each projection year, (11) changes in the relative price of food by year, (12) the share of food in the bundle defining the poverty line, (13) the share of food in the CPI, (14) the change in the Gini within each sector for each projection year, (15) changes in the average propensity to consume, and (16) the drift between surveys and national accounts.

# **Box 3: Unit Record Approach: PovStat Growth, Inequality, and Simulated Poverty Paths for Tanzania**

Tanzania achieved rapid growth in per capita GDP in the 1995–2001 period, but household budget survey data suggest that the decline in poverty between 1992 and 2001 was relatively small. Demombynes and Hoogeveen (2004) argue that a possible explanation for this outcome is that poverty increased during the period of economic stagnation in the early 1990s, but economic growth in the second half of the 1990s was able to offset only part of the early rise in poverty.

To test this hypothesis, the authors use PovStat to simulate the likely trajectory of poverty rates over the 1992–2002 period. They employ data from two household budget surveys (1991/2 and 2000/1), along with growth rates derived from national account data. These growth rates are then applied to unit record data to estimate the full evolution of poverty over the course of the nine-year period.

The simulated poverty trajectories show that poverty rates followed a hump-shaped path during the period. Under a variety of scenarios, poverty incidence first increased to above 40 percent in the early 1990s and then declined below 36 percent by 2000/1. The sectoral simulations suggest that the poverty reduction impact of economic growth in Tanzania was more significant in urban areas than in rural areas. For example, in Dar es Salaam, economic growth reduced poverty by about 16 percentage points, assuming no change in inequality. In reality, the growthinduced reduction in poverty was partially offset by increased income inequality, which caused poverty to increase by 9.8 percentage points. The sectoral decomposition of the poverty outcomes also indicates that only a small part (11.6 percent) of the decline in headcount poverty at the national level could be explained by a shift in the population from poorer rural areas to wealthier urban areas like Dar es Salaam (see the table below). The study concludes that achieving the poverty-related Millennium Development Goal by 2015 will require changing patterns of growth so as to include the rural areas where most Tanzanians live.

On the methodological side, the authors propose an extension of the basic projection method underlying PovStat. This extension involves the use of estimates of growth rates of consumption and population for the quantile and the sector to which the household belongs. Applying this method to the initial year survey would produce a final year distribution that is very close to the final year survey data.

# **Sectoral Decomposition of the Change in Poverty**

|||Contribution to C<br>Headc<br>|hange in the National<br>ount Rate<br>|
|---|---|---|---|
||Population Share, 1991/2|Absolute Change|% of Total Change|
|Dar es Salaam|5.5|−0.56|17.08|
|Other urban areas|12.6|−0.34|10.49|
|Rural areas|82.06|−1.82|55.41|
|Total intra-sector change||−2.72|82.98|
|Population-shift effect<br>||−0.38|11.60<br>|
|Interaction effect||−0.18|5.41|
|Total change in poverty||−3.28|100|
|_Source_: Demombynes|and Hoogeveen 2004.|||

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