1. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Food prices and poverty in the medium run: Stylized facts from international data Derek D. Headey Poverty, Health and Nutrition Division, IFPRI Washington DC

2. Outline 1.Introduction 2.Theoretical linkages between food prices and poverty 3.Data and methods 4.Results 5.Conclusions 2

3. Introduction  Sharp increases in international food prices since 2007 have widely been termed the “global food crisis”.  Indices of staple grains doubled or tripled over the course of 2007-08, fell precipitously in the second half of 2008, and then surged again over 2010-2011  Food prices likely to say high for many years to come; volatility may be the new norm  It is therefore critically important to understand the impacts of higher food prices on global poverty  Yet surprisingly, there is no consensus on this question 3

4. Introduction  Conventional approach has used Deaton’s (1989) workhorse model, the net benefit ratio  Prior to 2008, it was already known that many poor people seem to be net food consumers  So no surprise that subsequent simulations estimated increases in poverty under higher food price scenarios  Global estimates for 2007-08 price surge put the figure at 105 million (Ivanic and Martin, 2008), to 160 million (de Hoyos and Medvedev, 2009) people falling into $1.25/day povertyIvanic and Martin, 2008de Hoyos and Medvedev, 2009  2010-11 surge put a further 44 million into poverty (Ivanic et al., 2011).Ivanic et al., 2011 4

5. Introduction  But critics pointed out that the prevailing wisdom prior to the 2008 crisis was that high agricultural prices were good for the poor (Swinnen, 2010; Rodrik, 2008).Swinnen, 2010Rodrik, 2008  Headey & Fan (2010) express concerns over wage adjustments, measurement errors, supply response2010  First study to use historical data found no evidence of any global increase in food insecurity (Headey, 2013)Headey, 2013  Later corroborated by updated World Bank poverty estimates (The Economist, 2012).The Economist, 2012  Recent paper finds wages rose in response to higher food prices in India; poverty went down (Jacoby, 2013).Jacoby, 2013 5

6. Introduction  So partial equilibrium simulations sending one message, historical data sending another  In this paper I use historical poverty data on from the World Bank, which I combine with data on food inflation and total inflation from ILO  I conduct a barrage of reduced form (non-structural) tests of the relationship between changes in relative food prices and changes in various welfare indicators  I find that increases in food prices predict sizeable reductions in poverty  The results are highly robust to different specifications, different welfare indicators and different samples 6

7. Introduction  There are plenty of caveats with this kind of approach  Cross-country data are particularly limited for examining structural theories  My aims are more modest: to establish some basic “stylized facts” in the international data (Kaldor 1957; Pritchett 2000)  There’s a danger in reading too much into facts derived from faulty data and non-structural models  But there’s also the danger of structural models failing to predict genuine facts  In those circumstances the models themselves need a serious re-think 7

8. Existing theory and evidence 8

9.  Early criticisms of this approach pointed out that the prevailing wisdom prior to the 2008 crisis was that low agricultural prices were holding back poverty reduction  Evidence was largely from trade models, where trade liberalization would raises international agricultural prices, which was found to reduce poverty  Swinnen (2010) showed serious inconsistencies in statements by World Bank, Oxfam and IFPRI from before and after crisis 9

10. Existing theory and evidence  Headey and Fan (2010) offered somewhat more technical critique 1. Non-trivial to accurately measure net food consumption –production and consumption taken from different survey modules with different recall periods  Beegle et al. (2012) show that survey instruments can greatly affect expenditure estimates 2. Simulations conducted ceteris paribus predictions, but all else was not the same.  Food crisis partly triggered by high rates of growth in LDCs, and higher non-food commodity prices 10

11. Existing theory and evidence 3. Food prices can affect factor markets, particularly labor markets  Small literature using time series econometrics to test short and long run impacts of higher food prices on wages (Ravallion 1990; Palmer-Jones 1993; Rashid 2002; Lasco et al. 2008)  But several weaknesses: some studies use inappropriate techniques, and 3 of the 4 studies are from Bangladesh, 4 th from Philippines  Short run wage-food price elasticities vary from 0.2 to 0.6; long run elasticities are much larger 11

12. Existing theory and evidence 12

13. Existing theory and evidence  Jacoby’s model has several important predictions: 1.A larger ag. sector demands more labor in absolute terms from the smaller nonfarm sector, so larger wage adjustments required to extract this labor 2.Since services cannot be supplied from overseas, it is difficult to extract labor from services. So large wage responses needed to get labor out of nonfarm sector 3.If demand for services is sensitive to ag prices or ag income growth, then wage response is higher 4.Large shares of intermediate inputs raise returns to labor, which raises wages. 13

14. Existing theory and evidence 14

15. Existing theory and evidence  Jacoby focuses on “long run” (2004-09) and uses district price, wage and expenditure data for rural India  districts w/ higher food prices saw larger wage growth  Welfare improves across the entire distribution  van Campenhout et al. 2013 use a CGE model to separate short & long run effects in Uganda  Also find negative welfare impacts in short run, but positive impacts in the longer run  Rural populations are the main beneficiaries  Present paper is useful for testing the generalizability of these predictions, albeit non-structurally 15

16. Data and methods  Poverty, income and inequality data are from the World Bank’s POVCAL dataset  All measured at national level, so we cannot make rural-urban distinctions  POVCAL widely used in previous research, but current version is a major extension over previous versions.  As of mid 2013, POVCAL contained 592 poverty “episodes”, whereas previous cross-country studies of poverty utilize samples of a few hundred or less  Moreover, a large proportion of these observations fall in the window 2005-2010 when there was considerable volatility in food and non-food commodity prices 16

17. 17 Figure 1. A histogram of POVCAL observations Source: Author ’ s estimation from POVCAL data.

18. Data and methods  The usable increase in sample size is somewhat smaller however, because we need to make some exclusions  Very low poverty headcounts create problems when measuring percentage changes, so we exclude countries with initial poverty of less than 5%  Food prices are measured as changes in ratio of the food CPI to the total CPI, both sourced from the ILO  Advantageously, this frees us from having to make assumptions about international price transmission  On the other hand, domestic food prices may be heavily influenced by unobservables that also influence poverty 18

19. Data and methods  Some examples: 1.Drought increases food prices but independently increases poverty 2.Financial crisis leads to weakened currency and higher food prices, but independent creates unemployment and poverty (1998 Indonesian financial crisis) 3.Resource boom creates inflation for agricultural products, but independently reduces poverty 4.Food inflation correlated with general inflation, which is bad for the poor  Most of these stories lead to a downwards bias 19

20. Data and methods 20

21. Data and methods  In terms of methods, I use nonparametric techniques  These are particularly well suited to this case because: 1.Data are noisy and sensitive to outliers; several non- parametric techniques downweight outliers: LOWESS Kernel-weighted local polynomial; robust regressors 2.Non-parametric graphical techniques useful for exploring non-linearities  Another option is to add fixed (trend) effects to model, as in Christiaensen et al. (2011). I also test this  An ironic sidenote: Deaton’s (1989) paper was primarily focused on propagating the use of non-parametric techniques (but more famous for net benefit ratio!) 21

22. Results 22  I’m going to start with some very basic graphical results  Then show some basic robust regression results for different samples, including a sample where I crop the top and bottom 5% of food price changes  I’ll then take you through a wide range of robustness tests  Finally, since poverty changes are functionally the result of changes in mean income and changes in the distribution of income, I’ll look at these data briefly

23. 23 Figure 3. A Kernel-weighted local polynomial smoothing prediction of the relationship between changes in real food prices and changes in $1.25/day poverty headcounts, with 95% confidence intervals Source: Author ’ s estimates. Notes: This graph shows Kernel-weighted local polynomial predictions of the percentage changes in the $1.25/day poverty headcount against percentage changes in real food prices, along with the 95% confidence intervals in grey shade.

24. 24 Regression No.R1R2 Sample cropped?NoYes Food prices (%  p f ) -0.66***-1.01*** (0.25)(0.33) Time trend-0.41*-0.27 (0.23)(0.21) Log y t-1 -2.47-2.49 (2.05)(1.89) R-squared0.040.05 N297241 Table 2. Robust regressions of changes in $1.25/day poverty against changes in food prices Notes: The equations above are estimated with the robust regressor (rreg command) in STATA. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Standard errors are reported in parentheses. The r-squared is derived from ordinary least squares rather than OLS. %  p f is the percentage change in food prices, defined as the ratio of the food consumer price index to the total food consumer price index. y t-1 is the dependent variable in levels measured at the start of a poverty episode.

25. 25 No. of observations285 Food prices-0.68**Exchange rate0.12* (0.29)(0.07) CPI index-0.07Terms of trade-0.27*** (0.07)(0.10) Agricultural GDP-0.79***M2/GDP0.01 (0.19)(0.08) Cereal yields0.24**Population growth0.42 (0.11)(0.55) R-squared0.42 Table 3. Robust regressions of changes in poverty against changes in food prices with tests of sensitivity to potential confounding factors

26. 26 Figure 4. LOWESS predictions of poverty and food prices, Disaggregated by initial poverty levels Notes: These are Lowess predictions of percentage changes in the $1.25/day poverty headcount against percentage changes in real food prices, for three different samples based on poverty levels at the beginning of the episode.

27. 27 Figure 5. Switching to the $1.25/day poverty gap measure

28. 28 Figure 5. Switching to the $1.25/day poverty gap squared

29. 29 Figure 5. Switching to the $2/day poverty headcount

30. 30 Dependent variable $1.25/day poverty gap $1.25/day poverty gap, squared $2/day poverty headcount Sample cropped?NoYesNoYesNoYes Food prices-0.84***-1.84***-0.60***-1.14***-0.36**-1.04*** (0.30)(0.50)(0.22)(0.37)(0.16)(0.32) R-squared0.060.090.060.07 0.09 N225199233207235190 Table 5. Poverty-food prices elasticities for alternative Poverty indicators

31. 31 R1R2R3R4 Cropping?No Length of episodes1-4 years1-3 years1-2 years1 year Food prices-0.56**-0.72**-0.90**-0.66 (0.25)(0.28)(0.43)(0.52) R-squared0.040.060.05 N272251196156 Table 6. The impact of food price changes on poverty under different lengths of poverty episodes

32. 32 Regression No.R1R2R3R4R5R6 Initial structure Rural pop share (%) Agric pop share (%) Agric GDP share (%) Rural pop share (%) Agric pop share (%) Agric GDP share (%) CroppingNo Yes Food prices-1.55**-1.19**-1.11***-1.07-0.82-1.37** (0.64)(0.48)(0.42)(0.76)(0.62)(0.54) Prices*structure1.83*1.322.93*0.740.362.24 (1.04)(0.82)(1.72)(1.33)(1.23)(2.24) R-squared0.05 0.04 0.06 N341339328268267262 Table 7. Interacting food prices changes with structural indicators of the importance of agriculture

33. Figure 6. Predicted changes in poverty, by initial 33

34. Results  Finally, while cross-country data do not permit structural tests, we can look at whether higher food prices catalyze mean income growth, or shift the distribution of income in favor of the poor 34 Food pricesPovertyMean incomeGini coef. Food prices1.00 Poverty-0.19***1.00 Mean income0.05-0.54***1.00 Gini coef.-0.11*0.28***0.31***1.00 Table 6. Correlations with different welfare indicators Notes: These are pairwise correlation using the cropped sample. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively.

35. Results  So it looks like higher food prices have a stronger correlation with income redistribution rather than growth of mean income  If that’s the case, we can also add mean income growth to the poverty model under the assumption that higher food prices have little impact on mean income growth  This could also be interpreted as another robustness check, especially if we are concerned that income growth drives food prices whilst independently affecting poverty outcomes 35

36. 36 Regression No.1245 Sample cropped?No Yes Food prices (%)-0.67***-0.18-1.17***-0.73*** (0.25)(0.17)(0.34)(0.24) Change in income (%) -1.12***-1.05*** (0.06)(0.07) R-squared0.050.570.060.54 N295294239238 Table 4. Robust regressions of the impact of food price changes on changes in $1.25/day poverty headcounts, with and without controlling for income growth

37. 37 SampleFullCropped Cropped & LAC excluded Cropped & LAC, MNA, ECA exc. Food prices-0.13**-0.16*-0.44**-0.54** (0.06)(0.09)(0.20)(0.26) Lag Gini-5.05***-4.29***-14.83***-17.12*** (1.47)(1.49)(4.28)(5.07) Time trend-0.08-0.070.010.16 (0.05) (0.12)(0.14) R-squared0.060.050.120.17 N31328213483 Table 7. Robust regressions of the impact of food price changes on changes in the Gini coefficient (first differences)

38. 38 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

39. 39 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

40. 40 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

41. 41 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

42. 42 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

43. 43 Figure 8. Trends in food prices (black lines) and Gini coefficients (grey lines) in six large LDCs Source: Author ’ s construction from POVCAL and ILO data.

44. Results  Finally, we use these econometric estimates to very cautiously and crudely estimate the impacts of the increases in food prices witnessed over the late 2000s  We do so for 37 countries with one survey in first half of 2000s and one survey in second half of 2000s  Population of these countries is around 4 billion people  We take a poverty-food price elasticity from the model in which we added income growth (= -0.71)  Not claiming these estimates are accurate (they come with large confidence intervals, let alone other caveats)  But useful to demonstrate potential importance of higher food prices to poverty reduction 44

45. 45 FactorPoverty elasticity Predicted change in poverty (millions) Contribution to total predicted change (%) Food price movements-0.71-69.025.9% Income growth-1.05-196.974.1% Both factors-265.9100.0% Actual change-245.6 Table 8. Retrospective predictions of the poverty impacts of food price and income changes in the late 2000s

46. Conclusions  Economists have sharply disagreed about the impact of higher food prices on poverty reduction  Recent structural models suggest that the general equilibrium impacts of higher food prices are pro-poor once sufficient time has taken place for factor market adjustments  The results in this paper corroborate these theories  But there are plenty of caveats.... 46

47. Conclusions  Caveats:  data don’t tell us why we observe this result  not clear that indicators and specifications are (sufficiently) unbiased;  confidence intervals are large;  Result are generic: some countries, or population groups may suffer  In particular, POVCAL data do not allow us to say anything about rural-urban dichotomy  Cannot infer much about the short run impacts 47

48. Conclusions  What are the research implications of this?  First, we clearly need much more analysis of the general equilibrium effects of food prices on poverty: o Econometric studies of wages and food prices in rural areas are mostly derived from Bangladesh o These studies are somewhat vague about the short run-long run distinction. o Jacoby’s study could potentially be replicated for other countries o Conceivably there are other linkages: e.g. supply responses(*), consumption multipliers. 48

49. Conclusions  Assuming medium to long term impacts are positive, how should this finding alter policies?  In many respects it’s not obvious that international and domestic policymakers reacted inappropriately  There was understandable concern about the capacity of the poor to cope in the short run  Safety nets still relevant, especially conditional variety  Another response was investing in agriculture  Also justified because higher food prices signal scarcity, and because we want the poor to be part of the supply response 49

50. Conclusions  Also a case for some amount of price stabilization, especially in the absence of good safety net programs  The danger of these efforts is that they mute supply response, and end up protecting urban consumers to the detriment of the rural poor  There may be good political and economic reasons to protect the urban consumer, but we shouldn’t believe that higher food prices also hurt the rural poor  On the contrary, the evidence in this paper suggests that higher food prices have ultimately proved a boon for global poverty reduction 50

51.  Thank you 51