URBAN WAGE BEHAVIOR AND FOOD PRICE INFLATION IN ETHIOPIA

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International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI). Conference on "Towards what works in Rural Development in Ethiopia: Evidence on the Impact of Investments and Policies". December 13, 2013. Hilton Hotel, Addis Ababa.

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URBAN WAGE BEHAVIOR AND FOOD PRICE INFLATION IN ETHIOPIA

  1. 1. ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE URBAN WAGE BEHAVIOR AND FOOD PRICE INFLATION IN ETHIOPIA Derek Headey, Fantu Bachewe, Ibrahim Worku, Mekdim Dereje & Alemayehu Seyoum Taffesse IFPRI ESSP-II “Towards what works in rural development in Ethiopia: Evidence on the impact of investments and policies” A conference by IFPRI-ESSP II 13 December 2013 Addis Ababa 1
  2. 2. Outline 1) Background 2) Data and Methods 3) Results 4) Conclusion 2
  3. 3. 1) Background • Global food crises of 2007/08 and 2010/11 sparked efforts to understand the poverty impacts of high real food prices o World Bank simulation suggested global poverty rose by 160 million people o Subjective survey data from Gallup suggest substantial variation of impacts: (Headey 2011) o A third approach is to deflate wages by (food) prices to proxy for disposable income o Cointegration analyses: short and long-run adjustment of wages for changes in food prices- 3
  4. 4. 1) Background In this paper we have two objectives: 1. To track real wages (as per Mason et al.) 2. To formally test wage adjustment (as per Lasco et al., etc) Particularly interesting in the Ethiopian context : 1. Large population of urban poor  60% earns <$2/day and 20% unemployment rate 2. Understudied in World Bank & Gallup studies 3. Rich monthly panel data on informal or casual wages 4. Arguably one of rapid food inflation in 2008 & 2011 4
  5. 5. 2) Data and methods • CSA consumer price data covering  119 woredas, with 1 or more markets-from 3 respondents  The July 2001 to August 2012 • Prices on food & non-food items (more than 700 items): wages of daily laborers, and maids and guards salaries,…  Maids and guards are partly paid with food-in-kind • Food, non-food, and general price indices specific to the poor computed to create a better wage-welfare proxy, • The 2004/05 HICES expenditure data used to measure expenditure shares of food & non-food for the bottom 40% 5
  6. 6. 2) Data and methods (cont.) • Rural and urban areas of each region considered separately • Weights applied on CSA price data to derive spatially disaggregated “poor person’s price indices” (PPPIs) for food, non-food and all items • Laborer’s wages deflated by both food and total CPIs for the poor.  Deflating by total CPI appropriate for welfare interpretation,  Deflating by food prices more relevant for the poor • Deaton & Dreze (2002)-casual labor wage series are a good poverty indicator-represent reservation wage of the poor • We make the same argument for Ethiopia 6
  7. 7. 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 (PVEC) model & spatially disaggregated subsamples by town/city size & regions • PVEC effectively separates out a long run adjustment relationship (cointegrating relationships) and short run adjustments. • Short run adjustments more interesting as they are more welfarerelevant. 7
  8. 8. 2) Data and methods (cont.) • The theoretical model results in a relationship: W  (P , P , P , Q f n c Mc ) where W, Pf, Pn, and Pc stand for wages, food, non-food, and construction materials prices and QMC construction output. • Given all series are non-stationary we can’t use OLS • Cointegration analysis used Zit  1Zit 1  2 Zit 2    k 1Zit ( k 1)  Zit 1    X it   t  it where Z  [ w p f p n p c q M c ],    A , k k it it it it it it 1   Ak  Ak 1  ...  A2 , and  k 1   Ak  Ak 1 ,   ( I  A1  ...  Ak ). 8
  9. 9. 2) Data and methods (cont.) Table 1. Regional average daily laborers’ nominal wages and expenditure shares for the lowest 40% income quintile Region National Wages in USD Tigray Amhara Oromiya Somali SNNP Addis Ababa Average nominal wages Urban Rural 2001 2005 2010 2012 Food Non-food Food Non-food 6.9 8.1 23.4 34.3 67% 33% 69% 31% 0.82 9.0 6.0 7.3 10.7 5.7 6.7 0.93 10.0 7.6 8.2 10.7 6.6 9.1 1.62 28.2 23.7 21.2 30.7 20.3 25.6 1.95 45.8 32.1 31.9 47.8 28.7 35.2 65% 63% 67% 68% 65% 63% 35% 37% 33% 32% 35% 37% 72% 66% 71% 70% 67% _ 28% 34% 29% 30% 33% _ 9
  10. 10. Fig. 1. Price trends for the urban poor: 2001-2012 2 sharp food price spikes, but 400 350 300 250 Nominal wage index 2011 saw nonfood inflation too Poor persons' food CPI Poor persons' nonfood CPI Poor persons' total CPI 200 150 100 50 0 2001m7 2002m1 2002m7 2003m1 2003m7 2004m1 2004m7 2005m1 2005m7 2006m1 2006m7 2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1 2010m7 2011m1 2011m7 2012m1 2012m7 Price and wage indices (Dec. 2006=100) 450 10
  11. 11. Fig. 2. Comparing food price trends for the poor and general population: 2001-2012 Poor persons' food CPI 350 Food CPI 86% and 73% growth in PPFCPI & FCPI 300 250 200 150 100 Over 97% increase 50 2001m7 2002m1 2002m7 2003m1 2003m7 2004m1 2004m7 2005m1 2005m7 2006m1 2006m7 2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1 2010m7 2011m1 2011m7 2012m1 2012m7 Food CPI (Dec. 2006=100%) 400
  12. 12. Table 2. National and regional trends in daily laborers' wage (2006 birr), deflated by the poor person’s food CPI: 2001-2012 Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 %: 2007-08 %: 2010-11 National 12.0 11.9 10.8 10.8 10.9 10.7 11.3 9.8 10.8 12.0 10.1 10.5 -13.8% -15.4% Tigray Amhara Oromia Somali 15.1 10.0 12.0 14.7 14.8 9.5 11.7 15.5 12.8 8.7 10.6 14.5 12.6 9.3 10.2 13.9 13.1 9.9 10.3 12.5 11.9 10.6 9.8 12.2 12.8 9.8 10.1 14.7 11.6 8.8 7.8 13.4 11.6 9.8 8.8 16.5 13.1 10.6 9.7 17.9 13.1 8.7 8.3 13.7 13.1 8.6 8.2 16.2 -9.4% -10.1% -23.0% -8.4% 0.3% -18.1% -14.4% -23.2% SNNP 9.4 9.2 8.4 8.3 8.2 8.2 8.5 7.0 7.6 9.3 7.7 7.4 -17.8% -17.5% Addis 10.5 10.6 10.0 10.7 11.2 11.4 12.0 10.0 10.5 11.1 9.1 9.2 -17.3% -17.9%
  13. 13. 15 14 22% decline Wages deflated by poor persons' food CPI Wages deflated by poor persons' total CPI 13 12 11 10 9 8 16% decline in fooddisposable income relative to total disposable income 26% decline 7 2001m7 2002m1 2002m7 2003m1 2003m7 2004m1 2004m7 2005m1 2005m7 2006m1 2006m7 2007m1 2007m7 2008m1 2008m7 2009m1 2009m7 2010m1 2010m7 2011m1 2011m7 2012m1 2012m7 Real daily wage of laborers (Dec. 2006 birr) Figure 3. Trends in real daily laborer wages deflated by the urban poor’s food and total prices indices
  14. 14. 3) Results • Substantial long-run adjustment of wages to food prices, but lower to non-food prices: Wages =1.197*food CPI+0.484*non-food CPI - 0.564* CMU CPI +0.00003*t+0.424 Adjustment speed: 3.4% per month • Difficult to put a welfare interpretation I. It takes long time for wages to partially and fully adjust o 48 and 86 months for wages to partially and fully adjust for one SD change in poor persons’ food CPI o 61 and 101 months for one SD change in non-food CPI II. Short run adjustments are small
  15. 15. Table 3. Short run adjustment coefficients of panel vector error correction (PVEC), July 2001-October 2011 Variable Full sample ∆ FPIt-1 -0.029** -0.049* -0.033* -0.044 0.115 0.021 -0.058* ∆ FPIt-2 -0.029** -0.044* -0.034** -0.037 0.114 -0.023 -0.019 ∆ FPIt-3 0.005 -0.012 _ -0.037 _ 0.019 0.006 ∆ NFPIt-1 -0.027** -0.011 -0.035** 0.025 -0.042 -0.005 -0.0158 ∆ NFPIt-2 0.004 0.012 0.006 0.035 0.0004 0.006 0.02 ∆ NFPIt-3 -0.017 -0.021 _ 0.002 _ -0.015 0.017 ∆ CMRUPIt-1 0.091*** 0.011 0.142*** 0.139* -0.13 0.043 0.146* ∆ CMRUPIt-2 0.101*** 0.039 0.103*** 0.092 0.148 0.07 0.0072 -0.011 0.019 _ -0.081 _ -0.038 -0.098 ∆ CMRUIt-3 “Cities” >=30K Small towns <30K SNNP Addis Ababa Amhara Oromia
  16. 16. 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  2011-12 crisis seems worse than 2008 crisis  Short run results show scarcely any adjustment and “In the long run we are all dead”  Given households could have coping mechanisms (e.g. long work hours), these may be upper bound estimates of welfare impacts 16
  17. 17. 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,  The capacity to fully reduce inflation may be limited given higher international prices and growth scenarios  Does Ethiopia need an urban social safety net?  Many considerations here, but one option is to index cash transfers to poor person’s price index 17
  18. 18. Thank you! 18

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