Distributional Impact Analysis of Climate Variability in Rural Indonesia negligible, and the results are sufficiently precise to rule out negative effects greater than 1.5 percent. Since the estimated negative effects of delayed onset rise to 6 percent when limited to IFLS provinces, and 8 percent when limited to IFLS provinces and IFLS years, the IFLS results may overstate the negative effect of delayed onset for all Indonesian households.

# **_...delayed monsoon onset two years ago for all rural households..._**

In the IFLS, delayed onset two years ago is associated with an 11 percentage point increase in per capita expenditure for all rural households and this result is statistically significant at the ten percent level. Most of this positive association is due to an increase in total consumption rather than household size. This may partly be explained by a _La Niña_ effect, which normally follows _El Niño_ episodes and is associated with abnormally high rainfall levels and higher agriculture outputs (to the extent that the _La Niña_ is not captured by controlling for lagged onset). However, the positive effect of delayed onset two years ago does not extend to the SUSENAS data, which shows a tiny negative effect of around three tenths of a percent. Overall, the results rule out the possibility that delayed onset has a substantial negative effect on rural households two years later.

# **_...distributional impact of delayed monsoon onset..._**

**Delayed monsoon onset has substantially larger short-term effects the following year on poor households.** The effect of delayed onset on poor households can only be estimated cleanly using the IFLS data. Delayed monsoon onset the previous year likely reduced household per capita expenditure for the poor, by approximately 13 percent, but the majority of this effect occurs through an increase in household size. Specifically, delayed onset in the previous year decreases household consumption by roughly 4.3 percent while increasing household size by roughly 6.8 percent. Climatic shocks may increase household size through several mechanisms. Households may respond to negative shocks by combining households. This could occur, for example, if higher food prices push more urban Indonesians to migrate back to their families in rural areas. Households with older heads, who are over-represented in the IFLS as the panel ages, may be particularly likely to receive additional household members. A final possibility is that delayed onset increases household size by reducing the number and size of roving bands of crop harvesters, which are common in Indonesia.

**Poor households show the largest positive effects of delayed onset two years later.** Delayed onset two years ago is associated with a 23 percent rise in per capita consumption, which is larger than the 11 percent for all households. This suggests that the positive association between per capita consumption and delayed onset two years ago might be due to the positive effects of _La Niña_ or the ability of farm households to “catch up” following delays in planting two years ago.<sup>15</sup>

# _Caveats_

**An important limitation is the amount and quality of the rainfall data.** Only 36 rain stations can be matched to each wave of the IFLS, which reduces the variation in our main independent variable. Unfortunately the years prior to the IFLS interviews do not show wide variation in monsoon onset, and none of the years captured by the IFLS follow a strong _El Niño_ year. Finally, the measure of late onset is based on daily rainfall data, which is undoubtedly measured with error. Since measurement error in rainfall is independent of local household consumption, the true effect of delayed onset, both positive and negative, is likely greater than the estimates presented here.

> 15 The beneficial effects of La Niña could occur even controlling for the timing of onset the previous year, due to the imprecise nature of the quadratic approximation used in the econometric possibility. Another possibility is that the beneficial effects of La Nina are felt through other factors such as rainfall amount that are not included as control variables.

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