June 21, 2018

High-frequency Spending Responses to the Earned Income Tax Credit

Aditya Aladangady, Shifrah Aron-Dine, David Cashin, Wendy Dunn, Laura Feiveson, Paul Lengermann, Katherine Richard, and Claudia Sahm

Many households face large, high-frequency changes in income and have limited financial buffers to smooth their consumption through this income volatility (Murdoch and Schneider, 2017; Board of Governors, 2018). However, few studies have quantified spending responses to such timing shifts in income due to a lack of high-frequency spending data. We use a new dataset of anonymized daily, state-level spending to study a two-week delay in federal tax refunds with an earned income tax credit (EITC) in 2017. Using time-series and cross-state variation in refund receipt, we estimate that, on average, EITC recipients spend about 15 cents out of each dollar of their total refunds at retail stores and restaurants within two weeks of receipt. Thus the two-week delay in 2017 of over $40 billion in refunds--while short lived--led to a noticeable change in the timing of spending in February. Moreover, while previous studies, such as Barrow and McGranahan (2000) and Goodman-Bacon and McGranahan (2008), emphasize the link between the EITC and durable goods purchases, we find that EITC receipt also affects spending on nondurable necessities, such as groceries. Altogether, these findings suggest many households have limited access to liquidity, such that even a short-lived delay in income leads to notable changes in spending.

Background on Tax Refunds to EITC Claimants

The EITC is a refundable tax credit claimed by a large share of low- to moderate-income households. In 2017 (tax year 2016), 27 million households claimed the EITC--18 percent of all tax returns processed.1 Moreover, those claiming the EITC tend to be among the earliest tax filers each year, and federal income tax refunds often represent a substantial portion of their annual incomes. Maag et al. (2016) find that of all EITC claimants, 56 percent filed prior to February 15 in 2015 and 2016, receiving an average refund of $4,479--an amount equal to roughly two months of pay for a typical EITC claimant.

At the time a tax return is filed, tax filers learn the expected amount of their refund, but they do not receive the refund until after the tax return has been processed by the IRS. Prior to 2017, the length of time between the filing date and the date the IRS issued a refund was less than three weeks.2 But starting in 2017, legislation that was part of the Protecting Americans from Tax Hikes Act (PATH) prohibited the Internal Revenue Service (IRS) from issuing any federal tax refunds claiming the EITC before February 15.3 As a result, EITC claimants waited longer to receive their tax refunds in 2017 than in prior years.4 The left panel in Figure 1 shows weekly values of federal tax refund dollars issued during the 2014 to 2017 filing seasons that included an EITC.5 Refund issuance in early February 2017 was well below the levels observed in prior years, peaking about two weeks later than usual. However, the issuance of refunds without an EITC was similar to prior years (Figure 1, right panel). By adding exogenous variation to the timing of household income receipt, this legislated refund delay allows us to estimate the extent to which low- and moderate- income households smooth their spending through a large, but short-lived disruption to income.6

Figure 1: Weekly Issuance of Federal Tax Refunds with and without EITC Refunds (billion dollars)

Survey evidence from Maag et al. (2016) suggests that at least some of the early EITC claimants would have difficulty smoothing spending through the PATH Act's temporary delay in refund issuance. One-third of survey respondents said that even a one-week delay in their refund would "somewhat negatively" affect their household finances. Using tax filing data and a survey of early EITC filers, Maag et al. (2016) also document that the median family with children affected by the delay reported only $400 in liquid assets and $2,000 in credit card debt at the time of tax filing. Of course, from a prospective survey, it is hard to know how many EITC claimants were surprised in February 2017 by the delay. The rise in refund anticipation loans (RALs) suggests that those households using tax preparation services may have been told about the delay when they filed.7 However, news reports in early February suggest that many filers were caught by surprise.

In addition to variation in the timing of refund receipt across tax years, our analysis takes into account the considerable variation across the United States in EITC receipt each year. Figure 2 shows the fraction of federal tax returns in each state receiving the EITC in 2016, which ranges from over 30 percent of all returns in Mississippi to less than 15 percent in North Dakota.

Figure 2: Fraction of Federal Tax Returns with the EITC by State in 2016

Description of the New Spending Data and Summary Statistics

Central to our study of the two-week EITC delay are new daily, state-level indexes of spending, as introduced in Aladangady et al. (2016). These indexes were constructed using aggregated and anonymized credit, debit, and electronic transactions from First Data, a large payment processing company.8 Spending is categorized by the type of merchant where the payment transaction occurred (for example, at a restaurant or a department store) and by the location of the merchant. Our analysis here focuses on spending at retail stores and restaurants.9 This sub-aggregate covers consumer spending on most durable goods (excluding autos), most nondurable goods (excluding gasoline), and food services, accounting for one-third of total personal consumption expenditures in the National Income and Product Accounts. Given that a large fraction of retail purchases are made via card transactions, this spending is well measured with our data set and is comparable to the Census Bureau's Retail Trade Survey.

To roughly illustrate how the timing of EITC refunds affects consumer behavior, Figure 3 plots daily, national spending in recent years. We use a trailing seven-day moving average to smooth out the large, regular day-of-the-week variation in spending. The index of daily spending for each year is expressed relative to spending in the middle week of January. To the extent that EITC households were unable to smooth spending through the PATH Act's refund delay in 2017, we would expect to observe lower retail spending between late January and late February than in previous years; conversely, spending should be higher at the end of February into early March after the delayed refunds were issued. Indeed, we broadly observe this pattern at the national level: whereas retail sales in 2014 to 2016 peaked in early-to-mid-February--in conjunction with refund issuance (vertical lines)--sales during that period in 2017 were well below the previous years' levels. Similarly, retail sales in 2017 peaked soon after refund issuance and remained somewhat higher than the levels observed in 2015 and 2016 (though not 2014) through mid-March.

Figure 3: Daily Spending at Retail Stores and Restaurants, 2014 to 2017 Index, Seven-Day Trailing Average

Unlike prior years, spending in 2017 did not exhibit a pronounced hump-shaped pattern around the peak week of refund issuance to EITC recipients. Of course, other factors beyond refund issuance likely affected spending around this period. For example, severe winter weather often disrupts spending early in the year, muddling such summary statistics.

Regression Estimates of the Spending Response to the EITC Refund Delay

To quantify the high-frequency spending response to the EITC refund delay, we estimate the following model of retail spending per capita in state s on day t:

$$$$ \frac{{Spend}_{s,t}}{{Population}_{s,t}} = \omega_{s,t}\ (Week\ of\ Year)_t + \delta_{s,t}\ (Day\ of\ Week)_t + \Omega_{s,t}\ (Year)_t + \Psi_t\ (Holidays)_t \ \ \ \ \ \ \ (1)$$$$ $$$$ + \sum_j \beta_j \frac{{EITC\ Refunds}_{s,t+j}}{{Population_{s,t}}} $$$$



We include a broad set of variables that control for the regular variation in spending across states and over time: ω for week of year, δ for day of week, Ω for year, and Ψ for holidays such as Easter and Valentine's Day. The identification of the EITC spending response relies on the policy-driven, two-week delay in issuance in 2017. Our regressor of interest is a state's per capita weekly federal income tax refund issuance to EITC recipients. In addition to contemporaneous refund issuance, we include one- and two-week leads and lags to capture possible anticipatory spending effects along with any trailing spending effects. Summing over the $$\beta_j$$ coefficients yields an estimate of the cumulative increase in spending per dollar of EITC refund in the five weeks surrounding issuance. We exclude states that were strongly affected by harsh winter storms.10

As shown in Figure 4, we find that EITC recipients spend 14 cents of every refund dollar within two weeks of receipt at retail stores and restaurants. The largest increase in spending (8 cents per refund dollar) is in the week of issuance, with successively smaller increases in the two weeks following issuance. We find no evidence of anticipatory spending effects in the weeks prior to issuance, suggesting that EITC households had limited ability to smooth spending through this short-lived income disruption by either drawing down liquid assets, such as checking accounts, or accessing short-term credit.

Figure 4: Regression Results of Spending Response to EITC Refunds Fraction of refund spent at retail stores and restaurants

To interpret the magnitude of this spending increase and make comparisons to other spending propensities in the literature, we need to highlight a few unique aspects of our study. First, we study the response of low- and moderate- income consumers to a relatively limited (two-week) shift in income. As such, we focus on spending within a narrow window of only five weeks around income receipt. Second, we only examine the response in a subset of consumption categories that comprise about one-third of aggregate consumption. Thus, our estimate likely misses a sizeable portion of the spending out of refunds to EITC recipients. If we were to scale up our results to total spending, this would imply that EITC recipients spent a little less than half of their refund within two weeks. Such a response would be quite sizeable for an annual payment.11

Using weekly spending in the Nielsen Consumer Panel (NCP), Broda and Parker (2014) find that during the four weeks starting with the week of the 2008 economic stimulus payment receipt, spending on NCP-measured goods rose by 3.5 to 5.5 percent of the magnitude of the payment.12 The NCP captures a narrower subset of goods--only about 10 percent of aggregate consumer expenditures--than our spending indexes. Increasing the estimates from Broda and Parker by a factor of three, to roughly match our coverage of spending, we find a comparably large spending response to EITC refunds as to the economic stimulus payments. While EITC targets lower-income households than the 2008 stimulus payments, the EITC is also a more regular, predictable source of income.13 The sizeable, immediate spending response could reflect the low liquidity, on average, among EITC claimants and many stimulus recipients. In fact, in a separate study of the Nielsen data Parker (2017) finds that households with persistently low levels of liquidity (possibly due to impatience or poor planning skills) spend more out of the additional income.

Finally, in Figure 5 we separate the spending response into finer subcomponents: groceries, restaurants, electronics, general merchandise, and other retail stores. While previous studies have found that EITC refund spending is concentrated in vehicle purchases and repair, transportation, household durables, and electronics (Barrow and McGranahan, 2000; Goodman-Bacon and McGranahan, 2008), one striking aspect of this figure is that we find a non-trivial spending response at grocery stores and restaurants. Our estimates may, in fact, understate the response in grocery spending because general merchandise stores are often both a department and grocery store. Whereas durable purchases such as electronics can often be delayed without significantly reducing households' well-being, that is generally not the case for nondurable necessities purchased at grocery stores. As such, our results further suggest that EITC households' well-being may have been negatively affected by the disruption to income resulting from the refund delay.

Figure 5: Estimated Spending out of EITC Refunds by Store Category Fraction of refund spent at each type of store

Taken as a whole, our results suggest limited access to liquidity for low- to moderate-income households, such that even a short-lived delay in income of a few weeks can lead to notable changes in spending with potentially negative effects on these households' well-being.

References

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