G20 ´Compact with Africa´: Audacity of Hope

The Compact with Africa (CwA)[1] initiative is the main pillar of the G20 Partnership with Africa. It initially started in March 2017 as an initiative of the G20 Finance Track to promote private investment in the African continent, with a focus on levering private infrastructure finance via blended finance to facilitate subsequent foreign direct investment (FDI) flows. Ludger Schuknecht and coauthors, then affiliated at top positions with the German ministries of finance and cooperation, have succinctly outlined paradigm, motivation, work mechanics and objectives of the CwA[2]. The concept for the CwA is simple: Good governance is conceived as a prerequisite for enhanced private foreign investment for infrastructure, which in turn helps attract foreign direct investment inflows. To these ends, the international community contributes to the development of good economic institutions by investing in a "compact" with reform-minded poor countries.

Notwithstanding the existing Monitoring Reports posted on the CwA website, hard empirical evidence on output indicators for governance and instituitional quality, portfolio flows trigged for infrastructure as well as green-field FDI (and jobs) created in the Compact countries is still mostly absent, to my knowledge. Official CwA documents have rather focused on input indicators (such as meetings or investment plans) and anecdotal evidence to assess the progress of the Initiative. This blogpost aims at a first, preliminary (and, I admit, premature) presentation of output indicators for the Compact partner countries. (So informed comments are very welcome.). It is not intended (nor possible at this early stage) to present hard empirical evidence to establish causality. If anything, the data presented might be suggestive of a structural break of governance scores and private foreign flows in the compact countries before and after the CwA was launched in March 2017.

The CwA evokes Barrack Obama´s 2006 book ´The Audicity of Hope´, because the Compact postulates that its mechanics can spur private foreign investment and sustainable transition even in low-income countries. Kappel & Reisen (2017) have argued earlier that such premise is ´unsuitable´ for low-income countries[3]. The brilliant Graph 1 (from OECD, 2019)[4] seems to support that scepticism. The OECD graph presents the evolution of the external financing mix for developing countries during the transition process. Its focus is on the percentage contribution of external resources (left y axis), while including the relative importance of domestic resources (right y axis), and shows the evolution of the mix as income per capita increases (x axis).

Graph 1: Financing Mix during the Transition from LIC to HIC Status

Source: OECD (2019), Transition Finance: Introducing a New Concept, OECD Development Co-Operation Working Paper 54.

As the graph shows, the percentage share of private flows to total external flows for LICs and LMICs has on average been well below 10% during the 2012-2016 preceding the Compact. Private external flows start to dominate the external financing mix (with more than 50%) only once countries graduated from upper-middle income (UMICs) to high-income (HICs), with a annual GNI per capita of $12,056 or more[5]. 

To put the ´Audacity of Hope´ of the CwA into perspective, note that all twelve Compact countries produce a yearly income per head at such low levels that they are either classified as low-income countries (LICs) or lower-middle-income countries (LMICs), as shown in Table 1.

Although the mean annual per capita income varies widely across the group of Compact countries – from $590 (Burkina Faso) to $3,490 (Tunisia) – they all remain widely below income levels where an important contribution of private external flows can be reasonably expected.

Moreover, some Compact countries (Ethiopia, Ghana) have been assessed recently under ´high´ risk of debt distress in the IMF/WB Debt Sustainability Framework. Debt vulnerability should further mitigate the role of external private flows to fund a country, if it comes in the form of debt-creating flows, including through blended finance.

Table 1: African Compact Countries Fact Sheet

Compact Countries	GNI/cap a)	Income Status	EoDB 15-16	EoDB 17-18	CPIA 15-16	CPIA 17	Risk of Debt Distress
	2017	2018	Score	Score	Score	Score	2018
BENIN	800	LIC	49	51	3.45	3.50	moderate
BURKINA FASO	590	LIC	51	51	3.60	3.60	moderate
CÔTE D´IVOIRE	1580	LMIC	51	56	3.35	3.40	moderate
EGYPT	3,010	LMIC	55	57	n.a.	n.a.	moderate
ETHIOPIA	740	LIC	46	49	3.55	3.40	high
GHANA	1,880	LMIC	57	58	3.55	3.60	high
GUINEA	790	LIC	48	51	3.15	3.20	moderate
MOROCCO	2,860	LMIC	67	70	n.a.	n.a.	low
RWANDA	720	LIC	69	76	4.00	4.00	low
SENEGAL	1,240	LIC	49	54	3.80	3.80	low
TOGO	610	LIC	48	52	3.00	3.10	heightend
TUNISIA	3,490	LMIC	64	65	n.a.	n.a.	low
			54.5	57.5	3.49	3.51

Sources: https://data.worldbank.org/indicator/ny.gnp.pcap.cd?page=1; http://datatopics.worldbank.org/cpia/country; https://www.imf.org/en/Publications/REO/SSA/Issues/2019/04/01/sreo0419

Notes: a) GNI/capita, Atlas method (current US$); EoDB = Ease of Doing Business; CPIA = Country Policy and Institutional Assessment; Risk of Debt Distress = recent IMF/World Bank assessments.

Next to annual data for gross national income per capita (GNI/cap) and the Income Status classification by the World Bank, Table 1 presents governance scores for the two years preceding the CwA launch (2015 and 2016) and the two subsequent years (2017 and 2018, if available):

-          The World Bank´s controversial ´Ease of Doing Business´ (EoDB) index measures the degree to which the regulatory environment is conducive to the starting and operation of a local firm. The index runs from 0 to 100 (perfect). The ease of doing business score benchmarks economies with respect to regulatory best practice, showing the absolute distance to the best regulatory performance on each Doing Business indicator.  When compared across years, the ease of doing business score shows how much the regulatory environment for local entrepreneurs in an economy has caught up relative to best practice.

-          The World Bank's Country Policy and Institutional Assessment (CPIA) index measures the institutional strength of a country, with 1=low, and 6=high. It scores countries against a set of 16 criteria grouped in four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. Until mid 2018, IMF and World Bank have been relying exclusively on the CPIA to classify countries’ debt-carrying capacity in their joint Debt Sustainability Framework for low-income countries (DSF). While other economic variables have been added since then, the CPIA still provides a useful characterization of debt vulnerabilities (including those from domestic debt and market financing).

Encourageingly, the ´Ease of Doing Business´ (EoDB) indicators significantly improved on average (red numbers) in the Compact countries from the pre-CwA period 2015-16 to thereafter. However, the CPIA scores did not[6]. While we have to wait for newer CPIA scores, I cannot reject the null hypothesis that the first CwA objective has been reached – to provide incentives for compact countries to improve business conditions for private investment.

Table 2: Private Flows to African Compact Countries

- current US$ million-

Compact Countries	Risk Debt Distress	PPI 15/16	PPI 17	FDI 15/16	FDI 17/18
	2018
Benin	moderate	0	0	141	184
Burkina Faso	moderate	0	517	311	486
Côte d´Iv	moderate	0	471	536	675
Egypt	moderate	106	2898	7516	7392
Ethiopia	high	0	0	3308	4017
Ghana	high	3,205	550	3339	3255
Guinea	moderate	0	121	836	577
Morocco	low	2,014	460	2786	2680
Rwanda	low	0	422	245	293
Senegal	low	396	114	441	532
Togo	heightened	0	0	106	146
Tunisia	low	0	0	797	810
		5,721	5,553	20362	21047

Sources: http://www.worldbank.org/en/topic/debt/brief/dsa; https://ppi.worldbank.org/data; https://data.worldbank.org/indicator/BM.KLT.DINV.WD.GD.ZS

Table 2 presents more sobering findings as to the private flows, which the Compact may have triggered, by comparing the mean of the two years preceding the CWA launch with the mean of the two years thereafter. The table provides

·       preliminary data from the Private Participation in Infrastructure (PPI) Project database of the World Bank, a comprehensive indicator of credit and portfolio flows that fund 6,400 infrastructure project in 139 low- and middle-income countries; and

·       Foreign direct investment (FDI, net inflows), assembled from the International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.

The sum of PPI actually fell slightly after the CwA launch in the Compact countries. Exceptions are Rwanda and in particular Egypt where PPI soared in terms of size of annual flows and number of projects. A third of the dozen Compact countries – Benin, Ethiopia, Togo and Tunisia – did not receive any private foreign infrastructure funding after the CwA launch according to the PPI database.

The sum of mean annual FDI flows, by contrast, increased by US$ 700 million after the CwA launch, to a total of US$ 21 billion during the years 2017-18. Again, Egypt received the bulk of that sum (as it had done before the CwA launch), roughly US$ 7.5 billion. Compared to the two-year period preceding the CwA launch, ten out of twelve Compact countries recorded higher annual FDI flows.

Clearly, the CwA results as measured by output indicators are mixed two years after the launch of the initiative. But they are not outright negative. More perseverance by private firms and investors, risk-sharing development finance institutions and authorities both local and foreign is needed. Importantly, however, the audacity of hope that carries the Compact seems to be justified so far.

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[1] https://www.compactwithafrica.org/content/compactwithafrica/home.html

[2] Ludger Schuknecht, Johannes Wolff, Andreas Gies & Stefan Oswald (2018), “Der G20 Compact with Africa–ein neuer Ansatz der wirtschaftlichen Zusammenarbeit mit afrikanischen Ländern“, ifo Schnelldienst 4 / 2018.

[3] Robert Kappel and Helmut Reisen (2017), The G20 “Compact with Africa” – Unsuitable for African low-income Countries, FES Discussion Papers, Friedrich-Ebert-Stiftung, Berlin.

[4] OECD (2019), Transition Finance: Introducing a New Concept, OECD Development Co-Operation Working Paper 54, Paris. The excellent paper has been authored by Cecilia Piemonte (work stream lead), Olivier Cattaneo, Rachel Morris, Arnaud Pincet and Konstantin Poensgen.

[5] World Bank, How Does the World Bank Classify Countries?

[6] A two-tailed t-test of the two EoDB columns and for the two CPIA columns produced a t-value of 0,249787947, a value too low to reject the null hypothesis that the EoDB and CPIA means were equal before and after.

Headwinds for Emerging-Market Convergence

*This text has been prepared, with the help of Michael Stemmer, for the OECD Perspectives on Global Development 2019, which will be presented at Hertie School min Berlin, 20th March.

The emerging country convergence observed over the last two decades is at risk. The sustained growth that large emerging countries have experienced has conferred on them a considerable growth delta over the OECD average. Combined with very large populations, these growth differences have translated into a new world economy.  Today, the countries with the largest economic mass (in terms of PPP-adjusted GDP and net foreign assets) are not also the richest countries. The shorthand for this complex event is what we call “Shifting Wealth” – the recalibration of the world economy toward the East and the South –, well documented since 2010 in the OECD Perspectives on Global Development. 

Today, I want to reflect on four potential headwinds for the emerging countries:

·       Slower speed of GDP convergence as the distance to the advanced countries is shrinking;

·       The middle-income trap in emerging countries;

·       Technological innovations that might reduce labor demand in emerging countries;

·       Global trade, notably normalization and protectionism.

Convergence Speed

China´s growth has been the engine of Shifting Wealth since the 2000s. After almost four decades of sustained growth, the Chinese economy has grown from an average GDP_PPP/capita of 313 US dollar in 1980 to 15,417 US dollar in 2016. Its GDP per capita is currently equivalent to 55 percent of the world's average. China, initially capital poor, has saved and invested a large part of its annual GDP over the past 40 years. The neoclassical Solow-Swan growth model postulates diminishing returns to capital. This has led Krugman (1994)[1] to make one of the most blatant errors in the history of economic prognosis:

”If growth in East Asia runs into diminishing returns, however,  the conventional wisdom about an Asian-centered world economy needs some rethinking”…”From the perspective of the year 2010 (sic!), current projections of Asian supremacy  … may well look as silly as 1960s-vintage forecasts of Soviet industrial supremacy did from the perspective of the Brezhnev years”.

The hypothesis that poor economies tend to grow faster per capita than rich ones— without conditioning on any other characteristics of economies—is referred to as absolute convergence. However, it has held in the past only for a fairly homogenous group of countries, such as the OECD, or for incomes across US states (Barro and Sala-i-Martin, 2004)[2]. The key proposition of endogenous-growth models is the absence of diminishing returns to capital. The simplest version of a production function without diminishing returns is the AK function Y = AK, where A is a positive constant that reflects the level of the technology. The global absence of diminishing returns may seem unrealistic, but the idea becomes more plausible if we think of K in a broad sense to include human capital.

Acemoglu, Aghion and Zilibotti (2006)[3] produced a stochastic growth model in which a country‟s “distance to the frontier” matters for the selection of appropriate growth and industrialisation strategies. Countries at early stages of development (optimally) pursue an investment-based strategy, which relies on existing firms and managers to maximize investment under the guidance of the government; at this stage, local entrepreneurship and home-made innovation matter relatively less, a condition that carries implications in shaping appropriate institutions. The three authors show that relatively backward economies risk shifting out of the investment-based strategy too soon. Policies that encourage the investment-based strategy, such as limits on product market competition or investment subsidies, may be beneficial for a lengthy period in terms of facilitating economic convergence and poverty reduction.

Table 1: BRIICS Innovation Ranking

Country	China	Russia	S Africa	India	Brazil	Indonesia	Total
2017	22	45	57	60	69	87	127
2007	29	54	38	23	40	48	107

Source: World Intellectual Property Organization, Global Innovation Index 2017 and 2007.

Industrialisationt strategies may also have significant long-run costs, if the country holds on too long, failing to reach the world technology frontier and finding itself devoid of domestic entrepreneurs and innovation capacity. (This is the risk of Germany´s new industrial policy.) Reassuringly, China has managed to reach the world technology frontier and is blessed with millions of highly innovative entrepreneurs. The Global Innovation Index 2017 (published annually since 2007 at the World Intellectual Property Organization) provides detailed metrics about the innovation performance of 127 countries and economies around the world, with 81 indicators. China ranked at 22, the only middle-income country among the top 25 innovative countries. Note that, China and Russia aside, no other BRIICS country was able to move up the WIPO innovation ranking over the past decade (Table 1).

The Middle Income Trap

Closely related is the concern that middle-income countries can be ´trapped´ in growth slowdowns.  Gill and Kharas (2015)[4] had coined the term ´middle-income trap´ in 2005, because they had found growth theory wanting for the group of middle-income countries (which the World Bank in 2018 classifies as the large group of countries with GNI per capita between $1,005 and $12,235): endogenous growth theories addressed the problem in high-income economies, and the Solow growth model was still the work-horse for understanding the growth problem in low-income countries, but neither were satisfactory for understanding what to do in countries where 5 billion (out of 7billion) people in the world live.

The middle-income trap, whereby GDP per capita growth slows down once a country approaches an intermediate level of development, is particularly persistent in Latin America[5]. Although average per capita income in the region was relatively high in the mid-20th century, most Latin American countries have been unable to reduce significantly the income gap with advanced economies and reach high-income status. The few regional exceptions are Chile, Uruguay and some Caribbean countries. These trends contrast with European and Asian countries, much more effective in joining the high-income group during the last half of the 20th century. Even though ´Emerging Asia´ has made remarkable progress over the past four decades in raising income levels, it will take from several years (Malaysia, China) to decades (Indonesia, India) to join in the ranks of the advanced high-income countries, according to OECD (2014)[6] estimates (Fig. 1). The jury is still out as to whether middle-income ASEAN countries like Indonesia, Malaysia, the Philippines, Thailand or Vietnam can expect to replicate the growth experience of the Asian Tigers, or whether they will follow the trajectories of Latin America.

Figure 1: Estimated years required to become high-income country, 2014

Source: SEAO 2014, http://dx.doi.org/10.1787/888932937111

To be sure, there is nothing mechanical about the development transition from middle-income to high-income country, not even in Asia. Since 2000 (tiny island economies aside), six European and two Latin American countries have joined the ranks of high-income economies as classified by the World Bank, but no Asian country.

Technological Innovations and Labor Demand

Especially the 1990s opening phase of Shifting Wealth had been rooted in the abundance of labor with basic skills in poor countries that got connected to demand in advanced countries via trade and investment, resulting initially in manufacturing job creation in China above all. The world entered a new phase of globalisation: Information and communication technology, trade liberalisation and lower transport costs have enabled firms and countries to fragment the production process into global value chains (GVCs). However, the current proliferation of artificial intelligence, such as industrial robots, and other forms of worker-replacing technological progress have the potential to disrupt global labor markets (Korinek and Stiglitz, 2017)[7]. Technology innovation, such as 3D printing, may facilitate reshoring and would deepen the domestic (rather than global) division of labor for major economies, including China. Policymakers in middle-income countries have started to worry about the “end of global value chains” (Arbache, 2016)[8].

Robots could alter the global pattern of comparative advantage, for example in shoe production, which would shift from skill-scarce to skill-abundant countries if skilled workers with robots could make shoes more efficiently than unskilled workers[9]. The familiar benefits of industrialization as a development strategy might be eroded if robot-based automation makes industrialization more difficult or causes it to yield substantially less manufacturing employment than in the past (Mayer, 2017[10]). To be sure, technical feasibility of workplace automation does not imply economic profitability, especially in developing countries with low wage levels.

Figure 2: Vulnerability to robot-based automation in manufacturing

Source: UNCTAD (2017), "Robots, industrialization and inclusive growth", chapter 3 in Trade and Development Report 2017

Figure 2 suggests “that on current technological and economic indicators, developed countries and developing countries other than least-developed countries (LDCs) are exposed to robot-based automation in manufacturing to a larger extent than LDCs. It should be noted that this evidence only refers to exposure to robot-based automation and does not take account of the risks to employment from other forms of automation. But it suggests that robot-based automation per se does not invalidate the traditional role of industrialisation as a development strategy for lower income countries. Yet, the dominance of robot use in sectors higher up on the skill ladder implies greater difficulty for latecomers in attaining sectoral upgrading and may limit their scope for industrialisation to low-wage and less dynamic (in terms of productivity growth) manufacturing sectors. This could seriously stifle these countries’ economic catch-up and leave them with stagnant productivity and per capita income growth” (Wood, 2017).

For the past decade, 3D printing has become popular due to availability of low-cost 3D printers such Fab@Home and better software. The brand name Fab@Home is suggestive of the technological possibilities to move production closer to the consumer, which may impact on trade, global value chains and shifting Wealth. Ishengoma and Mtaho (2014)[11] discuss 3D printing from a developing-country perspective, both challenges and opportunities. In developing countries, 3D printers are increasingly used in fabrication laboratories and engineering faculties, for example to produce 3D-printed prosthetic limbs. Major (economic) challenges are the lack of 3D experts and losses in manufacturing employment substituted for home platforms. Opportunities are lie in moving developing countries close to world supply chains For example, developing countries can use 3DP technology to manufacture local equipment such as farming tools and goods such as clothing.

Slowdown of Global Trade

Policymakers in low- and middle-income countries worry about the slowdown of the most important vehicle of Shifting Wealth, global trade. World trade growth was rapid in the two decades prior to the global financial crisis but has halved subsequently. There are both structural and cyclical reasons for the slowdown. A deceleration in the rate of trade liberalisation post 2000 was initially obscured by the ongoing expansion of global value chains and associated rapid emergence of China in the world economy. Post the financial crisis global value chains started to unwind and Chinese weakened markedly (Haugh et al., 2016)[12].

Figure 3: Drop in China’s export openness since 2007

Source: Banque de France (2017)“The role of China in the trade slowdown”, Rue de la Banque #30, September.

Protectionism is clearly another concern for the future of Shifting Wealth. The Economist (2016)[17] asked YouGov, a polling outfit, to survey 19 countries to gauge people's attitudes towards immigration, trade and globalisation. The data revealed a split between emerging markets and the West. Unsurprisingly, the countries that are the biggest enthusiasts of globalisation are the ones that have benefitted most from it – the poorer nations of East and South East Asia. In Asia, belief that globalisation is a force for good reaches at least 70% in all countries surveyed, and as high as 91% in Vietnam. In France, the US, the UK, Australia and Norway, less than 50 per cent thought that globalization had been a “force for good”. From disillusionment with globalisation to populist protectionism is a short distance.

Figure 4: Global Trade Liberalisation Index

Source: Haugh et al (2016), OECD Economic Policy Papers No.18

Research has confirmed that the slowdown of trade liberalisation and rising protectionism is taking its toll on trade flows (Evenett and Fritz, 2015, op.cit.). Less international fragmentation of production processes than in recent decades cannot be excluded with labour saving innovations, increasing domestic capabilities to substitute for imported intermediate goods, and subsequent reshoring. And while support for globalization has dropped in some advanced countries below the 50 percent threshold, resort to protectionism and retreat from multilateralism, notably in advanced countries, are a potential threat for Shifting Wealth present and future.

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[1] Paul Krugman (1994), “The Myth of Asia´s Miracle”, Foreign Affairs 73.6, pp. 62-78.

[2] Robert Barro and Xavier Sala-i-Martin (2004), Economic Growth, 2nd edition, MIT Press: Cambridge, Mass.

[3] Daron Acemoglu, Philippe Aghion and Fabrizio Zilibotti (2006), “Distance to Frontier, Selection and Economic

Growth.” Journal of the European Economic Association 4.3, pp. 37–74.

[4] Indermit Gill and Homi Kharas (2015), “The Middle-Income Trap Turns Ten”, World Bank Policy Research Working Paper No. 7403, August.

[5] Angel Melguizo, Sebastián Nieto-Parra, José Perea and Jaime Perez (2017), “No sympathy for the devil!

Policy priorities to overcome the middle-income trap in Latin America”, OECD Development Centre Working Paper No. 340, September.

[6] OECD (2014), Economic Outlook for Southeast Asia, China and India 2014: Beyond the Middle-Income Trap

[7] Anton Korinek and Joseph Stiglitz (2017), “Artificial Intelligence and Its Implications for Income Distribution and Unemployment”, NBER Working Paper No. 24174, December.

[8] Jorge Arbache (2016), “The end of the global value chains”, Valor Econômico, June.

[9] Adrian Wood (2017), “Variation in structural change around the world, 1985–2015: Patterns, causes, and implications”, UNU-WIDER: WIDER Working Paper 2017/34, February.

[10] Jörg Mayer (2017), “Industrial robots and inclusive growth”, Vox, October.

[11] Fredrick Ishengoma and Adam Mtaho (2014), “3D Printing: Developing Countries Perspectives”, International Journal of Computer Applications, 104 .11, pp. 30-34.

[12] David Haugh, Alexandre Kopoin, Elena Rusticelli, David Turner, Richard Dutu (2016), “Cardiac Arrest or Dizzy Spell: Why is World Trade So Weak and What can Policy Do About It?”, OECD Economic Policy Papers No.18, September.

[13] Simon Evenett and Johannes Fritz (2016), “Global Trade Plateaus”, Vox, July.

[14] Guillaume Gaulier, Walter Steingress & Soledad Zignago (2016), “The role of China in the trade slowdown”, Rue de la Banque #30, Banque de France, September.

[15] Bart Los and Marcel Timmer (2016), “Peak trade? An Anatomy of the Recent Global Trade Slowdown”, University of Groningen, May.

[16] Christophe Degain, Bo Meng, and Zhi Wang (2017), Global Value Chain Development Report 2017, Chapter 2.

[17] The Economist (2016), What the world thinks about globalisation, 18^th November.

US Drivers of Emerging-Market Volatility

The US Federal Reserve turned ´dovish´ recently, somewhat surprisingly. Does that mean that emerging markets (EM) will cheer? Or won´t we have other EM currency crashes in 2019, like in Turkey and Argentina last Summer? The last OECD Economic Outlook (November 2018) stated recently that „Argentina and Turkey have been experiencing severe financial turmoil. Rising tensions in these economies, in the context of US monetary policy normalisation and idiosyncratic domestic factors, led to a sudden change in market sentiment toward semerging-market economies and triggered capital outflows.“[1]

EM cyclical fortunes do depend on US monetary policy, largely via three channels: global interest rates, the dollar, and global output. China, so far the global ´growth machine´, has become another cyclical determinant for emerging-market (EM) output, but so far mainly through trade, raw materials and, until a decade ago, ´unlimited supply of labour´. However, in historical perspective, the monetary shocks emanating from the US Federal Reserve Board (the Fed) have been comparatively minor in 2018 (Figure 1). With the Fed's recent dovish shift, the hunt for yield could be back; flows to EM should be picking up sharply.

Fig.1:  US Fed Funds Rate (blue) and US Dollar Index (red) 1974-2019

Source: https://fred.stlouisfed.org/series/TWEXBPA#0

·       To be sure, the world´s single most important interest rate – the Fed funds rate – did com back from the zero lower bound in 2018 in recent quarters. However, in historical perspective, we are a long shot off the levels reached in the early 1980s when the disinflationary Volcker shock led to the Latin American, African and Korean debt crises. Even compared to the period thereafter, the recent rise looks almost ´peanuts´.

·       The trade-weighted dollar index has recorded annual percentages changes of maximum +/- 15 % ever since the 1985 "Plaza Accord" when G7 finance ministers reached an agreement about managing the fluctuating value of the US dollar. The recent dollar surge in 2013 in the wake of Bernanke´s paper tantrum caused quite a bit of (short-lived) havoc in EM markets and raw material prices. With corporate balance-sheet assymetries typical of EM, dollar volatility can be fatal[2].

Figure 2: A Tale of Two Currency Crashes 2018

- Currency index, 1/1/2018 = 100 -

Source: www.finanztreff.de

The Turkish lira and the Argentine peso both crashed in 2018 (Figure 2), which immediately let to contagion in some emerging countries, notably those who run deficits on the current account and have a high dollar share in private and public debt. That EM contagion remained comparatively limited and short lived, may be attributable to market interpretation that the Turkey and Argentina crises were home-made to a large extent. Note that the Turkish lira has recovered quite well since August while the Argentine peso has stayed knocked down in 2018.

That Argentina and Turkey crashed was predictable. Both countries were expanding their current account deficits into a period of rising G-3 interest rates. Both currency crashes rather are variants of 1^st generation currency crises as  external budget constraints and solid FDI funding were ignored for too long:

·       Argentina: In Argentina, unlike Turkey, the big foreign currency borrower is the government. Her twin deficits – fiscal and external – have been widening from zero in 2010 to roughly 6% in 2018. While these number do not sound outlandish – and nor is public debt as a percentage of GDP – Argentina´s debt rests on a very narrow export base. Since the early 2000s, exports (and services)  have come back towards 10% of GDP, from more than 20% in the 2000s. During the 2010s, “exports of bonds” have been gradually replacing exports of goods and services[3]. Toxic.

·       Turkey: In order to maintain his rule via a strong economy, Erdogan used pro-cyclical monetary and fiscal policies to fuel overall economic demand, after the global financial crisis in 2009 and then again after the military coup in mid-2016. In addition to generous money supply and high deficits in the state budget, public loan guarantees for private companies fueled output. Infrastructure investment was booming but increasingly debt-financed. Although private banks and companies in particular have incurred increasing foreign currency debt, the private debt often represents state contingent liabilities[4].

Figure 3: The Global ´Drumpf´ Effect

Source: http://www.policyuncertainty.com/

Beyond ´US monetary policy normalisation and idiosyncratic domestic factors´ listed by the last OECD Economic Outlook, we must look for another determinant that has recently caused, is currently causing and will cause EM financial volatility: global economic policy uncertainty. The Global Economic Policy Uncertainty Index has reached extreme levels never measured since its creation. While Euro fragility and the return of military tensions have certainly added to global uncertainty, the shocks to the world economy caused by US President Trump's isolationism, his obsession with containing China (the EM growth machine) and his willingness to impose sanctions all over the world have driven the index to levels displayed in Figure 3. Let´s call it the global ´Drumpf´effect. This is the central source of EM market volatiliy for the foreseeable future, despite the US Fed having turned dovish.

The Global Economic Policy Uncertainty Index[5] has thus reached extreme levels never measured since its creation. The index is a GDP-weighted average of national indices for 16 countries that account for two-thirds of global output. Each national index reflects the relative frequency of own-country newspaper articles that contain a trio of terms pertaining to the economy, uncertainty and policy-related matters: uncertainty about who will make economic policy decisions, what economic policy actions will be undertaken and when, and the economic effects of policy actions (or inaction)—including uncertainties related to the economic ramifications of “noneconomic” policy matters, for example, military actions.

In order to measure various possible drivers of emerging-market volatility, Figure 4 compares the indicators for the US dollar index, the Federal Funds rate and global economic policy uncertainty index with their long-term average. For each price driver, the distance between the current data point and the ten-year average was plotted. In order to be able to compare the distances, they were compared with the respective standard deviation. Thus, the measurements are standardized with the fluctuation of the respective time series. In statistics, the measure is called the "Z score".

Figure 4: US Drivers of EM Volatility (z-scores), 2007-18

Figure 4 shows the index of economic policy uncertainty at a level of fluctuation triple its long-term average at the end of 2018. Although the Fed Fund rates has recently started to rise, its standardized fluctuation is pretty much at long-term average, while the strong US dollar has lifted its z-score to almost double its long-term average. In sum, emerging markets need currently worry much more about policies of the Trump administration than about the US Federal Reserve, as far as global factors are concerned. 

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[1] OECD Economic Outlook, Volume 2018, Issue 2, page 30.  

[2] V. Bruno and H.S. Shin (2018), “Currency depreciation and emerging market corporate distress”, BIS Working Papers No 753.

[3] B. W. Setser (2018), “Argentina: Sustainable, Yes, with Adjustment. But Sustainable with A High Probability?”, Council on Foreign Relations, 21.5.2018.

[4] See my “Erdogan's macro populism is far from over”, ShiftingWealth, 30. 8.2018. In contrast to other observers who saw Erdogan´s imminent demise in August last year, I pointed to ´heterodox´ alternatives to IMF funds and capital controls as lifeguards for the continuation of Erdogan´s populist rule.

[5] For a detailed presentation of the methodology, refer to Scott R. Baker, Nicholas Bloom and Steven J. Davis (2016), “Measuring Economic Policy Uncertainty,” Quarterly Journal of Economics, Volume 131, Issue 4, and Steven J. Davis (2016),“An Index of Global Economic Policy Uncertainty”, University of Chicago Booth School of Business.