2.3 Data and methodology
2.3.2 Estimation Strategy
My estimation strategy allows for non-linear effects of delayed onset, given the likelihood that the cost of delayed onset accelerates as the length of the delay increases. This would occur, for example, if farmers faced with small delays forego planting on their lowest-quality soil, or if larger delays inhibit informal insurance networks. My preferred method estimates a spline (a piecewise linear regressions) with one knot, which maintains some flexibility and is relatively straightforward to interpret. I check robustness by estimating flexible non-parametric regressions as well as quadratic functions. The knot of the spline was selected to be -0.6 standard deviations, based on the results of the non-parametric regressions. It is apparent that the number of linear splines could be more than two but this proved unnecessary in the current application.
To estimate the effect of monsoon onset on household per capita expenditure and farm profits, I estimate the following spline models with two lags of onset:
it
And above the threshold value Oit1 0.6: capita expenditure of household i in year t , while F is monthly per capita farm profits of it household i in year t . i represents a household i’s fixed effect, which captures all time-invariant characteristics of the household, including all household characteristics determined prior to 1993. D represents a vector of dummy variables for each survey year. t Farm profits per capita are expressed in levels rather than logs, in order to allow negative values for profits. it is a stochastic error term, which is robust to heteroscedasticity and clustered on rain station, to allow for unobserved correlation between households that have been matched to the same rain station. In all regressions, households are weighted by their mean sample weight over the course of the three survey waves.15
1
Oit stands for monsoon onset the previous year, and it indicates the timing of monsoon onset at the nearest weather stations in the previous year. As onset is standardized, a value of Oit1 equal to zero indicates that the previous year’s monsoon, according to the nearest rain station, arrived at the same time as the historical average. Values of Oit1 equal to one and negative one indicate that last year’s monsoon arrived one standard deviation (24 days) late or early, respectively. Parameters 1, 3, 5, 7 represent coefficients for the slope of the left linear segment of onset, while parameters 2, 4, 6, 8 represent coefficients for the slope of the right linear segment.
Two lags of onset are included because rainfall exhibits negative serial correlation in the sample.16 Including an additional lag of delayed onset reduces omitted variable bias if the
15 However, consistent results are obtained without weighting.
16 The correlation between rainfall and lagged rainfall in the data is -0.33.
second lag of monsoon onset influences per capita expenditure and/or farm profits. Finally, examining the effect of variation in onset two years ago can shed light on whether the effects of climactic shocks persist for two years. For these reasons, despite the reduction in the precision of the estimates, my preferred specification includes an additional lag of monsoon onset. However, in the Appendix tables I also present estimation results for specifications with the first lag of monsoon onset only.
The model is re-estimated separately for farm and non-farm households. Per capita farm profits are only estimated for farm households, defined as those owning a farm in 1993.
Owning a farm in 1993 is time-invariant, meaning that it is orthogonal to the residual it in equations (2.1) and (2.2).
In addition, I re-estimate the model separately for poor, middle-class and rich households. I use two methods to determine household class. The first involves taking the tercile of their average real per capita expenditure, over the course of the three surveys. If the impact of rainfall shock is constant across all households within an income group, stratifying the sample based on these time-invariant characteristics does not directly introduce bias into the estimates.17
The assumption that the effect of rainfall shocks is constant is strong, however.
Households that suffer a large loss following a rainfall shock are more likely to be in the bottom tercile according to their average per capita expenditure. This could lead the results to overstate the extent to which poor households are vulnerable to rainfall shocks. Thus, I employ an alternative strategy to group households, based on a welfare indicator that is predetermined with respect to rainfall shocks.
This alternative strategy involves estimating the portion of average household expenditure that is predetermined three years before the household enters the survey, prior to the earliest rainfall shock used in the analysis.18 To do this, I regress the average per
17 Two other sources of bias may be present: attenuation bias due to measurement error in average per capita expenditure measure, as well as correlation between the impact of the shock and welfare status if households’
response to shocks is heterogeneous. These issues are discussed below.
18 While dynamic panel data models are commonly utilized to control for past consumption (i.e. models with lagged dependent variable), they cannot be employed in this case because there is insufficient data to sacrifice one of the three years, and because the interval between panels is not constant across years.
capita expenditure of the household over the course of the panel on several retrospective variables as follows:
i,
i
i Z
C (2.3)
where Ci represents the per capita expenditure of household, averaged over each year in which the household appears in the panel survey. Zi is a vector of retrospective predetermined variables for household , including the age and education of the household head, as well as a set of indicators for his or her district of residence at age 12, a dummy for whether the head worked three years before entering the panel, and for those that did, the number of hours and weeks worked. Each of these variables was determined prior to the first rainfall shock considered in the analysis.19 Households were then classified into terciles based on their predicted per capita expenditure. Appendix table A3 displays the results of estimating equation (2.3).
This additional robustness, however, comes at a cost. First, a full set of predetermined variables is available for only 92 per cent of the sample, which limits the representativeness of the results. In addition, this procedure raises the prospect that predicted expenditure is an inaccurate indicator of household economic welfare, particularly given that the regressors in (2.3) only explain 11 per cent of the variation in average household per capita expenditure. Therefore, I treat this alternative method as an important robustness check.
Finally, identification of the causal effect of delayed onset is based on the assumption that monsoon onset is exogenous with respect to household expenditure for all households.
Several previous studies have assumed that rainfall is exogenous with respect to household behavior (see, for example, Paxon, 1992; Munshi, 2003; Newhouse, 2005; Jayachandran, 2006; the literature is surveyed in Rosenzweig and Wolpin, 2000). This identification assumption may be threatened if some households are able to anticipate rainfall shocks, but
19 Arguably, rainfall shocks could influence who the household identifies as the head, but there is no reason why the characteristics of the head would be systematically different for households residing in areas with a delayed onset in the past two years.
this is unlikely to be a serious concern in this context. Systematic dissemination of El Niño
Approximately 60% of rural households in the IFLS are farm households (see table 2.2).21 Farm households are slightly more likely to be poor than rich, as the share of farm households is 64 per cent in the bottom expenditure tercile and 57 per cent in the top tercile.
Approximately half of the farm households cultivate rice as their main crop.22
Table 2.2. Main variables, IFLS data.
Mean s.d.a
PCE (in logs)b 12.089 0.710
Share of farm households 0.596 0.491 Farm profits pc (in rupiahs)bc 31,711.8 56,959.3 Farm profits pc, 1st tercile 19,5628.3 28,526.5 Farm profits pc, 2nd tercile 29,174.2 47,762 Farm profits pc, 3rd tercile 48,366.2 81,455.4
Notes: a denotes for standard deviation. b Household per capita expenditure and farm profits are expressed in December 2000 Jakarta prices. c Conditioning that household owns a farm business.
20 This is in the form of Climate Change Field Schools organized for farmers. See for example:
http://www.agrometeorology.org/topics/accounts-of-operational-agrometeorology/climate-field-schools-in-indonesia-coping-with-climate-change-and-beyond. Accessed 10 October 2009. These schools may be beneficial for the farmers: Climate change field schools surveyed in the main rice production kabupatens in West and East Java in 2007-2009 indicated that formal climatic data were used in the timing of farming activities (Natawidjaja et al. 2009).
21 Households are classified as farm households if at least one member of the household was reported as working on a farm business on household-owned land in 1993.
22 This is taken from 2000 IFLS data. Unfortunately, farmers were not asked to list their main crops in prior waves.