Tag: Economic Growth
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.
Russia: Increasing Concentration of the Economy and Low Investment
Author: Oleg Shibanov, New Economic School and Corporate University of Sberbank.
The Russian economy became more concentrated in 2014. The new RBC-500 rating shows that the 643 largest companies in Russia produce 77% of the country’s GDP. Moreover, 94% of the net profit of these companies was generated in the oil and gas sector. This is up from 71% in 2013. This increasing concentration appears unstable at times of huge external shocks on commodity prices.
Evaluating the Political Man on Horseback – Coups and Economic Development
In a new paper (Meyersson, 2015) I examine the development effects of military coups. Coups overthrowing democratically elected leaders imply a very different kind of event than those overthrowing autocratic leaders, and these differences relate to the implementation of authoritarian institutions following a coup in a democracy. Although coups taking place in already autocratic countries show imprecise and sometimes positive effects on economic growth, in democracies their effects are distinctly detrimental to growth. Moreover, when coups overthrow democratic leaders, they fail to promote economic reforms, stop the occurrence of economic crises and political instability, as well as have substantial negative effects across a number of standard growth-related outcomes including health, education, and investment.
Do military coups matter for economic development? After all, successful coups – i.e. where the military or state elites have unseated an incumbent leader – have occurred 232 times in 94 states since 1950 (see Figure 1). Moreover, around a quarter of these overthrew democratically elected governments (Powell and Thyne, 2012). The prevalence of military coups has not been lost on researchers, yet despite an abundance of research aiming to explain the occurrence of coups (see for example Acemoglu and Robinson, 2001; Collier and Hoeffler, 2006 & 2007; Leon, 2014; Svolik, 2012) much less research has focused on its economic effects (two exceptions are the papers on covert US operations during the Cold War by Dube, Kaplan, and Naidu, 2011 and Berger, Easterly, Nunn, and Satyanath, 2013). Olsen (1963), for example, claimed that coups “often bring no changes in policy.” Londregan and Poole (1990), in their panel-data analysis, find no effects of coups on income.
By now, there is mostly a consensus that significant military influence in politics is detrimental for democracy (Dahl, 1971; Huntington, 1965; Linz and Stepan, 1996). Nonetheless, military coups overthrowing democratically elected governments are often met with ambiguity. Western governments have a long history of tacit support for military coups overthrowing democratic governments, be it left-leaning governments in Latin America or Islamist governments in the Middle East and North Africa (Schmitz 2006). Commentators expressing support for coups often do so invoking extreme outcomes to represent the counterfactual to the military coup; if Pinochet had not overthrown President Allende, the latter would have created a Castro-style regime in Chile; if the Algerian army hadn’t annulled the elections in 1992, the Islamist FIS would have turned Algeria into an Islamist dictatorship in the Maghreb, and so on (Los Angeles Times 2006, Open Democracy 2013). Similarly, the fault for the coup and preceding problems fall invariably upon the ousted leader, with the coup constituting an unfortunate, but necessary, means to rid the country of an incompetent, if not dangerous, leader (Foreign Policy, 2013).
Other commentators have pointed out the risks of allowing a military to intervene and dictate post-coup institutions to their advantage; a “Faustian” bargain likely to bring regime stability but no solution to the real underlying problems behind the conflict in the first place. Yet others lament the human rights abuses following coups, and the inherent ineptitude of military leaders in running the economy (NYT, 2013; New Republic, 2013; Washington Post, 2013).
Figure 1. Successful and Failed Coup Attempts by Country and Year
Notes: The graph shows successful (solid circles) and failed coup attempts (hollow circles) by country and year, and aggregated by country (right graph) as well as by year (top graph). A circle in blue means the political regime was classified by Cheibub et al 2010 as a democracy in the year before the attempt and a red circle means they classified the regime as an autocracy.
Military coups tend to be endogenous events, and establishing a causal relation between coups and development is therefore a challenge. The unobservable likelihood of a coup – often referred to as coup risk (Collier and Hoeffler, 2006 & 2007; Londregan and Poole, 1990; Belkin and Schofer, 2003) – may be driven by many factors also affecting a country’s development potential, such as weak institutions, the military’s political power, social conflict, and economic crises etc.
In order to address this problem, I employ several empirical strategies including comparing successful versus failed coup attempts, matching methods, as well as panel data techniques, using a dataset of coup attempts during the post-World War II era. These methods facilitate, in different ways, comparisons of development consequences of coups in situations with arguably more similar degrees of coup risk.
Of significant importance is distinguishing coups when they occur in clearly autocratic settings from those where they overthrow democratically elected governments. I show that a military coup overthrowing a regime in a country like Chad may have very different consequences than a military leader overthrowing a democratically elected president in a country like Chile. In the former, a coup appears to constitute the manner in which autocracies change leaders. In the latter, coups typically imply deeper institutional changes with long-run development consequences.
I find that, conditional on a coup-attempt taking place, the effect of coup success depends on the pre-intervention level of democratic institutions. In countries that were more democratic, a successful coup lowered growth in income per capita by as much as 1-1.3 percent per year over a decade. In more autocratic countries, I find smaller and more imprecisely estimated positive effects. This effect is robust to splitting the sample by alternative institutional measures, as well as to a range of controls relating to factors such as leader characteristics, wars, coup history, and natural resources. As Figure 2 illustrates, the economic effect of coups tend to worsen over time. Extending the analysis to matching and panel-data methods reveal these results to be highly robust.
Figure 2. Relationship between a Successful Coup and Growth in GDP per capita
Notes: The three graphs represent the coefficient on a successful coups on growth in GDP per capita (PPP) between year t-1 and t+s with s given by the x-axis for all regimes(left), autocracies (middle), and democracies (right). Controls include period t-1 values of log GDP per capita, annual growth, log population, PolityIV index, annual change in the PolityIV index military expenditures as a share of GDP, annual change in military exp/GDP, military personnel as a share of population, years since the last coup, total number of previous coups, social unrest, leader tenure, as well as continent and year dummies respectively. See Meyersson (2015) for details.
A commonly held view is that coups overthrowing democratically elected leaders often provide an opportunity for engaging in unpopular but much needed economic reforms. Not only do I show that coups fail at this, but also that they tend to reverse important economic reforms, especially in the financial sector, while also leading to increased indebtedness and an overall deteriorating net external financial position, and an increased propensity to suffer severe economic crises. A documented reduction in social spending suggests a shift in economic priorities away from the masses to the benefit of political and economic elites.
Whereas coups occur mostly in dire situations, their prescriptions, as shown, rarely constitute adequate remedies to the underlying problems, as the institutional changes brought by these events show clear detrimental development consequences. Any short-lived benefit of regime stability a coup brings, comes at a steep economic, political, and human cost in the longer run.
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References
- Acemoglu, Daron and James A. Robinson, “A Theory of Political Transitions,” The American Economic Review, Vol. 91, No. 4 (Sep., 2001), pp. 938-963
- Berger, Daniel, William Easterly, Nathan Nunn, and Shanker Satyanath. 2013. ”Commercial Imperialism? Political Influence and Trade during the Cold War.” American Economic Review, 103(2): 863-96.
- Belkin, Aaron, and Evan Schofer, 2003,“Toward a Structural Understanding of Coup Risk”, Journal of Conflict Resolution, Vol. 47 No. 5, October 2003 594-620
- Cheibub, Jos ́e Antonio, Jennifer Gandhi, and James Raymond Vreeland, 2010, “Democracy and dictatorship revisited,” Public Choice (2010) 143: 67-101.
- Collier, Paul and Anke Hoeffler, 2006, “Grand Extortion: Coup Risk and the Military as a Protection Racket,” working paper
- Collier, Paul and Anke Hoeffler, 2007, “Military Spending and the Risks of Coups d’ ́etat,” working paper.
- Dahl, Robert A., Polyarchy: Participation and Opposition, Yale University Press 1971.
- Dube, Arindrajit, Ethan Kaplan, and Suresh Naidu, “Coups, Corporations, and Classified Infor- mation”, Quarterly Journal of Economics, Quarterly Journal of Economics, 2011 (Vol. 126, Issue 3)
- Foreign Policy, “Blame Morsy,” Michael Hanna, July 10 2013,
- Huntington, Samuel P., 1965, “Political Development and Political Decay,” World Politics, 386- 429
- Leon, Gabriel, 2014, “Loyalty for Sale? Military Spending and Coups d’Etat,” Public Choice 159, 363-383
- Linz, Juan, and Alfred Stepan, Problems of Democratic Transition and Consolidation: Southern Europe, South America, and Post-Communist Europe, Johns Hopkins University 1996
- Los Angeles Times, “Iraq needs a Pinochet”, Jonah Goldberg, December 14, 2006
- Londregan, John B and Kenneth T. Poole, “The Coup Trap, and the Seizure of Executive Power,” World Politics, Vol. 42, No. 2 (Jan., 1990), pp. 151-183
- Meyersson, Erik, 2015, Political Man on Horseback – Military Coups and Development, working paper, http://erikmeyersson.com/research/
- Olsen, Mancur, “Rapid Growth as a Destabilizing Force,” The Journal of Economic History, Vol. 23, No. 4 (Dec., 1963), pp. 529-552
- Open Democracy, February 11 2013, https://www.opendemocracy.net/arab-awakening/hicham-yezza/how-to-be-different-together-algerian-lessons-for-tunisian-crisis.
- Powell, Jonathan M, and Clayton L Thyne, 2012, “Global instances of coups from 1950 to 2010: A new dataset,” Journal of Peace Research 48(2) 249-259
- Schmitz, David F. “The United States and Right-Wing Dictatorships”, Cambridge University Press 2006
- Svolik, Milan W., The Politics of Authoritarian Rule, Cambridge University Press 2012.
- The New Republic, “Egypt Officially Declares What Is and Isn’t Important”, Nathan J. Brown, July 9 2013, http://www.newrepublic.com/article/113792/egypt-president-adli-mansour-makes-constitutional-declaration.
- The New York Times, “A Faustian Pact: Generals as Democrats”, Steven A. Cook, July 5, 2013
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
The Aid Effectiveness Literature: Is It Over Yet?
Author: Maria Perrotta Berlin, SITE.
After several decades of studies, the academic community still does not have an answer to whether foreign aid affects growth, and in which direction. Part of the reason for such an outcome may lie in a wide variety of models, techniques and data used. However, the main reason is probably that the broad spectrum of effects is difficult to disentangle when looking at the question at an aggregated level.
Crisis and Trust
Authors: Maxim Ananyev and Sergei Guriev, CEFIR
Our research uses the 2008-2009-crisis experience in Russia to identify the relationship between income and trust. In 2009, Russian GDP fell an 8-percent drop in 2009. The impact of the crisis was very uneven among Russian regions because of their differences in industrial structure inherited from the Soviet times. We find that the regions that specialize in producing capital goods, as well as those depending on oil and gas, had a more substantial income decline during the crisis. The variation in the industrial structure allows creating an instrument for the change in income. After instrumenting average regional income, we find that the effect of income on generalized social trust (the share of respondents saying that most people can be trusted) is statistically and economically significant. Controlling for conventional determinants of trust, we show that a 10 percent decrease in income is associated with 5-percentage point decrease in trust. Given that the average level of trust in Russia is 25%, this magnitude is substantial. We also find that the post-crisis economic recovery did not restore the pre-crisis trust level. Trust recovered only in those regions where the 2009 decline in trust was small. In the regions with the large decline in trust during the crisis, trust in 2014 was still 10 percentage points below its pre-crisis level. This has straightforward policy implications: governments should pursue generous countercyclical policies especially in the areas that are the most vulnerable to macroeconomic shocks.
Stimulating Growth in Belarus: Selecting the Right Priorities
Belarus is suffering from a substantial decline in economic growth potential. Both the government and academic researchers are discussing a number of options for stimulating the growth rate and enhancing its stability. The government tends to focus on equipment investments as the priority for growth stimulation. However, academic researchers have revealed huge unused potential for growth in institutional environment in Belarus. In this brief, we deal with the issue of selecting the right priorities in growth stimulation policies.
Nowadays emerging markets as a whole, and especially countries of Central and Eastern Europe (CEE) and the CIS region suffer from the problem of declining growth potential (IMF, 2013). Belarus is not an exception from this trend. However, the situation in Belarus is distinct from the regional patterns since the majority of factors behind the declining growth potential in Belarus differ from those in other CEE and CIS countries. While the IMF (2013) emphasizes constraints for capital accumulation as the core challenge for the CEE region, the major problem in Belaurs is the lack of productivity growth. Capital accumulation has in fact been huge and ineffective in Belarus in recent years (Kruk and Bornukova, 2014). Hence, a key issue for Belarus for restoring output growth, and enhancing its sustainability, is total factor productivity. Some degree of consensus about this priority exists both in the academic sphere and among economic policy makers. However, further questions about the sources of productivity growth generate ambiguous solutions, which result in different growth strategies.
Embodied Technical Progress versus Neutral Productivity Growth
Two years ago, the Belarusian government initiated a so-called modernization campaign. The idea of this campaign was to accomplish rapid re-equipment of large Belarusian firms, which was expected to increase their productivity. The government considers this channel to be self-sufficient, hence staking on it almost exclusively.
At the same time, a number of both academic (World Bank, 2012; Cuaresma et al., 2012; Kruk and Bornukova, 2014) and economic policy studies (IMF, 2012) emphasize the necessity of institutional changes for productivity growth. Gains in productivity herewith are expected due to improved incentives by firms and more efficient allocation and usage of factor inputs by firms.
From an academic perspective, the first approach may be interpreted as one based on technical progress embodied in capital (embodied technical progress, ETC). In other words, equipment investments are to provide productivity growth per se (De Long and Summers, 1991; Greenwood et al., 1997; Hernstein and Krusell, 1996). More recent studies provide evidence on the importance of this mechanism for a modern transition agenda (Skare and Sinkovic, 2013).
The second approach deals with so-called neutral productivity growth (NPG), i.e. productivity gains independent of the quantity of either capital or labor inputs. NPG can be divided into a number of channels: neutral technical change, technical efficiency (characterized by the distance between the actual position of the firms and the production frontier), scale economies, and allocative efficiency (Coelli et al., 2005).
Impact of NPG and ETC on Productivity: Complementary or Substitutive?
As a rule, growth models do not assume any trade-off between NPG and ETC. For instance, a firm that succeeds to implement a new technology (independent on capital of labor inputs) will generate higher productivity. This will attract additional inputs – capital and labor – given higher factor returns due to productivity gains. New capital (equipment), in turn, may generate additional gains in productivity. Hence, productivity growth may stem from both tracks complementing each other. In this sense, the issue of decomposing actual sources of productivity growth – capital or technology itself – becomes largely meaningless.
The idea of the Belarusian modernization – that ETC comes first and other things do not matter – substantially changes this growth pattern. Rapid technical re-equipment makes the lack of financial sources for investments roughly inevitable, as national savings can hardly be enough for a surge in investments. The government in Belarus partially solves this problem through centralized reallocation of financial resources. However, this reallocation negatively impacts allocative efficiency (Kruk, Haiduk, 2013). Further, it is likely to have a similar adverse effect on technical efficiency and scale economies. Hence, in Belarus the trade-off between ETC and NPG arises: artificially pushing ETC suppresses NPG.
Criterions for Assessing Effectiveness of NPG and ETC
A misbalance between the ETC and NPG resulting from an artificial ETC stimulation raises serious concerns about the desirability of this policy. However, the ‘modernization ideology’ uses a counter-argument: productivity gains from ETC may be sufficiently large to allow sacrificing potential gains from NPG growth.
From this perspective, we can compare both channels through the following criterions:
- How large is the productivity effect from both channels
In order to get a quantitative assessment, we employ the model by Greenwood et al. (1997) that dissect NPG and ETC for a balanced growth path (the equilibrium trajectory when capital and output grow with the same rates). We apply our estimates of the Belarusian growth parameters to the model. For assessing ETC growth rate, we employ an approach by Hernstein and Krusell (1996). The latter produces an assessment of an average ETC productivity growth in 2005-2012 from -1.55 up 6.40% (depending on the measures of correspondent prices). The mean of the corridor seems to be rather close to the one Hernstein and Krusell (1996) estimate for developed countries (3-4%). Hence, in the current exercise we use a value of 3.5% for the Belarusian ETC. In this manner, we get the estimates of output growth-rate returns on growth rate of NPG (1.69) and ETC (0.41). This means that a change in the growth rate of NPG by 1 percentage point results in 1.69 percentage point increase of output growth rate, while the latter will increase by only 0.41 in case of 1 percentage point increase of ETC. However, the range in which NPG and ETC may vary due to government policies is highly important as well.
- How large is the sensitivity of NPG and ETC to government stimulation?
Economic modelling assumes that, once an economy is on a balanced growth path (the stock of capital grows by the same growth rate as output), the ETC growth rate is exogenously determined by global technology gains. In this case, an attempt to push ETC by excessive capital accumulation will only generate a savings-investment misbalance. Hence, this kind of stimulus policy makes sense only if the economy has not yet entered the balanced growth trajectory. Whether this is the case for Belarus is still an open question, although findings in Kruk and Bornukova (2014) signal that this path has already been achieved.
Existing options for stimulating NPG seem to be much more numerous. First, technical efficiency and scale economies may progress substantially due to a changing environment, with more intense competition and tighter budget constraints. Such environment will force firms to increase their flexibility and adaptability, which will finally result in more technical efficiency and more proper scaling. Second, Belarus has accumulated great growth potential in the sphere of allocative efficiency. Due to long periods of inefficient capital accumulation, its proper reallocation can provide up to 10% growth of output (Kruk and Bornukova, 2014).
- What are the costs of growth stimulation?
In the case of NPG, there are actually no direct costs. Enhancing more flexibility and adaptability for firms, along with establishing tough budget constraints does not require new financial injections. These goals may be achieved through legislative activity, implementing new practices and standards into business activities.
As for ETC, a number of undesirable outcomes may be interpreted as costs. First, while stimulating productivity growth due to technology background, artificial ETC stimulation may further dampen allocative efficiency in Belarus. Second, an attempt to boost it requires sources for additional investments, which typically exceed available savings. Hence, the country is likely to face a deficit of savings-investments balance. The latter is to determine current account deficit, the necessity of external borrowings, and vulnerability of financial market.
Conclusion
In the last two years, Belarus has spent considerable effort towards modernization and re-equipment of large industrial enterprises. However, the most important outcome from the Belarusian experience – artificial stimulation of ETC – is likely not worth the effort as it might hinder allocative efficiency. Because of such practices, Belarus has faced an unfavorable trade-off between ETC and NPG.
However, this trade-off should not be treated as a predetermined one. It is possible and desirable to avoid it. In the long term, the growth should stem from both tracks – NPG and ETC. However, in a shorter perspective, more returns in terms of welfare may be obtained through a more efficient allocation of resources, improvements in the institutional environment, and more flexibility and adaptability by firms.
References
- Cuaresmo, J., Oberhofer, H., Vincelette, G. (2012). ‘Firm Growth and Productivity in Belarus: New Empirical Evidence from the Machine Building Industry’, World Bank, Policy Research Working Paper No. 6005.
- De Long, J., Summers, L. (1992). ‘Equipment Investment and Economic Growth’, Quarterly Journal of Economics, 106, 2, pp. 445-502.
- Greenwood, J., Hercowitz, Z., Krusell, P.(1997). ‘Long-Run Implications of Investment-Specific Technological Change.’ American Economic Review, 87, 3, pp. 342–362.
- Hornstein,A., Krusell, P. (1996). ‘Can Technology Improvements Cause Productivity Slowdowns?” In NBER Macroeconomics Annual 1996, eds. Julio J.Rotemberg and Ben S. Bernanke. Cambridge, MA: MIT Press.
- IMF (2013). ‘Central, Eastern and Southeastern Europe: Faster, Higher, Stronger – Raising the Growth Potential of CESEE’, Regional Economic Issues, October 2013.
- IMF (2012). ‘Republic of Belarus: Selected Issues’, IMF Country Report No.12/114.
- Kruk, D., Bornukova, K. (2014). ‘Belarusian Economic Growth Decomposition’, Belarusian Economic Research and Outreach Center, Working Paper No.24
- Skare, M., Sinkovic, D. (2013). ‘The Role of Equipment Investments in Economic Growth: Cointegration Analysis’, International Journal of Economic Policy in Emerging Economies, 6, 1, 2013.
- World Bank (2012). ‘Belarus Country Economic Memorandum: Economic Transformation for Growth’, Country Economic Memorandum, Report No. 66614.
The crisis in Ukraine and the Georgian economy
We analyze how the crisis in Ukraine will likely impact the Georgian economy and distinguish between short-run and long-run effects. We argue that the short-run effects are transmitted through trade and capital flows and that they are rather negative for Georgia and can hardly be bolstered. In the long-run, however, the crisis could improve the competitiveness of the Caucasus Transit Corridor, an important trading route between Europe and Central Asia Georgia participates in. We give recommendations how political decision makers could support such a development in the wake of an impairment of the northern Ukrainian transit routes.
Introduction
When Ukrainian President Victor Yanukovich decided not to sign the association agreement with the European Union and instead opted for a Russian package of long-term economic support, many Ukrainians perceived this not to be a purely economic decision. Rather, they feared this to be a renunciation of Western cultural and political values, and – to put it mildly – were not happy about this development.
The Russian political system, characterized by a prepotent president, constrained civil rights, and a government controlling important parts of the economy through its secret service, is not exactly the dream of young Ukrainians. Russia can offer economic carrots, but these do not count much against the soft power of Europe that comes in the form of political freedom, good governance, and economic development to the benefit of not just a small group of oligarchs.
Hence, it was all but surprising when many young Ukrainians took their anger about Yanukovich to the streets. After protests that lasted for nearly three months, President Yanukovich fled the country, a temporary government took over, and chaos broke out on the Crimean peninsula.
The dispute about the Crimea has the potential to impede the relations between Russia and the West for a long time to come, in particular if Russia enforces an annexation of the territory. Moreover, the tensions could quickly turn into a military conflict. The aircraft carrier USS George H.W. Bush was moved into an operational distance to the Crimea, accompanied by 20 smaller U.S. warships, and 12 additional fighter planes will be stationed in Poland. Yet even if there will be no direct confrontation between official Russian and U.S. forces, Ukraine could become the battleground of a proxy war, a kind of conflict that was common in the Cold War era. In this respect, one can already read the writing on the wall: the new Ukrainian government begs the U.S. for supplying arms and ammunition, and while the Obama administration is still reluctant to give in to such requests, the call is supported by hawkish U.S. congressmen who might finally prevail.
Ukraine is a country that is geographically close to Georgia and, like Georgia, has vital economic stakes in the Black Sea area. Georgia will not be unaffected by whatever happens in Kiev and Simferopol. In this policy brief, we will inform policy makers about the likely short-run and long-run economic consequences of the turmoil in Ukraine, discuss the challenges and opportunities that may arise, and derive some policy recommendations.
Short-run economic consequences
The crisis in Ukraine will almost instantaneously affect trade and capital flows between Georgia, Ukraine, and Russia. The effects will likely be negative and hit Georgia in a situation of economic recovery.
The Georgian real GDP growth rates were 6.3% in 2010, 7.2% in 2011, and 6.2% in 2012, and the real GDP per capita evolved from about 2,600 USD to about 3,500 USD in this time, but the upsurge discontinued in 2013 (if no other source is mentioned, figures presented in this policy brief (including those in the graphs) come from the Georgian statistical office GeoStat). ISET-PI, in its February 2014 report on the leading GDP indicators for Georgia, estimates the GDP in 2013 to be 2.6%, while GeoStat, the statistical office of Georgia, believes it to be 3.1%.
The unsatisfactory performance of the Georgian economy in 2013 was arguably caused by political uncertainties resulting from the government change that took place in late 2012, and as these uncertainties are largely overcome, most economists believe that Georgia will get back to its remarkable growth trajectory in 2014. The IMF, in its Economic Outlook, predicts a real GDP Growth of 6% in 2014, and the government of Georgia expects this number to be 5%. With an escalating crisis in Ukraine, it is questionable whether these rosy forecasts are still realistic.
Effects on imports
In 2013, Ukraine and Russia were the 3rd and the 4th largest importers to Georgia, respectively. Graph 1 shows the top five importers to Georgia, which together make up about 50% of total imports. The imports from Ukraine and Russia are mainly comprised of consumption goods: of all goods that were imported between 2009 and 2013 from Ukraine and Russia, about 30% were foodstuff. The ten main import goods in this time (in order of monetary volume) were cigarettes, sunflower oil, chocolate, bread, cakes, meat other than poultry, poultry, and sugar.
If the supply of these goods would be reduced through a breakdown of production and logistics, roadblocks, damaged infrastructure etc., the consequences for Georgia would not be utterly severe. From Ukraine and Russia, Georgia receives few goods that are (1) needed for investment projects and (2) cannot be produced domestically (an example of sophisticated investment goods that need to be imported would be ski lifts for tourism projects). Moreover, as Ukraine and Russia supply primarily standard goods that are produced almost everywhere, it is unlikely that a cutback in their imports would lead to sharp price rises in Georgia. Very quickly, increased imports from other countries would close any supply gaps. In addition, many imported consumption goods, like Ukrainian orange juice, are but luxury for ordinary Georgians, who buy their food in cheap domestic markets that sell almost exclusively local products.
Effects on exports
A small anecdote may illustrate the status of Georgian products in the Russian market. In the late 1940s and early 1950s, Stalin used to invite his comrades to his Kuntsevo dacha almost every night. At these occasions, he drank only semi-sweet Georgian red wine. His clique, usually preferring Russian vodka, adopted this habit out of fear to displease the dictator. Yet the real highlight of these nightly gatherings took place after midnight, when an opulent feast began, featuring all the delicacies of the Georgian cuisine. Through Stalin (and the fact that Georgia was a preferred destination of Soviet tourism), Georgian food obtained an excellent reputation in most countries of the former Soviet Union, and, to the dismay of Georgians, some younger Russians even do not know that Khinkali is not an originally Russian dish.
As can be seen in Graph 2, Russia and Ukraine are among the top 5 destinations for Georgian produce, together absorbing about 14% of total Georgian exports in 2013. In 2006, two Georgian products that are traditionally highly popular in Russia, namely wine and mineral water (the famous “Borjomi” brand), were banned from the Russian market. Yet in the wake of the diplomatic thaw that set in after the new government assumed power last year, this ban was lifted, and in 2013, the export of these goods regained momentum. In 2013, 68% of all wine exported from Georgia was sold in Russia and Ukraine (44 and 24 percentage points, respectively). In both countries, Georgian wines are sold at the higher end of the price range and are typically consumed by people with middle and high income. It is likely that these exports, in particular those to Ukraine, will be affected considerably by the crisis. This may happen through decreased demand for luxury foods and through a possible depreciation of the Ukrainian hryvna and the ruble vis-à-vis the Georgian lari.
Another sector that may be affected by the situation in Ukraine is the car re-export business. Georgia imports huge numbers of used cars from the U.S., Europe, and Japan, and passes them on to countries in the region. While this business hardly yields potential for real economic progress, it accounts for roughly 25% of Georgian exports! Of these 25%, about 7 percentage points go to Russia and Ukraine. Moreover, many cars are imported to Georgia on the land route from Europe through Ukraine and Russia (often driven by private, small-scale importers). If it will become more difficult to cross the border between Russia and Ukraine, this business, providing income to many low-skilled Georgians, may be at risk.
It should also be noted that Ukrainians and Russians make up an ever-increasing share of the tourists coming to Georgia (though the biggest group of tourists are Israelis). Also through this channel, an economic downturn in Ukraine and Russia will have unpleasant consequences for Georgia.
Effects on capital flows
According to the National Bank of Georgia, in 2013 a total of 801 mln USD was flowing in from Russia (see Graph 3). Ukraine contributed 45 mln USD to the money inflows, still significant for an economy as small as Georgia’s. An economic downturn in Russia and Ukraine would hit many Georgian citizens, often pensioners and elderly people, who depend on remittances of their children and other family members sent from these countries. This may aggravate a trend that already exists: in January 2014, money inflows decreased by 4% from Russia and by 5% from Ukraine (compared to January 2013).
Long-run economic consequences
Most of the economic dynamics Georgia experienced since 2003 was “catch up growth”. A country permeated by corruption, with a dysfunctional police and judicial system, without protection of property rights and contract enforcement, will grow almost automatically when the government restarts to fulfill its basic functions. Yet once this phase of returning to normal economic circumstances is over (Georgia probably is already in this situation), high growth rates can hardly be achieved without a strong export orientation of the economy, in particular when an economy is as small as Georgia’s. Most economists concerned with Georgia are therefore struggling to identify economic sectors where Georgia is in a good position to develop export potential. The National Competitiveness Report for Georgia, written in 2013 by the ISET Policy Institute on behalf of USAID, therefore extensively discusses the question what Georgia can deliver to the world. Though not related to export in a classical sense, the report points out that one of the advantages Georgia has is its geographical location, providing for possibilities to transform Georgia into a logistics hub.
There are three main routes to transport goods from Europe to the Central Asian countries (e.g. from Hamburg to Taraz in Kazakhstan). One route goes via the Baltic ports of Klaipeda or Riga, and then through Ukraine and Russia, and another route goes overland through Ukraine. A third one, the so called Caucasian Transit Corridor, has the Georgian port city of Poti and Turkey as its Western connection points, then goes through Georgia, Azerbaijan, and the Caspian Sea, and further east it splits up into a Kazakhstan and a Turkmenistan branch.
According to the Almaty based company Comprehensive Logistics Solutions, the fastest and cheapest route is the one through the Baltic ports. The transport from Hamburg to Taraz takes around 33 days and costs 6,220 USD per standard container. The overland transport via Ukraine takes around 34 days and costs 7,474 USD. Finally, transport through the CTC currently takes the longest time, namely around 40 days, and costs 6,896 USD.
Unlike many other economic activities, competition for transportation is more or less a zero-sum game played by nations. If transport through Ukraine and Russia will be restrained due to closed borders and political and economic instability, the total transport volume will not change substantially. Rather, instead of going through the northern routes, the goods will flow through the CTC. A similar development could be observed when the embargo against Iran was tightened and shipping goods through Iranian ports became increasingly difficult for Armenia and Azerbaijan. As a result, Azerbaijan, traditionally importing through Iran and exporting through Poti, now facilitates both its imports and exports through Poti.
This is a great chance for Georgia if it wants to become serious about transforming into a logistics hub. In our policy recommendations, we will speak about how to utilize on this opportunity.
Policy recommendations
Georgia can do little to bolster the short-run effects that are transmitted through the trade and capital flow channels. Political decision makers should be aware of problems that might arise for particularly vulnerable groups in the population, like pensioners who lose income in case remittances from Russia and Ukraine run dry, and help out with social support if necessary.
Regarding the long-run impact, Georgia should use this opportunity for gaining ground in the competition with northern transit routes. The Caucasus Transit Corridor can become much faster and cheaper if (a) a deepwater port and modern port facilities with warehouses will be built in Poti, (b) the road and train infrastructure will be improved, and (c) it will be easier to bring cargo over the Caspian Sea. Regarding the latter point, it would be important to assist Azerbaijan in improving the port management at Baku (in particular reducing corruption), and in reforming the monopolistic Azerbaijani State Caspian Sea Shipping Company.
Azerbaijan invests 775 mln USD into the Georgian part of the Baku-Tbilisi-Kars railway, proving their serious interest to upgrade CTC. Given this impressive commitment of Azerbaijan, Georgia should not stand back.
Conclusion
The crisis in Ukraine yields short-run risks and long-run opportunities for the Georgian economy. While there is little that can be done about the risks, the opportunities call for courageous steps to improve the Caucasus Transit Corridor. If the countries that hold stakes in the CTC are now further reducing the cost of transportation and make the route faster and more customer-friendly, the CTC may establish itself as the main trading route connecting Europe and Central Asia. Once critical investments have taken place, CTC’s advantage could be sustained beyond the current crisis. It is a competitive route that simply needs upgrading, which can happen now as a fallout of the conflict between Ukraine and Russia.
References
- Leading GDP Indicators for Georgia, The ISET Policy Institute, February 2014, http://www.iset-pi.ge/index.php?article_id=711
- The National Competitiveness Report for Georgia, The ISET Policy Institute, 2013, http://www.iset-pi.ge/index.php?article_id=713
- World Economic Outlook, The International Monetary Fund, October 2013, http://www.imf.org/external/pubs/ft/weo/2013/02/
Decomposition of Economic Growth in Belarus
During the last decade Belarus was one of the leaders of growth in the CEE region. Kruk and Bornukova (2013) have analyzed the sources of growth and found that capital accumulation was the main contributor to growth. The contribution of total factor productivity (TFP) to growth was, on the contrary, quite modest. On the sectoral level, capital accumulation was not always accompanied by the increases in TFP. Hence, the new growth policy, modernization, with the bottom line “more capital” may not be the best option for enhancing productivity-based growth. The competitive advantages of Belarus lie in the resource-based and non-tradable sectors, while the majority of the manufacturing sectors are lagging behind in productivity. Belarus has symptoms of a Dutch disease without the trade surplus, and the devaluation of 2011 did not cure it.
During 2003-2012, Belarus had an average growth rate of 7.1%, and during the ‘fat years’, i.e. 2003-2008, it was even higher – 9.5%. Intuitively, this prominent growth is questionable, as it was achieved in the context of dominating state ownership, centralized allocation of resources, government’s control at the factor and goods markets, as well as poor infrastructural reforms (for instance, according to the indices of the EBRD). The Belarusian case challenges the mainstream paradigm of growth in transitional countries, which assumes that the progress in market reforms is the key factor for high and sustainable growth.
The simplest and most widespread explanation of the Belarusian phenomena is based on ‘non-standard’ gains in productivity. This approach assumes that productivity is the engine of growth (World Bank (2012); Demidenko and Kuznetsov (2012)). To a large extent, these gains in productivity are seen as “artificial”, resulting from Russian injections into the Belarusian economy: cheap gas, specific schemes of oil trade, and preferences in access to the Russian markets (Kruk (2010)). However, under this approach, decomposing the growth in productivity by ‘natural’ and ‘artificial’ parts is hardly possible, as the impact of these factors is already hidden in the available data.
The IMF (2010) gave a substantially different explanation of Belarusian growth. They claimed that the average growth of 8.3% over the period of 2001-2008 was mainly capital-based with a contribution of 4.8 percentage points, while the contribution of productivity growth was only 3.0 percentage points (the rest of growth was explained by labor and cyclical factors).
The main reason behind the substantial difference in the explanation of growth factors is the statistical data on capital used during the growth accounting exercise. Belarusian official statistics reports the data on capital stock based on a direct survey of capital assets according to both gross and net (wealth) capital concept. However, the growth rates of capital are reported only for the gross stock of capital. These growth rates are questionable as they demonstrate ‘unnatural stability’ – they fluctuate around 2% for the last 20 years, despite the fact that investments during this period has displayed huge and volatile growth. Statistical offices in other CIS countries have reported similar dynamics of the capital stock. Voskoboynikov (2012), and Bessonov and Voskoboynikov (2008) show that this trend is a consequence of the statistical methodology used in Russia (which the Belarusian methodology is very similar to). In particular, the trend is driven by biased capital investments deflators (which are overestimated) from the periods of high inflation (1990-s and early 2000-s).
If official data is used as the capital input for the growth accounting exercise, the contribution of TFP to growth will be overestimated. Hence, in the studies of the World Bank (2012) and Demidenko and Kuznetsov (2012), the leading role of TFP may be due to the use of the official data on the capital stock.
Motivated by this concern, we use two different methods to evaluate the value of capital inputs (see Kruk and Bornukova (2013) for more details). The first alternative to using the data from direct capital survey is to exploit a perpetual inventory method (PIM): the historical assessment of initial capital stock is further adjusted by the flow of investments and depreciation. However, if there is a bias in deflators within the sample, the series will also be distorted. This problem may be eliminated if the initial stock will be selected at the moment when there is no bias in investment deflator, in the period of moderate inflation. We call this approach PIM-backward.
The second approach to constructing capital series exploits the concept of productive capital and the data on the flow of capital. It assumes that the productive capacity of a capital good depends on its age. The productive stock of a capital good (i.e. the gross stock adjusted by the age-efficiency profile) generates a flow – capital services. The latter is the productive stock adjusted by the user cost of the individual capital good. For the total output of an industry (or economy) one should aggregate the inputs by different capital goods, which in contrast to the net (wealth) concept depends not only on the value of capital goods, but also on their user costs. This approach has solid theoretical foundations, which is the reason it is prioritized in productivity studies.
From the view of available data in the case of Belarus, this approach has a number of powerful advantages. First, we use individual deflators for individual capital goods, which are expected to be less biased than total deflators for the industry. Second, we use heterogeneous depreciation rates for each capital good in each industry based on actual data of ‘accounting depreciation’, while we would have to use homogenous assumptions for each industry in the case of net (wealth) concept. Third, we can exclude residential housing from our measure of capital input.
There are, however, also disadvantages. First, data of newly employed capital goods (in direct surveys of capital assets) and data on capital investments differ rather substantially. Traditionally, the data on capital investments is treated as more reliable, but based on the direct surveys of capital assets we have to use the series of newly employed capital goods as a flow variable when running PIM. Second, we use exogenous real interest rate for computing unit user costs, but the results are very sensitive to our assumptions on the real interest rates across industries. Third, the necessity to exclude residential housing from the data (because of ‘mixed historical prices’) may be interpreted as a loss of information. Given the strengths and weaknesses of the approach, we prioritize it on the industrial level, but prefer the PIM-backward approach for an aggregate economy analysis.
Based on the PIM-backward measure for the total economy (see Figure 1), we may argue that the contribution of TFP to growth was more modest during the last decade than what was reported in the majority of previous studies on Belarusian growth. This finding is of fundamental importance for the growth agenda: only productivity-based growth may be treated as sustainable, since capital growth will slow down as the capital approaches its stationary value. We argue that only the policy directed to promotion of productivity is vital for growth prospects.
Figure 1. Contribution of Production Factors and TFP to the Growth of Gross Value Added (PIM-Backward Approach) 
The dynamics of productivity divided according to industries (see Table 1) display that the leaders in productivity growth are either industries that produce non-tradable goods (communications, finance, construction) or those that have a chance of ‘artificial productivity gains’ (chemical and petrochemical manufacturing, and fuel).
Table 1. Initial Level and Growth Rates of Productivity in Major Industries 
However, the theory suggests that the leaders in productivity growth should be the industries producing tradable goods. . This contradiction may be interpreted in two ways. First, one may argue that a more competitive environment and larger share of private ownership (which are seen in the financial industry, trade and catering) are the core reasons for high productivity level and growth rates in ‘domestic industries’. Second, an attractive position of ‘domestic industries’ may reflect a high level of domestic prices rather than ‘natural’ productivity. The base year for our computations is 2009, in which both the real effective exchange rate of the national currency and income were relatively high. The devaluation of 2011 fixed the problem only temporarily, since the inflation in 2011-2013 quickly eroded the benefits of the devaluation. Therefore, the indicators, in terms of 2009 prices, may capture the changes in nominal values as the main component of the productivity gains, while from a longer-term perspective it would be seen as mainly price movements without substantial progress in productivity. In our view, the second explanation is the main reason for the non-standard disposition of productivity levels and growth rates among industries.
If that is the case, the bigger picture looks as follows. Industries producing tradable goods suffer from the lack of progress in productivity, i.e. lose their competitive advantage; enhancements in total productivity are mainly due to industries with ‘artificial productivity gains’. The latter allows domestic prices to grow, making a productivity illusion of domestic industries. All together these symptoms are quite similar to the Dutch disease.
One more finding from the productivity analysis at the national level is the lack of productivity gains from reallocation of resources from less productive industries to more productive ones. A scatter-plot between capital accumulation growth rates and TFP growth rates (see Figure 2) demonstrates no clear relationship between them.
Figure 2. Growth Rates of Capital Input vs. TFP Growth Rates in Manufacturing Branches, 2006-2010. 
Notes: The sizes of the circles correspond to industry shares in value added.
However, if there was a free allocation of resources, more productive industries would accumulate more capital. Moreover, the same indicators under the PIM-backward approach demonstrate clear negative relationship. A ‘soft’ interpretation of this phenomenon assumes that the lack of reallocation of capital restrains the development of total productivity. A ‘tighter’ interpretation assumes that at least in some industries there is a trade-off between capital accumulation and productivity gains. For instance, in Kruk and Haiduk (2013) it is shown that spurring capital accumulation through the practice of directed lending leads to losses in efficiency through a number of channels. Hence, the simplest way to increase aggregate productivity is to depart from the centralized allocation of capital and unblock capital inflows to more productive industries and vice versa.
Figure 3 documents the mobility of labor markets across the manufacturing industries in Belarus. While one can expect that labor flow into more productive industries, it is not completely true for the Belarusian manufacturing sector.
Figure 3: Labor growth and TFP growth in industries of Belarusian manufacturing, (capital services approach). 
Notes: The sizes of the circles correspond to industry shares in value added.
Two distinct trends emerge in the labor market. On the one hand, some industries exhibit textbook behavior: increases in TFP are associated with increases in the number of people employed. The best example here is the fuel industry, which experiences TFP increases due to preferential oil prices. However, there are industries that gain TFP and lose labor at the same time. The chemical industry, machinery manufacturing and woodworking are examples of this pattern. These industries have experienced rapid capital accumulation, which, coupled with high gains in TFP, should have contributed to the increases in labor productivity. Surprisingly, though, these industries did not attract more labor. A possible explanation for this counterintuitive pattern is the excessive employment at the beginning of the period in question. In this case, a decrease in the number of people employed may have contributed to the increases of TFP.
Indeed, Figure 4 confirms our hypothesis: labor was flowing from the industries with lower labor productivity to the industries with higher labor productivity in general. Industries in which TFP increased and which were accompanied by a labor decrease, featured low labor productivity in the beginning of the period in consideration, more precisely in 2005. Only the chemical industry exhibited the unexpected behavior: it lost labor despite high initial productivity. By getting rid of excessive employment they were contributing to an increase in TFP.
Figure 4: Labor shifts into the sectors with higher labor productivity. 
Notes: The sizes of the circles correspond to industry shares in value added.
How is Belarus doing relative to other countries? We have compared Belarusian TFP to the TFP of the leader of transition, the Czech Republic, and to the regional leader, Sweden. The Czech Republic is more developed than Belarus (in 2010 Czech GDP per capita (PPP-corrected) was 1.73 times higher than in Belarus), and, theoretically, it should be much more difficult and costly for it to continue approaching the technological frontier. However, our findings suggest that the Czech Republic is catching up with Sweden in terms of TFP, and doing it faster than Belarus (see Figure 5).
Figure 5: TFP of Belarus and the Czech Republic relative to TFP of Sweden, (PIM-backward approach).
Over the last 10 years, Belarus has closed only 5 percentage points of the gap with Sweden. The Czech Republic, where the contribution of TFP to growth was more substantial, has managed to close 8 percentage points of the gap.
In absolute numbers (in ‘international’ dollars of 2010), aggregate TFP in Belarus in 2010 was 2.92 versus 4.66 in the Czech Republic and 9.38 in Sweden (according to the PIM-backwards method). However, the aggregate picture does not reflect the situation in the sectors of the economy and industries of manufacturing.
Table 2: Comparative advantage of Belarusian industries: winners and losers (capital services approach) 
Table 2 documents the comparative advantages and disadvantages of the Belarusian economy in 2010 according to the capital services approach. Both the capital services approach and the PIM-backwards approach produce the same winners and losers list with the only difference being that the PIM-backwards method has the construction sector among winners. It is not surprising to see resource-based industries among the winners (mining and quarrying mainly reflects the extraction of potash, while the chemical industry benefits both from potash and from preferential process for Russian oil). Food manufacturing is among the winners mostly due to the price scissors in agriculture: food producers buy their inputs at very low prices. The non-tradable sectors are among winners, and the majority of the manufacturing sectors are among the losers. Again, this is similar to the symptoms of the Dutch disease. It is ironic that Belarus has symptoms of a Dutch disease without the trade surplus. Instead, the desire of the government to inflate wages combined with the preferences for Russia led to the development of the same diagnosis.
Belarusian economic growth is less TFP-led than is commonly believed. While the labor market proves to be relatively successful in its reallocation of employees and its contribution to aggregate increases in efficiency, the capital market is distorted by government interventions. Capital accumulation does not necessarily lead to increases in TFP, and the new modernization policy with the bottom line of “more capital” may not be the best option for enhancing growth. Our conclusion is that Belarus should find new sources for TFP-led growth.
▪
References
- Bessonov, V., Voskoboynikov.I. (2008). “Fixed Capital and Investment Trends in the Russian Economy in Transition.”, Problems of Economic Transition, 51(4), pp. 6-48.
- Demidenko, M., Kuznetsov, A. (2012). “Ekonomicheskiy rost v Respublike Belarus: factory i otsenka ravnovesiya” (Economic Growth in Belarus: Factors and Equilibrium Assessments), National Bank of the Republic of Belarus, Working Paper No.3.
- IMF (2010). “Sources of Recent Growth and Prospects for Future Growth”, IMF, Country Report No.10/16.
- Kruk, D., Bornukova, K. (2013). “Belarusian Economic Growth Decomposition”, unpublished manuscript.
- Kruk, D., Haiduk, K. (2013). “The Outcome of Directed Lending in Belarus: Mitigating Recession or Dampening Long-Run Growth?”, BEROC Working Paper Series, WP No.22
- Kruk, D. (2010). “Vliyanie krizisa na perspectivy dolgosrochnogo ekonomisheskogo rosta v Belarusi” (The Impact of Crisis on the Perspectives of Long-term Growth in Belarus), IPM Research Center Working Paper Seies, WP/10/07.
- World Bank (2012). “Belarus Country Economic Memorandum: Economic Transformation for Growth”, Country Economic Memorandum, Report No. 66614
- Voskoboynikov, I. (2012). “New Measures of Output, Labour and Capital in Industries of the Russian Economy”, Groningen Growth and Development Centre, Research Memorandum GD
Natural Resources, Intangible Capital and Sustainable Development in a Small, Oil-Rich Region
“Where scientific enquiry is stunted, the intellectual life of a nation dries up, which means the withering of many possibilities of future development.” – Albert Einstein, 1934 The rampant unemployment rates and the general contraction of economic activity in many western countries rekindled the fear of emigration and brain drain, which for a while seemed to be exclusively a developing-world problem. This brief illustrates a potential new approach to the issue, through a recent experience in a small but oil-rich region of Southern Italy.
Economic Growth and Brain Drain
Since the times of Solow, economic theory represents growth as the result of a process not unlike some sort of portfolio management. Just like any individual investor, countries own and need to manage certain assets, characterized by different properties and returns: some are exhaustible, others are renewable or living, and ensure a sustained stream of income. In the original formulations, the economy’s productive assets were identified in land, capital and labor, to which human capital was soon added. In 2006, the World Bank published estimates of 120 countries’ total wealth, in an attempt to introduce a broader view of what these assets really are [1]. The report classified a country’s capital into three main types: natural, produced (physical) and intangible. A striking pattern emerged. While the share of produced assets in total wealth is virtually constant across income groups of countries, the share of natural capital tends to fall with income, and the share of intangible capital rises. This means that rich countries are largely rich because of the skills of their populations and the quality of the institutions supporting economic activity.
There is an important relation between the different types of assets. In order to avoid illusory and temporary growth based on consuming the readily available natural capital, efficient management through saving and investment can transform one type of asset into another, achieving sustainability over time. Although this may sound as no big news, the analysis of the actual savings and rates of growth in the different form of capital reveals far from ideal situations all over the world. In many resource-rich developing countries, savings rates have been negative for many decades, meaning that resource rents have been at best used for consumption. In the worst cases, they have fueled corruption and private enrichment of small elites, as highlighted by the extensive literature on the “resource curse”.
Also, renewable natural resources are often exploited in an unsustainable fashion. One case in point is the thorny issue of fish stocks, but many more examples are discussed in the literature on ecosystem services. Even the intangible capital is under stress in many places. In the wording of the 2006 World Bank report, “intangible assets include the skills and know-how embodied in the labor force; social capital, that is, the trust among people in a society and their ability to work together for a common purpose; all those governance elements that boost the productivity of labor: an efficient judicial system, clear property rights, and an effective government.” Probably the first component in the list, what is traditionally indicated with the term human capital, is the most tangible, observable and relatively controllable part of it.
Controlling the Brain Drain?
Although there are many arguments in favor of international careers and general workforce mobility,[2] some regions experienced negative and prolonged net outflows – emigrants minus immigrants – to the extent that they now face a real risk of hold ups in their economic development. This, due to shortages of vitally needed high-skilled personnel. Even the economic sustainability of many basic services and businesses is in doubt due to the shrinking customer base.
Southern Italy is one of these regions. The net outflow of people with a bachelor or higher degree is negative[3] even at the national level, -2% over the latest ten years. In southern Italy, with a population of just above 13 million, the net balance of emigrants and immigrants over the same period amounts to -630,000. 70% of these people are aged between 15 and 34, and 25% hold at least a bachelor degree. To this figure, which is based on changes in official residence and therefore grossly underestimates the real size of the phenomenon, must be added the 150,000 that on average every year join the flow of internal migrants or long-distance commuters from the south to Northern Italy. Among these people, 47% are aged between 15 and 34, and almost 30% hold a bachelor or higher degree. The reason for these massive outflows can be identified in the labor market dynamics. If we break down the average 22% decline in job creation for youth between 2008 and 2011, new hires declined by 30% for youth with a bachelor degree and 14% for higher degrees, against 11% decline for youth with only secondary education.[4]
As opposed to physical capital, recent research shows that loss of human capital can have long lasting crippling consequences for economic growth (Waldinger, 2012). Among the policies that have been tried in order to stop or counterbalance the brain drain, a first set targets human capital as embodied in the workforce, i.e. tries to attract highly trained people. Probably the most popular are economic incentives in the form of tax rebates, higher wages or other job-related benefits and amenities. This kind of incentive regime exists in Italy since December 2010, though only targeting Italian nationals. However, for many high-skilled professionals, the important factors are others, such as a generally innovative and creative environment, a network with a critical mass, a transparent and competitive labor market not contaminated by politics, high quality support services, and other conditions that are not as easy and cheap to modify. Some countries have played the card of instead attracting prestigious foreign schools to their national territory to prevent their brilliant youth from leaving in the first place. Many famous western universities have already initiated partnerships with or lent their names to schools and universities in these countries and even built replicas of themselves – mostly in Asia – so as to get a toehold in the world’s largest education market, or in the Gulf States, where financial resources abound. There are successful examples of such partnerships in Italy, too.
A different approach has been taken by the new government, with the realization that the country can benefit from the pool of expatriated talents without moving them permanently back. A program of facilitation for visiting scholars and exchange students was thus launched in September 2012. But a step even further is actually possible. A network of scholars and high-skilled professionals that want to contribute to the development of a particular country or region, for example their place of origin, does not require physical presence on the territory, and not even any formal or institutional bond. The only needed ingredient is the Internet. Not removed from the environment and the conditions where they achieved success, these people can actually contribute even more. This is the idea behind, for example, Innovitalia.net and other smaller independent initiatives inspired by the concept of crowd-sourcing.[5]
The Experience of Basilicata
I recently witnessed (what I hope is) the birth of one such network in the region where I am from. Basilicata, also known as Lucania, is a small, poor region of less than 600,000 inhabitants scattered across 131 different municipalities on a territory of barely 10,000 squared kilometers, between the heel and the toe of the boot that the Italian peninsula resembles. Here, the crisis hit especially hard and migration outflows are since then even stronger, especially among youths. According to SVIMEZ (a think tank focused on entrepreneurship and economic activity in Southern Italy), Basilicata has lost 10% of its regional GDP since 2007, much more than the national average of -4.6%. Compared to other large European economies, Spain is currently at -2.7, while Germany and France, notwithstanding the low annual growth rates, are now back at the same level as in 2007. The youth employment rate (with the generous definition of 15-34) is alarmingly low at 30%, down by 15% since 2007, and only 24% for women. As a result, the consumption level of 27.5% of families is now below the poverty threshold, compared with 11% of families at the country level.[6]
Enter Europe’s largest onshore oil and gas reservoir; about 150,000 oil barrels are extracted in Basilicata every day, covering 12% of the national oil demand. The exploitation started in the late 1990s, although the reservoir has been known since at least the 1970s. It is expected that these oil fields will be operational until 2022, but at least one more reservoir with about the same estimated capacity remains unexploited. The regional government has for the time being blocked any new concession, hoping perhaps to negotiate better conditions. The truth is, there have been strong concerns – related to lack of transparency and in some cases to alleged corruption – voiced at the actual quantities of extracted oil and what is a fair distribution of revenues. After more than 10 years, it is hard to claim any major social impact of the project: there is a clear lack of funds to invest in local small and medium size businesses and, as observed above, unemployment in the area remains a problem while the regional population has plummeted.
Is this a case of “resource curse”? Not really. There is no clear evidence of corruption, or elite capture – the problem seems to be mostly poor management and a lack of ideas, mixed with the deeply rooted penchant of local politics for populism and the clientela system (patronage). To give an idea, creativity in using the oil money did not go much beyond the restoration of many of the small town’s pavements and facades. In 2009, in line with the so called “Development Action Plan” of the Berlusconi government, an 80 euro lump sum was distributed to all residents. After the crisis hit harder, the royalties have also been used to cover holes here and there in the current account. Data from the Ministry for Economic Development shows that capital investment in the region went down by 8.5% per year between 2008 and 2011, while current expenditure went up by 3%. Going back to the importance from the growth perspective of savings and investment versus consumption, it is worth remarking that current expenditure is (in most part) consumption.
Can this bounty instead become an answer to Basilicata’s troubles? This was the question driving the first Sustainable Development School, held at the end of October in Viggiano, a small town in the center of the oil field, hosting 23 oil wells. Sponsored by a number of institutions and associations, local or national,[7] the event attracted a group of 45 economists, sociologists, managers and entrepreneurs, engineers and culture sector specialists, in most part born in Basilicata and working or studying abroad. Seven of these participants were instead citizens of various countries in the Middle East and North Africa region, working or studying in Basilicata. This heterogeneous group worked together for two days on concrete proposals to be put on the administrator’s tables, in five main areas: Regional Economy in the new Euro-Mediterranean context, Energy and natural resources, Environmental protection, Infrastructure for environmental protection, Promotion of the historical, cultural and social heritage. Given the context, most projects focused on alternative proposals for how to use the royalties. The keyword was, however, sustainability. Everybody was well aware of the fact that for them to last longer than oil itself, these resources must be saved and earmarked to some productive use that, leveraging on other locally abundant resources, can start off a process of self-sustained development. The projects highlighted the stimulation of local small-scale entrepreneurship and the creation of employment opportunities as necessary ingredients for a fairer sharing of the revenues but most importantly for long-term sustainability.
Many local resources, not fully utilized at present, were brought in as examples: the abundant wood, the underexploited waterways, even the wastewater from bigger agricultural and animal farms, connected to the potential for small-scale generation of energy from renewable sources. On a slightly different note, the list continued with the historical and cultural heritage, natural beauty and the religious and culinary traditions that could support a much more developed tourism industry than what it does today. All of this, in the proposals of the participants, has the potential to support profitable businesses that bring employment to the community. This ingredient is considered crucial, in the perspective that the long-term survival of any (business) initiative requires tying its success to the welfare of the local communities. The focus was thus overwhelmingly on private initiative, with the public confined to the role of investing partner and provider of supportive infrastructure (material and immaterial) and services.
Overarching is undeniably the question of institutional quality, needed as the underlying canvas to support whatever initiative we hope to see blooming. A proposal that did not make it to the finals, though, involved the creation of a stable watchdog, either on local policies in general (and in particular on the use of the royalties) or more specifically focused on the environmental and health impact of the extractive activity. According to the more politically experienced participants, no administration would agree to finance an independent body with the explicit mandate to criticize them. Never mind that this type of institutions is common in other places. In Italy, the one body that currently operates with a watchdog function on the public administration, although limited to the financial aspect,[8] is facing threats of limitations of its powers. A lot remains to be learned. However, the perhaps most valuable outcome of this experience was, if not yet policy change at least a promising method to produce change, by mobilizing a latent ‘local’ resource and really transform oil rents in durable intangible capital.
References
- Where Is the Wealth of Nations? Measuring Capital for the 21st Century. Washington, DC: The World Bank, 2006
- The brain drain in Spain is mainly to Spain’s gain, The Economist, April 2012
- The Inclusive Wealth Report 2012, Cambridge University Press, 2012
- Rapporto sull’economia del Mezzogiorno, SVIMEZ, 2012
- Peer effects in science: evidence from the dismissal of scientists in Nazi Germany, Waldinger, F., The Review of Economic Studies, 2012
[1] Updates on these figures for a subset of 20 countries can be found in the newly released Inclusive Wealth Report 2012 , sponsored by a number of UN agencies, the first of what is intended to be an annual report looking at a broad measure of wealth. From the report: “Wealth is the social worth of an economy’s assets: reproducible capital; human capital; knowledge; natural capital; population; institutions; and time.”
[2] The Economist recently pointed out that “[w]hat some call “brain-drain” may in fact be a win-win situation for Europe’s economies. […I]n the short run, migration takes away pressure from budgets as the unemployed don’t claim benefits but move [abroad] instead. In the long run, there is a pool of highly skilled workers who have not fallen victim to hysteresis effects and can be re-activated for the [home] economy once the crisis is over.” However, it is not at all obvious that this migration is short-run, i.e. that these high-skilled workers will eventually go back. A survey of Italian scientists working aboard reveals, for instance, that the overwhelming majority excludes ever going back to Italy.
[3] The “import” of such people generally more than compensates the “export” in other big European countries.
[4] Source: SVIMEZ, 2012.
[5] A recent paper analyzing the experience of New Zealand (Davenport, 2040) reviews the waves of brain-drain response policies and calls this latest generation diaspora policies: “Diaspora policies are based on an assumption that many expatriates are not likely to return, at least in the short term, but represent a significant resource wherever they are located. This resource is not just embodied in the individual expatriate but also potentially includes their socio-professional networks. A key advantage of any diaspora option is that such connectivity initiatives do not require a large infrastructural investment in order to potentially mobilize this latent ‘national’ resource.”
[6] Source: ISTAT.
[7] Sponsors and partners included the municipal and regional administration, the Italian Institute for Asia and Mediterranean (ISIAMED) and its local branch, CeBasMed, the Val d’Agri National Park, the Regional Environmental Protection Agency, SVIMEZ and the University of Basilicata.
[8] The Corte dei Conti tribunal.
The Effect of Municipal Strategic Planning on Urban Growth in Ukraine
Authors: Denys Nizalov and Olena Nizalova, KEI.
In a downturn, the pressure is especially high on governments to produce sensible and effective development strategies to generate needed jobs and increased earnings. A large number of economic development tools were used in the past by local and national governments around the world, designed to facilitate regional and local economic growth. This brief presents the preliminary results from the evaluation of a program implemented in Ukraine.
Bradshaw and Blakely (1999) distinguish three historical waves of popularity for different tools used in economic development, with reference to the US states’ development policy:
- 1st wave – Incentive-based competition for industrial location, so called smokestack chasing (direct incentives to firms, reimbursement of relocation and infrastructure costs, tax-breaks);
- 2nd wave, from the early 80s – Cost-benefit-based assistance, focusing on internal growth (business incubators, start-up funds, trainings);
- 3rd wave, over the last two decades – Building of a “soft infrastructure” (institutions) conducive to economic growth (strategic planning, marketing, public-private partnerships, financing, regulation, intergovernmental collaboration).
While the effect of the first two waves on various growth outcomes was studied extensively (for reviews, see Bartik 1991; Fisher 1997; Wasylenko 1997; Goss and Phillips 1999; Buss 2001) the effect of the policies representing the third wave is less known. There are several reasons for that. These policies were developed relatively recently, they are hard to measure and compare and are most likely to have a long run effect.
A unique example of a third-wave policy was recently evaluated in Ukraine. The Local Economic Development (LED) Project in Ukraine, started by the USAID in 2004, introduced a process of municipal strategic planning into the practice of local government decision making. This Strategic Planning process involves setting goals and priorities for community economic development and coordination of activities in different areas of community life. It also allows the establishment of partnerships among various stakeholders and interest groups, and the mobilization of public and private resources to facilitate economic development.
Until recently, the effect of municipal strategic planning has been assessed exclusively by case-studies. See for example, the cases of Randstad (Priemus, 1994), Lisbon (Alden and Pires, 1996), London (Newman and Thornley, 1997), Hong Kong (Jessop and Sum, 2000), Guangzhou (Li, Yeung, Seabrooke, 2005; Wu and Zhang, 2007), and Hangzhou (Wu and Zhang, 2007). Although the above mentioned cases describe the planning process and the perceived benefits in great detail, they do not address the question of whether the Strategic Planning causes a higher rate of community economic growth or not. There are several reasons for these limitations. The procedure of planning, beyond general similarities, differs greatly in the implementation details from case to case, which makes any comparisons complicated. Moreover, the decision to start the planning process in those cases is thought to be endogenous since cities that are more likely to benefit from strategic planning are also more likely to get involved in this.
The LED example is much more suitable for evaluation. The implementation of the strategic planning system in the participating cities has been performed using a standardized procedure with the help of LED advisors. With one exception, the implementation took from 4 to 12 months. Also, the selection of the participating cities was done by LED personnel based on clear participation rules. Altogether, the LED activities targeted the same goal in each city – FDI growth and creation of new jobs. Moreover, a relatively large number of communities – 74 cities from all regions of Ukraine – were involved in the project by mid-2008.
Internal reports point to a great success of the project. More than 30 cities had by mid-2008 reported an increase in FDI. Collectively, the partner cities reported $700 million of inflowing investment and an addition of about 12,000 jobs.
The impact of the LED project on the following outcomes was evaluated using more rigorous statistical procedures:
- Number of businesses per capita;
- Fixed capital investment per capita;
- Number of jobs per capita;
- Unemployment rate; and
- FDI per capita.
It was found that the LED project had a positive overall effect on the number of businesses, fixed capital investment, and the number of jobs. In absolute values, the introduction of strategic planning lead to 6 to 12 new jobs per 1,000 of population, 18 to 50 new businesses per 100,000 of population, and 5 to7 million USD of investments in fixed capital per 100,000 (controlling for other factors of influence). However, differences in the project effect among the cities were found. The reasons for these differences in impact include:
- the effect was observed at different points of time after the implementation of planning (1 to 45 month by Dec. 2008);
- the cities had different implementation teams (composed of 6 advisors); and
- the municipalities had different administrative subordination (58 cities and 16 small towns of rayon subordination);
The project effects on the number of businesses, fixed capital investment, number of jobs, and the unemployment rate increased each month. The administrative subordination affects only the effectiveness of investments and job creation: the effect is larger for cities than for rural towns. Team-specific differences are evident on all outcomes. This confirms that the implementation have a significant impact on the success of this intervention.
Finally, the effect of LED was compared to the effect of a similar project implemented in Ukraine by UNDP. Provided that the results presented above are robust, it turns out that the effects of the two projects introducing strategic planning are very similar in magnitude and significance.
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References
- Bartik, T.J., 1991. Who Benefits from State and Local Economic Development Policies? Upjohn Institute for Employment Research: Kalamazoo, MI.
- Buss, T.F., 2001. “The Effect of State Tax Incentives on Economic Growth and Firm Location Decisions: An Overview of the Literature,” Economic Development Quarterly 15(1), 90-105.
- Fisher, R.C., 1997. “The Effect of State and Local Public Services on Economic Development,” New England Economic Review March/April, 53-67.
- Goss, E. and J. Phillips, 1999. “Do Business Tax Incentives Contribute to a Divergence in Economic Growth?” Economic Development Quarterly 13(3), 217-228.
- Wasylenko, M., 1997. “Taxation and Economic Development,” New England Economic Review March/April, 37-52.