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Ecological Economics, Regime Shifts

Food security and financial markets

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FAO says that Food price volatility a major threat to food security:

Concluding a day-long special meeting in Rome the experts recognized that unexpected price hikes “are a major threat to food security” and recommended further work to address their root causes.

The recommendations, put forward by the Inter-Governmental Groups (IGGs) on Grains and on Rice, came as FAO issued a report showing that international wheat prices have soared 60-80 percent since July while maize spiked about 40 percent.

The meeting said that “Global cereal supply and demand still appears sufficiently in balance”, adding, “unexpected crop failure in some major exporting countries followed by national policy responses and speculative behaviour rather than global market fundamentals have been the main factors behind the recent escalation of world prices and the prevailing high price volatility.”

Among the root causes of volatility, the meeting identified “Growing linkage with outside markets, in particular the impact of ‘financialization’ on futures markets”. Other causes were listed as insufficient information on crop supply and demand, poor market transparency, unexpected changes triggered by national food security situations, panic buying and hoarding.

The Groups therefore recommended exploring “alternative approaches to mitigating food price volatility” and “new mechanisms to enhance transparency and manage the risks associated with new sources of market volatility”.

In a recent IFPRI discussion paper, Recent Food Prices Movements: A Time Series Analysis, Bryce Cooke and Miguel Robles analyze the food price spike of 2008.  They asses multiple proposed explanations (from biofuels, oil prices, weather, trade barriers, and speculative markets) using econometric time series analysis.  They conclude that financial activity in futures markets and proxies for speculation can best explain crisis.  They write:

Results of our rolling windows Granger causality tests show the following:

(1) In the case of rice prices we find weak evidence that for few 30-month intervals between 2004 and 2007, the U.S. dollar depreciation rate has marginally Granger-caused the growth rate of rice price; and also the growth rate of real world money holdings seems to be more important in explaining the growth rate of rice prices after 2004, but this evidence is not really statistically significant.

(2) When we analyze the price of soybeans we find that, starting in mid-2005 (which implies a 30-month period ending December 2007), the growth rate in the world exports of soybeans shows evidence of Granger causing the growth rate of soybean prices.

(3) In the case of corn we find that starting in the second half of 2004 the growth rate of oil prices shows evidence of Granger causing the growth rate of corn prices, but with a negative relationship.

(4) When analyzing our speculation proxies we observe that the ratio of monthly volume to open interest in futures contracts indicates that for the case of wheat and rice, starting in 2005, it has influence in forecasting price movements.

Also we find that for the case of rice, the ratio of noncommercial long positions to total long (reportable) positions has an effect on prices, starting in 2004. When we analyze the same ratio for short positions we find additional evidence for speculation affecting the growth rate of corn and soybean prices. In the case of corn there are signs of causality between March 2004 and September 2006, and during the 30-month span from May 2005 to November 2007. In the case of soybeans we find weak evidence, in particular for the 30-month period ending February 2008.

Interestingly as the rolling samples include 2008 and 2009 data, picking the decrease of grain prices since mid 2008 and the adverse effects of the global financial crisis, the evidence of speculation activity affecting spot prices vanishes in all cases. This supports the view that during the food crisis agricultural grain markets were operating under a different regime in which speculation activity played a role in spot prices formation. The overall evidence points to the following interpretation: before and after the food crisis speculation activity had no effect on spot prices formation while during the crisis it did. This is not to say that before and after the crisis speculation was not present, it was (probably to a less extent) but didn’t granger cause spot prices.

Overall, we conclude from our time series analysis that when taking the four commodities analyzed here there is evidence that financial activity in futures markets and/or speculation in these markets can help explain the behavior of these prices in recent years. Other explanations are only partially supported for the particular case of one agricultural commodity or not supported at all. We do not claim, however, that these other explanations should be disregarded; all that we can say is that in using the variables considered in this study and the particular time series models herein, we do not find such evidence.

Frederick Kaufman wrote a Harper’s magazine in July 2010 The food bubble:
How Wall Street starved millions and got away with it that reports on finance and the food crisis. The Harper’s version is behind a paywall, but Kaufman was interviewed on Democracy Now.

More academic takes on the food crisis and the possible future of food price volatility are in:

C. Gilbert and C. Morgan’s article Food price volatility in Proc Royal Soc (DOI: 10.1098/rstb.2010.0139 ). They conclude:

We have highlighted the extensive evidence demonstrating interconnection of financial and food commodity markets as the result of speculative activity. Nevertheless, this contention remains controversial and, until the mechanisms are better understood, the policy debate will remain confused.

and

C. Gilbert’s How to Understand High Food Prices in Journal of Agricultural Economics (DOI: 10.1111/j.1477-9552.2010.00248.x) whose abstract states:

Agricultural price booms are better explained by common factors than by market-specific factors such as supply shocks. A capital asset pricing model-type model shows why one should expect this and Granger causality analysis establishes the role of demand growth, monetary expansion and exchange rate movements in explaining price movements over the period since 1971. The demand for grains and oilseeds as biofuel feedstocks has been cited as the main cause of the price rise, but there is little direct evidence for this contention. Instead, index-based investment in agricultural futures markets is seen as the major channel through which macroeconomic and monetary factors generated the 2007–2008 food price rises.

Inequality

Estimating welfare: another measure

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New NBER paper Beyond GDP? Welfare across Countries and Time by Charles Jones and Peter Klenow looks interesting.  They propose a new summary statistic for a nation’s flows of welfare that combines data on consumption, leisure, inequality, and mortality.  They do not include welfare gains from ecosystem services.

The authors explain their index:

… High hours worked per capita and a high investment rate are well known to deliver high GDP, other things being equal. But these strategies have associated costs that are not reflected in GDP. Our welfare measure values the high GDP but adjusts for the lower leisure and lower consumption share to produce a more accurate picture of living standards.

This paper builds on a large collection of related work. … We try to incorporate life expectancy and inequality and make comparisons across countries as well as over time, but we do not attempt to account for urban disamenities. The World Bank’s Human Development Index combines income, life expectancy, and literacy into a single number, first putting each variable on a scale from zero to one and then averaging. In comparison, we combine different ingredients (consumption rather than income, leisure rather than literacy, plus inequality) using a utility function to arrive at a consumption equivalent welfare measure that can be compared across time for a given country as well as across countries. Fleurbaey (2009) contains a more comprehensive review of attempts at constructing measures of social welfare.

They discover that while their index is highly correlated with GDP/capita (.95) there are still important differences among countries using this new measure.  They also find welfare growth is less correlated with GDP (0.82), and exhibits even larger differences among individual countries.  According to their index, welfare is being substantially increased by recent increases in life expectancy worldwide (with the major exception of sub-Saharan Africa).

Using this index many developing countries are poorer than GDP/capita alone suggests due to inequality, poor health and lack of leisure.

Inequality

How much is African poverty really falling?

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Martin Ravallion, Director of the Development Research Group of the World Bank,responds to Maxim Pinkovskiy and Xavier Sala-i-Martin’s NBER paper that estimates a decline in African poverty.  He agrees that poverty is decreasing, but believes they are overstating their case.

He writes Is African poverty falling? on the World Banks’ Africa can end poverty blog:

We must first be clear about what we mean when we say “poverty is falling”. What many people mean is falling numbers of poor. However, PSiM [Pinkovskiy & Sala-i-Martin] refer solely to the poverty rate—the percentage of people who are poor. (There is no mention of this important distinction in their paper.) And it is not falling over their whole period of their analysis, which goes back to 1970. Rather they find that the poverty rate has been falling since the mid-1990s.

Here we agree: aggregate poverty rates have fallen in Sub-Saharan Africa (SSA) since the mid-1990s.  Shahoua Chen and I came to exactly the same conclusion in our research, for the World Bank’s global poverty monitoring effort, although our methods differ considerably and (no surprise) I prefer our methods.

However, Chen and I also point out that the decline in the aggregate poverty rate has not been sufficient to reduce the number of poor, given population growth. …

Two points to note here: (i) Chen and I show that the poverty decline in SSA tends to be larger for lower poverty lines (in the region $1-$2.50 a day) and (ii) PSiM’s method attributes the entire difference between GDP and household consumption to the current consumption of households, and they assume that its distribution is the same as in the surveys. These assumptions are very unlikely to hold, and they give an overly optimistic picture.

In effect, PSiM are using a lower poverty line than us.

…  Another important difference is that Chen and I are more cautious about the data limitations. There are not enough good household surveys available yet to be confident that this is a robust new trend of a falling poverty rate for SSA. PSiM are not so restrained, as is plain from their title!

…Hopefully we will see a confirmation of the emerging downward trend for Africa in the years ahead, as more (genuine) data emerge.

via
Chris Blattman

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Economics as a complex systems science

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An interesting interview of Paul Krugman by Edward Hugh is on A Fistful of Euros:

E.H. : The late Sir Karl Popper used to contrast what he regarded as science with ideologies like Marxism and Psychoanalysis, because there seemed to be no way whatever of consenually agreeing with their practitioners a series of simple tests which would enable their theories to be falsified. Some critics of neoclassical economics – including Popper’s heir Imre Lakatos – have expressed similar frustrations. Do you think we economists are, as a profession, up to the challenge of formulating testable hypotheses in such a way that the public at large might come to have more confidence in what we are up to, or are we a lost cause?

P.K.: I really don’t think that’s a helpful way to pose this question. Economics is about modeling complex systems, and as such the models are always less than fully accurate. What economists do need, however, is some demonstrated ability to get big things right. They had that after the Great Depression, when Keynesian economics clearly made sense of both the depression and the wartime recovery. But now the profession needs to get back on track.

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Elinor Ostrom’s Nobel Prize in Economics

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Prize Award Ceremony

Elinor Ostrom receiving her Prize from His Majesty King Carl XVI Gustaf of Sweden at the Stockholm Concert Hall, 10 December 2009. Copyright © The Nobel Foundation 2009. Photo: Frida Westholm

Our colleague, Lin Ostrom was just in Stockholm to receive her Nobel Prize. I was fortunate to be able to congratulate Lin Ostrom before her Nobel Lecture.  Her prize Lecture, Beyond Markets and States: Polycentric Governance of Complex Economic Systems » (28 min.  ) is available on the Nobel website.

Her colleagues at Indiana University have been blogging her Stockholm trip, providing some insight into her very busy ittineary, which has included sidetrips to COP 15 in Copenhagen and Uppsala.

Lin Ostrom is on the board of the Stockholm Resilience Centre, and they write:

A sparklingly happy Elinor Ostrom arrived in Stockholm to receive the prize at the Nobel ceremony on the 10t December. Professor Ostrom, who currently serves on the board of  Stockholm Resilience Centre, is a long time research associate of Stockholm Resilience Centre and its partner the Beijer Institute of Ecological Economics.

“We need serious people with good theories to look at environmental problems and Stockholm Resilience Centre and the Beijer Institute has gathered extraordinary people to do this”, says Elinor Ostrom enjoying the traditional Nobel reception at the Royal Swedish Academy of Sciences.

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Paradoxes of efficient market theory

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Complex systems scientist Cosma Shalizi reviews economic journalist Justin Fox‘s book The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street for American Scientist magazine in the article Twilight of the Efficient Markets:

The Myth of the Rational Market, by Justin Fox, is an account—popular but thorough—of the roots, rise, triumph and ongoing fall of the theory of efficient markets in finance. This school of thought is an exemplary specimen of a type of social science that flourished after World War II: It has mathematical models at its center, has supposedly been empirically validated by statistical analyses, is indifferent to history and to institutions, and takes as an axiom that people are intelligent, farsighted and greedy. Unlike many economic theories, the efficient-market school has been influential beyond academia. It helped reshape ideas about how companies should be run, how executives should be paid, and indeed how the economy should be regulated (or not) to promote the general welfare. (In comic-book form: A mild-mannered social science by day, at night efficient-market theory puts on a cloak of ideology and struggles for the Capitalist Way.) The theory contributed, arguably, to setting up the crisis that has gripped the world economy since 2007. Its story is of much more than just scholarly interest.

The founding principles of efficient-market theory are easily described. The assumption on which all else rests is that, unless one has private knowledge, there is no way to profit from financial markets without risk. …

… Therefore, says efficient-market theory, securities prices are unpredictable. Current prices are supposed to be optimal forecasts, on the basis of currently available data, of the present value of future returns, because changes in optimal forecasts are, themselves, unpredictable. (If you know that tomorrow your forecast of next year’s gasoline price will be higher than today’s forecast by $1, you should raise your current forecast.) As Paul Samuelson put it, “properly anticipated prices fluctuate randomly.” The efficient-market hypothesis, as a technical term, is the claim that market prices cannot be predicted, either from past prices alone or from past prices combined with other publicly available information. One of the early triumphs of the school was the demonstration that stock prices look very much indeed like random walks.

… A vast superstructure was erected on these foundations, beginning in the 1950s and really taking off in the 1960s and 1970s. Particularly impressive wings of that edifice were devoted to the design of portfolios to balance risk against return and to the valuation of derivative securities (“contingent claims” or bets on the value of other securities), especially options to buy or sell stocks at given prices by given dates. As Fox notes, scholars of finance achieved acclaim, and were awarded substantial consulting fees, for solving pricing problems that by hypothesis were already being solved by the markets themselves! (Donald Mackenzie’s An Engine, Not a Camera explores this paradox in depth.)By the 1980s and 1990s, these ideas had led to changes in the way the investment industry worked, new concepts of corporate governance and new kinds of financial firms, which aimed to systematically identify arbitrage opportunities—deviations from what the theory said prices should be—and to earn a profit even as they eliminated those opportunities. More diffusely, the academic prestige of efficient-market theory provided, at the least, rhetorical support for deregulating markets, especially financial markets, and delegating more and more authority to them. This was aided by a conflation—subscribed to by many scholars—between those markets having informationally efficient prices (that is, unpredictable ones) and those markets allocating capital efficiently (directing savings to where the money can be used most profitably). The latter is the more usual economic notion of efficiency, but informationally efficient prices are neither necessary nor sufficient for efficient allocation.

The whole edifice, however, has turned out to be built, if not on sand, then at best on loose fill. More rigorous testing on larger data sets has shown that the capital asset pricing model does not fit the data; beta in particular does not predict returns at all. The response has been to identify variables that do predict returns and presume that they must be risk factors, although the extra risk has never been demonstrated. Prices are hard to predict, although not impossible, especially with high-frequency data (arriving minute-by-minute or faster). One reason markets are hard to predict is that they change much more than forecasts of future earnings should, and often they change on no detectable information at all. (Defenders claim that this just shows scholars aren’t smart enough to grasp information known to everyone in the market.) Economists taking a behavioral approach have shown that actual investors don’t act like the cool, farsighted calculators that efficient-market theory demands; worse, it turns out that having a handful of smart arbitrageurs around is actually not enough to swamp the “noise traders”—it really is the case that, as the saying goes, “markets can stay irrational longer than you can stay solvent.”

This leaves us at an impasse. Efficient-market theory ought, with any methodological justice, to be relegated to the Museum of Nice Tries. But there is no unified replacement theory, and developing one will be arduous, involving empirical and theoretical work on all scales, from the experimental psychology of individual investors, through the institutional constraints under which money managers work, to solving for the aggregated effects of market participants’ interactions. In the meantime, efficient-market theory provides a ready basis for precise calculations, and one that is moreover now built into the academic field of finance and into the practice and even infrastructure of the markets.

General

Income, fertility and the world’s demographic trajectory

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Avg. Income vs. Fertility from Gapminder

data from Gapminder

The Economists looks at recent declines in fertility discusses current projections of world population, and how changes in a country’s demographic structure shape its economic development (but it doesn’t mention the role of urbanization).  In Fertility and living standards it writes:

Sometime in the next few years (if it hasn’t happened already) the world will reach a milestone: half of humanity will be having only enough children to replace itself. That is, the fertility rate of half the world will be 2.1 or below. This is the “replacement level of fertility”, the magic number that causes a country’s population to slow down and eventually to stabilise. According to the United Nations population division, 2.9 billion people out of a total of 6.5 billion were living in countries at or below this point in 2000-05. The number will rise to 3.4 billion out of 7 billion in the early 2010s and to over 50% in the middle of the next decade. The countries include not only Russia and Japan but Brazil, Indonesia, China and even south India.

The move to replacement-level fertility is one of the most dramatic social changes in history. It manifested itself in the violent demonstrations by students against their clerical rulers in Iran this year. It almost certainly contributed to the rising numbers of middle-class voters who backed the incumbent governments of Indonesia and India. It shows up in rural Malaysia in richer, emptier villages surrounded by mechanised farms. And everywhere, it is changing traditional family life by enabling women to work and children to be educated. At a time when Malthusian alarms are ringing because of environmental pressures, falling fertility may even provide a measure of reassurance about global population trends. …

Higher standards of living, then, reduce fertility. And lower fertility improves living standards. This is what China’s government says. It is also the view that has emerged from demographic research over the past 20 years.  In the 1980s, population was regarded as relatively unimportant to economic performance. American delegates told a UN conference in 1984 that “population growth is, in and of itself, neither good nor bad; it is a neutral phenomenon.” Recent research suggests otherwise.

Cutting the fertility rate from six to two can help an economy in several ways. First, as fertility falls it changes the structure of the population, increasing the size of the workforce relative to the numbers of children and old people. When fertility is high and a country is young (median age below 20), there are huge numbers of children and the overall dependency ratio is high. When a country is ageing (median age above 40), it again has a high dependency ratio, this time because of old people.

But the switch from one to the other produces a Goldilocks generation. Because fertility is falling, there are relatively few children. Because of high mortality earlier, there are relatively few grandparents. Instead, countries have a bulge of working-age adults. This happened to Europe after the baby boom of 1945-65 and produced les trente glorieuses (30 years of growth). It is happening now in Asia and Latin America. East Asia has done better than Latin America, showing that lower fertility alone does not determine economic success. Eventually developing countries will face the same problems of ageing as Europe and Japan do. But for the moment, Asians and Latinos are enjoying fertility that is neither too hot, nor too cold. According to David Bloom of the Harvard School of Public Health, the “demographic dividend” (his term) accounted for a third of East Asian growth in 1965-90.

Slowing fertility has other benefits. By making it easier for women to work, it boosts the size of the labour force. Because there are fewer dependent children and old people, households have more money left for savings, which can be ploughed into investment. Chinese household savings (obviously influenced by many things, not just demography) reached almost 25% of GDP in 2008, helping to finance investment of an unprecedented 40% of GDP. This in turn accounted for practically all the increase in Chinese GDP in the first half of this year.

Lastly, low fertility makes possible a more rapid accumulation of capital per head. To see how, think about what happens to a farm as it is handed down the generations in a country without primogeniture. The more children there are, the more the farm is divided. Eventually, these patches become so tiny they cease to be efficient. …

This link between growth and fertility raises awkward questions. In the 1980s the link was downplayed in reaction to Malthusian alarms of the 1970s, when it was fashionable to argue that population growth had to be reined in because oil and natural resources were running short. So if population does matter after all, does that mean the Malthusians were right?

Not entirely. Neo-Malthusians think the world has too many people. But for most countries, the population questions that matter most are either: do we have enough people to support an ageing society? Or: how can we take advantage of having just the right number for economic growth? It is fair to say that these perceptions are not mutually exclusive. The world might indeed have the right numbers to boost growth and still have too many for the environment. The right response to that, though, would be to curb pollution and try to alter the pattern of growth to make it less resource-intensive, rather than to control population directly.

The reason is that widening replacement-level fertility means population growth is slowing down anyway. A further reduction of fertility would be possible if family planning were spread to the parts of the world which do not yet have it (notably Africa). But that would only reduce the growth in the world’s numbers from 9.2 billion in 2050 to, say, 8.5 billion. To go further would probably require draconian measures, such as sterilisation or one-child policies.

The bad news is that the girls who will give birth to the coming, larger generations have already been born. The good news is that they will want far fewer children than their mothers or grandmothers did.

Inequality

World distribution of income

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In a new paper, Parametric Estimations of the World Distribution of Income, Maxim Pinkovskiy and Xavier Sala-i-Martin revisit previous work by Sala-i-Martin, and estimate that globally income has substantially increased, reducing the number of people living in extreme poverty, and become more equally distributed (among individuals globally) over the past decades.

World distirbution of income 1970-2006 (Pinkovskiy & Sala-i-Martin 2009)

World distribution of income 1970-2006 (Pinkovskiy & Sala-i-Martin 2009). Red lines show 2006 and 1980 $1 day/income level.

In the paper they explain changes in income distribution:

In 1970, the WDI was trimodal (Fig. 19). There was a mode between the two $1/day lines, corresponding to the mode of the East Asian distribution (which, in turn, corresponds to the mode of the Chinese distribution which, in turn, corresponds to the mode of the Chinese rural distribution). The second mode is at about $1,000 and corresponds to the mode of South Asia which, in turn is slightly to the right of the mode of India. Finally, there is a third mode at around $5,000, which is somewhere between the mode of the USSR and that of the OECD. Note that a substantial fraction of the distribution lies to the left of the poverty lines, and that substantial fractions of the East Asia, South Asian, and African distributions lie to the left of the poverty lines. In 1970, $1/day poverty was large.

Figure 19: World Distribution of Income by Region, 1970

Figure 19: World Distribution of Income by Region, 1970

By 2006 things have changed dramatically (Fig. 20). First, note that the three modes disappeared. Instead, we have one mega-mode at an annual income of around $3,300, which roughly corresponds to the mode of East Asia and South Asia. To the right of the mode there is quite a substantial “shoulder” marked by the roughly 1 billion rich citizens of the OECD. At the other extreme, there is a thick tail at the bottom of the distribution marked by Sub-Saharan Africa. The fraction of the overall distribution to the left of the poverty lines has been reduced dramatically relative to 1970. Interestingly, most of the distribution to the left of the poverty line in 2006 is from Africa.

Figure 20: World Distribution of Income by Region, 2006

Figure 20: World Distribution of Income by Region, 2006

General

Krugman on Keynes and Uncertainty

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Paul Krugman reviews Keynes: The Return of the Master by Robert Skidelsky in the Observer.  He writes:

…there’s an alternative interpretation of what Keynes was all about, one offered by Keynes himself in an article published in 1937, a year after The General Theory. Here, Keynes suggested that the core of his insight lay in the acknowledgement that there is uncertainty in the world – uncertainty that cannot be reduced to statistical probabilities, what the former US defence secretary Donald Rumsfeld called “unknown unknowns”. This irreducible uncertainty, he argued, lies behind panics and bouts of exuberance and primarily accounts for the instability of market economies.

In this book, Skidelsky puts himself in the camp of those who argue, in effect, that Keynes 1937, not Keynes 1936, is the man to listen to – that Keynesianism is, or should be, essentially about uncertainty and how it leads to economic instability. And from this he draws some radical conclusions.

Most strikingly, Skidelsky declares that the traditional division between microeconomics and macroeconomics, which is based on whether one focuses on individual markets or on the overall economy, is all wrong; macroeconomics should be defined as the field that studies those areas of economic life in which irreducible uncertainty, uncertainty that cannot be tamed with statistics, dominates. He goes so far as to call for a complete division of postgraduate studies: departments of macroeconomics should not even teach microeconomics, or vice versa, because macroeconomists must be protected “from the encroachment of the methods and habits of mind of microeconomics”.

How far should we be willing to follow Skidelsky in this? I think we must trust the biographer in his assessment of Keynes himself; Skidelsky argues persuasively that Keynes spent much of his life deeply focused upon, even obsessed with, the question of how one acts in the face of uncertainty, which is why Keynes 1937 comes closer to the essence of the great man’s own thinking.

That’s not the same thing, however, as saying that Keynes was right – even about his own contribution. Surely it’s possible to make the case for a less profound reconstruction of economics than Skidelsky advocates. I’d point out that behavioural economists, who drop the assumption of perfect rationality but don’t seem much concerned by the essential unknowability of the future, have done relatively well at making sense of this crisis; I’d also point out that current disputes over economic policy, above all about the usefulness of government spending to promote employment, seem to be primarily about Say’s Law – that is, Keynes 1936.