Category Archives: Financial Economics

A Review of Mian & Sufi’s House of Debt

Lawrence Summers, a Professor of Economics at Harvard University and a Financial Times columnist, hailed Atif Mian & Amar Sufi’s book as ‘the most important economics book of the year’. The book was published in 2015 by the University of Chicago Press. This is a very readable book on issues of debt (particularly household debt in America), determination of activity levels, and on how to do good economics.

        Mian & Sufi begin by discussing the leverage ratio – ‘the ratio of total debt to total assets’ (p. 20). For the poorest homeowners, this ratio was near 80% and for the richest 20%, this ratio was only 7%. This is because the poor households borrow to purchase their assets (for example, a house). At the same time, the rich households deposit (credit) money with the banking sector to earn interest. The banking sector mediates the financial needs of the borrowers and the lenders. As Mian & Sufi write:

A poor man’s debt is a rich man’s asset. Since it is ultimately the rich who are lending to the poor through the financial system, as we move from poor home owners to rich home owners, debt declines and financial assets rise. (p. 20)

This observation immediately points to the need for looking at inequalities of income and wealth when studying debt or credit. Indeed, ‘[a] financial system that relies excessively on debt amplifies wealth inequality’ (p. 25). This is because when house prices fall, the decline in net worth for the indebted poor households will be more than proportional (p. 22-3).

       The authors rightly note that ‘the Great Recession was consumption-driven’ (p. 30) for ‘the decline in overall household spending in the third and fourth quarters of 2008 was unprecedented’ (p. 33). However, the dominant view in the US and across the world is what the authors term the ‘banking view’.

According to this view, the collapse of Lehman Brothers froze the credit system, preventing businesses from getting the loans they needed to continue operating. As a result, they were forced to cut investment and lay off workers. In this narrative, if we could have prevented Lehman Brothers from failing, our economy would have remained intact. (p. 31)

The dominant view locates the problem to be the lack of credit in the economy. And, they believe that if credit is made available at cheap rates (low rates of interest), the economy will revive. This view ignores the purpose of credit in an economy. Individual and firms demand money for consumption and investment (in a two-sector economy, aggregate demand is the sum of consumption and investment), and if aggregate demand falls so will the demand for credit. A fall in aggregate demand, as Keynes demonstrated in The General Theory, results in the reduction of activity and employment levels. This is precisely what happened during the Great Recession.

Job losses materialized because households stopped buying, not because businesses stopped investing. In fact, the evidence indicates that the decline in business investment was a reaction to the massive decline in household spending. If businesses saw no demand for their products, then of course they cut back on investment. (p. 34)

In other words, investment is not independent of consumption. This insight is of value in emerging economies like India where actual output is far below the potential output (large presence of disguised unemployment and underemployment), and political campaigns like ‘Make in India’ must be viewed with great caution. The dominant view is based on, what in growth theory is called, the supply-side growth theory. According to this theory, a growth in aggregate supply automatically generates an equivalent growth in aggregate demand. In House of Debt, the authors label this as the ‘fundamentals view’.

The basic idea behind the fundamentals view is that the total output, or GDP, of the economy is determined by its productive capacity: workers, capital, and the technology of firms. The economy is defined by what it can produce, not by what is demanded. Total production is limited only by natural barriers, like the rate at which our machines can convert various inputs into output, the number of working hours in a day per person, and the willingness of people to work versus relax. This is sometimes called the supply-side view because it emphasizes the productive capacity, or supply, of resources. (pp. 47-8)

That is, lower spending in the fundamentals view does not lead to contraction or job loss. Remember, output in the fundamentals view is determined by the productive capacity of the economy, not by demand. In response to a sharp decline in consumption, the economy in the fundamentals view has natural corrective forces that keep it operating at full capacity. These include lower interest rates and consumer prices … Obviously, however, these corrective forces weren’t able to keep the economy on track. (p. 49)

This view ignores the fundamental insight provided by Keynes in 1936. In a sense, the Say’s Law still lives on. And, in this theory, ‘[i]nvoluntary unemployment can only exist … if there are some “rigidities” that prevent wages from adjusting and workers from finding jobs’ (p. 56). These rigidities or frictions may be the following: presence of non-tradable jobs (that is, jobs which only cater to the local economy); wages do not fall; workers do not move; and the costs of reskilling if workers have to reallocate (p. 58, p. 63). For a critique and an alternative, see Thomas 2013.

       The marginal propensity to consume (MPC) varies across classes and therefore the assumption that everyone has the same MPC cannot be admitted. The MPC is high for poor households and low for rich households. ‘The larger the MPC, the more responsive the household is to the same change in wealth’ (p. 39; also p. 44). In fact, ‘the higher the leverage in the home, the more aggressively the household cuts back on spending when home values decline’ (p. 42). Therefore, debt matters. According to Mian & Sufi, ‘[t]he higher MPC out of housing wealth for highly levered households is one of the most important results from our research. It immediately implies that the distribution of wealth and debt matters’ (p. 42). Moreover, ‘[t]he MPC of households is also relevant for thinking about the effectiveness of government stimulus programs for boosting demand’ (p. 41).

       Very often, during recessions, the dominant policy response is the lowering of interest rates via monetary policy. But does the lowering of rates help? Is the problem a lack of availability of funds at cheap rates?

To help answer this, there is evidence from surveys by the National Federation of Independent Businesses (NFIB). Proponents of the bank- lending view are particularly concerned about credit to small businesses. Because small businesses rely heavily on banks for credit, they will be disproportionately affected. Large businesses, however, can rely on bonds or commercial paper markets for debt financing. The NFIB is informative because it surveys exactly the small businesses that should be most vulnerable to being cut off from bank lending. The survey asks small businesses to list their most important concern, where “poor sales,” “regulation and taxes,” and “financing and interest rates” are a few of the options. The fraction citing financing and interest rates as a main concern never rose above 5 percent throughout the financial crisis— in fact, the fraction actually went down from 2007 to 2009. It is difficult to reconcile this fact with the view that small businesses were desperate for bank financing. On the other hand, from 2007 to 2009, the fraction of small businesses citing poor sales as their top concern jumped from 10 percent to almost 35 percent. As indebted households cut back sharply on spending, businesses saw a sharp decline in sales. (p. 128)

As the survey indicated in the passage shows, the problem is a lack of aggregate demand, particularly consumption demand. ‘Companies laying off workers in these hard-hit counties were the largest businesses. This is more consistent with businesses responding to a lack of consumer demand rather than an inability to get a bank loan’ (p. 128). There is another issue here; this has to do with the effectiveness of the monetary policy mechanism. Hence, Mian & Sufi write: ‘[a]n increase in bank reserves leads to an increase in currency in circulation only if banks increase lending in response to the increase in reserves. If banks don’t lend more— or, equivalently, if borrowers don’t borrow more— an increase in bank reserves doesn’t affect money in circulation’ (p. 154) limiting the ‘effectiveness of monetary policy’ (p. 155). And there is no strict connection between interest rates and household spending; at the very least, a strong association cannot be assumed (see p. 161).

       This brings us to the end of this book review. It was noted in the introductory paragraph that this book is also about doing good economics. Mian & Sufi point to the need for have a good theory to make sense of the macroeconomic phenomena. This blog concludes with their view on the role of theory.

The ability to interpret data is especially important in macroeconomics. The aggregate U.S. economy is an unwieldy object – it contains millions of firms and households. … But unless an economist can put some structure on the data, he or she will drown in a deep ocean of numbers trying to answer these questions.

Which brings us to the importance of an economic model. Macroeconomists are defined in large part by the theoretical model they use to approach the data. A model provides the structure needed to see which data are most important, and to decide on the right course of action given the information that is available. (p. 47)

The Monopoly of Credit Rating Agencies

“After Fitch, Moody’s lowers India’s growth forecast” reads a headline in The Hindu on August 25. Who are these agencies? They are credit rating agencies responsible for assessing the creditworthiness of big borrowers – companies and governments. The market for credit rating is dominated by 3 big firms – Standard & Poor, Moody’s and Fitch. Basically, these credit rating agencies sell information about the debtors to the creditors.

How reliable are they? As the regulator of the Indian securities market, Securities and Exchange Board of India (SEBI) writes in its FAQ, ‘A credit rating is a professional opinion given after studying all available information at a particular point of time. Nevertheless, such opinions may prove wrong in the context of subsequent events. There is no contract between an investor and a rating agency and the investor is free to accept or reject the opinion of the agency.’ As a matter of fact, the credit ratings were proven to be completely wrong in the wake of the Great Recession because they grossly misrepresented the risk on the mortgage-backed securities. Joseph Stiglitz is quoted as saying: “I view the rating agencies as one of the key culprits.” And not surprisingly, between 2001 and 2007, the operating margins of Moody’s exceeded 50 per cent, three to four times those of Exxon Mobil Corp., the world’s biggest oil company. Also, as a CFR report states, the “EU governments and ECB policymakers accused the Big Three [S&P, Moody and Fitch] of being overly aggressive in rating eurozone countries’ creditworthiness, exacerbating the financial crisis”.

A financial market mediates between debtors and creditors through the buying and selling of financial instruments with varying risk and liquidity (to meet the different preferences and needs of the market participants). Unlike in a product market, say for tomatoes, it is difficult to assess the ‘value’ (let alone the quality) of a financial instrument. Suffice to note here that different financial theories exist which provide explanations for the ‘value’ of a financial instrument. The creditor needs to know whether the debtor is credit-worthy, i.e., whether the probability of the debtor to default is low. This information need is met by the credit rating agencies, of course, not very satisfactorily. For, they also seem to fall prey to the irrational exuberance characterizing the financial markets. More importantly, as during the Great Recession, evidence points to them as perpetrating a financial crime by aiding and abetting the housing bubble by issuing top ratings to bad mortgage-backed securities.

Global investors obtain information on investment avenues from multiple sources. And in the specific case of India, most of the financial savings are parked in time deposits, Post Office savings and with LIC and not in the stock market. Should a credit rating downgrade worry us? Are we worried because of how the stock market may react? Will it affect capital inflows? Rational investors make informed decisions by examining the macroeconomic situation, the ease of investing and the transparency and stability of macroeconomic policies. For example, any amount of mere rhetoric of ‘Make in India’ will not help – as seen by the exit of Jim Rogers, a global commodities trader and hedge fund manager, from India. As Rogers’ says, “one can’t invest just on hope.”

The argument of this blog post is not that all the assessments by credit rating agencies are incorrect. The argument is rather than we must critically appraise them and contextualize them. For instance, the lowering of Asia’s growth forecasts on account of slowing exports and subdued demand by Moody’s on 8th September 2015 should be a cause for concern. Why are we not focusing on policies which generate domestic demand?

I end with the financial commentator John Kay’s observation on the power of the bond markets in Britain. “So how do bond markets acquire their power to intimidate? Politicians spend too much time talking to people who take a daily interest in the bond market, and come to believe that their obsessions are important. Britain’s economic performance should be judged by benchmarks relating to employment, productivity, growth and innovation, not credit ratings.” This should be the case in India too.

On Financial Markets: The Problematic Assumptions

More than half of the dissertations and theses in India are on financial markets. Various aspects such as pricing of options, efficiency of markets, volatility of markets, its impact on the real sector, futures markets, effect of foreign trade, etc are analysed. Financial markets refer to the stock market, the derivatives market, the commodity markets, etc. For our purposes, we will take into account only the stock/share market as it is the one that is most well-understood in comparison to the rest. This blog post echoes a lot of my concerns with the way financial markets are analysed, and also indicates some of the broader concerns about econometric work in general. I have been greatly motivated and moved by Benoit Mandelbrot’s and Richard Hudson’s book The (Mis)Behaviour of Markets in writing this post. All quotations in this post are from their book.

On attending several pre-submission, post-submission, work-in-progress and viva-voce seminars, I have often wondered about economists fascination with the ‘normality assumption’. We assume that price changes follow a normal distribution, that is, outliers (both small and large) do not significantly affect the average/expected value. That is, standard theories of finance “assume the easier, mild form of randomness. Overwhelming evidence shows markets are far wilder, and scarier, than that.” Now, in natural sciences, this is a common enough assumption. Is there any empirical evidence supporting the use of such a distribution in economics, mainly the analysis of changes in prices and quantities? One wonders. In fact, it is this distribution which underlies the most commonly used tool in regression – the method of least squares. Most studies (academic and corporate) measure volatility using variance or standard deviation of the normally distributed variables. As Mandelbrot asks, “is this the only way to look at the world?”

Apart from the normality assumption, orthodox financial theory makes the following assumptions. This list is directly based on Mandelbrot’s book. (1) People are rational and aim only to get rich. (2) All investors are alike and they are price-takers, not makers. (3) Price change is practically continuous. (4) Price changes follow a Brownian motion, that is each price change appears independently from the last, the price changes are statistically stationary and that the price changes are normally distributed.

Assumptions (1) and (2) need no discussion, owing to their obvious falsity. Now it is assumption (3) that allows the use of continuous and differential functions; whereas, the reality is that “prices do jump, both trivially and significantly” and that discontinuity is an “essential ingredient of the market.” The meaning of independent price changes is that, price at t+1 is not dependent on price at t. In other words, prices have no memory. An example from tossing a fair coin will illustrate this better. Suppose a fair coin is tossed once, we get a head. The outcome of the next toss is not based on the outcome of the previous one. Again, how true this is of stock markets or of prices is questionable. How can such an assumption cope up with ‘expectations’ of investors? The statistical stationarity of price changes implies that the process generating the price changes stays the same over time.

Very often, in research, we do not have the time to question these assumption; not only that, these assumptions function as received wisdom. However, as Mandelbrot comments, “They work around, rather than build from and explain, the contradictory evidence” because “It gives a comforting impression of precision and competence.” For, a high kurtosis (the measure of how closely the data fits the bell curve) has been found in the prices of commodities, stocks and currencies.

To conclude, how does one as a researcher overcome such problematic/unreal/easy assumptions? Is this what academic “discipline” means? Or are we to learn adequate mathematics and statistics so that we can find a way around it? Or do we cooperate and seek help from mathematicians and statisticians? Mandelbrot has developed tools and concepts such as ‘fractal analysis’ and ‘long memory’ which can aid economics, which is inherently not a study of normally distributed variables.