Finance Has Become the Dominant Force in Shaping the Global Economy

Lynn Fries of The Real News—reporting from the 2017 launch of the United Nations Conference on Trade and Development’s Trade and Development Report—interviews the report’s main author, Richard Kozul-Wright, Director of UNCTAD’s Globalization and Development Strategies Division. In the interview, Kozul-Wright addresses the problems of a “hyperglobalized world … in which finance has essentially gained the upper hand in policy making.” And he discusses the prospects for building alternatives to neoliberalism and austerity, up to and including a “Global New Deal.”

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How Many Hurricanes Must It Take?

Edward B. Barbier

Edward B. Barbier is Professor in the Department of Economics and Senior Scholar, School of Global Environmental Sustainability, Colorado State University

In November 2013, I posted “Typhoon Haiyan and a Global Strategy for Protecting Coastal Populations” on Triple Crisis. I argued that:

“Given the scale and frequency of recent coastal disasters—Typhoon Haiyan, Hurricanes Sandy, Katrina, and Rita, the Fukushima and Indian Ocean Tsunamis—it is time to develop a global strategy for protecting coastal populations. There should be two elements to this strategy: a short-run emergency response and investments in long-term global adaptation.”

I developed this theme further for a Perspectives article in Science, “A global strategy for protecting vulnerable coastal populations,” which was published in September 2014. In 2015, on the tenth anniversary of the US Gulf Coast disasters, I was asked by Nature to reflect on “Hurricane Katrina’s lessons for the world.” Once again, I called for coastal protection plans, similar to the Louisiana Coastal Master Plan, for the world’s most vulnerable people.

As I stated in the Nature article:

“The most vulnerable are poor, rural populations in developing countries that live less than 10 metres above sea level, in low elevation coastal zones (LECZs). In 2010, around 267 million people lived in the rural areas of LECZs. By 2100, the figure is projected to be 459 million.”

The recent devastation wrought by Hurricanes Harvey, Irma, and Maria across the Caribbean, Puerto Rico, Florida, and Texas are yet another sober reminder that it is the poorest nations, regions and populations that are the most vulnerable to coastal disasters, and which need assistance in terms of immediate emergency response as well as long-term recovery efforts.

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Gross National Saving

Gokcer Ozgur

The saving-investment relationship is at the center of one the oldest debates in economics. Even though all economists agree on the existence of the saving-investment identity, disputes start once the direction of causality is sought. In fact, almost all the debates between (effective) demand-side vs. supply-side economics can be summarized in the saving-investment debate.

There are various theoretical as well as empirical studies on this topic, yet one aspect of this issue remains unnoticed; how saving is estimated. A close study of data shows that (gross) national saving data does not exist ex ante and it is derived from investment data (Ozgur, 2017). In a closed economy, national saving equals investment (S = I), and in an open economy national saving equals investment plus the current account balance (S = I + CAB) (Krugman et al., 2012: 303). Alternatively, gross national saving (S) equals net saving (NS) plus consumption of fixed capital (CFC) (SNA, 1993). A close study of how saving is estimated can show that, in both methods, saving is derived from investment data, and we can use the United Nation’s System of National Accounts (SNA, 1993) for this purpose. According to SNA, (gross national) saving data is estimated ex post as a residual, and (gross national) saving does not exist ex ante. The difference between ex post and ex ante saving is crucial for understanding the nature of macroeconomic events, and developing macroeconomic policies -as promoting national saving is often seen as a remedy for current account deficits, government and foreign debt, and sluggish economic growth. However, if saving is an outcome, not the cause, of economic events, the order of economic policies should be rearranged.

If we start with saving-investment identity, IMF’s World Economic Outlook (WEO) defines gross national saving as follows: “Gross national saving is gross disposable income less final consumption expenditure after taking account of an adjustment for pension funds (SNA, 1993). For many countries, the estimates of national saving are built up from national accounts data on gross domestic investment and from balance of payments-based data on net foreign investment” (IMF, 2014). Though the WEO database has limited information about the derivation of savings, SNA shows that gross national saving data can be derived through basic income accounting identities (SNA, 1993: 55, 256, 274). Following SNA methodology, gross domestic product equals the sum of consumption, investment, government spending, and net exports:

GDP = C + I + G + NX (1)
GDP + F = C + I + G + NX + F, (2)
where F is net factor incomes from abroad.
GNI = C + I + G + NX + F, (3)
where GNI = GDP + F is gross national income.
GNI + TR = C + I + G + NX + F + TR, (4)
where TR is all current transfers in cash or in kind receivable by resident institutional units from non-residents, net of those payable by residents to non-residents.
GNDI = C + I + G +CAB, (5)
where GNDI = GNI + TR is gross national disposable income, and CAB = NX + F +TR is current account balance
GDI – C – G = I + CAB, (6)
And, as a result,
S = I + CAB, (7)
where S = GDI – C – G is gross national saving.

Equation (7) is still an identity and it shows gross national saving can be derived through investment and current account balance. Anyone familiar with the WEO database can find this identity for various economies. It can still be argued that, even though saving can be derived ex post, it must have existed trough economic units’ decisions to spend and save. And for economic units’ decisions we should look at the relationship between net saving and gross saving. This relationship can be better understood from the U.S. Bureau of Economic Analysis (BEA) “NIPA Table 5.1. Saving and Investment by Sector.” As summarized in Table 1 below, gross national saving equals gross domestic investment, capital account transactions, and net lending (with a statistical discrepancy). The second column of Table 1 is similar to right-hand of equation (7). Net capital account transactions and net lending and borrowing equals the current account balance (Ozgur, 2017: 8). In this summary table, the second column represents the ex post data, and the net saving in the first column represents the net savings of domestic sectors such as domestic business, households, and government. For domestic business, net saving equals undistributed corporate profits, inventory valuation adjustment, and capital consumption adjustment; for households, it is disposable income minus consumption; for the government, taxes minus government spending.

Table 1. NIPA: Gross saving and Investment
Ozgur table 1

Figure 1 shows the two columns of Table 1 for the U.S. economy in nominal terms and as a ratio of GDP between 1952 and 2012. Gross saving and gross investment in this graph represents both sides of equation (7).

Figure 1. Saving-investment identity, U.S. , 1952-2012

Ozgur figure 1

Source: NIPA, Table 5.1

The relationship between these variables becomes more interesting once the details of Table 1 are opened up. Gross saving has two components: net saving of domestic sectors plus consumption of fixed capital (CFC). Net saving represents actual or ex ante savings of domestic sectors whereas consumption of fixed capital is a residual and an estimated value for depreciation; it is a major balancing item, and “is one of the most important elements in the System” (SNA, 1993: 187).[1] Figure 1 shows that the saving-investment identity holds—with statistical discrepancy—since 1952, yet net saving has continuously declined and it has never been sufficient to cover investment. The largest component of gross saving is CFC, an imputed value, to balance the equality of gross saving and investment.

Figure 2. Components of gross saving and investment

Ozgur figure 2

Source: NIPA, Table 5.1

Thus, equation (7) holds at the aggregate level only after the CFC is imputed. Moreover, CFC does not represent a saving decision and it is an imputed value to represent depreciation. In the U.S. economy, BEA uses a geometric pattern to estimate the CFC, or depreciation of all U.S. fixed assets for the overall service life of assets (Fraumeni, 1997). BEA uses a table for the rate of depreciation and service life of all types of fixed assets (Fraumeni, 1997: 18-19). Depreciation is high in the early years of an asset, it declines as the asset gets older, and follows a geometric pattern. For one dollar of investment, depreciation, d_(i,G), of an assets is as follows (equation 8):Ozgur equation 1i = 1, 2, 3, …, where i is the age of the asset.

In equation (8), δ represents the rate of depreciation, and d represents the depreciation of a physical asset in a given year. Over the course of its lifetime, an asset will lose a fraction of its value each year, and its value will become zero at the end of its lifetime. Here, CFC or the largest component of gross saving is imputed based on past investment data.[2] In order to show that CFC is built on past years’ investment data we can use a simple exercise based on BEA’s methodology. In this exercise, let’s assume the average lifetime of all the physical capital in the U.S. is twenty years, and then we can randomly pick depreciation rates between 1 and 10 percent for all these capital assets. In NIPA Table 5.1, U.S. investment data starts in 1952, and as a result, our estimated CFC starts in 1972. That includes the CFC of physical assets invested in 1952, 1953, and all other years up to 1971. In our exercise, the investments of 1952 were 20 years old, the investments of 1953 were 19 years old, and the investments of 1971 were 1 year old by year 1972. Starting from 1972, we can estimate depreciation for all the age groups by using the BEA’s formula and add them together in order to find overall depreciation or CFC for that year. If we repeat this exercise for every year between 1972 and 2012, we can find our estimated CFC. We can estimate different CFC series based on different depreciation rates. Finally, we can plot the estimated series of three different depreciation rates together with the BEA’s CFC for comparison.

As can be seen in the graphs of Figure 3 below, using past years’ investment data can give very similar results with actual CFC even under very unrealistic assumptions, i.e. all assets have the same depreciation rate and service life. Out of these three, a 8% depreciation rate for a 20-year lifetime gave an estimated value of CFC very similar to that of the BEA. The BEA is using different depreciation rates and life times for various assets, but this simple exercise shows that CFC can be estimated by using past values of investment. As a result, the biggest component of gross saving does not depend on what economic units actually save, but on investment data of previous years. And in this sense, neither CFC nor gross national saving can be sources of funds.

Figure 3. Consumption of fixed capital

Ozgur figure 3

Source: Author’s estimations from NIPA, Table 5.1

After reviewing the details of saving data, a review of SNA methodology can be helpful in understanding the conceptual basis of saving. Similar to any economic data, saving data is based on the methodology of System of National Accounts of the United Nations, which was first developed in 1953. Even though this methodology evolved and changed in 1968 and 1993, standard macroeconomic analyses did not follow these developments. According to Godley and Lavoie (2007: 23) The 1953 version of SNA had “left the monetary and financial phenomena in dark” as the focus was “saving must equal to investment.” Even though this notion is valid at the aggregate level, the real issue is who finances whom, and through which instruments. In SNA methodology, financial markets and institutions are not passive but active participants of an economic system. In 1968, a new SNA “provided a theoretical scheme that stressed the integration of the national income accounts with financial transactions, capital stocks and balance sheets” (Godley and Lavoie, 2007: 24), and this new system was also updated in 1993 (SNA, 1993). However, National Income and Product Accounts (NIPA) in the U.S. and similar macro data sources all around the world did not incorporate such developments into their systems. And many economists were similarly reluctant to use this new methodology in their models (Godley and Lavoie, 2007: 25). As a result, the financial transactions remained outside of the system, and these transactions were represented under saving as if it were a black box. This approach also enabled the classical dichotomy between real and monetary variables to survive (Godley and Lavoie, 2007: 24). The questions of who finances whom, and how an investment is financed are often ignored.

In SNA framework, saving can emerge for an economic unit as a negative or positive amount as a residual. And once it emerges, the next step is the direction of change in terms of a change in liabilities or assets. As a result, saving, by itself, is not a constraint for any economic unit.

As a result, building macroeconomic policies on the concept of saving can be misleading. Promoting national saving is often seen as a solution for excessive government debt, current account deficits, and boosting economic growth. However, lack of gross national saving is usually lack of investment or current account deficits or, usually, both of these. Spending less cannot make a nation’s goods more competitive in international markets; these issues should be addressed individually. The concept of national saving hides many macroeconomic problems. A deficit unit—a firm or a national economy—can always spend above its disposable income. Yet, it does not mean that such an economic unit can continuously increase its liabilities. Even though running a deficit may not be a problem, running chronic deficits can lead to accumulation of liabilities, and eventually creating financial instability. In a macroeconomic framework, financial positions of domestic private sectors, government, and the rest of the world are interdependent (Parenteau, 2004; Zezza, 2009). The interaction between these sectors, and the changes in assets and liabilities of these sectors can yield more information than gross national saving.


[1] CFC “does not represent the aggregate value of a set of transactions. It is an imputed value whose economic significance is different from entries in the accounts based mainly on market transactions. (…) Its value may deviate considerably from depreciation as recorded in business accounts or as allowed for taxation purposes, especially when there is inflation. Consumption of fixed capital should reflect underlying resource costs and relative demands at the time the production takes place. It should therefore be calculated using the actual or estimated prices and rentals of fixed assets prevailing at that time and not at the times the goods were originally acquired” (SNA 1993: 182).

[2] For details of CFC, see Ozgur, 2017.


Godley, W. and M. Lavoie (2007) Monetary Economics: An Integrated Approach to Credit, Money, Income Production and Wealth, New York: Palgrave MacMillan.

Krugman, P. R., M. Obstfeld, and M. J. Melitz (2012) International Economics, 9th Ed., Boston, MA.: The Pearson Series in Economics.

IMF (2014) World Economic Outlook April, Washington, D.C., IMF.

Ozgur, G. (2017) How Saving Data is Estimated?, University of Massachussetts-Amherst Political Economy Research Institute, Working Paper 436.

Parenteau, R. (2004) “Exploring the Economics of Euphoria: Using Post Keynesian Tools to Understand the US Bubble and Its Aftermath.” In L. R. Wray and M. Forstater (eds.), Contemporary Post Keynesian Analysis. Northampton, MA: Edward Elgar, pp. 44-66.

United Nations (1993) System of National Accounts. Washington, D.C.: United Nations.

Zezza, G. (2009) “Fiscal Policy and the Economics of Financial Balances.” Levy Economics Institute of Bard College Working Paper 569. (

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The De-Digitisation of India

Jayati Ghosh

So it’s official: cash use is back in almost full force in the Indian economy. Cash withdrawals from ATM machines – a reasonable if incomplete proxy for the use of cash in the economy – are nearly back to the level of just before the demonetisation shock of 8 November 2016. RBI data on use of debit and credit cards to withdraw money from ATMs show that such withdrawals, which had collapsed to only Rs 850 billion in December 2016 largely because of the sheer unavailability of cash with such machines, amounted to Rs 2.27 trillion in July 2017, only slightly below the Rs 2.55 trillion withdrawals recorded for October 2016.

It is worth noting that this reliance on cash is back despite the fact that the RBI is yet to remonetise the economy fully: currency with the public on 15 September 2017 was still 11 per cent below its level of a year earlier. It cannot simply be assumed (as was done in the Economic Survey 2016-17 Volume II) that this reflects lower demand from currency by the public, since there is no evidence that it is not supply-constrained. Rather, the aggressive return of cash use suggests that it has only been the lack of supply of cash that has constrained people from using it in payments and exchange settlement.

Indeed, it is likely that if the RBI does fully remonetise, then cash use will increase further, since the economy is still growing and therefore the volume and value of total transactions must increase. What is more surprising is that total digital payments have not increased more along with economic growth. In fact such payments, which peaked dramatically in December 2016, are also back to the levels broadly seen in September-October 2016, despite the many incentives provided for such payments through official policy.

This makes it apparent that demonetisation failed on this front as well, in addition to the spectacular failure of not being able to flush out “black money” from the system since almost all the banned notes were returned to banks. The aim of digitisation of the economy by forcing a comprehensive shift to cashless electronic means of payment was declared to be one of the primary goals of that expensive and economically damaging exercise. But now it seems that such a coercive process was untenable: the shift to cashlessness cannot be forced upon people, especially in the absence of other enabling and supporting conditions.

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World Bank Must Stop Encouraging Harmful Tax Competition

Jomo Kwame Sundaram and Anis Chowdhury

One of the 11 areas that the World Bank’s Doing Business (DB) report includes in ranking a country’s business environment is paying taxes. The background study for DB 2017, Paying Taxes 2016 claims that its emphasis is “on efficient tax compliance and straightforward tax regimes.”

Its ostensible aim is to aid developing countries in enhancing the administrative capacities of tax authorities as well as reducing informal economic activities and corruption, while promoting growth and investment. All well and good, until we get into the details.

Tax less

First, the Report advocates not only administrative efficiency, but also lower tax rates. Any country that reduces tax rates, or raises the threshold for taxable income, or provides exemptions, gets approval.

Second, it exaggerates the tax burden by including, for example, employees’ health insurance and pensions and charges for public services like waste collection and infrastructure or environmental levies that the businesses must pay. The IMF’s Government Financial Statistics Manual correctly treats these separately from general tax revenues.

Third, by favourably viewing countries that lower corporate tax rates (or increase threshold and exemptions) and negatively considering those that introduce new taxes, DB is essentially encouraging tax competition among developing countries.

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Economic Development for Transformative Structural Change, Part 4

Economist and Triple Crisis contributor Jayati Ghosh recently moderated, at a book launch event in Geneva, a discussion of the new book The Handbook of Alternative Theories of Economic Development.  This is the final part in a four-part series, from The Real News Network, featuring that discussion. The full series is available here.

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From Japanese Bubble to Chinese Time Bomb

An Interview with Walden Bello

Asia’s financial crisis came a decade before the global crisis, but it has had a lasting influence and left a legacy that could sow the seeds for the next global crisis.

How did the Asian financial crisis in 1997-1998 differ to the one that broke in the US and Europe in 2008?

The Asian financial crisis in 1997-98 was similar to the 2008 global financial crisis in that it was also the product of speculative bubbles in real estate and the stock market created by the search for high profits by finance capital. The difference was the role of currency speculators and hedge fund operators in hastening the bursting of the bubble and the collapse of the real economy; these actors played a negligible role in the 2008 crisis. These speculators, led by George Soros’ Quantum Fund, targeted the overvalued currencies of the Asian economies, particularly the Thai baht, betting on the probability that they would be devalued relative to the dollar owing to investor fears that the bubbles would burst, thus accelerating the devaluation of the currencies and making tremendous profits once the Asian currencies were devalued. Had the speculators not been active, the bubbles would still have burst and the real economy would still have entered into severe crisis, but the “landing” would have probably been less rough.

Where the uniqueness of the 2008-2009 crisis lay was in the fatal marriage of a real estate bubble with financial engineering. Tremendous amounts of cash flowed into real estate that were plowed into loans, a great many of them of dubious quality because the debtors’ capacity to repay the loans was questionable– thus the term subprime loans. Financial engineering allowed mortgage originators to slice, dice, and package these loans into securities that were then sold to banks and other financial institutions, which then resold them to other banks and financial institutions. When the mortgage holders could no longer service their mortgages, the quality of the loans was drastically impaired. But billions of dollars of these now toxic securities were circulating in the global financial system, upending the balance sheets of the US and foreign banks and institutions that held them and driving many, like Lehman Brothers, to bankruptcy, and others, like Citi and the German regional banks, requiring a massive government rescue.

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