otherwise. The key parameter in Equation (1) is $ \beta_{2} $ , which captures the additional effect of inheritance receipt on changes in the probability of working and is expected to be negative. It is important to add the control variables to avoid endogeneity issues. For example, if receiving an inheritance leads individuals to be more inclined to have (more) children because it eases the financial burden of child-rearing, and those who have more children are more likely to quit their job, the estimate for $ \beta_{2} $ would be downward-biased if the number of children was not controlled for. Similarly, some individuals may exit the labor market due to a mental health problem caused by the death of their parent death and, as a result, might be treated favorably when the inheritance is divided among heirs. That is, given that in Japan it is common for inheritances to be divided up in negotiations among surviving family members, such individuals may be treated favorably in such negotiations and receive a larger inheritance as a result (see Hamaaki, Hori, and Murata 2016 for custom of intra-family division of bequests in Japan). Thus, ignoring such health issues could result in downward biased estimates.

All time-constant factors including unobservables such as the preference for work are captured by $ \alpha_{i} $ as an individual fixed effect, while aggregate effects are controlled for by year dummies, $ \mu_{t} $ . Note that $ Inheri_{i0} $ alone cannot be incorporated in Equation (1), since it is time-constant so that it is included in $ \alpha_{i} $ . The idiosyncratic error $ \varepsilon_{it} $ is assumed to be uncorrelated with the explanatory variables. We regard Equation (1) as a linear probability model and estimate it using the fixed effect approach.

Table 2 shows the regression results obtained estimating Equation (1). To check the robustness of the results, the pre-inheritance period is defined in two ways, namely as t = -1 and $ t \leq -1 $ . In the former case, we simply drop observations for $ t \leq -2 $ when estimating Equation (1). Columns (1) to (4) show the benchmark model estimates excluding the control variables. Except in column (1), coefficients have the expected sign, although only in column (2) are the coefficient estimates statistically significant. Taken together, the results provide weak evidence that, in the case of women, receiving an inheritance has a negative effect on labor supply. To check whether the coefficient estimates are contaminated by other factors, we add control variables including year dummies in columns (5) to (8). Columns (5) and (7) suggest that inheritance receipt does not have a statistically significant impact on men's labor supply. $ ^{7} $ In sharp contrast, columns (6) and (8) show that receiving an inheritance reduces women's labor supply despite the fact that the average amount inherited by women (excluding zero inheritances) is smaller than men's.

Looking at the results in more detail, column (6) suggests that women whose parent passed away but who did not receive an inheritance were 6.50 percentage points more likely to be working after the death of the parent than before. One possible explanation for this phenomenon is that these women