Consumer Forecast Revisions: Is Information Really so Sticky?

My paper "Consumer Forecast Revisions: Is Information Really so Sticky?" was just accepted for publication in Economics Letters. This is a short paper that I believe makes an important point. 

Sticky information models are one way of modeling imperfect information. In these models, only a fraction (λ) of agents update their information sets each period. If λ is low, information is quite sticky, and that can have important implications for macroeconomic dynamics. There have been several empirical approaches to estimating λ. With micro-level survey data, a non-parametric and time-varying estimate of λ can be obtained by calculating the fraction of respondents who revise their forecasts (say, for inflation) at each survey date. Estimates from the Michigan Survey of Consumers (MSC) imply that consumers update their information about inflation approximately once every 8 months.

Here are two issues that I point out with these estimates:

I show that several issues with estimates of information stickiness based on consumer survey microdata lead to substantial underestimation of the frequency with which consumers update their expectations. The first issue stems from data frequency. The rotating panel of Michigan Survey of Consumer (MSC) respondents take the survey twice with a six-month gap. A consumer may have the same forecast at months t and t+ 6 but different forecasts in between. The second issue is that responses are reported to the nearest integer. A consumer may update her information, but if the update results in a sufficiently small revisions, it will appear that she has not updated her information. 

To quantify how these issues matter, I use data from the New York Fed Survey of Consumer Expectations, which is available monthly and not rounded to the nearest integer. I compute updating frequency with this data. It is very high-- at least 5 revisions in 8 months, as opposed to the 1 revision per 8 months found in previous literature.

Then I transform the data so that it is like the MSC data. First I round the responses to the nearest integer. This makes the updating frequency estimates decrease a little. Then I look at it at the six-month frequency instead of monthly. This makes the updating frequency estimates decrease a lot, and I find similar estimates to the previous literature-- updates about every 8 months.

So low-frequency data, and, to a lesser extent, rounded responses, result in large underestimates of revision frequency (or equivalently, overestimates of information stickiness). And if information is not really so sticky, then sticky information models may not be as good at explaining aggregate dynamics. Other classes of imperfect information models, or sticky information models combined with other classes of models, might be better.

Read the ungated version here. I will post a link to the official version when it is published.

Happiness as a Macroeconomic Policy Objective

Economists have mixed opinions about the degree to which subjective wellbeing and happiness should guide policymaking. Wouter den Haan, Martin Ellison, Ethan Ilzetzki, Michael McMahon, and Ricardo Reis summarize a recent survey of European economists by the Centre for Macroeconomics and CEPR. They note that the survey "finds a reasonable amount of openness to wellbeing measures among European macroeconomists. On balance, though, there remains a strong sense that while these measures merit further research, we are a long way off reaching a point where they are widely accepted and sufficiently reliable for macroeconomic analysis and policymaking."

As the authors note, the idea that happiness should be a primary focus of economic policy is central to Jeremy Bentham's "maximum happiness principle." Bentham is considered the founder of utilitarianism. Though the incorporation of survey-based quantitative measures of subjective wellbeing and life satisfaction is a relatively recent development in economics, utilitarianism, of course, is not. Notably, John Stuart Mill and many classical economists including William Stanley Jevons, Alfred Marshall, and Francis Edgeworth were deeply influenced by Bentham.

These classical economists might have been perplexed to see the results of Question 2 of the recent survey of economists, which asked whether quantitative wellbeing analysis should play an important role in guiding policymakers in determining macroeconomic policies. The responses, shown below, reveal slightly more negative than positive responses to the question. And yet, what macroeconomists and macroeconomic policymakers do today descends directly from the strategies for "quantitative wellbeing analysis" developed by classical economists.

Source: http://voxeu.org/article/views-happiness-and-wellbeing-objectives-macroeconomic-policy

In The Theory of Political Economy (1871), for example, Jevons wrote:

"A unit of pleasure or pain is difficult even to conceive; but it is the amount of these feelings which is continually prompting us to buying and selling, borrowing and lending, labouring and resting, producing and consuming; and it is from the quantitative effects of the feelings that we must estimate their comparative amounts."

Hence generations of economists have been trained in welfare economics based on utility theory, in which utility is an increasing function of consumption, u(c). Under neoclassical assumptions-- cardinal utility, stable preferences, diminishing marginal utility, and interpersonally comparable utility functions-- trying to maximize a social welfare function that is just the sum of all individual utility functions is totally Benthamite. And a focus on GDP growth is very natural, as more income should mean more consumption.

The focus on happiness survey data I think stems from recognition of some of the problems with the assumptions that allow us to link GDP to consumption to utility, for example, stable and exogenous preferences and interpersonally comparable utility functions (that depend exclusively on one's own consumption). One approach is to relax these assumptions (and introduce others) by, for example, using more complicated utility functions with additional arguments and/or changing preferences. So we see models with habit formation and "keeping up with the Joneses" effects.

Another approach is to ask people directly about their happiness. This, of course, introduces its own issues of methodology and interpretation, as many of the economist panelists point out. Michael Wickens, for example, notes that the “original happiness literature was in reality a measure of unhappiness: envy over income differentials, illness, divorce, being unmarried etc” and that “none of these is a natural macro policy objective.”

I think that responses to subjective happiness questions also include some backward-looking and some forward-looking components; happiness depends on what has happened to you and what you expect to happen in the future. This makes it hard for me to imagine how to design macroeconomic policy to formally target these indicators, and makes me tend to agree with Reis' opinion that they should be used as “complements to GDP though, not substitutes.”

Post-Election Political Divergence in Economic Expectations

"Note that among Democrats, year-ahead income expectations fell and year-ahead inflation expectations rose, and among Republicans, income expectations rose and inflation expectations fell. Perhaps the most drastic shifts were in unemployment expectations:rising unemployment was anticipated by 46% of Democrats in December, up from just 17% in June, but for Republicans, rising unemployment was anticipated by just 3% in December, down from 41% in June. The initial response of both Republicans and Democrats to Trump’s election is as clear as it is unsustainable: one side anticipates an economic downturn, and the other expects very robust economic growth."

This is from Richard Curtin, Director of the Michigan Survey of Consumers. He is comparing the economic sentiments and expectations of Democrats, Independents, and Republicans who took the survey in June and December 2016. A subset of survey respondents take the survey twice, with a six-month gap. So these are the respondents who took the survey before and after the election. The results are summarized in the table below, and really are striking, especially with regards to unemployment. Inflation expectations also rose for Democrats and fell for Republicans (and the way I interpret the survey data is that most consumers see inflation as a bad thing, so lower inflation expectations means greater optimism.)

Notice, too, that self-declared Independents are more optimistic after the election than before. More of them are expecting lower unemployment and fewer are expecting higher unemployment. Inflation expectations also fell from 3% to 2.3%, and income expectations rose. Of course, this is likely based on a very small sample size.

Source: Richard Curtin, Michigan Survey of Consumers

Whose Expectations Augment the Phillips Curve?

My first economics journal publication is now available online in Economics Letters. This link provides free access until November 9, 2015. It is a brief piece (hence the letter format) titled "Whose Expectations Augment the Phillips Curve?" The short answer:

"The inflation expectations of high-income, college-educated, male, and working-age people play a larger role in inflation dynamics than do the expectations of other groups of consumers or of professional forecasters."

Update: The permanent link to the paper is here.

Surveys in Crisis

In "Household Surveys in Crisis," Bruce D. Meyer, Wallace K.C. Mok, and James X. Sullivan describe household surveys as "one of the main innovations in social science research of the last century." Large, nationally representative household surveys are the source of official rates of unemployment, poverty, and health insurance coverage, and are used to allocate government funds. But the quality of survey data is declining on at least three counts.

The first and most commonly studied problem is the rise in unit nonresponse, meaning fewer people are willing to take a survey when asked. Two other growing problems are item nonresponse-- when someone agrees to take the survey but refuses to answer particular questions-- and inaccurate responses. Of course, the three problems can be related. For example, attempts to reduce unit nonresponse by persuading reluctant households to take a survey could raise item nonresponse and inaccurate responses if these reluctant participants rush through a survey they didn't really want to take in the first place.

Unit nonresponse, item nonresponse, and inaccurate responses would not be too troublesome if they were random enough that survey statistics were unbiased, but that is unlikely to be the case. Nonresponse and misreporting may be systematically correlated with relevant characteristics such as income or receipt of government funds. Meyer, Mok, and Sullivan look at survey data about government transfer programs for which corresponding administrative data is also available, so they can compare survey results to presumably more accurate administrative data. In this case, the survey data understates incomes at the bottom of the distribution, understates the rate of program receipt and the poverty reducing effects of government programs, and overstates measures of poverty and of inequality. For other surveys that cannot be linked to administrative data, it is difficult to say which direction biases will go.

Why has survey quality declined? The authors discuss many of the traditional explanations:

"Among the traditional reasons proposed include increasing urbanization, a decline in public spirit, increasing time pressure, rising crime (this pattern reversed long ago), increasing concerns about privacy and confidentiality, and declining cooperation due to 'over-surveyed' households (Groves and Couper 1998; Presser and McCullogh 2011; Brick and Williams 2013). The continuing increase in survey nonresponse as urbanization has slowed and crime has fallen make these less likely explanations for present trends. Tests of the remaining hypotheses are weak, based largely on national time-series analyses with a handful of observations. Several of the hypotheses require measuring societal conditions that can be difficult to capture: the degree of public spirit, concern about confidentiality, and time pressure...We are unaware of strong evidence to support or refute a steady decline in public spirit or a rise in confidentiality concerns as a cause for declines in survey quality."

They find it most likely that the sharp rise in the number of government surveys administered in the US since 1984 has resulted in declining cooperation by "over-surveyed" households. "We suspect that talking with an interviewer, which once was a rare chance to tell someone about your life, now is crowded out by an annoying press of telemarketers and commercial surveyors."

Personally, I have not received any requests to participate in government surveys and rarely receive commercial survey requests. Is this just because I moved around so much as a student? Am I about to be flooded with requests? I think I would actually find it fun to take some surveys after working with the data so much. Please leave a comment about your experience with taking (or declining to take) surveys.

The authors also note that since there is a trend toward greater leisure time, it is unlikely that increased time pressure is resulting in declining survey quality. However, while people have more leisure time, they may also have more things to do with their leisure time (I'm looking at you, Internet) that they prefer to taking surveys. Intuitively I would guess that as people have grown more accustomed to doing everything online, they are less comfortable talking to an interviewer in person or on the phone. Since I almost never have occasion to go to the post office, I can imagine forgetting to mail in a paper survey. Switching surveys to online format could result in a new set of biases, but may eventually be the way to go.

I would also guess that the Internet has changed people's relationship with information, even information about themselves. When you can look up anything easily, that can change what you decide to remember and what facts you feel comfortable reporting off the top of your head to an interviewer.