few observations per _jati_ -state-year cell.<sup>20</sup> Thus, the variation in our key explanatory variable, expected dowry, is at a more aggregate level (caste-state-year) than our savings data, which is at the household level. With 17 states and 22 years, we have 1,285 caste-state-year groupings or marriage markets in our sample.<sup>21</sup> Figures A.2 and A.3 show the expected dowry variation by year of birth of the firstborn child and by caste groups.<sup>22</sup> Although our caste categories are broad, our objective of using them is simply to distinguish between marriage markets. While it is true that the SCs, for example, do not operate as one marriage market, it is also true that the likelihood of an SC individual marrying another SC individual is substantially and significantly higher than an SC individual marrying an upper-caste individual, making our categorization still useful for a marriage market analysis. To illustrate, in REDS 2006, 91 percent of SC individuals were married to another SC individual. The corresponding numbers for STs, OBCs, and other castes were 93 percent, 95 percent, and 92 percent respectively, implying that our caste categorization captures the marriage markets reasonably well.

Does our expected dowry variable <u>predict</u> actual dowry? Our expected dowry variable is a significant predictor of the actual dowry paid at the time of marriage. Using data on individuals whose year of birth and year of marriage fall within our data, we run an ordinary least-squares (OLS) regression of the actual dowry paid at the time of marriage on the expected dowry in the year of birth (using our definition). Table A.3 shows that the “first stage” relationship between our constructed expected dowry measure and actual dowry that the household pays, including state fixed effects, year-of-birth fixed effects, caste fixed effects and their interactions, is highly significant.<sup>23</sup> Figure A.4 shows the scatter plot overlaid with the linear prediction plot without any controls. Moreover, the correlation between expected and actual dowry is positive and significant for all four of our caste categories.

Recall bias in dowry data: Like most survey data, one may be concerned about the recall bias in the reported dowry variable, especially when the year of marriage is too far back in time. To examine the extent of the recall error, we utilize data from the 1999 round of REDS and compare average dowry paid by year of marriage for years that are available in both rounds. Note that the question about dowry was asked differently in both rounds. Nonetheless, Figure A.5 shows that, for the sample period of our savings analysis (1986-2007), the two rounds report similar dowry amounts. Thus, recall bias is unlikely to be a significant issue for our regression estimates. Given

> 20The same issue prevents us from using district-level variation—the average number of marriages in a district-caste-year cell is just four.

> 21Due to missing data, the bulk of which are for ST, we do not have all 1,496 cells.

> 22On average, 100 observations are used to construct expected dowry for each caste-state-year of birth cell.

> 23We cannot include the firstborn girl interactions here because we do not know the sibling composition and birth order of individuals for whom actual dowry information is available. One may also worry that the significant relationship in Table A.3 is driven by outliers. To test if this is the case, we ‘residualized” the fixed effects included in Table A.3 from expected dowry as well as actual dowry and then regressed actual dowry on expected dowry after removing 6 outlier observations—the coefficient estimate remains almost exactly the same as in the last column of Table A.3.

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