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

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

2

Outline

• Model

• Data

• Calibration

• Results

• Counterfactuals

3

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

8

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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