marriage decreases with the increase in the value of father’s farmland (the results are statistically signi fi cant at the 10 percent level), con fi rming that consanguinous marriages are more common among poorer households. The estimates also con fi rm that the relationship between farmland and consanguinity may be non-linear. We turn now to an examination of the relationship between dowry and wealth. Figure 5 shows that the relationship between dowry and wealth is also non-monotonic and that as the theory predicts, is the exact inverse of the relationship between consanguinity and wealth: as wealth levels rise, dowries fi rst increase, reach a peak and then decrease. Table 5B explores the relationship between dowry and wealth econometrically. Panel (A) regresses the log of dowry values on father’s farmland value and its squared term, and Panel (B) regresses dowry values on a dummy variable indicating father’s completion of primary school. The coefficients are statistically signi fi cant even with the inclusion of individual, household, rainfall and decade of marriage controls. Standardization of the coefficients from the farmland regression indicate that a 1 percent increase in the value of farmland causes the dowry paid to increase by approximately 35 percent. Similarly, a father’s completion of primary school increases the value of the dowry by about 500 taka, which is about 25 percent of the average value of a dowry in this sample. The estimates for the squared term of farmland are also statistically signi fi cant, con fi rming the non-linearities that are predicted by the theoretical model.

Taken together, the results of Table 5A and Table 5B suggest that as families get wealthier (starting from an initial condition of low levels of wealth), credit constraints may weaken and the family is able to search for a groom outside the kinship network by providing higher levels of dowries for their daughters. These results are also consistent with the results of Mobarak, Kuhn and Peters (2006), who use a difference-in-differences framework to show that the construction of an embankment in Matlab (several years prior to the 1996 data) created a positive wealth shock for some households, who were then able to pay higher dowries for their daughters and were less likely to enter into consanguinous marriages. Our results con fi rm this fi nding but also raise the additional possibility that the relationship is non-linear. At very high levels of wealth for example, households may once again prefer to contract marriages within the kinship network and resist the transfer of wealth to unrelated individuals through either dowries or future inheritances.

As an additional test of the hypothesis of credit constraints, we explore the relationship between cousin marriage, dowry and age at marriage. As noted earlier in this section, marriages to fi rstcousins take place at younger ages than marriages to unrelated individuals. The results in Table 6B suggest that the relationship between dowry and age at marriage is positive. In particular, the estimates from column (4) suggest that a one standard deviation delay in the age at marriage (i.e. almost 3 years) increases the value of the dowry by 0.7 standard deviations, or approximately 2800 taka. The speci fi cation where the value of the dowry is regressed on the age at marriage suffers from a possible endogeneity problem. It is possible for example, that the age at marriage is in fact an outcome rather than a determinant of a household’s ability to pay a dowry. We deal with this issue by instrumenting the age at marriage with the age at menarche. Since most marriages in rural Bangladesh are contracted at, or just after, the time of the onset of puberty, we assume that this variable will be highly correlated with the age at marriage. We thus assume that the age at menarche affects parental credit constraints only through the the time of a woman’s marriage. This exclusion restriction is motivated by the observation that in rural Bangladesh, as in much of South Asia, the age at menarche is a lower bound on the age at which marriages are contracted, with the lag between the onset of menarche and the contracting of a marriage increasing steadily over time (Iyer 2002).<sup>24</sup> We concede, however, that our exclusion restriction relies on the assumption that

> <u>24</u> In our dataset, women aged 35 and younger married at an average age of 17. Only 103 marriages (out of 6294

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