Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seminar Series, May 24, 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
Contact: d.headey@cgiar.org
1

2. Outline
1) Background and Objective
2) Data and Methods
3) Results
4) Conclusion
2

3. 1) Background
• The global food crises of 2007-08 and 2010-11 sparked a number o
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

4. 1) Background (cont.)
• Some precedent on agricultural wages & food prices
• Literature is almost solely confined to Bangladesh (Ravallion 1992;
Palmer-Jones; Rashid 2002), and Philippines (Lasco et al. 2008).
• 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.
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5. 2) Objectives
In this paper we have two objectives:
1. To track real wages in (as per Mason et al.)
2. To formally test wage adjustment (as per Lasco et al., etc)
Our context – Ethiopia - is a particularly interesting one:
1. Very poor (60% of urban pop. with <$2/day; 20% uN rate)
2. Very understudied in World Bank & Gallup studies
3. Unusually, we have monthly panel data on informal or casual
wages (much better than previous data)
4. Arguably the most rapid food inflation in the world in 2008 and
2011
5

6. 2) Objectives (cont.)
Figure 1. Average monthly inflation in Ethiopia relative
to other developing countries: 2004-2011
4.0 3.0
Food inflation (%) Relative food inflation (%)
2.0
3.0
1.0
2.0
0.0
1.0
-1.0
0.0 -2.0
2005
2004
2006
2007
2008
2009
2010
2011
2011
2004
2005
2006
2007
2008
2009
2010
-1.0
Other developing countries Ethiopia
Source: ILO (2012).
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7. 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,… (more than 700 items)
• 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

8. 2. Data and methods (cont.)
• 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 both food prices and total prices for
the poor.
8

9. 2. Data and methods (cont.)
• In principle, deflating by total prices is most appropriate for
welfare interpretation, but deflating food prices may be more
relevant for ultra-poor who may spend almost all of their income
on food
• More generally, are daily laborer’s wages are a good welfare
indicator for the poor?
• For India, Deaton and Dreze argue that wage series for casual
labor are a good poverty indicator, because they represent the
reservation wage for the poor
• For urban Ethiopia we make the same argument
9

10. 2. Data and methods (cont.)
• Finally, we use panel regressors to see whether wages react
to food prices in the short run
• We use a panel vector error correction model (PVEC) &
spatially disaggregate by town/city size & regions
• PVEC effectively separates out a long run adjustment
relationship (cointegrating relationships) and short run
adjustments.
• We are more interested in the short run adjustments as they
are more welfare-relevant.
10

11. 3. Results
Fig. 2. Price trends for the urban poor: 2001-2011
350
Price and wage indices (Dec. 2006=100)
2 sharp food price spikes, but
300
Poor person's food CPI 2011 saw nonfood inflation too
250
Poor person's nonfood CPI
200
Nominal wage index
150
100
50
0
2001m7
2001m12
2002m5
2002m10
2003m3
2003m8
2004m1
2004m6
2004m11
2005m4
2005m9
2006m2
2006m7
2006m12
2007m5
2007m10
2008m3
2008m8
2009m1
2009m6
2009m11
2010m4
2010m9
2011m2
Source: Author’s calculations from CSA (2011b) data. See section 2 for methods used.
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12. Food CPI (Dec. 2006=100)
100
150
200
250
300
350
50
2001m7
2001m12
2002m5
2002m10
2003m3
2003m8
2004m1
2004m6
General food CPI
2004m11
2005m4
Poor persons' food CPI
2005m9
general population: 2001-2011
2006m2
2006m7
2006m12
3. Results (cont.)
2007m5
2007m10
2008m3
2008m8
2009m1
2009m6
2009m11
2010m4
Fig. 3. Comparing food price trends for the poor and
2010m9
2011m2
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13. 3. Results (cont)
Figure 3. Trends in real daily laborer wages deflated by the
urban poor’s food and total prices indices
13
Wages deflated by poor person's food CPI
21%
Real daily wage of laborers (Dec. 2006 birr)
Wages deflated by poor person's total CPI fall
12
10%
fall
11
10
9
26% fall 26% fall
8
7
Source: Author’s calculations from CSA (2011b) data. See section 2 for methods used.
13

14. 3. Results (cont)
Table 1. National and regional trends in daily laborers' wage
(2006 birr), deflated by the poor person’s food CPI: 2001-2011
Year National Addis Amhara Oromia SNNP Somali Tigray
2001 11.7 10.6 10.0 11.8 9.2 14.2 14.5
2002 11.4 10.4 9.3 11.5 8.9 14.9 13.9
2003 10.5 9.4 8.7 10.4 8.5 14.2 12.4
2004 10.7 10.2 9.3 10.2 9.1 13.5 12.3
2005 10.8 11.1 9.7 10.0 8.9 12.3 12.7
2006 10.7 11.3 10.5 9.8 8.8 11.5 11.5
2007 10.9 11.6 9.6 9.9 8.7 14.2 12.3
2008 9.2 10.2 8.5 7.7 6.8 12.6 11.4
2009 10.0 10.8 9.7 8.5 7.4 14.4 11.4
2010 11.5 11.3 10.4 9.6 9.3 15.4 12.9
2011 9.7 9.3 8.7 8.2 7.6 12.2 13.0
%D: 2007-08 -15.5% -11.8% -11.5% -22.4% -21.8% -11.2% -6.8%
-15.8% -17.4% -16.5% -14.2% -17.4% -20.7% 0.8%
%D: : 2010-11
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15. 3. Results (cont)
Table 2. National and regional trends in daily laborers' wage
(2006 birr), deflated by the poor person’s total CPI: 2001-2011
year National Addis Amhara Oromia SNNP Somali Tigray
2001 9.5 9.0 8.6 9.7 7.6 12.9 12.1
2002 9.5 8.8 8.3 9.7 7.3 13.3 11.9
2003 9.3 8.6 8.1 9.5 7.4 13.3 11.0
2004 9.7 9.2 8.6 9.4 8.3 13.0 10.7
2005 9.9 10.3 9.1 9.4 8.5 12.0 11.5
2006 10.4 10.7 10.1 9.7 8.9 11.7 11.3
2007 11.3 12.3 10.0 10.5 9.2 15.3 13.5
2008 10.8 11.9 9.9 9.4 8.3 15.1 13.6
2009 11.2 12.1 10.8 9.8 8.6 16.0 13.4
2010 12.2 11.7 10.9 10.6 10.0 17.3 14.6
2011 10.9 10.0 9.5 9.9 9.1 15.9 14.7
%D: 2007-08 -4.9% -3.4% -1.5% -11.0% -9.8% -1.4% 0.9%
%D: : 2010-11 -10.4% -15.0% -13.0% -7.3% -8.5% -8.3% 0.3%
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16. 3. Results (cont)
• The long run relationship shows substantial adjustment of
wages to food prices (elasticity of greater than 1), but not to
non-food prices:
Wages = -2.9 +1.2*Food CPI -0.1* Nonfood CPI -0.001* t
• However, it is obviously difficult to put a welfare
interpretation on this equation
• Especially, since the short run results show scarcely any
adjustment . . .
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17. Table 3. Short run adjustment coefficients of panel vector
error correction (PVEC), July 2001-October 2011
Small
Full “Cities” towns Addis
Variable sample >20K <20K Ababa Amhara Oromia SNNP
∆ FPIt-1 -0.039*** -0.038** -0.041*** -0.057** -0.062** -0.038* 0.023
∆ FPIt-2 -0.028** -0.012 -0.037** -0.067** -0.045* -0.013 -0.032
∆ FPIt-3 0.014 0.019 0.01 -0.037 -0.001 0.035 0.055*
∆ NFPIt-1 -0.004 0.006 -0.013 0.011 -0.004 -0.003 -0.0130
∆ NFPIt-2 0.007 0.002 0.011 0.029 -0.008 0.022 0.009
∆ NFPIt-3 0.002 0 0.003 0.009 -0.003 -0.005 0.005
Number
of
observati
ons 13571 5343 8228 3,549 2,240 2,839 719
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18. 4. Conclusions
Main findings:
 Casual workers in urban Ethiopia have been hit hard by rapid food
inflation in 2008 & 2011, particularly ultra-poor:
10-26% loss of disposable income year, region, indicator
 2011 crisis (ongoing) seems worse than 2008 crisis
 Short run results show scarcely any adjustment and “In the long
run we are all dead”
 Given that households could have many coping mechanisms (e.g.
longer working hours), these may be upper bound estimates of
welfare impacts
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19. 4. Conclusions (cont.)
Policy questions:
 GOE has focused on trying to directly curb food inflation through
price controls & some subsidization of food.
 Efforts to reduce domestic inflation are sensible, but the capacity
to fully reduce inflation may be limited given higher international
prices and growth scenarios
 So, does Ethiopia need an urban social safety net?
 Many considerations here, but one option is to index cash
transfers to our poor person’s price index
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20. 4. Conclusions (cont.)
Research implications:
 Further work could try to validate the wage series as a relevant
and accurate welfare indicator for poor
 CSA could consider asking about food-in-kind for maids salaries,
guards salaries
 Arguably, the collection of wage series by statistical agencies
elsewhere should be scaled up
 They appear to be a cost-effective and very useful high frequency
indicator of urban welfare, and in some contexts, agricultural
welfare (e.g. South Asia)
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21. Thank you!
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