The Implications of Behavioral Research for the Phillips Curve

San Francisco Fed President Janet Yellen discusses the use of behaviorally based macroeconomic models incorporating features such as money illusion, rules of thumb, and concern for issues such as fairness and equity to improve the ability of the New Keynesian Phillips curve to explain macroeconomic data:

Implications of Behavioral Economics for Monetary Policy [1], Janet Yellen, SF Fed: I want to congratulate the Federal Reserve Bank of Boston for organizing a fascinating and thought-provoking conference. I applaud the Bank’s decision to establish a center to promote and support research in behavioral economics and concur wholeheartedly with the judgment that motivates these initiatives—that research in behavioral economics is broadening and enriching our understanding of decisionmaking. This research has the potential to strengthen the conceptual and empirical underpinnings of macroeconomic policy.

The Federal Reserve is one of a growing number of organizations that have already taken some implications of behavioral research to heart. This year, we began to automatically enroll new employees into our System’s savings plan, defaulting them into an asset allocation fund that includes fixed income, domestic, and international equity investments. Employees who do not want to participate can, of course, easily opt out. But our early experience mirrors well-known research findings: so far, an overwhelming fraction of employees who were defaulted in remain in. Of course, this choice reflects the Federal Reserve System’s appreciation of the striking findings of behavioral economics concerning the sensitivity of saving decisions to default enrollments.

In terms of the Federal Reserve’s public policy responsibilities, I can easily envision other ways in which explorations in behavioral economics could be of practical use. For example, one of the Federal Reserve’s responsibilities is to design consumer disclosures, including the information that borrowers receive from lenders when they take out a mortgage, apply for a credit card, or lease a new vehicle. As we have unfortunately seen recently, such disclosures have not always been effective in conveying the key information that is relevant to such decisions in a salient, understandable, and timely way. Indeed, recent research by the Federal Trade Commission[2] documents that a large fraction of mortgage borrowers fail to understand the financial implications of prepayment penalties and other complex loan features. To improve the effectiveness of such disclosures, the Federal Reserve has begun to use consumer testing techniques,[3] but there remains substantial scope for behavioral research to contribute to the design of more effective practices in the consumer disclosure area.

Today, however, I would like to focus on some implications of behavioral economics for the conduct of monetary policy. I will concentrate on implications of behavioral research for the Phillips curve, although the papers at this conference demonstrate that behavioral economics has implications for many other aspects of macroeconomic modeling, including the behavior of housing and other asset prices, and the specification of crucial components of aggregate demand, such as the consumption function.

The Phillips curve is a core component of every realistic macroeconomic model. It plays a critical role in policy determination, because its characteristics importantly influence the short- and long-run tradeoffs that central banks face as they strive to achieve price stability and, in the Federal Reserve’s case, maximum sustainable employment—our second, congressionally mandated goal. I will argue that behavioral economics can enhance our understanding of the Phillips curve, and this is important for two reasons: First, better models of the inflation process help improve our forecasts and clarify limitations on what monetary policy can do. Second, the theoretical underpinnings of the Phillips curve are important in understanding what central banks should do. In other words, beyond determining the “constraints” governing what is feasible, models underpinning the Phillips curve have implications for the way in which central banks should interpret their price stability mandate and for assessing the welfare costs of fluctuations in output and inflation.

The New Keynesian model provides theoretical microfoundations for a Phillips curve that relates actual inflation to expected inflation one period ahead as well as to marginal cost.[4] This model has become a standard workhorse for policy analysis, and provides loose justification for empirical implementations of the Phillips curve, which typically relate actual inflation to lags of inflation (as a proxy for expected inflation), a measure of the output or unemployment gap (which proxies for cyclical fluctuations in marginal cost), and other variables reflecting “supply shocks” such as the prices of energy and imported goods. The coefficient on the unemployment gap in the Phillips curve determines the slope of the “short-run” Phillips curve relationship between unemployment and inflation and is a crucial parameter for monetary policy because it influences the sacrifice ratio—the cost in terms of unemployment or lost output to lower inflation. Virtually all empirical research on the inflationary process finds that the “short-run” Phillips curve is flat enough to generate a significant short-run tradeoff.

Of course, the existence of this empirical short-run tradeoff between inflation and unemployment also helped motivate the development of the New Keynesian model in the first place. In particular, with no frictions and with fully maximizing agents, markets should always clear, and the labor market should be no exception. Thus, the short-run Phillips curve “should be” vertical.[5] This divergence between theory and reality was the original motivation for Keynesian economics. But in contrast to the ad hoc behavioral assumptions underlying old-style Keynesian theory, modern researchers have “amended” the neoclassical model with well-specified assumptions concerning the nature of preferences, the process of decisionmaking, the frictions characterizing markets, and the details of expectation formation. The objective has been to build models on sound microfoundations that are not only rigorous but also realistic.

Viewed in this light, the now standard New Keynesian approach explains the short-run Phillips curve tradeoff by introducing a key “friction” into neoclassical theory, namely, price-stickiness; such a friction is often justified as a menu cost of changing nominal prices. The consequence is that firms change the prices they charge only periodically, not continuously. With staggered decisionmaking across price setters, the aggregate price level exhibits inertia, rationalizing the short-run Phillips curve tradeoff. Other frictions, such as wage rigidity and habit persistence in consumption, are typically added to improve the fit of the model.

Behavioral macroeconomic models have extended this agenda, both by providing new justifications for wage and price rigidity and by incorporating additional departures from the frictionless benchmark. Of course, the jury is still out on which modifications are most important empirically for understanding the macroeconomy. Nevertheless, the evidence presented throughout this conference regarding how people behave is too compelling to simply ignore. Let me discuss a few examples of behavioral macroeconomics.

Some behavioral models assume that people follow simple heuristics or rules of thumb that require relatively little cognitive effort or time (such as focusing on only a few salient details of a problem). Indeed, the psychology and economics literature that builds on the work of Kahneman, Tversky, and others generally concludes that people do not make decisions in the fully rational way commonly envisioned in standard macroeconomic models. As Benjamin and Laibson (2003) summarize the findings of this literature: “Economic agents make good decisions but not perfectly rational ones.”

Other behavioral models, including those surveyed by Fehr, Goette, and Zehnder (2007) and by Rotemberg (2007) at this conference, go much further, arguing that individual behavior is affected by a reliance on nominal frames of reference and by considerations such as fairness, envy, social status, and social norms. As Rotemberg makes clear, such assumptions can also rationalize the phenomenon of “price-stickiness” embodied in the Phillips curve.

Of course a logical question is why such additional complexities are worth incorporating into macro models if the New Keynesian approach, based on costly price adjustment, is empirically satisfactory. The problem is that the New Keynesian Phillips curve is not fully satisfactory. For example, it is not consistent with contractionary disinflations or with the inflation persistence observed in the postwar period. It also is not consistent with empirical estimates of the joint responses of unemployment and inflation to monetary shocks.[6]

Behaviorally based macroeconomic models help address these concerns about the New Keynesian Phillips curve, notably by modifying the process of expectations formation, the feedback between expected future inflation and current inflation, the link between labor market conditions and firms’ marginal cost, and the impact of “supply shocks” on the inflation process. They also offer new insights. For example, Mankiw and Reis (2002) assume that decision makers form expectations using “sticky” or stale information, an assumption they justify on behavioral grounds. To keep their model more tractable, they assume that all agents act as if they had rational expectations, but most use outdated information. With this amendment of the standard New Keynesian model, the Mankiw-Reis version generates a short-run Phillips curve that is downward-sloping and that is consistent with inflation persistence and costly disinflation.

Of course, the assumption of rational expectations, which Mankiw and Reis maintain, is a clear, but probably unrealistic, benchmark. Ball (2000) suggests, on near-rationality grounds, that perhaps people forecast with optimal univariate estimation rather than acting as if they knew the entire model.[7] For the postwar period, this approach makes expected inflation close to being last period’s inflation—so expectations depend heavily on recent experience. Inflation is thus persistent, but this persistence is not structural. An important implication for policy is that, if policymakers change their behavior, the empirical dynamics of inflation could change markedly.

Let me next turn to the long-run properties of the Phillips curve. Most macroeconomists accept that the long-run Phillips curve is vertical, so that steady-state unemployment is unaffected by the average level of inflation. Intriguingly, some behavioral models raise the possibility that steady-state unemployment might depend on the inflation rate.[8] For example, Akerlof, Dickens, and Perry (2000) explore the implications of a model with money illusion, a phenomenon which, according to surveys and other empirical evidence, appears to be both widespread and significant in decisionmaking. In their model, when inflation is sufficiently low, most agents don’t focus on the difference between real and nominal variables, so inflation is relatively unimportant for nominal wage bargains and for prices. As inflation rises, however, it becomes salient to a growing fraction of agents who take it fully into account. This hypothesis gives rise to a long-run Phillips curve that is bowed in at very low inflation rates, backward bending at slightly higher rates, and ultimately vertical at the “natural rate” when inflation is sufficiently high. The implication is that a very small amount of inflation may lower equilibrium unemployment. Beyond a point, however, higher inflation raises equilibrium unemployment, since inflation becomes an increasingly salient factor in decisionmaking. Akerlof et al. argue that, in the late 1990s, as inflation fell to low levels, it became less salient to wage bargains, reducing the effective natural rate of unemployment.

Closely related to the idea of money illusion is downward nominal wage rigidity which, as Fehr et al. discuss, may reflect considerations of fairness. Pervasive evidence of such nominal rigidity was identified, for example, by the International Wage Flexibility project (see Dickens et al., 2007). As Tobin (1972) originally showed, such downward nominal wage rigidity means that, at sufficiently low inflation rates, a significant fraction of firms would optimally cut nominal wages. This possibility is explored in another paper by Akerlof, Dickens, and Perry (1996). In their model, if productivity growth and steady-state inflation are low, then long-run unemployment might be relatively high. The reason is that some firms might need to cut real wages which, at very low inflation, requires nominal wage cuts. If they’re unwilling or unable to implement such cuts, then they may lay off workers instead. This reduction in labor demand leads to an increase in unemployment. Of course, if productivity growth is high, as it has been on average since the mid-1990s, then downward nominal wage rigidity becomes a less important issue.[9] Behavioral considerations thus point to the possibility of a long-run tradeoff between inflation and unemployment at very low inflation rates.

Downward nominal wage rigidity, as well as downward real wage rigidity, may also affect the linkages in the Phillips curve among unemployment, marginal cost, and inflation. In particular, norms governing the pay increases that are deemed fair may result in a short-run Phillips curve that is convex rather than linear. The nonlinearity is due to the fact that, even with high unemployment rates, firms are unwilling to treat workers in ways they consider unfair—either cutting nominal wages or raising nominal wages by less than workers think they should receive, causing inflation to “bottom out” as unemployment rises. For the United States, Clark, Laxton, and Rose (1996) find evidence of nonlinearity, although tests to discriminate among alternative functional forms of the Phillips curve suffer from extremely low power, making a reliable assessment of the degree of convexity impossible. The degree of convexity of the short-run Phillips curve is potentially important, however, because the volatility of unemployment and mean unemployment are inversely related along paths with constant expected inflation. This means that policies to stabilize unemployment produce the payoff of lowering it on average.

Another implication of behavioral economics for the Phillips curve relates to the impact of productivity growth on equilibrium unemployment when real wages exhibit some rigidity, a phenomenon found by the International Wage Project to be prevalent in many countries. Ball and Moffitt (2001), for example, have shown that shifts in productivity growth, like other supply shocks, can shift the Phillips curve and thereby change, at least for a time, the equilibrium unemployment rate, or NAIRU. Behavioral economics suggests that norms may govern the real wage increases that workers consider fair, and these norms or aspirations may be historically rooted. Shifts in productivity growth make it easier or more difficult for firms to meet these norms, altering, at least for a time, the unemployment rate that is consistent with growth in real wages that is in line with productivity. During the 1990s, faster productivity growth enabled firms to more easily meet norms for real wage growth that were depressed by the post-1973 productivity decline. In this view, the sluggish upward adjustment of norms enabled unemployment to fall to 40-year lows without igniting inflation. In essence, the short-run NAIRU was below its long-run level. By contrast, the poor experience of the 1970s reflected the collision of inherited norms for rapid real wage growth with the unpleasant reality of a sharp productivity slowdown.

Let me wrap up these remarks on the implications of behavioral research for the properties of the Phillips curve by noting that at least some of the behaviorally based insights have already crept into our internal analysis and forecasts. For example, Federal Reserve policymakers often attributed favorable inflation performance in the late 1990s to fast productivity growth and its effect on the short-run NAIRU. And policy simulations with FRB/US, the Board of Governors’ main model, sometimes assume that agents form expectations by estimating reduced-form vector autoregressions rather than using model-consistent expectations.  Moreover, issues related to communications and credibility figure prominently in FOMC discussions, because members recognize that well-anchored inflation expectations, as we have had in the United States since the mid-1980s, can reduce the sacrifice ratio and the sensitivity of inflation to supply shocks. More generally, the Federal Reserve recognizes that public understanding of its reaction function can help people form expectations in ways that are likely to enhance the stability of the economy. Given the importance that expectations formation plays in all aspects of modern macroeconomic models, I see a high payoff to further behavioral research on how people actually form expectations. Moreover, behavioral research could be very useful in helping us understand how best to communicate our views on the economy and on policy.

So far, I have discussed how behavioral research affects our understanding of what policy can do. I now want to draw on some of this discussion to address the question of what policy should do—namely, what we can learn about the appropriate objectives of monetary policy.

I’ll start with inflation. In the long run, everyone agrees that inflation primarily reflects the actions of the central bank. But what inflation rate should we strive for as a long-run objective? Existing theoretical work, grounded in neoclassical models, provides surprisingly little guidance. It points to the importance of “shoe-leather” costs, since individuals tend to economize on their use of cash as inflation rises. However, these costs are probably small at low to moderate inflation. More important, in all likelihood, is the impact on the incentive to save and invest stemming from the interaction of inflation with the tax code. But behavioral economics brings other considerations into play. Empirically, the evidence from surveys performed by Shiller (1997) and those discussed by Di Tella and McCulloch (2007) at this conference, reveal that individuals strongly dislike inflation. It appears to reduce reported happiness. Such evidence, along with evidence suggesting that individuals heavily rely on nominal frames of reference in decisionmaking, reinforce the desirability of keeping inflation quite low. After all, zero inflation, correctly measured, means that the distinction between real and nominal variables is unimportant; indeed, targeting a constant price level would make it easier for people to plan for the future. However, some considerations highlighted by behavioral research point in the opposite direction. For example, the tendency of workers to ignore inflation in wage bargaining until it becomes salient and the prevalence of downward nominal wage rigidity suggest that there may be potential benefits from choosing an inflation target that is low but positive. These arguments reinforce a case for some small inflation cushion to guard against deflationary risks due to the “zero nominal bound” on interest rates. Although empirical work suggests that downward nominal wage rigidity is prevalent in the United States, its importance diminishes when productivity growth is high, as it has been since the mid-1990s.

Let me next turn to some implications of behavioral economics for the Fed’s role in stabilizing the real economy. Along with price stability, output stabilization has been an important policy objective during the postwar period, and fluctuations in both output and unemployment have diminished. The questions for policymakers are how large are the welfare losses that result from such output volatility and how beneficial would further reductions be?

Perhaps surprisingly, standard economic theory suggests that the losses associated with output volatility of the magnitude experienced during the postwar period are quite small. Lucas (1987, 2003) spawned a large literature by arguing that the welfare gains from additional stabilization of the economy are tiny. Given standard preferences and the observed variance of consumption around a linear trend since 1947, he calculates that the representative American consumer would be willing to reduce his average consumption by a trivial amount, only ½ of 1/10th of a percent, to eliminate all remaining consumption volatility. Lucas concluded that stabilization of output, even if possible, should not be a[10] macroeconomic priority.

If Lucas’s calculation were correct, then the average person in the United States would value consumption stabilization (complete insurance) by only around $16 a year.[11] Compared with the premiums we pay for very partial insurance (e.g., for collision coverage on our cars), this seems implausibly low. The New Keynesian model offers one basis to conclude that the costs may be larger. For example, Galí, Gertler, and López-Salido (2007) argue that, because of wage and price markups, steady-state employment and output are inefficiently low. In their model, the welfare effects of booms and recessions are asymmetric because marginal increases in employment result in diminishing welfare gains. In good times, with low unemployment, the marginal gain from additional job creation may be low, because marginal employees may be close to indifferent between work and leisure. In contrast, job creation in bad times may yield a sizeable welfare surplus. As a result, recessions are particularly costly—welfare falls by more during a business-cycle downturn than it rises during a symmetric expansion. If good policy reduces the frequency and severity of recessions, then the analysis of Galí et al. suggests that the resulting welfare gains may be substantial.

Behavioral considerations suggest some additional reasons why output stabilization may raise welfare. In particular, some of the behavioral phenomena discussed previously create the tantalizing prospect that a more stable economy may benefit from higher average levels of employment, output, and consumption. As DeLong and Summers (1988, p. 434) once put it, stabilization might “…fill in troughs without shaving off peaks.”[12] Or, as in Barlevy (2004), stabilization might increase the economy’s long-run growth rate. In contrast, both the neoclassical model, analyzed by Lucas, and the New Keynesian model, analyzed by Galí et al. predict that mean consumption, output, and unemployment are unaffected by the volatility of these variables.

One behavioral reason that a more stable economy might enjoy lower average unemployment relates to the convexity of the short-run Phillips curve. If this relationship is convex, rather than linear, higher volatility in unemployment is associated with a higher mean. Recall that such convexity could reflect the influence of downward rigidity in either nominal or real wages. Interestingly, using U.S. data for the period 1971 to 1995, Debelle and Laxton (1997) estimate that the increase in mean unemployment associated with the volatility in unemployment over this period amounted to a nontrivial 0.33%.[13] Yellen and Akerlof (2004) show that a similar argument applies if the long-run Phillips curve is not vertical at low inflation rates.

For policymakers, the bottom line of such research is that behavioral economic models tend to reinforce the priority that policymakers should attach to the goal of stabilizing output. But the magnitude of the possible gains are difficult to infer from existing empirical estimates of the Phillips curve. In principle, the happiness literature might give us some more direct evidence on these benefits. As Di Tella and McCulloch emphasize in their paper, there is persuasive evidence that happiness is inversely correlated with both unemployment and inflation. The finding that lower unemployment raises satisfaction even when it is fairly low to start with is consistent with the New Keynesian assumption that equilibrium unemployment is inefficiently high. But this finding sheds little light on how policymakers should assess the welfare consequences of fluctuations. This hinges on the more subtle issue of how volatility in unemployment affects well-being for a given mean. On this point Wolfers (2003), using subjective measures of satisfaction, found evidence of nonlinearity in the relationship between life satisfaction and unemployment, implying that unemployment volatility does undermine well-being. Even so, Wolfers found that the welfare benefits of reducing volatility are subject to rapidly diminishing returns, so that further reductions in the volatility of unemployment would raise welfare by only a relatively small amount, albeit by more than Lucas’s estimate.

There’s a lot more work to be done here to validate and confirm that happiness responses correspond to well-being.[14] In addition, we care about more than just whether people are happy; we’d like to understand why. There is considerable scope for more refined survey evidence that focuses more precisely on what it is that people dislike about unemployment and inflation, and why.[15]

Let me conclude by summarizing what I think policymakers can learn from behavioral research bearing on the Phillips curve. This research provides clear-cut evidence that people often deviate from the way that benchmark neoclassical theories assume. People have money illusion, follow rules of thumb, and care about issues like fairness and equity. As I’ve discussed, there’s a growing body of literature that shows that macroeconomic theories built on behavioral foundations have strikingly different implications from more standard theories. Behavioral research thus offers the promise of unified theories that can explain microeconomic behavior as well as the movements of macroeconomic aggregates.

With respect to the Federal Reserve’s dual mandate, behavioral research supports the view that inflation is costly, although very modest inflation might help protect against downward nominal wage rigidity. Behavioral macroeconomic models also provide theoretical underpinnings for the view held by most policymakers that, in the short run, monetary policy can and should strive to stabilize the real economy.

In sum, research on behavioral economics is as exciting for policymakers as it is for academics. It helps policymakers understand what they should care about and improves the quality of our economic models. The work at this conference highlights some of the progress that has been made, but also suggests that the marginal product of further research in behavioral economics is still likely to be very high.

Endnotes

1 I am deeply indebted to staff in the Economic Research Department of the Federal Reserve Bank of San Francisco, and most particularly to John Fernald, for their help in preparing these remarks.

2 See Lacko and Pappalardo (2007).

3 See Kroszner (2007).

4 The New Keynesian intuition for such a relationship is that firms that are readjusting their prices today will want higher prices if the marginal cost of production is relatively high; but they are also concerned that they might be unable to change their price in the future. Hence, if they expect inflation to be high in the future, they will want to raise their price by more today in order to keep from being stuck with a price that is too low.

5 In a simple version of the New Keynesian Phillips curve, Mankiw (2001) shows that the slope of the curve is αλ2/1-λ, where λ is the fraction of agents that adjust their prices each period and α is the response of the desired real price to movements in the unemployment gap with a small value of α reflecting greater real rigidity. With perfectly flexible wages and prices, λ = 1 and the curve is vertical.

6 Mankiw (2001) highlights these critiques.

7 A key motivation for Ball (2000) is that inflation appears very persistent in the postwar period but not persistent under the gold standard, which was a very different monetary regime. Common features of New Keynesian models, such as backward-looking agents or price indexing, can yield more persistence but not its apparent regime-specific nature.

8 Technically, in standard Phillips curve models, this relates to whether the coefficient on expected inflation is one.

9 Recent productivity data have been, on balance, weaker than the average since the mid-1990s. But most, if not all, estimates of trend productivity growth remain above the average growth rate from 1973-1995.

10 As Lucas (2003) makes clear, even taking his estimates at face value, such a calculation does not imply that the Federal Reserve should ignore fluctuations. Very long, very deep downturns, such as the Great Depression, are costly, and policy has avoided such episodes during the postwar period, presumably averting sizeable welfare costs.

11 Reis (2007) suggests this way of framing the benefits of stabilization.

12 Yellen and Akerlof (2004) survey this literature.

13 0.33% is the estimated difference between the average historical rate of unemployment and the deterministic NAIRU, defined as the unemployment rate consistent with nonaccelerating inflation in the absence of shocks.

14 Responses do appear correlated with things like income, employment status, education, marital status, and so forth. And there is some evidence that these measures are, in turn, mirrored in suicide data (see Daly, Wilson, and Johnson, 2007), which is clearly of a very objective nature.

15 Shiller (1997) took this approach in asking people about inflation.

References

Akerlof, George A., William T. Dickens, and George L. Perry. 1996. “The Macroeconomics of Low Inflation.” Brookings Papers on Economic Activity 1996(1), pp. 1-59.

Akerlof, George A., William T. Dickens, and George L. Perry. 2000. “Near-Rational Wage and Price Setting and the Long-Run Phillips Curve.” Brookings Papers on Economic Activity 2000(1).

Ball, Laurence. 2000. “Near-Rationality and Inflation in Two Monetary Regimes.” NBER Working Paper 7988.

Ball, Laurence, and Robert Moffitt. 2001. “Productivity Growth and the Phillips Curve.” In The Roaring Nineties: Can Full Employment Be Sustained? edited byAlan B. Krueger and Robert Solow. New York: Russell Sage Foundation and Century Foundation.

Barlevy, Gadi. 2004. “The Cost of Business Cycles under Endogenous Growth.” American Economic Review 94(4), pp. 964-990.

Benjamin, Daniel J., and David I. Laibson. 2003. “Good Policies for Bad Governments: Behavioral Political Economy.” Manuscript prepared for Federal Reserve Bank of Boston Behavioral Economics Conference, June.

Clark, Peter, Douglas Laxton, and David Rose. 1996. “Asymmetry in the U.S. Output-Inflation Nexus: Issues and Evidence.” IMF Staff Papers 43(1) (March), pp. 216-250.

Daly, Mary C., Daniel J. Wilson, and Norman J. Johnson. 2007. “Relative Status and Well-Being: Evidence from U.S. Suicide Deaths.” Working Paper Series 2007-12, Federal Reserve Bank of San Francisco.

Debelle, Guy, and Douglas Laxton. 1997. “Is the Phillips Curve Really a Curve? Some Evidence for Canada, the United Kingdom, and the United States.” IMF Staff Papers 44(2) (June), pp. 249-282.

DeLong, J. Bradford, and Lawrence H. Summers. 1988. “How Does Macroeconomic Policy Affect Output?” Brookings Papers on Economic Activity 1988(2), pp. 433-480.

Dickens, William T. , Lorenz Goette, Erica L. Groshen, Steinar Holden, Julian Messina, Mark E. Schweitzer, Jarkko Turunen, and Melanie E. Ward. 2007. “How Wages Change: Micro Evidence from the International Wage Flexibility Project.” Journal of Economic Perspectives 21(2) pp. 195-214.

Di Tella, Rafael, and Robert MacCulloch. 2007. “Happiness for Central Banks.” Paper prepared for the Federal Reserve Bank of Boston Behavioral Policy Conference, September.

Fehr, Ernst, Lorenz Goette, and Christian Zehnder. 2007. “The Behavioral Economics of the Labor Market: Central Findings and Their Policy Implications.” Manuscript (September).

Galí, Jordi, Mark Gertler and J. David López-Salido. 2007. “Markups, Gaps, and the Welfare Costs of Business Fluctuations.” The Review of Economics and Statistics 89(1) (November), pp. 44-59.

Kroszner, Randall S. 2007. “Creating More Effective Consumer Disclosures.” Speech delivered May 23 at the George Washington University School of Business, Financial Services Research Program Policy Forum, Washington, D.C.

Lacko, James J., and Janis K. Pappalardo. 2007. “Improving Consumer Mortgage Disclosures.” Federal Trade Commission, Bureau of Economics Staff Report (June). http://www.ftc.gov/os/2007/06/P025505MortgageDisclosureReport.pdf

Lucas, Robert E., Jr. 1987. Models of Business Cycles. Oxford: Basil Blackwell Ltd.

Lucas, Robert E., Jr. 2003. “Macroeconomic Priorities.” American Economic Review 93(1) (March), pp. 1-14.

Mankiw, N. Gregory. 2001. “The Inexorable and Mysterious Tradeoff between Inflation and Unemployment.” The Economic Journal 111(471) (May) pp. 45-61.

Mankiw, N. Gregory, and Ricardo Reis. 2002. “Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve.” Quarterly Journal of Economics 117(4) (November), pp. 1295-1328.

Reis, Ricardo. 2007. “The Time-Series Properties of Aggregate Consumption: Implications for the Costs of Fluctuations.” Manuscript, Princeton University.
Shiller, Robert. 1997. “Why Do People Dislike Inflation?” In Reducing Inflation: Motivation and Strategy, edited by Christina D. Romer and David H. Romer. Chicago: University of Chicago Press.

Rotemberg, Julio J. 2007. “Behavioral Aspects of Price Setting, and Their Policy Implications.” Manuscript (September).

Tobin, James. 1972. “Inflation and Unemployment.” American Economic Review 62(1-2) (March), pp. 1-18.

Wolfers, Justin. 2003. “Is Business Cycle Volatility Costly? Evidence from Surveys of Subjective Well-Being.” International Finance 6(1) (March), pp. 1–26.

Yellen, Janet, and George Akerlof. 2006. “Stabilization Policy: A Reconsideration.” 2004 Presidential Address to the Western Economic Association, Economic Inquiry 44(1), pp. 1-22.

Comments

says...

In view of most people's extreme dislike of constant inflation, even low inflation, research into alternative methods of encouraging full employment would seem to be in order.

[robertdfeinman]

Yellen uses the word "model" 37 times. She uses the word "data" once.
Economics is not a theoretical subject. The extent that it is treated as one parallels the degree to which those doing the work are generating something with no real world applicability.

If economics wants to be treated as a science and not as voodoo then papers should start with data. The data should be analyzed using conventional statistical tools. Correlations should be teased out, if possible. After all this is done then there is a place for formal equations. In general these should be simple functions of a few limited variables.

At that point one can drift into some speculation as to why the correlations exist and whether this due to some characteristic of human nature or social structure.

A plan of action based upon these conclusions should then be bolstered by evidence of how such a plan worked in similar cases (or the converse).

Any other approach risks being viewed as an ex post facto justification for following ones ideological bent.

[Mark Thoma]

If you are going to try to be a critic with your broken record comments on this, at least search for the right word. Try empirical rather than data for a start.

And no, papers should not start with data. This statement is simply wrong:

"If economics wants to be treated as a science and not as voodoo then papers should start with data. The data should be analyzed using conventional statistical tools. Correlations should be teased out, if possible. After all this is done then there is a place for formal equations. In general these should be simple functions of a few limited variables."

If you think you can interpret raw correlations without theory, then you don't understand the issues well enough to set yourself up as a critic. Most of the time, I let you say whatever here, but every once in a while I think it's important to lay down a marker that makes it clear that your criticisms appear to come largely from a lack of thorough understanding of what economists do (and the relationship between theory and data) rather than from some deeper insight into how economists ought to move knowledge forward.

On this particular point, you should look at people like Sims and LeRoy a few decades ago and their debate over "atheoretical macroeconomics."

[Mark Thoma]

The point being made by Yellen and others is that currently, the New Keynesian Phillips Curve does not adequately explain the data. A long time ago, someone did what you suggested and looked at raw correlations between wages changes and changes in unemployment (Phillips). That motivated a search for explanations - theoretical models that could explain the results.

There have been various attempts to provide a theoretical basis for the statistical relationship, and thus to provide an interpretation of what it means (the relationship cannot be interpreted independent of a theoretical model) Old Keynesian models, New Classical models, New Keynesian models for example, but all of them have places where they lack agreement with actual data and the Phillips curve is one, but not the only example of places where the models are in need of improvement.

The goal of this strand of literature - incorporating behavioral economics into macro - is to try to bridge the remaining gaps between theory and evidence. We might be able to explain, say, price stickiness from behavioral models rather than from transactions costs or contracting stories that employ traditional rational behavior (to name just two theories of rigidities). We shall see. In any case, there has been progress. To say that this literature is proceeding without consulting the evidence misunderstands the motivation underlying this line of research.

[Mark Thoma]

I reacted a bit stronger that I probably should have above, so let me add one more note on all of this to explain a bit further:

A scientist in a laboratory can control the conditions - hopefully - to the extent where the "all else equal" assumption is true (or true enough anyway). Thus, looking at bivariate relationships (correlations) is both possible and informative in this setting. If X goes up, what happens to Y? (all else equal) is a question that can be asked and answered. Notice though that even in that setting, what is held constant is not independent of theory (e.g. you may not worry that the experiment is conducted at different times of the day because, theoretically, time of day has nothing to do with the outcome).

Economists do not have this luxury. We get our data as the world gives it to us and the all else equal assumption is, except in rare cases, invalid (this is a key difference between econometrics and statistics). Disentangling all of the shocks embedded in a particular piece of data and making sense of the relationships between pieces of data cannot be done independent of theory. Any correlation you look at is a relationship between a large collection of underlying structural shocks and without theory there is no way to sort one structural shock from another and make sense of what the data are saying.

It is for this reason that simply starting with correlations is inadequate, or at least potentially misleading. That might work if you've controlled the laboratory conditions so that the relationship is definitely bivariate and causal (between treatment and effect), but that approach will not work when the data are from the real world and subject to more than one influence. To sort things out, some sort of structure must be imposed on the relationships.

[robertdfeinman]

The only reason anyone is paying any attention to what Yellen has to say, is because everyone is focused on what the Fed will or should do.

There is no consensus on either. This, to me, indicates that the uses to which economic "theory" are being applied are inadequate to the task. Either the theories are poor, or are still under development, or are not sufficiently connect to "empirical" data (is there another kind?).

So here's the nub of the issue. What should the Fed do? How are they to decide? Should they even be left to make the decision or should there be some other mechanism used instead (say legislation)? Even if the Fed is clear on its course of action, is it based upon solid analysis or is it influenced by ideology? What happens if they are wrong?

We think we live in a democracy, but institutions like the Fed were explicitly set up to be immune to the will of the people. Presumably this was to prevent panic, but there has been plenty of evidence since Volcker on that the Fed hasn't been immune to the influence of the executive branch. Is this a good thing?

Perhaps you find my pedantic comments on the need to make economics more data driven tiresome, but there seems to still be a large segment of the profession which is still overly abstract. Even if you don't like what I'm saying, there are many others who are taking on the same theme. It seems that there will be a reconsideration of the appropriate balance between theory and experimentalism. Dani Rodrik posed a question about this recently and several economists commented critically on the amount of mathematics that has been incorporated into the requirements for advanced degrees. One even commented on requiring topology.

The field is changing with environmental, ecological, and behavioral subdisciples. I find this a hopeful sign.

[kio]

Dear Professor Thoma,

I would recommend you ( and Dr. Yellen) to visit some physical seminars and present your economic research as a (physics) B.S. dissertation. Guess that you would not get the sought degree because of formal violating principal rules of any physical research.
That's what R.D.F. wanted to say.
I join his opiniojn in sense that any study has to be anchored by data. In physics, it sometimes looks like that scientist base their study on pure theories. It is right to the extent that the theories do not contradict data.
There is no theory accepted by physical community which is not based on data or contradicts data.
there is no economic theory which does not contradict data, although.

[Mark Thoma]

I always forget there's not a single disagreement between theory and evidence in physics, and that the theory will explain everything. What do they do all day?

[Ivan Kitov]

In physics, the disagreement always comes as the difference between prediction and data. data always come first. In economics, the disagreement always come from theories and theories. Data come never.
That's the difference.
Also, being a geophysicist (specifically seismologist)I have to say that there is no laboratory conditions in many hard sciences. But data always come first.
What I am doing every day is trying to get better measurements or better approach to get better measurements. only increasing measurements accuracy allows to get a more reliable theory. Nothing else.

[Mark Thoma]

Isn't that what Yellen (and the Fed, etc.) is doing a lot of the time?

"Data come never"? Are you serious?

[Ivan Kitov]

I would like stress that I do not consider economics as not a hard science and my own results confirm the existence of equilibrium long-run relations between macroeconomic variables, as I mentioned many times in our blog.
These are, however, relationships in sense of R.D.F. not Yellen.
This is not bad for the relationships, although. If one looks into physics a bit deeper, s/he could find that all fundamental laws are approximate and nobody know what such findamental terms as mass, time, distance, charge really mean. These terms are given us as results of measurements and are restricted to be results of measurements.
Moreover, they are not correct to the extent that such new findings as dark matter, dark energy, extra dimesions might violate these fundamental relationships (laws).

[Ivan Kitov]

Data come never.
I would appreciate some examples when data come first and are used to select and deny economic theories.

[Ivan Kitov]

"Isn't that what Yellen (and the Fed, etc.) is doing a lot of the time?"

Formally, I would agree that the process of the Fed's (etc.) research looks like scientific. Moreover, I am sure that the intention behind this approach is absolutely benign in terms of science.
My problem is that it does not result in what I would name reliable scientific finding. The latter has thorough and deep roots in physics.

[Mark Thoma]

"I would appreciate some examples when data come first and are used to select and deny economic theories."

My last entry on this - how long a list do you want? Start with labor supply elasticities, real wage output and productivity correlations, the fact that the New Classical model could not explain both the magnitude and duration of actual business cycles, and go from there. Why do you think past models were discarded in favor of newer versions if had nothing to do with how well they fit actual data? That's the whole point of Yellen's talk, to find models that overcome disagreements with the data, but it seems to have been missed.

I'm going to have to leave it at that and move along to other things...

[Ivan Kitov]

I would like to withdraw my comments.
Just try to present any of aforementioned studies as a physical research to physical seminar in your University.
These studies will get what they studies deserve.

[Gabriel M.]

It's too late, Dr. Thoma! They're on to us! Let's retreat to the underground lair and come up with another strategy to serve our corporate, class-warfare-practicing masters! :-)

[robertdfeinman]

I'm with Mark Thoma that we have gotten off the topic, so (also) my last comment.

I agree with his point that ones needs some sort of framework if one is to even begin to know where to look for correlations. I would call this a hypothesis. After further work has increased the probability that one is on the right track then one can call it something else. Physicists seem to prefer "theory" while economists like "model". But what is one to make of this extract from Yellen?
Of course, the existence of this empirical short-run tradeoff between inflation and unemployment also helped motivate the development of the New Keynesian model in the first place. In particular, with no frictions and with fully maximizing agents, markets should always clear, and the labor market should be no exception. Thus, the short-run Phillips curve “should be” vertical.[5] This divergence between theory and reality was the original motivation for Keynesian economics.

One can't have "should be" (even in quotes) in a valid theory. We, on the left are quick to point out the difficulties with the Laffer curve, what would happen if the credibility of the Phillips curve was also to be called into question?

Anyway, I repeat my question. What's to be done when even the Fed is unsure of what actions will have what effect? Is appealing to theory the best way to go about making a decision? Doctors were sure that bloodletting was a viable treatment for hundreds of years, yet they were wrong. Overconfidence in one's beliefs can lead to poor decisions.

This is one of the fundamental contradictions of democracy. Majority rule is subject to abuse, but so is delegating decision making to insulated agencies. People may be free of political pressure (although that seems unlikely with the Fed) and still be wrong. The trouble is that if they are wrong there is no corrective mechanism in place to force a change when this become clear. I don't have a solution to these weaknesses, but I think they should be acknowledged. People tend to put to much faith in individuals, whether Petraeus or Bernanke.

Perhaps Stiglitz would like to shift from exploring incomplete information in markets to exploring incomplete information in policy making.

[Mark Thoma]

If you look into work of people like Sargeant and Cogley, you will see a whole theoretical structure applied to the problem of model uncertainty. And it's not just the Fed. You also need to ask what happens if private sector agents have similar uncertainties, and this too has received quite a bit of attention. There's quite a bit of work in this area. It's intense theoretically, but it exists.

The Fed has taken this research to heart and runs policies and probable scenarios through a variety of models. You then, essentially, pick the policy that is robust across the models so that whichever model is actually true, policy won't be a disaster. Anyway, just becasue there is model uncertainty does not mean policy has no role, there are ways to incorporate the uncertainty into the decision-making structure.

[Laurent GUERBY]

In 2004 according to OECD, normalized unemployment for men aged 25 to 54 was 4.6% in the USA and 7.4% in France. At the same time and for the same population the employment rate (number of workers divided by population) was 86.3% in the USA and 86.7% in France.

This example shows that the unemployment rate is 60% higher in France than in the USA, yet more people in this demographic are working in France than in the USA.

Economists should at least try to forget about garbage data, here "unemployment".

As we say in my field (hard science), garbage in, garbage out.

[ndd]

Prof. Thoma, as one who reads articles on behavioral economics with an "I told you so" chip on my shoulder from training in ancient experimental psychology, I have to say I appreciate the interchange on this subject between you and RDF and Ivan Kitov. Much as it might exercise you, it is enlightening to the rest of us.

I do read Yellen as making every effort to let data speak and theory follow. If we had to shoot down a killer asteroid tomorrow, and the survival of the species depended on the unification of quantum mechanics and relativity theory, I daresay all we could ask of physicists is to give it their best shot. So with the Fed, interest rates, and the phillips curve.

I also give credit to Yellin for being positive ("here's how people appear to behave") rather than normative ("here's how people should behave if they were to be rationally efficient maximizers") . Still, it would be nice to see more falsifiable predictions, and tests thereof, in economist's reports.

So, thank you very much even if sometimes the chorus is most useful by trying your patience.

[Lee A. Arnold]

Come off it, this is precisely the way that physics is done. You come up with a model, a theory, an hypothesis, and you see if it works. Empirical data is the decider, but physics begins with theories. Galileo didn't have terrestrial data to confirm continuous momentum without friction, and Newton came up with numerical results that agreed "pretty nearly" (his words) with the observations. Physics became more expert since, partly by matching math to cognition, not necessarily to reality! Eg., quantum theory results are more precise than Galileo or Newton -- by including the observing instrument, excluding one of the variables, and introducing probability. There are ways in which physics and economics differ, but the relation between empirical data and theoretical model isn't really one of them.

The two biggest differences between physics and economics are (A) that economics appears to still be in a search for some of its fundamentals, and (B) that it requires human behavior to be fairly predictable. On the fundamentals, I think this is because economics has not yet realized its full scope, and that is very exciting. As for human behavior, I'm still not sure that there is much that is constant about it, beyond short aggregated periods, (and of course for the requirements of one's physical life, such as eating and sleeping and getting to work.) For example, the idea that default enrollment into retirement savings works now, does not mean that it will work as well in the future. In other words, will we have exited the expectations loop which is described by "rational expectations?"

The relationship of economics to policymaking is intimate and constant and will always be in constant flux, while this is a relationship that physics does not have to deal with at all.

Behavioral economics as a policymaking input is likely (my prediction) to lead to a few major classes of action: (1) defaults of various sorts, which are sometimes called "social engineering" or "institutions," and which work by saving on costs; and (2) information improvements of various sorts, which run into the time constraints of individuals to digest it all, (sometimes even when the information is "salient, understandable, and timely" as Yellen writes,) and may be part of the reason why people often refer to heuristics and social norms and so on. Yellen's article is fascinating and I wish I had the time right now to study it further. My inclination is to suspect that behavioral economics takes us into an expanded realm of contracting stories and transactions costs, and that may go right into neurological mechanisms.

[ndd]

Lee A. Arnold, one area where human behavior appears pretty constant, and thereby psychology ought to be most useful in economic theory is w.r.t how people learn. I asked Prof. Thomas a few weeks ago if there is any such analog in economics, and I don't think there is. Incorporating insights about the manner and rate at which people learn (both the similarities among people and the differences) should lead to important results in economics (imho as a layperson of course).

[Mark Thoma]

Learning models:

http://economistsview.typepad.com/economistsview/2006/07/adaptive_expect.html

http://economistsview.typepad.com/economistsview/2006/05/learning_about_.html

http://economistsview.typepad.com/economistsview/2006/08/learning_about_.html

[ndd]

Prof. Thoma, thanks but your web addresses go off the right end of the page (so I can't pull them up). Much appreciated.

[Mark Thoma]

Fixed.

If you triple click on an address, it will select it even if it scrolls off the visible page. You can then copy and paste it as desired.

[ScentOfViolets]

If I may, since everyone else is going off-topic, I would like to comment on earlier comments:

The two biggest differences between physics and economics are (A) that economics appears to still be in a search for some of its fundamentals, and (B) that it requires human behavior to be fairly predictable. On the fundamentals, I think this is because economics has not yet realized its full scope, and that is very exciting.

I don't know what sort of reputation he enjoys these days in economics circles, but no less a personage than von Neumann (I really would be interested if someone like Mark could comment on his current reputation) stated that economics is to hard science what alchemy was to chemistry. His sense was that, as mentioned in the quote above, there really isn't even a clear sense of what the 'fundamentals' really are.

So, yes, as a new field, yes, this makes it very exciting. There's lot's of room for the young Turks to come in and shake the house. The flip side, of course, is that economics is hard, very hard, very very hard, very . . Not only are the measurements themselves intrinisically harder, but it may well be that the mathematical formulations to describe a valid theory have not been invented yet. So there can be some successful predictions made in some limited, constrained situations, but as a rule, these situations do no not occur all that often (think rational expectations, for example, which seems to work pretty well in a boutique farmers market)

Which goes to the other observation:

Any other approach risks being viewed as an ex post facto justification for following ones ideological bent.

Does anyone seriously believe that there is not a lot of that going on in a lot of influential policy-making circles? Consider, for example, the mantra 'correlation does not imply causation'. This is certainly true. But it is equally true that 'causation _does_ imply correlation', and so of course, the contrapositive logically follows that 'lack of correlation implies lack of causation'. Consider for example the hullabaloo around the Omnibus Budget Reconciliation Act (I think that's the right descriptor, but in case I'm wrong, I'm referring to the '93 tax increase.) Critics of the day howled that 'the largest tax increase in history' would surely send the country into a deep recession, if not outright depression should it be passed. Articles emanating from such influential organizations (well, influential to some people) as the Cato Institute and the Heritage Foundation pounded the drums relentlessly, explaing in considerable theoretical detail just why this was the case.

We all know what happened, of course. The predictions weren't true, yet the people making those predictions did not alter their economic models in the slightest. And to this day they remain highly influential - the West's version of Lysenko. Hey, everybody knows tax cuts increase revenue, right? And tax increases decrease them.

Is it any wonder that some people remain highly skeptical of the pronouncements? I am curious, btw: how do economists conduct internal policing? What is done to sideline these types of people out of the academic mainstream?

[robertdfeinman]

Mark Thoma:
If you are referring to this paper:
The Conquest of U.S. Inflation: Learning and Robustness to Model Uncertainty then I think it doesn't satisfy my objections.

The authors attempt to apply a mathematical model to decision making:
Cautious behavior induced by model uncertainty can explain why the central bank presided over the inflation of the 1970s even after the data had convinced it to place much the highest probability on the natural rate model.

"Cautious behavior" is a judgment about human behavior. A Marxist would say that what Volcker was trying to do at the time was destroy the power of labor by pushing up unemployment. (Unemployed workers can't fund union political battles). Others might claim the Fed's actions were as the result of incompetence. Still others may claim that there were political considerations at work to damage the Democrats. I'm sure there are other claims that could be made.

The authors have their own interpretation:
Our paper assembles evidence that confirms this timing puzzle. Using recursive Bayesian techniques, we find that posterior probabilities strongly favored a version of the Lucas-Sargent model as early as 1975. Nevertheless, the central bank did not implement that model's recommendations for inflation. This paper shows that a concern for robustness across a variety of models can explain why. According to our calculations, despite its high probability weight, the Lucas-Sargent model had little influence on policy because its recommendations were not robust across some other recently popular models.

In other words they feel that the Fed was behaving rationally and the only failures were do to delay in adopting new models. Today's NY Times throws some cold water on that assumption.

Can We Turn Off Our Emotions When Investing?

but most of us have some smarts, and we know, absolutely, what we are supposed to do with our money. We’re supposed to diversify, shut out all the white noise of the market, rely mainly on low-expense index funds, sell when stocks are high and buy when they are going down. We should avoid the herd instead of becoming part of the herd. That what economic man would do.

But do we do that? Hardly. When it comes to investing, most of us simply don’t act rationally.

It would be amazing if the folks at the Fed were any more immune to emotion when making decisions than the rest of us. I don't think ignorance of the future can be quantified. What can be done is to "diversify". In the case of the Fed (and perhaps congress) this means not doing anything too rash or that will affect one sector of the economy more than another. Robert Reich wrote about who will suffer explicitly this week. I'm afraid that history has shown that the poor suffer the most in any reversal and I don't see any explicit steps being taken to forestall this right now either.

[Mark Thoma]

Your interpretation of their work is quite creative. I'm sure the authors would get a good laugh out of it.

So you object to their taking their model to the data to see if it conforms? Figures.

[Lee A. Arnold]

ScentOfViolets, are you aware that von Neumann co-authored The Theory of Games and Economic Behavior?

...Really off-topic, by the way, but have a quick look at THIS:

http://www.physorg.com/news110191847.html

(via Slashdot)

[ScentOfViolets]

I have it on my shelves, so, yes, I am very aware of his authorship. A most amazing man. Thus my statement, 'no less a personage . . .' Hence my question to Mark (or any other practising economists) as to the professional assement of von Neumann fity years later. Oh, btw, I'm a math teacher, if this helps to give you an idea of where I'm coming from in regards to the theory and practice of economics.

[robertdfeinman]

Mark Thoma:
First of all I only made some conjectures, not assertions as to the Fed's possible motivations. The authors, on the other hand, said "cautious" behavior. That is an assertion.

A fact without any interpretation would be "the Fed didn't institute a new policy until such and such a time". I don't think they proved their psychological argument.

If you want to maintain that the Fed's action was later that it "should have been" or "might have been" then they may have demonstrated that, but they can't prove "caution".

On the other hand ex post facto analysis is not a strong case. It's like the investor who says "if I'd only sold yesterday". The test of a model is its predictive ability.

Using prior data only means the model hasn't been disproven so far. Karl Popper has the most succinct statement. It only takes one failure for a theory to be invalidated, an unlimited number of positive results do not constitute proof of validity.

[Mark Thoma]

You do realize that cautious behavior is an outcome of uncertainty in the model don't you? It's not an assertion, it follows from the assumptions of the model. It's a logical result. Are you sure you read the paper closely?

Feel free to continue if you want, but I'm done.

[robertdfeinman]

I think we have a semantic difference. I view caution as a state of mind.
From the dictionary:

1a. Careful forethought to avoid danger or harm.
1b. Close attention or vigilance to minimize risk:
2. Prudence or restraint in action or decision

Notice words like forethought, attention and prudence. These are human characteristics. One could do interviews to determine the state of mind of someone. One can attempt to infer it from their actions, but this is just a guess.

What one can observe is how quickly a new course of action is undertaken. Perhaps the term is being used metaphorically or anthropomorphically.

In that case I would prefer to see the conclusion stated as: "the existence of such and so factors correlates well with the rate at which policy changes were observed to be undertaken."

The problem with metaphorical use of terms is that they end up being treated literally. Thus we have the "war" on drugs/terror/crime/poverty. The metaphorical use of war has allowed our civil liberties to be eroded in the first three cases. It is exactly because the metaphorical use of words can be so powerful that these terms are chosen so carefully by propagandists.

Perhaps you think this is a minor issue in this case since, the authors results make the use of the term less important, but I don't see it that way. Too much of what passes for received knowledge these days is blurred by imprecision like this.

[Winslow R.]

I find it interesting that Yellen's introduction to behavioral economics,

"This year, we began to automatically enroll new employees into our System’s savings plan...."

is also a very good way to control inflation. Where are savings incentives mentioned in her new models?

With low employment, if worker's wages are to increase (think phillips curve), automatically shifting the increase into savings will reduce spending as would increasing consumption taxes (think laffer curve) reduce incentives to work.

Low employment would seem to shift saving desires as employers continue to consume by reducing saving levels and employees increase consumption rather than savings.

[Patricia Shannon]

Lee A. Arnold says...
Come off it, this is precisely the way that physics is done. You come up with a model, a theory, an hypothesis, and you see if it works. Empirical data is the decider, but physics begins with theories.

W/o some data/observations, how could you come up with a theory at all? Unless maybe you are mentally ill, you don't come up with theories out of thin air. (I'm not trying to imply anything about the mental health of anybody, just being my almost obsessively exact self). It would be a waste of time. Here's a theory I just created : The earth is a giant turtle, and we are parasites on it. Should I waste time testing whether this is true?

[Lee A. Arnold]

Patricia, you are right. You need a reason to start. And this applies to both physics and economics.

But you aren't creating science until you have an hypothesis to test.

And it should be pointed out that some mathematical theories weren't formulated from the data, or indeed any "realistic" ideas, although they proved successful. Several key equations in physics were found by trial and error: would this qualify as being "pulled out of thin air?" It is part of the reason that Einstein, who followed very careful epistemological reasoning to formulate his theory of relativity, objected so strongly to the expression of quantum theory.

(It should also be pointed out: You can interpret standard ecological theory as saying that we ARE parasites on the larger biosphere, which has some of the formal characteristics of a living organism. The current hypothesis is whether we are going to kill the host.)