Inhoe Ku,Sunyu Ham,Heyjin Moon
Means-tested COVID-19 stimulus payment and consumer spending: Evidence from card transaction data in South Korea
COVID-19,Stimulus payment,Consumption,Transaction data
This study examines the effect of a means-tested COVID-19 stimulus payment, which was provided by the Seoul Metropolitan Government in South Korea, on consumer spending. The Seoul government issued a one-off payment in the spring of 2020 for residents in the city living below the national median income. We use daily card transaction data aggregated by users’ age, income and location of residence, and apply a difference-in-differences approach to assess the effect of the stimulus payment. We compare consumption for the treatment (eligible for the payment) and control (ineligible but with a similar level of income) groups before and after the implementation of the payment. The results show that the payment increased consumer spending for the treatment group by about 12%. Recipients of the means-tested payment have a marginal propensity of consumption at no less than 59%, larger than that found for a universal emergency payment made by the Korean government and similar stimulus checks in other countries.
Means-tested COVID-19 stimulus payment and consumer spending: Evidence from card transaction data in South Korea

This study examines the effect of a means-tested COVID-19 stimulus payment, which was provided by the Seoul Metropolitan Government in South Korea, on consumer spending. The Seoul government issued a one-off payment in the spring of 2020 for residents in the city living below the national median income. We use daily card transaction data aggregated by users’ age, income and location of residence, and apply a difference-in-differences approach to assess the effect of the stimulus payment. We compare consumption for the treatment (eligible for the payment) and control (ineligible but with a similar level of income) groups before and after the implementation of the payment. The results show that the payment increased consumer spending for the treatment group by about 12%. Recipients of the means-tested payment have a marginal propensity of consumption at no less than 59%, larger than that found for a universal emergency payment made by the Korean government and similar stimulus checks in other countries.

Although the COVID-19 pandemic inflicted unprecedented pains on the lives of citizens across the world, different countries responded in different ways to the spread of the virus and its economic consequences. While many industrialized countries extended unemployment benefits and job retention subsidies, some including China, Japan, Singapore, South Korea (hereafter Korea) and the United States also relied on economic stimulus packages to support household consumption and boost business revenue (IMF, 2020, OECD, 2020, OECD, 2021). Facing severe economic losses induced by the pandemic and related quarantine measures, Korea’s central and local governments provided stimulus payments starting in April 2020. This study examines the effect of a stimulus payment issued by the Seoul Metropolitan Government on consumption. While the permanent income hypothesis suggests that a temporary increase in income due to stimulus payments does not change consumption, empirical studies find that stimulus payments during an economic crisis increase consumer spending (Canbary and Grant, 2019). Recent studies also demonstrate the positive effect of stimulus payments in the context of the COVID-19 pandemic in the U.S. (Baker et al., 2020, Chetty et al., 2020, Karger and Rajan, 2020). However, few studies show that COVID-19 associated stimulus payments have a similar effect on consumption expenditure in different socioeconomic and cultural milieus. This study fills the vacuum in the literature by investigating the effect of a stimulus payment during the pandemic in Korea. Along with some other industrialized countries in East Asia, Korea heavily relied on a unique type of stimulus payment during the pandemic. The stimulus payment under review shares similarities with Japan’s 1999 shopping coupon program and Taiwan’s 2009 shopping voucher scheme in that the usage was limited to immediate consumption spending. While studies have already evaluated such programs (Hsieh et al., 2010, Kan et al., 2017), research on COVID-19-related stimulus packages in East Asia has only just begun (see Lee and Hong, 2021, Woo et al., 2022 for Korea, and Liu et al., 2021, for China). Furthermore, this study investigates the means-tested program implemented by the Seoul Metropolitan Government, which distributed the payments to less affluent households with income up to the country’s median level. Means-tested subsidies may have a different impact on spending than universally provided stimulus payments regardless of income levels as often examined in the literature. The means-tested nature of the program provides a unique opportunity to evaluate the causal effect of a stimulus payment by naturally creating eligible and ineligible groups whose outcomes can be compared cross-sectionally. Most previous studies examine variations in consumption expenditure over time before and after the disbursement of payments, which may be confounded by other changes occurring simultaneously. We estimate the effect of the emergency payment by the Seoul Metropolitan Government (this payment is hereafter referred to as ‘SEP’) on the consumption expenditure of citizens of Korea’s capital, Seoul. We apply a difference-in-differences approach to credit card transaction data. Credit cards play a major role in consumer finances in Korea. Our study joins a growing number of studies using high-frequency data to track changes in consumer behavior (Baker et al., 2020, Chetty et al., 2020, Karger and Rajan, 2020). The data allow us to pinpoint the time of the implementation of the stimulus payment to measure its causal effect in Korea.

As COVID-19 infections spread and quarantine measures were implemented, employment and income dropped and, consequently, consumption declined. In April and May 2020, the employment shock developed in earnest, and was concentrated in face-to-face service occupations, such as lodging and restaurants, wholesale and retail, education, performance, sports and leisure activities, and personal services. According to Statistics Korea (2020), the employment shock decreased family income on average by 6% during the second quarter of 2020 compared to the same quarter of 2019. Family income continued to decline during the third quarter. In particular, low-income families at the bottom quintile of the income distribution experienced the largest drop. In response to the economic shock, the central and local governments distributed emergency payments. The majority of local governments provided emergency payments in April. Among them, the Seoul Metropolitan Government spent 542.3 billion Korea Wons (hereafter KRW) to provide the SEP to 1.59 million households with income up to 100% of the national median income (1 USD was equivalent to 1236 KRW as of April 2020).1 The income threshold differed by household size. For example, it was 1.75 million KRW for one-person households while it was 4.74 million KRW for four-person households. The payment also varied depending on household size: 300,000 KRW for households with one or two members, 400,000 KRW for households with three or four members, and 500,000 KRW for households with five or more members.2 On the other hand, the central government provided its first stimulus payment, ranging from 400,000 KRW (for a one-person household) to one million KRW (for a household with four or more members), for all households regardless of income level starting in May 2020. The government spent 14.2 trillion KRW nationwide. The expenditure included 2.3 trillion KRW delivered to households in Seoul. The main motivations behind the provision of the SEP and other stimulus payments were to support consumption among citizens and, in turn, restore business revenue. So, was the stimulus effective in increasing consumption? We hypothesize that the SEP increased disposable income and subsequently improved consumption spending for its recipients. The SEP, provided in pre-paid cards or mobile coupons, functioned almost like cash because it could be used for purchasing a broad set of goods and services, as will be discussed in more detail later. Given that the SEP targeted low-income households that suffered income losses due to the pandemic, we expect that its effect on consumption expenditure may be significant. On the contrary, the permanent income hypothesis suggests that only permanent income shocks result in a major revision in consumption spending (Friedman, 1957). Maintaining that a temporary change in income affects consumption to the extent that it changes the permanent income, the hypothesis predicts that a one-time stimulus payment may not significantly increase consumption (Canbary and Grant, 2019). A contrasting argument here places an emphasis on liquidity constraints, taking into account situations where those without liquid assets may not have extra economic resources to smooth consumption after losing regular income due to an unexpected event. In such cases, temporary income support may lead to increased consumption, a proposition supported by empirical evidence. Johnson et al. (2006) and Parker et al. (2013) find that households responded strongly to the receipt of stimulus checks during the recessions in 2001 and 2008 in the U.S. Baker et al. (2020) and Chetty et al. (2020) report a similar finding in the context of the COVID-19 pandemic. Thus, we expect that the emergency payments led to increased consumption. Furthermore, the SEP like other stimulus payments in Korea may have a strong effect on consumption, due to its programmatic components which required immediate consumption. The SEP was paid not in cash but in pre-paid cards or mobile coupons, neither of which could be cashed out or used for paying down debt, saving, or payments like rent, mortgage repayments and other bills. They were redeemable only for the purchases of goods and services at small-sized local businesses in the recipient’s neighborhoods. The SEP was initially designed to be valid until the end of June 2020, but the expiration date was later extended to the end of August. These features of the payments may have strongly contributed to an increase in consumption. On the other hand, a boost in spending using the stimulus payment may not have increased consumption overall if offset by a reduction in spending using other sources of income. However, because the payment was not large enough to make up for lost income, the SEP is likely to have resulted in a net increase in consumption. Finally, we expect that the SEP had a stronger positive effect on consumption because it targeted low-income families. Maintaining consumption when one experiences a loss in income may be more difficult for low-income households who often lack an adequate level of liquid assets. Credit constraints are more serious for low-income groups (Canbary and Grant, 2019). Studies note that lower-income households tend to respond more strongly to stimulus checks (Baker et al., 2020, Chetty et al., 2020, Parker et al., 2013). Based on conceptual grounds, we empirically examine the effect of the SEP on consumption expenditure. We further evaluate the effect of the SEP by categories of consumption. The effect may depend on whether consumption items are necessities or luxury goods, durables or nondurables, or services or goods.

Most studies focusing on stimulus payments during past economic crises have relied on survey data for consumers (Johnson et al., 2006, Parker et al., 2013). In contrast, recent studies on COVID-19 related subsidies increasingly use high-frequency transactions data. For instance, while Baker et al. (2020) analyze bank account transactions data at an individual level, Chetty et al. (2020) use aggregated card transactions data.3 We also analyze consumer spending using aggregated data on card spending. Our data come from transaction-level data collected by the Shinhan Card Co., a credit card company with the country’s largest market share of 22.8% among eight Korean credit card companies as of 2019 (see Woo et al., 2022 for a study using the same data source). The Shinhan card collects information on every transaction conducted by individual card holders. For each transaction, the data provide information on the date of transaction, the amount of expenditure, and the goods or services purchased in addition to the characteristics of individual users. Thus, the data allow for a precise estimation of daily consumer spending for different groups of individuals including those eligible for the SEP and those ineligible. It is important to note that only the Shinhan card transaction data contain information on whether a transaction is made using a SEP pre-paid card account or a regular credit card account.4 Our data come from more than 2 million Shinhan card account holders living in Seoul. We exclude individuals aged 65 and over, low-income individuals with an annual income of less than or equal to 24 million KRW, and individuals without information on income from the sample. Analyses based on card transaction data may not be suitable for these groups of individuals who do not use credit cards as a main means for consumption.5 We use aggregated card transaction data. For the protection of privacy, the card company only releases cell-based grouped data on card transactions to the public. In this study, cells are jointly defined by whether the account holders of the transaction linked the SEP pre-paid card to their card account, income bracket for the account holder (one of three brackets of annual income: more than 24 million to 48 million KRW; more than 48 million to 72 million KRW; or more than 72 million KRW), residential area (one of 25 metropolitan boroughs in Seoul), age (one of three age groups: those aged less than 35; those between 35 and 49; or those between 50 and 64), date of transaction, and year of transaction. Cells with transactions made by fewer than three card holders are discarded for the protection of privacy. For each cell, the dataset includes the following information: total number of card users for the month; the amount of expenditure across consumption categories; and whether the purchases were made by the SEP pre-paid card or a regular credit card. We construct a daily average amount of spending for each cell as an outcome variable for the main analyses. Our data cover the periods between January 1 and October 31 for 2019 and 2020.

Studies on COVID-19 stimulus payments often exploit the differences in the timing of stimulus payments to examine high-frequency changes in behavior brought about by the receipts of the payments. The approach relying on the timing of payments is applicable because those stimulus payments were deposited on a specific date. Some studies adopt an event study framework to measure the immediate effect of stimulus payments on daily consumer spending (Baker et al., 2020, Karger and Rajan, 2020). A regression discontinuity design is also adopted to see whether spending levels rise discontinuously on the date of the arrival of the payments (Chetty et al., 2020). Our study uses a different approach to measure the causal effect of the SEP, because we cannot use information on the exact timing of the payment. The Seoul Metropolitan Government received applications for the SEP between March 30 and May 15. After reviewing each application, which took about one week, the city disbursed the SEP from early April. This study utilizes the means-tested nature of the SEP to examine the difference in consumption expenditure between those eligible for the SEP and those ineligible. We apply a difference-in-differences (DiD) approach to compare the differences in the two groups before and after the beginning of the disbursement of the SEP. Focusing on the group difference at a particular point in time, our approach overcomes a weakness found in alternative methods based on a change in consumption over time, which may be confounded by other changes over the same period. We construct the treatment group from applicants who were determined to be eligible for the SEP and received the payment in pre-paid cards. About 80% of all the recipients, amounting to 1.27 million individuals representing their households, received the SEP in a pre-paid card. Among the pre-paid card recipients, 112,669 individuals opted to register their cards to spend the SEP using their Shinhan credit cards. After eliminating older individuals and individuals with low income or no information on income, the final sample size for the treatment group was 89,032. Individuals in our treatment group may have been less poor and more familiar with the use of credit cards and thus unrepresentative of SEP recipients as a whole. In other words, our results may not be generalized to the whole population of SEP recipients. We construct the control group from 2.07 million Shinhan card holders among citizens of Seoul. We include the card holders ineligible for the SEP in the control group. While we exclude individuals already selected as the treatment group, the remaining card holders may include those who received the SEP in mobile coupons or in pre-paid cards without registering it at the Shinhan card company. To eliminate such potential misclassification, we select individuals with annual incomes ranging from 48 million KRW to 72 million KRW as the control group. These individuals are not likely to belong to the treatment group given that their income was above the median level. Since the control group excludes rich individuals, it may also be similar to the treatment group regarding consumption behaviors. As illustrated in Figure A1, the extent of the spread of COVID-19, the strictness of the social distancing measures adopted, and the implementation of emergency payments varied together over time. We define the post-treatment period as starting from the second week of April and running to the end of August because most card spending of the SEP occurred during this period (see Figure A2). To distinguish the effect of the SEP from the effects of other changes clearly, we segment the period into seven sub-periods (see the Appendix for more details). The period before the implementation of the SEP consists of the first and second sub-periods. We select the first sub-period before the outbreak of the COVID-19 pandemic as the pre-treatment one because the effects of the spread of the virus are easily shown along with the effect of the SEP. Even though selecting the second sub-period after the outbreak instead may be more appropriate for assessing the effect of the SEP, later results show that the selection does not make a difference in the estimation (we will discuss this later based on the results presented in Table 2). The post-treatment period includes the third to fifth sub-periods. We focus on the third sub-period as this was when the SEP started to be distributed. During the fourth and fifth sub-periods, the stimulus payment of the central government was also delivered. Meanwhile, the sixth to seventh sub-periods show changes after the post-treatment period. Given the possibility that the effect of the SEP on consumption continued after the expiration of the pre-paid cards, we examine a more extended period. We are interested in the difference in differences between the treatment and control groups over time in 2020. However, the difference over time may reflect the influences of unobserved characteristics affecting the two groups differently, such as seasonal factors. In order to remove potential biases due to such factors, we subtract the difference between the treatment and control groups in 2019 from the difference between the two groups in 2020. To carry out the triple differences method, we matched sub-periods in 2020 to corresponding periods in 2019 by week based on calendar dates. We adjusted matched periods by allocating holidays, such as the Lunar New Year holiday, to the same matched pair given their potentially large effect on consumption. Our triple differences estimates can be produced by running the regression Eq. (1) below: where indicates an individual cell, jointly defined by age, income group and residence of area, indicates whether an individual cell belongs to the treatment group or the control group, indicates the sub-period (for Table 2, Table 3) or week (for Fig. 3), when consumption expenditure occurred, and indicates whether consumption expenditure for a cell occurred in 2019 or 2020. In the above equation, , a coefficient of the triple interaction between the treatment status (), dummies for sub-period (), and a year dummy () provides an estimate of the effect of the SEP in each sub-period. indicates a set of characteristics for a cell, such as age and area of residence, controlled in the model. We use as an outcome variable. The expenditure indicates daily consumption spending (for pre-paid cards and regular credit cards) per capita for each cell. Given the different number of individuals included in each cell, we use per capita expenditure calculated by dividing the expenditure of each cell by the number of individuals in each cell. We apportion a weight to each cell according to the number of individuals when we conduct the regression analysis. Our outcome measure does not include the expenditure using other means of payment such as credit cards other than the Shinhan card, cash, and bank account deposits. It is possible that the consumption expenditure using the SEP payment may have crowded out expenditure using other means, partly offsetting the increased expenditure using pre-paid accounts. For this reason, our estimates should be accepted as an upper bound for the effect of the SEP.

Fig. 1 shows the trends in the weekly average of daily per capita card expenditure in 2019 and 2020. We use seven-day averages to smooth daily fluctuations. The vertical red lines demarcate the seven sub-periods. During the first sub-period, the levels of the expenditure for the two years do not differ, ranging between 40,000 and 60,000 KRW. During the second and third sub-periods, the level of the expenditure in 2020 is below the level in 2019. In contrast, the level of expenditure in 2020 is higher than the level in 2019 during the fourth sub-period. In the early weeks in this period, the expenditure soared to higher than 70,000 KRW in 2020, while it stayed at 60,000 KRW in 2019. During the remaining sub-periods, we do not find a large difference in the expenditures between 2020 and 2019, although a significant uptick in the expenditure in 2020 compared to that in 2019 is found at the end of the seventh sub-period. The trend is consistent with the ups and downs in COVID-19 infection rates and the timing of the first and second payments of the central government. On the other hand, we cannot identify the effect of the SEP, which started to be distributed from the third sub-period in the figure. Fig. 2 shows the trends in the weekly average of daily per capita card expenditure in 2019 and 2020 separately for the treatment and control groups. For the treatment group, we find that the level of expenditure in 2020 is much lower than in 2019 except for the short period between weeks 20 and 24 in the fourth sub-period. For the control group, the level of expenditure in 2020 is lower during the second and third sub-periods while it is noticeably higher during the fourth sub-period. There were no clear differences in consumption for other sub-periods between the two years. Table 1 shows that the consumption expenditure during all the examined sub-periods for both groups is lower in 2020 than in 2019. As presented in the first row, the expenditure in 2020 is lower than in 2019 by 2.45% for the control group while it is much lower (by 23.2%) compared to the level in 2019 for the treatment group. The lower expenditure in 2020 can be understood as reflecting the negative effect of the COVID-19 pandemic. However, even during the first period before the first outbreak, the level of the expenditure in 2020 was lower than in 2019. For the treatment group, in particular, the level of the expenditure during the first period was lower by 27.1% than in the same period in 2019. One of the reasons for the lower expenditure in 2020 may be that the card users in the year include more individuals who do not use credit cards often. For example, the treatment group includes 21,000 individuals who did not use credit cards other than for expenditure using the pre-paid cards, thereby lowering the expenditure for the treatment group in 2020. These individuals may not have used the Shinhan card in 2019 (note that the figures in parentheses in the right panel show daily per capita expenditure calculated for the treatment group excluding those individuals). According to the figures, the expenditure in 2020 is slightly higher (by 1.3%) than the corresponding expenditure in 2019. The last column shows that the consumption expenditure for the third to fifth sub-periods is higher in 2020 based on the adjusted treatment group.6 The compositional change of the card users seems to lower the level of consumption expenditure in 2020 relative to the level in 2019. However, the change would not affect our DiD estimates which only rely on within-year variations in the expenditure.

In Table 2, we present our DiD estimates of the effect of the SEP on consumption expenditure. The first sub-period is defined as the pre-treatment period. Thus, coefficients on the three-way interaction terms for the second to seventh sub-periods show the difference in the expenditure between the treatment and control groups in each sub-period relative to the difference in the first sub-period after considering a corresponding difference in 2019. The coefficients for the second sub-period are close to zero, suggesting that the COVID-19 outbreak did not change the difference in the expenditure between the two groups. Given the zero coefficients, we know that all coefficients would remain unchanged if we choose the second sub-period as the pre-treatment one. The first model only includes variables of treatment dummies and period dummies and their interactions, while the second model also includes control variables such as age and area of residence. Estimates for each period are virtually the same in the two models. The coefficients for the post-treatment sub-periods indicate the difference in the expenditure between the treatment and control groups potentially affected by the SEP. The results demonstrate significantly higher expenditure among the treatment group during the post-treatment sub-periods except for the seventh one. The expenditure for the treatment group was 11.7% higher than that for the control group during the third sub-period. During the fourth sub-period, the increase in the expenditure among the treatment group became even larger, reaching 19.6%. An increase in expenditure of about 5% among the treatment group was also detected during the fifth sub-period. Even during the sixth sub-period, the treatment group shows expenditure 3.2% higher than among the control group. The increase in the expenditure among the treatment group cannot entirely be attributed to the contribution of the SEP. For example, the large increase in the expenditure among the treatment group during the fourth sub-period may reflect the combined effects of the SEP and the first emergency subsidy of the central government launched in this sub-period. Although the subsidy universally provided benefits for all families regardless of income level, it may have had a larger effect on consumption expenditures for the treatment group. For low-income families, the payment may have been larger relative to their income level. These families may have spent a larger share of the payment on consumption than high-income families, due to their larger propensity of consumption. Another example is the small but significant coefficients found during the sixth sub-period, which seems associated with a drop in consumption among the control group after the second wave of the pandemic started, rather than increased consumption among the treatment group, as illustrated later in Fig. 3. In Table 3, we categorize consumption items into “essentials” versus “non-essentials,” “durables” versus “non-durables,” and “services” versus “goods,” and compare the effect of the SEP on spending in these consumption categories. We expect that the SEP has had a larger effect on spending on essentials than on non-essentials and on non-durables than on durables among the treatment group. Individuals in the treatment group are more likely to be in need of daily necessities which may fall under essentials and non-durables. The SEP may also have had a greater effect on spending on services among the treatment group if wealthier families were more concerned about the risk of infection associated with the consumption of services (Chetty et al., 2020). According to Table 3, coefficients are mostly significant for the third and fourth sub-periods, which we focus on here. As expected, coefficients for the third sub-period are significantly positive in the models of expenditure on essentials and non-durables. In contrast, corresponding coefficients are insignificant and smaller, if positive, or significantly negative in the models of expenditure on non-essentials and durables. Contrary to our expectation, however, the SEP does not have a larger effect on expenditure for services than for goods. As insignificant coefficients for the second sub-period in the models of services and goods reveal, Table 3 does not support the expectation that wealthier families would have been more concerned about the risk of infection associated with the consumption of services. In the fourth sub-period, coefficients are significantly positive in all models of consumption categories, and their sizes are larger than those in the third sub-period. The larger effects may be due to the combined effects of the SEP and the central government’s subsidy, as mentioned earlier in the discussion of the results in Table 2. Similar to the results for the third sub-period, the effect on expenditure on essentials is larger than on expenditure on non-essentials. On the other hand, an interesting result is found with regard to the effects on expenditure on durables, as the coefficient for the fourth sub-period is significantly positive (0.299) in contrast to the significantly negative coefficient for the third sub-period (−0.423). In fact, coefficients for all sub-periods except for the fourth one are negative. The control group could have increased the consumption of durable goods as its members purchased home appliances and furniture for telecommuting or online schooling after the COVID-19 broke out. It seems that the treatment group held off on durables until sufficient income became available for such spending. During the fourth sub-period, when the emergency subsidy was implemented by the central government in addition to the SEP, the treatment group boosted spending on durables. Meanwhile, further explanation is sought as to why the coefficient for goods is substantially larger than the coefficient for services in the fourth sub-period, even though the coefficient for goods was close to the coefficient for services during the third sub-period. The larger coefficient for goods in the fourth sub-period could be explained by increased spending on durable goods during this sub-period. The increased spending on goods among the treatment group simply reflects the increased spending on durable goods. Fig. 3 illustrates the effect when we use week, instead of sub-period, as a unit of time in the regression models. In this graph, the bar chart shows how coefficients for the three-way interaction terms vary across weeks within the post-treatment sub-periods. For example, the coefficients gradually became larger during the third sub-period while they peak in the early fourth sub-period and then fade away in the middle of the fifth sub-period. During the sixth sub-period, the coefficients again increased and then decreased, which may not have been associated with the SEP. The graph shows that, in the early sixth sub-period, the expenditure dropped steeply among the control group while the expenditure gradually declined among the treatment group. The increase in the expenditure of the treatment group follows the slow and gradual pattern of the SEP rollout over time, which is in contrast to a sharp increase in consumption at the time of payments being made in the U.S. On the other hand, the line graphs during the pre-treatment period including the first and second sub-periods illustrate that consumption expenditures for the treatment and control groups experienced more or less parallel ups and downs. In contrast, non-parallel trends for the two groups clearly appear in the post-treatment period including the third to fifth sub-periods. The uptick in the expenditure for both groups around week 20 may be a reflection of the effect of the first stimulus payment of the central government. The effect of the SEP during the third to fifth sub-periods showed up because the expenditure for the treatment group rose more steeply than it did for the control group. The marginal propensity of consumption, a ratio of increased consumption to increased income, can be calculated from the above estimates of the effect of the SEP on consumption during the third to fifth sub-periods. Since the stimulus payment of the central government in addition to the SEP was delivered to households during the fourth and fifth sub-periods, we focus on the effect of the SEP during the third sub-period. We begin by calculating the increased consumption due to the SEP. As presented in Table 2, the SEP increased the consumption spending of the treatment group by 11.7% during the third sub-period. From Table 1, we know that the daily per capita expenditure of the treatment group was 29,100 KRW during the third sub-period. This means that the SEP increased consumption by 3100 KRW from 26,000 KRW, which would have been spent had there had not been the SEP. If we multiply 3100 KRW by 35, the number of days during the third sub-period, we get 108,500 KRW, representing the increased amount of per capita spending during the third sub-period. To calculate the increase in income due to the SEP during the third sub-period, we closely examined documents released by the city administration. The total amount of the SEP distributed was 542.3 billion KRW. We find that the amount of the SEP distributed was 273.7 billion KRW by 10 May, which increased to 498.6 billion KRW by 25 May. Given that the amount increased linearly, we estimate that approximately 303 billion KRW was distributed during the third sub-period (between 8 April and 12 May). Then, we get 184,400 KRW as the average payment per household who received the SEP using pre-paid cards. Our estimate of the MPC is 58.8% (=108.5/184.4). Information from the city administration shows that it takes eight days on average for recipients to spend 50% of the payment, which means that some of the increased income due to the payment during the third sub-period was spent later during the fourth sub-period. Considering this potential underestimation of the increased spending, our estimate of the MPC should be accepted as the lower bound. Our estimate is much larger than those for the universal stimulus payment by Korea’s government reported in other studies which range from 25% to 40% (Kim and Oh, 2020, Kim et al., 2020). Our finding of a large MPC is also supported by ancillary data on saving behaviors. According to a survey conducted with 10,000 SEP recipients, most respondents used the extra income generated from the payment for consumption spending while a small share of respondents used the income for saving (4.5%) or paying down their debt (13.9%) (Seoul Welfare Foundation, 2020). Elsewhere, studies of the US stimulus payments report a larger substitution effect. The effect is estimated to be 80.1% by Shapiro and Slemrod (2009) and 70%–87% by Sahm et al. (2012). The substitution effect of the SEP seems to be much smaller than suggested by those estimates for US stimulus payments. Sahm et al. (2012) argue that the substitution effect can vary depending on the delivery method. Since the SEP is more like a shopping coupon that can be redeemed only for the purchases of goods and services at local businesses and is only valid during a limited period of time, it may have had a small substitution effect.

In Table 4, we examine whether estimates of the effect of the SEP differ when applying the same model but using differently defined control groups. In Table 2, we reported the result when the control group was defined as those in the annual income bracket between 48 million and 72 million KRW. Those in this income bracket are not likely to be eligible for the SEP but still have an income relatively close to that of the treatment group. If their higher income leads to consumption behaviors different from those of the treatment group, the estimates may be biased. Thus, we use different control groups to check whether estimates are significantly different. In the first column, we reproduce the basic estimates as presented in the first column of Table 2 as a reference for comparison. In the second column, we report results when using those in the income bracket between 24 million and 48 million KRW. Individuals in the control group have income similar to those in the treatment group. Because of the change in income ranges, however, the control group may include some SEP recipients, potentially reducing the effect size of the SEP. The result shows that the estimates are not smaller than those in the first column over the post-treatment period consisting of the third, fourth, and fifth sub-periods. The third column reports estimates when the control group is defined as those with an income of 48 million KRW and more. The estimates are slightly smaller than the corresponding base estimates although the control group includes high-income individuals. In the last column, the result shows estimates when the control group consists of high-income individuals with an income of 72 million KRW and more. The control group is not likely to include SEP recipients but may show consumption behaviors different from the treatment group. The estimates are generally the smallest, suggesting that using the control group with less economic constraints may hinder the full capture of the effect of the SEP. Another source of potential bias is that since the SEP was disbursed to an individual representing the household, individuals in the treatment group are more likely to have been household heads than those in the control group. Crucially, some household heads would have been living with other adults like spouses. If this is the case, their increased card expenditure due to the SEP may have been offset by a decrease in spending by other adult members in the household. To examine this possibility, we estimate the same model as the first one in Table 2 using the samples split into two age groups: those aged under 35; and those aged between 35 and 64. The idea here is that estimates based on the sample of those aged under 35, who may have been unmarried and who were unlikely to live with other adults, are less prone to upward bias due to the crowding-out of spending of other household members. In Table 5, we restrict the sample to those aged under 35 in the second column. For comparison, we present the base estimates using the original sample, as demonstrated in Table 2, in the first column. We find that the sample restriction does not remove the effect of the SEP found in the base estimates, although it decreases the size of the effects and renders estimates for some sub-periods insignificant. In the second column, we find that estimates based on the sample restricted to those aged 35 to 64 become larger than the base estimates, while they show a similar pattern across the sub-periods. The results presented in Table 5 suggest that our estimates may overstate the effect of the SEP on consumption in some models but remain valid even when taking a more conservative approach.

In Korea, the central and local governments provided stimulus payments for households as a response to the COVID-19 pandemic in the spring of 2020. Among them, the Seoul Metropolitan Government targeted its one-off payment to residents with income below the national median in the city. This study has examined the effect of the means-tested stimulus payment on consumer spending. We apply a DiD approach to aggregate card transactions data. Previous studies in the U.S. focus on daily transactions at the time of the payments which were deposited to the accounts of all the recipients at the same point in time (Baker et al., 2020, Chetty et al., 2020). Yet, our DiD approach may be appropriate for assessing the effect of subsidy programs which gradually roll out the payment over a month-and-a-half, like the one under examination in this study. We rely on cross-sectional differences between the treatment and control groups to discern the effects of payments from the influences of the spread of COVID-19, and the implementation of social distancing measures which often covary with the payment. The high-frequency data enable us to observe dynamic changes on a weekly basis to separately identify the causal effects of the payment. The results show that the emergency payment increased consumer spending, just as other studies of stimulus payments during the pandemic find (Baker et al., 2020, Chetty et al., 2020, Lee and Hong, 2021, Woo et al., 2022). We found that the marginal propensity of consumption for the means-tested payment is at least 59%, larger than those found for less targeted stimulus payments in the U.S. ranging from 10% to 50% (Baker et al., 2020, Karger and Rajan, 2020, Parker et al., 2022). Studies in Korea report that the marginal propensity of consumption for the national stimulus payment ranges from 25% to 40% (Kim and Oh, 2020, Kim et al., 2020). The increase in the expenditure was larger for non-durable, essentials. This finding is similar to that of Baker et al. (2020) for the U.S who found that stimulus payments from the 2020 CARES Act led to a larger increase in spending on food, household goods, personal care and non-durables than on durables. Findings from our study suggest that consumption increases with a temporary increase in income due to stimulus payments, which is inconsistent with the permanent income hypothesis. In particular, stimulus payments targeting families in need are more effective in supporting consumption than a universal payment. The large impact on consumption may also be explained by the design features of the payments promoting immediate consumption and the successful containment of the virus in the early stages of the pandemic. During the pandemic, governments across the world addressed the economic difficulties of citizens by different means. Unlike developed countries in the West with well-developed social safety nets, industrialized East Asian countries mainly relied on economic stimulus packages to support the consumption of families and increase the sales revenue of small businesses. Overall, stimulus payments helped citizens to smooth their consumption and prevented aggravated economic distribution (Ku, 2022). Our analysis suggests that an effective approach to mitigating economic hardship may be to provide benefits to low-income families at a higher risk of economic loss. At the same time, it is worth mentioning that a stimulus payment has its limitations. It is a blunt ad hoc measure to address employment loss and accompanying financial strain caused by an economic shock. As exemplified by Western countries, job retention subsidies effectively prevent employment losses while unemployment benefits target financial support at those who lose their job. Korea still faces the challenge of strengthening its regular social safety nets, which if accomplished will serve as automatic stabilizers in future economic crises.