specific top quantiles such as the top 1% or 10% (Burkhauser _et al._ , 2016), we follow the same approach as Czajka (2020): for each year, we replace the survey with tax data starting from the point where the quantile functions from both sources cross.<sup>12</sup> We first collapse the tax records in up to 37 quantiles - nine quantiles between the top 10% and top 1%; nine quantiles for the top 1% to top 0.1%; 9 quantiles between the top 0.1% and 0.01%; and the highest ten quantiles up to the top 0.001%.<sup>13</sup> As mentioned before, for the period 2003 - 2010 we have very limited information on third-party reporting, and are thus unable to capture a large share of income from individuals who do not file PIT. We adjust the level of income in top quantiles in those years to match the ratio between income declared in PIT forms and total income for the 2011-2019 period, where we observe all sources of income.<sup>14</sup> We then compute, for a range of income levels, the share of individuals with incomes above that level in tax and survey data – as a rule, for low income levels we observe more individuals with higher incomes in the survey data, so this ratio is below unit, and at very high income levels that ratio often far exceeds unit. We choose as the merging threshold the lowest point when that ratio equals one, and replace survey data with tax records above that income level.<sup>15</sup> We present a diagnostic assessment of our merging procedure in Table 2. In column (1) we illustrate the total reference adult population in each year, computed using the household survey. In 2019 there were approximately 5.4 million adults over 20 yearsold in Honduras out of a population of over 9 million individuals. For that year, the merging point between the two sources was L465,000, the equivalent of the income of the top 0.6% highest-earning individuals in both datasets. Despite the fact that administrative data includes over 600,000 individuals for that year, our merging process led us to use fewer than 40,000 taxpayers to complement the income distribution obtained from survey data – this share ranges from 0.4% - 1% in the 2012-2019 period, and is significantly lower at 0.1 - 0.2% before that, when the availability of tax records is more limited.

> 12We recognize the limitations of this procedure as laid out in Blanchet _et al._ (2018). In future versions of this working paper we expect to also present results using alternative methods to complement survey data with tax records.

> 13The top decile of the adult population in Honduras represents 300,000 - 450,000 individuals, depending on the year. In some years, our entire sample of individuals in tax records is lower than those numbers, so we are only able to construct a smaller share of quantiles. In practice that does not impact our results, since we document that we never replace the survey below the top 1% level.

> 14 We provide more information on this adjustment in Appendix G.

> 15In Figure A.4, we illustrate these ratios across income levels for three years. We note that the ratio is not monotonic – since we collapse the tax data in quantiles and the survey data is sparse at high income levels, sometimes the ratio decreases as income increases and even dips below one. But the trend of more income in the tax data at higher levels is clear in all years presented, so we replace survey with tax records at the first income level at which the ratio crosses the value of one.

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