Census data
Indonesia conducted a Census of its population in June 2000. The dependent variable, the cohort size for a subdistrict-month, is the count of all enumerated people born in a given month who reside in the subdistrict. The specific date of birth is not available. The popula-tion weight for each subdistrict is the total number of people born in 1994 to 1996 enumerated in the Census.
I link mothers to children for the analyses by mother’s 1995 residence and education and for constructing the measure of predicted births. Using a household identifier, I link each child to women who are 14 to 42 years older than the child. When there are multiple matches, I give preference to household heads or spouses of heads and to women closer to the peak of the fertility age distribution. To construct predicted fertility, I perform this matching for children born in 1999 and 2000 (through May) and calculate the mother-child age gap.
Then for each age in months of women between 14 and 42, I calculate the number of children these women give birth to divided by the total number of women of that age. This gives the fertility rate (net of infant mortality) for each age. I make two adjustments to the fertility rate. First, I smooth the distribution using values for the 4 ages in months before and after each data point. Second, after the age of 38, I replace the estimate with a linear extrapolation from the estimated value at age 38 to 0 at age 42 to correct for the fact that the matching process mistakenly assigns grandmothers as mothers in some cases (giving an implausibly high fertility rate for older ages). The next step is to calculate the number of women by age for each district in the sample period, and multiply it by the age-specific fertility rate.
Summing across all the ages of women of childbearing age gives the predicted number of births for each district-month in the sample.
TOMS pollution data
In addition to the information provided in the text, further details on the Total Ozone Mapping Spectrometer can be found at http://toms.gsfc.nasa.gov.
Fire location data and rainfall data
The European Space Agency compiles the ATSR World Fire Atlas, a list of “hot spots”
(date, time, latitude and longitude) identified by nighttime infrared measurements by the ATSR-2 instrument onboard the ERS-2 satellite. A hot spot corresponds to a reading of >
312 Kelvins at 3.7 micron wavelength. For each subdistrict-month, hot spot-days within 50 km of the subdistrict’s center are used to calculate the number of fire-days, or the sum over discrete fires of the duration of the fire in days.
The rainfall data set, Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series, is from the Center for Climatic Research, University of Delaware. The rainfall measure for each node on a 0.5◦ latitude by 0.5◦ longitude grid is interpolated from 20 nearby weather stations using a spherical version of Shepard’s distance-weighting method.
The rainfall measure for a subdistrict uses the closest node.
SUSENAS data
I use household level data from the 1996 and 1999 SUSENAS core modules which aggregate item-by-item consumption data to two categories, food and non-food. For each household, per capita consumption is calculated weighting children by 0.75 and infants by 0.6 compared to adults who are weighted by 1. The subdistrict log consumption measure is the median across all households of log food consumption per capita.
PODES data
The PODES is a census of all villages and towns in Indonesia. I use the population, fuel use, and health facilities questions for 1996. One question asks what cooking fuel the majority of the village uses, where I group the answers as wood fuel (wood plus other biomass) or other (kerosene and gas). The population-weighted average of this indicator variable across villages in a subdistrict is the fuel use variable. Health care measures are unweighted per capita measures for the subdistrict, based on the reasoning that people have access to facilities throughout the subdistrict.
Notes
1The Indonesian Minister of Forestry estimated that “[commercial] plantations caused some 80% of the forest fires,” and that small farmers caused the remainder (Straits Times, September 3, 1997). Rabindran (2001), using satellite data on land use, finds that the 1997 incidence of fires on plantations was higher than the “natural” level (based on a benchmark from conservation areas), but the incidence of fires on small farms was at its natural level.
2This value of a statistical life (VLS) is calculated by using $5 million in 1996 dollars as the value in the U.S. from Viscusi and Aldy (2003), Murphy and Topel (2005), and the U.S. Environmental Protection Agency (2000), and applying an income elasticity of a VSL of 0.6 from Viscusi and Aldy (2003) and the fact that Indonesia’s per capita gross domestic product was one tenth of the U.S’s.
3Recent work suggests that the health costs could include long-term consequences among surviving fetuses (Barker 1990, Almond 2005).
4Other natural experiments used to measure health effects of air pollution include the temporary closure of a steel mill in Utah during a labor dispute; the reduction in traffic during the 1996 Olympics in Atlanta; and involuntary relocation of military families (Pope et al. 1992, Friedman et al. 2001, Lleras-Muney 2005).
5For research on pollution and infant mortality outside the U.S., see for example Bobak and Leon (1992) on the Czech Republic, Loomis et al. (1999) on Mexico, and Her Majesty’s Public Health Service (1954) on the 1952 London “killer fog” (smog) episode.
6In 1996 forest products accounted for 10% of Indonesia’s gross domestic product, and Indonesia supplied about 30% of the world palm oil market (Ross 2001).
7One reason the Indonesian fires produced so much pollution is that many were peat fires which produce large amounts of smoke.
8The appendix verifies that for more aggregated area-time cells, population counts from the Census move one-for-one with births and deaths in survey data.
9The literature on “missing women” in developing countries, most often associated with Sen (1992), uses population sex ratios to infer excess female mortality caused by gender discrimination.
10As shorthand I describe deaths of children in the sample as infant mortality even though they could occur as late as age three and a half. The common definition of infant mortality is deaths before age one. Note that one advantage of observing survival more than two years after the due date is that for deaths that occur around birth, the estimates are less likely to reflect simply short-term “harvesting.”
11The Census covers 3962 subdistricts which make up 336 districts. For subdistricts dropped from the sample, either the latitude and longitude could not be determined or
there were no enumerated children for more than 15% of the monthly observations due to missing data or very small subdistrict size. In addition, I drop four districts that make up Madura since the East Javanese island received a large influx of return migrants in 1999 (in response to ethnic violence against them in Kalimantan), and also Aceh province where separatist violence is thought to have affected the quality of the Census enumeration. The results are robust to dropping Irian Jaya, another area where unrest could have affected data quality.
12Negative values represent non-absorbing particulates such as sulfates.
13See Table A1 in the appendix for an instrumental variable estimate of the effect of P renatalSmoke on cohort size. The instrument for P renatalSmoke is a dummy for Kali-mantan or Sumatra interacted with a dummy for October 1997 to January 1998. The differences-in-differences estimate, which uses only coarse variation in pollution attributable to the fires, is -0.040.
14The estimates using high-smoke days imply a 1.1% aggregate effect. (The mean of the prenatal high-smoke variable is 0.14 during the 1997-8 episode and 0.01 for the same calendar months a year earlier, and the coefficient in Table 2, column 4, is -0.085.) The decline in cohort size for the affected subdistrict-months is not visible in the raw data, given the noisiness of the outcome variable.
15The government estimates of under-1 and under-5 mortality rates at this time are 5%
and 7%, respectively. I assume that half of deaths between age 1 and 5 occur before age 2.
16The month-by-month patterns, unlike the results with the three-month measures, are somewhat sensitive to using a different sample period or a different threshold for high-smoke days, for example.
17For 9% of the sample, district of residence differs from district of birth, for 7% it differs from mother’s residence five years earlier, and for 12% it differs from one or the other.
18Within-district migration is unlikely to be driving the results since there is very little within-district variation in pollution, and most of it derives from interpolation so is noisy.
In a model with district-month fixed effects, the coefficient for P renatalSmoke is -0.013, smaller than in the main specification (Table 2, column 1), and imprecise, suggesting that between-district variation is dominant in the main estimates.
19Table A2 addresses another potential concern about fertility, namely that the seasonality of births or deaths could differ for areas more affected by the pollution, generating a spurious result. As shown in columns 4 and 5, the results are robust to restricting the sample to the months with high P renatalSmoke plus the same calendar months one year earlier.
20Considering only September, this phenomenon should also generate a positive correlation between Smoke (pollution in the month of birth) and cohort size, but averaged with October and November which have high values of both Smoke and P renatalSmoke, the net effect is
indeterminate.
21Table A2 restricts or expands the sample to other time periods, and the results are robust to this change. One noteworthy finding is that the estimated effect of P renatalSmoke is smaller when the window extends more than 8 months after the fires, suggesting that the fires may have reduced fertility.
22Since the crisis accelerated a few months after the fires, I also estimated models that control for the crisis measure for the three months following the month of birth. This generates more variation in the crisis measure during the period of interest. The estimated effect of P renatalSmoke remains unchanged.
23Rukumnuaykit (2003) finds a 3% increase in infant mortality in 1997-8 in the Indonesia Family Life Survey, which is interpreted as due to the financial crisis, drought and smoke.
24This is unlikely to explain the effects since the drought began only in June. However, if rainfall shortages began in May in some areas, then cohorts conceived in May who entered the third trimester in November could be affected in this way.
25Most of the variation in rainfall is absorbed by the month fixed effects. The results show that the residual variation in rainfall cannot explain the “missing children,” but the findings should not be interpreted as showing that rainfall has no effect on fertility or mortality. The 1997 drought may have had an Indonesia-wide effect.
26In unreported results, when the sample is divided into infants born to mothers who work in agriculture, work in other industries, or do not work, it does not appear that children whose mothers work in agriculture experience larger effects.
27In unreported results, the effect of prenatal smoke is smaller in areas where more houses are constructed with bricks and concrete (compared to wood, palm leaves, etc.). Worse road quality, which could be a proxy for higher costs of evacuating, is also associated with larger mortality effects of pollution. Another approach to measuring evacuation would be to use distance to the nearest low-smoke area, but in practice this variable is too highly correlated with the pollution level to be useful as an interaction term. Use of surgical masks might also vary with income. Kunii et al. (2002) surveyed 532 people during the fires and found that use of surgical masks was associated with fewer respiratory problems, but most medical experts believe that surgical masks are ineffective at blocking out fine particles.
28The income gradient in health for survivors could go in the same direction or, if the weakest individuals were “harvested,” in the opposite direction.
29Ideally, the DHS would have recorded pregnancies that ended in fetal deaths. Some of the missing children in the Census are not among the live births measured by the DHS. Also, ideally, the validation exercise would use the same unit of observation and sample period as the main analysis, but the survey data are then too noisy to obtain meaningful results.
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Figure 1: Rainfall at Palembang Airport meteorological station, South Sumatra, 1990-97
Figure2:SatelliteimagesofsmokeoverIndonesia September 5, 1997 September 25, 1997 October 15, 1997 November 4, 1997
Figure 3: Timing and location of the pollution
Figure 4: Kernel regression of log cohort size on prenatal exposure to pollution
The solid line is the relationship between log cohort size and pollution (P renatalSmoke).
The dashed lines mark the bootstrapped 95% confidence interval, with errors clustered within an island-month. The model estimated is a locally weighted non-parametric regression of log cohort size on pollution conditional on linear year and district fixed effects, following Robinson (1988). Log cohort size has been offset by a constant so that its value is 1 at an aerosol index of 0.
Figure 5: Timing of the fires and the financial crisis
Table 1 Descriptive Statistics
Mean Std. Dev.
Cohort size variables
Cohort size (for subdistrict-month) 95.6 89.7
Ln(cohort size) 4.8 .8
Pollution variables
Pollution variables