Distributional Impact Analysis of Climate Variability in Rural Indonesia respondents”. In addition, not all new split-off households are successfully re-contacted and reinterviewed. As a result, successive rounds of the IFLS increasingly over-represent households with older heads. If households with older head are more likely to accept new members in response to delayed onset, this may also lead the IFLS results to overstate the negative effect of delayed onset on per capita consumption.

**The SUSENAS surveys’ limited information on household consumption, in contrast, may not accurately capture the effect of delayed onset on consumption.** The IFLS consumption measure is likely to be more accurate, for three reasons. First, The IFLS consumption questionnaire is far more detailed than the SUSENAS core. It asks households to report expenditures on 37 food items and 19 nonfood items, whereas the SUSENAS core questionnaire only asks about 15 food items and 8 non-food items. Second, the IFLS questionnaire asks separately about purchased food, and food that was given or self-produced. In contrast, the SUSENAS asks households to estimate the total amount that is purchased, self-produced, or given. Third, the IFLS likely uses more accurate deflators. In 1997 and 2000, price deflators were calculated for each specific IFLS village. For the first IFLS round, as well as all SUSENAS rounds, consumption was deflated using price data published by the Central Bureau of Statistics (BPS). Unfortunately, price data is only published for major cities, meaning that the SUSENAS data is deflated by the capital city in the provinces, and may not accurately reflect changes in rural prices as a result of delayed onset.

**In sum, analysis of the SUSENAS core provides additional evidence that the effect of delayed onset on all rural households is small.** In the SUSENAS, delayed onset in the previous year has virtually no correlation with per capita consumption, and delayed onset two years ago is associated with one-half percent lower per capita consumption. Furthermore, rural households in IFLS provinces appear to be more vulnerable to delayed onset than rural households in provinces not covered by the IFLS. Unfortunately, it is impossible using the SUSENAS data to test the hypothesis that poor households are more vulnerable to late onset.

# **IV. Summary and Conclusions**

**To our knowledge, no study has focused on how climatic variability has affected household welfare in rural Indonesia** . The topic is important for at least two main reasons. First, climate variability appears to be an important determinant of agricultural productivity, particularly rice production. In addition, the agricultural sector contributes the second largest share of GDP, employs more than 60 percent of the poor nationwide, and accounts for 60 percent of all rural workers (Kishore _et al_ : 2000; World Bank: 2008). We fill this gap by estimating the effect of delayed monsoon onset on household per capita consumption.

## **_...delayed monsoon onset one year ago for all rural households..._**

**Estimates consistently show small effects on all rural households.** To assess the effect of delayed onset, we consider the average effect of experiencing a one standard deviation delay, rather than an average date of monsoon onset, on per capita farm profits and per capita consumption. Delayed onset the previous year reduces per capita farm profits by a small amount -- nearly 4,000 rupiah, or roughly 40 cents per month. This is equal to six percent of average monthly profits and two percent of a standard deviation. This estimate is sufficiently precise to rule out a loss greater than 20,000 rupiah. Turning to household capita consumption, delayed onset the previous year is associated with a 1.4 percent reduction in per capita consumption. The SUSENAS cross-sectional data, however, include many more years and provide more precise estimates. In these data, the estimated effect of late onset the previous year is

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