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# **6 Conclusions**

The starting point for the analysis in this paper was the observation that despite rapid growth in per capita GDP in the late 1990s, survey data shows that the drop in poverty nationally between 1992 and 2001 was small. A plausible explanation for this pair of facts is that poverty first increased during the period of economic stagnation that ended in 1995 and only declined once rapid growth was achieved in the second half of the decade. Consequently, recent growth has reduced poverty, even though the change observed between the two surveys shows only a small net drop in the fraction who are poor.

The simulated poverty trajectories in this paper support this view. Under a variety of scenarios, the simulations imply that poverty rates have followed an inverted U-pattern, increasing to over 40% or higher in 1994 and then dropping, down to below 36% in the 2000/01 survey. Variations of the analysis which take into account separate urban-rural growth rates show small increases in rural poverty in 1997 and 1998, when rural per capita income declined while urban per capita output grew, followed by declining rural poverty.

We employ both the Datt-Walker macro-micro projection technique and a modified “two survey” version of their method. In the Datt-Walker analysis there are instances where growth patterns diverged substantially from the general pattern. These are the simulations using Dar-specific growth data, aggregate household consumption estimates from the national accounts, and alternative urban/rural population growth rates. In all three cases, it is likely that the results are the consequence of rough approximations in the national accounts data.

In most cases, relative to the survey estimates, the simulations with the Datt and Walker method overestimate drops in poverty in rural areas and the nation as a whole, while underestimating the drop in poverty in the capital. This reflects the particular distributional incidence of growth (in the survey data) which is illustrated by the growth incidence curves. Our modified “two survey” method provides a close match between the simulation and the final year survey data, irrespective of the particular distributional incidence of growth. Consequently, we take the two versions of the analysis using this method (Figures 12 and 13) as our preferred simulated poverty trajectories.

We also presented some supporting evidence that suggests it is very likely that poverty declined since 1994. The alternative hypothesis—that poverty has _not_ declined—would require that inequality dropped significantly from 1992 to 1994.

It is important to recognize that the precision and accuracy of the presented results is only as good as the underlying data. Uncertainty is associated with the macro data, the micro data, and the assumptions that go into the simulation analysis. Only the sampling error arising from the household survey can be readily quantified, and the standard errors on the survey-based estimates provide an extreme lower bound on the standard errors on the simulated poverty rates. This suggests that the year-to-year pattern of changes in poverty may differ substantially from what is implied by the simulations. Nonetheless, the analysis presented here provides the best estimates of poverty rate trajectories in Tanzania with available data.

In terms of the Millennium Development Goal of cutting poverty in half by 2015, growth at the end of the last decade has put Tanzania roughly on the path necessary to meet the goal for urban areas. This is despite the fact that the poverty-reducing impact of growth has been partly offset by increasing inequality, particularly within Dar es Salaam. Rural areas, where the large majority of Tanzanians live, are lagging behind their MDG target.

Reaching the MDG goal for poverty reduction by 2015 will require sustaining and improving upon the rural growth rates achieved in recent years. Given that only a tiny minority of the population lives in the capital, even rapid urban growth will have only a small impact on poverty in