1. How Pro-Poor Growth Will Affect the Global Demand for EnergyAlan FuchsPaul GertlerOrie Shelefand Catherine WolframUC Berkeley December 2012

2. Wolfram 1 3. Wolfram 2 4. Wolfram 3 5. The developing world accounts for most expected growth inenergy and CO2 emissions.Source: Energy Information Administration. Wolfram 4 6. Pro-poor growth Many countries have made progress addressing povertyrecently. China, for instance, saw the share of its population living inpoverty fall from 53% in 1981 to 8% in 2001. Brazil and Mexico have aggressive anti-poverty programs. The success fighting poverty varies around the world.Wolfram 5 7. This projectHow important is rising income among the worlds poor toexplaining growth in the demand for energy? - Model: We develop a simple model of household asset acquisition when income is growing. - Micro empirics: We show that predictions of our model are born out in data from Mexico, where there were plausibly exogenous shocks to income. - Macro empirics: We show that the effects we identify have large impacts on macro-level projections.Answers: pro-poor growth is important ANDthe speed at which people come out of poverty matters Wolfram 6 8. Wolfram 7 9. Figure 1: Refrigerator Ownership and Household IncomeWolfram 8 10. Benchmarking EIAs projections Wolfram 9 11. China example We compared the 2000 World Energy Outlook forecast of Chinese total energy demand for 2005. to actual Chinese total energy demand in 2005:A 25% underestimate. Even adjusting for under-estimated GDP growth, still a 15% underestimate. 12. Why its important to project energy demand Fossil fuel use is the major contributor to climate change.- Emissions projections inform likely damages.- Country-by-country emissions projections are used toestablish baselines for global negotiations. Infrastructure investment require long lead times.- Incorrect demand forecasts can lead to local shortages andglobal price spikes.Wolfram 11 13. Outline Model describes tradeoff between lumpy asset (refrigerator)and continuous consumption (food). Empirical evidence from Mexico.- Acquisition of refrigerators and other assets.- Energy use conditional on asset holdings. Cross-country estimates of energy income elasticities, whichare typical inputs to forecast models.Wolfram 12 14. Model setup Two periods, no discounting. Each period, household consumes at most two goods: - Consumption good (food) with utility uf(.), uf(.)>0, uf(.) 4 Wolfram 39 41. Discrete-time hazard specification results 2 & 3h(kit ) = Pr(kit = 1 kit 1 = 0) =1 + 2 cumulative it + 3early i + 4 earlyi cumulative itaa a + 1 X i + Rrt + itOur model predicts: 2 > 0 ; 3 , 4 < 0Wolfram 40 42. Results for refrigerators prediction 1Table 4: Basic Results - Refrigerator - Income Effects(1) (2)(3) (4) (5) (6)OLSIV IV OLSIVIV Discrete Time HazardHousehold FE Discrete Time Hazard Household FE Cumulative Transfers0.023*** 0.029***0.048***[0.004] [0.005][0.005] Cumulative Transfers X 0.020*** 0.024***0.043*** Bottom 75% of [0.004][0.005] [0.005] Baseline Assets Cumulative Transfers X 0.032*** 0.040***0.058*** Top 25% of Baseline [0.006][0.007] [0.007] AssetsRelatively better off are more N30,41430,41430,258 30,414 30,41430,258 sensitive, R-squared 0.1000.100consistent w/ prediction 1. F Stat on Excluded Variables - 3156 2262 Cumulative Transfers F Stat on Excluded Variables - Cumulative Transfers X Bottom 75%3161 3767 F Stat on Excluded Variables - Cumulative Transfers X Top 25% 1635 1596 Number of Households6,6556,655Note: All specifications include state by round- fixed effects and household controls. Wolfram 41Robust standard errors clustered by village in brackets. 43. Results for refrigerators predictions 2&3Table 5: Basic Results Refrigerator - Timing (1) (2) (3) (4) (5) OLS OLSOLSIVIV Discrete Time HazardHousehold FE Cumulative Transfers0.023***0.028***0.039***0.056***0.061*** [0.004] [0.004] [0.007] [0.007] [0.007] Early -0.016*** -0.007-0.009* [0.005] [0.005] [0.005] Cumulative Transfers X Early-0.015**-0.021*** -0.018**Consistent w/ [0.006] [0.007] [0.007] prediction 3. Net Early Effect at 2003-0.025*** -0.033*** Median Cumulative Transfers [0.008] [0.008] Consistent w/ prediction 2. N 30,41430,41430,41430,41430,258 R-squared 0.100 0.100 0.101 F Stat on Excluded Variables - Cumulative Transfers 1,554 1,226 F Stat on Excluded Variables - Cumulative Transfers X Status1,974 1,889 Number of Households6,655Note: All specifications include state by round- fixed effects and household controls. Wolfram 42Robust standard errors clustered by village in brackets. 44. Additional results Patterns seems to hold with other durables. Placebo tests: cumulative transfer amounts do notconsistently predict asset ownership at baseline. If anything future income is negatively correlated with assetacquisition, consistent with a complementary savingsexplanation. Using these results as a first-stage, we see that assetownership is the only way in which increased transfers driveenergy use. - i.e., theres no effect of transfers on electricity use once you condition on appliance ownership.Wolfram 43 45. The developing world accounts for most forecast growth inenergy use. Wolfram 44 46. What would our model suggest about EIAs forecasts?First need to understand how EIA develops its projections EIA uses the World Energy Projections Plus (WEPS+) modelingsystem. TOTQUADt= TOTQUAD(t-5) * (((GDPGRt* ELASTt)/100)+1)5 - TOTQUADt is total final energy consumption in quads. - GDPGR is projected growth in GDP. - ELAST is assumed income elasticity of energy.Wolfram 45 47. Does EIAs methodology allow for differences betweencountries with pro-poor growth? Notably, ELAST does not vary for high and low GDP growthscenarios, or by whether a country has had pro-poor growth. Our model suggests that income elasticity varies with GDPgrowth, and with whether or not growth is pro-poor.Wolfram 46 48. Our model and income elasticity estimatesTo evaluate the size of the effects we have identified, we estimateversions of the following equation (based on numerous previous papers): (5)To evaluate our model, we include interactions between pro-poor growthand income growth:Prediction 1: coefficient on ln(Incomeit) x ProPoorGrowth positivePrediction 2: coefficient on Income Growthit x ProPoorGrowth positivePrediction 3: coefficient on ln(Incomeit) x Income Growth x ProPoorGrowth positiveWolfram 47 49. Cross-country income elasticity estimatesT a b le 9 : A gg re g at e Co u nt ry -L e ve l E n e rg y C on su m p tio n (1 ) (2 ) (3 )V A R IA B L E Sln (In c o m e ) 0 .9 2 5 * * * 0 .8 5 6 * * * 0 .9 0 9 ** *[0 .0 8 7 ][0 . 1 0 4 ] [0 .0 8 8 ]In c o m e G ro w th -0 .3 2 4 0 .3 0 7 [0 . 2 0 1 ] [0 .7 2 6 ]ln (In c o m e ) X In c o m e G ro w t h 0 .0 9 7[0 .1 1 6 ]ln (In c o m e ) X P ro P o o rG ro w th 0 .0 7 3 * * *0 .0 5 7 ** *[0 .0 1 7 ] [0 .0 1 6 ]In c o m e G ro w th X P ro P o o rG ro w th0 .1 2 2 * * 0 .8 9 3 ** *[0 . 0 5 3 ][0 .2 3 3 ]In c o m e X I n c o m e G ro w th X 0 .1 4 6 ** *P ro P o o rG ro w th [0 .0 3 7 ]C o u n try F ix e d E f fe c tsYE S YESYESY e a r F ix e d E ffe c ts YE S YESYESO b s e rv a tio n s90 7 8 92 89 2R -s q u a re d0 .9 8 1 0.9810 .9 8 3R o b u s t s ta n d a rd e rro rs c l u s te re d b y c o u n t ry i n b ra c k e ts .* * * p < 0 .0 1 , * * p < 0.0 5, * p < 0.1 0. Wolfram 48 50. Conclusions Nonlinear relationship between income and asset acquisition. Timing of income transfers or growth may affect assetacquisition. We need to be mindful of this in: - Designing policies that encourage the adoption of energy efficient durables. - Designing transfer programs. - Forecasting future growth in energy demand and GHG emissions.Wolfram 49 51. Wolfram 50