Silviase/jgov_v1.4_full_ann huggingface dataset:
month.

We stress the following three advantages of the FIES in performing our analyses. First, the sample size is relatively large, increasing the precision of our parameter estimates. Our main result is based on about 4,400 households, a larger number than those in the earlier studies, i.e. roughly 800 households in Hsieh (2003) and 2,300 households in Browning and Collado (2003). $ ^{7} $ Second, we emphasize that our spending data is reliable and of high quality, which is particularly important for the first-difference specification analyses. In the FIES, Japanese households are asked to keep a spending diary that is collected twice a month. Thus, it is likely that the measurement errors in FIES spending data would be considerably lower than those in datasets used in earlier studies, such as CEX which asks households to recall their spending over the previous three months. Third, the FIES asks households to include the bonus amount in the daily diary, reducing the likelihood of misidentification of the bonus classification, which contrasts to Browning and Collado (2001) in which bonus receipts are inferred from the income pattern. The correctly recorded large amount of bonus payments enhances the reliability of our analyses.

The power of our test relies on the institutional nature of Japanese wage payments. In our sample, most workers are paid monthly. Firms usually pay their workers a regular salary each month of the year and pay out more in the pre-scheduled “bonus” months, typically in summer (June or July) and in winter (mid-December). Therefore, in the bonus months, workers receive a bonus payment in addition to their regular salary.
Although amounts vary by job, industry, and firm, the bonuses paid out in the summer and winter bonus months each roughly equal two monthly salaries. Some workers, including government employees, in addition receive a smaller third bonus in spring (March). $ ^{8} $ While the term “bonus” may give the impression that it has a performance-related aspect, bonuses in Japan have become largely institutionalized and are an integral and anticipated component of workers’ compensation. Thus, there is virtually no uncertainty (especially in the short run within a year) as to whether or not a bonus will be paid or when it is paid, and the amount is fairly predictable for each individual worker (see a description of Japanese bonus payment in the chapter 3 of Aoki [1988]). $ ^{9} $

In the following analyses, we focus only on the effects of the winter and spring bonuses. Among bonus recipients, the average ratio of the bonus to the household head's monthly regular income is very high (more than twice the amount) in Japan, making our test using the Japanese bonus scheme very powerful. We note that roughly three-quarters of households receive bonus payments in December, but the share varies substantially across job and firm types. $ ^{10} $ In addition, there are large differences in average monthly
income levels across job and firm types. Given the heterogeneity observed above and possibility that the sample selection across groups under different bonus scheme may not be random, we confine the data for our regression analyses to the samples of white-collar workers who are employed by large private firms or government. $ ^{11} $ In addition, we restrict our sample to those who are aged less than 65 since the income and consumption pattern may be very different for the households beyond the normal retirement age.

From all available observations from 1990 to 1999, we pull out different sets of panels to perform our two empirical analyses: Browning and Collado's graphical analysis and estimation of the size of consumption reaction to bonus incomes. For the graphical analysis, which compares the seasonality in consumption of households that receive a bonus and those do not, we further divided the sample households into three groups: non-bonus households, December-bonus households, and December and March-bonus households, based upon their bonus receipt entries in the FIES. To sort households into these three categories, we utilize the panels that cover both December and March. Among the twelve panels, only three, i.e., October–March, November–April, and December–May panels, can meet this requirement. Summary statistics for those panels and that for each bonus group are reported in Table 1. Reported sample statistics endorse that three groups look generally homogeneous except for the bonus related attributes. While regular monthly income and monthly consumption are slightly smaller for non-bonus group households, the reported statistics (except for the bonus payments) are about equal across the groups. Average family size and head of household age are comparable, and the levels of monthly average income are indistinguishable. In contrast to the homogeneity in other
aspects, the differences in the bonus income are striking. An average household in the bonus groups receives a bonus of more than a million yen in December. December and March bonus group households receive March bonus of more than 200 thousand yen on average, while households in other groups receive nothing. It is clear that the difference in bonus payments across the three groups is dominant over other differences.

For our monthly-based regressions to estimate the size of consumption reaction to bonus incomes in December, we use all the panels whose information on monthly spending growth is available in November–December, December–January, or January–February. As explained in Section 4, our regression specification allows for two month lags of bonus receipt and we use the panels beginning between July and December, i.e., July–December, August–January, September–February, October–March, November–April, and December–May panels. The summary statistics for these panels are reported in the last column of Table 1.

### 3. Comparison of Seasonality in Income and Consumption across Groups

By employing the graphical analysis presented by Browning and Collado (2001), this section examines the observed differences in seasonality of household head income and consumption among the different bonus groups.

Figure 1 depicts the average monthly changes in log nominal income for the three groups. We observe that the income of households in a bonus scheme displays a strong seasonal pattern, while the income of those not in a bonus scheme is smooth. The December-bonus group exhibits a strong positive swing in December and a negative swing in January, after which the average monthly change converges to the same path as
that of the non-bonus group. The line for the December & March-bonus group follows the same pattern as that for the December-bonus group up until February, but then exhibits a smaller up and down cycle in March and April.

The next step is to see whether the consumption patterns for each of the three groups display the same patterns as observed in the income patterns. Consumption may display seasonality due to other exogenous factors such as demand variations caused by the New Year holiday, by Christmas, or by the start of the fiscal year. $ ^{12} $ To control for those bonus-unrelated seasonality, we run regressions with the following specification:

$$ \begin{aligned}\ln(C_{i,t}/C_{i,t-1})&=\sum_{m=November}^{May}a_{m}\times MonthDummy(m)\\&+\sum_{m=November}^{May}b_{m}\times MonthDummy(m)\times DecemberBonusDummy\\&+\sum_{m=November}^{Mayl}c_{m}\times MonthDummy(m)\times December\&MarchBonusDummy\\&+\beta Z_{t}+\varepsilon_{i,t}\\ \end{aligned} $$

The dependent variable is the log difference of monthly real consumption (total consumption excluding durables and three categories of consumption: semi-durable goods, non-durable goods, and services). As our control variables ( $ Z_{t} $ ), we include nominal interest rate, age and age-squared of household head, and the number of family members in our regressions. $ ^{13} $ We then examined the month dummy coefficients for each of the three groups of households. If consumers behave as predicted by the standard LC/PIH model, we would not detect any significant difference among the month dummy coefficients for the three groups. If we find stronger December consumption for the two bonus groups and stronger March consumption for the December & March group, this
would contradict the standard LC/PIH model.

Figure 2 plots the estimated consumption seasonality for the three groups $ (a_{m}, a_{m} + b_{m}, \text{and } a_{m} + c_{m}; m = 11, 12, 1, 2, 3, 4) $ from the total consumption regression (excluding durables) and from the regressions with the three categories of consumption, i.e., semi-durables, non-durables, and services. In the case of all three groups, there are seasonal surges in all categories of consumption in December and March. Moreover, as expected, the estimated seasonal variations are smaller for non-durables and services and larger for semi-durables, which probably contain some durable components. However, what matters for us here is whether the seasonality differs for the different bonus groups, and in contrast with earlier studies on large and regular income changes, there appear to be differences in the consumption seasonality of the three groups. $ ^{14} $

While we can observe differences in all types of consumption, the pattern is most clearly seen in the case of services and total consumption excluding durables. Consumption in December is substantially higher for the two bonus groups and that in March is higher only for the December & March-bonus group. If consumption of the two bonus groups were higher only in December, we might not be able to rule out the possibility that the consumption surge resulted from an unobserved other factor correlated with bonus receipts, since our sample selection may not be strictly random. However, since we additionally observe that March consumption is higher only for the December & March bonus group, it is highly unlikely that yet another unobserved other shock is working again to generate consumption seasonality. Furthermore, the difference
between the December bonus group and the December and March bonus group appeared to be very small as illustrated in Table 1. Therefore, we can reasonably expect that the observed differences in the consumption seasonality between the bonus groups result not from the non-random sample selection but from some responses of consumption to the anticipated bonus payments.

### 4. Response of Consumption to Bonus Payments

Having found a statistically significant deviation from the LC/PIH, our interest now turns to the size of the deviation. To complement our graphical analysis and evaluate the economic significance of our findings, we run the following simple regression to estimate elasticity of consumption with respect to bonus income and marginal propensity to consume (MPC) out of bonus receipt.

$$ \ln\left(\frac{C_{_{h,t}}}{C_{_{h,t-1}}}\right)=\sum_{n=0}^{2}\alpha_{_{n}}\left(\frac{Bonus_{_{h,t-n}}}{MonthlyIncome_{_{h,\tau-1}}}\right)+Z_{_{h,t}}^{^{\prime}}\cdot\gamma+Month_{_{t}}^{^{\prime}}\cdot\omega+\varepsilon_{_{t}} $$

The dependent variable is the log difference in monthly real consumption (total consumption and three categories of consumption goods) by household h in a given month t relative to the real consumption in the previous month. The main independent variables are $ \frac{Bonus_{h,t-n}}{MonthlyIncome_{h,\tau-1}} $ , i.e., the amount of the bonus payment expressed as a fraction of household’s average monthly income in the previous year. $ MonthlyIncome_{h,\tau-1} $ is calculated as the previous year’s pretax income divided by 12. Since the length of the panel is six months, and we allowed for lagged terms up to two months, there are at most three observations of the dependent variable for each household. $ Z_{h,t} $ is a vector of control variables (nominal interest rate, age and age-squared of the household head, and
number of family members). $ Month_{t} $ is a vector of dummy variables for each month. The coefficients of interest are the $ \alpha_{n} $ s, which measure the percent change in consumption that is associated with a one percent boost to monthly income from a bonus payment, that is, the elasticity of consumption with respect to bonus income. The marginal propensity to consume can be calculated as MPC= $ \alpha_{n} $ x (C/Y).

We run the regressions using the observations in the panels beginning between July and December, which contain data on spending growth in November–December, December–January, or January–February. We estimate the specification above for the three categories of consumption and total consumption excluding durables. The results are presented in the left half of Table 2 (Model I). The standard errors are clustered at the household and robust to heteroskedasticity. The coefficients on the contemporaneous bonus variable are significantly positive for all categories, consistently with our finding that Japanese consumers react to bonus receipt. However, the estimated coefficients appear to be generally small. For example, the elasticity coefficient of 0.047 for total consumption excluding durables implies that the short-run MPC out of contemporaneous bonus income is only about $ 0.02\,(=0.047\times0.5) $ , as an average household typically consume roughly half of its monthly income (see Table 1).

The right half of Table 2 reports the estimates from our following extended model (Model II).

$$ \ln\left(\frac{C_{h,t}}{C_{h,t-1}}\right)=\sum_{n=0}^{2}\alpha_{n}\left(\frac{Bonus_{h,t-n}}{MonthlyInome_{h,\tau-1}}\right)+\sum_{n=0}^{2}\beta_{n}\left(\frac{Y.E.A d j u s t m e n t_{h,t-n}}{MonthlyInome_{h,\tau-1}}\right)+Z_{h,t}^{^{\prime}}\cdot\gamma+M o n t h_{t}^{^{\prime}}\cdot\omega+\varepsilon_{t} $$

The newly added $ \frac{Y.E.Adjustment_{h,t-n}}{MonthlyIncome_{h,\tau-1}} $ are the values of the year-end tax adjustments

received by household h relative to the household’s average monthly income in the
previous year. The year-end tax adjustment refunded to each household can be estimated from the FIES. $ ^{15} $ Table 1 shows that the adjustment amount is relatively small in comparison with the bonus payments and the year-end tax refund is generally perceived as an unpredictable windfall. $ ^{16} $ Therefore, the comparison between the bonus payments and the end-of-year income adjustments constitutes a comparison between large and regular income changes and small and unpredictable income changes. An advantage of our analysis is that we use the same household observations to examine consumer reactions to income shocks of different types.

The coefficients on the bonus terms are generally unchanged after the inclusion of the end-of-year tax adjustment terms, as the amount of bonus receipts and the end-of-year tax adjustments are nearly orthogonal. As for the coefficients on the end-of-year tax adjustments, they are economically larger, though insignificantly estimated in the statistical sense. $ ^{17} $ Although the impact of the yearend adjustments on lifetime income is negligible, such adjustments are surprises to which, according to the LC/PIH, consumers should react. The sizes of the MPC estimates for contemporaneous end-of-year tax adjustments are three or four times larger than those for the bonus for the total consumption (excluding durables) and service consumption. The difference in the size of
the coefficients on the bonus and the yearend adjustment indicates that the same households respond more strongly to unanticipated income changes than to anticipated ones.

We further examined whether the reactions of consumers to the bonus income differ between the rich and the poor, as reported in Table 3. The LC/PIH assumes that people can allocate their assets freely during their lifetime but this may not be always the case (see Stephens (2006)) especially for Japan where consumer credit was less easily available until the 1990s. To examine the difference in consumption reaction to bonuses between the rich and the poor, we run the equation regressions with different levels of assets. $ ^{18} $ While the FIES does not collect information on assets, the Family Savings Survey (FSS) every year collects financial assets data as of December 31 from the households that were in the FIES sample in August, September and October. $ ^{19} $ We can therefore create a matched dataset from the FIES and FSS. Since we need data covering both December and March, the only panel we can use is the October–March panel.

The first two columns show the estimates on households with an asset-income ratio above and below the median, and the last two columns estimates on households with a ratio above and below the twenty-fifth percentile. $ ^{20} $ Estimated coefficients indicate that the contemporaneous impact of bonus income on consumption is smaller for rich households than for poor households, while the estimates are still positive and significant even for the rich. One interpretation is that while the lack of a more developed consumer credit market in Japan or other factors separating the poor from the rich may have amplified the reaction of consumption to the bonus payments, such factors do not fully
account for the observed reactions.

So far, we utilized monthly data from FIES to examine consumer reaction to bonus payments. Our finding that the consumption seasonality of households in the bonus scheme is significantly affected by the bonus payments seems to contradict the findings by earlier studies on large and regular income changes. While the difference could simply be an issue of precision, it is possible that differences in the model specification, including the different data frequency, i.e., monthly vs. quarterly, are playing a role. To explore the possibility, we converted our FIES monthly data into quarterly periods and used a regression specification which resembles that of Hsieh as closely as possible. Since our focus is again on the December bonus, we use the September–February panel and created two quarterly (September–November and December–February) datasets to compute quarterly consumption growth. $ ^{21} $

Table 4 reports the results. First, we find that the coefficient on the ratio of bonus payment to monthly income is still positive and statistically significant when using all the households in the sample (Column (1)). The estimated elasticity of is approximately 0.10, implying a MPC of 0.05, which is larger than that reported by Hsieh and smaller than those reported in many studies on smaller and predictable income changes. Second, to compare our results with Hsieh more closely, we ran the same specification using only the households which are bonus recipients (column (2)) $ ^{22} $ , and obtained the results that the size of the elasticity is smaller and only marginally significant. We obtained almost the same elasticity estimate by including a non-bonus dummy variable in the all sample regression (column (3)), while the negative coefficient on the non-bonus dummy was not
statistically significant. Third, we further split the bonus recipients into those with a small bonus and those with a large bonus (relative to their annual income in the previous year) at the median, and estimated the elasticities for the two parties separately (column (4)). We observe that the estimated elasticity is larger for the households with a smaller bonus amount, while the estimate is statistically insignificant. For the households with a larger bonus amount, the estimated elasticity is slightly smaller and marginally significant.

In summary, we find that the consumption response to the anticipated large bonus is still positive and significant either using the monthly or quarterly based data, which contrasts to Hsieh (2003). While the deviation from the LC/PIH appears to be less significant among the bonus recipients, the estimated elasticities are consistently positive and the point estimates are qualitatively stable.

### 5. Conclusion

In this study, we exploited a notable institutional feature of salary payments in Japan, the bonus system, to examine whether households in Japan react to fairly predictable and large changes in household income within a year. Using a large household-level dataset from the FIES, we find statistically significant evidence that bonus-induced changes in income affect the seasonal pattern of consumption in Japan, despite the fact that Japanese bonus payments are large and fairly predictable.

Our findings may at first sight appear to contradict earlier studies on large and regular income changes, particularly Browning and Collado (2001) and Hsieh (2003), since the consumption seasonality of households in the bonus scheme is significantly affected by the bonus payments and the estimate of the marginal propensity to consume
from bonus income is positive and significantly different from zero. However, the economic significance of the deviation from the LC/PIH, i.e., the estimated instantaneous MPC of 0.02, is very small, indicating that households facing large and predictable income changes have engaged in consumption smoothing even though this smoothing is not complete. The finding that the same households respond more to a small and unpredicted income change is also consistent with the LC/PIH and findings of earlier studies.

Therefore, while we find nominal deviations from what the standard LC/PIH model predicts, we would like to emphasize that the deviations could be detected only by a very powerful statistical test, such as that provided in this paper with a large sample and precisely measured income and spending.
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