Silviase/jgov_v1.4_full_ann huggingface dataset:
labor earnings of the household head; the average of this variable is 5.80 million yen. The second one is households' life cycle wealth, which is defined as their net worth after subtracting gifts and inheritances received. The average of this variable is about 20 million yen.

### 3. Empirical analysis

### 3.1 Econometric approach

This section describes the econometric approach used to estimate the parameters of our intergenerational asset transfer function. We employ three different econometric models: (1) a Tobit model; (2) an interval regression model; and (3) an ordered probit model.

The Tobit model, the standard regression model for dealing with the kind of censored data that we have here, is our first choice, because our data of intergenerational asset transfers is censored from below at 0 yen and from above at 50 million yen. However, our dataset is not only censored but also coded in intervals. Therefore, we use the median value of each interval (except for the highest and lowest intervals) when applying the Tobit model.

As our second choice, we try the interval regression model, i.e., an ordered probit model with known boundaries, which takes the interval-coded structure of our dataset explicitly into account. By regarding transfer receipts as a qualitative choice problem where household i falls into one of the j ordered categories, we can apply the interval regression model for our dataset. Let $ y_{i} $ denote the number of an ordered category of transfers taking a value from 1 to 8. Assume that a latent variable $ y_{i}^{*} $ corresponding to different categories is determined by the following equation:

$$ y_{i}^{*}=x_{i}\beta+u_{i}, $$

$$ u_{i}\mid x_{i}\sim N(0,\sigma), $$
where $ x_{i} $ is a vector of explanatory variables and $ u_{i} $ is a normal error term. Let $ \kappa_{0} < \kappa_{1} < \cdots < \kappa_{8} $ be known cut off points. The relationship between $ y_{i} $ and $ y_{i}^{*} $ can be defined as follows:

$$ y_{i}=\left\{\begin{aligned}&1\quad&if\quad\kappa_{0}
boundaries). While the interval regression estimates both the standard deviation ( $ \sigma $ ) of the error term and $ \beta $ by taking the boundary values ( $ \kappa $ ) as given, the ordered probit model assumes standard normality in the error term ( $ \sigma=1 $ ) to identify $ \kappa $ and $ \beta $ . Analogous to equations (1) and (2), the log-likelihood function of the ordered probit model can be written as follows:

$$ \ln L(\beta,\kappa,\sigma;y,x)=\sum_{i=1}^{N}\sum_{j=1}^{8}1\cdot\big[y_{i}=j\big]\cdot\ln\Big(\Phi\big(\kappa_{j}-x_{i}\beta\big)-\Phi\big(\kappa_{j-1}-x_{i}\beta\big)\Big). $$

In Section 4, we compare the regression results of the above-mentioned three models to confirm the robustness and plausibility of our empirical findings.

### 3.2 Specification of the models

We use the nine independent variables described in Section 2.2 in our regression analysis. $ ^{5} $ They can be classified into six categories: (i) basic attributes of the household head and his/her spouse (i.e., variables (2) to (4)), (ii) their sibling status (i.e., variables (5) and (6)), (iii) whether their parents are alive or not (i.e., variable (7)), (iv) the educational attainment of their parents (i.e., variable (8)), which we use as a proxy for the financial strength of the parents, $ ^{6} $ (v) annual labor earnings of the household head and household life cycle wealth (i.e., variable (9)), two alternative proxies for the financial strength of respondents, and (vi) region-specific effects (i.e., variable (10)), which may reflect local traditions, customs and culture.
We expect the following relationships between the explanatory variables and the amount of intergenerational transfers. First, we expect that the age of the household head will have a positive effect on the amount of intergenerational transfers received, as people are more likely to have received bequests the older they get. Next, we consider the dummy for the occupational status, which takes one for those who are self-employed or working in a family business. The reason for including this variable is that those working in a family business may inherit that business, in addition to the personal assets of their parents (or other relatives), so that we would expect the coefficient on this dummy to be positive. It should be noted, however, that since separate information for family employees is not available, the dummy also includes those who are self-employed (e.g., doctors, lawyers, writers, and artists), so that the results for this variable need to be interpreted with caution. The relationship between intergenerational transfers and the educational attainment of the household head and his/her spouse, which we use as a proxy for human capital investment by their parents, could be either positive or negative. If parents treat intergenerational transfers in the form of human capital investment and transfers in the form of bequests as substitutes, then the relationship would be negative. On the other hand, if wealthy parents tend to make intergenerational transfers in both forms, then the relationship would be positive. With regard to the sibling status, bequests are likely to be smaller the greater the number of siblings, if the total amount of intergenerational asset transfers is independent of the number of children in a household and bequests are divided equally among heirs in accordance with inheritance law. On the other hand, based on traditions, a specific child with certain attributes, such as the oldest son, may receive a disproportionate amount of assets, either because parents stipulated this in their will or by agreement among siblings. With regard to the death of parents, we use two dummies for the household head and his/her spouse, respectively, indicating (1) whether one parent had deceased, or (2) whether both parents had deceased.
Moreover, in order to take into account the hike in asset prices during Japan’s bubble period (1986-1991), which may have inflated the amount of intergenerational transfers, we add interaction terms of the dummies just mentioned and a dummy for the years around the bubble period (we use 1986 to 1995) to reflect whether one or more parents died during this period. As for households’ financial strength (as represented by the labor earnings of the household head and the household’s life cycle wealth), this could also have either a positive or a negative effect on the amount of asset transfers, depending, for example, on parents’ attitudes toward asset inequality among their children, or on children’s consumption/saving behavior. Therefore, the sign of the coefficient cannot be predicted. Finally, we expect that the amount of asset transfers will be positively related to parents’ educational attainment, since more educated parents are likely to have had higher lifetime earnings and therefore have more assets to transfer to their children.

### 4. Results

In this section, we present the regression results of our intergenerational asset transfer function. In order to identify the determinants of the amount of intergenerational asset transfers with as large a sample as possible, we start by estimating our models without the variables related to households' financial strength (Section 4.1). The reason is that the information necessary to construct this variable is missing for many households, resulting in a substantial reduction in the size of the sample. We then re-estimate the models to examine the link between the amount of intergenerational asset transfers and household financial strength in order to examine whether those transfers contribute to a widening in inequality between the rich and the poor (Section 4.2).
### 4.1 Determinants of the amount of intergenerational asset transfers

We start by examining the determinants of the amount of intergenerational asset transfers. The results are shown in Table 3, which reports the coefficients for three different specifications: (A) including all explanatory variables (except for households' financial strength); (B) excluding variables for the spouse; and (C) further excluding the educational attainment of the household head's and his/her spouse's parents. The reason for trying specification (B) is that in about 70 percent of the households that participated in the survey, it was the household head who filled in the questionnaire, and we wanted to allow for the possibility that household heads may have reported only asset transfers they themselves received rather than the household as a whole. Finally, the reason for trying specification (C) was to use as large a sample as possible, since information on parental educational attainment was missing for a considerable number of households, constraining the sample size for specifications (A) and (B).

We estimate the three different specifications using the three different empirical models outlined in Section 3.1, i.e., the Tobit, the interval regression, and the ordered probit model. While the interval and ordered probit regressions treat the amount of asset transfers as categorical data, the Tobit model treats it as a continuous variable that is censored at 0 yen and 50 million yen. The coefficients of the ordered probit model are not comparable with those of the other two models, because the standard deviation of the error term ( $ \sigma $ in eq. (2)) is normalized to be 1. In order to make the coefficients comparable, we multiply the coefficients of the ordered probit model by the standard deviations of the error term obtained from the other two models. The two rightmost columns under (A) to (C) in the table report the resulting coefficients.

Table 3 shows that the type of occupation of the household head, the educational attainment of household head, and that of his/her parents have a significant impact on the
amount of asset transfers received. Specifically, if the household head is self-employed or a family employee, the household is likely to receive larger intergenerational asset transfers than other households. This is probably because some of the household heads who are/were a family employee received not only personal assets but also inherited the family business. As for educational attainment, the negative coefficient on the junior high school dummy and the positive coefficient on the university dummy mean that better educated households are more likely they are to receive larger bequests. In addition, the positive coefficient on the university dummy implies that intergenerational asset transfers and educational investment by parents are not used as substitutes for each other. Finally, household heads whose parents are highly educated and therefore earn more tend to receive larger bequests.

Turning to the results for specifications (B) and (C), we find that excluding the variables for the spouse and parents’ educational attainment makes the coefficients on the key variables above more significant, probably due to the larger sample size. In addition, the positive coefficient on the age of the household head also becomes significant, meaning that the total amount of asset transfers households receive increases as they get older. The pattern of the coefficients on the dummies for the number of siblings also looks more reasonable, especially in the case of specification (C), since it implies that the amount of asset transfers received gets smaller as the number of siblings increases. $ ^{7} $

Table 3 also shows that intergenerational transfers are strongly associated with the death of parents. That is, compared with couples whose parents are all alive, couples whose four parents have all died tend to have received significantly larger intergenerational transfers,
clearly indicating that inheritances are far more important than inter vivos gifts in intergenerational transfers. As for the interaction terms indicating whether one or more parents had died during the bubble period, which may have inflated transfers, we find that most coefficients are positive but insignificant (not shown in Table 3 to conserve space). $ ^{8} $

### 4.2 The relationship between asset transfers and household financial strength

In order to examine the impact of intergenerational asset transfers on asset inequality, we next include proxy variables for households' financial strength – their labor income and their life cycle wealth – in our estimation. If the amount of intergenerational asset transfers is positively correlated with households' financial strength, asset inequality across households will be perpetuated or may even increase. However, at least hypothetically, there are a number of reasons why the correlation may in fact be negative. For example, if parents tend to bequeath more to less well off children to reduce inequality among their children, this would result in a negative correlation between households' financial strength and the intergenerational transfers they receive. In addition, at least with regard to households' life cycle wealth, we may not find a positive correlation, since – according to the life cycle/permanent income hypothesis (LC/PIH) – we would expect households receiving larger asset transfers to consume more of their existing assets.

Before we present our results, a few comments on our proxy variables for households' financial strength are in order. The first is that while labor earnings are a relatively straightforward proxy for financial strength, households' life cycle wealth, which we define as households' net wealth after subtracting gifts and inheritances received, is not necessarily a
direct measure of households' financial strength, since present wealth depends not only on past earnings but also on past consumption. The second comment is that the earnings and wealth of households in Japan tend to increase with age due to factors such as seniority-based wages and saving for retirement. Therefore, to examine the effects of financial strength on the size of intergenerational asset transfers, we need to identify individuals' real financial strength, that is, controlling for any age-related increase in labor earnings or asset holdings, since failing to do so would yield biased results. We gauge households' real financial strength controlling for age-related effects by regressing labor earnings or life cycle wealth on household heads' age and its square and then use the residuals from this regression. $ ^{9} $ The residuals for the labor earnings of the household head range from -6.15 million to 13.13 million yen, and those for households' life cycle wealth from -62.10 million to 103.79 million yen.

Table 4 shows the results when regressing intergenerational transfers on age-adjusted labor earnings and life cycle wealth using specification (B) in Table 3. $ ^{10} $ Panel I shows that the estimated coefficients on labor earnings are positive and significant regardless of which regression approach is used. The positive coefficients suggest that better off households tend to receive larger intergenerational asset transfers. Turning to Panel II, the coefficients on life cycle wealth are positive but not statistically significant. A possible interpretation is that when households receive intergenerational asset transfers, they try to at least partly offset the asset increase by consuming more of their past income (or life cycle wealth), as suggested by the LC/PIH.

### 5. The quantitative importance of intergenerational transfers
The positive coefficient on the income variable suggests that, contrary to the finding by Horioka (2008, 2009), households that are already economically advantaged are also likely to receive larger intergenerational asset transfers. However, so far, we have not yet examined the quantitative importance of such transfers in widening asset inequality among households in Japan. Therefore, we next calculate the amount of intergenerational transfers for households with different attributes, using the ordered probit estimates from the previous section. $ ^{11} $

### 5.1 Household characteristics and the probability of receiving intergenerational transfers

We start our analysis by examining the probability of receiving intergenerational transfers by focusing on a hypothetical household consisting of individuals toward the end of their lives. For this purpose, we calculate the probability that a household consisting of a 70-year-old household head with a 67-year-old spouse has received intergenerational asset transfers. Further, we assume that the household head was a salaried worker, that both he/she and his/her spouse are the first-born child, that they each have one sibling and that their parents are high school graduates. $ ^{12} $ Focusing on this household, we calculate the probability of receiving asset transfers varying the following characteristics: (1) the educational attainment of the household head and his/her spouse, and (2) whether all parents are alive or dead.

Table 5 reports our estimates for the expected probability, based on the regression results for the specification presented under (A) in Table 3. The results indicate, first, that the probability of having received intergenerational transfers is higher if all parents are dead. For
example, for a couple where both the household head and the spouse are junior high school graduates, the probability that they had received intergenerational transfers was twice as high when all four parents were dead as when all parents were alive. Second, the probability appears to be considerably higher the higher the educational attainment of the couple. For instance, the probability for a couple where both were university graduates was about two to three times as high as that for a couple where both were junior high school graduates.

### 5.2 Household characteristics and the amount of asset transfers

Next, let us consider how much the expected amount of intergenerational asset transfers depends on a variety of household characteristics. Since the estimated ordered probit model provides us with the probability of each observation falling into a certain interval of asset transfers, we can calculate the expected value of intergenerational transfers by summing up the product of the probability and the median amount of a particular interval. We calculate the expected amount for the same age and number of siblings as for the hypothetical household considered in the previous subsection, varying the following two household characteristics: (1) the educational attainment of the household head and his/her spouse, and (2) the former occupation of the household head. We conduct this calculation of the expected amount for all households (i.e., E[asset transfers]), and only for households that report having received intergenerational asset transfers (i.e., E[asset transfers | asset transfers>0]).

Table 6 presents the results obtained using the specification shown under (A) in Table 3. They indicate that the expected amount of asset transfers varies substantially with household characteristics. First, the expected amount appears to be correlated with the educational attainment of the couple receiving transfers. While the difference between senior high school graduates and university graduates is relatively modest (e.g., 5.69 million yen vs. 8.56 million).
yen for former salaried workers), that between junior high school graduates and senior high school graduates appears to be sizable (e.g., 1.68 million yen vs. 5.69 million yen). Second, the expected amount is larger when the household head was self-employed or worked in a family business.

Next, in the lower panel of Table 6, we look at the expected amounts for those that reported actually receiving any bequests. Unsurprisingly, the figures are considerably larger than for all households in the upper panel. For instance, the conditional expectation for households where both the household head and the spouse only graduated from junior high school and the household head worked as a salaried worker was 6.67 million yen compared with an unconditional expectation of only 1.68 million yen. Thus, to a considerable extent, the lower figures for junior high school graduates in the upper panel simply reflect the fact that, as seen in Table 5, junior high school graduates are less likely to receive intergenerational transfers. However, what the results of the lower panel indicate is that even when taking this into account, there is still a considerable difference in the amount bequeathed: university graduates tend to have received about twice as much as junior high school graduates.

### 5.3 The impact of intergenerational transfers on asset inequality

Finally, we examine how the expected amount is correlated to households' financial strength, i.e., the level of labor earnings and accumulated life cycle wealth. The expected amounts are calculated based on the regression results reported under (C) in Table 4. Table 7 shows the results, focusing on households where the head was employed as a salaried worker, his/her parents (though not necessarily those of the spouse) have already died, and he/she is the first-born child and has one sibling. In contrast with the previous two sections, we do not assume any specific characteristics for the spouse here, since specification (C) in Table 4 does
not include any variables for the spouse. Panel I reports the amount of intergenerational asset transfers by level of age-adjusted labor earnings of the household head, ranging from -5 million to 5 million yen. $ ^{13} $ Asset transfers considerably increase with labor earnings; that is, a 10 million yen increase in labor earnings is associated with a 3.2-4.3 million yen increase in transfers received. The increase with labor earnings is particularly pronounced in the case of couples where both are university graduates. Panel II reports the amount of the intergenerational asset transfers by the level of age-adjusted households' life cycle wealth, ranging from -25 million to 75 million yen. The panel confirms the pattern observed for age-adjusted labor income; that is, intergenerational transfers tend to be larger the greater households' life cycle wealth. However, quantitatively, the direct effect of transfers on asset inequality appears to be limited. For example, even if life cycle wealth increases by 100 million yen, intergenerational transfers increase only by 3.1-3.5 million yen at most (as shown in the bottom two rows of Panel II).

In sum, examining how the expected amount of asset transfers is correlated with households' financial strength, we find that asset transfers tend to be larger the higher a household's income and life cycle wealth. However, the size of the increase in transfers associated with greater life cycle wealth is not substantial. These results can be interpreted as follows. First, parents who make greater investments in their children's education, a form of intergenerational transfer, which in turn tends to raise children's earning capacity, also tend to leave more assets behind. Second, the fact that the increase in asset transfers with increasing life cycle wealth is relatively minor hints at a pattern of consumption behavior following the LC/PIH, with households that can expect larger bequests from their parents tending to offset such wealth transfers by consuming more of their existing assets in order to smooth their