4.2 Data and Summary Statistic
4.2.3 Average Household Characteristics and Baseline Differences 92
Table 4.2 shows the key descriptive statistics of the data before the program. I report the average values and its standard deviation of households’ characteristics in the control group, weighted with the survey weights, in column 1 and 2.
In column 3 and 4, I report the average values and its standard deviation of households’ characteristics in the treatment group, weighted with the survey weights. In column 5 and 6, I report the within-household difference between the average values of the treatment and the control households at baseline, controlling for household fixed effects and province-year dummies.
Primary cooking fuel. On average at the baseline years, about 30%-43% of households are using kerosene as their main cooking fuel, while 45%-68% of
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holds are using wood as their main cooking fuel. There are very few households use either LPG or electricity. The trend to use these fuels between treatment and control group are very similar as shown in column 5.
Households characteristics. Households size is three on average, with total monthly expenditure around 70 USD. About 40% is spent on food and 8% is spent on the utility bills. Total working hours for all households members are around 23 hours per week. Average last year income, an estimate for household members that worked last year and for whom respondent knew earnings of last year, is 300 USD, or 25 USD per month. Average yearly income during the survey year is calculated from reported monthly salaries, about 130 USD, which are consistently about two of the fifth of reported last year income. More than 80% of households own their house and do not move.
4.3 Empirical Methodology
I use difference-in-differences estimation strategy to exploit variation across time of program implementation on household level panel data, following Eq. 4.1.
Chrt= β1h+ β2t+ β3Pr2014 + β40Xht+ hrt (4.1) where h indexes households, r indexes district, and t indexes year of survey, C is household consumption or their log; αh, β1c are household, and time fixed effects. Following the literature, I add 1 if the dependent variable is zero before taking log transformation. Xht is a set of covariates that capture household characteristics (age, family size, interview month and year). Pr2014 is a dummy of the program implementation, that is the interaction between treated district and year of 2014. Using a dummy variable for the program implementation guards against measurement error. I use ordinary least squares and cluster the standard errors by district to allow for heteroskedasticity and serial correlation in within district as the implementation of the program is varied by district.
Key coefficient of interest is β3 which measures the average response of house-hold consumption to the program implementation. This reduced form effect
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Table 4.2: Baseline Household Characteristics Before the Program
Mean SD Mean SD Mean SE
(1) (2) (3) (4) (5) (6)
Primary cooking fuel:
Electricity 0.00 0.05 0.00 0.07 0.02** (0.01)
LPG 0.01 0.11 0.11 0.32 0.03 (0.03)
Kerosene 0.29 0.46 0.43 0.50 -0.02 (0.05)
Wood 0.68 0.46 0.45 0.50 -0.02 (0.04)
Percapita kerosene (litre) 1.68 7.32 1.34 6.03 -0.12 (0.46)
Kerosene price (USD) 0.2 0.53 0.16 0.3 -0.11 (0.20)
Household characteristics:
Husband age 36.90 22.06 38.82 21.69 -0.34 (1.85)
Wife age 41.00 14.61 41.50 14.94 -0.55 (1.26)
Household size 2.92 1.32 3.01 1.34 -0.03 (0.11)
Number of adults 0.33 0.56 0.29 0.52 -0.10 (0.07)
Number of children 2.41 1.17 2.52 1.22 -0.02 (0.10)
Number of elderly members 0.02 0.15 0.02 0.16 -0.00 (0.01)
Use electricity 0.84 0.36 0.92 0.27 -0.14* (0.08)
Own house 0.86 0.34 0.87 0.34 0.00 (0.04)
Have fridge 0.16 0.36 0.17 0.37 0.02 (0.06)
Boil water to drink 0.83 0.38 0.93 0.26 -0.03 (0.07)
Did not move 0.75 0.43 0.88 0.33 -0.03 (0.18)
Percapita total expenditure (USD) 72.13 315.13 71.42 381.76 17.57 (17.30) Percapita non-durables (USD) 57.25 284.38 58.47 360.54 18.09 (15.49) Percapita food exp. (USD) 31.04 25.86 28.99 49.40 3.28 (3.06) Percapita utility bills (USD) 6.58 124.50 10.87 159.93 11.70 (12.56) Working hours per capita/week 23.08 18.95 23.41 19.23 -5.06** (2.13) Percapita last year income (USD) 302.99 431.12 281.37 617.36 -99.95 (68.15) Percapita this year income (USD) 129.10 296.46 138.46 464.67 -11.01 (24.51) Head of household:
Female 0.16 0.37 0.15 0.35 -0.03 (0.03)
Uneducated 0.12 0.32 0.13 0.33 0.02 (0.02)
High school educated 0.81 0.39 0.81 0.39 -0.00 (0.03)
Diploma or higher 0.07 0.25 0.06 0.24 -0.02 (0.01)
Worked last year 1.21 0.61 1.22 0.63 0.07 (0.08)
Notes: All regressions use the sample prior to the program. Control group is households living in the districts that get the program after 2011 (less than three years). Treatment group is househoolds living in the district that get the program during 2007-2010. In column 1 and 2, I report the average values and its standard deviation of control households at baseline. In column 3 and 4, I report the average values and its standard deviation of the treatment households at baseline. Each row in column 5 and 6 is the estimated differences from a regression of each household characteristic on an indicator variable whether the households are in the treatment group, controlling for household fixed effects and province-year dummies. The standard error is clustered by district. 1 USD = Rp 13,000.
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contains two components: substitution effects and income effects. Substitution effects arise when the prices of kerosene increase due to removal of the subsidy, and households will substitute towards other fuel alternatives as they become relatively cheaper than kerosene. Income effects arise when the lower effective price for other alternative fuels increases household’s purchasing powers, leading to a further increase in consumption of those alternative fuels (assuming it is a normal good) and other normal goods consumption.
The main empirical challenge is that households in the treated districts may differ systematically from households in the untreated districts, that is the timing of program implementation might have been associated with unobserved factors that otherwise influence households consumption trend in the targeted districts.
To address this issue, first, I show that the pre-implementation trends in con-sumptions between treatment and control groups are very similar. In Table 4.3, I show regression coefficient of each outcome variable on the district and year dum-mies. Table A.1 in appendix confirms that the results are very similar, with or without household fixed effects. In other words, households in the treatment and control groups are very similar before the program on their percapita kerosene quantity, nondurable expenditure and their utility bills. They also face similar price of kerosene.
Secondly, a probit for being in the treatment group against a variety of ob-servable characteristics did not reveal any strong systematic correlations (Table 4.4). This result is consistent with (Andadari et al., 2014; Imelda, 2018) which study the same program. They show that the program induced by the program has been largely independent of household characteristics. Although households in the treatment group shows that they are more likely to have less household members, this is actually driven by the increase in household members in the control group. There is also a weak correlation that households with less uned-ucated members are likely to be in the treatment group, the coefficient are very small, 5%. Overall, there is little evidence on any systematic difference between households in the treatment group and the control group. For the robustness checks, I include these control variables and the main conclusion stays.
To some extent, the results from Table 4.3 and Table 4.4 help to address 95
Table 4.3: Test of parallel time trends
ProgramX2007 0.261* 0.128 0.123 0.728 0.066 0.211 0.291
Standard error (0.142) (0.109) (0.294) (0.827) (1.036) (0.215) (0.312)
Obs. 15,058 15,097 15,097 15,097 15,097 15,097 15,097
R-squared 0.642 0.599 0.579 0.505 0.612 0.407 0.448
(8) (9) (10) (11) (12) (13)
ProgramX2007 0.265 -0.002 0.287** 1.017*** -0.542 0.418
Standard error (0.197) (0.094) (0.144) (0.385) (0.433) (0.531)
Obs. 15,097 15,097 14,995 15,097 15,097 15,129
R-squared 0.499 0.353 0.666 0.524 0.455 0.548
Sample is prior to the program. All regressions include district fixed effects and month-year dummies. The standard error is clustered by district.
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Table 4.4: Is the program correlated with the observables?
ProgamX2014 -1.25 -1.32 -0.36*** -0.29*** -0.01 -0.07 0.01 0.01 -0.01
(1.860) (1.039) (0.107) (0.099) (0.070) (0.054) (0.023) (0.049) (0.064)
Obs. 20,094 20,094 20,094 20,094 20,094 20,094 20,094 20,094 20,094
R2stat 0.573 0.570 0.506 0.470 0.302 0.576 0.568 0.566 0.460
(10) (11) (12) (13) (14) (15) (16) (17)
ProgamX2014 -0.01 -1.88 -23.50 -0.00 -0.05** 0.03 0.02 -0.00
(0.121) (2.578) (93.590) (0.036) (0.024) (0.038) (0.021) (0.085)
Obs. 20,094 20,051 20,094 20,094 20,094 20,094 20,094 20,086
R2stat 0.424 0.364 0.451 0.598 0.671 0.671 0.757 0.455
Each column reports the estimated differences from a regression of each household characteristic on an indicator variable whether the household is in treated region, controlling for household fixed effects and province-year dummies. Column 1 - 13 report the main household characteristics. Column 14 - 17 report the head of household characteristics. The standard error is clustered by district.
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some concern about possibly unobserved shocks that might be correlated with household’s consumption. Thus, in further analysis, I consider that the control group as a valid counterfactual for the treatment group in the absence of the program, conditional on household fixed effects, district-year fixed effects, and the other time-varying household characteristics.