Urban Wage Behavior During Food Price Hikes In Ethiopia

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

• 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

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.

Background

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: •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

Background

Figure 1. Average monthly inflation in Ethiopia relative to other developing countries: 2004-2011

Source: ILO (2012).

• 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)

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.

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)

2. Data and methods

3. Results

Fig. 2. Price trends for the urban poor: 2001-2011

2 sharp food price spikes, but

2011 saw nonfood inflation too

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

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.

year National Oromia SNNP Amhara Addis Tigray Somali2001 11.7 11.8 9.2 10.0 10.6 14.5 14.22002 11.4 11.5 8.9 9.3 10.4 13.9 14.92003 10.5 10.4 8.5 8.7 9.4 12.4 14.22004 10.7 10.2 9.1 9.3 10.2 12.3 13.52005 10.8 10.0 8.9 9.7 11.1 12.7 12.32006 10.7 9.8 8.8 10.5 11.3 11.5 11.52007 10.9 9.9 8.7 9.6 11.6 12.3 14.22008 9.2 7.7 6.8 8.5 10.2 11.4 12.62009 10.0 8.5 7.4 9.7 10.8 11.4 14.42010 11.5 9.6 9.3 10.4 11.3 12.9 15.42011 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%

Table 1. National and regional trends in daily laborers' wage (2006 birr), deflated by the poor person’s food CPI: 2001-2011

VariableFull sample

“Cities”>20K

Small towns<20K SNNP

Addis 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 -0.005Number of observations 13571 5343 8228

719 3,549 2,240 2,839

Table 2. Short run adjustment coefficients of panel vector error correction (PVEC), July 2001-October 2011

• So unsurprisingly given the descriptive evidence, we observe very small short run adjustments

• 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?

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)

4. Conclusions