH). It shows that Turkey’s authoritarian turn under the ruling AKP is not a recent phenomenon. Instead, the country’s institutional erosion – especially in terms of freedoms of expression and political pluralism – in fact began much earlier, and the losses in the earlier periods so far tend to dwarf those occurring later.
Introduction
A growing field in economics emphasizes the importance of the media in shaping economic outcomes. Whereas the role of the media in informing voters is well established (Strömberg, 2015), recent research also points to the link between the rise of illiberal democracies (Mukand and Rodrik, 2016) and authoritarian control over media (Guriev and Treisman, 2015).
In few countries is the link between authoritarianism and restrictions on freedom of expression as pervasive as in Turkey. The Turkish government under the Erdoğan-led Justice and Development Party AKP has gutted the judiciary of most of its independence, and set it loose to crack down on critical media. These dire circumstances, however, contrast markedly with descriptions of Turkey from of a few years ago. Quite recently many analysts still deemed Turkey a “vibrant democracy”, and as late as in 2013 the foreign minister of Sweden proclaimed: “Erdoğan’s Turkey is on the right path.” This media shift raises concerns over the public portrayal of Turkey’s institutional development up until the last few years as well as the extent to which analysts may have misinterpreted Turkey’s institutional development over the past decade.
Measures of Freedoms
To this date, the most prominent source of measuring freedoms in the world is the Freedom House’s annual Freedom of the World reports (Freedom House, 2015), which designates countries into one of three statuses: Free, Partly Free, and Not Free, in ascending order in freedoms. In constructing these statuses, FH uses subscores for 7 subcategories, aggregated into 2 categories — Political Rights (hereby PR), with a range of 0 to 40 increasing in freedoms, and Civil Liberties (hereby CL), with a range 0 to 60 increasing in freedoms – for which each then gets its own 1-7 (with a low value indicating more freedoms and vice versa), and in turn these are used to classify a country as having a particular Freedom status. From the FH’s methodology section:
A country or territory is awarded 0 to 4 points for each of 10 political rights indicators and 15 civil liberties indicators, which take the form of questions; a score of 0 represents the smallest degree of freedom and 4 the greatest degree of freedom. The political rights questions are grouped into three subcategories: Electoral Process (3 questions), Political Pluralism and Participation (4), and Functioning of Government (3). The civil liberties questions are grouped into four subcategories: Freedom of Expression and Belief (4 questions), Associational and Organizational Rights (3), Rule of Law (4), and Personal Autonomy and Individual Rights (4).
The PR category thus focuses more on rights pertaining to politics, and is used also to construct an indicator variable for whether a country constitutes an electoral democracy or not. CL, as the name indicates, is more focused on liberties, including the right to freedom of expression, including media freedoms.
Somewhat curiously, Turkey’s ratings have barely budged over the last decade, and have been consistently classified as a Partly Free country. In 2005, FH assigned Turkey a 3 in both PR and CL, and the only change since then was a one-point drop in 2012’s CL rating down to 4.
The PR and CL, as well as their subcategory, scores are available on FH’s website. Using these, the graphs below plot the evolution of the PR and CL total scores for Turkey between 2005-2015 (which is the period for which FH provides this data), as well as the 25th, 50th, and 75th global percentiles from the annual world distribution of the respective scores. The latter allows gauging not just the absolute performance of Turkey but also that relative to the rest of the world.
Figure 1. Freedom House Category Scores
Note: The red lines in the upper (lower) graph indicate the Political Rights (Civil Liberties) score. The dashed gray lines indicate the 25th, 50th, and 75th percentiles.
Turkey’s PR score (upper graph) is relatively flat over the decade with a 4-point drop after 2013, while the CL score (lower graph) has been falling since 2010 and was stagnant in the period before. Whereas Turkey started the period with a CL score just below the median country, it ended closer to the 25th percentile. None of the years show any indication of expanding freedoms during AKP’s rule.
The disaggregation of these scores into subcategories sheds further light on which of the latter have driven the former. For Turkey, especially relevant subcategories are the political pluralism and freedom of expression. The former refers to the degree to which people can organize in political groupings, credible opportunities for a political opposition, freedom from outside interference, and political rights for ethnic minorities. (This has clear links with the political barriers to entry such as the ten-percent threshold, the banning of political parties, and the persecution of Kurdish political activists in Turkey.) The latter subcategory refers to freedoms related to media, culture, religion, and academics, as well as the degree of freedom from government surveillance. Turkey’s subscores are plotted below, grouped by PR and CL:
Figure 2. Freedom House Subcategory Scores
In Figure 2, there is rather striking fall in both the political pluralism (red in upper graph) and freedom of expression (blue in lower graph). The other subscores tend to be more stagnant over time, and the only subcategory that exhibited any significant positive momentum during the period is Electoral Process, although by 2015 it had reverted to its 2005 value. The falls in political pluralism and freedom of expression are large in magnitude, from 12 to 9 in the former, and 12 to 8 in the latter. Taking the latter decrease at face value would imply that Turkish citizens in 2015 have two-thirds of the freedoms of expression that they enjoyed in 2005. Moreover, the clear majority of the falls in both these variables occured before 2013 – the post-2013 fall in freedom of expression accounts for only one fourth of the total fall observed since 2007.
Given the degree to which civil liberties have eroded faster than that of political rights in Turkey, this begs the question how its inherent degree of illiberalism has evolved relative to other democracies. For this purpose, I plot the Freedom of Expression subcategory (a part of the CL score) against the PR score for the 2015. This latter score is used by FH to classify countries into whether they can be called ‘electoral democracies’ or not (CL subcategories have no bearing on this classification):
“An ‘electoral democracy’ designation requires a score of 7 or better in subcategory A (Electoral Process) and an overall Political Rights score of 20 or better.”
FH thus classifies Turkey as an electoral democracy – its Electoral Process is consistently above 7 (see upper graph in Figure 2) and PR score above 20 (see Figure 1). But are there many FH-classified electoral democracies with similar levels of freedom of expression? Figure 3 answers this question:
Figure 3. Freedom House Subcategory Scores
Note: Green = Autocracy, blue = Democracy, Red = Turkey. The dashed green/blue lines indicate the median values for autocracies/democracies.
In 2015, Turkey is much closer to the median autocracy than the median democracy in terms of freedom of expression. Numerous autocracies have higher levels of freedom of expression than Turkey, and only two other electoral democracies have lower values of this variable: Bangladesh and Pakistan.
Consequently, Turkey today is a clear outlier due to its freedom of expression deficit among the countries classified as electoral democracies by FH. Yet the slide in Turkeys’ freedoms did not start in the last couple of years but has remained a pervasive feature of its institutional trajectory during the last decade.
Much of Turkey’s erosion of freedoms would not be as visible using only the most aggregate seven-point scaled ratings, as these obfuscate important changes within the rating scores. A shift in analysts’ focus to more disaggregated data may thus be useful in order to detect warning signs in a more timely fashion.
Finally, the severe deficit in freedoms in countries nonetheless classified by Freedom House as electoral democracies raises the issue of whether there should be a lower freedom bound to inclusion into the group of democracies in the world. And if so, what should determine such a lower bound?
References
- Freedom House, 2016, “Freedom of the World 2016.”
- Guriev, Sergei, and Daniel Treisman, 2015, “How Modern Dictators Survive: An Informational Theory of the New Authoritarianism”, NBER Working Paper No 21136.
- Meyersson, Erik, and Dani Rodrik, “Erdogan’s Coup”, Foreign Affairs, May 26th, 2014,
- Mukand, Sharun, and Dani Rodrik, 2016, “The Political Economy of Liberal Democracy”,working paper.
- Rodrik, Dani, 2014, “A General’s Coup”, mimeo.
- Strömberg, David, 2015, “Media and Politics”, Annual Review of Economics, 7: 173-205.
The Economic Complexity of Transition Economies
‘Diversification’ is a constant concern of policy-makers in resource rich economies, but measurement of diversification can be hard. The recently formulated Economic Complexity Index (ECI) is a promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system. The ECI is based on the diversity and ubiquity of a country’s exports. This brief uses ECI to discuss the economic diversity of transition economies in the post-Soviet decades, and the relationship between economic diversification and per capita income.
The search for and construction of appropriate predictors of economic development are among the main goals of economists and policy-makers. Education, infrastructure, rule of law, and quality of governance are all among the commonly used indicators based on inputs. The recently formulated Economic Complexity Index (Hidalgo and Hausmann, 2009) is a new promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system.
Indeed, the importance of production and trade diversification for economic development has been highlighted by the economic literature. Numerous studies have found a positive relationship between diversified and complex export structure, income per capita and growth (Cadot et al., 2011; Hesse, 2006; Hausmann et al., 2007). In line with this, Hausmann et al. (2014) demonstrate the predictive properties of the ECI for economic development and GDP per capita, which implies that the ECI can serve as a useful complement to the input-based measures for policy analysis by reasoning from current outputs to future outputs.
This brief uses the ECI to discuss the evolution of economic diversification, its relationship to per capita income in transition economies in the post-Soviet decades, and its policy implications.
How is economic complexity measured?
The economic complexity index (ECI) is a novel measure that reflects the diversity and ubiquity of a country’s exports. The index considers the number of products a country exports with revealed comparative advantage and how many other countries in the world export such goods. If a country exports a high number of goods and few other countries export these products, then its economy is diversified (a wide range of exports products) and sophisticated (only a few other countries are able to export these goods). Thus, the measure tries to capture not a specific aspect of the economy, but rather its overall sophistication.
For example, Japan, Switzerland, Germany and Sweden have been in a varying order at the top of the ranking of the Economic Complexity Index from 2008 until 2013. This means that these countries export a large number of highly sophisticated products.
In contrast, Tajikistan is among the countries at the bottom of the world ranking by the ECI with raw aluminum, raw cotton and ores making up 85% of all Tajikistan’s exports in 2013. However, not only are Tajikistan’s exports concentrated among very few narrow products, these products are also ubiquitous and the ability to export them does not require knowledge and skills that can be used in the production and exports of many other products.
As the index for each country is constructed relative to other countries’ exports, it is comparable over time.
What can we learn from the economic complexity of transition economies?
The economic complexity index can serve as a useful indicator for understanding transition economies in the post-Soviet period. A strong relationship between GDP per capita and economic complexity is found in the sample of transition economies in Figure 1. This figure presents the relationship for the last year for which data is available for the sample of 13 post-Soviet states and Poland. As can be seen in Figure 1, the economic complexity is positively related to income per capita. This is especially true for Poland, Estonia, Lithuania, Latvia and Russia, who all have higher than average economic complexity and high levels of per capita income. While Belarus and Ukraine also have diverse and complex economies, they have somewhat lower income per capita than the first group.
Figure 1. Economic Complexity and GDP per capita
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Natural resource-rich, or rather, oil-rich countries are the exception from the abovementioned correlation. Most transition countries with below than average economic complexity are characterized by low income per capita levels, except for Kazakhstan and Azerbaijan, which are oil-rich countries. Still, the overall picture is straightforward: countries with a complex export structure have a higher level of income.
One of the advantages of a systemic measure like export complexity is its straightforward policy application. The overall diversity and sophistication of the economy can thus be a complementary measure for the assessment of economic progress and development to GDP and GDP per capita, which are more susceptible to the volatile factors such as commodity prices.
Figure 2 shows the development of economic complexity for 14 post-Soviet countries and Poland between 1994 and 2013 (due to data availability issues, only one year is available for Armenia).
First, we see that the economic complexity has diverged over time, although there is some similarity in the rankings among countries over time. The initial closeness is likely related to the planned nature of the Soviet economy that aimed to distribute production among Soviet Republics. In the post-Soviet context, however, the more complex economies (Estonia, Belarus, Lithuania, Ukraine, Latvia, Russia) kept or increased their sophistication and diversity of exports. Poland is the leading economy in terms of complexity, both in the beginning and towards the end of the sample period. Belarus, the second most complex economy in 2013 and the most complex economy in several years prior, shows an increasing trend in its sophistication of exports. Although its GDP per capita is noticeably lower than what would be expected from such a sophisticated economy, the complex production structure may explain its ability to withstand a permanent high inflation and external macroeconomic shocks. Some others, e.g., Tajikistan and Azerbaijan, saw a decreasing trend in economic complexity; Georgia and Kazakhstan, notably, lost in economic complexity but also in their ranking among their peers.
Figure 2. Economic Complexity of Transition Economies
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Conclusion
This brief revisited the economic complexity of transition economies and its evolution since the 1990s. The post-Soviet and other transition countries have had diverging economic development paths: Some have managed to build complex production economies, while others’ comparative advantage remains in raw materials. These differences are also reflected in their income levels.
Across the world, economic diversification is associated with higher per-capita income. As the brief showed, this relationship also holds for the post-Soviet countries; policy-makers should take economic diversification seriously. Increasing economic complexity may well pave the path to higher income levels.
References
- Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What’s behind the hump?. Review of Economics and Statistics, 93(2), 590-605.
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of economic growth, 12(1), 1-25.
- Hesse, H. (2006). Export diversification and economic growth. World Bank, Washington, DC.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. proceedings of the national academy of sciences, 106(26), 10570-10575.
Important Policy Lessons from Swedish-Russian Capital Flows Data
A recent study of capital flows between Sweden and Russia provides many policy lessons that are highly relevant for the current economic situation in Russia. In line with studies on other countries, bilateral FDI flows were more stable than portfolio flows, which is important for a country looking for predictable external sources of funding. However, much of the FDI flows came with trade and growth of the Russian market. The sharp decline in imports and fall in GDP is therefore bad news also when it comes to attracting FDI. The conclusion is (again) that institutional reforms and reengaging with the West are crucial policies to stimulate both the domestic economy and encourage much-needed FDI.
In a recent paper (Becker 2016), I take a detailed look at the trends and nature of bilateral capital flows between Sweden and Russia over that last 15 years. Although the paper focuses on the capital flows of a relatively small country like Sweden with Russia, it sheds some light on more general theoretical and empirical issues associated with FDI and portfolio flows that are highly relevant for Russia today.
Measuring Bilateral FDI
One general qualifier for studies of bilateral capital flows is however the reliability of data; Not only is a significant share of international capital flows routed through offshore tax havens which makes identifying the true country of origin and investment difficult, but also many investing companies are multinationals (MNEs) with operations and shareholders in many countries so it is hard to have a clear definition of what is a “Swedish” or a “Russian” company. In addition, when different official data providers, in this case Statistics Sweden (SCB) and the Central Bank of Russia (CBR), report capital flows on the macro level, there are large discrepancies.
Private companies also gather company level data on FDI that can be aggregated and compared with the macro level FDI data. This data is on gross FDI flows and should not be expected to be the same as the net macro level FDI flows data but is a bit of a “reality check” of the macro data.
Figure 1. Average annual FDI flows
Sources: SCB, CBR, fDi Market, MergerMarkets
The reported annual average flow of FDI from Sweden to Russia varies from around USD500 million to USD1.2 billion depending on the data source. Russian flows to Sweden are rather insignificant regardless of the source but the different sources do not agree on the sign of the net flows (Figure 1).
The differences between data sources suggest that some caution is warranted when analyzing bilateral FDI flows. With this caveat in mind, there are still some clear patterns in the capital flows data from Sweden to Russia that emerge and carries important policy lessons in the current Russian economic environment.
FDI vs. Portfolio Investments
There is a large literature discussing the distinguishing features of FDI and portfolio flows (see Becker 2016 for a summary). Some of the key macro economic questions include which type of flows provides most international risk sharing; are most stable over time; or most likely to contribute to balance of payments crises when the flows go in reverse. In addition, there are potential differences in terms of the amount of international knowledge transfers and how different types of capital flows respond to institutional factors.
Figure 2. FDI and portfolio investments
Source: SCB
Figure 2 shows that FDI has been much more stable than portfolio flows in the years prior to and after the global financial crisis as well as in more recent years. Although all types of capital flows respond negatively to poor macroeconomic performance, and the stock of portfolio investments swing around much faster than FDI investments, i.e., portfolio flows go in reverse more easily and can contribute to external crises. This makes FDI a more preferable type of capital flow for Russia.
FDI and Trade Go Together
Since FDI is a desired type of capital flow, it is important to understand its driving forces. The first question to address is whether FDI and trade are substitutes or complements. Since the bulk of FDI comes from MNEs that operate in many countries, we can imagine cases both when FDI supports existing trade and cases when it is aimed at replacing trade by moving production to the country where the demand for the goods is high.
In the case of Sweden and Russia, the macro picture is clear; FDI has increased very much in line with Swedish exports to Russia (Figure 3). Both of these variables are of course closely correlated with the general economic development in Russia, but even so, the very close correlation between FDI and trade over the last 15 years suggests that they are compliments rather than substitutes.
Figure 3. Swedish Exports and FDI to Russia
Source: SCB
Most FDI is Horizontal
FDI flows are often categorized in terms of the main motivating force for MNEs to engage in cross-border investment: vertical (basically looking for cheaper inputs), horizontal (expanding the customer base), export-platform (producing abroad for export to third countries) or complex (a mix of the other reasons) FDI.
Looking at the sectoral composition of FDI from Sweden to Russia (Figure 4), most investments have come in sectors where it is clear that MNEs are looking to expand their customer base. Even in the case of real estate investments, a large share is IKEA developing new shopping centers that host their own outlets together with other shops. Communication and financial services are also mostly related to service providers looking for new customer. Only a small share is in natural resource sectors that would be more in line with vertical FDI, while there are very few (if any) examples of MNEs moving production to Russia to export to third countries.
Figure 4. Sectors of Swedish FDI to Russia
Source: SCB
Policy conclusions
The above figures on bilateral capital flows from Sweden to Russia carry three important policy messages: 1) FDI is more stable than portfolio flows; 2) Trade goes hand in hand with FDI; and 3) FDI to Russia has mostly been horizontal and driven by an expanding customer base.
In the current situation where Russia should focus on policies to attract private capital inflows, the goal should be to attract FDI. Instead, the government is now looking for portfolio inflows in the form of a USD3 billion bond issue. But FDI is a more stable type of international capital than portfolio flows and also come with the potential of important knowledge transfers both in terms of new technologies and management practices.
However, as we have seen above, FDI inflows have in the past been correlated with increased trade and an expanding Russian market. In the current environment, where imports with the West declined by 30-40 percent in the last year, GDP fell by around 4 percent, and the drop in consumers’ real incomes have reached double digits in recent months, it is hard to see any macro factors that will drive FDI inflows.
Instead, attracting FDI in this macro environment requires policy changes that remove political and institutional barriers to investments. The first step is to fulfill the Minsk agreement and contribute to a peaceful solution in Ukraine that is consistent with international laws. This would not only remove official sanctions but also provide a very serious signal to foreign investors that Russia plays by the international rulebook and is a safe place for investments from any country.
The second part of an FDI-friendly reform package should address the institutional weaknesses that in the past have reduced both foreign and domestic investments. It is telling that many papers that look at the determinants of FDI flows to transition countries include a ‘Russia dummy’ that is estimated to be negative and both statistically and economically significant (see e.g. Bevan, Estrin and Meyer, 2004 and Frenkel, Funke, and Stadtmann, 2004). One factor that reduces the significance of the ‘Russia dummy’ is related to how laws are implemented. Other studies point to the negative effect corruption has on FDI.
Reducing corruption and improving the rule of law are some of the key reforms that would have benefits far beyond attracting FDI and has been part of the Russian reform discussion for a very long time. It was also part of the reform program that then-President Medvedev presented to deal with the situation in 2009 together with a long list of other structural reforms that would help modernize the Russian economy and society more generally.
As the saying goes, don’t waste a good crisis! It is time that Russia implements these long-overdue reforms and creates the prospering economy that the people of Russia would benefit from for many generations.
References
- Becker, T, 2016, “The Nature of Swedish-Russian Capital Flows”, SITE Working paper 35, March.
- Bevan, A, Estrin, S & Meyer, K 2004, “Foreign investment location and institutional development in transition economies”, International Business Review, vol. 13, no. 1, pp.43-64.
- Frenkel, M, Funke, K & Stadtmann, G 2004, “A panel analysis of bilateral FDI flows to emerging economies”, Economic Systems, vol. 28, no. 3, pp. 281-300.
Highly Educated Women No Longer Have Fewer Kids
This policy brief summarizes evidence that the cross-sectional relationship between fertility and women’s education in the U.S. has recently become U-shaped. The number of hours women work has concurrently increased with their education. The theory that the authors advance is that raising children and home-making require parents’ time, which could be substituted by services such as childcare and housekeeping. By substituting their own time for market services to raise children and run their households, highly educated women are able to have more children and work longer hours. The authors find that the change in the relative cost of childcare accounts for the emergence of this new pattern.
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. Some countries such as Norway, France, Italy, Belgium and the Netherlands have already taken legal measures in that direction. Trends in women’s education give hope that such goals may be achieved as women are increasingly occupying more prestigious and demanding careers. Indeed, in today’s world, women have surpassed men in higher education in most developed countries (Goldin et al 2006; Hazan and Zoabi 2015a).
What are the consequences of this important development for fertility? Historically, highly educated women have had fewer kids than less educated women (see, for example, Jones and Tertilt 2008). This relationship is deep rooted in the economic and sociological literature to the extent that many theories have already been proposed to explain this relationship. Leading explanations rely on the difficulty to combine children and career (Mincer, 1963; Galor and Weil, 1996) and the quantity-quality tradeoff (Becker and Lewis, 1973; Galor and Weil, 2000; Hazan and Zoabi 2006). The shift in women’s education coupled with more demanding careers for women means that if the cross-sectional relationship between women’s education and fertility is stable over time, then future fertility rates will continue to decline from their already historically low levels.
In Hazan and Zoabi (2015b) we find, however, that the cross-sectional relationship between women’s education and fertility has changed from monotonically declining until the 1990s to a U-shaped pattern during the 2000s. This change is due to a substantial increase in fertility among women with advanced degrees who increased their fertility by 0.7 children, or by more than 50%. This is illustrated in Figure 1, which plots the cross-sectional relationship between fertility and women’s education in 1980 and during the period 2001-2011.
Figure 1: Fertility Rates by Women’s Education, 1980 and the 2000s.
What can explain the rise of fertility among highly educated women during the period that saw the largest increase in the labor supply of highly educated women? We argue that the rise in college premium increased the demand for child-care and housekeeping services by highly educated women and a rise in the supply for such services by low educated women. This ‘marketization’ weakened the tradeoff between career and family life and enabled highly educated women to pursue demanding career without giving up on their desired family size.
To establish the relationship between the rise in the college premium and fertility of highly educated women, we use data from the March CPS for the period 1983-2012. We estimate the average hourly wage in the “child day-care services” industry and allow it to vary by state and year. In addition, we compute the hourly wage of all women in the 25-50 year-old age group who reported a positive salary income. Figure 2 presents the fitted values of the average of this variable for each of our five educational groups. The figure shows that childcare has become relatively more expensive for women with less than a college degree but relatively cheaper for women with a college or an advanced degree.
Figure 2: Linear Prediction of the Log of the Ratio of Average Wage in the Childcare Industry to Average Wage in the Five Educational Groups 1983–2012
To utilize the micro data we estimate regression models where the dependent variable is a binary variable that takes the value of one if a woman, living in a specific state in a specific year gave birth during that year and zero otherwise. Our main explanatory variable is the labor cost in the child daycare industry divided by the own wage of that woman. We show that there is a highly statistically significant and economically large negative correlation between this measure of childcare cost and the probability of giving a birth. In our empirical analysis we find that this change in the relative cost can account for about one-third of the increase in the fertility of highly educated women. We use a battery of tests to show that this correlation is not driven by selection of women into the labor market, by the endogeneity of wages, or by specific years over the last three decades.
Figure 3: 2000s Actual and Hypothetical Fertility under the 1980s prices of Childcare
Figure 3 uses the estimates from the regression models described above and shows a hypothetical fertility for 2001-2011 under the 1983-1985 relative childcare cost. The figure shows that the hypothetical fertility curve is obtained by a clockwise rotation of the actual fertility curve around the group of women that has some college education.
Direct evidence on paid childcare services is consistent with this view. Figure 4 shows the average weekly paid childcare hours by all women aged 25-50 in 1990 and 2008. The figure has two salient features. First, in each of these years, paid childcare is increasing with women’s education. Secondly, between 1990 and 2008, paid childcare hours have stagnated for women with up to some college education but have sharply increased for highly educated women.
Figure 4: Paid Childcare Weekly Hours for Women aged 25-50.
We then rule out potentially other explanations. What if the increase in labor supply stems from women who did not give birth during that year? To address this concern we shows that the cross-sectional relationship between education and usual hours worked for the sub-sample of women age 15-50 who gave birth during the reference period exhibit the same positive correlation. Another concern might be that it is in fact the spouses who respond to a birth by lowering their labor supply enabling their wives to work more. Find that men who are married to highly educated women work more than men who are married to women with lower levels of education. Interestingly, fathers to newborns work more than husbands who do not have a newborn at home, regardless of the education of their wives. More importantly, usual hours worked by fathers to newborns monotonically increased with their wives’ education. Thus, the spouses of highly educated women are not the ones substituting in childcare for their working wives.
Another concern our model may raise is that marriage rates differ across different educational groups. If married women have higher fertility rates and if more educated women have higher marriage rates, more educated women’s higher fertility rates may not be caused by the availability of relatively cheaper childcare and housekeeping services, but rather simply by their higher marriage rates. We find that the fraction of women with advanced degrees who are currently married is somewhat lower than that of women with college degree.
Figure 5: Number of birth per 1,000 White Women in the US in Age Groups 35-39, 40-44 and 45-49: Women with Advanced Degrees (2001-2011) and Historical Rates.
A final potential explanation might be related to recent advancements in Assisted Reproductive Technology (ART) that enable women to combine long years in school without scarifying parenthood. We address this possibility in three ways. First, we show that historical levels of fertility rates among women above age 35 were higher than the levels during the 2000s (see Figure 5). This stands in contrast to the argument that highly educated women were not able to have higher fertility rates in the past due to medical reasons. Secondly, we note that ART accounts for less than 1% of births occurred during the 2000s. Finally, fifteen U.S. states have infertility insurance laws that provide coverage to infertile individuals. We compare fertility patterns by women’s education in these states to the rest of the country and find no difference in fertility rates during the 2000s between the two groups of states.
The results of this study have several implications. For public policy, it highlights potential benefits from pro-immigration policies. Unskilled immigrants can potentially have positive effect on fertility via an increase in the supply of cheap home production substitutes. For many developed countries that are facing aging and shrinking population this may be something to consider. It also has consequences for economic growth. Given the strong correlation between parents’ education and kids’ education, an increase in the relative representation of kids coming from highly educated families means that the next generation is going to be relatively more educated. These are good news for economic growth.
References
- Gary S. Becker and Gregg H. Lewis. On the interaction between the quantity and quality of children. Journal of Political Economy, 81:S279–S288, 1973.
- Oded Galor and David N. Weil. The gender gap, fertility, and growth. American Economic Review 86(3): 374–387, 1996.
- Oded Galor and David N. Weil. Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. American Economic Review 90(4): 806–828, 2000.
- Claudia Goldin, Lawrence Katz, and Ilyana Kuziemko. The homecoming of American college women: A reversal of the college gender gap. Journal of Economic Perspectives 20(4): 133–156, 2006.
- Moshe Hazan and Hosny Zoabi. Does longevity cause growth? A theoretical critique. Journal of Economic Growth, 11 (4), 363-376, 2006.
- Moshe Hazan and Hosny Zoabi. Sons or Daughters? Endogenous Sex Preferences and the Reversal of the Gender Educational Gap. Journal of Demographic Economic, Vol 81, pp: 179-201, 2015a.
- Moshe Hazan and Hosny Zoabi. Do highly educated women choose smaller families? Economic Journal, 125(587):1191–1226, 2015b.
- Larry E. Jones and Michele Tertilt. An economic history of fertility in the u.s.: 1826-1960. In Peter Rupert, editor, Frontiers of Family Economics, pages 165 – 230. Emerald, 2008.
- Jacob Mincer. Market prices, opportunity costs, and income effects. In Carl F. Christ, editor, Measurement in economics: Studies in mathematical economics and econometrics in memory of Yehuda Grunfeld. Stanford University Press, pages 67-82, 1963.
Strategic Aid Financing
Donor countries often face a Samaritan’s dilemma when trying to implement political conditionality in bilateral aid. Giving through multinational organizations can mitigate this problem: Recipient nations are de-facto competing for funds, which restores their incentives to comply even with non-enforceable conditions. Donor nations might therefore find it useful to set up multinational aid funds, rather than to disseminate aid bilaterally, even if they have to give up control over where the money is spent.
In the last decade, global challenges like climate change or the fight against epidemics have become the focus of aid projects. In order to make progress on these large-scale issues, donor nations increasingly recognize that simply giving money to a developing region does not ensure success. Instead, the impact of aid spending depends crucially on efforts undertaken in the partner countries. Reforms of the local economic and judicial system, fighting corruption and general good governance are just a few of the demands on donor countries’ wish lists.
However, many of the conditions set by donating governments are hard to enforce, especially since they nowadays often fall in the category of “political conditionality,” aiming at broader political improvements rather than simple, measurable economic indicators (see for example Molenaers et.al., 2015). The crucial question for aid giving countries therefore remains how to strategically structure aid, so that recipient governments can be incentivized to cooperate also on intangible or non-enforceable conditions.
The Samaritan’s Dilemma
Indeed, the theoretical literature on aid conditionality finds that optimal contracts should be self-enforcing, i.e. the threat of aid sanctions should be large enough to ensure that the recipient government has an interest in fulfilling the conditions (see for example Scholl, 2009). That, however, might be easier said than done: Svensson (2000) argues that the threat of cutting aid in case conditions are violated is hard to credibly sustain, at least for individual donor countries. They often face political constraints to spend a certain amount of money on aid. This opens the possibility for a classic hold-up problem: If the donor country cannot commit to giving aid only conditional on reform efforts, the recipient country, knowing it will receive assistance in any case, has no incentive to implement costly reforms. From the donor’s perspective, this is also known as the Samaritan’s dilemma.
Multinational Funds Can Help
Svensson goes on to argue that transferring the responsibility of allocating aid to a multilateral organization might solve the Samaritan’s dilemma outlined above. He notes that if donors are lacking a commitment technology (that helps them to actually implement aid sanctions in case a recipient government shirks on the agreed aid conditions), “delegation of part of the aid budget to an (international) agency with less aversion to poverty will improve welfare of the poor.” Such organizations will have less of a commitment problem and should therefore better be able to enforce aid conditionality.
Competition Restores Incentives
There is, however, no a priori reason to think that multilateral organizations have a different objective than individual donor countries in terms of eliminating poverty and achieving growth and prosperity for developing countries. After all, these organizations’ founding principles are set by the donor countries that fund them. An international organization’s objective, as represented by how it actually allocates funds across causes and recipient countries, should reflect an aggregation of the individual preferences of donor nations.
In a new study, Simon and Valasek (2016) argue that precisely this stage of preference aggregation enables multilateral organizations to better implement aid conditionality. How non-earmarked funds given to multilateral organizations are allocated is determined in a bargaining process between representatives of the different donating nations. Individual preferences might differ; the bargaining outcome thus has to reflect a compromise between them. The bargaining position of each donor will in part depend on intrinsic values, but importantly also on the reform efforts and willingness to cooperate of the potential recipient nations. Intuitively, the better the government of an aid receiving country behaves, the better the bargaining position of donor countries lobbying on its behalf.
This constitutes a new strategic reason for pooling resources in large aid funds rather than implementing aid bilaterally: When resources are pooled, recipient nations have to compete for their share of aid. It is precisely the heterogeneity of donor country preferences that induces (or enhances) such competition. Bilateral aid relations thus cannot replicate the effect to the same extend. This competition restores incentives to invest in costly reforms and circumvents the hold-up problem.
Conclusion
Donor nations should consider pooling their resources in multinational funds when they fear that their partner governments are reluctant to implement political reforms. This is especially relevant for aid aimed at common global issues like climate change or disease control.
Simon and Valasek show that the payoff from joining an aid fund is especially high when donor nations represented in the fund have relatively homogenous goals for their foreign aid programs, but differ in terms of where in the world they would like to send their aid money. Then the disadvantage from losing the direct say over which recipient nations get the most funds is far outweighed by the gain from inducing investment and reform incentives in the aid receiving nations.
References
- Molenaers et al. (2015): “Political Conditionality and Foreign Aid,” World Development Vol. 75.
- Scholl (2009), “Aid Effectiveness and Limited Enforceable Conditionality,” Review of Economic Dynamics 12(2).
- Simon & J.M. Valasek (2016), “The Political Economy of International Aid Funds,” Working Paper.
- Svensson (2000): “When is Foreign Aid Policy Credible? Aid Dependence and Conditionality,”, Journal of Development Economics Vol. 61.
Non-Tariff Barriers and Trade Integration in the EAEU
It is a commonly held view that the Eurasian Economic Union (EAEU) is a political enterprise (Popescu, 2014) that has little economic meaning other than redistribution of oil rents (Knobel, 2015). With a new reality of low oil prices and reduced rents, a legitimate question is how stable this Union is, or whether there is any hope for mutual economic benefits that can provide incentives to all the participants to maintain their membership in the Union? Our answer is yes, there is hope, but only if countries, especially Russia, make progress on deep integration such as services liberalization, trade facilitation, free movement of labor and especially in the reduction of the substantial non-tariff barriers (NTBs). NTBs are hampering trade both within the Union (World Bank, 2012; Vinokurov, 2015), as well as against third country imports. Our research shows (see Knobel et al., 2016) that all the EAEU members will reap benefits from a reduction of NTBs against each other, but they will obtain considerably more substantial gains from a reduction in NTBs against imports from the EU and China.
Since the early stages of creation of the Customs Union (CU) between Belarus, Kazakhstan, and Russia back in 2010, the economic benefits of the CU have been questionable. The main reason for this in Kazakhstan was the increase in its import tariffs in order to implement the common external tariff of the CU, which initially was Russia’s external tariff (Tarr, 2015). Kazakhstan almost doubled its average tariff from 5.3% to 9.5% (Shepoltylo, 2011; Jondosov and Sabyrova, 2011) in the first year of its CU accession. Belarus did not increase its average tariff, but the structure of its tariffs shifted toward a protection of Russian industry.
In 2015, the CU was transformed into the EAEU, and Armenia and Kyrgyz Republic have joined the EAEU. These two countries are WTO members; Kyrgyzstan entered the WTO in 1998, and Armenia in 2001. In 2014, the simple average most-favored nation (MFN) applied tariff rate in Armenia was 3.7%, and 4.6% in the Kyrgyz Republic. Due to differences between Armenia and Kyrgyzstan’s WTO commitments and the EAEU tariff schedule, the new members of the EAEU are not implementing the full EAEU tariff schedule. That is, they have numerous exemptions. However, they have started a WTO commitments modification procedure.
Despite adverse impacts from the higher import prices from implementing the common external tariff of the EAEU in Armenia and the Kyrgyz Republic, there are potentially offsetting gains. Given the importance of remittances to the Kyrgyz Republic, the benefits coming from the right of workers to freely move and legally work inside EAEU likely dominate the tariff issues. Armenia also benefits from the free movement of labor, receives Russian gas free of export duties, and wants to preserve the military guarantee granted by Russia through the six-country Collective Security Treaty Organization.
In the case of Belarus, it receives Russian oil and natural gas free of export-duties, which, when oil prices were high, tended to dominate their calculus. Kazakhstan hopes for more FDI as a platform for selling to the EAEU market; but President Nazarbaev has expressed concerns that the EAEU is not providing net benefits to his country.
To date, the members have judged participation to be in their interest, but with the plunge in the price of oil and gas, the calculus could swing against participation in the EAEU. That is why it is so important to achieve progress with deep integration in the EAEU. One of the most important areas of deep integration for the EAEU is the substantial reduction of non-tariff barriers in goods trade, both between the EAEU members and against third countries. Estimates by the Eurasian Development Bank (Vinokurov et al., 2015) reveal that NTBs account are 15% of the value of intra-union trade flows.
In our paper, Knobel et al (2016), we estimate substantial gains to all the EAEU members from a reduction of NTBs. We employ a global computable general equilibrium model with monopolistic competition in the Helpman-Krugman style based on Balisteri, Yonezawa, Tarr (2014). Estimates of the ad-valorem equivalents of NTBs were based on Vinokurov et al (2015) for the EAEU member countries and Kee, Nicita, Olarreaga (2009) for non-members.
We find that the effects of deep integration are positive for all countries of the EAEU. Armenia’s accession to the EAEU will have a strong positive effect only if coupled with a decrease of non-tariff barriers. Armenian accession is associated with an increase in external tariffs, which causes a negative economic impact and decrease in output.
The effect of deep integration in the EAEU will be even greater if any spillovers effect reducing NTBs for EAEU’s major trading partners are present. Knobel et al. (2016) simulate a 50% decrease in “technical” NTBs inside the EAEU and a 20% spillover effect of reduction NTBs toward either the EU and USA or China. Reduction of NTBs in trade with the EU and the USA dominates the comparable reduction of NTBs with China for all countries of the EAEU in terms of the welfare gain. Armenia’s welfare gain with a spillover effect towards the EU is 1.1% of real consumption compared to 1.02% with a spillover effect towards China. Growth in welfare in Belarus will be 2.7% with a EU spillover versus 2.5% with a spillover effect towards China. Kazakhstan’s gain in real consumption is also greater in the first (EU+USA) case: 0.86% versus 0.66% (with spillover towards China). Russia’s gain in real consumption in the case of a spillover effect with the EU is 2.01% versus 0.63% in the case of China.
Summing up, our findings suggest an answer to the recent concern about stability of the EAEU. We think that eliminating NTB, hampering mutual trade, and decreasing NTBs in either European or Chinese direction could provide mutual economic benefits that could tie countries of the EAEU together, thereby giving a much needed solid economic ground for the Union.
References
- Balistreri, Edward J., Tarr, David G. and Hidemichi Yonezawa (2014). Reducing trade costs in east Africa : deep regional integration and multilateral action (No. 7049).
- EEC (2015) Eurasian economic integration: facts and figures, (in Russian).
- Kee, Hiau Looi, Nicita, Alessandro, and Marcelo Olarreag (2009) Estimating Trade Restrictiveness Indices, Economic Journal, 119, 172–199.
- Knobel, Alexander (2015) Eurasian Economic Union: Prospects and Challenges for Development, Voprosy Ekonomiki, 2015, No. 3, pp. 87—108. (in Russian).
- Knobel, Alexander, Andrey Lipin, Andrey Malokostov, David G. Tarr, and Natalia Turdyeva (2016) Non-tariff barriers and trade integration in the EAEU, mimeo
- Plekhanov, Alexander and Asel Isakova (2012) Customs Union and Kazakhstan’s Imports (July 1, 2012). CASE Network Studies and Analyses No. 422.
- Popescu, Nicu (2014), “Eurasian Union: the real, the imaginary and the likely,” Chaillot Paper – No. 132, European Union Institute for Security Studies, September 9.
- Shepotylo, Oleksandr (2011), “Calculation of the tariff rates of Kazakhstan before and after the imposition of the customs union common external tariff in 2010.” Available as part of World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012.
- Tarr, David G. (2015) The Eurasian Economic Union among Russia, Belarus, Kazakhstan and Armenia: Can it succeed where its predecessor failed? Paper prepared for the BOFIT conference of the TIGER project, Helsinki, Finland, September 16, 17, 2015
- Vinokurov, Evgeny, Mikhail Demidenko, Igor Pelipas, Irina Tochitskaya, Gleb Shymanovich, Andrey Lipin (2015) Measuring the Impact of Non-Tariff Barriers in the Eurasian Economic Union: Results of Enterprise Survey. EDB Centre for Integration Studies Report no. 30, EDB: Saint-Petersburg.
- World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012
The Inevitable Social Security Reforms in Belarus
In 2016, Belarus will face the need to reform its social protection policy. The three main directions of reforms will be to departure from subsidized tariffs, to reform the pension system, and to increase unemployment benefits. Needless to say, some of these reforms will be highly unpopular. The government needs not only to cut expenditures, but also to think about new ways of providing targeted social support.
Faced with an anemic growth over the last 5 years and a GDP decline of 3.9 percent in 2015, Belarus has to rethink its economic policy. While the government is so far reluctant to undertake serious structural reforms, the decrease in budget revenues and lack of access to international financing leaves the authorities with few other options than to reform the social security system. This push might actually be a good thing, as social security in Belarus needs to depart from its current non-sustainable model of subsidies for everyone, to a model of focused means-tested social support.
Subsidized Tariffs
A lot of government-set tariffs in Belarus are currently subsidized. Utility service tariffs and transport fees are lower than the costs of providing these services. This is especially true for the heating tariffs, which currently cover 10-20 percent of total costs.
The subsidization policies are inefficient, as they benefit the rich (who consume more) rather than the poor in need of government support. Moreover, in the case of energy tariffs, cross-subsidization leads to higher energy costs for the firms, making them less competitive. Both prospective creditors, the IMF and the Eurasian Fund for Stabilization and Development, demand that the subsidies are gradually removed.
Zhang and Hankinson (2015) estimate the effects of an increase of the heating tariff on welfare. They find that the burden of higher tariffs will mostly fall on low-income groups. In particular, if the heating tariffs increase to 100 percent of the costs, households from the lowest income quintile will spend over 16 percent of their income on energy. Therefore, the authors conclude that the government should introduce a targeted social assistance together with the tariff increase.
While tariff increases were already introduced in the beginning of 2016, a targeted social assistance is still only a project.
Unemployment Benefits
Despite calling itself a social economy, Belarus has inexplicably low unemployment benefits (currently below 10EUR per month in Minsk). These low unemployment benefits contribute to a very low registered unemployment rate – 1 percent in November 2015. The more adequate measure of unemployment, based on labor force surveys, is classified in Belarus. However, large-scale job cuts in the biggest state-owned enterprises suggest that unemployment is a real threat.
Akulava (2015) argues, that given the current situation on the labor market, unemployment benefits should be increased to at least the minimal subsistence level. However, unemployment benefits per se will not solve all the problems in the labor market, and Belarus needs more active labor market policies facilitating the retraining and reallocation of workers.
The IMF has also emphasized the need to introduce proper unemployment insurance. The government has already pre-announced an introduction of increased unemployment benefits, but the details and dates are still unclear. There is a risk that, as many other policies in Belarus, the unemployment support will favor the state-controlled part of the economy and only offer increased support for those laid off from state-owned enterprises.
Pension Reform
The Belarusian pension system has not change much since the Soviet times: it is still a pay-as-you-go redistributive system, with a pension age among the lowest in Europe (55 for women and 60 for men). The Pension Fund first registered a deficit in 2013, and given the ageing population, deficits will only deepen in the future.
Due to relatively low fertility rates (1.6 per woman) and increasing life expectancy, the Belarusian population is quickly ageing: In 2015, there are 4 persons of retirement age per 10 persons of working age, but in 2035, this ratio will be 6 per 10.
Lisenkova and Bornukova (2015) build a demographically accurate overlapping generations model of Belarusian economy to estimate the stability of the pension system. They find that if the current parameters do not change, the deficit of the Pension Fund will explode up to 9% in 2050 (line 55/60 in Figure 1).
Figure 1. The Pension fund deficits under different scenarios, in % of GDP
Source: Lisenkova and Bornukova, 2015
The authors also estimate different reform scenarios. An increase in the contribution rate and a decrease in the replacement rate (ratio of average pension to the average wage) do not seem feasible, as the current contribution rate of 29 percent is already too high, and the replacement rate is near the minimum set by the National Development Strategy 2020. The most obvious reform is then to increase the pension age for women, who retire 5 years earlier than men, despite having 10 years longer life expectancy. However, as can be seen from line 60/60 in Figure 1, equating the pension ages for women and men will not be enough to curb the deficits. Another simulated reform is to gradually increase pension age to 65 years for everyone, after increasing it to 60 for women only. This reform would mean that the deficits would be kept below 1 percent of GDP (and even generate a small proficit by 2035, although in a very long perspective the deficit will increase to 2 percent again (line 65/65)). In the very long run, Belarus needs to build a fully funded pension system.
The need to increase the pension age is already on the public debate agenda, and the authorities recognize the need for reforms. Needless to say, however, this move will be very unpopular.
Conclusion
The current economic crisis gives an opportunity and incentive to make Belarusian social policy more efficient. This policy brief describes the three major fields of reform. Subsidized tariffs are unfair and inefficient, but before removing subsidies the government should create a targeted system of social assistance to those in need. Increasing unemployment (and demands from the creditors) may force the government to change its unemployment benefit policy. The pension system needs reforms, but these would be difficult to implement due to unpopularity.
▪
References
- Akulava, M. (2015), ‘Unemployment insurance as a tool of social protection’, BEROC policy paper series, PP no. 32 (in Russian)
- Lisenkova, K., and Bornukova K. (2015), ‘Effects of Population Ageing on the Pension System in Belarus’, BEROC working paper series, WP no. 28
- Zhang, F., and Hankinson, D. (2015), ‘Belarus Heat Tariff Reform and Social Impact Mitigation,’ World Bank Publications, The World Bank, number 22574
The Issue of Repeat Cartel Offences
Leniency policies have become an important antitrust tool but it is not clear whether they have effectively prevented recidivism or whether firms have learned to collude under, and even make strategic use of them. If “recidivism” is really an industry-level phenomenon, the appropriate policy measures are very different from what is necessary if individual firms, having been detected and punished for colluding, engage in the behavior again. Following Levenstein et al. (2015), this brief discusses the recidivism question as one about post-cartel behavior, i.e. the set of policies required to assure that effective competition emerges post-cartel breakup.
Measuring Recidivism
Cartels are one of the main concerns of the European Commission (EC) and the US Department of Justice (DOJ) and so, the US and EU Leniency Programmes (LPs) were designed, in 1978 and 1996 respectively, as a device for the deterrence and dissolution of collusive agreements (see Marvão and Spagnolo (2015a) for an in-depth review on the available evidence of the effects of LPs).
In the analysis of cartel formation, recidivism is an important issue. In the set of 510 cartel members fined by the EC in 1998-2014, Marvão (2015) identifies 89 “multiple offenders” (firms fined for collusion more than once), 10 “repeat offenders” (firms which initiate a cartel after being investigated for another cartel), and 5 recidivists following the definition from Werden et al. (2011): firms which initiate a cartel after being fined for another cartel.
The DOJ dataset compiled by Levenstein and Suslow (2015), spanning 1961-2013, preliminarily finds 113 “multiple offenders” but only 14 “repeat offenders”. Of these 14 firms, 5 that had been previously indicted were caught in the 1990s, but none was indicted again by the DOJ in the 2000s.
Although the number of (discovered) “true recidivists” is not zero, it is less than 1% in these two samples (EU, US). Recidivism seems to arise when there are lapses in enforcement; not surprisingly, some firms take advantage of these lapses to return to old behaviors. Designing policies that are able to prevent recidivism requires understanding whether this is an industry or firm-level phenomenon.
Industry Recidivism
Levenstein et al. (2015) use the above-mentioned EU and US datasets to show that collusion occurs in virtually all sectors of the economy, but with discernable patterns.
In the US, construction and chemicals are frequently cartelized (pre and post leniency). There are a large number of cartels in local markets in some industries, such as retail gasoline stations and dealers and ready-mix concrete. While collusion in these local markets is frequently uncovered, it is not necessarily amongst the same firms.
In the EU, chemicals and transport cartels are also frequent areas of collusive activity (although cartels that are strictly within national boundaries and prosecuted by national competition authorities are not included in the sample).
The authors show that there is a large share of repeat and multiple offenders in chemicals and a surprisingly high proportion of repeat offenders in the manufacture of transport and electrical equipment. The highest proportion of multiple offenders is found in pharmaceuticals and refined petroleum products. The transportation and storage market is a sector with a high incidence of collusion (83 convicted cartel members), but no repeat offenders.
While the determinants of cartel activity are varied and endogenous, some correlations with industry-driven recidivism can be discussed:
- Industry concentration. It increases the ease of tacit collusion and it should increase the likelihood of explicit collusion, but there are many cartel examples in unconcentrated industries. In some industries, it has been argued that high fixed costs make competition unstable, so that, absent collusion, firms price below long-run marginal cost and are unable to cover fixed costs (Pirrong, 1992).
- Culture and history. Spar (1994) argues that the cooperative culture necessary for survival for diamond miners facilitated collusion as the industry matured. Policy fluctuations can also contribute to this problem, as was the case in the US during the Great Depression.
- Inelastic demand. This is empirically challenging to capture if the observed prices have been affected by monopoly power, thus potentially raised to a level at which demand is elastic. In many cases, the direct consumer is a producer, so the downstream cost function and competitive intensity also influence elasticity of demand for the cartelized product. Grout and Sonderegger (2005) estimate the likelihood of collusion in the US and EU and rank industries accordingly. This could be used to target competition authority resources to select industries.
Firm Recidivism
Once a cartel breaks-up, cartel members may decide to compete in the market, merge, tacitly collude, or explicitly collude again. The latter does not mean that the cartel re-forms: a firm may collude in a new industry or product line or with a new set of co-conspirators.
U.S. Steel was involved in 6 different US cartels between 1948 and 1969, with different cartel partners and in different steel products. VSL construction was similarly involved (including as a leader) in multiple US cartels across several decades with distinct, but overlapping partners.
In the EU, Akzo Nobel N.V. has been convicted for 9 cartels, which lasted between 1987 and 2007, and in which its co-conspirators were mostly overlapping – e.g. collusion with Arkema in 6 instances (although the latter changed its name during the period). Many of the other co-conspirators were also multiple offenders. While Akzo only received one fine increase for recidivism, it received 7 leniency reductions, of which 3 were full immunity.
Other EC repeat offenders are ABB and Degussa Evonik – both convicted 4 times and received full immunity twice – as well as Brugg and Sumitomo. The latter was convicted for 7 cartels, of which 5, in the automotive wire harness, were self-reported.
What may influence repeated cartel participation, at the firm level?
- Firm’s corporate culture. In such a case, the leadership of the organization expects managers to collude, and collusion occurs in many markets in which the firm operates. Firm norms and expectations of managerial behavior can repeatedly encourage collusion and “disregard” previous fines, as illustrated in the ADM case (Eichenwald, 2000).
- Firm structure. Multi-market collusion literature focuses on the ability of firms to target punishments in particular markets. Multi-market firms may also encourage the spread of collusion if they have learned to collude in one market and share their “best practices” in another. This seems to have been the case, for example, in the spread of the vitamin cartel from vitamins A and E to other vitamins (Connor, 2008). Multi-market collusion is encouraged not only by multi-product multinationals, but also multi-market relationships between what appear to be smaller firms in local markets. For example, if gas stations are owned by multi-market firms such as large oil firms or chains of stations, that may facilitate repeated collusion over time and/or across geographic locations.
Policy Tools
In complementarity with LPs, Levenstein et al. (2015) discuss additional (possibly) effective post-cartel policies, aimed at preventing firm-driven recidivism.
- Company Fines and Leniency. Theoretical research has emphasized the aptitude of well-designed and well-run LPs to improve cartel detection and deterrence (for a survey, see Spagnolo, 2008). However, Marvão and Spagnolo (2015b) note the generosity of the current EU LP: the average LP reduction is 45% and leniency is granted to 52% of convicted cartel members. In addition, Marvão (2015) shows that repeat offenders appear to receive larger EC leniency reductions, which suggests that firms can learn the “rules of the game”, colluding repeatedly and reporting the cartel to reduce their penalties. As such, fines need to be tougher and recidivism needs to be dealt with differently.
- Individual Accountability. Senior management in EU cartels does not seem to suffer from their participation in cartels. For example, Robert Koehler became CEO of SGL Carbon in 2012, after being convicted in 1999 of price-fixing in the graphite electrodes cartel. Imposing tougher sanctions, such as individual prison sentences or disqualification of senior executives from employment in their sector or role, may prevent repeated collusive behaviors (in new firms) and thus, increase deterrence levels.
- Follow-On Damages. Private damage suits may increase deterrence. In the US, private litigation plays a major role in the enforcement of antitrust law. Conversely, access to private damages is relatively new in the EU. A recently adopted EU Directive on damages (11/2014) prevents the use of LP statements in subsequent damage actions. However, Buccirossi et al. (2015) show that the effectiveness of damage actions can be improved if the civil liability of the immunity recipient is minimized and claimants receive full access to all evidence collected by the competition authority. Access to previous cartel decisions, for a given firm, will increase the amount of available information and can increase the likelihood and/or amount of successful damage claims.
- Consent Decrees. These impose conditions on the behavior of convicted firms (e.g. maximum price, and transparency). If these are violated, the authorities intervene, thus lowering the cost of prosecuting recidivists. In the US, decrees were routinely used by the DOJ in the 1960s and 1970s, but the practice was abandoned due to concerns of effectiveness and large costs. More recently, in September 2007, the Brazilian Administrative Council for Economic Defense enacted a resolution that allows for the use of consent decrees with the aim to settle cartel investigations. Two have already been executed.
If recidivism is industry-driven, its prevention may require a different set of tools, including those below, to complement leniency.
- Structural Remedies. Competition authorities have repeatedly permitted mergers among former cartel members, often without review, let alone structural intervention. Davies et al. (2014) examine mergers among former cartel conspirators and conclude that only 29% of the mergers were investigated by the EC. Remedies such as disclosure, divestiture of assets, selling minority shares in competitors, or licensure of intellectual property to competitors may change the nature of competition in the market and make collusion more difficult (see Marx & Zhou, 2015 regarding post-cartel mergers). This is particularly relevant if recidivism is industry-driven.
- Monitoring and screening. Some antitrust authorities have implemented monitoring and screening techniques to identify anticompetitive behavior in a given industry. These initiatives involve the analysis or monitoring of the characteristics of products or market structures that are thought to be more prone to collusion (mostly due to repeated offenses). Some examples are watch lists (e.g. Australia, UK, Chile), price observatories (e.g. Belgium, Spain, France), statistical screens (e.g. US FTC, Korea FTC), gasoline retail in Brazil and public procurement in Sweden (see Abrantes-Metz (2013) for further details on screens).
Conclusions
While literal recidivism, i.e. the formation of a cartel after having been convicted of illegal collusion, appears to be rarely detected in the EU and US, there remain policy gaps closing which could improve competition post-cartel.
A variety of post-cartel policies should be explored for their ability to increase the likelihood that workable competition, rather than tacit collusion or single firm dominance, will emerge. These reduce the reliance of competition authorities on leniency-driven self-reports, which will in turn make leniency more effective and less amenable to strategic use by firms determined to collude.
▪
References
- Abrantes-Metz, Rosa (2013). “Proactive vs Reactive Anti-Cartel Policy: The Role of Empirical Screens.” Available at SSRN: http://ssrn.com/abstract=2284740.
- Buccirossi, Paulo, Catarina Marvão, and Giancarlo Spagnolo (2015). “Leniency and Damages,” CEPR Working Paper DP 10682.
- Connor, John M. (2008). Global Price Fixing, 2nd ed. Berlin: Springer.
- Eichenwald, Kurt (2000). The Informant. New York: Random House.
- Grout, Paul and Silvia Sonderegger (2005) “Predicting Cartels,” Office of Fair Trading, Economic Discussion Paper.
- Levenstein, M., Marvão, C., Suslow, V., 2015. Serial Collusion in Context: Repeat Offenses by Firm or by Industry? OECD Global Forum on Competition. DAF/COMP/GF(10/2015)
- Levenstein, Margaret C., and Valerie Y. Suslow (2015). “Price Fixing Hits Home: An Empirical Study of U.S. Price-Fixing Conspiracies,” working paper.
- Marvão, C., 2015. The EU Leniency Programme and Recidivism. Review of Industrial Organization, 48(1), 1-27
- Marvão, Catarina and Giancarlo Spagnolo (2015a). “What do we know about the effectiveness of leniency policies? A survey of the empirical and experimental evidence,” in Beaton-Wells, C and C Tran (eds.), Anti-Cartel Enforcement in a Contemporary Age: The Leniency Religion, Hart Publishing.
- Marvão, Catarina and Giancarlo Spagnolo (2015b). “Pros and Cons of Leniency, Damages and Screens”. Competition Law and Policy Debate (forthcoming)
- Marx, Leslie M., and Jun Zhou (2015). “The Dynamics of Mergers among (Ex) Co-Conspirators in the Shadow of Cartel Enforcement,” working paper.
- Pirrong, Stephen Craig (1992). “An application of core theory to the analysis of ocean shipping markets” Journal of Law and Economics, 35(1): 89-131.
- Spar, Debora (1994). The Cooperative Edge: The Internal Politics of International Cartels, Ithaca: Cornell University Press.
- van Driel, Hugo (2000). “Collusion in Transport: Group Effects in a Historical Perspective.” Journal of Economic Behavior and Organization, 41(4): 385–404.
- Werden, Gregory, Scott Hammond, and Belinda Barnett (2011). “Recidivism Eliminated: Cartel Enforcement in the United States since 1999,” Georgetown Global Antitrust Enforcement Symposium, Washington DC, Sept. 22, 2011.