Consistent with household survey data in many developing countries, our calculations confirm the lack of representativeness of households in the upper tail of the income distribution. This is reflected in the degree to which we can capture the tax base and thus evaluate the allocation of the tax burden across the income distribution and the true implications of the tax and benefit system for redistribution. For example, there is no one in the data with earnings high enough to be paying salary tax in the highest tax bracket. Similarly, underrepresentation of the highest income households suggests some caution is needed in the interpretation of inequality measures under different income definitions and changes in inequality level over time.

Several elements of the tax system are simulated relatively well when compared to administrative statistics. For example, the exercise captures about 96 percent of employment injury insurance and 99.2 percent of property tax.

Our method of allocating education benefits allows us to assign 99.2 percent of education expenditure and 93.1 percent of health care costs. In the case of education, this is because transfers per student are given their value based on the official statistics. For health transfers, this validates the adopted approach to imputation of the value of health care benefits and indicates that the source of health costs used for the valuation of various procedures is very accurate. It is also worth noting that in this case underrepresentation of top income households may be less damaging for the precision of our simulations since many of the highest income households are likely to opt for the use of private health care.

Table 3: Simulation results and validation

||**Amount simulated,**<br>**(billion KHR)**|**Macro data,**<br>**(billion KHR)**|**Proportion of**<br>**simulated to macro**<br>**(%)**|
|---|---|---|---|
|Direct transfers|111.6|101.7|<br>109.8|
|Salary tax|196.1|847.8|<br>23.1|
|SSC: Employment Injury Insurance|93.4|96.4*|<br>96.9|
|Health Insurance|303.6|154.2*|<br>196.9|
|Taxes on interest, royalties, and dividends|n.a.**|n.a.|---|
|Property tax|148.3|149.5|<br>99.2|
|Means of transportation tax|91.7|259.6|<br>35.3|
|Tax on income from rental of movable and|77.0|268.6|<br>28.7|
|immovable property||||
|Registration tax|n.a.***|n.a.|---|
|VAT tax|3,707.7|6,259.9|<br>59.2|
|Specific tax, excise|644.5|1,130.5|<br>57.0|
|Public lighting tax|43.8|182.5|<br>24.0|
|Accommodation tax|4.4|20.2|<br>21.8|
|Indirect subsidies (electricity subsidy)|90.7|134.9|67.2|
|Education transfers|2,950.9|2,974.8|<br>99.2|
|Health transfers|1,400.5|1,498.8|<br>93.4|
|Disposable Income|107,335.6|81,589.7|<br>131.6|

Source: Amounts of taxes paid are taken from 2019 Table of Government Financial Operations (TOFE); for SSC values come from NSSF, Report on Ten-Year Achievements 2008-2017 and Action Plans 2018; Education transfers are taken as MoEYS budget, health transfers are taken as MoH expenditures less donor funding. Notes: Values in yearly amounts. *External statistics of SSC: Employment Injury Insurance and Health insurance not available for the most recent years, value provided for 2017 and uprated to account for inflation to 2019. **There are no observations in the CSES data of income from interest, royalties, or dividends. ***Only one case of property sale in the CSES data.

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