binary interacting variables, all possible splits are tried out and the split with the greatest rejection of equality of the passthrough rates is chosen.

Our source for growth in per capita HFCE and per capita GDP is the World Development Indicators (WDI). We use surveys reported in PovcalNet to calculate growth in survey means. Growth between any two consecutive surveys for a country is referred to as a survey spell. Our main sample consists of a total of 1,671 survey spells if the per capita GDP aggregate is used, and 1,511 spells if per capita HFCE is used. After accounting for survey comparability, 1,429 of 1,671 spells remain when per capita GDP is used and 1,323 of 1,511 spells for per capita HFCE.

# **4.2. Results**

Figure A.1 shows the results of the MOB algorithm. There is significant evidence in favor of the welfare measure in the survey (income or consumption) being relevant for passthrough rates. Using the per capita HFCE growth rates, the MOB algorithm suggests that no other variable besides this yields a significantly different pass-through rate.<sup>23</sup> Table 4 reports the pass-through estimates for each sub-sample in Figure A.1. Observations using income-based surveys have a pass-through rate of 1.00 with a 95 percent confidence interval between 0.89 and 1.12, while observations using consumption-based surveys have a passthrough rate of 0.66 with a 95 percent confidence interval between 0.58 and 0.75.<sup>24</sup> With a p-value of 0.024, we can reject that the coefficients are identical for these two subgroups at a 5 percent significance level.

Given the results from MOB and the fact that the CES are consumption-based surveys, we use the consumption-based partition as the sample to calculate a pass-through rate for India. Our preferred estimate of the pass-through rate thus uses the global sample of comparable consumption survey spells in combination with per capita HFCE growth. There are 471 spells in this sample, and the regression using this sample yields a pass-through rate of 0.67 with a 95 percent confidence interval between [0.59, 0.75].<sup>25</sup> While it is impossible to know the true pass-through rate for India over this period, 95 percent of pass-through rates using global consumption-specific survey spells with the per capita HFCE aggregate will fall within this confidence interval. As determined by the

> 23 As noted above, Lakner et al. (Forthcoming) use per capita GDP growth as their main dependent variable (as opposed to per capita HFCE growth used here). They find that in addition to partition by data type, further partitions by median income, level of inequality, and geographic regions yield significantly different pass-through rates when using income-based surveys. However, similar to what we find, they find that within the sample of consumption surveys, there are no further significant splits.

> 24 Lakner et al. (Forthcoming), using per capita GDP growth rate, calculate a slightly higher pass-through rate of 0.72 for global consumption-specific comparable survey spells.

> 25 Table A.5, which is further discussed in the Appendix, compares the estimate from the global consumption-specific comparable sample with estimates using various alternative samples. The difference in the pass-through estimate between the MOB of 0.66, reported in Table 4, and the 0.67 estimate reported here and in Table A.5, is due to the difference in sample size – 457 observations in the former and 471 in the latter. The MOB is more taxing on the data as we use several variables to check for partitioning and there might be missing values in some cases. In this paper, we have used the MOB primarily as a method to confirm the partitions. In what follows, we use 0.67 with a confidence interval of [0.59, 0.75] as the pass-through rate of the preferred sample.

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