Distributional Impact Analysis of Climate Variability in Rural Indonesia consumption quintile. These 12 datasets were then combined into a single dataset consisting of 118,500 households.

**Both datasets were matched to local daily rainfall data.** Rainfall data is taken from the NOAA (National Oceanic and Atmospheric Administration) Global Summary of the Day combined with additional data obtained from the Indonesian Meteorological Agency ( _Badan Meteorologi dan Geofisika, BMG_ ). Imputed values for the missing values in the Global Summary of the Day data were provided by CEREGE, _Centre Europeen de Recherche et d’Enseignement des Geosciences de l’Environnement_ . Rainfall data contains daily rainfall data for 52 stations, of which 36 stations match with the IFLS data and 49 stations match with the SUSENAS data. In the IFLS data households were matched with the closest weather station at the community level whereas in the SUSENAS they were matched at the kabupaten (district) level.

**The definition of monsoon onset is based on daily rainfall data.** The start of the monsoon is defined as the number of days past August 1 that cumulative rainfall exceeds 200 mm, following Naylor _et al._ (2007). For each station, we calculated the start date of monsoon defined as the number of days past August 1 when cumulative rainfall exceeds 200 mm.  Onset was then standardized using each station’s “leave-out” mean and standard deviation across years. In other words, data from the onset year was excluded when calculating the mean and standard deviation used to standardize each year’s onset.

**Monsoon onset is sufficiently variable across Indonesia to allow for detailed analysis.** Annex 2 displays district-level maps of Indonesia that indicate the timing of monsoon onset from 1990 to 1999. These maps show that the 36 rain stations used to match the IFLS data generate meaningful variation in monsoon onset.

# **To estimate the effect of late monsoon onset on household expenditure, we estimate the following simple model.**

Where _Cit_ represents the per capita expenditure of household **_i_** in year **_t_** , and  _i_ is a household-specific fixed effect that captures all time-invariant characteristics of the household, including all household characteristics determined prior to 1993. _Dt_ represents a vector of dummy variables for each year.

_Oi_ , _t_  1 represents standardized monsoon onset, at the nearest weather station in the previous year. A value

> of _Oi_ , _t_  1 equal to zero would indicate that the nearest station’s monsoon onset last year was equal to its historical average, while a value equal to one would indicate that last year’s monsoon  arrived one standard deviation late. The standard deviation of monsoon onset across the entire sample is 24 days.

The key estimated parameters are  1 and  2 ,  3 and  4 , which define a quartic function that approximates the effect of monsoon onset on per capita consumption the following year.

 _it_ is a stochastic error term, which is robust to heteroscedasticity and clustered on weather station in order to allow correlation between households that have been allocated the same rain station.

8/35