Zambia’s pre-fiscal income measure includes income received from the public pension system. Market income reflects income before any transfers (including public spending on health and education, farming inputs, fuel and energy subsidies, and unconditional cash transfers) or taxes (including personal income taxes, VAT, and alcohol and tobacco excises) have been added. However, two scenarios for treating contributory pensions usually are elaborated in the standard CEQ fiscal incidence methodology. These two scenarios are (1) contributory pensions as deferred income (and pension contributions as voluntary saving) or (2) contributory pensions as a government transfer (and pension contributions as a tax on income). Zambia’s contributory pension system is available only to civil servants while employee contributions make up an insignificant portion of government and pension-system revenues. In other words, in Zambia, the contributory pension system functions much as a non-wage salary payment (to civil servants). Therefore, the team’s pre-fiscal income measure becomes Market Income + Pensions (second blue box from top in Figure 9).

# Data Sources

The primary data set providing the individual- and household-level information necessary to allocate fiscal policy elements<sup>22</sup> is the 2015 Zambia Living Conditions Monitoring Survey. This study focuses solely on fiscal year 2015 because that is the last year for which LCMS is available. LCMS (2015) includes modules covering health, education, economic and labor market activity, household consumption expenditure, agricultural production, and rent (or, for owner-occupied housing, imputed rent). LCMS also provides a roster per household that provides individual, demographic, and dwelling characteristics. The 2015 LCMS uses the 2010 Census of Population and Housing as the sampling frame and is representative at the national level, by urban and rural areas, and by province. The survey was administered to approximately 12,250 households, who comprised almost 63,000 individuals.

<mark>The team uses the 2013–14 Zambia</mark> Demographic and Health Survey <mark>(DHS) to impute a propensity to visit public health care providers as well as to calculate shares of all recorded visits by household type.</mark> The LCMS 2015 health care utilization module asks household members to recall whether they had an illness or medical condition over the past two weeks for which they sought attention at a health care facility. The Zambia 2013–2014 DHS includes more complete information on health care use––over a 12 month recall period, and not conditional upon illness or injury ––in <mark>a nationally representative sample survey of women and men of reproductive age. The team uses propensity scores and shares of total clinic and hospital-level visits estimated within the DHS to generate a household</mark> _<mark>expected</mark>_ <mark>public health care benefit received within the LCMS 2015.</mark><sup>23</sup> The source for total revenues collected by the government from households––via the personal income tax, VAT, and excise taxes––is the 2015 Annual Economic Report published by the Ministry of Finance. To impute “effective” or actual, prevailing rates (which may differ from statutory rates), the team first scales down the expected tax take from LCMS households. Scaling is done so that the ratio of tax revenues in final or audited budget reports to Private Final Household Consumption Expenditure in Zambia National Accounts data are equivalent to the ratio of VAT collections from LCMS households to the value of cumulative LCMS household consumption expenditure.

> 22 The allocations––including the assumptions and choices implicit in them––are described in the following section.

> 23 The DHS-to-LCMS imputation is made by estimating propensity scores (describing the likelihood of choosing a public health care provider when health care services are acquired) in the DHS based on individual and household characteristics (education, household size, and region) and income rank and then creating fitted values of that propensity score in the LCMS using the analogous LCMS characteristics.  We also use the DHS to estimate a decile’s share of all clinic-level health care visits and all hospital-level health care visits over a 12-month period; in that estimation DHS households are ranked according to a household wealth variable recorded in the DHS itself.  We then assume that the share of total visits for a DHS decile are equivalent to an LCMS decile’s share of total visits.

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