The Impact of World Oil Price Shocks on

The Impact of World Oil Price Shocks on

The Impact of World Oil Price Shocks on Maize Prices in East Africa Brian M. Dillon and Christopher B. Barrett Presentation to the Australian Agricultural & Resource Economics Society New South Wales Brank Sydney, May 23, 2013 Motivation 2008 and 2011 global food and oil price spikes: Widespread, high-level concern about the impact of global commodity market price shocks on developing countries and what to do about it. Considerable pundit and scholar focus on oil-food price links, especially due to ethanol production and fertilizer prices. Yet the relevance of those links is questionable in poor countries that use little fertilizer or biofuels. Motivation Inter-commodity spatio-temporal price transmission: Deaton (1999, p.24) the understanding of commodity prices and the ability to forecast them remains seriously inadequate. Without such understanding, it is difficult to construct good policy rules. That concern remains today. Our results underscore importance of post-harvest distribution systems as well as ag productivity. Also emphasize the prominent role of variable transactions costs in determining equilibrium food Motivation Central questions of this paper Do global oil price shocks impact food prices in local (sub-national) markets in low-income countries where subsistence food production is widespread?

If so, how much and by what mechanisms? We tackle those questions, focusing on maize markets within Ethiopia, Kenya, Tanzania and Uganda, using a newly assembled data set of local, monthly average maize and petrol prices from 17 sub-national markets, January 2000 November 2012. Motivation 3 Prospective oil price maize price linkages: 1) Farmers input costs fertilizers, machinery fuel - We ignore this b/c use rates negligible in all but Kenya. - With available data, we test if fertilizer could be a channel in Kenya, but find no support for that hypothesis. Thats the expected result as Kenya is a price-taker on international grain markets. Motivation 3 Prospective oil price maize price linkages: 2) Biofuels and US ethanol mandate - Historically, weak oil and maize price connection. But 2005 US Energy Policy Act thought to link them now due to corn ethanol conversion into biofuel. - Yet, literature often finds no causal effects. - We similarly test but find no causal effect of oil prices on maize prices. So conservatively assume no effects. Motivation 3 Prospective oil price maize price linkages: 3) Fuel and transport costs - Prospectively important b/c of low value-to-weight of grains, and in east Africa, rudimentary transport infrastructure, heavy dependence on truck/lorry service, and long distances to some markets. - We find global oil prices significantly influence maize prices in local markets in East Africa through fuel prices. In more remote markets, global oil prices have

a larger marginal effect than global maize prices. Background Maize is the key food grain of east Africa Largest single crop in terms of area planted and a major source of income for farmers. Single largest source of calories in diets Very sensitive issue politically, so government interventions are routine, albeit far less than pre2000, following market liberalization All countries trade (near-)continuously on intl markets, but volumes small share of consumption/ Background East African economies are pure oil importers Only Kenyan has any domestic fuel refining capacity Ethiopia administratively fixes fuel prices, market reigns in other three economies Data Monthly price series, January 2000 November 2012 Petrol and maize for each of 17 subnational urban markets, which we assembled from various sources. Global oil and maize price data from World Bank CPI and USD exchange rate data from IMF 500 km Global oil-maize prices Global oil and maize prices are strongly correlated (r=0.83 in nominal terms, =0.45 in real 2005 terms) Figure 15. Global Maize and Oil prices, Oct 2006 Nov 2012 (Nominal)

Global crude oil national petrol prices Global crude oil and East African port of entry (POE) petrol prices are strongly correlated Figure 10. Global oil prices and fuel prices in Addis Ababa, ET, 2000-2012 Figure 12. Global oil prices and fuel prices in Dar es Salaam, TZ, 2002-2012 Figure 11. Global oil prices and fuel prices in Mombasa, KY, 1998-2012 Figure 13. Global oil prices and fuel prices in Kampala, UG, 1998-2012 Global national maize prices Global and East African POE maize prices are also strongly correlated, but w/more (seasonal) deviations Figure 6. Global maize prices and maize prices in Addis Ababa, ET, 2000-2012 Figure 8. Global maize prices and maize prices in Dar es Salaam, TZ, 2000-2011 Figure 7. Global maize prices and maize prices in Mombasa, KY, 2000-2011 Figure 9. Global maize prices and maize prices in Kampala, UG, 2000-2012 Empirical strategy Identifying Assumptions 1) All four countries are price-takers on intl markets. So global market prices weakly exogenous. 2) Within region, no feedback from maize prices to fuel prices (no ethanol production; maize haulage modest share of freight), so fuel prices are weakly exogenous. 3) Within countries, disequilibrium between POE prices and those in another market j is resolved through adjustment in market j, reflecting that these are pricetaking markets routinely connected through trade. 4) Exchange rates weakly exogenous to POE prices. (Verified in Appendix.)

Empirical strategy Sequence of bilateral price transmission models - Allows for country-specific links to global markets, and differential price transmission along distinct within-country links. Figure 14. Diagram of Empirical Strategy Global oil price 1 ? Global maize price 3 POE petrol price 2 Petrol price in market j Includes exchange rate POE maize price 4

Maize price in market j Empirical strategy Estimate global price shock pass-through - Use estimated cointegrating equations to estimate long-run equilibrium price effects of global price shocks. - Use estimated error correction models to estimate time to convergence to new long-run equilibrium following global price shocks. Empirical strategy 1. Estimating the global oil-maize price relationship - Both are I(1) series. - No evidence of cointegration using any of multiple specifications and tests. Others have found similar result. - These results hold even for the post-Oct 2006 subsample after US ethanol mandate begins. - In the absence of any clear, stationary long-run equilibrium relationship between the two series, we estimate a reduced form VAR (in 1st diffs). Empirical strategy 2. Estimating global-POE price relationships - In all 4 countries and for both commodities, global and POE prices are cointegrated I(1) series. - For each country-good, estimate two-stage asymmetric error correction model (ECM), allowing the long-run equilibrium POE price (F,M) to be determined jointly by global market price (FG, MG) and exchange rate (ER) and, in the case of maize, fuel prices to cover transport:

- This cointegrating vector represents the long-run equilibrium price relationship of central interest. Empirical strategy 2. Estimating global-POE price relationships For each country-commodity pair, we then estimate short-run, asymmetric error correction model, controlling for CPI changes: where the error correction term (ECT) and and reflect (asymmetric) speed of convergence parameters, whose reciprocal absolute values , |1/| and |1/|, represent rate of decay estimates. The estimated speed of adjustment is also of interest. Empirical strategy 3. Estimating domestic price transmission - For each market/commodity, follow same approach as in step 2 now with POE price as weakly exogenous. - All non-POE market price series are also I(1) series and cointegrated with POE series. - Estimate cointegrating vector (w/o exchange rate), controlling for fuel prices associated with variable transport costs of maize to get long-run price relationship. - Then estimate asymmetric ECM to estimate the short-run price adjustment dynamics. Results 1. Global price linkages - We find no effect of lagged or contemporaneous oil prices on maize prices. - We find positive changes in maize prices do tend to drive up oil prices (consistent with Serra et al. 2011). - We interpret these results conservatively for our purposes, inferring no meaningful causal relationship from exogenous global crude oil price increases to global maize prices. If

such a relationship does exist, it only reinforces our core findings. - Strong correlation in global crude oil and maize prices appear due to correlated shocks (Gilbert 2010, Enders and Holt 2012, Byrne et al. 2013). Results 1. Global price linkages Table 5. VAR results, global oil and maize prices (Nominal) Jan 1990 - Oct 2006 Nov 2012 Nov 2012 Oil price equation LD.Oil price ($/bl) 0.365*** 0.376*** 0.057 0.11 LD.Maize price ($/mt) 0.080*** 0.107* 0.024 0.048 Constant 0.129 0.087 0.231 0.73 R2 0.216 0.295 Maize price equation LD.Oil price ($/bl) -0.105 0.001 0.151 0.3

LD.Maize price ($/mt) 0.211*** 0.122 0.063 0.131 Constant 0.657 2.391 0.611 1.994 R2 0.04 0.015 N 272 73 Results 2. Global oil POE petrol price linkages - On average, a 1% increase in the price of oil on world markets leads to an increase in the long-run POE petrol price of 0.38-0.46%, with estimates remarkably similar across countries. - Petrol price elasticities wrt exchange rate are higher, ranging from 0.85 in Kenya to 1.52 in Ethiopia. - Adjustment back to the long-run equilibrium is not instantaneous, but is still reasonably fast on average, ranging from 2-7 months. - Increases in global oil prices transmit faster than decreases, although differences often not stat. signif. Results 2. Global oil POE petrol price linkages Table 6. POE fuel and global oil, first-stage ECM results Ethiopia

Kenya Tanzania Uganda 0.053 0.621 8.667 14.507 0.004 0.014 0.451 0.531 1.194 0.792 1.262 1.182 0.041 0.059 0.069

0.06 -7.322 -22.018 -839.344 -911.665 0.325 4.251 66.468 104.065 R2 0.955 0.94 0.96 0.94 N 141 177 126

147 Pass-through elasticity (oil) 0.380 0.463 0.435 0.383 Pass-through elasticity (ER) 1.519 0.854 1.219 1.036 Mean dep. variable 8.14 69.55 1282.60 2175.71 Global oil ($/bl) Exchange rate (local/$) Constant

Results 2. Global oil POE petrol price linkages Table 7. POE fuel and global oil, second-stage asymmetric ECM results Ethiopia Kenya Tanzania Uganda L.ECTneg -0.187*** -0.140*** -0.562*** -0.298*** L.ECTpos -0.132*** -0.144*** -0.097 -0.186*** 0.013 0.192*** -4.804

2.62 0.360*** 0.203*** 0.023 0.180** LD.Global oil ($/bl) 0.008 0.164*** 1.035 -1.172 LD.ER (Local/$) 0.177 0.305*** -0.024 0.270* LD.Domestic CPI 0.001 -0.022

-1.853 -0.400 R2 0.51 0.65 0.36 0.25 N 139 145 121 145 0.447 7.90 0.937 67.95 0.001 1240.75 0.24 2146.19

D.Domestic CPI LD.POE price (Local/L) F test: asymmetric (p-val) Mean POE price (Local/L) Results 3. Global POE maize price linkages - Estimated pass-through elasticities a bit higher than for oil, but also more heterogeneous across countries, ranging from 0.22-0.82 (mean=0.44). - Long-run pass-through elasticities of POE maize wrt global oil prices range 0.20-0.36. In Kenya, by far the biggest maize importer in the region, elasticity wrt global oil prices > wrt global maize prices, underscoring transport costs importance. - Short-run adjustment not affected by oil prices. - Adjustment slower than for oil and asymmetric. Higherthan-equilibrium POE maize prices never persist beyond the next harvest, disappearing in 5-6 months in each country. Lower-than-equilibrium prices persist longer. Results 3. Global POE maize price linkages Table 8. POE maize and global maize, first-stage ECM results Ethiopia Kenya Tanzania 0.012 0.026 0.733 Global maize ($/mt) 0.003 0.014 0.198 0.013

0.096 0.847 Global oil ($/bl) 0.004 0.03 0.41 0.041 0.491 0.081 Exchange rate (local/$) 0.038 0.071 0.017 -0.779 -29.131 Constant 0.246 5.28 0.721 0.604 0.664* R2 144 143 144 N 0.823 0.215 0.436 Pass-through elasticity (maize) Pass-through elasticity (oil) 0.356 0.306 0.195 Pass-through elasticity (ER) 0.202

2.140 0.383 2.039 17.538 244.617 Mean dep. variable Uganda 1.201 0.414 1.546 0.779 0.272 0.056 -408.556 89.542 0.682 135 0.467 0.235 1.334 394.433 Results 3. Global POE maize price linkages Table 9. POE maize and global maize and oil, second-stage asymmetric ECM results Ethiopia Kenya Tanzania Uganda L.ECTneg L.ECTpos D.Domestic CPI LD.POE price (Local/L) LD.Global maize ($/mt) LD.Global oil ($/bl)

LD.ER Local/USD LD.Domestic CPI R2 N F test: asymmetric (p-val) Mean POE price (Local/L) -0.062 -0.047 -0.063 -0.134* -0.202*** 0.035*** 0.13 -0.004** 0.001 -0.029 -0.003 -0.178*** 0.184** 0.266*** 0.029* -0.04 0.145 0.035 -0.124*** 4.115** 0.333*** -0.211 -0.298

-0.065 0.525 -0.172*** 5.333* 0.275*** 0.381 -0.918 -0.073 0.167 0.47 142 0.031 2.04 0.23 141 0.105 17.54 0.25 142 0.354 244.62 0.20 133 0.676 394.43 Results 4. Domestic fuel price transmission - Fuel markets are very well integrated across space within the study countries. - Long-run equilibrium price relationship estimates

correspond quite closely with the law of one price. - In short-run adjustment, POE price increases transmit faster (<2 months in most cases) than POE price decreases, although differences not always stat sign. Results 4. Domestic fuel price transmission Table 10. Within-country fuel price transmission, ECM stage 1, 2000-2012 Country Ethiopia Market Bahir Dar Dire Dawa M'ekele Kenya Kisumu Nairobi Nakuru Tanzania Arusha Dodoma Kigoma Mbeya Uganda Gulu Mbale Mbarara POE fuel price 1.034 1.099 1.06 0.972 0.977 1.001 1.015

1.023 1.114 1.054 1.027 1.012 1.010 Constant -0.108 -0.752 -0.304 2.790 3.271 0.244 17.470 -10.941 9.474 1.358 23.772 -33.272 21.820 R2 0.996 0.998 0.998 0.988 0.991 0.992 0.984 0.990 0.980 0.990 0.992 0.993 0.994

N 141 141 141 171 171 171 126 126 126 126 147 147 147 Pass-through elasticity 1.013 1.092 1.037 0.959 0.953 0.996 0.987 1.008 0.993 0.999 0.989 1.015 0.990 Results 4. Domestic fuel price transmission Table 11. Within-country fuel price transmission, asymmetrical ECM stage 2, 2000-2012 F test:

LD.POE LD.own asymmetric Market L.ECTneg L.ECTpos price price R2 N (p-val) Ethiopia Bahir Dar -0.671*** -0.205 0.388* 0.134 0.31 139 0.065 Dire Dawa -0.444** -0.307 0.276 0.281 0.29 139 0.686 M'ekele -0.586** -0.276 0.381 0.165 0.30 139 0.294 Kenya Kisumu -0.109 0.126 0.463*** -0.057 0.23 169 0.072 Nairobi -0.220* 0.080 0.129 0.315** 0.24 169

0.052 Nakuru -0.323** -0.264** 0.455*** -0.052 0.27 169 0.708 Tanzania Arusha -0.671*** -0.424*** -0.001 0.059 0.23 124 0.189 Dodoma -0.430*** -0.273 0.241* 0.067 0.19 124 0.499 Kigoma -0.637*** -0.223* 0.433*** -0.121 0.37 124 0.017 Mbeya -0.488*** -0.323** 0.098 0.106 0.16 124 0.428 Uganda Gulu -0.508*** -0.001 0.719*** -0.573*** 0.27 145 0.023 Mbale -0.737*** -0.404* 0.414** -0.325** 0.24 145 0.237 Mbarara -0.461** -0.110 0.342** -0.152

0.15 145 0.154 Results 5. Domestic maize price transmission - As with domestic fuel price transmission, most estimates indicate strong spatial market integration, in most cases corresponding to the law of one price in long-run equilibrium. - Within-country maize price transmission elasticities are lower for the four markets in TZ/UG furthest from coastal POE markets. - The remote markets also have the highest pass-through elasticities wrt local fuel prices: 0.29-0.76, often POE grain price pass-through. - Short-run adjustment back to equilibrium is quick, typically < 3 months and fuel prices play little or no role in short-run dynamics, just as with global-POE price dynamics. Results 5. Domestic maize price transmission Table 12. Within-country maize price transmission, ECM stage 1, 2000-2012 Pass-through elasticities Country Market POE maize Own petrol Ethiopia Bahir Dar 0.991 0.005 Dire Dawa 0.916 0.030 M'ekele 0.894 -0.046

Kenya Kisumu 1.028 0.209 Nairobi 0.928 0.109 Nakuru 1.144 0.014 Tanzania Arusha 0.937 0.051 Dodoma 1.011 0.033 Kigoma 0.633 0.447 Mbeya 0.803 0.285 Uganda Gulu 0.659 0.410 Mbale 1.137 -0.618 Mbarara 0.482 0.761 Results 5. Domestic maize price transmission

Table 13. Within-country maize price transmission, asymmetrical ECM stage 2 Market Ethiopia Bahir Dar Dire Dawa M'ekele Kenya Kisumu Nairobi Nakuru Tanzania Arusha Dodoma Kigoma Mbeya Uganda Gulu Mbale Mbarara L.ECT -0.928*** -0.779*** -0.289** -0.521*** -0.468*** -0.413*** -0.404*** -0.188* -0.296*** -0.337*** -0.188* -0.353** -0.236** neg L.ECT -0.543***

-0.397** -0.251* -0.510*** -0.320*** -0.341*** -0.419*** -0.385*** -0.293*** -0.419*** -0.164 0.050 -0.396*** pos LD.POE price 0.184 0.330** 0.481*** 0.154 0.252** 0.066 0.151 0.131 0.251** 0.199*** 0.195*** 0.273** 0.312*** LD.own maize price 0.129 -0.071 -0.044 0.293***

0.063 0.278*** 0.272*** 0.382*** 0.249** 0.364*** -0.035 -0.136 0.275*** F test: LD.own fuel asymmetric price (p-value) 0.021 0.109 -0.013 0.041 0.095** 0.839 0.143* 0.955 0.077 0.377 0.055 0.622 0.027 0.925 0.009 0.174 0.013 0.984 -0.010 0.553 -0.011 0.868

-0.025 0.022 0.002 0.209 Summary Discussion Long-run price pass-through estimates: Scenario 1: Scenario 2: Scenario 3: Scenario 4: For many markets, Country Ethiopia esp. the most remote and importdependent ones, Kenya pass-through wrt global crude oil Tanzania prices wrt global maize prices. Uganda Table 16. Cumulative impacts Only global oil price increase of 1% Only global maize price increase of 1% Global oil and global maize prices both increase 1% Global oil, global maize, and exchange rate all increase 1% % change in local maize price Market Scen. 1 Scen. 2 Scen. 3 Scen. 4 0.82

1.18 1.38 Addis Ababa 0.36 0.82 1.17 1.38 Bahir Dar 0.35 0.75 1.09 1.33 Dire Dawa 0.34 0.74 1.04 1.14 M'ekele 0.30 0.22 0.63 3.00 Kisumu 0.41 0.22 0.52 2.66 Mombasa 0.31 0.20 0.53 2.61 Nairobi 0.33 0.25 0.60

3.06 Nakuru 0.36 0.41 0.61 1.03 Arusha 0.20 0.44 0.63 1.01 Dar es Salaam 0.20 0.44 0.65 1.08 Dodoma 0.21 0.28 0.59 1.38 Kigoma 0.32 0.35 0.63 1.29 Mbeya 0.28 0.31 0.62 1.92 Gulu 0.31 0.47 0.70 2.04

Kampala 0.24 0.53 0.56 1.42 Mbale 0.03 0.23 0.63 2.05 Mbarara 0.40 Summary Discussion And adjustment is much faster to oil price shocks Table 14. Speed of adjustment to global price increases (months) Fuel Maize Maize GlobalPOEGlobalPOEGlobal-local POE local POE local Country Market (1) (2) (3) (4) (3) + (4) Ethiopia Addis Ababa 5.3 16.1 16.1 Bahir Dar

5.3 1.5 16.1 1.1 17.2 Dire Dawa 5.3 2.3 16.1 1.3 17.4 M'ekele 5.3 1.7 16.1 3.5 19.6 Kenya Kisumu 7.1 9.2 21.3 1.9 23.2 Mombasa 7.1 21.3 21.3 Nairobi 7.1 4.5 21.3 2.1 23.4 Eldoret/Nakuru 7.1

3.1 21.3 2.4 23.7 Tanzania Arusha 1.8 1.5 15.9 2.5 18.3 Dar es Salaam 1.8 15.9 15.9 Dodoma 1.8 2.3 15.9 5.3 21.2 Kigoma 1.8 1.6 15.9 3.4 19.3 Mbeya 1.8 2.0 15.9 3.0 18.8 Uganda Gulu 3.4 2.0 7.5

5.3 12.8 Kampala 3.4 7.5 7.5 Mbale 3.4 1.4 7.5 2.8 10.3 Mbarara 3.4 2.2 7.5 4.2 11.7 Fuel-Maize Global-local (1) + (2) + (4) 5.3 7.9 8.9 10.5 18.2 7.1 13.8 12.7 5.7 1.8 9.4 6.7 6.8 10.6

3.4 7.5 9.8 Conclusions Our findings indicate 10 key points: 1. Oil and maize prices co-move strongly on global markets, but oil price shocks do not seem to cause maize price changes at that scale of analysis. 2. Within-country, POE price changes in fuel and maize largely transmit to other markets according to law of one price in long-run equilibrium. 3. Global price changes impact POE prices more slowly, likely b/c of policy-induced and infrastructure frictions, following commonplace border effects. 4. Cross-border maize price adjustment is slower than oil/fuel price adjustment, consistent with local production and policies buffering pass-through rates. 5. Oil/fuel prices play little role in short-run price dynamics, but big role in long-run eqln relationships. Conclusions Our findings indicate 10 key points: 6. Across the 17 markets we study, average long-run local maize price elasticity wrt to global oil prices is 0.29 and stable among markets (0.20-0.41 for 16/17). 7. Average local price elasticity wrt global maize is 0.44, but considerably more dispersed across markets. 8. If global maize and oil prices both increase 1%, average local maize market change is up to 0.73% without any exchange rate adjustment (higher with depreciation). 9. In the most remote and import-dependent markets, global oil price changes have larger, quicker impact on local maize prices than do global maize price shocks. 10. Transport costs are the main channel through which global oil prices affect food prices in this region.

Thank you for your time, interest and insights

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