RDP 2021-08: Job Loss, Subjective Expectations and Household Spending 2. Measuring Subjective Employment Expectations and Household Spending

2.1 Subjective employment expectations

The analysis in this paper relies on household-level data sourced from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Survey is a longitudinal study that tracks a representative sample of more than 17,000 individuals from 2001 to 2019. The panel nature of the HILDA Survey allows us to exploit heterogeneity in household spending patterns and labour market outcomes while controlling for individual time-invariant characteristics.

The HILDA Survey asks employed respondents about their subjective employment expectations. Specifically, respondents are asked the following question on the subjective probability of job loss:

What do you think is the per cent chance that you will lose your job during the next 12 months? (That is, get retrenched or fired or not have your contract renewed.)

Responses are given as integer values from 0 to 100 where 0 indicates no chance, and 100 absolute certainty. We use this probabilistic response as our measure of individuals' subjective probability of job loss.

Looking at the distribution of job loss probabilities, there is clear ‘bunching’ in responses at 0, 50 and 100 per cent (Figure 2). This distribution is commonly observed in studies of subjective job loss expectations (Manski and Straub 2000; Stephens 2004). The bunching is at least partly due to respondents rounding their answers (Kleinjans and van Stoest 2010).

Similar to the variable on job loss expectations, individuals who are not employed but are looking for work are asked the following question on the probability of finding suitable employment:

What do you think is the per cent chance that you will find a suitable job during the next 12 months?

Again responses range from 0 to 100 where 0 indicates no chance, and 100 absolute certainty. We take this variable as our measure of unemployed individuals' subjective probabilities of finding a job.

The distribution of the unemployment duration variable also displays evidence of bunching, particularly at 50 per cent (Figure 3). The implied distribution suggests that most unemployed persons expect to find work within a year. But there is considerable dispersion among individuals' expectations of the duration of unemployment.

We are able to examine the realisation of job loss events by using the response to the question:

Did any of these (events) happen to you in the past 12 months … fired or made redundant by an employer?

Given households are surveyed every 12 months, the response to this question corresponds perfectly with the wording in the expectations of job loss question. This allows us to closely explore the links between expected and actual job outcomes.

2.2 Household spending

The HILDA Survey collects detailed information on household spending each year. This includes spending on non-durable goods and services (for the years 2006 to 2019) and durable goods (for a limited sample window between 2006 and 2010).

To measure different types of spending, survey respondents are asked to complete a questionnaire. Respondents are first asked if they have any responsibility for the payment of household bills (such as household groceries, electricity, gas and water). If so, respondents are then directed to the following question:

For each type of expenditure below, write in your best estimate of the total amount spent on that item by all people in the household.

Respondents fill out expenditure amounts for a list of items, and are asked to report how much they spent on each item in the last 12 months. Our measure of annual expenditure comes from aggregating these items, as shown in Table 1. For weekly expenditure, respondents are asked to report how much they spend on each item per week on average. Weekly expenditure is recorded for a limited number of items, as shown in Table 1.

We mainly focus on food spending (including groceries and meals eaten out) for a few reasons.^[2] First, food expenditure is one of the few survey items that is recorded on a weekly basis. Using weekly expenditure should give more insight into the contemporaneous effects of job loss on spending. For example, a small decline in annual spending in the year of job loss would not tell us if the household reduced spending a little but spread it out over the full year, or if they reduced spending a lot for a short period of time. These 2 possibilities have different implications for the welfare effects of unemployment. Second, food expenditure has been shown to suffer from less measurement error than other spending categories in survey data.^[3] Third, food expenditure, particularly groceries, is also commonly used in existing research, making it easier to compare our results to the literature. Food spending can also be disaggregated into grocery expenditure and expenditure on meals eaten outside of the home. This split can offer some insight into how the response of more essential expenditure (groceries) compares to the response of discretionary spending (meals outside of the home).

The HILDA Survey also contains information on household income and wealth. These variables are partly used as controls in the model estimation but are also used to explore the heterogeneous effects of job loss on household spending. Household income data is available for every sample year, but wealth data are available only every 4 years from 2002 and 2018.

Footnotes

Technically, the ‘food’ spending measure includes food, meals eaten out and other grocery spending (which includes cleaning and personal care products). [2]

Previous research has found that there can be large discrepancies amongst survey respondents within the same household in terms of reporting total spending in the HILDA Survey (Wilkins and Sun 2010). Grocery expenditure has been found to be the most consistent estimate within households. [3]