What’s in a wedge? Distortions in the Oil Industry. · 2018-06-19 · University of St Andrews...
Transcript of What’s in a wedge? Distortions in the Oil Industry. · 2018-06-19 · University of St Andrews...
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What’s in a wedge? Distortions in the Oil
Industry.
Rados law (Radek) Stefanski
University of St Andrews and OxCarre
Gerhard Toews
OxCarre, University of Oxford
15th June, 2018
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Introduction
• Misallocation of capital and labor explains a large part of cross-
country productivity differences.
• Hsieh and Klenow (2009) quantify this misallocation by extrac-
ting price ‘wedges’
• These measure extent to which marginal revenue products of
inputs fail to equalize across countries and firms
• Although wedges ’look’ like taxes in models, often motivated
as being broader and also capturing differences in geography,
trade costs, borrowing constraints, institutional differences etc.
• Interpreting wedges difficult since pinning down specific sources
usually impossible due to a lack of data.
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• Question: What accounts for extracted price wedges?
→ Are they driven by direct taxation or other, broader factors?
• To address gap, use proprietary firm-year-country-concession
level database of oil industry (eg BP-1984-US-Texas)
• Crucially database contains info on concession-level taxation
•We extract H&K wedges before & after controlling for taxation
• Answer: Almost all the variation in extracted wedges is acco-
unted for by variation in direct tax policies
• So What? If generalizable then the misallocation driving pro-
ductivity differences may stem from differences in taxation
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Outline
• Model
• Data
• Calibration
• Results
• Counterfactuals
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Model
• Oil firms: f = 1, . . . , F .
• Each firm f owns nf oil concessions: i = 1, . . . , nf
• Concessions grant firms right to extract oil/gas in a geog. area
& ownership in exchange for royalty/income tax payments
• Assume static problem:
- No exploration/development: focus on production
- All concessions operational: exclude possibility of entry/exit
→ Like H&K - focus on intensive margin
• Each concession i characterized by a production function:
Yfi = A
fi (Kf
i )γ(Lfi )α. (1)
• α, γ constant across concessions and firms
• 0 < α+ γ < 1: Span-of-control model (DRS)
• Positive profits: Fixed asset(oil reserves); managerial ability
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Model
• Firm’s objective: maximize total profits from all concessions:
πf = max{Lfi ,K
fi }
nf∑i=1
(P (1− τfV i)Y
fi − w(1 + τ
fLi)L
fi − r(1 + τ
fKi)K
fi
)
• P , w, r: oil prices, wages and rents - same for all concessions
- Oil traded
- Labor and capital very mobile in the oil sector >
• τfLi, τfKi: distortions to labor or capital (e.g. borrow constraints)
• τfV i: distortions to labor and capital (e.g. transportation)
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Measuring Distortions
• From FOC calculate before-tax MRP of L and K:
MRPLfi ≡ P
∂Yfi
∂Lfi
= αPY
fi
Lfi
=(1 + τ
fLi)
(1− τfV i)w
MRPKfi ≡ P
∂Yfi
∂Kfi
= γPY
fi
Kfi
=(1 + τ
fKi)
(1− τfV i)r.
• Before-tax MRP higher in concessions facing disincentives, lo-
wer in concessions benefiting from subsidies
• After-tax MRP (i.e. w and r) of concessions must be equalized
→ variation in MRPs indicative of distortions or ‘wedges’
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Extracting Wedges
1) Total wedges:
1 + τfLi ≡
(1 + τfLi)
(1− τfV i)= α
PYfi
wLfi
and 1 + τfKi ≡
(1 + τfKi)
(1− τfV i)= γ
PYfi
rKfi
2) After-tax wedges:
1 + τfLi = α
Pt(1− τfV i)Yfit
wtLfit
and 1 + τfKi = γ
Pt(1− τfV i)Yfit
rtKfit
• Compare τ and τ to measure importance of direct taxation
• To operationalize need: revenues, labor costs, capital costs,
revenue taxes, α and γ
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Outline
• Model
• Data
• Calibration
• Results
• Counterfactuals
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Overview: Data
• Proprietary data on 32 private and public oil companies (So-
urce: BP, Wood Mackenzie):
- 62 countries, 1966-2013
- Oil and LNG
- Data by production type: mainly PSC and Concessions
• Baseline sample: Ross (2012) - Largest oil firms; producing,
profitable, concessions.
• Avoids investment phase companies
• Results unchanged with entire sample
• This leaves us with 3381 observations, 214 country-firm-concession
combinations (168 when excl. the US), 41 countries, 24 firms,
and on average 26 years.
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Numbers of Concessions by firm (Baseline)
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Numbers of Concessions by country (Baseline Sample)
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Data
For each firm f , concession i and period t we have:
• Total Revenues: Quantity produced in barrels of oil-equivalent
multiplied by the current price per barrel
• Capital Costs: Amount spent, US$, on durable goods (assets
with lifetime > 1 year)
• Operational Costs: Amount spent, US$, on labor and non-
durable goods e.g. (mostly) salaries and wages but also mate-
rials, insurance, maintenance
• Profits: Revenues less capital costs, operational costs and
taxes, US$
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Taxation Data
• Taxes in the data consist of two measures: Royalties and
‘Government Take’
• Royalties: almost always levied on revenues of a field
• Government Take: either a revenue tax or a profit taxes. In
general we cannot distinguish which. In most countries takes
the form of revenue taxes and - due to complexity - very few
countries implement profit taxes (Mintz and Chen, 2012)
• Throughout we shall treat Royalties and GT as revenue taxes
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Descriptives
variable mean p50 sd max min
Gross Revenue (mil. US$) 4759.12 766.00 19935.74 436419.10 1.80Capital Costs (mil. US$) 508.05 92.00 1530.09 19756.90 0.10Oper. Costs (mil. US$) 585.38 108.30 2377.98 45709.20 0.10Profit (mil. US$) 940.83 161.30 3287.70 47858.50 0.00Taxes/Revenues (%) 0.43 0.41 0.18 0.93 0.00Taxes/Profits (%) 0.63 0.63 0.19 1.00 0.00Duration (years) 26.81 33.00 8.73 33.00 0.00
Source: Own Calculations
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Outline
• Model
• Data
• Calibration
• Results
• Counterfactuals
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Calibration
1) Total wedges:
1 + τfLi ≡
(1 + τfLi)
(1− τfV i)= α
PYfi
wLfi
and 1 + τfKi ≡
(1 + τfKi)
(1− τfV i)= γ
PYfi
rKfi
2) After-tax wedges:
1 + τfLi = α
Pt(1− τfV i)Yfit
wtLfit
and 1 + τfKi = γ
Pt(1− τfV i)Yfit
rtKfit
• PY fi - revenues; τfV i - royalties + Government Take
rKfi - Capital expenditure; wtL
fit - Operational costs
• To operationalize: we still need estimates of α and γ
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Calibration
• Use US to calibrate our parameters α and γ
• Assume: besides direct taxes, no additional distortion to ‘me-
dian’ US concession
- Set median, after-tax K and L wedges in US to zero
- This will effectively be a normalization
• Why?
- Follow HK (2009)
- US consistently tops WB “Doing Business” Survey
- Large geological variation, many concessions
α =wtL
fit
Pt(1− τfV i)Yfit
= 0.259 and γ =rtK
fit
Pt(1− τfV i)Yfit
= 0.227
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US: Labor and capital wedges
• By construction, median after-tax wedges in US zero• Distribution interpreted as mis-measurement (like in HK) orgeological variation >
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Outline
• Model
• Data
• Calibration
• Results
• Counterfactuals
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Main Result: Labor Wedge• Compare τL and τL to measure importance of direct taxation- Both relative to each other and between US and ROW
Median US ROWτL (total) 0.46 0.68τL (after taxes) 0.00 −0.00
• After controlling for direct taxation, labor wedges in ROW arezero and have same dist as the US
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Main Result: Capital Wedge• Compare τK and τK to measure importance of direct taxation- Both relative to each other and between US and ROW
Median US ROWτK (total) 0.48 0.71τK (after taxes) 0.00 0.02
• After controlling for direct taxation, capital wedges in ROWare zero and have same dist as the US
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Main Result: Private Firms
• After controlling for direct taxation, labor/capital wedges in
each firm statistically indistinguishable from zero
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Main Result: Public Firms
• After controlling for direct taxation, labor/capital wedges in
each firm (mostly) statistically indistinguishable from zero
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So?
• Most of the variation in wedges across countries and con-
cessions (relative to the US) as well as firms accounted for by
variation in direct taxation!
• Firms seem to be very good at allocating capital and labor
across concessions
• Distortions in MRP seem to stem from het. tax policies
• What is the effect of these distortions on output?
• Assume direct taxes can be changed exogenously without ef-
fecting wedges
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Outline
• Model
• Data
• Calibration
• Results
• Counterfactuals
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Closing the model• Assume fixed amount of labor and capital (static model!):
F∑f=1
nf∑i=1
Lfi = L and
F∑f=1
nf∑i=1
Kfi = K.
• Then:
Lfi =
Afi
AL and K
fi =
Afi
AK,
Yfi = A
fi
AfiA
γ AfiA
α KγLα
where, Afi ≡(
Afi
(1+τfKi)
γ(1+τfLi)
1−γ
) 11−α−γ
, Afi ≡
(Afi
(1+τfKi)
1−α(1+τfLi)
α
) 11−α−γ
A ≡∑f,i A
fi and A ≡
∑f,i A
fi .
• The more distorted a concession relative to the mean → thesmaller its share of labor, capital and output
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Closing the model
• We do NOT have data to construct Afi• Instead we can calculate a measure of revenue productivity:
Bfi ≡
Yfi
(rKfi )γ(wLfi )α
. (2)
• This is then enough to measure relative distortions since:
Bfi
Bfj
=Afi
Afj
• We compare current output to two counterfactual worlds:
1) Direct taxes are set to US median: τfLi = τfL,US; τfKi = τ
fK,US
2) Total wedges are set to US median: τfLi = ˆτfL,US; τfKi = ˆτfK,US
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Counterfactuals
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Conclusions
• Once we control for direct taxation, the median wedge is zero
• All the variation in extracted wedges (above and beyond the
US) comes from variation in direct taxation.
• So, firms are good at allocating capital and labor
• Wedges extracted using HK methodology largely capture dif-
ferences in tax policies rather than indirect distortions
• If generalizable, model suggests that harmonizing taxes may
strongly contribute to eliminating misallocation driving income
differences
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Variation in shares <
• Different types of fields: shale vs. deep sea vs. surface:- Different labor and capital requirements!
• Consider detailed economy with production function:
Yfi = A
fi (Kf
i )γi(Lfi )αi
Proposition: The equilibrium allocations of the detailed - . - economy areidentical to the equilibrium allocations in the baseline economy if productivitiesin the baseline are specified by:
Afi =Afi (Kf
i )γi(Lfi )αi
(Kfi )γ(Lfi )α
(3)
and distortions in the baseline, are specified by:
τ fV i = τ fV i (4)
1 + τ fLi = (1 + τ fLi)α
αi(5)
1 + τ fKi = (1 + τ fKi)γ
γi. (6)
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Mobility<
• Oil and gas sector is very quick in responding to change in
market conditions. ⇒ 10% increase (decrease) in the oil price
increases (decrease) global drilling activity by 4% within a year
(Toews and Naumov, 2015).
• “Many work as so-called FIFOs, who ‘Fly In and Fly Out’ for
their jobs, often on 7-7-7 rosters of seven days on, seven nights
on, before flying home for seven days off.” (The Telegraph,
2011)
• Geology determines which places are affected first. For in-
stance, Africa was abandoned during the recent slump in the oil
price (WSJ, 2014).
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Mobility<
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Geology in the US and ROW
• Logged median per square mile in the US versus ROW
(1) (2) (3) (4)Variables US ROW p-valueLog giant disc/mi2 since 1900 -11.44 -11.68 0.36Log size giant disc/mi2 since 1900 -4.34 -4.67 0.16Log reserves per sq.mi. in 2005 7.77 7.45 0.43
• Data on giant discoveries from Horn (2011)
• Data on estimated reserves from Nationmaster
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