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Financial Expectations and Household Consumption: Does Middle-Inflation Matter?

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Sarah Brown explores the finding that households often expect their financial position to remain unchanged compared to other alternatives.

Image courtesy of interviewee. April 24, 2024

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This video video discusses the use and implications of survey data, particularly in economics and social sciences, focusing on individuals' financial expectations. It highlights a common tendency for respondents to select neutral or middle options in surveys, a phenomenon referred to as "middle inflation." The authors present a study examining financial expectations by analyzing survey responses from the British Household Panel Survey from 1999 to 2008, focusing on how individuals anticipate their financial situation a year ahead.

The findings reveal that a significant portion (27%) of responses in financial expectation surveys can be attributed to middle inflation, suggesting that respondents often align with middle-ground sentiments when expressing their outlook. The study introduces a new statistical framework (generalized middle inflated ordered probit model) to better capture the role of these middle responses and to assess their impact on household consumption behaviors.

The research contrasts traditional methods, which treat financial expectations as fixed variables, by allowing these expectations to be influenced by various psychological and personal factors. This video video consideration leads to findings that existing models may overestimate the impact financial expectations have on consumption, particularly highlighting a 38% overestimate regarding expenditure on durable goods when ignoring middle inflation.

The authors emphasize the importance of accurately modeling financial expectations for better predictions of household consumption patterns, emphasizing implications for policymakers, especially in the context of economic changes and consumer behavior during crises like inflation. This video video concludes by suggesting future research directions to investigate how recent discussions on inflation might affect household expectations.

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Here are some key quotes from the video you that highlight the main themes, findings, and implications of the research:

1. **Convideo of Survey Data**:
- "So survey data is widely used in economics and right across the social sciences more generally."

2. **Neutral Responses**:
- "There is a tendency for 'em to choose a middle category, a kind of neutral category."
- "People sort of just go in the middle."

3. **Focus on Financial Expectations**:
- "We focus on financial expectations... looking ahead, how do you think you'll be financially a year from now?"

4. **Middle Inflation Concept**:
- "That's what was referred to as middle inflation, whereby we get this grouping, this concentration in the middle category."

5. **Statistical Modeling Contribution**:
- "We introduce a new statistical modeling framework whereby we can quantify how much of a survey response is likely to be related to overinflation rather than...a true belief."

6. **Implications on Consumption**:
- "If I think things are going to get better financially... I might go on the spending spree now... however, if I think things are gonna get worse financially, I may start cutting back now."

7. **Estimation of Middle Inflation Impact**:
- "We find that 27% of responses that we observe in our expectations data can be attributed to the impact of middle inflation."

8. **Overestimating Financial Expectations**:
- "Specifications...which do not explicitly control for middle inflation, tend to overestimate the effect of sentiment consumer expectations on expenditure."

9. **Research Importance**:
- "If we think about policymakers...we're trying to work out the relationship between expectations and things like household consumption."

10. **Future Research Directions**:
- "It will be interesting to see whether or not households’ financial expectations on more or less accurate going forward."

These quotes encapsulate the main findings of the research regarding the significance of middle inflation in survey responses related to financial expectations, how this affects consumption behavior, and the implications for policymakers.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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So just a little bit of context.

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So survey data is widely used in economics

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and right across the social sciences more generally.

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So we think about surveys when households

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and individuals could be asked their opinions

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and attitudes about all sorts of things,

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political preferences, immigration,

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as well as their finances.

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So you name it. Um, anything could be in the survey.

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So when individuals are faced with survey questions,

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there is a tendency for 'em to choose a middle category,

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a kind of neutral category.

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So what we tend to observe is a concentration of responses

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by individuals in the middle category.

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So if you present somebody with a statement,

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they very often say they neither agree

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or disagree with a particular statement.

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So in our paper, as you say, we met,

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we focus on financial expectations

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and specifically, individuals are asked a, a,

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a question looking ahead, how do you think you'll,

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you'll be financially a year from now?

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So it's a very general question,

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and when we look at the responses to this, people can say

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that they're, they think they're gonna be worse off about

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the same or better off.

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And what we find is about 10%

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of individuals think they're gonna be worse

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off in a year's time.

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About 61% say they think they're gonna be about the same,

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and the remainder about,

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28% think they're gonna be better off.

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So this clump in the middle whereby people say,

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I just think I'm gonna be about the same.

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That's what was referred to as middle inflation,

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whereby we get this grouping,

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this concentration in the middle category.

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And that's, something that happens with lots

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of different survey questions,

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not just ones about financial expectations,

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but quite generally, people sort of just go in the middle.

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So there's a very small literature

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that looks at this characteristic of survey data.

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There are loads and loads of papers

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that use survey data in economics and social sciences,

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but there's very few papers actually think about the

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modeling implications of it.

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So what we actually do in our paper is we introduce a new

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statistical modeling framework whereby we can quantify

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how much of a survey response is likely to be related

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to overinflation rather than, for example, a true belief

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that I really think my income will be unchanged.

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So the other literature which we relate to is,

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that literature on individual and household consumption.

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So the contribution is kind of twofold.

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The first thing is to actually think about

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how should we model this type of survey data

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where we get this kind of clump in the middle response.

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The second thing that we do is

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that we look at the implications of how we model survey data

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for a particular outcome

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Such as household consumption.

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So if we think about expectations in household consumption,

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you know, the, the ex the, the, the relationship is,

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is quite kind of quite intuitive.

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If I think things are going to get better financially

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for me in a year's time,

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I might go on the spending spree Now,

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however, if I think things are gonna get worse

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for me financially, I may start cutting back now.

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So what we do in our paper is in contrast

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to the existing literature, which looks at the relationship

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between expectations

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and how household financial behavior IE consumption,

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what we do is we deviate from the standard approach,

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which is that financial expectations are treated as

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what we call an exogenous or a fixed variable.

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So we actually model expectations.

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We allow expectations

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to be endogenous within our framework rather than being

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exogenous fixed when we look at their effect on consumption.

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So there's two types of, of, of contribution.

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The first is, so statistical modeling contribution.

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How should we model financial expectations?

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And the second con contribution is if we model financial

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expectations appropriately allowing for this, concept,

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this phenomenon of mid middle inflation,

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what implications does that have for the relationship

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between expectations and household consumption?

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So let me say something about the data first,

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and then I'll, I'll try

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to explain the modeling framework

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is a little bit complicated.

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Um, but, but hopefully I'll be able to,

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explain the intuition.

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So we use data from the British household Panel survey,

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which is a longitudinal survey which covers the period 1999

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to 2008.

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So we conduct our analysis on what's called a balanced

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sample of individuals.

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And by balanced sample, I mean individuals are in the,

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in the sample for the full 18 year period.

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So we look at, 1,417 individuals

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over the 18 year period.

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And the, the fact that the ba the, the panel is balanced,

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we look at everybody over 18, years is very important

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because if we think about expectations

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data, it could be the case.

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The longer you are in the panel,

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the more you learn about your expectations,

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you get better at making predictions.

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So we want to make sure

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that everybody in our analysis is there for the,

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the same 18 years.

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So we take this longitudinal data

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and rather than model expectations data

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with just taking individuals

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and putting them in one of three groups, so optimistic,

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which means that I think things are gonna get better

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in the middle, which means that they're gonna stay the same,

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same or pessimistic, which means

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that it things are gonna get worse for me financially.

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What our modeling framework does,

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the generalized middle inflated ordered probate model,

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it introduces what we call splitting equations.

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And the splitting equations provide a potential mechanism

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whereby individuals can be steered away from saying

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that they're optimistic

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or pessimistic, towards this middle category.

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Whereby I think, expect my,

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I think my financial expectations are such

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that I think my income is gonna stay the same next year.

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So when we model expectations,

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we do things which are quite standard in the literature.

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We have a wide range of individual

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and household characteristics, age, gender, education,

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number of kids, marital status, home ownership,

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labor market status, household income, savings, and wealth.

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So when we do our standard model of expectations, we want

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to keep as close to the existing literature as possible.

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So there's nothing in there, I think,

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which is particularly different to

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what other researchers have used in the

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past, including myself.

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We then introduce these splitting equations,

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and in these equations, we, we use a wide range of,

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explanatory variables, things related

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to psychological factors, cognitive, factors,

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things which might contribute to generating an excess

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of observations within a particular category.

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So we still have variables such as age, gender,

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and education in there,

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but we also have information such as individual's,

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personality traits, the big five, agreeableness,

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extroversion, neuroticism, op, openness to experience

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and conscientiousness.

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We also, control for things such as the number

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of times within this 18 year period that individuals,

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are correct in their financial expectations.

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Remember, it's longitudinal data.

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So I can look at the first year of my panel,

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I can see whether the individuals think they're going

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to be better off financially,

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then I can use my longitudinal data

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to find out whether they're correct,

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they were correct in their prediction.

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The other things that we use in our spitting equation,

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which are relevant for today, is we use information related

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to interview conditions.

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So when the individual was making these expectations,

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were there problems related to language reading,

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interpretation, how long were the interviews?

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Because in a sense

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that there's a literature out there which states sat,

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the longer our interviews,

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the the more trust an individual has in the person

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that's interviewing them,

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and the more likely they are to give accurate responses.

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So the key part, part about our model is

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that we allow individuals to be steered away

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from their initial expectations.

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And the word allow is very important.

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We don't enforce it if the data doesn't support it.

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We just allow this mechanism whereby individuals may be

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steered away from a particular category.

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And once we have modeled our expectations appropriately

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allowing for this middle inflation,

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we can then look at the implications of this

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for the relationship between expectations and consumption.

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So what we find is that when we,

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set up our generalized ordered probate model

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and we do our analysis, we find

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that 27% of responses

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that we observe in our expectations data can be attributed

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to the impact of middle inflation.

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So just to repeat, 27% of responses can be attributed

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to the impact of middle inflation.

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So that's a high percentage in terms

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of the data that we are looking at.

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We find that the probability associated

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with being steered away from the pessimistic category,

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the worse off category, is greater than the probability

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of our splitting equations,

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steering people away from the better off the

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optimistic category.

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So given that middle inflation seems

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to be important in shaping individuals' financial

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expectations, the second stage of our analysis is

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to think about the implications of this when we're trying

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to look at the, patterns

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or model, the patterns of UK household consumption behavior.

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So we know that

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however we model financial expectations,

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there is a statistical relationship

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between financial expectations

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and consumption regardless of the modeling approach.

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But our results reveal that specifications models,

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statistical models, which do not explicitly control

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for middle inflation, tend to overestimate the effect

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of sentiment consumer expectations on expenditure.

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And this could be expenditure as measured by the probability

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of, for example, buying durable goods

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and the amount of the, the amount of, expenditure

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that households incur on, for example, a durable good.

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So our findings suggests that if you ignore the role

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of middle inflation, we get a situation whereby the impact

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of financial expectations on household consumption

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are overstated.

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So if we just take one example, there's, there's,

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there's lots of different types of expenditure,

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which are looked at in the paper,

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but if we just look at the amount

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of expenditure on durable goods,

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the overestimate from a standard modeling approach compared

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to our new statistical framework is approx is approximately

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38%.

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So it is a large effect.

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Um, when I talk about the fact that the relationship

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between, expectations

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and consumption is, is overstated, it's overstated

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by about 38%.

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So why is this important?

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Well, if we think about policymakers,

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we can think about the, the recent budget,

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when we are thinking about the effect of, tax changes,

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tax cuts and so on.

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In order to model the effects of such policy changes,

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what we're interested in is thinking about how individuals

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and households respond.

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So for example, if the budget takes place,

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and I think I'm gonna be better off in a year's time, then

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as I said before, I may go on a spending spree.

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If on the other hand, I think that's not gonna happen,

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I may cut down in terms of my expenditure.

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So we're trying to work out the relationship between,

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expectations and things like household consumption.

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What we're trying to do is to work out the,

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the true relationship between these two things.

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And our paper shows.

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Our research shows that you need

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to appropriately model financial expectations if you want

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to get an accurate prediction of the extent

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to which expectations may affect things like consumption,

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and things like changes in monetary policy,

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fiscal policy, all of these sorts of things,

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are introduced in order to affect, for example, households,

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behaviors, their financial, decisions and so on.

13:29.125 --> 13:31.625
And if we think about the current living standards crisis,

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I think, you know, going forward in terms

13:33.865 --> 13:37.185
of future research, one thing that's very interesting to me

13:37.185 --> 13:39.945
as an economist is over the last, year

13:39.945 --> 13:41.265
or so, there's been a lot

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of discussion about inflation in the news.

13:44.605 --> 13:46.425
So in a sense, households,

13:46.425 --> 13:49.785
because terms like inflation, are, are

13:50.645 --> 13:51.945
now common terms, which,

13:51.945 --> 13:53.585
which households are faced in the news

13:53.845 --> 13:57.185
and media on a regular basis going, going forward.

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I think it will be interesting to see whether

13:58.965 --> 14:02.985
or not households, financial expectations on more

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or less accurate going forward.

14:05.845 --> 14:07.625
Um, but that's just something to kind

14:07.625 --> 14:10.425
of highlight possible areas for future research.

Understanding Middle Responses in Survey Data

00:00:00 - So just a little bit of context. So survey data is widely used in economics and right across the social sciences more generally. So we think about surveys when households and individuals could be asked their opinions and attitudes about all sorts of things, political preferences, immigration, as well as their finances. So you name it. anything could be in the survey. So when individuals are faced with survey questions, there is a tendency for 'em to choose a middle category,
00:00:32 - So what we tend to observe is a concentration of responses by individuals in the middle category. So if you present somebody with a statement, they very often say they neither agree or disagree with a particular statement. So in our paper, as you say, we met, we focus on financial expectations and specifically, individuals are asked a, a, a question looking ahead, how do you think you'll,
00:01:04 - So it's a very general question, and when we look at the responses to this, people can say that they're, they think they're gonna be worse off about the same or better off. And what we find is about 10% of individuals think they're gonna be worse off in a year's time. About 61% say they think they're gonna be about the same, and the remainder about, 28% think they're gonna be better off. So this clump in the middle whereby people say,
00:01:35 - That's what was referred to as middle inflation, whereby we get this grouping, this concentration in the middle category. And that's, something that happens with lots of different survey questions, not just ones about financial expectations, but quite generally, people sort of just go in the middle. So there's a very small literature that looks at this characteristic of survey data. There are loads and loads of papers that use survey data in economics and social sciences,
00:02:06 - modeling implications of it. So what we actually do in our paper is we introduce a new statistical modeling framework whereby we can quantify how much of a survey response is likely to be related to overinflation rather than, for example, a true belief that I really think my income will be unchanged. So the other literature which we relate to is,
00:02:38 - So the contribution is kind of twofold. The first thing is to actually think about how should we model this type of survey data where we get this kind of clump in the middle response. The second thing that we do is that we look at the implications of how we model survey data for a particular outcome Such as household consumption. So if we think about expectations in household consumption,

Modeling Financial Expectations and Consumer Behavior

00:03:08 - is quite kind of quite intuitive. If I think things are going to get better financially for me in a year's time, I might go on the spending spree Now, however, if I think things are gonna get worse for me financially, I may start cutting back now. So what we do in our paper is in contrast to the existing literature, which looks at the relationship between expectations and how household financial behavior IE consumption,
00:03:40 - which is that financial expectations are treated as what we call an exogenous or a fixed variable. So we actually model expectations. We allow expectations to be endogenous within our framework rather than being exogenous fixed when we look at their effect on consumption. So there's two types of, of, of contribution. The first is, so statistical modeling contribution. How should we model financial expectations?
00:04:11 - expectations appropriately allowing for this, concept, this phenomenon of mid middle inflation, what implications does that have for the relationship between expectations and household consumption? So let me say something about the data first, and then I'll, I'll try to explain the modeling framework is a little bit complicated. but, but hopefully I'll be able to, explain the intuition.
00:04:41 - which is a longitudinal survey which covers the period 1999 to 2008. So we conduct our analysis on what's called a balanced sample of individuals. And by balanced sample, I mean individuals are in the, in the sample for the full 18 year period. So we look at, 1,417 individuals
00:05:12 - And the, the fact that the ba the, the panel is balanced, we look at everybody over 18, years is very important because if we think about expectations data, it could be the case. The longer you are in the panel, the more you learn about your expectations, you get better at making predictions. So we want to make sure that everybody in our analysis is there for the, the same 18 years. So we take this longitudinal data
00:05:44 - with just taking individuals and putting them in one of three groups, so optimistic, which means that I think things are gonna get better in the middle, which means that they're gonna stay the same, same or pessimistic, which means that it things are gonna get worse for me financially. What our modeling framework does, the generalized middle inflated ordered probate model, it introduces what we call splitting equations.

Modeling Financial Expectations and Middle Inflation

00:06:16 - whereby individuals can be steered away from saying that they're optimistic or pessimistic, towards this middle category. Whereby I think, expect my, I think my financial expectations are such that I think my income is gonna stay the same next year. So when we model expectations, we do things which are quite standard in the literature. We have a wide range of individual and household characteristics, age, gender, education,
00:06:48 - labor market status, household income, savings, and wealth. So when we do our standard model of expectations, we want to keep as close to the existing literature as possible. So there's nothing in there, I think, which is particularly different to what other researchers have used in the past, including myself. We then introduce these splitting equations, and in these equations, we, we use a wide range of,
00:07:20 - to psychological factors, cognitive, factors, things which might contribute to generating an excess of observations within a particular category. So we still have variables such as age, gender, and education in there, but we also have information such as individual's, personality traits, the big five, agreeableness, extroversion, neuroticism, op, openness to experience
00:07:50 - We also, control for things such as the number of times within this 18 year period that individuals, are correct in their financial expectations. Remember, it's longitudinal data. So I can look at the first year of my panel, I can see whether the individuals think they're going to be better off financially, then I can use my longitudinal data to find out whether they're correct, they were correct in their prediction.
00:08:22 - which are relevant for today, is we use information related to interview conditions. So when the individual was making these expectations, were there problems related to language reading, interpretation, how long were the interviews? Because in a sense that there's a literature out there which states sat, the longer our interviews, the the more trust an individual has in the person that's interviewing them, and the more likely they are to give accurate responses.
00:08:55 - that we allow individuals to be steered away from their initial expectations. And the word allow is very important. We don't enforce it if the data doesn't support it. We just allow this mechanism whereby individuals may be steered away from a particular category. And once we have modeled our expectations appropriately allowing for this middle inflation, we can then look at the implications of this

Impact of Middle Inflation on Financial Expectations

00:09:29 - So what we find is that when we, set up our generalized ordered probate model and we do our analysis, we find that 27% of responses that we observe in our expectations data can be attributed to the impact of middle inflation. So just to repeat, 27% of responses can be attributed to the impact of middle inflation.
00:09:58 - of the data that we are looking at. We find that the probability associated with being steered away from the pessimistic category, the worse off category, is greater than the probability of our splitting equations, steering people away from the better off the optimistic category. So given that middle inflation seems to be important in shaping individuals' financial expectations, the second stage of our analysis is
00:10:29 - to look at the, patterns or model, the patterns of UK household consumption behavior. So we know that however we model financial expectations, there is a statistical relationship between financial expectations and consumption regardless of the modeling approach. But our results reveal that specifications models, statistical models, which do not explicitly control
00:11:00 - of sentiment consumer expectations on expenditure. And this could be expenditure as measured by the probability of, for example, buying durable goods and the amount of the, the amount of, expenditure that households incur on, for example, a durable good. So our findings suggests that if you ignore the role of middle inflation, we get a situation whereby the impact
00:11:32 - are overstated. So if we just take one example, there's, there's, there's lots of different types of expenditure, which are looked at in the paper, but if we just look at the amount of expenditure on durable goods, the overestimate from a standard modeling approach compared to our new statistical framework is approx is approximately 38%. So it is a large effect. when I talk about the fact that the relationship
00:12:02 - and consumption is, is overstated, it's overstated by about 38%. So why is this important? Well, if we think about policymakers, we can think about the, the recent budget, when we are thinking about the effect of, tax changes, tax cuts and so on. In order to model the effects of such policy changes, what we're interested in is thinking about how individuals and households respond. So for example, if the budget takes place,

Impact of Expectations on Household Consumption

00:12:36 - as I said before, I may go on a spending spree. If on the other hand, I think that's not gonna happen, I may cut down in terms of my expenditure. So we're trying to work out the relationship between, expectations and things like household consumption. What we're trying to do is to work out the, the true relationship between these two things. And our paper shows. Our research shows that you need
00:13:07 - to get an accurate prediction of the extent to which expectations may affect things like consumption, and things like changes in monetary policy, fiscal policy, all of these sorts of things, are introduced in order to affect, for example, households, behaviors, their financial, decisions and so on. And if we think about the current living standards crisis, I think, you know, going forward in terms
00:13:37 - as an economist is over the last, year or so, there's been a lot of discussion about inflation in the news. So in a sense, households, because terms like inflation, are, are now common terms, which, which households are faced in the news and media on a regular basis going, going forward. I think it will be interesting to see whether or not households, financial expectations on more or less accurate going forward.
00:14:07 - of highlight possible areas for future research.

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