WHE MARKETS FAIL: ASSET PRICES, GOVERMET EXPEDITURES, AD THE VELOCITY OF MOEY
WARBURTON, Christopher E.S.*
Abstract
This paper examines the contributions of financial and macroeconomic variables to the revitalisation of a depressed US economy. Using time series data from 1957 to 2011 and a binary logistic regression model, it finds that government consumption expenditure and gross investment, real personal consumption expenditure, and the velocity of money provide robust possibilities for improving economic growth after the failure of financial and real markets. It concludes that policies that are oriented towards the revitalisation of economic growth should seriously consider the speed at which money circulates in contradistinction to the money stock and asset prices or the wealth effect.
JEL: E1, E12, E32, E44, E51
Keywords: Asset Prices, Liquidity Trap, Logit, Market Failure, Velocity of Money 1. Introduction
This paper examines the contributions of financial and macroeconomic variables to the revitalisation of the recessionary or depressed US economy. Using time series data from 1957 to 2011 and a binary logistic regression model, it finds that government consumption expenditure and gross investment, real personal consumption expenditure (RPCE), and the velocity of money provide robust possibilities for improving economic growth after the failure of financial and real markets. It concludes that policies that are oriented towards the revitalisation of economic growth should seriously consider the speed at which money circulates in contradistinction to the money stock and asset prices or the wealth effect.
In this paper, market failure refers to the episodic failure of financial and real markets to allocate resources efficiently, implying that markets are not immune from inherent inefficiencies. The realisation of this problem is not a novel issue, and various scholars have devoted a lot of time and energy to the study of market operations, their periodic failures, the consequences of failures, and the most desirable responses for economic revitalisation. As a result, the literature on market failure and recovery is phenomenally replete with assorted theoretical underpinnings.
This study extends these theoretical and empirical analyses by focusing on the very pointed and somewhat unsettled issues related to market inefficiency and the role of wealth, government expenditure, and the velocity of money in achieving expeditious economic stabilisation. Specific variables and models are of interest, with an important objective of estimating the probabilistic contributions of these variables to the attainment of economic growth. This approach diverges from conflicting vector autoregressive forecasting algorithms, but the findings generally augment the evidence from some alternative models and present cogent reasons for the re-evaluation of the contributions of some macro and financial variables and sample sizes. The remainder of the paper is structured as follows: first, a review of the literature on market efficiency, household
*Christopher E.S. Warburton is an economist of the Economics Department at John Jay College of Criminal Justice, City University of New York, USA. Email: [email protected]
consumption, and market failure is provided in Section 2; second, a discussion of the data and macroeconomic variables is provided in Section 3; third, the econometric models and findings are reported in Section 4; followed by a conclusion and identifiable policy implications.
2. Review of the Literature on Market Efficiency, Consumption, and Market Failure There is an extensive literature on market efficiency or the lack thereof. The notion that markets generate efficient prices was first popularised by Fama (1965), with a limiting caveat that markets “fully reflect” all available information. The efficacy of the theory relies on the assumption that markets are made up of intelligent traders who process information about past market occurrences and what could reasonably occur in the future.
This category of traders must be distinguished from the uninformed noisy traders whose actions would not ensure efficient prices in the marketplace, especially since the extraction of material information for market transactions is usually costly (Grossman and Stiglitz, 1980). Therefore, the activities of noisy traders could easily subdue or camouflage those of informed or intelligent traders (Kyle, 1985).
The prototype theory of efficiency is inherently deficient, for which some fundamental arguments have been revisited in previous studies. For example, LeRoy (1976) pointed out that the theory of market efficiency as was propounded lacked a convincing assessment of intrinsic value (equilibrium price). Fama (1976) responded by introducing the concept of the true expected return on any security, which is also the market’s assessment of its expected value, namely the use of equilibrium price to replace the notion that market prices fully reflect all available information (Guerrien and Gun, 2011).
However, the adjustment created further consternation in the literature, as evidenced by the gulf between two market efficiencies: that perceived as the economic equilibrium and the other interpreted as a martingale model of falsifiable asset prices (LeRoy, 1989).
The issue of whether or not asset prices follow a random walk is a prominent discussion in the literature, and a good historical overview can be found in the work of Dimson and Mussavian (1998). The literature on the random walk hypothesis is duplicitous. Part of it suggests that the time series of successive returns are independent or serially uncorrelated (Kendall, 1953; Fama, 1965), while another segment of the literature suggests that there might be evidence of some correlation, such as in the subset of stock and commodity prices (Cowles and Jones, 1937; Kendall, 1953). Yet, the random walk hypothesis has been associated with market efficiency when properly anticipated prices fluctuate randomly in competitive markets (Samuelson, 1965). “Properly anticipated prices” suggests the rational foresight of economic risks in the marketplace.
By the 1970s, the literature on market efficiency had evolved to include the magnitude of profits that could be obtained from financial markets. In effect, it became synonymous with trading on available information that could not yield abnormal profits (Fama, 1970);
this is what Dimson and Mussavian (1998) characterised as an introduction of the dual test for market efficiency, namely market behaviour (based on past information) and asset pricing models incorporating all available information.
This dichotomous representation provided opportunities for event studies of pricing models that accommodated the speed of adjustment to new information by studying the average performances of stocks before and after an event. However, Shiller (1981) discovered that stock price volatility for over a century was excessively high for it to be attributed to new information about future dividends. Additionally, using price/earnings
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ratios, Basu (1977) observed that low price/earnings securities outperformed high securities by more than 7 percent annually, an indication of market inefficiency (Dimson and Mussavian, 1998). It is unclear whether excessive volatility should be attributed to market inefficiency or risk aversion. Mehra and Prescott’s (1985) equity premium puzzle, which tracks the substantial premia differential between risk-free assets and risky assets, is probably indicative of the substantial degree of risk aversion in the marketplace.
Equity price movements are integral to the value of wealth and expected household consumption, and this issue has been extensively dealt with by Poterba (2000). Wealth is usually presented in the literature as a composite variable, net of liabilities. Household wealth, or net worth, is generally computed as the value of assets such as homes, bank accounts, physical capital, and stocks minus debts such as mortgages and credit cards (see also Weicher, 1997). Wealth creation was one of the dominant themes of the US economy in the 1990s as the real net worth of US households increased.1 The rapid expansion of wealth generated several issues for economic analysis, of which stock market wealth has gained prominence for a variety of reasons: (a) its effect on household consumption; (b) its impact on monetary policy; and (c) the government revenue effect or deficit reduction (Poterba, 2000). However, the wealth effect is confounding because of asset composition and skewed equity ownership in the US, which includes race and other demographic considerations. The skewed ownership of wealth makes it relevant to estimate household consumption that would be reflective of equity ownership differentials.
The issue of the wealth effect may be misguided because of the issue of timing or lags. “If the lag between a favorable shock to household balance sheets and an increase in consumption spending takes many years to develop, then stock market fluctuations may have a limited impact on aggregate spending. However, if the link from net worth to consumption is powerful and immediate, then sharp changes in asset values may translate into sharp changes in consumer spending” (Poterba, 2000, 103). Yet, apart from the fact that consumers may wish to engage in minimal spending that is reflective of their preferences for precaution, bequest, and inter-temporal considerations, Sabelhaus (1998) discovered that the Consumer Expenditure Survey, the usual source of information on household consumption, shows that households in the top 5 percent of the income distribution (i.e., those with incomes of more than $100,000) account for roughly 12 percent of aggregate consumer spending (see also Poterba, 2000, 108).
Poterba (2000) also observed that the co-movement of consumption and household net worth is usually examined and estimated by aggregate time series data, for which consumption is often conceptualised as expenditures on nondurables and services, since the consumption of durable goods and services does not equal the flow of expenditures on such durables. It is unclear whether the resulting estimates of the structural models should be considered to be indefinite structural parameters. Lawrence Meyer and Associates (1994) estimated that a marginal increase in equity values (e.g., an increase of $1) increases consumption in the next quarter by about 2 cents, while a similar increase in nonstock market wealth raises next-quarter consumption by 1.4 cents. The long-run impact of a $1 increase in stock market wealth is a consumption increase of 4.2 cents,
1 Also, see Table 4 for the inflection that occurred in 1989.
while an increase in nonequity wealth raises consumer spending by 6.1 cents (see also Poterba, 2000, 105). Similarly, Brayton and Tinsley (1996) found a smaller marginal propensity to consume (MPC) out of changes in equity values (.030) relative to changes in other components of net worth (.075).
MPC parameters have been discovered to be contingent on model selection. Ludvigson and Steindel (1999) found that the effect of total wealth on consumption was .040 for their full sample, 1953–1997, but that the result was inconsistent with estimates for different subperiods. They estimated a much larger effect (.106) for the 1976 to 1985 sample and a smaller effect (.021) for the post-1986 period. Given the short periods of these subsamples, however, it is hard to know if these differences reflect fundamental changes in consumer behaviour (Poterba, 2000, 106).2 Empirical work in the literature generally suggests that the MPC value associated with the effect of stock market performance on consumption is likely to be in the range of .01 to .05 with a greater consensus on a value that is somewhere in between (.03). The co-movement of equity wealth and consumption may espouse a wide array of uncertainty. For example, Temin (1976) showed that the decline in consumer spending between 1930 and 1932 was substantially greater than what could be attributed to the stock market decline.
Consumption and the revitalisation of markets have a functional relationship to the speed at which money circulates in an economy, otherwise known as the velocity of money. Velocity is the number of times that a unit of currency can be used to purchase final goods and services—a practical measure for the transaction velocity of money. It is usually measured as the ratio of nominal gross domestic product (GDP) or gross national product (GNP) to a measure of the money stock. The literature reflects two categories of money stock: (i) a relatively narrower measure of the money stock (M1) (Tatom, 1983) or money as a medium of exchange and (ii) a comparatively broader measure of the money stock (M2) (Hetzel, 2009; Bridges et al., 2011), which incorporates money as a store of value.
The growth of the velocity of money may be conceptualised as constant for the implementation of monetary policy, but the data for a broader category of the money stock, namely money with zero maturity (MZM), show both trend and volatility (Figures 1 and 2). This variable will be discussed later. The relevance of velocity to economic recovery rests on the notion that an increase in the money supply (stock) should intensify or increase economic transactions in the macroeconomy. The theory that velocity declines relative to its trend during a recession because of the retardation of money growth that causes the recession was developed and subjected to one of its earliest tests by Warburton (1950). However, it is usual for an increase in the money stock to have a less than desirable impact on economic transactions and for a slowing of money growth to result in increased transactions (Bridges et al., 2011).
Tatom (1983) found that although the US velocity of money (M1 to GNP ratio) grew steadily from 1959 to 1981, averaging at about a 3.2 percent rate of increase, it fell to 2.3
2 Poterba (2000) suggested three reasons why the MPC out of wealth may vary over time: (i) changes in the mix of wealth shocks, especially when equities are owned by fewer households; (ii) equities may be in tax- favoured accounts such as Individual Retirement Accounts and 401Ks; and (iii) reduction in the cost of leaving bequest. For further reading on the MPC, see the works of Zandi (1999), Gale and Sabelhaus (1999), and Parker (1999).
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percent in 1982. This decline was unusual when the rate of GNP growth in 1982 is juxtaposed with the increase in the money supply. The retardation of velocity is conventionally attributed to declining inflation and the reduction of the opportunity cost of holding money as interest rates decline. However, retardation may also be associated with financial innovation (i.e., the creation of new financial instruments), such as the creation of negotiable order of withdrawal accounts. Bridges et al. (2011) observed that an increase in competitiveness within the banking sector (such as that which occurred in the 1980s and early to mid-2000s) is likely to lead to a permanent fall in the velocity of circulation because greater banking sector competitiveness would act to lower the interest rate on bank loans and increase deposit rates. The lack of responsiveness of transactions to money growth could also be linked to the decline in real income, when income elasticity of demand for money is inelastic, and lags, since GNP growth could easily lag behind increases in the supply of money (Tatom, 1983).3
One of the most hotly contested arguments in the literature is the role of government spending in the stabilisation of aggregate economies after the failure of financial and real markets. The size of the Keynesian spending multiplier relative to the tax multiplier, or increases in transfers and tax rebates, makes a compelling case for a review of the discussion on government spending. For example, Romer and Bernstein (2009) estimate that the largest multiplier effect is expected to come from government purchases. They estimate that an increase in government purchases of 1 percent of GDP would induce an increase in real GDP of 1.6 percent compared with what it otherwise would be. Direct purchases and similar spending as part of the euro area stimulus come to 0.48 percent of 2008 GDP in 2009 and 0.20 percent in 2010. Among such purchases, those with an investment character offer the possibility of a longer-term improvement in the productive capacity of the euro area economy (Cwik and Wieland, 2010, 11; see also Cogan et al., 2010).
The literature generally provides two broad paradigmatic structures for appraising government expenditure: (a) the classical Keynesian and (b) the neo-Keynesian (Cogan et al., 2010; Cwik and Wieland, 2010).4 On both occasions, it is assumed that by infusing cash into the macroeconomy, governments can mitigate the impact of disastrous market failures through a multiplier effect. Of course, the paradigm choices and the results of model selection are ultimately contingent on the assumptions that are made about leakages in the form of savings, imports, and taxes as well as the models of choice. For example, based on divergent assumptions, Cwik and Wieland (2010) found crowding-in (for backward-looking behaviour or the role of lagged values for forecasting) and crowding-out (for forward-looking behaviour in anticipation of tax hikes) effects of
3 Tatom (1983) suggested that velocity could be viewed as real income (x) per unit of real money balances (m); so that an inelastic income elasticity of demand for money would result in lower demand for real money balances, which will propagate procyclical velocity. He noted that firms could also fail to adjust quickly and appropriately to the growth in sales when an expansion is ebbing, namely during the early stages of recessions, which can result in overproduction. For further discussions of such lag effects, see Bridges et al.
(2011).
4Neo-Keynesians evaluate the relationships between: the real interest rate and consumption (postponement of consumption when the real interest rate is high); inflation and production (reduction in aggregate demand depresses prices); and inflationary pressures and interest rates (increasing price expectations proactively contracts monetary policy); see also Cogan et al. (2010) for discussions about rational expectations.
expansionary fiscal policy in the case of the euro area. The effects of fiscal expansion are supposed to be stimulative in the short-run, but inflationary in the long-run.
The literature indicates that government spending is especially relevant to stabilisation when monetary policy fails. The failure of monetary policy is typical when a negative shock leads to a very strong reduction in private demand when a reduction in asset prices makes households poorer, when there is risk aversion, when there is a loss of confidence in future economic prospects, or when financial turbulence increases credit spreads and risk premiums (Kuester, 2011). In such situations, central banks reduce the nominal interest rate to counteract or negate the recessionary impulse as inflationary pressures dissipate.5
A severe or depressed state of affairs is at the core of why government spending multipliers may be bigger than one and therefore bigger than usual if the zero lower bound is binding. However, this classical theory presupposes that households and firms are not forward looking. Anticipated future spending can affect investment and savings contemporaneously because of pending price increases, potential tax increases, and increases in long-term interest rates (Kuester, 2011). As such, announcing future government spending could “crowd in” private demand today if the zero lower bound is expected to be binding in a future that coincides with higher spending, but not if the zero lower bound is not binding or expected. When it comes to the US, the displacement features of forward-looking reactions to fiscal stimulus can be found in the work of Cogan et al. (2010). The pertinence of this type of argument in the literature suggests that there could be a rational link between government expenditure and personal consumption expenditure (PCE). To the extent that the benefit of fiscal stimulus is contingent on the persistence of economic disruption and the zero-bound rule, there is an implicit neo- Keynesian assumption that it may be difficult for policymakers to ascertain the appropriate timing and amount of fiscal stimulus, except fiscal stimulus is well targeted.
Yet, fiscal stimulus is prone to inside (implementation) and outside (effect) lags.
Acrimonious debates that are reflective of political polarisation constitute implementation lags, while the implementation of appropriation laws to renovate or create infrastructure such as roads, bridges, and canals usually takes time (effect lags). Yet, Cwik and Wieland (2010) observed that if discretionary fiscal stimulus is intended to offset recessions, then timeliness is of the essence.
This paper makes some noteworthy contributions to the literature. It finds: (a) that US government consumption expenditure supports private consumption expenditure during periods of recessions and/or depression (a crowding-in effect); (b) that the velocity of money shows probable dominance when it comes to the revitalisation of economic growth in the US; and (c) that although asset prices (wealth) and monetary policy could contribute to the cessation of a recession or depression, their effects are comparatively less pronounced in the US. The next section discusses the pertinent financial and macroeconomic variables.
5Kuester (2011) noted that if the recessionary impulse is severe, a central bank would want to reduce the nominal interest rate to less than zero. But it cannot do so: the lower bound on nominal interest rates becomes binding. Consequently, unless the central bank resorts to nonconventional monetary policy means (e.g., quantitative easing), the real rate of interest is higher than what the central bank would like to achieve, and aggregate demand is lower than that desired.
79 3. Data and Macroeconomic Variables
The literature reflects some key variables of interest for the empirical analysis of economic recovery. These variables of interest include a binary variable to capture market failure (the recessionary state), asset prices (wealth effect), RPCE, a broader measure of money supply (i.e., MZM) to examine the velocity of money, and government consumption expenditure and investment demand. All financial and macroeconomic variables were obtained from the Federal Reserve Bank of St. Louis. The samples contain exhaustive data, covering periods of 52 and 54 years (1957/9–2011) to avoid the problem of sampling bias. Biannual data are generally used to minimise the problem of serial correlation, while maintaining a reasonable amount of dynamism for the econometric analysis.
Economic downturn: The recession dummy variable
This variable is indicative of macroeconomic downturn and expansion in the US economy from 1957 to 2012. As could be expected, the data reveal uneven periods of expansions and contractions. Recessions are notable periods of intolerable reduction in production and relatively high unemployment that are tracked by the Business Cycle Dating Committee of the National Bureau of Economic Research (NBER). The variable is of interest because it is a useful indicator of the failure of financial and/or real markets.
It turns out that its severity has varied over the duration of the time period considered in this paper, but that its longevity has detrimental effects on economic performance, including the depression of prices and very high levels of unemployment.6 Milder forms of recessions (slumps) may not necessarily be the result of financial and real market failures, but the more serious forms. For example, the Great Depression of the 1930s and the much more recent [depression] “Great Recession” are usually indicators of the joint failure of financial and real markets to allocate economic resources efficiently. The sequence of these market failures is not of particular interest in this paper, but it is unequivocal that the financial crises of the 21st century generated a contagion of failures in the US and the global economy.7
The binary time series data for the recession variable represent periods of recessions (denoted or quantified as 1) and periods of expansion (denoted as 0). The NBER identifies the months and quarters of turning points without stipulating a date within the period that such turning points occurred. The dummy variable adopts an arbitrary convention that the turning point occurred at a specific date within the period. The data for this time series indicate that a recession begins on the first day of the period following a peak and ends on the last day of the period of the trough. The data for the variables are provided by the NBER and made accessible for the purposes of this work by the Federal Reserve Bank of St. Louis.
6 The short-run effects of a recession hurt the long-run prospects of economic performance through multiple channels, including employability or the diminution of the effective workforce in the long-run (the result of long-term unemployment), low business investment, and the neglect of human capital. For further discussion of the corrosive effects of a recession, see Krugman (2012).
7 These financial crises could be more broadly defined in terms of asset price deflation and banking crises, which involved commercial and investment banks as well as shadow banking institutions. See Reinert (2012) for a fuller discussion of the forms of financial crises.
The money conundrum: The role of money and interest in the macroeconomy Contemporary money is usually defined as anything that is generally accepted as a medium of exchange, store of value, unit of account, and standard of deferred payment.
The measurement of money may vary to reflect its limited or general properties. For example, M1 defines money as a medium of exchange, and it characterises the relative liquidity of money in contradistinction to broader categories. In addition to the medium of exchange, M2 defines money as a store of value. The broader categories of money include the inter-temporal use of money via the storage of purchasing power in assets and the foreign holdings of money. A measure of money that incorporates the aforementioned concepts, asset holdings, and the liquidity with which assets can be converted into cash is MZM. As such, MZM is a broader measure of money that includes those financial assets that can be quickly converted into cash. This concept of money also takes into consideration decisions about inter-temporal consumption (consumption in between two arbitrary time periods).The link between assets and money is an important observation, because when the nominal interest rate and traditional measures to facilitate increased consumption fail to attain the desired results, the Fed may engage in quantitative easing (the purchase of longer-term assets to depress interest rates and discourage saving). This has implications for asset valuation and the decisions of households and firms to hold assets in lieu of spending. Of course, households or investors have to evaluate the inter- temporal consequences of their choices against the risk-free rates. In effect, money performs a useful role in the macroeconomy because it facilitates transactions that could reduce unemployment and facilitate growth (assuming that employment and growth are positively correlated). Consequently, the speed at which money circulates in an economy (the velocity of money) is critically important to getting out of a recession. The velocity of money is conventionally defined as the average number of times that a unit of a currency can be used to make purchases of goods and services that have been produced in an economy per year between buyers and sellers. Consider equation 1, generally referred to as the “quantity equation:”
Mt*Vt=Pt*Yt; (1)
where Mt is the aggregate money supply (a stock of money rather than flow, assumed to be MZM in this case), Vt is the velocity of money for a particular time period, Pt is the general price level at a particular time, and Yt represents real GDP estimated at a particular time.8 It is reasonably straightforward to solve Equation 1 for the definition of velocity:
t t t
t M
Y
V P *
= ; (2)
where Vt is the ratio of nominal GDP to the money supply. If Vt is assumed to grow in a stable manner in the so-called “short-run,” then it can be verified theoretically that growth in the money supply (Mt) must correspond to growth in nominal output, thereby
8 Real output is normally substituted for macroeconomic transactions because of the difficulties associated with the computation of actual transactions. For further reading, see Mankiw (2013, pp. 102–105).
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suggesting a stable relationship among spending, growth in the money stock, and nominal output. This realisation is intricately linked to rule-based monetary policy.9
The argument that velocity grows at a fairly constant rate is contingent on a number of factors: (a) the rate at which wages/income are received; (b) the rate of consumption;
and (c) the frequency with which firms are able to make claims on consumers. It is implicitly assumed that these factors do not change in a significant way over time.
Figures 1 and 2 present some interesting revelations about the MZM velocity of money:
(a) between 1959 and 2011 the velocity ratio is about 2.24; (b) that velocity could also have conflicting discernible trends over a longer time horizon (upward trend from 1959 to 1982) and downward trend after 1982); (c) that from 1959 to 1969 and 1999 to 2011, the velocity ratios were below the long-run average; and (d) that the growth rate of MZM velocity shows considerable trendless volatility.
Figure 1: MZM Velocity of Money (1959 to 2011) Figure 2: Growth Rate of MZM Velocity of Money (1959 to 2011)
Data Source: Federal Reserve Bank of St. Louis.
Figure 2 is similar to any differenced time series in quest of stationarity—a zero mean and constant standard deviation. Yet, it typifies considerable volatility or gyrations in the intervening time periods of a relatively longer time horizon. This suggests that during the intervening time periods of a relatively longer time horizon, say two decades or even less, the velocity of money may not be stable enough to accommodate monetary policy expectations. Consequently, when the velocity of money deviates from the anticipated theoretical prognosis, monetary policy could become very ineffective. In effect, monetary policy is only effective when consumers and investors respond favourably to monetary policy expectations. What happens when money does not circulate in the aftermath of expansionary monetary policies? When money is hoarded, a macroeconomy falls into a liquidity trap, which is illustrated by the shift of the money supply curve from MS1 to MS2 along the flat segment of the money demand curve in Figure A1 in the Annex.
9 For further reading on the velocity of money and monetarism, see Krugman and Wells (2009, pp. 893–894).
See also the Taylor Rule as a criterion for responding to inflation and economic performance:
) (
5 . 0 ) 2 ( 5 . 0
2 GDPgap
i=π + + π − + ; where i is the nominal Fed Funds Rate,πis the inflation rate, and GDP gap is the percentage by which real GDP deviates from an estimated natural level. The rule-based argument suggests that the nominal rate must respond to inflation and the GDP gap. That is, i = 2% when
πis 2% and GDP is at its natural level (Taylor, 1993); see also Mankiw (2013, pp. 435–436).
RPCEs
RPCE is the household consumption of durable and nondurable goods and services adjusted for price changes. Other forms of consumption are also relevant to the evaluation of economic performance, but RPCE is of special interest because of the size of its impact on the aggregate economy. It constitutes about 65 to 70 percent of US GDP, while gross private domestic investment spending on fixed investment and private inventories, the official measure of investment expenditures on GDP by the business sector, averages about 15 percent of GDP. Net exports (the difference between exports and imports) of goods and services average about 2 percent of GDP (http://www.Amosweb.com).
Household consumption is intricately related to wealth, income, the speed at which money circulates, price expectations, and inter-temporal consumption decisions that maximise utility. Financial markets usually implode when the prices of assets can no longer be set above their market clearing equilibria. This market disequilibrium subsequently triggers a crisis of the asset price deflation genre (Reinert, 2012) with serious consequences for consumption and investment in the face of falling prices and risk aversion. Yet, the wealth effects emanating from financial market failures may be less precise because of variations in asset holdings and racial and demographic distributions. The wealth–consumption relationship will be discussed more fully in the next section. RPCE is measured as a seasonally adjusted annual chain-type price index, with a 2005 reference year in billions of dollars. The data are also obtained from the Federal Reserve Bank of St. Louis.
The issue of wealth and consumption
In this paper, the value of wealth (assets) is measured by price changes. Implicitly, as prices fall, the value of assets becomes relatively worthless. The S&P Index, which is a broad measure of asset prices, is used as a proxy for changes in the value of wealth. Stock prices and real exchange rates have been found to increase consumer confidence (Gӧrmüs and Günes, 2010). Asset price determination is critical not only for consumer confidence in financial markets and the smooth operations of financial markets or their recovery, but also for household consumption levels. As stated earlier, household wealth, or net worth, is generally computed as the value of assets such as homes, bank accounts, physical capital, and stocks minus debts such as mortgages and credit cards (see also Weicher, 1997). Lottery winnings provide one potentially exogenous source of variation in household net worth. Housing has been estimated to have greater importance for the wealth positions of most families (Bricker et al., 2012, 4). However, Tracy et al. (1999) noted that the change in household net worth associated with a change in house prices is larger than the change from a comparable change in stock values for the vast majority of households. This study is oriented to price changes in asset value as proxied by the S&P Index to determine changes in the value of wealth.The index is also a useful and unbiased variable for estimating the wealth–consumption relationship because changes in asset prices may not affect all asset categories. As such, the index controls for the substitution effects and conveniently sidesteps the 52 years net worth estimation problem. Poterba (2000) found important differences across net worth components in the concentration of ownership. Part of the deleterious wealth effects of the stock market failure in 2000 was replaced or substituted by housing equity; this was not the case for the 2008 collapse involving esoteric mortgage-backed securities.
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Between 2006 and 2009, house prices declined by approximately 19 percent.10 In 2008, the S&P lost 40 percent of its value (Farmer, 2010, 127, 129). Invariably, with the growth of the securitisation of mortgages, one would expect the prices of mortgage-backed securities in financial markets to be significantly and positively correlated with the value of wealth, but that safer assets may compensate for aggregate household consumption, assuming no risk aversion and systemic market failures for the duration of the sample period. The effect of wealth on consumption must appropriately consider demographics and other limiting factors that may significantly enhance aggregate household consumption. First, it must be noted that stock ownership usually generates a substantial amount of returns on wealth ownership when financial markets do not behave badly. As a result, it might not be entirely surprising that stock ownership is often immediately and exclusively associated with wealth, albeit erroneously. Table 1 shows that stock ownership declined from 21.5 percent in 2001 to 14 percent in 2010, even though it constituted a greater percentage of the financial assets considered. Of stock holdings, one survey of consumer finances indicated that 21.4 percent of stocks were owned by white non-Hispanics and 9.4 percent by nonwhite or Hispanics (Bricker et al., 2012, 25).Poterba (2000) discovered that across-the-board increases in asset values might have only a modest effect on the behaviour of most households, since most households hold relatively few assets to begin with. The Survey of Consumer Finances indicated that the bottom 80 percent of asset owners held 1.7 percent of common stock, excluding pensions, and 4.1 percent of all common stock (Poterba, 2000, 102), suggesting that the absolute magnitude of household net worth at different points in the wealth distribution can be an important consideration in evaluating wealth effects. The consumption patterns and decisions of the very wealthy and relatively less wealthy are usually heterogeneous, and asset wealth may not necessarily trigger the same consumption preferences that are necessary for the movement out of a recession to an expansion. Indeed, wealth is usually concentrated (Weicher, 1997), but households are generally interested in maximising inter-temporal utility.
Table 1: Value of the financial assets of all families, distributed by type of asset, 2001–10 surveys (percent)
Type of financial asset 2001 2004 2007 2010
Transaction accounts (TAs) 11.4 13.1 10.9 13.3
Certificates of deposit (CDs) 3.1 3.7 4.0 3.9
Savings bonds 0.7 0.5 0.4 0.3
Bonds 4.5 5.3 4.1 4.4
Stocks 21.5 17.5 17.8 14.0
Source: Bricker et al. (2012, 24). See the source for further information on pooled investment, retirement accounts, cash value life insurance, and other assets.
Table 2 shows that a greater percentage of stockholders fall between the ages of 55 and 75 years or greater than 75 years. Assuming that time-sensitive assets such as bonds and CDs cannot be quickly liquidated without a loss of value, society must also rely on the relatively older population who are entering the final stages of their life spans to transform their wealth into consumption when investment spending is inadequate; this is not very probable. Moreover, elderly expenditure seems to be skewed towards health
10 See Financial Crisis Inquiry Commission (2011, 215).
care, housing, and cash contributions. BLS data reveal that in 1998, the older population spent less than the other age brackets and had less disposable income. This demographic segment also owned between 45 and 48 percent of stock in 2010 (see Table 2).
Table 2: Family holdings of financial assets, by selected characteristics of families and type of asset (2007 and 2010 surveys)a
Age of head TAs CDs Savings bonds Bonds Stocks
< 35 2.1 5.2 0.5 * 5.4
35–44 2.5 7.0 0.9 10.0 10.0
45–54 3.5 16.0 0.8 150 30.0
55–64 5.0 20.0 1.2 250 35.0
65–74 5.7 25.0 4.0 100 48.0
≥75 7.2 32.2 1.0 141 45.0
aMedian value of holdings for families holding assets (thousands of 2010 dollars). * Ten or fewer observations. Source: Bricker et al., (2012, p. 30).
Proper wealth valuation is directly related to price distortions and financial market failure. The reasons for price distortions and financial market failures, which result in wealth depletion, are numerous, but for simplicity, they can be categorised as shocks and information asymmetry. Consequently, the reasons for market failures may not be as cryptic as they seem. They can be the result of unanticipated sudden but temporary disruptions or of fraudulent activities that are deliberately intended to distort prices and deceive investors in the quest for profit maximisation. Fraudulent activities are symptoms of endogenous market inefficiencies that generate risk aversion (the development of a market psychology) and ultimately lead to market failures. These inefficiencies (inherent asymmetric problems) are usually not considered appropriately when markets are presumed to be efficient allocators of resources, namely when asset prices are presumed to be indicators of all the available information required to provide the best estimate of future discounted cash flows. These inefficiencies come across as exogenous ex-post occurrences that could belatedly be incorporated into the decision-making processes of market participants to determine the fair values of assets. In the presence of these response lags, financial markets are inefficient. Yet, the adverse effects of the temporary price distortions that are not attributable to shocks are usually colossal if not irreparable and adversely detrimental to the presumption of market efficiency. Since the prices of riskier assets are not adequately discounted, they tend to be overvalued. Consider the Cochrane (2001) pricing model for risky and risk-free assets:
= 1 ( +1) >
* Xt
t Rf P
1) 1 (
*
* = Xt+
tRf t E
P ; (3)
where Pt is the price of an asset at time t, Rf is the risk-free rate, 1/Rf is the discount factor, Et/ Rf is the risk-adjusted discount factor, and Xt+1 is the expected payoff for an asset (X) in the future or at time t+1. It is instantly revealing that the price of the risk-free asset (on the left-hand side of the inequality sign) is greater than the price of a risky asset (on the right-hand side) because the contemporaneous price of the risky asset reflects a value of discounted cash flow that incorporates the risks that are associated with the asset.
Pricing risky assets as risk-free assets when they are known to be risky makes markets inherently inefficient and inter-temporal consumer utility suboptimal. Asset prices are related to inter-temporal consumer utility maximisation when households consider consumption for two periods, today and tomorrow. In such situations, households
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typically like to smooth consumption across both periods in order to prevent wild volatility in consumption during both periods.
Table 4: S&P Prices (1957 to 2011)
Year S&P prices Variations Variations (subsample)
Year S&P prices Variations Jan. 1957 44.42 -0.85539 -0.96996 Jan. 1993 451.61 0.004674 Jan. 1958 46.20 -0.85163 -0.95181 Jan. 1994 460.42 0.023283 Jan. 1959 57.41 -0.82795 -0.83754 Jan. 1995 541.72 0.195004 Jan. 1960 55.85 -0.83125 -0.85344 Jan. 1996 670.50 0.467013 Jan. 1961 66.27 -0.80924 -0.74722 Jan. 1997 873.43 0.895641 Jan. 1962 62.32 -0.81758 -0.78749 Jan. 1998 1085.50 1.343575 Jan. 1963 69.86 -0.80166 -0.71062 Jan. 1999 1327.33 1.854367 Jan. 1964 81.37 -0.77735 -0.59329 Jan. 2000 1427.22 2.065355*
Jan. 1965 88.15 -0.76303 -0.52418 Jan. 2001 1194.18 1.573128 Jan. 1966 85.18 -0.7693 -0.55445 Jan. 2002 993.94 1.150182 Jan. 1967 91.96 -0.75498 -0.48534 Jan. 2003 965.23 1.089541 Jan. 1968 98.37 -0.74144 -0.41999 Jan. 2004 1130.65 1.43894 Jan. 1969 97.77 -0.74271 -0.42611 Jan. 2005 1207.23 1.600692 Jan. 1970 83.18 -0.77352 -0.57484 Jan. 2006 1310.46 1.818735 Jan. 1971 98.31 -0.74157 -0.42061 Jan. 2007 1477.19 2.170901*
Jan. 1972 109.09 -0.7188 -0.31071 Jan. 2008 1220.04 1.62775 Jan. 1973 107.44 -0.72228 -0.32753 Jan. 2009 948.05 1.053253 Jan. 1974 82.78 -0.77437 -0.57892 Jan. 2010 1139.97 1.458626 Jan. 1975 86.18 -0.76719 -0.54426 Jan. 2011 1267.64 1.72829 Jan. 1976 102.04 -0.73369 -0.38258
Jan. 1977 98.18 -0.74184 -0.42193 Jan. 1978 96.12 -0.74619 -0.44293 Jan. 1979 102.99 -0.73168 -0.3729 Jan. 1980 118.71 -0.69848 -0.21265 Jan. 1981 128.04 -0.67877 -0.11754 Jan. 1982 119.71 -0.69636 -0.20245 Jan. 1983 160.47 -0.61027 0.213051 Jan. 1984 160.46 -0.61029 0.212949 Jan. 1985 186.81 -0.55464 0.48156 Jan. 1986 236.39 -0.44991 0.986977 Jan. 1987 287.00 -0.34301 1.502894 Jan. 1988 265.88 -0.38762 1.287597 Jan. 1989 323.05 -0.26687 1.870386 Jan. 1990 334.63 -0.24241 1.988432*
Jan. 1991 376.19 -0.15463 2.412093*
Jan. 1992 415.75 -0.07107 2.815367*
1957–2011(sample) mean = 449.40; sample standard deviation = 473.44; subsample (1957-1992) mean= 139.57; and subsample standard deviation=98.1. Variations are annual standard deviations from sample means.* Denotes significant differences from the respective sample means at the 95 percent level of confidence. The cut-off point of the subsample takes into consideration inflection in the historical series. Source of raw data: Federal Reserve Bank of St. Louis.
In relating wealth to consumption, economic theory suggests that households expect wealth (the price of assets) to reflect the relative expected changes in utility for the two periods (i.e., the expected change in utility in the future relative to the expected change in today’s utility).This marginal rate of substitution is conventionally derived from optimisation problems in economics.11 Cochrane (2001) summarised this relationship as:
+ +
= ( 1)
' ' 1
Xt Ct U
Ct U t E
P β ; (4)
where
) ( '
1) ( '
Ct U
Ct
U +
β is the discounted marginal rate of substitution associated with the current market price of an asset and the expected return of the asset at a future date is Xt+1. The price (Pt) may not reflect all available information in the market period, especially on occasions of fraud, insider trading, conspiracy, and antitrust practices, including covert collusion.
In brief, the asset price variable performs a useful empirical function: (a) it addresses the issue of market failure that could be attributed to sluggish response lag and loss of wealth; (b) it considers the substitutability of wealth that could be derived from assorted categories of assetswithout any loss of generality about value and net worth when market prices change; (c) it tracks the effect of asset price changes on RPCE; and (d) it examines the extent to which variations in prices (changes in the value of wealth) can facilitate the probable recovery from a recession on a comparative basis. Table 4 provides a record of annual variations.
An attractive property of the data is that they reflect the low variability consistent with the general theory of stock market returns over a longer time horizon, which is also not prejudicial to the measurement of wealth over a considerable period of time. Stock market returns over a longer time horizon are expected to show little variability (see Table 4). Only two (2000 and 2007) and three (1990, 1991, and 1992) years reflect significant variations (at the 95 percent level of confidence) from the averages of the general sample and subsample (35 years).12
Real government consumption expenditure and gross investment demand
According to the US National Income and Product Account, government consumption expenditures and gross investment averages between 15–20 percent of GDP, but this percentage is volatile and contingent on political appetite and winds of change. While
11 Optimisation problems are generally divided into two interrelated parts: (a) an objective function and (b) a specific constraint, or multiple constraints. It is straightforward to recall that the objective is generally to maximise a benefit or minimise a cost or loss subject to a constraint, or multiple constraints. Price is usually an important component of such constraint(s) to derive the optimal outcome. However, price distortions have deleterious effects on inter-temporal consumption. See Warburton (2009) for a discussion of corporate crime and macroeconomic performance.
12 The probability that these deviations would occur on average two to five times for the sample period (about 54 years) is fairly high, 0.69. The Poisson distribution could be used to determine such a probability without any loss of generality:
! )
; ( ) (
k e k k f k X
P λλ
λ −
=
=
=
=∑
! 5
! 2 ! i
for k i; where e is the usual base of a natural log, k is the hypothesised number of departures from a sample mean over a period of time, and λ is the number of observed occurrences. For further reading on the Poisson distribution, see Hatekar (2010).
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some political leaders favour expansionary fiscal policy on the demand side, others may favour expansionary fiscal policy on the supply side or stringent and targeted government spending that may have nothing to do with investment. The percentage is also contingent on a state of war or peace. “US government consumption expenditures and gross investment is the official government measure of government purchases undertaken by the government sector. It seeks to quantify that portion of gross domestic product that is purchased by the government sector and which is used to pursue government functions.
These expenditures purchase a wide range of goods and services, from aircraft carriers to army boots, from administrative salaries to ammunition, from school buildings to street lights” (www.Amos web.com).
US government expenditures cover both consumer and capital goods, including transfer payments (all purchases by the government sector). Consumer goods provide immediate utility, but capital goods are investment goods, such as roads, bridges, airports, and canals. Transfer payments are expenditures on households for which goods and services are not created, provided, or purchased. Government consumption expenditures and gross investment is the official measure of the government purchases component of aggregate expenditures used in the calculation of US GDP. This term reflects the fact that the US government sector purchases both consumption and capital goods.13
This variable is important because of its potential to resuscitate a sluggish economy, especially reducing unemployment and increasing consumer expenditures. It should be recalled from the literature review that it also has the capacity to generate a multiplier effect under the regularity condition that leakages are not detrimental to the stabilisation process. The variable is denominated in billions of chained 2005 dollars to accommodate price changes and is seasonally adjusted.
4. Econometric Models and Empirical Findings
Two econometric models are used to estimate the variables of interest: (i) the Newey–
West estimator, which is a Heteroskedasticity and Autocorrelation Consistent Covariance (HAC) estimator (Equation 5), and (ii) a logit estimator (Equation 6). EViews 6 Quantitative Micro Software (2007) is used for the estimation of both models. Newey and West (1987) proposed a more general covariance estimator that is consistent in the presence of both heteroskedasticity and autocorrelation of an unknown form. The author finds the proposition of the HAC method appropriate for time series data and macroeconomic variables that are known to have some form of autocorrelation. The HAC coefficient covariance estimator is given as:
∑
^ 2W = (X 'X )−1TΩ(X 'X )−1 ; (5)
13 About a third of total US government purchases are made by the federal government and about two-thirds of total government purchases are made by state governments, municipal governments, county governments, and other local government authorities. By definition, government purchases exclude transfer payments and accounts for purchases of final goods and services. For a detailed discussion of this variable, government consumption and gross investment, visit www.amosweb.com and the Federal Reserve Bank of St. Louis.
where E is an expectation operator, X is a matrix of stimulus variables,
ε
stands for the residuals, T represents the number of observations, and Ω is the long-run covariance estimator. Four regression models are estimated, using a stepwise and bivariate procedure to evaluate the stimulus effect of the exogenous variables on US RPCE (dependent variable) for the sampling period. The results are reported in Table 5, where MZM Vel.represents the velocity of money, MZM is a broad measure for the stock (supply) of money, including asset holdings, and GCE & GI is the US real government consumption expenditure and gross investment demand.
Overall, although stock prices show a positive correlation with RPCE, they do not show a very robust impact on PCE for economic recovery. The evidence shows that incremental price increases (the wealth effect) generate an increase of about 0.05 in the PCE index when other variables are considered to be invariant for the period 1957 to 2011. This presupposes that other variables, including consumer confidence, wages, and real income, account for about 95 percent of the changes in the PCE index. This is not entirely surprising, because a considerable amount of households are not wealthy enough to hold stocks, although they might have other forms of financial assets. Demographic realities also complicate the analysis. The paucity of the contribution of equity wealth to household consumption is consistent with earlier studies of the 1990s, such as Lawrence Meyer and Associates (1994), Brayton and Tinsley (1996), and Ludvigson and Steindel (1999). The estimates of the marginal dollar impact emanating from equity wealth range from about 2 cents to 4.2 cents.
Table 5: Determinants of RPCE: ewey–West OLS (p-values in parentheses, 1957–2011)*
Ind. Variable Stock Prices (i) MZM (ii) MZM Velocity (iii) GCE & GI (iv)**
Stock Prices (i) 0.05(0.00) x X x
MZM (ii) 0.02(0.00) 0.005(0.00) X x
MZM Vel. (iii) 0.05(0.00) x 6.99(0.00) x
GCE & GI (iv) 0.01(0.00) x 1.81 (0.00) 3.57(0.00)
R-Squared 0.87 0.94 0.89 0.99
Adj. R-Squared 0.87 0.93 0.89 0.99
Observations 110 106 106 106
* The Breusch–Godfrey Serial Correlation LM Test with two lags used to reflect the biannual structure of the data indicates the presence of serial correlation. This is usual for macro time series data, and it is usually a welcomed occurrence for forecasting. The following are the F-stats with the corresponding p-values for the test: Reg. (i), 407.6 (0.00); Reg. (ii), F-Stat. 680.5 (0.00); Reg. (iii), F-Stat. 301.7(0.00); and Reg. (iv), 260.5 (0.00). The Newey–West estimator is used to correct for serial correlation. However, it produces results that are not significantly different from the White heteroskedasticity-consistent standard errors and covariance. To avoid multicollinearity, Regression (iii) does not estimate MZM and MZM velocity concurrently. NB: Taxes do not usually go up during a recession and the effects of national income and government consumption expenditure on PCE are too conflated to be estimated jointly. ** The R-squared of the government consumption expenditure variable is suspicious, especially because there is a relationship between government consumption expenditure and the velocity of money (e.g., through a crowding-out effect). As such, the variance inflation diagnostic test is conducted to detect probable multicollinearity. The test shows that 2 percent of the coefficient is inflated as a result of the combination with the other regressors, suggesting that the variable is positively and highly correlated with PCE. It should be recalled that the Newey–West estimator controls for all serial correlation.
It must be noted, however, that although subsamples may generate alternative results, such samples exclude all available information. However, the relationship between financial intermediation and economic growth may be robust even with sample variations (Hodges and Knabb, 2010). It is striking to note that the velocity of money (Regression 3)
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and government consumption expenditures (Regression 4) have the greatest impact on RPCEs when the other variables are presumed to be ineffective or excluded. An increase in government spending and the money supply attenuates the impact of the wealth effect on RPCEs by about 3 to 4 percent (Regressions 2 and 4). Without undue impediments or disturbances, marginal increases in the velocity of money and government spending are capable of increasing the household consumption index by about 6 and 4 points, respectively. The data reflect that such increases (stimuli) are likely to occur within three to four years.
US household net worth rose 4.7 percent to $62.9 trillion in the first quarter of 2012, according to a June 2012 Federal Reserve report. The main reason has been attributed to a 12 percent jump in the S&P 500 index, which padded the wealth of Americans who own stocks. House values increased by 2.3 percent, and Americans have been gradually recovering the wealth they lost to the recession, which remains about 5 percent below its pre-recession peak of $66 trillion (www.Federal Reserve.gov; June 2012); yet, it is unclear whether this wealth will be transformed into significant consumption.
To examine the probable contributions of the variables to the economic recovery the logistic regression model (Equation 6) is the preferred estimating tool:
β ε
ε β
ε
β +
− = + =
+ − + +
= X
e Pt Pt e X
e X Rt
1 1
1
{
Ln e=β
X +ε
; (6)where Ri denotes the odds of ending a recession; recall that the recession variable is binary and obtained from the Federal Reserve Bank of St. Louis, e stands for the usual natural log representation,
ε
is the heteroskedastic and binomial error expression, for which the use of maximum likelihood is in order, Ptis the probability that the effects of a recession could be reduced at time t, and the ratio of Pt to (1-Pt) is the usual odds representation, denoted in natural log form as Ln e. As such, β is the logit coefficient associated with the variables (Xs)—RPCE, asset price increases, the money supply, the velocity of money, and government consumption expenditure—in the models.14 In its linearised or log transformation form, the model estimates the odds of an event occurring;however, for practical purposes the probabilities that the variables enhance the economic recovery are also calculated and reported in Table 6.
To derive better estimates and greater precision, the intercept expression is suppressed.
This approach also makes it reasonable to assess the direct or bivariate relationship between the variables of choice. It must be noted that an explanation of the variation in the binary dependent variable serves no useful purpose. Notable examples of such applications include the relationship of security risk premium to systematic risk (as evidenced by capital asset pricing models), the relationship of permanent consumption to permanent income (Friedman’s permanent income hypothesis), the relationship between inflation and the money supply, and the relationship between unemployment and real output growth rate (the so-called Okun’s law).15
14 For further discussions of the distribution of binomial errors, see Gujarati and Porter (2010, 392); see also Wooldridge (2006) for discussions about the maximum likelihood estimation of logit and probit models (pp. 586–587).
15 For further discussions of greater precision without an intercept, see Theil (1978, 76).
