Silviase/jgov_v1.4_full_ann huggingface dataset:
monthly by the Ministry of Internal Affairs and Communications and is an important source of information for the construction of aggregate data such as the Consumer Price Index.

The FIES provides detailed information on household characteristics, income, expenditures, and financial asset holdings for a nationally representative sample of about 8,000 households each month. Each household is surveyed for a period of six consecutive months, and one-sixth of the sample households are replaced by new households every month. $ ^{8} $ As data on financial assets are not available for single-person households, we use multiple-person households only in the empirical analyses below. We also dropped households whose housing tenure status changed during the six-month period as well as households whose head is less than 20 years old, since their consumption patterns are likely to contain a considerable amount of noise. The resulting total number of households is 497,661 (consisting of roughly 360,000 homeownings and 140,000 renting households).

The FIES requests survey respondents to keep a diary to report monthly income and expenditures, which is believed to be more accurate than information obtained from retrospective question-based surveys. The FIES also contains information on the previous year's pretax annual household income and a wide variety of household characteristics such as the age of the household head, the number of household members, etc. Information on financial assets and liabilities is also collected once during the six-month survey period, for a limited number of households before 2002 (approximately 18.2% of 316,390 households) and for almost all sample households from 2002 onward. As for housing wealth, while the FIES unfortunately does not contain information on housing wealth itself, we can obtain information on the approximate residential address, the housing tenure status (homeowner or renter) for all households, and the floor space, land area, structure of the dwelling, and year of construction of the dwelling for homeowner households. Therefore, the next subsection briefly describes how we estimated the value of housing wealth for individual households from that information.

#### 2.2.2 Estimation of housing wealth

Housing wealth consists of two parts: a house (building) and the land on which it sits. Since approximately 85% of housing wealth in Japan derives from the value of land (see Table 1), we describe the estimation methodology for the latter (land assets) first and then turn to the estimation of the former (house assets).

Although households may hold multiple houses, the FIES does not provide any information about additional houses. Thus, we restrict the estimation of household real assets in this study to the value of households' current home. $ ^{9} $ To estimate the value of residential land assets owned by individual
households, we determine the approximate address where sample households lived (to the greatest level of precision possible using multiple official statistics $ ^{10} $ ) and use the price of residential land at the closest survey location $ ^{11} $ in the “Land Market Value Publication (Chika-koji)” provided by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) $ ^{12} $ as the relevant land price. Finally, we estimate the nominal value of survey households’ residential land assets by multiplying the land price per square meter at the location closest to households’ approximate address determined in the manner just described and the land area (square meters) of their home reported in the FIES.

Turning to the estimation of the value of survey households’ house, the FIES provides information on the floor space (in square meters), the structure of the dwelling (wooden, reinforced concrete, etc.), and the year of construction. Following the method employed by Takayama et al. (1989), we match the FIES data with data on average construction costs (by type of building structure, municipality, and construction year) reported in the “Annual Report of Building Construction” (1953–2012) to calculate the value of house assets. The house value is basically constructed by multiplying the construction cost per square meter by the total floor space homeowners report. Then, the house value evaluated in this manner is depreciated up to the survey year using certain depreciation rates that depend on the structure of the dwelling. $ ^{13} $

Table 1 presents the basic statistics of major variables in the dataset we constructed in the manner described above. The mean of housing wealth for homeowners is about 32.6 million yen, and 85.7% of it derives from the value of land, as highlighted in Section 1. The annual average of capital gains from land for homeowners, calculated as the current year's land value minus the previous year's land value, is negligible. However, if the sample period is split into the bubble period (1983–1991) and the post-bubble period (1992–2012), the annualized average capital gains (losses) are 2.48 million yen and -1.01 million yen, respectively. These relatively large capital gains (losses) mean that it is possible that housing wealth effects can be observed.

## 3 Methodology

To examine housing wealth effects, we employ the theoretical prediction of the standard LC/PIH that current consumption depends on current asset holdings and human wealth (i.e., the discounted sum of expected future income). Since human wealth is unobservable, it is proxied by household income, $ ^{14} $
the industry in which the household head works, and the type of work $ ^{15} $ of the household head. Thus, the consumption function takes the following form:

$$ C_{i}=\beta_{0}+\beta_{1}H W_{i}+\beta_{2}F W_{i}+\beta_{3}{^{\prime}}Z_{i}+\varepsilon_{i}, $$

where $ C_{i} $ is the annualized $ ^{16} $ household consumption, $ HW_{i} $ is the housing wealth, and $ FW_{i} $ is the net financial wealth (financial wealth minus outstanding debt) of household i.

$ Z_{i} $ is a vector of control variables that include the previous year's (pre-tax) household income, $ ^{17} $ the industry in which the household head works, and the type of work of the household head in order to control for heterogeneous human wealth; the age of the household head, family size, the number of household members aged 65 and over, and the number of household members aged 15 and under to control for household needs at different life stages; and the sex of the household head and the prefecture of residence to control for other factors affecting household consumption. Dummies for the first month in which a household was surveyed as well as year dummies are also included to adjust for seasonality and eliminate the effects of common macroeconomic factors. In addition, following Attanasio and Weber (1994) and Attanasio et al. (2009), cohort dummies are created based on 19 ranges for the year of birth (-1919, 1920–1922, ..., 1968–1970, and 1971–) and added as independent variables to control for cohort effects. All monetary variables are converted into real terms using prefectural consumer price indexes (CPIs). Prefectural CPIs instead of the nationwide CPI are employed to eliminate regional differences in price levels as much as possible. $ ^{18} $ For the same reason, an urban dummy, which takes unity if the household lives either in the capital, Tokyo, or one of the government-designated cities, $ ^{19} $ is also included in the regression to control for the difference in price levels between urban and rural areas. Equation (1) is estimated using ordinary least squares (OLS).

Before presenting the benchmark results, we should draw attention to potential endogeneity issues with regard to Equation (1). We believe that the error term may contain at least three possible omitted variables. The first of these is whether households just purchased a new house. Households that recently purchased a new house may be likely to also buy durable goods such as a refrigerator and/or washing machine as well as nondurable goods such as curtains. In this case, the impact of new housing purchases would be picked up by the error term, resulting in omitted variable bias. Fortunately,
the FIES contains information on the year of construction of homeowners' dwelling. We therefore dropped from our sample homeowners that live in a house that is no more than one year old in order to eliminate the greatest extent possible the potential endogeneity problem caused by recent housing purchases. $ ^{20} $

The second possible source of omitted variable bias is that other factors apart from those included in our estimation may affect permanent income. As mentioned, we include household income, the industry in which the household head works, and the type of work of the household head in the regression as proxies for households' permanent income. Although the industry of occupation dummies enable us to control for differences in average permanent incomes between, say, the mining industry on the one hand and finance and insurance on the other, it does not allow us to consider differences in permanent income among workers within such broad industry classifications, such as between workers at major city banks and small regional banks. $ ^{21} $ In this study, we try to overcome this potential omitted variable bias in two ways. The first is based on the fact that, in the framework of the standard LC/PIH, lifetime resources consist of (i) housing wealth, (ii) net financial wealth, and (iii) human wealth, and that the share of human wealth becomes negligible after retirement. Since the former two variables are already included as explanatory variables in our analysis, this means that we can essentially control for households' entire permanent income if we confine our sample to retired households. The estimation result of this additional exercise will be reported in Table 2 below. The second way in which we try to alleviate possible omitted variable bias is to utilize the (pseudo)panel, the construction of which is described in Section 4.2 below and which enables us to take the first difference of logged variables. According to the standard consumption Euler equation, the growth rate of consumption depends on the subjective discount factor and real interest rates, but not permanent income. Thus, employing pseudo-panel data may be another possible solution to address the omitted variable bias.

The third possible omitted variable in Equation (1) is household preferences. For instance, more frugal households tend to forgo consumption today in order to accumulate wealth. Thus, unobserved household frugality potentially causes the OLS estimator for $ \beta_{1} $ to be downward-biased. Employing the pseudo-panel data already mentioned will eliminate the impact of household preferences and other time-invariant unobserved factors.

Overall, while we think that most of the possible omitted variable bias due to the first two sources is controlled for by employing the methodologies mentioned above, we recognize that the last
source, household preferences, may still be present in the error term in Equation (1) unless the pseudopanel data are employed. Nevertheless, employing the cross-sectional analysis provides a useful starting point, for the following two reasons. First, while the pseudo-panel data allow us to eliminate the effects of time-invariant unobserved factors, constructing the panel from the cross-sectional data we end up with a much smaller sample (consisting of about 1,000 observations compared to 200,000–500,000 observations in the cross-sectional data) and hence much smaller household heterogeneity. Second, as discussed in detail in Section 4.2, Attanasio et al. (2009) and Campbell and Cocco (2007) reach different conclusions regarding the most plausible explanation for the positive correlation between housing prices and household consumption based on the same British cross-sectional micro data, the Family Expenditure Survey (FES). Specifically, while Attanasio et al. (2009) use the data for cross-sectional analyses and show that, among the different age groups, the MPC is largest for the young, leading them to conclude that the common factor hypothesis is the most plausible, Campbell and Cocco (2007) construct a pseudo-panel from the same dataset and show that the MPC is largest for older homeowners, which is consistent with the pure wealth effect hypothesis. It is worth investigating whether the same conflicting results depending on the type of analysis employed are also observed when using Japanese household microdata.

## 4 Results

### 4.1 Cross-sectional analysis

The estimate of $ \beta_{1} $ can be interpreted as the MPC out of housing wealth, since both consumption and housing wealth in Equation (1) are in levels. Table 2 reports the estimation results using nondurable and total consumption (excluding housing-related expenditure) as the dependent variable. In specifications (1) and (5), which make full use of the large sample size, the MPC out of housing wealth is 0.0010 for nondurable and 0.0033 for total consumption. Specifications (2) and (6) show that controlling for net financial wealth leaves the estimated MPCs essentially unchanged. The results for specifications (3) and (7), which, as in Campbell and Cocco (2007), include a dummy for young households where the head of household is aged 40 or under, indicate that the MPC of younger households is smaller than that of older households and that the difference is statistically significant at the 1% level, which is consistent with the pure wealth effect hypothesis. $ ^{22} $ Finally, in specifications (4) and (8), the sample is confined to households whose head is aged 65 or older to eliminate the potential bias arising from imperfectly capturing the human wealth component of permanent income.
wealth, and cohort characteristics, respectively. $ ^{23} $ As in Section 4.1, all monetary variables are in real terms. $ ^{24} $ Note that net financial wealth is dropped from the right-hand side of Equation (2) to ensure that the number of households in each cohort cell is at least 100. $ ^{25} $

As for any remaining possible omitted variable biases, since we take first differences in Equation (2), all time-invariant factors that might affect the dependent variable, such as household preferences, are controlled for. In addition, in the standard consumption Euler equation, the growth rate of consumption is not dependent on permanent income, which was a potential source of endogeneity in the cross-section analysis. However, there is still at least one variable the omission of which in Equation (2) gives rise to concern, namely, endogenous changes in the quantity of housing (or land area in our case). That is, as highlighted by Paiella and Pistaferri (2016), changes in wealth can be decomposed into (a) changes in asset prices for a given quantity of assets, and (b) changes in the quantity of assets at a given price. While the former can be considered as exogenous to households, the latter, in most cases, is not exogenous. As discussed in Section 3, although we already dropped households from our sample that purchased a house that is no more than a year old, households that purchased a used house that is more than one year old are still left in our sample. This could be problematic since such households may also have engaged in purchases of durable goods such as a refrigerator or washing machine or of nondurable goods such as curtains when they moved in. It is crucial to hold the quantity component of assets (in our case, the land area measured in square meters) constant to obtain a consistent estimator for housing wealth effects. In what follows, we take advantage of our data structure, which allows us to decompose the growth rate of housing wealth ( $ \Delta ln HW_{i,t+1} $ ) into two parts: the change in land prices per square meter ( $ \Delta ln P_{i,t+1} $ ) $ ^{26} $ and the land area measured in square meters ( $ \Delta ln Q_{i,t+1} $ ). $ ^{27} $ This data structure enables us to eliminate the spurious housing wealth effects driven by endogenous changes in land area. Note that many previous studies relying on self-reported and regional average values of housing wealth fail to distinguish between these two changes, i.e. exogenous price changes for a given quantity and endogenous quantity changes at a given
price (the notable exception is Paiella and Pistaferri, 2016). $ ^{28} $

Against this background, Equation (2) is modified as follows:

$$ \Delta l n C_{i,t+1}=\beta_{0}+\beta_{1}r_{t+1}+\beta_{2}\Delta l n P_{i,t+1}+\beta_{3}\Delta Z_{i,t+1}+\varepsilon_{i,t+1}, $$

where the quantity component of housing wealth, the land area in our case (given that housing wealth is the product of the quantity and price of land) is fixed at the average over time for cohort i, so that $ \Delta \ln Q_{i,t+1} = 0 $ for all t. As a robustness check, we also estimate the following first difference versions of Equation (3):

$$ \varDelta C_{i,t+1}=\beta_{0}+\beta_{1}r_{t+1}+\beta_{2}(\varDelta P_{i,t+1})\bar{Q}_{i}+\beta_{3}\varDelta Z_{i,t+1}+\varepsilon_{i,t+1}, $$

$$ \Delta C_{i,t+1}=\beta_{0}+\beta_{1}r_{t+1}+\beta_{2}(\Delta P_{i,t+1})Q_{i,t}+\beta_{3}\Delta Z_{i,t+1}+\varepsilon_{i,t+1}, $$

where $ \bar{Q}_{i} $ is the average land area over time for cohort i. In Equation (5), changes in land prices per square meter from time t to $ t+1 $ are multiplied by the land area at time t, which is another possible way to keep the land area constant. $ ^{29} $

Figure 2 provides a graphic illustration of the relationship between the rate of change in land prices per square meter and the rate of change in nondurable (left panel) and total consumption (right panel). The sample regression lines clearly show a positive relationship between the variables. However, we can only draw definitive conclusions about the relationship after controlling for other factors such as changes in household characteristics and aggregate effects.

Table 3 displays the estimation results of Equations (2) and (3) using the pseudo-panel we constructed. The estimates can be interpreted as elasticities of consumption with respect to housing wealth, and the implied MPCs are reported at the bottom of Table 3. In specifications (1) through (4), in which the key independent variables are the rate of change of housing wealth ( $ \Delta ln HW_{i,t+1} $ ), the implied MPCs out of housing wealth using the estimated elasticities are 0.0013 and 0.0049 for nondurable and total consumption, respectively. Given that the estimated MPCs derived from the cross-sectional analysis in Table 2 are 0.0012 and 0.0035, the pseudo-panel estimates are slightly larger, which suggests that the OLS estimator in Equation (1) is downward biased due to the omission of time-invariant household preferences. As for differences in the responses of the young and the old, specifications (2) and (4) show that older households' consumption responds to a greater extent to changes in housing wealth than younger households' consumption and that the MPCs for the young are slightly negative. Given that about 28% of households in our sample are renters, a possible interpretation of the negative MPC in terms of the LC/PIH is that it reflects forward-looking behavior in that renters increase their saving (and cut back consumption) in order to be able to purchase more expensive houses in the future. In specifications (5) through (8), our main explanatory variable, the
rate of change of housing wealth, is replaced by the rate of change of the price of land per square meter to avoid the possible endogeneity arising from endogenous changes in the land area that households own. The implied MPCs are 0.0012 and 0.0082 for nondurable and total consumption, respectively, and the consumption responses are again larger for older households.

Table 4 presents the estimation results based on the first difference specification given in Equations (4) and (5). In this table, the estimates are interpreted as MPCs out of housing wealth. In specifications (1) to (4), where the land area is fixed at the average over time for each cohort, the MPCs are 0.0008 for nondurable and 0.0059 for total consumption, and again the estimates are larger for older households in both cases. In specifications (5) to (8), in which the land area takes the value at time t, the estimated MPCs are approximately 0.0010 for nondurable and 0.0068 for total consumption. Although the differences between the MPCs for younger and older households in specifications (6) and (8) are not statistically significant, the point estimates for younger households are smaller than for older households.

In summary, the finding that a larger effect of housing wealth on consumption is observed for older households remains unchanged even in the pseudo-panel analysis, which differs from the conflicting results obtained by Attanasio et al. (2009) and Campbell and Cocco (2007). In addition, the MPCs out of housing wealth in the pseudo-panel analysis are 0.0008–0.0013 for nondurable consumption and 0.0059–0.0082 for total consumption, values that are almost equal to or slightly larger than the estimates obtained in the cross-sectional analysis and are smaller than the MPCs reported for other developed countries (see Section 2.1).

Regarding the estimates obtained in this study, it should be noted that the FIES only contains information on households' home. That is, if households own other properties that are rented out, only the value of the house in which they live is included in our estimated value of housing wealth. As already mentioned in footnote 9, according to the 2009 NSFIE, the estimated mean value of owner-occupied homes was approximately 20 million yen, while the estimated value of non-owner occupied homes was about 5 million yen, which is a non-negligible amount. In fact, Gan (2010), using microdata for Hong Kong, found that housing wealth effects are larger for households owning multiple houses than a single house. It should therefore be kept in mind that our estimates are somehow downward-biased.

#### 4.3 Further evidence: homeowners vs. renters

In the previous subsections, MPCs out of housing wealth were estimated for all sample households and compared across age groups, following the empirical strategy by Attanasio and Weber (1994). However, a more prudent method to examine housing wealth effects is to estimate MPCs for homeowner and renter households separately. While higher house prices may encourage renter households to save more in order to be able to afford their own home in the future, higher house prices