also be that it is the illness of the parent that really causes the health or educational deprivation of the child, and not the actual death of the parent.

One avenue of investigating causality is to classify the sample of non-orphans used in the previous section by their _future_ orphan status. That is, we know in later survey rounds which non-orphans in the sample _became_ orphans by the second round (2004). We would expect that future orphan status would not be correlated with current outcomes unless (i) orphanhood captures some unobservable characteristics for which we are unable to control or (ii) morbidities associated with deaths affect outcomes before becoming an orphan. This approach follows Case and Ardington (2006) who make a causal interpretation of maternal orphanhood in a panel (2001 to 2003/04) by showing no effect of future orphan status on current schooling. Likewise, Evans and Miguel (2007) make note of the same issue in the context of censoring of the last round of their panel data since they include post-orphan variables which by definition are unknown for non-orphaned children in the final survey round.

Table 6 presents the regressions results of the correlates of height and years of education completed at the time of the baseline survey. The child and baseline control variables are identical to table 4, including a control for initial community fixed effects. Future orphanhood is not correlated with baseline height or education, suggesting that unobservables correlated with health and education are not correlated with becoming an orphan in the future.

Finally, what do these losses in terms of health and education mean for the overall loss in living standards brought on by orphanhood? To quantify these consequences, ideally, we should estimate earnings functions to obtain the returns to height and education. However, we do not have income data within the data set. Instead, we can estimate returns using total consumption expenditure in 2004, providing a useful proxy for overall living standards, based on the sample studied in this paper, who are young adults in 2004. We first use a simple specification, consistent with the standard Mincerian earnings regression, in which consumption expenditure per capita is regressed on the logarithm of height and years of education, controlling for a full set of age dummies and sex. However, this is unlikely to measure correctly the marginal effect of height and education, as factors such as health and education endowments and family background, likely to be correlated with height and education, are not included. We therefore include a second specification, using all household and parental level baseline controls (including wealth variables, parental education and mother’s height), and community fixed effects, i.e. a full set of controls as used in table 4.

Table 7 reports the coefficients of interest of these regressions, finding very strongly significant effects from both health and education in both specifications. When background controls are not included, the bias in estimated effects is in the direction expected, although small; returns are lower when controls are included. A one percent height gain adds 2.2 percent to consumption per capita, while an extra year of 12