Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI). Seminar Series, March 20, 2012

1. URBAN WAGE BEHAVIOR DURING
FOOD PRICE HIKES:
THE CASE OF ETHIOPIA
Derek Headey, Fantu Bachewe, Ibrahim Worku,
Mekdim Dereje & Alemayehu Seyoum Taffesse
Ethiopia Strategy Support Program (ESSP),
International Food Policy Research Institute (IFPRI),
Addis Ababa
1

2. Background
• The global food crises of 2007-08 and 2010-11
sparked a number of efforts to understand the
poverty impacts of higher real food prices
• On the one hand World Bank simulation approaches
suggested global poverty rose by 160 million people
• However, subjective survey data from Gallup suggest
substantial variation of impacts, and that strong
economic growth in developing countries limited the
impacts of higher prices (Headey 2011)
• A third less common approach is to deflate wages by
(food) prices as a proxy for disposable income

3. Background
• Some precedent here on agric. wages & food prices
• Literature is almost solely confined to Bangladesh
(Ravallion; Palmer-Jones; Rashid), and Philippines (Lasco et al.).
• Bangladesh: limited short run impacts of prices on
wages; Philippines: fairly large short run impacts
• More recent study by Mason et al. (2010) looks at
urban manufacturing wages in Zambia and Kenya.
No econometrics, but “food-disposable” wages fell in
2008, but were still high by historical terms because
of strong economic growth.

4. Background
In this paper we have two objectives:
•To track real wages in (as per Mason et al.)
•To formally test wage adjustment (as per Lasco et al., etc)
Our context – Ethiopia - is a particularly interesting one:
•Very poor (60% of urban pop. with <$2/day; 20% uN rate)
•Very understudied in World Bank & Gallup studies
•Unusually, we have monthly panel data on informal or
casual wages (much better than previous data)
•Arguably the most rapid food inflation in the world in
2008 and 2011

5. Figure 1. Average monthly inflation in Ethiopia relative
to other developing countries: 2004-2011
Source: ILO (2012).

6. 2. Data and methods
• CSA consumer price data from 115 “urban” markets
around the country, from July 2001 to October 2011
• In addition to prices on food & non-food items, CSA
asks about daily laborer wages, maids wages, guards
• But since maids and guards are partly paid with food-
in-kind, we only focus on laborers (trends the same)
• Prices and wages collected for 3 respondents (firms
or households) in each market and then averaged
• Enumerators try to measure the same respondents
(kind of a panel)

7. 2. Data and methods
• To create a better wage welfare proxy, we create food
and non-food price indices specifically for the poor
• We used the 2004/05 HICES expenditure data, and
measure expenditure shares just for the bottom 40%
• We do this for rural and urban areas of each region,
then apply these weights to the CSA price data to
derive a set of spatially disaggregated “poor person’s
price indices” (PPPIs) for food, non-food and all items
• We deflate laborer’s wages by the urban PPPIs for
food and total, as our welfare measures. Deflating by
food prices more relevant for ultra-poor.

8. 2. Data and methods
• Finally, we use panel regressors to see whether
wages react to food prices in the short run
• We use panel VEC, also fixed effects & IV, and we
spatially disaggregate by town/city size (20K
threshold) and regions
• We will just report PVEC here (choice of regressor
does not affect results much)

9. 3. Results
Fig. 2. Price trends for the urban poor: 2001-2011
2 sharp food price spikes, but
2011 saw nonfood inflation too

10. Fig. 3. Comparing food price trends for the poor and
general population: 2001-2011

11. Figure 3. Trends in real daily laborer wages deflated by the
urban poor’s food and total prices indices
Source: Author’s calculations from CSA (2011b) data. See section 2 for methods used.

12. Table 1. National and regional trends in daily laborers' wage
(2006 birr), deflated by the poor person’s food CPI: 2001-2011
year National Oromia SNNP Amhara Addis Tigray Somali
2001 11.7 11.8 9.2 10.0 10.6 14.5 14.2
2002 11.4 11.5 8.9 9.3 10.4 13.9 14.9
2003 10.5 10.4 8.5 8.7 9.4 12.4 14.2
2004 10.7 10.2 9.1 9.3 10.2 12.3 13.5
2005 10.8 10.0 8.9 9.7 11.1 12.7 12.3
2006 10.7 9.8 8.8 10.5 11.3 11.5 11.5
2007 10.9 9.9 8.7 9.6 11.6 12.3 14.2
2008 9.2 7.7 6.8 8.5 10.2 11.4 12.6
2009 10.0 8.5 7.4 9.7 10.8 11.4 14.4
2010 11.5 9.6 9.3 10.4 11.3 12.9 15.4
2011 9.7 8.2 7.6 8.7 9.3 13.0 12.2
%∆: 2007-08 -15.5% -22.4% -21.8% -11.5% -11.8% -6.8% -11.2%
%∆: : 2010-11 -15.8% -14.2% -17.4% -16.5% -17.4% 0.8% -20.7%

13. Table 2. Short run adjustment coefficients of panel vector
error correction (PVEC), July 2001-October 2011
Full “Cities” Small towns Addis
Variable sample >20K <20K SNNP Ababa Amhara Oromia
∆ FPIt-1 -0.039*** -0.038** -0.041*** 0.023 -0.057** -0.062** -0.038*
∆ FPIt-2 -0.028** -0.012 -0.037** -0.032 -0.067** -0.045* -0.013
∆ FPIt-3 0.014 0.019 0.01 0.055* -0.037 -0.001 0.035
∆ NFPIt-1 -0.004 0.006 -0.013 -0.0130 0.011 -0.004 -0.003
∆ NFPIt-2 0.007 0.002 0.011 0.009 0.029 -0.008 0.022
∆ NFPIt-3 0.002 0 0.003 0.005 0.009 -0.003 -0.005
Number of
observatio
ns 13571 5343 8228 719 3,549 2,240 2,839
• So unsurprisingly given the descriptive evidence,
we observe very small short run adjustments

14. 4. Conclusions
• Casual workers in urban Ethiopia have been hit hard
by rapid food inflation, particularly ultra-poor:
10-20% loss of disposable income (upper bound?)
• 2011 crisis (ongoing) seems worse than 2008 crisis
Policy questions:
• GOE has focused on trying to directly curb food
inflation through price controls & some subsidization
of food. Price controls don’t seem to have worked.
Capacity to directly curb inflation seems limited
• So does Ethiopia need an urban social safety net?

15. 4. Conclusions
• If an urban safety net is needed, then should transfers
be price indexed or confined to food?
Research questions
• Cannot say much about external validity (no study we
know of is directly comparable)
• Did not discuss theory much, but Ethiopia is likely to
be a surplus labor economy
• Fast growing Asian countries seem to be exhausting
surplus labor, so effects may be different there
(e.g. Zhang’s work on China, ongoing work in Bangladesh)