who’s getting globalized? the size and implications of

Who’s Getting Globalized? The Sizeand Implications of Intranational

Trade Costs

David Atkin1 Dave Donaldson2

1MIT

2Stanford

Who’s Getting Globalized?

• Massive reductions in barriers to international trade(tariffs, shipping costs, logistics, etc) in past decades.

• But trade does not start or stop at borders and ports.

• Large intra-national trade costs and uncompetitive trading sectormean that the incidence of globalization varies within a country.

• This may be especially true in Sub-Saharan Africa.

• Unfortunately we know very little about how large theseintranational trade costs are and what they imply forthe incidence of globalization.

Who’s Getting Globalized?

• Questions:

1. How large are intra-national trade costs in Sub-Saharan Africa?

2. How does Sub-Saharan Africa compare to the US?

3. What do these costs imply for the incidence of globalization?

Estimating Trade Costs from Price Gaps• Domestic traders buy an import at port-of-entry o for

price Po, then sell it at destination d for price Pd :

Pd − Po = τ(Xod) + µd

where τ(·) are intranational trade costs and Xod arecost shifters (such as distance).

• Voluminous literature seeks to uncover ∂τ(Xod )∂Xod

from thedistribution of prices over space.

• Challenge #1: Spatial price gaps may reflectdifferences in unobserved product characteristics.

• Solution: We collect new sample of micro-data onnarrowly defined (i.e. barcode-level) products.

Estimating Trade Costs from Price Gaps• Literature generally assumes µd = 0 and then aims to

estimate trade costs from the arbitrage inequality:

Pi − Pj ≤ τ(Xij)

• Challenge #2: This is only an equality if locations iand j are actually trading the product. But virtuallynever have data on trade flows at this level.

• Solution: We collect new data on the source locationfor each product in our sample and only useorigin-destination pairs:

=⇒ Pd − Po = τ(Xod)

Overcoming Challenge #2Spatial price gaps are only rarely informative about the level (rather than range) oftrade costs:

0.0

5.1

50 100 250 500

(15 products, 103 towns, 106 months)

Ethiopia

0.0

5.1

50 100 250 500


Nigeria

0.0

5.1

50 100 250 500

(46 products, 1881 towns, 72 months )

USA

Cos

ts (

2004

US

$)

Distance from origin location to destination market (miles, log scale)

all pairs (i.e. Pi − Pj) trading pairs (i.e. Pd − Po)

Estimating Trade Costs from Price Gaps

• Challenge #3: But what if trading sector is notperfectly competitive? Price gaps then reflect bothtrade costs and intermediaries’ mark-ups:

Pd − Po = τ(Xod) + µod

• Solution: We show that there exists an observablesufficient statistic (price pass-through) that allows∂τ(Xod)∂Xod

to be identified from ∂(Pd−Po)∂Xod

.

• By-product: Will characterize how markups vary across space(ie ∂µod

∂Xod).

Overcoming Challenge #3Spatial price gaps reflect both trade costs and spatial differences in mark-ups:

0.0

5.1

50 100 250 500


Ethiopia

0.0

5.1

50 100 250 500


Nigeria

0.0

5.1

50 100 250 500


USA

Cos

ts (

2004

US

$)


all pairs trading pairs only µ−adjusted, trading pairs only

Implications for Incidence of Globalization

• Thought experiment: suppose the port price of animport falls due to “globalization”.

• Two implications of our estimates for incidence ofthis globalization shock:

1. Higher τ(X) =⇒ Smaller quantity of social surplusgenerated.

2. Higher τ(X) locations feature less entry, less competition.

• =⇒Whatever surplus is generated, see smaller share of thissurplus accrues to consumers.

• As in Weyl and Fabinger (2013), pass-through provides a sufficientstatistic for calculating these shares without need for markup/priceelasticity estimation.

Incidence of Globalization

1.6

1.8

22.

2

50 100 250 500

Ethiopia

1.5

22.

53

50 100 250 500

Nigeria

.5.6

.7.8

.9

50 100 250 750 2000

USA

Rel

ativ

e ra

tio o

f int

erm

edia

rypr

ofits

to c

onsu

mer

sur

plus

Distance from source location to destination market (miles, log scale)95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Related literature• Using spatial price gaps to measure trade costs:

• e.g. Engel and Rogers (1996); Parsley and Wei (2001); Broda and

Weinstein (2008); Donaldson (2010); Simonovska and Waugh (2012).

• Implications of pass-through for market power:

• e.g. Bulow and Pfleiderer (1983); Goldberg and Hellerstein (2008);

Burstein and Jaimovich (2009); Nakamura and Zerom (2010); Li et.

al. (2011); and Weyl and Fabinger (2013).

• Estimating gains from trade with variable markups:

• e.g. Feenstra and Weinstein (2010); Edmond et al (2011); Arkolakis

et al (2012); de Loecker et al (2012); and Cosar et al (2013).

• International trade carried out by intermediaries:

• Ahn et al (2011); Antras and Costinot (2011); Bardhan et al (2011).

• International trade with intranational trade costs:• Cosar and Fajgelbaum (2013); Ramondo et al (2012); Redding (2012).

Outline of Talk

Introduction

Data

Model Environment and Estimation Strategy

Empirics: How large are intranational trade costs?

Implications for the incidence of globalization

Concluding Remarks

New Data: 3 Requirements1. Retail price of identical products at many locations in

space (challenge 1).

2. Source location (factory location or port of entry) of each ofthese goods in each country (challenge 2).

3. High frequency price data observed over a long duration(challenge 3).

• A core component of this paper is the creation of a newdataset satisfying these 3 requirements.

• NB: no data on quantities consumed (or produced or traded)is available for such narrowly-defined products.

• Our methodology is motivated by the need to understandintranational trade costs in absence of such quantity data.

Dataset 1: CPI micro-data

• 2 Sub-Saharan African countries:

• Ethiopia (2001-2010): 15 products, 103 towns• Nigeria (2001-2010): 18 products, 36 towns

• Products are those for which:

1. An exact product (with brand name) can be identified.2. Data are available over majority of towns and years.

• Comparison with developed country:

• United States (2004-2009): 46 leading brands, 2856counties (Nielsen Homescan data)

• Top barcode in every ‘module’; company told us factorylocation; observed 7 times out of 72 months.

Dataset 2: Source Locations

• Conducted telephone surveys with the firms thatproduce (or distribute) each product.

• For domestically-produced goods:

• Ask producers where is product made each year.

• For imported goods:

• Ask distributors and retailers what is country of origin andport of entry.

• Corroborate port with trade statistics.

Map of Ethiopian Sample LocationsEthiopia

0 500 Miles

#* Source Locations

!( Market Observations

Primary Roads

Secondary Roads

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Map of Nigerian Sample LocationsNigeria

#* Source Locations

!( Market Observations

Primary Roads

Secondary Roads

0 500 Miles

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#*#*#*#*#*#*#*#*#*#* #*

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Map of US Source Locations

0 500 Miles

#* Source Locations

Primary Roads

#*

#*

#*#*

#*#*

#*#*#*

#*#*

#*

#*

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Descriptive Statistics

• Data cover a range of food and non-food items:

• Ethiopia: Rothmans Cigarettes (20 pack), Harar Beer (330cc bottle),Zahra Detergent (50g packet), Lux Toilet Soap (90g packet), ZenithHair Oil (330cc bottle), Saris Wine (750cc bottle) etc.

• Nigeria: Titus Sardines (125g tin), Bournvita Drink (450g tin),Cerelac Baby Food (400g tin), Omo Detergent (100g packet), LiptonTea (226g box), Peak Evaporated Milk (170g tin) etc.

• USA: Dawn Liquid Ultra Dish Soap Original Scent (12.6oz), KikkomanSoy Sauce (10oz), Crunch ’N Munch Butter Toffee (4oz) etc.

• Mean (and SD) source price, 2001 US$:

• Ethiopia: $0.40 (0.48), Nigeria: $0.97 (1.27), US: $0.62 ($0.82)

• Mean of distance metric xod , log great circle distance betweenlocations in miles:

• Ethiopia: 5.24 log miles (221 miles), Nigeria: 5.69 log miles (350miles), US: 6.14 log miles (674 miles)

Distribution of Population by Distance

01,

000,

000

2,00

0,00

03,

000,

000

50 100 250 500

Ethiopia

02,

000,

000

4,00

0,00

06,

000,

000

8,00

0,00

0

50 100 250 500

Nigeria

05,

000,

000

10,0

00,0

0050 100 250 750 2000

USAD

estin

atio

n m

arke

t pop

ulat

ion

(mid

sam

ple

perio

d)

Distance from source location to destination market (miles, log scale)

Outline of Talk

Introduction

Data




Concluding Remarks

Model Environment

• Consider market for a single product in a single timeperiod in destination location d

• Origin location (port or factory): indexed by o

• Basic scenario:

• Consumers in location d demand product:

• Inverse demand curve Pd = P(Qd ;Dd), where Qd is total quantitypurchased and Dd parameterizes demand.

• Intermediaries specialize in:

• Buying product at location o for (exogenous) wholesale price Po .

• Transporting and selling product to consumer at location d forretail price Pd .

Intermediaries: Technology and MarketStructure

• A1 [Technology]: The cost to an intermediary of trading qdunits is:

C (qd) = [Po + τ(Xod)︸︷︷︸=cod

]qd + Fod .

• A2 [Market structure]: md identical intermediaries tradingthe product from location o to d choose qd to maximizeprofits subject to a perceived response of other firmssummarized by the parameter θd ≡ dQd

dqd. The ‘competitiveness

index’, φd ≡ md

θd, is fixed and exogenous.

Price gaps and trade costs• From intermediaries’ FOCs obtain:

µd ≡ Pd − cod = cod [φd

−η(Dd , cod , φd)− 1]−1

= µ (cod , φd ,Dd)

• Hence:

Pd − Po = τ(Xod) + µ (cod , φd ,Dd)

• Perturb this with small change in xod :

d(Pd − Po)

dxod=

(1 +

∂µod

∂cod

)∂τ(Xk

odt)

∂xkodt+∂µod

∂φd

∂φd

∂xod+∂µod

∂Dd

∂Dd

∂xod

Estimating trade costs from price gaps

d(Pd − Po)

dxod=

(1 +

∂µod

∂cod

)∂τ(Xk

odt)

∂xkodt+∂µod

∂φd

∂φd

∂xod+∂µod

∂Dd

∂Dd

∂xod

Note 1:

• Only if mark-ups are constant across locations (ie∂µod

∂cod= ∂µod

∂φd= ∂µod

∂Dd= 0), will the variation in price

gaps with distance identify the true marginal costsof distance,

d(Pd − Po)

dxod=∂τ(Xk

odt)

∂xkodt

Estimating trade costs from price gapsd(Pd − Po)

dxod=

(1 +

∂µod

∂cod

)∂τ(Xk

odt)

∂xkodt+∂µod

∂φd

∂φd

∂xod+∂µod

∂Dd

∂Dd

∂xod

Note 2:• The nuisance parameter

(1 + ∂µod

∂cod

)is equal to the equilibrium

pass-through rate ρd :

ρd ≡∂Pd

∂cod= 1 +

∂µod

∂cod• But in our case A1 implies:

ρd =∂Pd

∂Po

• We therefore use estimates of ρd obtained from Po shocks (in

time series) to correct for fact that intermediaries may not

pass on costs of distance one for one (in cross section).

Estimating trade costs from price gaps

d(Pd − Po)

dxod=

(1 +

∂µod

∂cod

)∂τ(Xk

odt)

∂xkodt+∂µod

∂φd

∂φd

∂xod+∂µod

∂Dd

∂Dd

∂xod

Note 3:

• Also need controls for possibility that mark-ups varyover space through effects that are independent ofthe effect of distance on marginal costs.

• i.e., control for ∂µod∂φd

∂φd∂xod

and ∂µod∂Dd

∂Dd

∂xod.

• e.g., long-run entry decisions may mean remote locationsare less competitive, ∂µod

∂φd

∂φd∂xod

< 0.

Further Progress...Constant pass-throughdemand

• Previous expressions always locally true (assumingspatial smoothness).

• For these to be global (and not require spatial smoothness)we need a specific demand system.

• A demand system also allows us to specify explicit controlsfor fact that competitiveness/tastes may vary with distance(i.e. ∂µod

∂φd

∂φd∂xod

and ∂µod∂Dd

∂Dd

∂xod).

Further Progress...Constant PT demand• For any demand system

ρd =∂Pd

∂Po=

dPd

dτ(Xod)=

[1 +

(1 + Ed(Pd))

φd

]−1

where Ed(Pd) ≡ Qd

∂Pd/∂Qd

∂(∂Pd/∂Qd )∂Qd

Q 0.

• A3 [Bulow-Pfleiderer demand]:

Pd(Qd) = ad − bd (Qd)δd

• For this (and only this) demand system, pass through is

constant over quantities: ρd =[

1 + δdφd

]−1

Q 1.

• CES, linear and quadratic demand are special cases.

From theory to estimation• Extend model to multiple products k ∈ K selling in locations

d ∈ D at multiple time periods t ∈ T .

• A4 [Static Pass-Through]: The pass-through rate is fixed

over time within a product-destination (ρkdt = ρkd ∀t ∈ T ).

• Step 1: Under A1-A4:

Pkdt = ρkdP

kot + ρkdτ(Xk

odt) + (1− ρkd)akdt

Regression of Pkdt on Pk

ot identifies ρkd (conditional on τ(·), akdt controls).

• Step 2: Under A1-A4, “adjusted price gap” satisfies:

Pkdt − ρ̂kdPk

ot

ρ̂kd

= τ(Xkodt) +

(1− ρ̂kd)

ρ̂kd

akdt

Use ρ̂kd to calculate adjusted price gap and regress on distance to uncover

marginal costs of distance (conditional on ((1− ρ̂kd)/ρ̂kd)akdt controls).

Outline of Talk

Introduction

Data




Concluding Remarks

A first look at spatial price gaps• Recall the equilibrium pricing formula:

Pkdt = ρkdP

kot+ ρkdτ(Xk


• Begin with perfect competition, ρkd = 1:

Pkdt − Pk

ot = τ(Xkodt)

• We compare:

• ‘trading pairs’: origin-destination (o, d) pairs for whichgoods traded; theory: Pk

dt − Pkot = τ(Xk

odt)

• to ‘all pairs’: any pair of locations (i , j) which may or maynot be trading; theory:Pkit − Pk

jt = τ(Xkoit)− τ(Xk

ojt) Q τ(Xkijt)

A first look at spatial price gaps

0.0

5.1

50 100 250 500


Ethiopia

0.0

5.1

50 100 250 500


Nigeria

0.0

5.1

50 100 250 750 2000


USC

osts

(20

04 U

S$)


all pairs (i.e. Pi − Pj) trading pairs (i.e. Pd − Po)

Step 1: Pass-Through Estimates

• Under A1-A4: Pkdt = ρkdP

kot + ρkdτ(Xk


• Empirical analogue to estimate ρkd (∀d and k),

Pkdt = ρkdP

kot + γkod + γkodt + εkdt ,

where γkod are fixed-effects, εkdt = ρkdζkodt + (1− ρkd)νkodt is error term.

• Orthogonality: Temporal variation in (detrended) product-locationτ(Xk

odt) or akdt must be independent of Pkot .

• Identifying variation: product-specific input price shocks,origin-product specific demand shocks.

• Formally: τ = βk1od + βk

2od t + ζkodt , E[Pkotζ

kodt

]= 0 and

akdt = αk1d + αk

2d t + νkdt , E[Pkotν

kdt

]= 0.

• Robustness: Omit towns <100/200 miles from source to avoidspatially correlated νkdt ; include time-destination fixed effects;instrument import Pk

ot with exchange rates; oil price controls; estimateρkd every 2.5/5 years, with 3 lags or quarterly.

Step 1: Pass-Through Estimates

0.2

5.5

.75

1

50 100 250 500

Ethiopia

0.2

5.5

.75

1

50 100 250 500

Nigeria

0.2

5.5

.75

1

50 100 250 750 2000

USAP

ass−

thro

ugh

rate

Distance from source location to destination market (miles, log scale)95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Step 2: How large are intranational tradecosts?

• Under A1-A4:Pkdt−ρ̂kdPk

ot

ρ̂kd= τ(Xk

odt) +(1−ρ̂kd)

ρ̂kdakdt

• Empirical analogue traces out the true marginalcosts of Xk

odt (e.g. distance, etc):

Pkdt − ρ̂kdPk

ot

ρ̂kd︸︷︷︸∂µ/∂c 6=0 correction

= τ(Xkodt) + γkt

(1− ρ̂kdρ̂kd

)+ γd

(1− ρ̂kdρ̂kd

)︸︷︷︸∂µ/∂φ 6=0, ∂µ/∂D 6=0 correction

+γkt + εkdt ,

where ρ̂kd is an estimate of pass-through, γkt and γd are fixed-effects, and

εkdt =(1−ρkd )

ρkdνkdt is an error term.

Step 2: How large are intranational tradecosts?• Empirical analogue traces out true marginal costs of Xk

odt :

Pkdt − ρ̂kdPk

ot

ρ̂kd

= τ(Xkodt)+γkt

(1− ρ̂kdρ̂kd

)+γd

(1− ρ̂kdρ̂kd

)+γkt +εkdt ,

where ρ̂kd is an estimate of pass-through, γkt and γd are fixed-effects, and

εkdt =(1−ρkd )

ρkdνkdt is an error term.

• Orthogonality: Spatial variation in (product-time and destinationdemeaned) demand shifters akdt must be independent of Xk

odt .

• Identifying variation: variation across both goods and locations indistance between o and d .

• Formally: akdt = αkt + αd + νkdt and E

[Xk

odtνkdt

]= 0.

• Robustness: destination-time interactions, include destination FEdirectly.


0.0

5.1

50 100 250 500


Ethiopia

0.0

5.1

50 100 250 500


Nigeria

0.0

5.1

50 100 250 750 2000


US

Cos

ts (

2004

US

$)


trading pairs only µ−adjusted, trading pairs only

Channel decomposition: variation in tradecosts and demand shifters across spaceUnder A1-A3: µd = (1− ρd)(ad − τ(Xod)− Po)

−.1

0.1

50 100 250 500


Ethiopia

−.1

0.1

50 100 250 500


Nigeria

−.1

0.1

50 100 250 750 2000


US

Cos

ts (

2004

US

$)


µ(x) −τ(x) a(x)


(1) (2) (3) (4) (5) (6)

Dependent variable: Price Gap

(Trading Pairs)

Adjusted Price Gap

(Trading Pairs)

Price Gap

(Trading Pairs)

Adjusted Price Gap

(Trading Pairs)

Price Gap

(Trading Pairs)

Adjusted Price Gap

(Trading Pairs)

Log distance to 0.0248*** 0.0374*** 0.0254*** 0.0558*** 0.00437*** 0.0106***

source (miles) (0.00125) (0.00223) (0.00437) (0.00759) (0.000731) (0.00100)

{0.00370} {0.0115} {0.00142}

[0.00594] [0.0221] [0.00140]

Time-Product FE Yes Yes Yes Yes Yes Yes

Time-Product No Yes No Yes No Yes

Destination No Yes No Yes No Yes

Observations 100,761 100,761 26,025 26,025 175,782 175,782

R-squared 0.252 0.932 0.500 0.964 0.408 0.928

USANigeriaEthiopia

×1 − 𝜌

𝜌

×1 − 𝜌

𝜌

×1 − 𝜌

𝜌

×1 − 𝜌

𝜌

Notes: Time-product clustered standard errors in round parentheses. Time-product block bootstrapped standard errors in curly parentheses,

product-destination block bootstrapped standard errors in square parentheses.

• Additional cost to reach the most remote locations (500 miles away, 99thpercentile in Ethiopia, 77th in Nigeria, 45th in USA) compared to the leastremote locations (50 miles away, 3rd-4th percentile):

• 9 cents (20% of mean Po) in Ethiopia, 13 cents in Nigeria (12% of meanPo), 2 cents in USA (4% of mean Po).

Decomposing the costs of distance

(1) (2)

Ethiopia Nigeria

Great circle distance 3.53 5.26

Quickest route road distance 3.19 5.40

Quickest route travel time 2.46 4.01

Ratio relative to US marginal cost of distance

• Ratios also similar (2.31 and 4.19) with destination FE so not justlocation-specific factors correlated with distance (productivity, wages).

• High African costs plausibly due to: waiting times for loading andcheckpoints; bribes and theft; low payload utilization and total milestraveled; high fuel costs; old inefficient trucks; poor roads; bad logistics.

(3) (4)

East Africa

(Mombasa-Nairobi)

West Africa

(Bamako-Accra)

Per km for one truckload 1.88 3.28

Ratio relative to US trucking costs

(Teravaninthorn and Raballand, 2009)

Outline of Talk

Introduction

Data




Concluding Remarks

Who is capturing the gains fromglobalization?

• Thought experiment: suppose the port price of an import fallsdue to “globalization” (e.g. tariffs or shipping prices fall).

• We consider the implications of our estimates for the impactof this globalization event on different groups (consumers andintermediaries).

• Two caveats:

1. We are constrained by the lack of quantity data here.

2. All estimates so far obtained from a sample of largely (but not

entirely) domestic goods. (But many of the domestic goods are

produced at a main port city and the same intermediaries are likely to

trade domestic and foreign goods.)

Implication 1: Remoteness and the size ofthe surplus

• In the extreme, high MC of distance may preventgoods from reaching remote locations.

• No increase in quantity of surplus from price reduction atborder.

• We lack quantity data, but provide suggestiveevidence from product availability regressions:

• CPI Enumerators record no price when cannot find productin particular month-location.

Implication 1: Remoteness and the size ofthe surplus

.75

.8.8

5.9

.95

50 100 250 500

Ethiopia

.75

.8.8

5.9

.95

50 100 250 500

Nigeria

Pro

duct

Fou

nd b

y E

num

erat

ors

(Bin

ary)


Implication 2: Remoteness and thedistribution of surplus

• Inland consumers benefit from price reduction at port/factory.

• But how much of this additional surplus do they enjoy, asopposed to intermediaries or deadweight loss?

• Once again, the pass-through rate is key determinant.

• Following logic in Weyl and Fabinger (2013), under A1-A3intermediary profitsconsumer surplus is:

IΠd

CSd=

1

ρd+

1− φod

φod

• Can also derive expression for consumer surplussocial surplus (see paper).


• Under A1-A3, profits are decreasing in distance, ∂Πod

∂xod< 0, if

δod > −1 (e.g. ρd < 1 sufficient).

• Competitiveness index likely to be decreasing in distance due tolong-run entry.

• A5 [Identification of δkd and φkod ]: The competitiveness

index parameter is location-specific, φkod = φd , the “super

elasticity” parameter is product k-specific, δkd = δk .

• Result 3(b): Under A1-A5, can use K × D estimated ρkds to

estimate K + D unknown φd and δks from ρkd =(

1 + δk

φd

)−1

.

• Transform ln(ρ̂kd−1

− 1) = ln δk − lnφd and normalize lowest φd = 1.

Estimated competitiveness index

1.6

1.8

22.

22.

4

50 100 250 500

Ethiopia

22.

53

3.5

50 100 250 500

NigeriaR

elat

ive

com

petit

iven

ess

inde

x φ

of in

term

edia

ries

at d

estin

atio

n m

arke

t (w

ith lo

wes

t φ n

orm

aliz

ed to

1)

Distance from location to commercial capital (miles, log scale)

Comparison of competitiveness indexestimates with outside dataEthiopia Distributive Trade Surveys (2001 and 2008)

β=0.0057 (t−stat=4.49)

12

34

Rel

ativ

e C

ompe

titiv

enes

s In

dex

of in

term

edia

ries

at d

estin

atio

n m

arke

t

0 50 100 150 200Number of wholesale firms

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, optimal bandwidth).Plot of competitiveness index against number of wholesalers from 2001 and 2008 Distributive Trade Survey.


1.6

1.8

22.

2

50 100 250 500

Ethiopia: Intermediary profitsover consumer surplus

1.5

22.

53

50 100 250 500

Nigeria: Intermediary profitsover consumer surplus

.5.6

.7.8

.9

50 100 250 750 2000

US: Intermediary profitsover consumer surplus

.38

.4.4

2.4

4

50 100 250 500

Ethiopia: Share of consumersurplus in total surplus

.35

.4.4

5.5

50 100 250 500

Nigeria: Share of consumersurplus in total surplus

.74

.76

.78

.8

50 100 250 750 2000

US: Share of consumersurplus in total surplus


(1) (2) (3) (4) (5)

Dependent variable:

Product Availability

1=Price Record,

0=No Price Record

(Time-Prod-Loc)

Competitiveness

Index of

Intermediaries

(All Locations)

Ratio of

Intermediary Profits

to Consumer Surplus

(Good-Location)

Ratio of

Deadweight Loss to

Consumer Surplus

(Good-Location)

Consumer's Share

of Total Surplus

(Good-Location)

Log distance between -0.0959*** 0.229*** 0.0368*** -0.0185**

source and destination (0.00309) (0.0598) (0.00908) (0.00763)

Log distance between -0.344***

location and Addis Ababa (0.127)

Constant 1.308*** 3.766*** 0.683** 0.120** 0.509***

(0.0163) (0.668) (0.312) (0.0475) (0.0403)

Observations 145,682 100 1,418 1,418 1,418

R-squared 0.14 0.059 0.009 0.009 0.004

(6) (7) (8) (9) (10)

Dependent variable:

Product Availability

1=Price Record,

0=No Price Record

(Time-Prod-Loc)

Competitiveness

Index of

Intermediaries

(All Locations)

Ratio of

Intermediary Profits

to Consumer Surplus

(Good-Location)

Ratio of

Deadweight Loss to

Consumer Surplus

(Good-Location)

Consumer's Share

of Total Surplus

(Good-Location)

Log distance between -0.0490*** 0.347*** 0.0833*** -0.0546***

source and destination (0.00410) (0.113) (0.0138) (0.0155)

Log distance between -0.444***

location and Lagos (0.114)

Constant 1.035*** 4.970*** 0.356 -0.162** 0.693***

(0.0234) (0.640) (0.651) (0.0774) (0.0903)

Observations 35,406 36 526 526 526

R-squared 0.298 0.168 0.019 0.063 0.023

Ethiopia

Nigeria

• Additional share of surplus going to consumers in the least remote locations (50 milesaway) compared to most remote (500 miles away):

• 4% in Ethiopia, 13% in Nigeria, 1% in US.

Outline of Talk

Introduction

Data




Concluding Remarks

Concluding Remarks• How large are intra-national trade costs in Sub-Saharan

Africa (Ethiopia and Nigeria)?

• The marginal costs of distance appear to be very high.

• (Approximately 4-5 X larger than US costs.)

• Under-estimated by standard spatial price gap methods.

• MC of distance doubles when only use trading pairs, doubles when accountfor spatial variation in µ using sufficient statistic (ρ) approach.

• Two Implications for the incidence of globalization:

1. Trade generates less surplus for remote consumers/intermediaries.

2. Additionally, consumers in remote locations capture a smallershare of whatever surplus is generated.

• Methodological Contribution:

• Results do not require estimating price elasticities or markups.

• Valuable where price data available but quantity data scarce.

who’s getting globalized? the size and implications of

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