13 cumulative consumption growth in the survey data, the simulated year-by-year changes in mean consumption will not be equal to the year-by-year GDP per capita changes. In Tanzania, the cumulative GDP per capita growth (6.1%) is close to growth in consumption per adult equivalent in the survey (6.5%). As a result, the difference between the GDP per capita changes and simulated mean consumption changes in each year is small.

In general, use of a large number of quantiles will cause the simulated distribution in the final year to more closely match that in the final survey year data. However, when using weighted data, complications may arise that force the use of a smaller number of quantiles. The simulation requires that at least one household be associated uniquely with each quantile. Because the quantiles are calculated by individual, with weighted data a household may be classified as being in two adjacent quantiles, even though the number of households exceeds the number of quantiles. If this is the case, a smaller number of quantiles should be used. For the analysis in this paper, 50 quantiles are used.

# **4.3 Reconciling National Accounts and Household Survey Data**

The approach taken in this paper is to apply per capita GDP growth rates from the national accounts data to unit-record household survey data to simulate changes in household per capita consumption. This approach assumes some correspondence between the micro-level household data and the macro national accounts data. Because the two sets of data are drawn from entirely different sources and are designed to capture different phenomena, it is to be expected that the match-up between the two sources of data is rough at best.

In general, it is often difficult or impossible to reconcile differences between the national accounts and household survey data. As Ravallion (2003) says in a review of comparisons between national accounts (NAS) and survey data, “It is evident that when the levels or growth rates from these two data sources differ, there can be no presumption that the NAS is right and the surveys are wrong, or vice versa, since they are not really measuring the same thing, and both are prone to errors.” In terms of growth rates in Tanzania for the period between the household surveys (19932001), there is surprisingly good correspondence between the HBS data and the national accounts. The cumulative GDP per capita growth of 6.1% is very close to the 6.5% growth in consumption per adult equivalent in the household survey. This close match between the general picture painted by the micro and macro data provides support for the validity of the simulation exercise undertaken for this paper.

It is also possible to calculate the growth rate of per capita consumption directly from the national accounts data. Figures from these calculations are shown in the last column of Table 2 and are plotted in Figure 3 below, along with the GDP per capita growth rate figures. At 8.3%, the cumulative change in per capita household consumption is only slightly larger than the cumulative change in per capita GDP. Mathematically, this is because household consumption changed just slightly, from 90% to 91% of GDP, between 1993 and 2001.

While details on the particular methodology used to develop the Tanzania national accounts data are not available, typically consumption figures in national accounts statistics are estimated as a residual in the national income calculation. As a consequence, there is great uncertainty in the estimates, particularly in year-by-year fluctuations. For this reason, growth rates in GDP per capita are used in this paper, except in one variation of the simulation analysis.