The U.S. Shale Oil Boom, the Oil Export Ban, and the Economy: A General Equilibrium Analysis Nida Çakır Melek Federal Reserve Bank of Kansas City Michael Plante Federal Reserve Bank of Dallas Mine K. Yücel Federal Reserve Bank of Dallas September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 1 / 36
The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Dallas, the Federal Reserve Bank of Kansas City or the Federal Reserve System. September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 2 / 36
Motivation ◮ Shale oil boom dramatically increased U.S. oil production ◮ U.S. oil imports declined ◮ Key feature of the boom: production of primarily “light” oil ◮ Refining sectors specialize to some degree across oil types ◮ U.S. crude oil export ban in place through 2015 Production Refining Export ban background September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 3 / 36
Questions we address ◮ What are the implications of the shale oil boom? ◮ for oil prices? ◮ for the refining sector? ◮ for the broader economy? ◮ What are the implications of an export ban on crude oil? ◮ Was the ban actually binding at any point in time? ◮ Are the effects limited to the energy sector? September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 4 / 36
What we do in this paper ◮ Develop a two-country DSGE model with three sectors (oil, refining, non-oil) ◮ What’s new : different types of oil, refining sector , and the U.S. crude oil export ban ◮ Calibrate the model using oil market and macro data (pre-shale boom) ◮ Use the model to examine the effects of the U.S. shale oil boom ◮ Start with case of no ban ◮ Consider implications of the export ban September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 5 / 36
Literature ◮ DSGE literature with oil ◮ Backus and Crucini (2000), Leduc and Sill (2007), Bodenstein et. al. (2011), Nakov and Nuno (2013), Plante (2014) ◮ International business cycles and trade ◮ Backus, Kehoe, and Kydland (1992, 1994), Crucini and Kahn (1996), Kose and Yi (2001, 2006), Farrokhi (2016) ◮ U.S. shale oil boom ◮ Manescu and Nuno (2015), Mohaddes and Raissi (2016), Walls and Zheng (2016), Kang et. al. (2016), Kilian (2016, 2017) ◮ Studies on the export ban (mostly non-academic) ◮ CRS (2014), ICF (2014), IHS (2014), Bordoff and Hauser (2015), Medlock (2015), Brown et. al. (2014), etc. September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 6 / 36
Introduction to crude oil quality ◮ Oil generally viewed as a homogenous commodity ◮ But can vary across a number of dimensions, such as ◮ Density (light, medium or heavy) (API gravity) ◮ Sulfur content (sweet or sour) Characteristics of several crudes ◮ We split oil into three broad types ◮ Light (API gravity ≥ 35 ◦ ) ◮ Heavy (API gravity < 26 ◦ ) ◮ Medium (everything in between) September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 7 / 36
U.S. light oil production almost tripled ◮ Total oil production increased by 72% U.S. Crude Oil Production by Type Million barrels per day 10 9 8 7 6 5 4 3 2 1 0 2010 2011 2012 2013 2014 2015 Light Medium Heavy SOURCE: Eni. September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 8 / 36
U.S. light oil imports down substantially ◮ Shifts in the quantity and types of oil imported ◮ Total oil imports down by 20% Table: U.S. imports of crude oil, mb/d U.S. crude imports Light Medium Heavy 2000 2.2 4.6 2.3 2005 2.3 4.3 3.5 2010 2.1 3.3 3.8 2011 1.7 3.3 4.0 2012 1.4 3.1 4.0 2013 0.9 3.0 3.9 2014 0.6 2.7 4.1 2015 0.6 2.6 4.2 September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 9 / 36
Oil inputs to refineries up ◮ U.S. light oil input to refineries up by 52% ◮ U.S. refineries are geared toward processing heavy crude relative to the ROW Table: Refiner inputs by type, U.S. and ROW, mb/d U.S. refiner inputs ROW refiner inputs Light Medium Heavy Light Medium Heavy 2000 4.3 7.5 3.1 17.8 30.4 5.3 2005 4.0 7.1 4.2 17.5 36.0 5.9 2010 4.2 6.1 4.4 18.6 35.9 6.0 2011 4.2 5.7 4.6 18.0 37.6 5.8 2012 4.9 5.5 4.5 18.7 38.3 5.7 2013 5.3 5.3 4.5 18.9 37.9 5.8 2014 5.9 5.1 4.7 18.8 38.8 5.9 2015 6.4 5.1 4.8 19.0 39.5 6.6 Details September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 10 / 36
Model - Households Countries denoted i = 1 , 2 (U.S., ROW) ◮ A typical household in country i maximizes utility subject to budget, time and capital accumulation constraints, where i , t L 1 − µ ∞ β t ( c µ ) γ � i , t E 0 γ t =0 c i , t is aggregate consumption and L i , t is leisure. ◮ Aggregate consumption given by � − ρ � − 1 � − ρ + (1 − ω i ) � c a c f ρ � � c i , t = ω i i , t i , t ◮ Key feature: ρ allows for inelastic demand of petroleum products September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 11 / 36
Model - Firms - Oil sector Representative firms maximize profits subject to production technologies ◮ Introduce a cost function to model production of oil, as in Balke, Plante, and Yucel (2015) ◮ Assume it costs C k i units of the non-oil good to produce a unit of oil type k ( k = L , M , H ) in country i ◮ Production cost is a convex function of the level of output � 1+ 1 � y ok η k i , t i z ok C k i , t i , t = 1 + 1 η k i ◮ Key feature: η k allows us to make oil supply price-inelastic September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 12 / 36
Model - Firms - Refining sector ◮ Refined petroleum products are produced using labor, capital and oil ◮ Production function, y f i , t , is a nested CES of value-added and oil 1 � � − ρ f � i + (1 − w f − ρ f − ρ f � i , t ) χ f i , t ) 1 − χ f w f z f i ( n f i ( K f i ) G ( o f L i , t , o f M i , t , o f H i , t ) i i i i allowing us to model differences between U.S. and ROW refining sectors in ◮ labor-share of value-added ◮ value-added share of gross output ρ f allows us to model the fact that it is hard to substitute between oil and other inputs when it comes to producing fuel September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 13 / 36
Model - Firms - Refining sector ◮ Different types of oil are imperfect substitutes as inputs into the refining process ◮ The aggregator G ( o f L i , t , o f M i , t , o f H i , t ) is given by 1 � ρ oil − ρ oil i i � H i , t ) − ρ oil L i , t ) − η oil M i , t ) − η oil η oil w o i ( o f + (1 − w o ω o i ( o f + (1 − ω o i )( o f i ) i i i i allowing us to ◮ capture differences in use of the oil types across countries ◮ emphasize the fact that substitution is easier between light and medium oils September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 14 / 36
Model - Firms - Non-oil sector ◮ Production function is a nested CES of value-added and fuel 1 � − ρ a � i + (1 − w a � − ρ a � i , t ) χ a i , t ) 1 − χ a i , t ) − ρ a y a w a z a i , t ( n a i ( K a i )( m f i i , t = i i i ◮ Key feature: ρ a allows for inelastic demand of petroleum products ◮ Key feature: χ a and w a let us match cost-shares September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 15 / 36
Model - Trade ◮ Free trade in all goods in baseline case ◮ Free trade in all goods except crude oil in export ban case ◮ The export ban in country 1 is introduced by three inequality constraints o f k 1 , t − y ok 1 , t ≥ 0 ◮ LOOP and PPP hold for all goods when the ban does not bind ◮ If the ban binds for type k , oil prices for type k can diverge in U.S., ROW ◮ To solve the model with inequality constraints, we use the Guerrieri and Iacoviello (2015) OccBin toolkit September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 16 / 36
Calibration: Overview ◮ Use data on the oil market, refining sectors and macroeconomy to guide our calibration ◮ annual frequency, match data from 2010 List of data ◮ U.S. calibration ◮ ROW calibration ◮ Model parameters ◮ ◮ Productivity shocks September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 17 / 36
Calibration: Refining sector Table: Calibration of refining sector parameters U.S. ROW Data source Labor share .16 .30 WIOD Capital share .84 .70 WIOD Share of light oil .29 .31 Calculations Share of medium oil .41 .59 Calculations Share of heavy oil .30 .1 Calculations September 6, 2017 IAEE Conference Presented by Mine K. Yücel - Dallas Fed 18 / 36
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