Given the importance of upstream forest cover loss to DD, it is possible that upstream forest cover loss could also influence stunting rates. However, thus far the discussion around targeting drivers of stunting has largely ignored the role of forests. To investigate whether upstream forest cover loss can also have long-term health impacts via stunting, we modify our empirical strategy to account for quasi-random variation in upstream forest cover loss around the time of each child’s birth in the DHS sample. For our main specification we estimate equation (2) on the pooled sample. Since children are born at different times, each child has their own exposure based on their birth year. This contrasts with the contemporaneous analysis presented in Table 3 where exposure to forest loss was the same for all children of the same cluster and was solely based on the year they were surveyed. This specification also includes country-by-birth-year fixed effects to control flexibly for changes over time in stunting and forest cover loss.

The results in column 2 of Table 5 indicate that when the share of forest loss in the year preceding birth, i.e., when the child is in-utero, up to the year of birth increases from 0 to 1 (a 100 percentage point change), the likelihood of severe stunting increases by 0.71 percentage points, a result that is statistically significant at the 5 percent level. In other words, a 1 percent decline in upstream forest cover significantly increases the likelihood of severe stunting by 0.0071 percentage points. Using the sample average of the probability of severe stunting of 0.172, a 1 percent decline in upstream forest cover around the time of birth increases the probability of severe stunting by 4.14 percent. The final row of the table interprets the magnitude of the estimated impact of a 1 percent forest loss in terms of mean levels of severe stunting in the sample. Other lags of upstream forest cover loss are positive, but insignificant, suggesting that the transmission channel is indeed through low water quality when the child is in-utero.

**Table 5: Impact of upstream forest loss on childhood stunting, 2004-2020 (global rural non-migrant sample)**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||**D**|**ependent varia**|**ble: Incidence fo**|**r Severe Stuntin**|**g**|
|Upstream Forest Loss||||||
|Birth Year b-1 to b|0.626**|0.713**|0.608*|||
||(0.312)|(0.322)|(0.335)|||
|Birth Year b-2 to b-1||-0.158|-0.279|||
|||(0.281)|(0.264)|||
|Birth Year b-3 to b-2|||0.379<br>(0.279)|||
|Birth Year b-3 to b||||0.236*<br>(0.133)||
|Downstream Forest Loss||||||
|Birth Year b-3 to b|||||0.209<br>(0.181)|
|N|372,040|372,040|372,040|372,040|372,040|
|Fixed Effects|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|
|Demographic Controls|Y|Y|Y|Y|Y|
|R-sq|0.042|0.042|0.042|0.042|0.042|
|Mean stunting incidence|17.22%|||||
|Impact of a 1 percent loss in upstream|3.64%|4.14%|3.53%|||
|forest cover 1 year ago as a percent of<br>mean stuntingincidence||||||

_Notes_ : Table presents estimates of equation (2) where the dependent variable is the stunting incidence for non-migrant children under the age of 5 between years 2004-2020. Forest loss is measured in terms of the share of forest loss over the upstream catchment area relative to birth year 'b' of a child. See notes to Table 3 for details on the variable construction 19