|
on Utility Models and Prospect Theory |
| By: | Craig McLaren (Department of Economics, University of California Riverside) |
| Abstract: | This paper presents an alternative formulation of consumer theory that allows consumer behavior to be modeled as a dynamic process. Rather than simply predicting the optimal choices a consumer will make, this formulation provides a time dependent process by which the consumer arrives at equilibrium with the market and maintains stability with it. This formulation is built upon multivariate integral (vector) calculus and is formally analogous to the theory of electric fields in classical physics. This approach allows the consumer’s Marginal Rates of Substitution (MRS) to be accepted as a theoretical given, rather than derived from hypothetical quantities such as utility or preference. Using a basic set of axioms, a vector function giving the consumer’s (observable) Marginal Values is defined from his (her) MRS. Using an additional axiom regarding the reciprocity of substitute and/or complementary goods, a scalar Use Value function is defined as the integral of the Consumer’s Marginal Values using Stokes’ Theorem. While functionally equivalent to utility, the consumer’s Use Value is measurable and unique to constants of integration that correspond to observable quantities. With an additional assumption that guarantees convexity of Use Value’s isotimic surfaces, the formulation developed here is used to solve the traditional consumer choice problem. It is shown that, whenever the consumer holds a bundle of goods that is not his or her “optimal†one, the consumer will undergo a tatonnement–like process consisting of a series of incremental exchanges with the market until her optimal bundle is obtained. |
| Keywords: | Dynamic Consumer Theory, Integrability, Convex Indifference Surface, Engle’s Law, Antonelli Conditions, Marginal Demand, Willingness to Pay, Contingent Valuation, Vector Analysis, general equilibrium, existence, stability, tatonnement |
| JEL: | B21 B41 C50 C60 D01 D11 D50 |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201613&r=upt |
| By: | Falk, Armin (University of Bonn); Zimmermann, Florian (University of Zurich) |
| Abstract: | Beliefs are a central determinant of behavior. Recent models assume that beliefs about or the anticipation of future consumption have direct utility-consequences. This gives rise to informational preferences, i.e., preferences over the timing and structure of information. Using a novel and purposefully simple set-up, we experimentally analyze preferences for information along four dimensions. We find evidence that the majority of subjects prefers receiving information sooner. This preference, however, is not uniform but depends on context. When the environment allows subjects to not focus attention on (negative) consumption events, later information becomes more attractive. We also identify an aversion towards piecemeal information. Variations in prior distributions do not seem to affect information preferences. |
| Keywords: | beliefs, anticipatory utility, news utility, information preferences, attention, reference-dependent preferences, experiments |
| JEL: | C91 D03 D12 D83 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp10172&r=upt |
| By: | Barnett, Richard (School of Economics Drexel University); Bhattacharya, Joydeep (Department of Economics Iowa State University); Bunzel, Helle (Department of Economics Iowa State University) |
| Abstract: | This paper studies a model economy populated with agents of differing incomes that get a utility boost when their consumption keeps up with their neighbors, the proverbial Joneses. The resulting utility function is non-concave. In this setup, participation in a fair consumption lottery has the potential to make some agents ex-ante better off but more financially vulnerable. More people of different incomes join the lottery pool when the ‘kick’ from keeping up increases. Worsening income inequality may increase the number of financially vulnerable people. The analysis offers broad-brushstroke insights into the connection between inequality and financial vulnerability. This paper studies a model economy populated with agents of differing incomes that get a utility boost when their consumption keeps up with their neighbors, the proverbial Joneses. The resulting utility function is non-concave. In this setup, participation in a fair consumption lottery has the potential to make some agents ex-ante better off but more financially vulnerable. More people of different incomes join the lottery pool when the ‘kick’ from keeping up increases. Worsening income inequality may increase the number of financially vulnerable people. The analysis offers broad-brushstroke insights into the connection between inequality and financial vulnerability. |
| Keywords: | Keeping up with the Joneses; consumption externalities; non-concave utility; lotteries; inequality |
| JEL: | D01 R21 |
| Date: | 2016–08–31 |
| URL: | http://d.repec.org/n?u=RePEc:ris:drxlwp:2016_011&r=upt |
| By: | Jim Engle-Warnick; Sonia Laszlo |
| Abstract: | We apply an instrument to measure ambiguity preferences in an experiment and show that revealed ambiguity preferences, but not risk preferences, predict behavior in a separate game that involves exploitation vs. exploration of a maximization problem. We provide direct evidence of ambiguity preferences acting on decision making separately from risk preferences, and advance knowledge regarding how ambiguity preferences operate on decision-making. |
| Keywords: | Learning-by-doing; Technology choice; Risk preferences; Risk measurement instruments; Ambiguity Aversion; Experimental economics, |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-46&r=upt |
| By: | John Geanakoplos (Cowles Foundation, Yale University); Kieran Walsh (University of Virginia; Darden School of Business) |
| Abstract: | We offer new sufficient conditions ensuring demand is downward sloping local to equilibrium. It follows that equilibrium is unique and stable in the sense that rising supply implies falling prices. In our setting, there are two goods, which we interpret as consumption in different time periods, and many impatience types. Agents have the same Bernoulli utility function, but the types differ arbitrarily in time preference. Our main result is that if endowments are identical and utility displays nonincreasing absolute risk aversion, then market demand is strictly downward sloping local to equilibrium. We discuss implications for the Diamond-Dybvig literature. |
| Keywords: | uniqueness of equilibrium, absolute risk aversion, excess demand functions, stability of equilibrium, Diamond-Dybvig models |
| JEL: | C62 D51 D58 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2050&r=upt |
| By: | Kaywana Raeburn; Jim Engle-Warnick; Sonia Laszlo |
| Abstract: | We present a decision-making experiment, conducted in the field, that explores the extent to which reduction of ambiguity can be a public good. We find evidence that people with a preference to avoid ambiguity contribute to the public good. We find that risk averse people free-ride. Cheap talk erases the predictability of who free rides, but does not affect the overall public good provision, either in a positive or a negative direction. Finally, we find that people draw appropriate inference from the evidence that the public good provides. We relate our findings to the issue of new technology adoption. |
| Keywords: | Ambiguity, Public Good, Technology Choice, |
| JEL: | C90 O33 Q16 |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-41&r=upt |
| By: | Jim Engle-Warnick; Diego Pulido; Marine de Montaignac |
| Abstract: | This paper reports results from an on-line economics experiment with heads of households that explores the link between a sample of survey questions on the Canadian Know-Your-Client survey form and several incentivized laboratory instruments, both aimed at measuring risk attitudes and loss aversion. We find that the instruments significantly predict responses to risk questions, with the exception of a question that includes a time dimension. By contrast, the loss aversion instruments do not predict responses to loss questions. Indeed, if anything, risk instruments predict the majority of the loss questions. We conclude that the survey appears to be successful in eliciting attitudes towards risk; that the survey appears to be less successful with regard to loss aversion; and that it may be useful to include survey questions about higher order risk preferences on the form. |
| Keywords: | Risk preferences, Risk preference elicitation, Prudence, Temperance, Ambiguity, Investing behavior, |
| JEL: | G02 D03 G11 C90 |
| Date: | 2016–08–23 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-40&r=upt |
| By: | R. Ferreira, Alexandre; A. P. Santos, Andre |
| Abstract: | Two crucial aspects to the problem of portfolio selection are the specification of the model for expected returns and their covariances, as well as the choice of the investment policy to be adopted. A common trade-off is to consider dynamic covariance specifications vis-a-vis static models such as those based on shrinkage methods. This work empirically shows that these two aspects are intrinsically attached to the impact of transaction costs. To address this question, we implement a broad range of covariance specifications to generate a set of 16 portfolio selection policies in a high dimensional sample composed by the 50 most traded stocks of the S\&P100 index. We find that GARCH-type dynamic covariances yield portfolios with superior risk-adjusted performance only in the absence of transaction costs. In more realistic scenarios involving alternative levels of transaction costs, portfolios based on static covariance models outperform. In particular, we find that a risk-averse investor with quadratic utility function is willing to pay an annualized fee of 368 basis points (bp) on average in order to switch from the dynamic covariance models to a static covariance specification when the level of transaction costs is 20 bp. Finally, portfolio policies that seek to alleviate estimation error by ignoring off-diagonal covariance elements as those proposed in Kirby and Ostdiek (2012) are more robust specially in scenarios with higher transaction costs. |
| Keywords: | Composite likelihood, conditional correlation models, factor models, multivariate GARCH |
| JEL: | C53 G11 G17 |
| Date: | 2016–08–21 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:73259&r=upt |
| By: | Jim Engle-Warnick; Diego Pulido; Marine de Montaignac |
| Abstract: | This paper reports results from an on-line economics experiment with head of household participants that explores the connection between trust and investment behavior. We show that trust is correlated with both the degree to which an investor makes decisions independently and the willingness to invest in an ambiguous asset. Our experiment is the first to suggest a link between trust, ambiguity, and investor independence. |
| Keywords: | trust, ambiguity, investment decisions, portfolio theory, artefactual field experiment, |
| JEL: | C91 C93 G02 G11 |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-44&r=upt |
| By: | Lucia Marchegiani (University of Rome 3); Tommaso Reggiani (LUMSA University); Matteo Rizzolli (LUMSA University) |
| Abstract: | A consistent empirical literature shows that in many organizations supervisors systematically overrate their employees’ performance. Such leniency bias is at odds with the standard principalagent model and has been explained with causes that range from social interactions to fairness concerns and to collusive behavior between the supervisor and the agent. We show that the principal-agent model, extended to consider loss-aversion and reference-dependent preferences, predicts that the leniency bias is comparatively less detrimental to effort provision than the severity bias. We test this prediction with a laboratory experiment where we demonstrate that failing to reward deserving agents is significantly more detrimental than rewarding undeserving agents. This offers a novel explanation as to why supervisors tend to be lenient in their appraisals. |
| Keywords: | Performance appraisal, TypeI and TypeII errors, Leniency bias, Severity bias, Economic experiment, Loss aversion, Reference-dependent preferences. |
| JEL: | C91 M50 J50 |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:lsa:wpaper:wpc11&r=upt |
| By: | Bahman Angoshtari |
| Abstract: | We consider the problem of optimal investment in a market with two cointegrated stocks and an agent with CRRA utility. We extend the findings of Liu and Timmermann [The Review of Financial Studies, 26(4):1048-1086, 2013] by paying special attention to when/if the associated stochastic control problem is well-posed and providing a verification result. Our new findings lead to a sharp well-posedness condition which is, surprisingly, also the necessary and sufficient condition for the optimal investment to be market-neutral (i.e. having offsetting long/short positions in the stocks). Hence, we provide a theoretical justification for market-neutral pairs-trading which, despite having a strong practical relevance, has been lacking a theoretical ground. |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1608.08268&r=upt |
| By: | Fuhai Hong; John List; Tanjim Hossain |
| Abstract: | Exploiting findings that losses loom larger than gains, studies have shown that framing manipulations can increase productivity of workers. Using a natural field experiment that exogenously manipulates wage bonuses within contests in a Chinese high-tech manufacturing facility, we show that how loss aversion affects worker behavior critically depends on the incentive scheme as well as the framing manipulation. Four sets of two identical teams competed against each other to win a bonus given to the team, within a set, with the higher average hourly productivity over the week. In each set, the bonus was framed as a reward or gain for one team and as a punishment or loss for the other. Average weekly productivity was slightly higher under the loss treatment, but this increase was statistically insignificant. However, the team under the loss treatment was at least 35% more likely to win the contest. As teams' payoffs are based on relative productivity under a contest, framing effect is much stronger in terms of relative productivity. Finally, workers seemingly responded to the bonus by increasing the quality of production as well as quantity-defect rate fell as productivity increased. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:feb:natura:00453&r=upt |
| By: | Munoz, F. D.; van der Weijde, A. H.; Hobbs, B. F.; Watson, J-P. |
| Abstract: | We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models. |
| Keywords: | risk aversion, stochastic programming, transmission planning, generation planning |
| JEL: | C61 D80 L94 Q40 |
| Date: | 2016–08–24 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1647&r=upt |
| By: | Silvia Delrio |
| Abstract: | This paper investigates the effects of a global uncertainty shock in open economies and the role of country relative risk exposure in the transmission of the shock. We employ an Interacted VAR model to take the time- varying dimension of country relative risk exposure into account. Evidence of nonlinearities in the real effects of a global uncertainty shock is found. The reduction in real activity is larger when the country is more exposed to aggregate risk. These findings support recent theoretical contributions on the role of risk exposure in the transmission of uncertainty shocks. |
| Keywords: | Global uncertainty shocks, Country relative riskiness, International analysis, Interacted VAR, Generalized Impulse Response Functions |
| JEL: | C32 E32 F41 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2016:19&r=upt |
| By: | Johannes Emmerling (Fondazione Eni Enrico Mattei (FEEM) and Centro Euromediterraneo sui Cambiamenti Climatici (CMCC)); Vassiliki Manoussi (Fondazione Eni Enrico Mattei (FEEM)); Anastasios Xepapadeas (Athens University of Economics and Business) |
| Abstract: | Climate Engineering, and in particular Solar Radiation Management (SRM) has become a widely discussed climate policy option to study in recent years. However, its potentially strategic nature and unforeseen side effects provide major policy and scientific challenges. We study the role of the SRM implementation and its strategic dimension in a model with two heterogeneous countries with the notable feature of model misspecification on the impacts from SRM. We find that deep uncertainty leads to a reduction in SRM deployment both under cooperation and strategic behavior, which is a more relevant issue if countries act strategically. Furthermore, we demonstrate that the heterogeneity in impacts from SRM has an asymmetric effect on the optimal policy and could typically lead to unilateral SRM implementation. We also consider heterogeneous degrees of ambiguity aversion, in which case the more confident country only will use SRM. |
| Keywords: | Climate Change, Solar Radiation Management, Uncertainty, Robust Control, Differential Game |
| JEL: | Q53 Q54 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2016.52&r=upt |
| By: | John List; Omar Al-Ubaydli |
| Abstract: | A commonly held view is that laboratory experiments provide researchers with more "control" than natural field experiments, and that this advantage is to be balanced against the disadvantage that laboratory experiments are less generalizable. This paper presents a simple model that explores circumstances under which natural field experiments provide researchers with more control than laboratory experiments afford. This stems from the covertness of natural field experiments: laboratory experiments provide researchers with a high degree of control in the environment which participants agree to be experimental subjects. When participants systematically opt out of laboratory experiments, the researcher's ability to manipulate certain variables is limited. In contrast, natural field experiments bypass the participation decision altogether and allow for a potentially more diverse participant pool within the market of interest. We show one particular case where such selection is invaluable: when treatment effects interact with participant characteristics. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:feb:artefa:00458&r=upt |
| By: | Gossner, Olivier; Steiner, Jakub |
| Abstract: | We study perception biases arising under second-best perception strategies. An agent correctly observes a parameter that is payoff-relevant in many decision problems that she encounters in her environment but is unable to retain all the information until her decision. A designer of the decision process chooses a perception strategy that determines the distribution of the perception errors. If some information loss is unavoidable due to cognition constraints, then (under additional conditions) the optimal perception strategy exhibits the illusion of control, overconfidence, and optimism. |
| Keywords: | Information Processing; overconfidence; perception |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:11478&r=upt |
| By: | Andrew Y. Chen; Eric Engstrom; Olesya V. Grishchenko |
| Abstract: | Print{{p}}April 4, 2016{{p}}Has the inflation risk premium fallen? Is it now negative?1{{p}}Andrew Chen, Eric Engstrom, Olesya Grishchenko{{p}}Inflation compensation is defined as the extra yield investors require to hold nominal assets that are exposed to inflation risk as opposed{{p}}to those that offer a safe inflation-adjusted return such as Treasury inflation protected securities (TIPS). Inflation compensation is widely{{p}}used by market commentators to gauge the expectations of investors regarding the outlook for inflation. Figure 1 depicts the daily time{{p}}series of one market-based measure of inflation compensation, defined as the difference between zero-coupon nominal and TIPS yields{{p}}of corresponding maturities.2 Since August 2014, measures of inflation compensation have trended down noticeably and, for many{{p}}months now, have been below 2 percent (the red line)--the rate of consumer price inflation targeted by the Federal Open Market{{p}}Committee (FOMC) over the longer-term.3{{p}}Figure 1: TIPS-based measures of inflation compensation.{{p}}Accessible Version{{p}} Source: Federal Reserve Board staff estimates{{p}}A straight read of these declines in inflation compensation might suggest that market participants expect inflation to fall significantly short{{p}}of the target rate of inflation, even at long horizons. However, other factors in addition to expected inflation likely affect inflation{{p}}compensation. In this note, we examine the theoretical determinants of one important component of inflation compensation, the inflation{{p}}risk premium, and argue that a secular decline in the inflation risk premium may be responsible for a substantial portion of the decline in{{p}}inflation compensation in recent years.{{p}}Measures of inflation compensation such as TIPS breakeven rates and inflation swap rates are related to market participants' expected{{p}}rate of inflation by the relationship:{{p}}Inflation compensation = expected inflation + inflation risk premium + other factors{{p}}Identifying the inflation risk premium is useful for measuring expected rate of inflation that is embedded in market prices, but it is also a{{p}}crucial quantity in its own right.4 For instance, if the premium is positive, then the government must pay an implicit positive premium for{{p}}issuing nominal Treasury securities relative to inflation-protected securities such as TIPS. However, if the inflation risk premium is{{p}}negative, then the relationship flips and issuing nominal bonds may be more cost effective for the Treasury.{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/has-the-infla...{{p}}1 of 4 4/4/2016 12:01 PM{{p}}Many market commentators appear to simply assume that the inflation risk premium is positive or ignore it altogether. According to these{{p}}commentaries, long-term nominal interest rates have generally exceeded short-term nominal interest rates, on average, to a greater{{p}}degree than is true for yields on securities that are protected from inflation risk such as TIPS. In the parlance of fixed-income analysts,{{p}}the nominal term structure has tended to be more steeply upward-sloping than the real term structure. This evidence is said to suggest{{p}}that assets like nominal bonds whose prices and returns suffer when inflation is unexpectedly high are viewed as risky by market{{p}}participants, who have then required a positive inflation premium, pushing up long-term nominal yields.{{p}}This logic is sound, but the available time series of data to support this claim is relatively short (in the United States, TIPS were first{{p}}issued in 1997). Further, as explored further below, there are good reasons to suspect that a structural change may have taken place{{p}}and that the slope of the nominal yield curve may be somewhat flatter in the future.{{p}}Conventional asset pricing theory suggests that the sign of risk premiums depends on the sign of the covariance of the returns of those{{p}}assets with the typical investors' consumption or wealth. For example, stocks require a high positive risk premium because equity prices{{p}}tend to fall during recessions, precisely when consumption also falls. Assets with payoffs tied to inflation are often modeled in this way{{p}}too.{{p}}Figure 2: Estimated correlations between 10-year forward consumption growth and long-run inflation.{{p}}Accessible Version{{p}}Figure 2 shows that the estimated correlation of long-run future inflation with long-run future consumption has not been stable over{{p}}time.5 The correlation was deeply negative in the 1980s, when periods of high inflation were associated with poor economic outcomes,{{p}}suggesting that the inflation risk premium was likely positive at that time. The negative correlation in the early sample is consistent with a{{p}}predominance of economic shocks that move inflation and real growth in opposite direction, such as oil price ("supply-side") shocks that{{p}}simultaneously raised inflation and lowered real economic activity.{{p}}However, Figure 2 shows that the correlation trended up over time and switched signs recently, implying that the risk premium may now{{p}}be negative. The change is consistent with an increasing role for "demand-side" shocks that instead push inflation and real economic{{p}}activity in the same direction. For instance, onset of the Great Recession saw both inflation and real activity plummeting simultaneously.{{p}}Using a-back-of-the-envelope calculation, we can roughly gauge the plausible magnitude of the negative risk premium. To do so, we{{p}}appeal to conventional asset pricing theory using a standard utility function with constant relative risk aversion and risk aversion{{p}}parameter, γ. Under these conditions, the inflation risk premium is{{p}}Inflation risk premium = -γ × covariance(inflation, consumption growth).{{p}}Using a risk aversion parameter of 20,6 the implied inflation risk premium at 10-year and 5-year 5 years ahead horizons at the end of the{{p}}sample is negative 17 basis points and negative 5 basis points, respectively, compared with positive average levels of about 100 basis{{p}}and 25 basis points for the two series in the 1980s.{{p}}However, there are important caveats to the above analysis. First, consumption-based asset pricing models have, at best, a mixed{{p}}record of fitting risk premiums across assets. Second, the particular statistical model of the correlation between consumption growth and{{p}}inflation depicted above may not coincide with investor perceptions.{{p}}To take a closer look at potential changes in the inflation risk premium over the past few years, we use higher-frequency data from asset{{p}}prices to estimate correlations between consumption growth and inflation depicted in Figure 2. We can use the capital asset pricing{{p}}model, according to which the risk premium associated with a position in inflation compensation (e.g. in inflation swaps) is:{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/has-the-infla...{{p}}2 of 4 4/4/2016 12:01 PM{{p}}Holding period inflation risk premium = market risk premium x beta(inflation compensation),{{p}}where the function "beta" is the usual concept that is proportional to the correlation between inflation compensation and equity returns.7{{p}}As Figure 38 depicts, the estimated betas of inflation compensation plunged into negative territory in 2009, similar to the results in Figure{{p}}2, and have remained negative ever since. Moreover, the betas have moved down sharply over the past few months.9 Also plotted is the{{p}}option-implied one month-ahead implied volatility on the S&P 500 index – the VIX – a measure plausibly related to the equity market risk{{p}}premium. The VIX has moved up somewhat, on net, over the past year as the betas have trended down, suggesting that the risk{{p}}premium associated with inflation compensation has become substantially more negative.{{p}}Figure 3. Beta of one-year ahead positions in inflation compensation with respect to the S&P500 index.{{p}}Accessible Version{{p}}To sum up, this note points out that standard consumption-based asset pricing models and the capital asset pricing model suggest that{{p}}the long run inflation risk premium has trended down over time, and is likely to be negative in the current macroeconomic environment.{{p}}Moreover, a nontrivial portion of the decline in far-forward inflation compensation over the past year may reflect a decline in the inflation{{p}}risk premium rather than a drop in investors' expected inflation rate.{{p}}A burgeoning academic literature has also investigated this issue, providing estimates of the inflation risk premium. For example,{{p}}Chernov and Mueller (2012) argue that the inflation risk premium estimates are model-dependent and can switch sign from positive to{{p}}negative in a model that accounts for survey-based inflation forecasts vs. the one that does exclude the survey forecasts from the{{p}}estimation. Grishchenko and Huang (2013) find that the inflation risk premium implied by the nominal yields and the TIPS-based{{p}}measure of inflation compensation was positive in the early 2000s but switched signs around the Great Recession. D'Amico, Kim, and{{p}}Wei (2014) also find that the inflation risk premium has been trending down. Finally, recent papers in the macro-finance literature have{{p}}made strides in incorporating the fundamental insights from consumption-based asset pricing into fully-fledged models of the term{{p}}structure of interest rates.10{{p}} References:{{p}}Bansal, R., Shaliastovich, I., 2013, "A long-run risks explanation of predictability puzzles in bond and currency markets," the Review of{{p}}Financial Studies, 26(1), pp. 1-33.{{p}}Chernov, M., Mueller, P., 2012, "The term structure of inflation expectations", Journal of Financial Economics, 106, pp. 367-394{{p}}Grishchenko, O., Huang, J., 2013, "The inflation risk premium: Evidence from the TIPS market", Journal of Fixed Income, 22(4), pp.5-30{{p}}Grishchenko, O., Song, Z., Zhou, H., 2015, "Term structure of interest rates with short-run and long-run risks," Finance and Economics{{p}}Discussion Series 2015-095. Washington: Board of Governors of the Federal Reserve System{{p}}Gurkaynak, R., Sack, B., Wright, J., 2007, "The U.S. Treasury yield curve: 1961 to present", Journal of Monetary Economics, 54, pp.{{p}}2291-2304{{p}}Gurkaynak, R., Sack, B., Wright, J., 2010, "The TIPS yield curve and inflation compensation", American Economic Journal:{{p}}Macroeconomics, 2(1), pp. 70-92{{p}}D'Amico, S., Kim, D., Wei, M., 2014, "Tips from TIPS: The informational content of Treasury Inflation-Protected Security Prices", Finance{{p}}and Economics Discussion Series 2014-24, Board of Governors of the Federal Reserve System (U.S.){{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/has-the-infla...{{p}}3 of 4 4/4/2016 12:01 PM{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}}Kocherlakota, N., 1996, "The equity premium: It's still a puzzle", Journal of Political Literature, 34(1), pp. 42-71{{p}}Koop, G., Korobilis, D., 2013, "Large time-varying parameter VARs", Journal of Econometrics, 177(2), pp. 185-198{{p}}Wachter, J., 2006, "A consumption-based model of the term structure of interest rates," Journal of Financial Economics, 79, pp. 365-399{{p}}1. Board of Governors of the Federal Reserve System. The views expressed in this note do not necessary reflect those of the Board of Governors, or its staff.{{p}}The note benefitted from helpful comments from Francisco Palomino, Anthony Diercks, and Michael Palumbo. Return to text{{p}}2. Zero-coupon nominal and TIPS yields are estimated using Svensson functional form of the yield curve. See Gurkaynak et al. (2007, 2010) for details. Return{{p}}to text{{p}}3. The FOMC targets Personal Consumer Expenditures (PCE) index while TIPS are indexed to Consumer Price Index (CPI). However, existing discrepancy{{p}}between the two indices is not likely to explain a recent downward trend in inflation compensation. Return to text{{p}}4. "Other factors" may include liquidity premiums and technical, usually transitory, trading effects. Return to text{{p}}5. These estimates were produced using a time-varying parameter vector autoregression (TVP-VAR) including the variables: the output gap (CBO estimate),{{p}}4-quarter core PCE inflation, real consumption growth, headline PCE inflation, and the Federal funds rate. Data are quarterly from 1954-2015. The TVP-VAR{{p}}methodology follows Koop (2013). Figure 2 shows the correlations between 10-year ahead consumption growth and 10-year ahead inflation (or 5-to-10 year{{p}}ahead inflation) implied by the TVP-VAR. Return to text{{p}}6. Risk aversion of 20 is a level typically found in the academic literature to be sufficient to fit the equity risk premium. See, for example, Kocherlakota (1996).{{p}}Return to text{{p}}7. The measured holding period risk premium is a slightly different concept from the inflation risk premium because it represents the excess return to holding a{{p}}position in inflation compensation for just one year. Return to text{{p}}8. The S&P 500 VIX ("Index") is a product of S&P Dow Jones Indices LLC and/or its affiliates and has been licensed for use by the Board. Copyright © 2016{{p}}S&P Dow Jones Indices LLC, a subsidiary of the McGraw Hill Financial Inc., and /or its affiliates. All rights reserved. Redistribution, reproduction and/or{{p}}photocopying in whole or in part are prohibited without written permission of S&P Dow Jones Indices LLC. For more information on any of S&P Dow Jones{{p}}Indices LLC's indices please visit www.spdji.com. S&P® is a registered trademark of Standard & Poor's Financial Services LLC and Dow Jones® is a{{p}}registered trademark of Dow Jones Trademark Holdings LLC. Neither S&P Dow Jones Indices LLC, Dow Jones Trademark Holdings LLC, their affiliates nor{{p}}their third party licensors make any representation or warranty, express or implied, as to the ability of any index to accurately represent the asset class or{{p}}market sector that it purports to represent and neither S&P Dow Jones Indices LLC, Dow Jones Trademark Holdings LLC, their affiliates nor their third party{{p}}licensors shall have any liability for any errors, omissions, or interruptions of any index or the data included therein. Return to text{{p}}9. We also use a TVP-VAR for this analysis. The endogenous variables include real and nominal Treasury yields at the 5- and 10-year maturities, the VIX{{p}}index, and returns on the S&P 500 index. Data are weekly from January 2010 - February 2016. Return to text{{p}}10. See Wachter (2006), Bansal and Shaliastovich (2013) and Grishchenko, Song and Zhou (2015). Return to text{{p}}Please cite this note as:{{p}}Chen, Andrew Y., Eric C. Engstrom, and Olesya V. Grishchenko (2016). "Has the inflation risk premium fallen? Is it now negative? ,"{{p}}FEDS Notes. Washington: Board of Governors of the Federal Reserve System, April 4, 2016, http://dx.doi.org/10.17016{{p}}/2380-7172.1720.{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Last update: April 4, 2016{{p}}Home | Economic Research & Data{{p}} FRB: FEDS Notes: Has the inflation risk premium fallen? Is it now negative?https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/has-the-infla...{{p}}4 of 4 4/4/2016 12:01 PM |
| Date: | 2016–04–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2016-04-04&r=upt |
| By: | Samuel Dodini |
| Abstract: | FEDS Notes Print{{p}}June 29, 2016{{p}}Findings on Relative Deprivation from the Survey of Household Economics and{{p}}Decisionmaking{{p}}Sam Dodini{{p}}The Federal Reserve's Survey of Household Economics and Decisionmaking (SHED) contains questions designed to ascertain overall{{p}}economic well-being and fragility, which can be used to gauge both the perceptions individuals have about their own economic status and{{p}}an approximation of their actual financial health.1 The SHED contains valuable information that can be used to explore how the well-being{{p}}of others in their community may impact an individual's assessment of their own well-being and their choices.{{p}}Typical measures of financial well-being often focus on income distributional statistics such as one's income or wealth level. Under the{{p}}standard consumer utility framework, income is important for well-being by determining the budget constraint and impacting the level of{{p}}consumption that one can afford. Since well-being is based solely on one's own income and consumption, well-being will have no direct{{p}}relation to the income or consumption of others.{{p}}However, under theories first developed by Duesenberry (1949), individual utility may not depend on consumption subject to absolute{{p}}income constraints and prices, but also upon relative income. As one's relative income falls, their level of relative deprivation increases, as{{p}}does their disutility, even if their own income or consumption remains unchanged.{{p}}Under this theory of relative deprivation, not only do quantities consumed matter to utility, but in order to combat the disutility of lower{{p}}relative income, individuals reallocate their income toward consumption goods designed to keep up with their higher-income peers.{{p}}Spending is particularly focused on "positional goods"--a term first coined by Hirsch (1977) to mean goods that represent more expensive{{p}}outward signals of success and whose utility comes from exclusivity. Examples include expensive cars, the newest electronic gadgets, or{{p}}fashionable clothing. The prediction of relative deprivation theory is that lower relative income households attempt to maintain a higher{{p}}quality lifestyle in order to keep up with higher relative income households in a kind of expenditure arms race (the proverbial "Keeping up{{p}}with the Joneses"), as discussed extensively by Frank (2007). Based on relative deprivation theory, increases (decreases) in inequality{{p}}can reduce (increase) individual well-being beyond that which would be expected from changes in any individual's own income.{{p}}While relative deprivation is one way in which inequality levels can impact individual well-being, it is not the only avenue by which an{{p}}impact may occur. To the extent that inequality increases the cost of living due to competition for scarce resources--which often occur{{p}}simultaneously with the economic growth of cities and gentrification--the higher prices will reduce the consumption possibility frontier for{{p}}an individual with a given level of income2. As an example, Glaeser, et al (2012), relate their findings that city centers with more poverty,{{p}}coupled with higher-income periphery areas, saw the greatest house price growth from 1996-2006 as the central cities revitalized (and{{p}}local inequality increased), which may have negatively affected the well-being of those lower-income residence who occupied those city{{p}}centers.{{p}}In this case, the relationship between consumption and well-being from classical economic theory will adequately predict lower well-being{{p}}for those in high-cost areas due to price effects without relying on this relative deprivation explanation. Local prices are key to{{p}}understanding the amount of weight to place upon relative deprivation theory vis-à-vis price theory.{{p}}For this analysis, I focus on two main questions in the SHED related to this theory of relative deprivation. Respondents are asked how{{p}}they perceive themselves to be faring financially, ranging from "Finding it difficult to get by" to "Living comfortably" and also about their{{p}}expenses relative to their income as a measure of savings. If relative deprivation leads to overspending or viewing one's household as{{p}}financially worse off than others, we would expect those with lower relative incomes to report lower levels of financial health and savings.{{p}}In making comparisons that may affect one's perceptions of relative economic standing, individuals depend heavily upon their social{{p}}interactions and their immediate environment. They draw from their experiences in communities, not across countries. For this reason,{{p}}analyses that attempt to fully explore these issues should examine them at the local rather than national level.{{p}}The restricted use version of the 2014 SHED data contain demographic information measuring age, income, education, household size,{{p}}marital status, and race/ethnicity along with the ZIP code information of each of the approximately 5,800 survey respondents across more{{p}}than 4,600 ZIP codes. Using the ZIP code information of the respondents, we can compare the reported income of the respondent to the{{p}}ZIP code level 5-year estimates of income as measured by the American Community Survey (ACS) in order to measure how one's own{{p}}income compares to the incomes of those above them in the income distribution.{{p}}Measuring Relative Deprivation{{p}}Gini-based indexes of relative deprivation account for the concentration of income as well as the proportion of the income distribution{{p}}above a household of interest (e.g. Yitzhaki, 1979), i.e. the distribution of income. The SHED, however, reports household-level incomes{{p}}and other important facets of well-being. In terms of individual knowledge of their own position in a community, I argue that households{{p}}are unlikely to readily perceive the distribution of income that surrounds them as much as they perceive the difference in the levels of{{p}}income between themselves and those that are relatively well off in the community.3 Thompson (2016) makes a similar argument that Gini{{p}}measurements do not adequately capture changes at the top of the income distribution nor address the true relative income of those at{{p}}the middle and bottom. To measure this perception in the SHED, I construct a metric of each household's income as a percentage of the{{p}}90th percentile income in their ZIP code. The lower the percentage below one, the more relative deprivation applies to the respondent{{p}}and/or their household. I adjust the measure for those whose income is greater than the 90th percentile to 1, as they face no relative{{p}}deprivation in connection to the reference group.4{{p}} FRB: FEDS Notes: Findings on Relative Deprivation from the Survey of Household Econ... Page 1 of 5{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/findings-on-relative-deprivati... 6/29/2016{{p}}A Snapshot of Current Well-Being{{p}}First, I examine the relationship between both absolute and relative income and self-reported financial well-being in the SHED. Figure 1{{p}}shows the straightforward relationship between stated living conditions and absolute income. The results are exactly what we would{{p}} expect: those in lower income buckets are more likely to respond that they are finding it difficult to get by.{{p}} Source: 2014 Survey of Household Economics and Decisionmaking (SHED){{p}}Accessible version{{p}} Source: 2014 Survey of Household Economics and Decisionmaking (SHED) and American Community Survey (ACS){{p}}Accessible version{{p}}Figure 2 shows a similar relationship between financial well-being and relative income. Among those whose income is less than 25{{p}}percent the 90th percentile value, only eight percent responded that they are "living comfortably" compared to 34 percent of those whose{{p}}income is 75 to 100 percent of the 90th percentile income. In the lowest category, 24 percent of those whose incomes are less than 25{{p}}percent of the 90th percentile say they are "finding it difficult to get by", compared to 4-5 percent of those whose incomes are near or{{p}}above the 90th percentile.{{p}}The similarities between Figures 1 and 2 underscore the need to adequately control for absolute income when examining the effects of{{p}}relative income. I use an ordered probit regression model to isolate these effects. I also include other controls that may influence{{p}}Figure 1: Which of the following best describes how well you are managing financially these days? by absolute{{p}}income, percent{{p}}Figure 2: Which of the following best describes how well you are managing financially these days? by relative{{p}}income, percent{{p}} FRB: FEDS Notes: Findings on Relative Deprivation from the Survey of Household Econ... Page 2 of 5{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/findings-on-relative-deprivati... 6/29/2016{{p}}household perceptions of well-being and financial behavior. These are personal demographics that reflect life cycle and preferences, i.e.{{p}}ethnicity, age, and education; the household characteristics of household size and marital status; employment status; and the ZIP-level{{p}}considerations of average household size and cost of living measured by median house value. Table 1 shows the incremental results of{{p}}including these controls.{{p}} Note: The regression includes unreported categories for race/ethnicity of Hispanic, Other/Non-Hispanic, 2+ Non-Hispanic and for education groups of high{{p}}school and some college (no degree) and work status of not employed, each of which are not statistically significant. Absolute income is controlled by including{{p}}categorical buckets so as to allow for nonlinear effects of absolute income.{{p}} Source: Author's calculations of SHED data.{{p}}These regressions control for absolute income. However, other characteristics are also important factors in what social comparisons{{p}}people may draw and the financial decisions they may make. The age of the respondents in particular plays an important role because{{p}}individual incomes vary predictably over the life cycle. The education of the respondent is also important because higher levels of{{p}}education are correlated with preferences for patience and financial literacy, which factor into financial well-being. In addition, nominal{{p}}dollars of income do not buy someone the same level of consumption across different locations. For example, housing and other{{p}}expenses are far higher in Manhattan than they are in Ithaca, NY. Someone making $60,000 per year in Manhattan is not necessarily{{p}}better off than someone making $58,000 in Ithaca. In order to effectively compare these individuals to each other and to others within their{{p}}cities, we must control for the cost of living. While a measure of local consumer prices is not directly available, house prices are available,{{p}}which constitute a major contributor to local cost of living calculations.5 Columns 2 and 4 include the log of median house price as a proxy{{p}}for local costs.{{p}}In all specifications, there is no statistically significant relationship between relative deprivation and self-reported financial well-being{{p}}(columns 1-2).6 This may be due to the fact that there are only four response categories that may miss more narrow changes in{{p}}Table 1: Ordered and standard probit regression results controlling for absolute income: effects of relative income{{p}}on self-reported financial well-being and probability of saving{{p}}Overall Financial Well-Being Income Greater than Expenses (Savings){{p}}(1) (2) (3) (4){{p}}Relative Income as % of 90th Percentile 0.0783{{p}}(0.149){{p}}-0.00133{{p}}(0.188){{p}}0.527***{{p}}(0.175){{p}}0.264{{p}}(0.226){{p}}Relative Income as % of 90th Percentile Squared 0.141{{p}}(0.0914){{p}}0.144{{p}}(0.0930){{p}}0.125{{p}}(0.108){{p}}0.151{{p}}(0.108){{p}}Age 30-44 -0.175***{{p}}(0.0578){{p}}-0.291***{{p}}(0.0605){{p}}-0.105{{p}}(0.0735){{p}}-0.161**{{p}}(0.0767){{p}}Age 45-59 -0.0851{{p}}(0.0581){{p}}-0.256***{{p}}(0.0637){{p}}0.0512{{p}}(0.0709){{p}}-0.0612{{p}}(0.0776){{p}}Age 60+ 0.456***{{p}}(0.0569){{p}}0.273***{{p}}(0.0715){{p}}0.173***{{p}}(0.0669){{p}}0.0699{{p}}(0.0829){{p}}Black, Non-Hispanic -0.153**{{p}}(0.0611){{p}}-0.112*{{p}}(0.0631){{p}}-0.167**{{p}}(0.0769){{p}}-0.161**{{p}}(0.0778){{p}}Bachelor's degree or higher 0.345***{{p}}(0.0751){{p}}0.282***{{p}}(0.0765){{p}}0.239***{{p}}(0.0916){{p}}0.150{{p}}(0.0930){{p}}Divorced/Widowed/Separated/Living with Partner -0.265***{{p}}(0.0480){{p}}-0.180***{{p}}(0.0588){{p}}Never Married -0.238***{{p}}(0.0564){{p}}-0.0698{{p}}(0.0700){{p}}Household Size -0.0811***{{p}}(0.0154){{p}}-0.0967***{{p}}(0.0210){{p}}Not Employed -0.0524{{p}}(0.0467){{p}}-0.113**{{p}}(0.0551){{p}}Average Household Size (ZIP) -0.0890*{{p}}(0.0460){{p}}-0.0545{{p}}(0.0603){{p}}Log Median House Value (ZIP) -0.0277{{p}}(0.0420){{p}}-0.107**{{p}}(0.0533){{p}}Observations 5,815 5,801 5,779 5,767{{p}}Personal Controls Yes Yes Yes Yes{{p}}Household, Work Status, ZIP Characteristics No Yes No Yes{{p}}Log Likelihood -6806 -6746 -3711 -3665{{p}}Pseudo R2 0.0903 0.0966 0.0535 0.0635{{p}} FRB: FEDS Notes: Findings on Relative Deprivation from the Survey of Household Econ... Page 3 of 5{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/findings-on-relative-deprivati... 6/29/2016{{p}}perception or may reflect the idea that these differences may simply not enter their financial "big picture" but may play a role in their day-to-{{p}}day decisions. More concrete measures of financial health would help us understand what other factors may be related. If relative{{p}}deprivation had a negative association with utility or self-perception, we would expect to see a negative relationship. These results are{{p}}inconsistent with the positional goods theory of relative deprivation.{{p}}Concrete Financial Status{{p}}In addition to asking about the way individuals perceive their overall financial well-being, the SHED also asks questions designed to{{p}}understand the financial health of the household in a more concrete way.{{p}}To understand overall and emergency savings, the SHED asks respondents how their spending compares to their income over the{{p}}previous 12 months. Prolonged household budget deficits can lead to the accumulation of debt, increased stress and economic fragility,{{p}}and limit upward mobility. In addition, if prices are driven upward by the spending of high-income households, or if concerns about relative{{p}}position in the community drive households to spend more on positional, nonessential, or other goods, they are less likely to have money{{p}}set aside for emergency expenses. Therefore, the relationship between savings and relative deprivation can inform the overarching{{p}}consumption decisions of these individuals. In a similar vein, using payment to income ratios as a measure of debt and state-level income{{p}}distributions in the Survey of Consumer Finances (SCF), Thompson (2016) finds that as the income of the state's 95th percentile increase{{p}}relative to the rest of the distribution, payment to income ratios for those in the middle of the distribution also increase. These effects are{{p}}not observed in the bottom of the distribution. As with other studies, state-level distributional analyses leave considerable room for{{p}}heterogeneity in cost within states and within metro areas, which this analysis seeks to address.{{p}}Similar to the ordered probit regression discussed above, I also run a probit analysis of the binary outcome of whether or not a{{p}}household's income was greater than their expenses, or in other words, whether or not the household set aside any real savings. Table 1,{{p}}columns 3-4 outline the results of interest for each specification.{{p}}Column 3 indicates that, even after controlling for absolute income, those who are further away from the 90th percentile income are less{{p}}likely than those who are closer to the 90th percentile to report that their income was greater than their expenditures, a relationship similar{{p}}to Thompson's results. This is consistent with the sociological positional goods argument. Taken alone, this would suggest that relative{{p}}deprivation is resulting in an increase in spending relative to income as individuals are attempting to keep up with the "Joneses."{{p}}However, Column 4 shows that this relationship between relative income and spending beyond one's means declines and becomes{{p}}statistically indistinguishable from zero upon inclusion of log median house value in the regression. This indicates that cost of living is{{p}}highly connected to how relative deprivation relates to savings.7 Column 4 results are inconsistent with relative deprivation theory.{{p}}These results cast some doubt on the "Keeping up with the Joneses" and positional goods explanation of consumption. After controlling{{p}}for absolute income, other important demographic covariates, and cost of living, those who face larger amounts of relative deprivation are{{p}}not necessarily less likely to set aside savings over the course of a year. Rather, those with lower relative income appear to have lower{{p}}levels of savings because they live in areas with higher prices and not necessarily because they are spending more in reaction to the{{p}}higher-income "Joneses". Controlling for cost of living in these analyses is key to understanding the possible effects of relative deprivation{{p}}on well-being independent of price effects.{{p}}Conclusion{{p}}Taken together, these analyses suggest that relative deprivation is weakly associated with lower marginal savings, but these influences{{p}}may simply be manifestations of the effects of costs. Relative deprivation does not appear to be a large enough factor to shift individual{{p}}perceptions of their overall financial well-being. Further analyses may utilize more measures of relative deprivation to investigate other{{p}}components of the relationship between well-being and relative deprivation such as the percentage of income saved, their perceptions of{{p}}mobility, and their preferences for work.{{p}}References{{p}}Cruces, Guillermo, Ricardo Perez-Truglia, and Martin Tetaz. 2013. Biased Perceptions of Income Distribution and Preferences for{{p}} Redistribution: Evidence from a Survey Experiment. Journal of Public Economics 98: 100-112.{{p}}Duesenberry, J.S. 1949. Income, Saving and the Theory of Consumer Behavior. Harvard University Press, Cambridge, Mass.{{p}}Frank, Robert H. 2007. Falling Behind: How Rising Inequality Harms the Middle Class. University of California Press.{{p}}Glaeser, Edward L., Joshua Gottlieb, and Kristina Tobio. 2012. Housing Booms and City Centers. American Economic Review: Papers &{{p}}Proceedings 102(3): 127-133.{{p}}Hirsch, Fred. 1977. Social Limits to Growth. Routledge & Kegan Paul Ltd.{{p}}Thompson, Jeffrey. 2016. Do Rising Top Incomes Lead to Increased Borrowing in the Rest of the Distribution? Finance and Economics{{p}}Discussion Series 2016-046. Board of Governors of the Federal Reserve System.{{p}}Yitzhaki, Shlomo. 1979. Relative Deprivation and the Gini Coefficient. The Quarterly Journal of Economics 93(2): 321-324.{{p}}1. For the full report of survey results from each year, the instrument, and technical notes, see https://www.federalreserve.gov/communitydev/shed.htm. Return{{p}}to text{{p}}2. For this reason, national income distributional statistics routinely adjust for cost of living. However, for local analyses, changes in cost of living will still play a{{p}}role to the extent that local cost trends diverge from national averages. Return to text{{p}}3. Research from Argentina suggests that individuals are poor judges of their place in and the shape of the income distribution (Cruces, Perez-Truglia, and{{p}}Tetaz, 2013). However, individuals are able to see positional goods or conspicuous consumption from which to infer ostensible differences in income levels.{{p}}Return to text{{p}}4. I also compare these values using the 75th, 85th, and 95th percentiles for robustness. Return to text{{p}} FRB: FEDS Notes: Findings on Relative Deprivation from the Survey of Household Econ... Page 4 of 5{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/findings-on-relative-deprivati... 6/29/2016{{p}}Last update: June 29, 2016{{p}}Home | Economic Research & Data{{p}}5. BEA produces a regional price parity index. The correlation between median house price and this price parity for all goods at the state level is very high at{{p}}0.91. For more information, see http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm. Return to text{{p}}6. This also holds when comparing the 75th and 85th percentile levels to household income. However, distance from the 95th percentile appears to give mixed{{p}}results. Return to text{{p}}7. Comparing these results to regressions using the gap between the household's income and the 75th, 85th, and 95th percentiles yields similar results,{{p}}although column 3 results are not statistically significant for the 95th percentile. Return to text{{p}}Please cite this note as:{{p}}Dodini, Samuel (2016). "Findings on Relative Deprivation from the Survey of Household Economics and Decisionmaking," FEDS Notes.{{p}} Washington: Board of Governors of the Federal Reserve System, June 29, 2016, http://dx.doi.org/10.17016/2380-7172.1791.{{p}} Disclaimer: FEDS Notes are articles in which Board economists offer their own views and present analysis on a range of topics in{{p}}economics and finance. These articles are shorter and less technically oriented than FEDS Working Papers.{{p}}Accessibility Contact Us Disclaimer Website Policies FOIA PDF Reader{{p}} FRB: FEDS Notes: Findings on Relative Deprivation from the Survey of Household Econ... Page 5 of 5{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/findings-on-relative-deprivati... 6/29/2016 |
| Date: | 2016–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2016-06-29&r=upt |