Silviase/jgov_v1.4_full_ann huggingface dataset:
### 1. Introduction

Most economists agree that Japan’s lost decade of the 1990s started with the collapse of the “Bubble” in asset markets. As its peak at the end of 1989, the Nikkei 225 Stock Average, a leading price weighted index of 225 top rated Japanese companies listed in the First Section of the Tokyo Stock Exchange, was at 38,915 yen. By spring of 2003 it had fallen to about 8,000 yen, the same level before the Bubble in 1983. Similar crash also can be seen in land prices, which fell by almost half from its peak (see Figure 1) $ ^{1} $ .

These unprecedented crashes in asset prices should have had adverse effects on Japanese household consumption in the 1990s. However, surprisingly, the channels and magnitude of the effect of these asset price movements on consumption have not been seriously explored in Japan. Since asset markets still remain stagnant and do not foresee the recovery of the economy, a quantitative evaluation of the “wealth effect” on Japanese consumption is of interest to both academics and policy makers.

In previous studies on the wealth effect, asset price movements have been found to affect consumption through two different channels. The first channel, called the “direct effect”, focuses fact that a change in asset prices alters the budget constraint on asset holders and thus their consumption. For example, if stock price declines, it makes stockholder’s budget constraint more stringent and thus dampens their spending. This hypothesis is studied in the large literature on the “elasticity of intertemporal rate of substitution (EIS)” or empirical tests of the Consumption CAPM theory. The second channel, called the “indirect effect”, focuses the fact that the asset markets predict the future prospects of the economy as a whole and therefore affect consumption through anticipation or mental accounting. Under the second channel, even households that do not hold stocks may perceive the stock price decline as a predictor of future income losses and thus shrink their spending.

In this paper, we consider both channels in quantitatively evaluating the “wealth gain/loss effects” on Japanese consumption in 1990s. In order to obtain precise estimates, this study takes advantage of micro-level data from the Japanese Panel Survey of Consumption (henceforth, JPSC) conducted by the Institute of Household Economy (Kakei-Keizai-Kenkyu-Sho). The JPSC data set has unique place in the Japanese household data with rich information on households’ characteristics and financial status. Initiated in 1993 and surveyed annually, the data can be constructed into the annual panel, which enables us to control for heterogeneity of households to evaluate the capital gain/loss effects for many years.

The main findings of this paper are summarized as follows. First, stockholders' consumption is responsive to stock market movements while this is not the case for
non-stockholders, suggesting the importance of “direct” wealth effects. The “indirect” effects are not clearly observed in our analyses. Second, we compared the marginal propensity to consume (MPC) out of stock price changes and that out of real estate price changes, and found that they are roughly comparable. The MPC out of wealth gains or losses is estimated to be 0.05 to 0.1, slightly higher than estimates reported in previous studies on Japanese consumption using aggregate data.

This paper is organized as follows. The next section reviews some previous studies on the effect of capital gains or losses on consumption. The third section describes the data set used in this study. The fourth section presents our empirical results that explore the channels and magnitude of the capital gain/loss effect on consumption in Japan, and estimates the marginal propensity to consume out of wealth gains/losses. The final section concludes.

### 2. Literature Review

As noted above, there are two channels through which asset market developments affect household consumption: the “direct” effect and the “indirect” effect. Thus we review the previous studies through two different streams in the literature.

The first stream is a large literature of theoretical and empirical studies on the elasticity of intertemporal substitution (EIS). Using aggregate data, Hall (1988) found the EIS is not significantly different from zero. However, more recently, researchers started to take advantage of micro-level data to estimate the elasticity based on the Consumption CAPM, and a key finding of those micro data studies is that consumption of stockholders is more sensitive to market returns than non-stockholders, that is, the “direct” channel is important.

Mankiw and Zeldes (1991) is a pioneering study that investigates how behavior of stockholders' spending differs from that of non-stockholders. They use annual panel data from the PSID (Panel Study of Income Dynamics) to observe that stockholders' spending is more volatile and more sensitive to the excess return of stock market, which contributes to solve the “equity premium puzzle” to some extent. Most of the later studies followed the strategy of Mankiw and Zeldes, and tried to divide the sample into stockholders and non-stockholders to see which type of household is more sensitive to the stock market movements. For example, Vissing-Jorgensen (2002) finds a large and significant difference in estimates of EIS between holders and non-holders showing that EIS is estimated to be 0.3 to 0.4 for stockholders and 0.8 to 1.0 for bondholders. Attanasio et al. (2002) employs U.K. household-level data to report that consumption growth of stockholders is more volatile and more sensitive to excess returns to stocks. Poterba (2000) argues that the marginal propensity to consume out of stock market wealth is at most 0.05.

In contrast to the first stream that exclusively focus on the “direct effect” by estimating
assets are less liquid. However, how real asset is liquid may depend on the types of ownership. We use rich data described in the next section to evaluate the consumption effect of real asset prices by type. As far as we know, the JPSC is the only available data set to be used to calculate the effect of capital gains at household level in Japan.

### 3. Data

The JPSC was initiated by the Institute of Household Economy (Kakei-Keizai-Kenkyu-Syo) in 1993, and follows the same households every year. The sample is randomly chosen from all over Japan and contains 2,000 households. The survey has very detailed variables related to consumption, income, assets, labor supply as well as household demographics. At this point, this is only a long-term panel data currently available for Japanese households and the data for 1993 to 1999 is open to researchers outside the Institute.

The first sample of this panel data (with 1,500 households) covers both married and unmarried women whose age is between 24 and 34 in 1993. The second sample of 500 households was added in 1997. If there were no omissions, the theoretical number of observations would be $ 12,000 (=1500 \times 7 + 500 \times 3) $ ; the actual number of observations with valid responses is 10,504, however.

To improve the reliability of our empirical analyses, we make following cuts. First, we eliminated 537 households (3,005 observations) with unmarried. This is because Japanese single young women often live on parents' income, but the survey does not contain the information about their parents' consumption. Second, we restrict our analysis to households with wage earners. As noted in the earlier studies on Japanese micro-level consumption, income data for self-employed is untraceable and possibly manipulated with their business account. Third, we exclude households in which members other than the household head and spouse earn their own income. We do this because the existence of other members who have their own source of income may blur the completeness of surveyed households. Fourth, we exclude observations if they respond the key items, i.e., consumption, income, and asset holdings, for our analyses improperly. These cuts reduce the sample to 1,164 households (or 3,778 observations).

The summary statistics of the main variables used in our analyses is reported in Table 1. The average household monthly consumption (in September) is 215 thousand yen and the average annual income is 6.1 million yen. The age of head of household is 34.4 on average, relatively young due to the sample design of the JPSC described above. The average family member is about 3.9, and roughly 40% of the head of household are university graduates. These figures are consistent with those in Table 2, the result of the National Survey of Family Income and Expenditure (NSFIE) if we look at the average of the figures in the age ranges of 30-34 and 35-39. Since the NSFIE is a large sample household data collected from all over Japan, our
sample represents the population and is less biased.

For the purpose of this paper, availability of the asset holding information, i.e., the information that can be used to identify assetholders and non-assetholders, is critical. Unfortunately, there is no question that explicitly asks about equity holdings in the JPSC. In each interview, however, households are asked: "Do you own any securities, such as stocks, bonds, investment trusts, loan trusts, etc?" Respondents who answer "yes" are also asked for the value of all securities, and the total purchase and sales value of any security trade during the past one year.

To identify stockholders, we need to set criteria to classify the observations and to assume a certain fraction of securities reflects stock holdings. In our analyses below, we classify households as stockholders if they have security holdings greater than a million yen $ ^{2} $ . As for real assets (land and real estate), there are questions regarding residential status, i.e., individual house, condominium, rented house, house provided by company, etc. every year in the JPSC. Respondents who answer they own their individual house are also asked for the market value of land and building respectively. About 42% of all sample households hold some amount of securities; and 32% own their individual house, 11% own a condominium and remaining households are supposed to rent house. The sample size is slightly reduced (to 3,753 or 3,527 observations) if we require the availability of the information of equity and real assets value. The average value of securities held by a household is 376 thousand yen, and that of real assets is 15.7 million yen (8.3 million for land and 7.4 million for house respectively).

If we compare stockholders and non-stockholders based on our million yen criteria (Table 1-2), household consumption and annual income are greater for holders than non-holders as expected. The average age and level of education is also higher for holders. The average value of securities for holders is 2.8 million yen. Stockholders also have larger amounts of real assets. The value of land/house for stockholders is about two times higher than those for non-holders.

If we compare different types of house ownership (Table 1-3), annual income and share of university graduates are higher for condominium owners than other categories, followed by individual house owners. There is no large difference in consumption, but individual house owners have a larger number of family members. The average value of land for individual house owners is 29.9 million yen. The value of house building is 13.0 million for individual house owners, 28.0 million for condominium owners.
4. Direct vs. Indirect Effects on Consumption by Assetholders and Non-holders

First, we employ the Dynan and Maki (2001) framework to evaluate the relative importance of the direct and indirect wealth effects on consumption. The idea is to divide the sample by asset ownership and to see the correlation between households' consumption growth and aggregate asset market returns. If we observe higher correlation for asset holders, we can interpret it as a supportive evidence for direct effects on consumption. If wealth value has no direct effects, the correlation should be no higher for assetholders.

## Specification

We run the following regression to see the correlations separately for assetholders and non-assetholders.

$$ \begin{aligned}\frac{\Delta C_{i,t}}{C_{i,t-1}}=&\sum_{j=1}^{2}a_{1i}\frac{\Delta StockIndex_{t-j+1}}{StockIndex_{t-j}}+\sum_{j=1}^{2}a_{2i}\frac{\Delta StockIndex_{t-j+1}}{StockIndex_{t-j}}*DStockholder_{i,t}\\&+\sum_{j=1}^{2}b_{1i}\frac{\Delta LandIndex_{t-j+1}}{LandIndex_{t-j}}+\sum_{j=1}^{2}b_{2i}\frac{\Delta LandIndex_{t-j+1}}{LandIndex_{t-j}}*DINDIV_{i,t}\\&+\sum_{j=1}^{2}b_{3i}\frac{\Delta LandIndex_{t-j+1}}{LandIndex_{t-j}}*DCOND_{i,t}+c X_{i,t}+\varepsilon_{t}\end{aligned} $$

The dependent variable is annual growth of real consumption for each household. For our regressions, we construct an annual growth rate of (or change in) consumption, using the total spending of all household members in September, which is the surveyed month. In other words, we use growth of spending in September as annual change in household consumption $ ^{3} $ . We dropped newly-married observations since the change in marital status surely makes our household spending data noisier. We also exclude any observations if the annual consumption growth rate exceeds 100% or falls short of -50%. These cuts and growth rate calculation reduce the samples in the following regressions to 685 households (or 1,643 observations). The first explanatory variable $ \frac{\Delta StockIndex_{t-j+1}}{StockIndex_{t-j}} $ is the annual (from September to September) growth of the aggregate real stock value, which is defined as the growth rate of the total market real value in the 1st and 2nd sections of the Tokyo Stock Exchange. Similarly, the third variable $ \frac{\Delta LandIndex_{t-j+1}}{LandIndex_{t-j}} $ is the annual growth rate of nationwide urban residential land price index,
which is regularly published by the Japan Real Estate Institute.

$ DStockholder_{i,t} $ is a dummy variable that takes one for stockholder observations (zero for otherwise). Thus, the second terms refer to the interaction between changes in stock value and status of stockholders. If stock value correlates holders' spending more than that for the non-holders' (probably due to the direct wealth effects), this interaction term is to be significantly positive. Similarly, $ DINDIV_{i,t} $ and $ DCOND_{i,t} $ are dummies for individual house owners and condominium owners respectively. Therefore, the fourth terms refer to the interaction between changes in land value and the individual house ownership, and the fifth terms to the interaction between changes in land value and the condominium ownership. If capital gains/losses on land have more of an impact on real estate holders' consumption than on the non-holders', these terms would be significantly positive. Following the specification of Dynan and Maki, we include lagged growth in our specification in order to capture wealth effects that probably occur gradually over a period of several years.

Our set of control variables, $ X_{i,t} $ , includes the growth of real annual income and the log of previous year real income to control for income effects. We also control for head of household age, age-squared, family size and its change, dummy variables corresponding households with loans, i.e., 1 for households with some loans and 0 for households without any loans, and dummy variables corresponding to educational attainment of head of household. Moreover, we include lagged inter-bank rate to allow for some of aggregate shocks. $ ^{4} $

## Results

Table 3 reports the results from estimating some variations of equation (1). We followed the general procedure of linear regression model for panel data estimation, and compared results from a pooled model (plain OLS), a random effects model, and a fixed effects model. Results of F tests generally support the use of plain OLS. We report the OLS and random effects model results, which is preferred to a fixed effects model based on a Hausman specification test.

The first two columns show the results based on our one million yen criteria to identify stock holdings when we include only stock price movements and ignore land price developments. Signs of estimated coefficients on the current and lagged annual growth of the aggregate real stock value are indefinite and insignificant, thus suggesting current or lagged stock price movements do not affect consumption as a whole. However, the coefficients on the cross terms with the stockholder dummy (the third and fourth row in the table) are positive and significant, indicating that the consumption pattern of stockholders are clearly
contemporaneously correlated with stock price movements. Together with the findings on the first two rows, our results support the existence of a direct effect of stock capital gain/loss on consumption.

The third and fourth columns show the estimated coefficients when we include only land price movements in the regression. The coefficients on the annual growth rate of urban residential land price are also indefinite and not different from zero. The signs of the estimated coefficients for the cross terms with the individual house owner dummy and the condominium owner dummy are positive but not significant. Thus, current or lagged land price movements do not correlate with consumption as a whole, though it has a slightly positive effect on real estate owner consumption. So, the direct effect observed in the stockholders is weakly observed for the real asset holders.

The last two columns in the table report the estimated results of the full specification (1). The full model produces similar coefficient pattern as those of the models in which the effects of stock and real estate price changes are dealt separately (probably due to the orthogonality between stock and land price short-run movements). Therefore we conclude that the direct wealth effects on consumption exist at least for stockholders.

As for the indirect channel, our finding that asset prices do not significantly affect non-stockholder consumption might be understood as negative evidence. Further that, if the indirect effect were working seriously, lagged asset prices would positively affect consumption growth since asset prices serve as predictors for future income. However, despite the inference, the coefficients on lagged terms are generally small and often turn to negative. Finally, estimates in Table 3 could reflect indirect wealth effects (rather than direct effects), if the assets market were a better leading indicator of the future incomes of assetholders than that of non-assetholders. However, the regression results in Table 4, in which we replace consumption growth with growth in household annual income as the dependent variable $ ^{5} $ , provide no evidence that assetholders' income move together with asset market movements more closely than the income of non-assetholders.

To sum up, we find stockholders' consumption is significantly correlated with stock prices, probably due to direct wealth effects. The direct effects from real estate capital gains/losses are somewhat more ambiguous. The indirect effect through the predictive power of asset prices is not clearly observed. In the next section, we use wealth gains or losses from asset markets for individual households to estimate the marginal propensity to consume (MPC) out of capital gains/losses.
### 5. Estimates of the MPC out of wealth gains/losses

In this section we estimate the MPC out of capital gains/losses to evaluate the effect of the asset price movements on household consumption. Straightforward expansion of the standard consumption model leads to a simple equation that ties the level of consumption with the level of total wealth, i.e., the sum of financial and real wealth and the present discounted value of future income in the form:

$$ C_{it}=MPC\times W_{it} $$

Thus, the appropriate specification to estimate MPC out of wealth is a linear regression in levels (rather than logs) as documented in Parker (1999) and Dynan and Maki (2001): a change in wealth, i.e., capital gains/losses is expected to produce a proportional change in the level of consumption. Our strategy is to use cross-sectional variations to estimate equation (2), and obtain the information about the long-run marginal propensity to consume.

## Specification and Data

We use the following specification to estimate the MPC out of stock and real estate separately.

$$ \Delta C_{i,t}=\sum_{j=1}^{2}a_{1j}CgStock_{i,t+1-j}+\sum_{j=1}^{2}a_{2j}CgLand_{i,t+1-j}+\sum_{j=1}^{2}a_{3j}CgApart_{i,t+1-j}+cX_{i,t}+\pmb{\varepsilon}_{i,t} $$

The dependent variable is the change in September consumption by a household from that of previous year. The first explanatory variables, $ CgStock_{i,t+1-j} $ , are households' capital gains or losses from stock market in current and lagged years. Similarly, the second variables, $ CgLand_{i,t+1-i} $ , are capital gains/losses from the change in land prices. The third variables, $ CgApart_{i,t+1-j} $ , are capital gains/losses from the change in house building prices. $ X_{i,t} $ represents a vector of control variables.

In the JPSC, households report the current value of securities, land, and housing as of September each year. Thus, a natural strategy is to calculate the change in the value of asset holdings. However, households may liquidate part of their wealth to consume, and this behavior may result in a spurious negative correlation between wealth and consumption changes. Fortunately, the JPSC also includes information about the purchases and sales values in households' security transactions during the past one year. We construct $ C_{gStock_{i,t+1-j}} $ by taking the reported change in security holding value, subtracting reported purchase value, and adding reported sales value. For the case of real asset capital gains/losses, we can use the changes in the market values reported, since we restricted our observations to the households whose residential status does not change over the year.