1) Malawi experienced rapid economic growth from 2004-2011 led by strong agricultural growth, but official poverty estimates surprisingly showed rural poverty increasing. 2) The study refined the poverty estimation methodology used by the national statistics office, including using regional poverty lines and revising consumption conversion factors. 3) The revised analysis found significantly larger declines in rural poverty over time compared to official estimates, especially in southern rural areas, suggesting agricultural growth did reduce rural poverty contrary to official results.

1. Did rapid smallholder-led
agricultural growth fail to reduce
rural poverty? Making sense of
Malawi’s poverty puzzle
Ulrik Beck, University of Copenhagen
Richard Mussa, Chancellor College, University of Malawi
Karl Pauw, Malawi Strategy Support Program, IFPRI
9 April 2014
Crossroads Hotel, Lilongwe

2. Background
 2004/05 – 2010/11 characterized by rapid
economic growth, averaging 7.1% per annum
 Led by strong growth in agric. GDP (10.1%
growth; 34.2% contribution to GDP expansion)
 Significant rise
in maize yields
(fig.)
 Legitimate
expectation of
rapid poverty
reduction

3. Official poverty results (NSO)
 Estimates from “interim” WMSs suggested
systematic decline in poverty (in line with
expectations) between 2004/05 and 2009
 But 2010/11
estimates
disappointing:
− Overall poverty
declined 52.4
to 50.7%
− Rural poverty
increased from
55.9 to 56.6%

4. How can this be explained?
 Growth did not trickle down to the poor?
− Some evidence of this in rising inequality
− Yet growth originated among smallholder maize producers who also
account for the bulk of the poor
 Growth is overestimated?
− Questionable crop production estimates underlying agricultural GDP
estimates: price behavior (Jayne et al. 2008); fertilizer use efficiency
(Dorward et al. 2008; Pauw & Thurlow 2014)
 Poverty reduction is understated (Pauw et al.
2011)?
− Revisiting poverty estimation is the focus in this
study… leave national accounts validation for later

5. Poverty estimation refinements
 Introduce several refinements compared to NSO
poverty estimation procedure, including:
− Cost of basic needs approach used in both periods with price data
from survey rather than external data source
− Nonfood consumption share permitted to change over time
− Entropy-based method used to ensure that chosen bundles are
utility-consistent across space and time (Arndt and Simler 2010)
 Above have marginal implications; major
differences between revised and NSO poverty
results explained by:
− Our use of regional poverty lines to account for differences in
preferences and prices (urban, and rural north, center and south)
− Systematic checking and revision of consumption conversion factors
(following Ecker & Qaim 2011)

6. Inflation faced by the poor (%)
 National average fairly comparable (132.8% vs. 128.9%)
 Analysis of implicit prices of consumption baskets of poor
people across different regions suggest:
− Higher urban poverty line inflation (186.6%), driven by nonfood
inflation (212.6%)
− Lower rural poverty line inflation (119.8%), with food inflation
(136.5%) much higher than nonfood inflation
Food Nonfood Total
Compare
NSO
National 137.8 127.7 132.8 128.9
Urban 151.3 212.6 186.6 128.9
Rural 136.5 101.5 119.8 128.9
North rural 129.0 124.0 126.8 128.9
Central rural 144.9 102.7 123.9 128.9
South rural 127.0 91.9 111.9 128.9
 NSO’s 128.9%
inflation
estimate from
revised CPI
series; same
rate applied for
food/nonfood
and urban/rural

7. Poverty results
2004/05
(IHS2)
2010/11
(IHS3)
National 55.1 49.0 -6.1 ± 2.7 -1.7 ± 2.4
Urban 38.2 36.0 -2.2 ± 9.1 -8.1 ± 6.8
Rural 57.3 51.3 -6.0 ± 2.9 0.7 ± 1.4
North rural 66.1 60.6 -5.5 ± 6.2 3.6 ± 6.5
Central rural 49.7 46.0 -3.7 ± 4.6 2.0 ± 4.2
South rural 62.3 53.6 -8.7 ± 4.3 -1.1 ± 3.7
Compare official change
in poverty (NSO)
Change in regional
poverty
 Our rural poverty rates comparable with NSO, but much
larger and significant declines over time in all rural areas,
especially south
 Urban poverty significantly higher using our method, and
also smaller (but statistically insignificant) decline compared
to NSO

8. Cumulative distribution
functions and poverty lines
 Difference in consumption
CDFs explained by conversion
factor adjustments and way in
which food consumption is
aggregated; nonfood
components similar
 Slightly higher consumption
level in rural and urban areas,
but also higher cost/poverty
line (same applies in 2004/05)
 Rural areas: higher
consumption compensates for
higher estimated cost of living
 lower poverty rate
 Urban areas: cost of living well
above national average  also
higher poverty estimate

9. Conclusions
 Evidence of rapid rural poverty reduction and only marginal
decline in urban poverty is consistent with
− Pro-poor smallholder-focused growth strategy coupled with
experience of rapid urban (nonfood) inflation
− Available non-monetary poverty indicators (Mazunda et al. 2012)
 Poverty measurement is challenging at best, and results
highly sensitive to assumptions
 However, our analysis addresses several shortcomings of
NSO method relating inter alia to price data, regional
preferences, utility-consistency, consumption conversion
factors, costing of poor households’ consumption bundles,
and estimating the related poverty lines
 Ultimately we believe poverty outcomes in rural areas not
as bleak; FISP may in fact have had a significant impact