Who’s Getting Globalized? The Size and Nature of

Intranational Trade Costs∗

David Atkin† and Dave Donaldson‡

July 2012

Abstract

This paper uses a newly collected dataset on the prices of narrowly defined goodsacross many dispersed locations within multiple developing countries to address thequestion, How large are the costs that separate households in developing countries from theglobal economy? Guided by a flexible model of oligopolistic intermediation with vari-able mark-ups, our analysis proceeds in four steps. First, we measure total intrana-tional trade costs (ie marginal costs of trading plus mark-ups on trading) using pricegaps over space within countries—but we do so only among pairs of locations thatare actually trading a good by drawing on unique data on the location of productionof each good. Second, we estimate, separately by location and commodity, the pass-through rate between the price at the location of production and the prices paid by in-land consumers of the good. Our estimates imply that incomplete pass-through—andtherefore, intermediaries’ market power—is a commonplace, and that pass-through isespecially low in remote locations. Third, we argue that our estimates of total tradecosts (Step 1) and pass-through rates (Step 2) are sufficient to infer the primitive effectof distance on the marginal costs of trading; after correcting for the fact that mark-upsvary systematically across space we find that marginal costs are affected by distancemore strongly than typically estimated. Finally, we show that, in our model, the esti-mated pass-through rate (Step 2) is a sufficient statistic to identify the shares of socialsurplus (ie the gains from trade) accruing to inland consumers, oligopolistic inter-mediaries, and deadweight loss; applying this result we find that intermediaries inremote locations capture a considerable share of the surplus created by intranationaltrade.

∗We thank Rohit Naimpally for excellent research assistance, and Alvaro González, Leonardo Iacovone,Clement Imbert, Horacio Larreguy, Philip Osafo-Kwaako, John Papp, and the World Bank Making MarketsWork for the Poor Initiative for assistance in obtaining segments of the data. We have benefited greatlyfrom conversations with Pol Antras, Arnaud Costinot, Michal Fabinger, Marc Melitz, Glen Weyl and manyothers, as well as from comments made by seminar participants at Bergen, Berkeley, Harvard, the IGC TradeGroup, the IGC Infrastructure and Urbanization Conference in London, the LSE, MIT, Stanford, UC SantaCruz. Finally, we thank the International Growth Centre in London for their generous financial support.†Yale University and NBER. E-mail: david.atkin@yale.edu‡MIT and NBER. E-mail: ddonald@mit.edu

1 Introduction

Recent decades have seen substantial reductions in the barriers that impede trade be-tween nations—a process commonly referred to as ‘globalization’. But trade does not startor stop at national borders. The trading frictions faced by many households, especiallythose in developing countries, include not only the international trade costs that havefallen in recent times, but also the intra-national trade costs that separate these house-holds from their nearest port or border. The goal of this paper is to use newly collecteddata to provide an answer to an important and primitive empirical question: How largeare the costs that separate households in developing countries from the global economy?

Like a voluminous existing literature (reviewed below), we seek an answer to thisquestion by drawing inferences from the equilibrium distribution of prices over space—prices that are observed monthly and over several decades, for hundreds of consumerproducts, across thousands of local markets. in a sample of representative developingcountries.1 But unlike this literature we use new data and new tools to overcome threewell-known challenges that plague such inferences:

1. Spatial price gaps may reflect differences in unobserved product characteristics (such asquality) across locations. The overriding principle guiding our data collection effortsand resulting sample is to use exclusively products that are extremely narrowlydefined (analogously to barcodes).

2. Spatial price gaps only rarely are directly informative of trade costs. A standard argu-ment maintains that: if trading (ie arbitrage) is perfectly competitive, bilateral pricegaps for any pair of locations always place a weak lower bound on the cost of trad-ing between that pair of locations; and further, this inequality is binding if trade isactually occurring among the pair. Unfortunately, in practice the (highly detailed,intra-national) trade data required to apply this argument is unavailable. We havetherefore collected unique data on the precise source location of production (in thecase of domestically produced goods) or importation (in the case of imported goods)for each product in our sample. As we demonstrate below, without this informationour estimates of intranational trade costs would be biased downwards by a factorof almost two.

3. Spatial price gaps may reflect variable mark-ups across locations, not just the marginal costof trading. Put simply, arbitrage may not be perfectly competitive (ie arbitrage may

1Our current sample comprises Ethiopia and Nigeria and in ongoing work we are extending this sampleto include 5-10 additional developing countries.

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not be free). If traders possess market power then spatial price gaps will be contam-inated by variation in mark-ups across locations. However, we demonstrate thatestimates of pass-through—which we estimate nonparametrically for each locationand product in our sample—can be used to correct for this contamination. The keyinsight is that estimated pass-through is a sufficient statistic for the extent to whichmark-ups vary across locations. Using our pass-through-based correction the es-timated marginal cost of trading is substantial, approximately twice as large as itwould be under the assumption of perfectly competitive trading (an assumptionthat is rejected by our data).

In short, we find that total intranational trade costs are large, and that a substantial com-ponent of inter-spatial price variation is due to varying levels of mark-ups that increas-ingly remote, uncompetitive locations pay for the delivery of goods produced abroad orafar.2

Our analysis has two important implications for how remoteness affects consumersand intermediaries situated in the interior of developing countries. The marginal cost ofdistance in our sample is extremely high, approximately seven to 15 times larger than themarginal cost of distance found by Hummels (2001) for truck shipments between Canadaand the US. The ability to consume goods at locations other than the port or the factorycreates a (partial equilibrium, that is holding prices in other markets contant) social sur-plus. These high marginal costs of distance imply that smaller quantities of this socialsurplus are available to consumers and intermediaries in remote locations (compared tothose in less remote locations).

In addition, because our estimates trace out how mark-ups vary over space we areable to speak to the share of surplus on traded goods that accrues to intermediaries (andto deadweight loss) in remote locations. A natural approach to measuring the distribu-tion of surplus along these lines would involve estimating mark-ups but in our devel-oping country context data on consumption quantities (for narrowly defined goods) istypically unavailable; standard methods for computing mark-ups (as described in, eg,Pakes (2008)) are therefore not possible. Fortunately—by an extension of the analysis inWeyl and Fabinger (2011)—if we restrict attention to demand systems that take a partic-ular form in which pass-through is constant (and hence CES demand is a special case)the rate of equilibrium pass-through (which we estimate for each location and product in

2Two important caveats, however, arise due to the nature of our restriction to a sample of only extremelynarrowly defined consumer products (a restriction which, to us, seems unavoidable in any attempt to mea-sure trade costs with price dispersion). First, these goods may not be representative of the entire nationalconsumption basket in our sample countries. And second, it is plausible that consumer products that canbe identified, essentially, at the unique barcode level are distributed differently than other products.

2

Step 2 above) is a sufficient statistic for the distribution of surplus, so estimates of mark-ups are unnecessary. Using this result we find that remote markets are less competitive,and that whatever social surplus from trade exists in remote locations sees larger sharesaccruing to intermediaries and DWL (relative to consumers).

The work in this paper relates to a number of different literatures. Most relevant isa recent and voluminous literature uses aspects of spatial price dispersion in order toidentify trade costs.3 Various segments of the literature have dealt with each of thesethree challenges in isolation, but we believe that our work is unique in attempting tocircumvent all three of these challenges. We discuss the response to these three challengesin the existing literature here in turn.

First, a large strand of this literature argues that inter-spatial arbitrage is free to enterand hence that inter-spatial price gaps place lower bounds on the marginal costs of trade,where these lower bounds are binding among pairs of locations that do trade. See, for in-stance, Eaton and Kortum (2002), Donaldson (2011), Simonovska (2010) and Simonovskaand Waugh (2011a), as well as the work surveyed in Fackler and Goodwin (2001) and An-derson and van Wincoop (2004).4 A central obstacle in this literature has been the need towork with narrowly defined products and yet also know which location pairs are actu-ally trading those narrowly defined products; our approach exploits unique data on thelocation of production of each product in our sample.

A second and recent strand of the literature draws on proprietary retailer or consumerscanner datasets from the US and Canada in order to compare prices of extremely nar-rowly identified goods (that is, goods with unique barcodes) across space (see for exampleBroda and Weinstein (2008), Burstein and Jaimovich (2009) and Li, Gopinath, Gourinchas,and Hsieh (2011)). However, this work typically lacks information on the region or coun-try of origin so point-identifying the level of trade costs is typically not the focus.5

3Another body of work to which our study relates is the rapidly growing literature on intermediationin trade, including (Ahn, Khandelwal, and Wei, 2011; Antras and Costinot, 2011; Bardhan, Mookherjee, andTsumagari, 2011; Chau, Goto, and Kanbur, 2009). This work aims to understand when trade is conductedvia intermediaries rather than by producers directly. Our work is instead focused on the consequencesof intermediaries—who potentially possess market power—for the magnitude of intranational barriers totrade, the pass-through of world price changes, and the distribution of the gains from trade.

4An additional body of work, (see for example Engel and Rogers (1996), Parsley and Wei (2001), Brodaand Weinstein (2008) and Keller and Shiue (2007)) uses moments derived from inter-spatial price gaps toinfer trade costs without data on which location pairs are actually trading. Because these moments pooltogether information from location pairs that are trading (on which price gaps are equal to trade costs) andlocation pairs that are not trading (for which price gaps understate trade costs) it is not clear how thesemoments estimate the level of trade costs without knowledge of the relative proportions of trading andnon-trading pairs in the sample.

5Li, Gopinath, Gourinchas, and Hsieh (2011), however, point out that their price gap estimates forlocation pairs on either side of the Canada-US border do place a lower bound on the cost of trading acrossthis border.

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Finally, a third strand of this literature considers, as we do, the possibility that pro-ducers or intermediaries have market power (that is, arbitrage is not free to enter) andhence that firms may price to market. (See, for example, Feenstra (1989), Goldberg andKnetter (1997), Goldberg and Hellerstein (2008), Nakamura and Zerom (2010), Fitzgeraldand Haller (2010), Li, Gopinath, Gourinchas, and Hsieh (2011), Burstein and Jaimovich(2009), Atkeson and Burstein (2008), Alessandria and Kaboski (2011), and Berman, Mar-tin, and Mayer (2012)). In particular, this literature has placed heavy emphasis on theextent of exchange rate pass-through and its implications for market power. We apply asimilar logic to the market for each good and location in our sample with the goal beingto infer how intermediaries’ market power and equilibrium mark-ups vary across loca-tions, as well as how variable mark-ups over space cloud inference of how the marginalcosts of trading vary over space. In this sense, the paper is related to a recent literaturethat explores the interaction between the gains from trade and variable markups. (See,for example, Arkolakis, Costinot, Donaldson, and Rodriguez-Clare (2012), De Loecker,Goldberg, Khandelwal, and Pavcnik (2012), Edmond, Midrigan, and Xu (2011), Feenstraand Weinstein (2010) and Melitz and Ottaviano (2008)).

The remainder of this paper proceeds as follows. Section 2 describes the new datasetthat we have constructed for the purposes of measuring and understanding intranationaltrade costs in our sample of developing countries. Section 3 outlines a theoretical frame-work in which intranational trade is carried out by intermediaries who potentially enjoymarket power, as well as how we use this framework to inform empirical work that aimsto estimate the size of intranational trade costs as well as the distribution of the gainsfrom trade between consumers and intermediaries. Section 4 discusses the empirical im-plementation of this methodology and presents our findings. Section 5 concludes.

2 Data

Our study draws on two main sources of data: (i) the retail prices of products atvarious points in space (within developing countries), in time, and for many narrowly-defined products; and (ii) the location(s) of production or import of each product in oursample. We describe these two types of data here in turn.

2.1 Data on retail prices

The key requirement for studying the extent to which households in developing coun-tries are integrated with global markets is high quality price data. The methodology we

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propose below requires observations of retail prices prevailing at many points in time,across many geographically segmented markets, for extremely narrowly defined prod-ucts (such that within-product differences in quality over space can be presumed to besmall). Fortunately the national statistical agencies of many developing countries collectexactly such data in the process of compiling a consumer price index. The data collectionexercise underpinning this paper has involved a search for a set of countries that collectdata that meets these standards and are willing to share their raw data (rather than thetypically publicly available aggregates) with researchers.

The typical country in our sample conducts a monthly price survey across tens of typ-ically fixed locations throughout the country, usually with representative regional cover-age. Enumerators are asked to survey particular retail establishments (the type of whichis often also available in the data that they share with us) and write down the postedprice (or typical sale price if a posted price is not available) for a fixed set of very nar-rowly defined products, and to record no observation if the product is not for sale atthe enumeration location on the enumeration date. Because the product list is typicallychosen to provide wide coverage of the a typical consumption basket many of the prod-ucts surveyed—such as rice, bread or haircuts—are not narrowly defined and we excludethese products from our analysis. We instead work with a sample of consumer productsthat are uniquely identified by their product descriptions. These descriptions include theproduct type, brand name, and specific size—such that the descriptions are akin to bar-codes that uniquely identify products in consumer scanner datasets in developed coun-tries. (Many countries that have made their data available to us contain product descrip-tions that lack unique, brand-level product identifiers and so these countries are excludedfrom our study.)

In the current version of this paper we use the following sub-samples of data only:

• Ethiopia (2001-2010): Covers 103 towns, the locations of which are illustrated in themap in Figure 1. Fifteen products are covered, which are detailed in Table 1.

• Nigeria (2001-2010): Covers 36 town markets (one per state), the locations of whichare illustrated in the map in Figure 2. Seventeen products are covered, which aredetailed in Table 2.

Our data collection efforts have lead us to the following additional sources of data (thoughthese data are not ready for analysis at present):

• Philippines (2000-2010): Covers all the provinces and major cities of the country (89unique geographic points in total). The data span 2000 to 2010 for close to 90 nar-rowly defined goods.

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• India (1985-2010): We work with the raw data used to compute the rural CPI only.This data covers over 650 villages distributed across the 28 states of India and in-cludes price data on over 100 narrowly identified products. Some of these goodsvary by state and region, while others are present across the various states. For theperiod from 1985-1993 we obtained this data from archival records in the Indian BLSand from 1994-2010 we obtained this data from the NSSO.

• Zambia (1996-2005): Covers 48 district centers and 60-70 narrowly identified prod-ucts. This dataset also includes information on the typical quantity of sale, and thecharacteristics of the product (weight, volume, etc.).

• Bangladesh (2004-2010): Covers locations in all 64 districts of the country, each ofwhich is further divided into an urban and rural region. The number of commodi-ties in the data vary between rural and urban centers, from 30-50 narrowly identi-fied products. In addition, the dataset contains information on the type of retailerat which the price observation was made, as well as the weight and quantity of atypical unit of the good sold.

• Rwanda (2009-2011): Covers 48 towns over 5 regions (4 of which are further splitinto urban and rural centers) and 60 narrowly identified products.

• Senegal (2006-2010): Covers over 5 town markets and 20-30 narrowly identifiedproducts. As with the Nigerian data (from 2007-2010), the dataset includes infor-mation on the type of retailer at which a particular price observation was noted.

• In addition to the countries noted above, we have acquired price data on Guinea-Bissau and efforts are underway to acquire similar price data from other countriesincluding Pakistan, Mexico and Ghana.

2.2 Data on production source (factory or import) locations

For the case of domestically-produced products, we have conducted a telephone in-terview with the firms that produce each of the products in our sample. We ask each firmfor the location(s) of production that serve markets in each country in our sample, andask this information retrospectively so as to cover all of the years in our sample. We havealso sought to corroborate this information by surveying distributors.

For the case of imported products we have contacted distributors to learn the portof entry of each imported product in each country (and year) in our sample. Again, wecorroborate this information with official trade statistics data where possible.

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3 Theoretical Framework

In this section we first describe a model of intranational trade carried out by interme-diaries who potentially enjoy market power. We then go on to discuss how this frame-work can be used to inform empirical work that aims to estimate the size of intranationaltrade costs as well as the distribution of the gains from trade between consumers andintermediaries.

3.1 Model Environment

We consider an environment in which there are D isolated locations6 indexed by dand K products indexed by k. Products k can be either domestically produced or importedfrom abroad. Domestically produced products are made at a factory location indexed by oand imported products are imported into the country through a port or border crossing atlocation o; regardless of whether the products are made at home or abroad, the domestic‘origin’ of the product is location o. (Note that we use the mnemonic o for origin and dfor destination.) We treat the market for product k traded from location o to location d asan isolated market—that is, we abstract for now from general equilibrium considerationsthat would introduce interactions in product or factor markets across or within locations.

We assume that any product k is sold on wholesale markets at the source (ie factorygate or port) location o for a price Pk

ot at date t. This product is then bought at the originlocation o wholesale market and traded from location o to any destination location d bydomestic intermediaries. These intermediaries specialize in the activity of purchasing aproduct in bulk at a wholesale market, transport the product to a destination location,and then finally selling the product to consumers at that location.7

Intermediaries incur costs of trading. Each intermediary has a total cost function,C(qk

odt), which is the cost of trading qkodt units of product k from location o to location d

at date t. Total costs are the sum of fixed costs of entry into the distribution sector, Fkodt,

and per-unit costs of trading. Per-unit costs are themselves the sum of the cost of buyingthe product at the origin location (which is simply the origin price, Pk

ot) and the marginalcosts of trading, denoted by τ = τ(Xk

odt). We assume (in Assumption 1) that marginalcosts are ‘specific’ (ie charged per unit of product shipped) and constant; future work will

6Locations are isolated in the sense that consumers do not travel to economies other than their own topurchase items. More generally, we simply require that intermediaries’ marginal costs are sufficiently low(relative to consumers’ travel costs) that consumer always buy goods locally from an intermediary ratherthan traveling themselves to other locations to make their purchases.

7Note that we assume that there is just one integrated sector that intermediates trade between producers(or importers) and final consumers, combining distribution and retail into one activity.

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explore extensions to this basic case. Finally we let Xkodt denotes a set of marginal cost

shifters specific to the route from origin o to destination d (such as the quality of roadsalong the route) and the product k shipped.

Assumption 1. The cost to an intermediary of buying qkodt units of product k from location o at

date t (for an origin price Pkot) is given by the sum of fixed and (constant, specific) marginal costs:

C(qkodt) =

[Pk

ot + τ(Xkodt)]

qkodt + Fk

odt.

Intermediaries maximize profits by choosing the amount of the product to sell, qkdt.

Let Qkdt denote the total amount sold to the market by all intermediaries. The essential

strategic interaction across intermediaries is the extent to which an intermediary’s ac-tions (his quantity choice, qk

dt) affect other intermediaries’ profits through the aggregatequantity Qk

dt. We follow the ‘conjectural variations’ approach to oligopolistic interactions(e.g. Seade, 1980) and assume (in Assumption 2) that this relationship is summarized by

the parameter θkdt

.=

dQkdt

dqkdt

. The case of symmetric Cournot oligopoly corresponds to θ = 1,

the case of a pure monopolist corresponds also to θ = 1, while perfect competition corre-sponds to θ = 0. In what follows we will not take a stand on the value of θk

dt because ourempirical application will not need—nor be able—to identify this model parameter.

Assumption 2. Intermediaries selling product k in location d at date t perceive the effect of their

sales decision qkdt on aggregate sales Qk

dt to be given by the parameter θkdt

.=

dQkdt

dqkdt

that is fixed in

any period. Alternative values of this parameter span a range of market structure assumptions.

Given the above notation, and denoting consumers’ aggregate inverse demand curveby Pk

dt(Qkdt), each intermediary’s first order condition for profit maximization implies that:

Pkdt =

[Pk

ot + τ(Xkodt)]− θk

dt∂Pk

dt(Qkdt)

∂Qkdt

qkdt. (1)

As is the case for any producer, the price that intermediaries’ charge here (ie Pkdt) is equal

to the intermediaries’ total marginal costs (the sum of the purchase price at the origin, Pkot,

and the marginal cost of trading, τ(Xkodt)) plus the markup that intermediaries potentially

charge (which we denote by µkdt

.= −θk

dt∂Pk

dt(Qkdt)

∂Qkdt

qkdt).

It remains to specify the process of entry into the intermediary activity. The stock ofpotential intermediaries may potentially be constrained by credit constraints, reputationissues, caste or ethnic traditions etc. For this reason we assume (in Assumption 3) thatall intermediaries are identical and that the stock of intermediaries buying product k at

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location o and selling it at location d on date t, denoted by mkodt, is fixed and exogenous

within any period.

Assumption 3. The number of identical intermediaries trading product k from location o tolocation d on date t, denoted by mk

odt, is fixed and exogenous.

Below we make extensive use of the concept of pass-through. This is definedas the amount by which intermediaries’ equilibrium prices respond to a change in their

marginal costs; that is, we define pass-through as ρkodt

.=

dPkdt

dPkot=

dPkdt

dτ(Xkodt)

. Differentiating

equation (1) it can be shown that, in general, pass-through takes the form

ρkodt =

[1 +

(1 + Ekdt)

φkodt

]−1

, (2)

where we define φkodt

.=

mkodt

θkdt

as the ‘competitiveness index’ (since it rises in the number

of intermediaries, mkodt, and falls in these intermediaries’ perceived individual influence

on aggregate supply, θkdt) and Ek

dt.=

Qkdt(

∂Pkdt

∂Qkdt

) ∂

(∂Pk

dt∂Qk

dt

)∂Qk

dtQ 0 is the elasticity of the slope of

inverse demand. (Note that Ekdt = 1

ekdt− 1 − Qk

dtek

dt

∂ekdt

∂Qkdt

, where ekdt

.=

∂Qkdt

∂Pkdt

Pkdt

Qkdt≤ 0 is the

elasticity of demand.) As this expression makes clear, pass-through depends on only twomarket characteristics: the competitiveness (φk

odt) of the market (where importantly it is

only φkodt

.=

mkodt

θkdt

that matters, not mkodt or θk

dt individually) and the second-order curvature

of the demand curve (ie Ekdt, the elasticity of the slope of demand).

The above results hold for any demand curve (or, more generally, to the single-itemconditional demand relationships in any demand system). However, in order to simplifya number of results below we will at times make the additional assumption (in Assump-tion 4) that consumer preferences belong to a particular class of demand for which Ek

dtis constant. We refer to this as ‘constant pass-through demand’ (though note that equi-librium pass-through, ρk

odt would only be constant under this demand class if the com-petitiveness index, φk

odt, were also constant). Constant pass-through demand was firstidentified by Bulow and Pfleiderer (1983) and is a natural generalization of isoelastic de-mand. Indeed, Bulow and Pfleiderer (1983) prove that the only demand system withconstant pass-through is the class introduced here.

Assumption 4. Consumer preferences take the constant pass-through demand form such that

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total demand Qkdt depends on price Pk

dt in the following manner:

Qkdt(Pk

dt) =

(

akdt−Pk

dtbk

dt)

1δkdt if (Pk

dt ≤ akdt, bk

dt > 0and δkdt > 0)or (Pk

dt > akdt, bk

dt < 0and δkdt < 0)

0 if Pkdt > ak

dt, bkdt > 0and δk

dt > 0

∞ if Pkdt ≤ ak

dt, bkdt < 0and δk

dt < 0

with akdt ≥ 0. Accordingly, inverse demand is:

Pkdt(Q

kdt) = ak

dt − bkdt

(Qk

dt

)δkdt . (3)

For this demand system we have ekdt = − 1

δkdt

(Pk

dtak

dt−Pkdt

)≤ 0 and Ek

dt = δkdt − 1Q 0;

that is, by design, Ekdt is equal to a (constant) model parameter, but this parameter is

free to vary. Note that the case of isoelastic demand corresponds to a restriction of thisdemand class in which ak

dt = 0. Hence from equation (2) equilibrium pass-through underAssumption 4 is equal to

ρkodt =

[1 +

δkdt

φkodt

]−1

(4)

Equilibrium pass-through can be ‘incomplete’ (ie ρkodt < 1) for δk

dt > 0 and ‘morethan complete’ (ie ρk

odt > 1) with δkdt < 0. Hence nothing in this class of preferences

restricts whether pass-through will rise or fall with the remoteness of locations within acountry; the only restriction is that pass-through is constant. Finally, note that, whateverthe demand parameter, the state of competitiveness (summarized by φk

odt) matters forequilibrium pass-through; in particular, if competition were perfect (ie φk

odt → ∞) thenequilibrium pass-through is ‘complete’ (ie ρk

odt = 1) for any demand parameters.

3.2 Using the Model to Measure Intranational Trade Costs

In what follows our goal is to describe how the theoretical framework introducedabove can be used, in conjunction with the data described in Section 2 above, to estimatethe magnitude of intranational trade costs. Additionally, we describe how our theoreticalframework can be used to estimate the distribution of surplus (among consumers andintermediaries) for each location in our sample. We break down our analysis into threesteps as follows.

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3.2.1 Step 1: Using price gaps to measure total intranational trade costs

We define total intranational trade costs—denoted here by Tkodt—in a manner that we

feel is relevant from the perspective of final consumers: total trade costs are the price thata final goods consumer must pay for an intermediary to deliver a good from its originlocation to the consumer’s location (the intermediary’s destination). Equation (1) abovedescribes how in this framework intermediaries are potentially charging a mark-up ontrades such that total trade costs are the sum of intermediaries’ marginal costs and inter-mediaries’ mark-ups. The following result is then immediate.

Result 1: Under Assumption 1 the total cost, Tkodt, of trading product k from its origin loca-

tion o to its destination location d at date t is equal to the price of this product across those twolocations on that date (ie Pk

dt− Pkot). Further, this total trade cost is equal to the sum of the marginal

costs of trading, τ(Xkodt), and the mark-up charged by intermediaries, µk

dt. That is:

Pkdt − Pk

ot = Tkodt

.= τ(Xk

odt) + µkdt. (5)

However, the difference in prices for product k among two distinct destination locations, i and jfor i 6= o and j 6= o, is uninformative about the total cost of trading among those locations. Thatis:

Pkjt − Pk

it R Tkijt for i 6= o and j 6= o. (6)

Result 1 is extremely simple, yet it offers a powerful guide to empirical work. Armedwith a dataset of prices prevailing for a given product at several locations, Result 1 sug-gests which price gaps over pairs of locations are informative of total trade costs andwhich are not. This is important because most researchers do not observe the origin loca-tion of each product in each time period, so they do not know which location(s) in theirdataset, if any, correspond to the origin location o, which is required to apply Result 1.In Section 4.1 below we use unique data on the production/importation location(s) ofeach product and year in our dataset and thereby apply Result 1 in order to estimate themagnitude of intranational trade costs for a group of developing countries. We also dis-cuss the size of the bias one would obtain in our dataset without knowledge of originlocations.

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3.2.2 Step 2: Estimating pass-through rates

As discussed in the Introduction, our method for inferring the extent of mark-up vari-ation over space (a necessary input into estimating the marginal costs of intranationaltrade) relies on flexible estimates of the extent of equilibrium pass-through for each prod-uct in each location. We discuss here how the theoretical framework outlined above canbe used to provide these estimates. As long as exogenous variation in the origin (or bor-der) price can be isolated, a reduced-form pass-through parameter can be estimated foreach product k, destination location d and time window (noting that at least two dates twould be required to study how a change in the origin price affects destination prices).

It is straightforward to show, using Equation (4) above, the following result, whichcharacterizes the relationship between destination and origin prices—that is, equilibriumpass-through—as well as the relationship between pass-through and underlying struc-tural parameters (ie mk

odt, θkdt, and δk

dt).

Result 2: Under Assumptions 1-4 the relationship between destination prices Pkdt and origin

prices Pkot for any product k and at any date t satisfies

Pkdt = ρk

odtPkot + ρk

odtτ(Xkodt) + (1− ρk

odt)akdt, (7)

where ρkodt =

(1 + δk

dtφk

odt

)−1

and φkodt

.=

mkodt

θkdt

.

This implies that a regression of destination prices (Pkdt) on origin prices (Pk

ot), con-ditional on controls for both the marginal cost of trading (ie τ(Xk

odt)) and local demandshifters (ie ak

dt), would reveal the equilibrium pass-through rate (ρkodt) inherent to each des-

tination market and product. Unfortunately, both the marginal cost of trading and localdemand shifters are unobservable to researchers—indeed, if these were observable thenanswers to the questions we pose in this paper would be immediately available. Nev-ertheless, in Section 4.2 below we propose an empirical strategy that aims to control forthese variables and hence provide consistent estimates of the equilibrium pass-throughrate (ρk

odt) prevailing in each destination location d and product k separately. While theprincipal reason for obtaining these estimates is, as described in the next sub-section, toinfer how mark-ups vary over space, Result 2 demonstrates that an additional use for

pass-through estimates is to estimate φkodt via the formulaρk

odt =

(1 + δk

dtφk

odt

)−1

.

12

3.2.3 Step 3: Using pass-through estimates to estimate the marginal costs of distance

A central aim of much of the literature on the estimation of trade costs has been tounderstand the determinants of the marginal costs of trading. In a setting of perfect com-petition, total trade costs—as we have defined them above—are equal to the marginalcosts of trading since mark-ups are zero. In such a setting the simple price gap methodsdescribed above in Step 1 are sufficient for determining the magnitude and determinantsof the marginal costs of trading.

However, once it is possible that intermediaries enjoy market power, price gaps reflectnot just the marginal costs of trading over space but also the mark-ups that intermediariescharge when carrying out trades over space. If these mark-ups vary over space (for ex-ample because the state of competition in the intermediary sector varies over space, orsimply because preferences are such that pass-through is anything but complete), thenthe change in price gaps over space reflects not just how space imposes marginal costsbut also how mark-ups vary over space.

The central challenge here is to separate out these two effects without the ability to sep-arately estimate marginal costs and mark-ups (a challenging prospect in any setting, butone that is especially challenging here where researchers lack access to data on consumerquantities or to producers’ input choices). In this section we describe a method—whichdraws on the estimates of equilibrium pass-through ρk

od obtained in Step 2 above—thataddresses this challenge. The key intuition is that pass-through describes how pricesrespond to any marginal cost shock. We have described above a way to estimate pass-through by using the observed response of destination prices to origin price shocks. Wenow use these pass-through estimates to deduce the extent to which the marginal costsof trading affect prices (which, by definition, they do via the extent of pass-through) andhence infer the true marginal costs of trading over space from observed price variationover space. This logic is formalized in Result 3 below which is a direct implication ofResult 2 above.

Result 3: Under Assumptions 1-4 the relationship between ‘adjusted price gaps’ between ori-

gin and destination locations for any product k and at any date t (ie Pkdt−ρk

odtPkot

ρkodt

) and the marginal

costs of trading that product between those locations on that date (ie τ(Xkodt)) satisfies:

Pkdt − ρk

odtPkot

ρkodt

= τ(Xkodt) +

(1− ρkodt)

ρkodt

akdt. (8)

13

Recall from Result 1 above that origin-destination price gaps (ie Pkdt − Pk

ot) are equal tothe sum of marginal costs of trade (ie τ(Xk

odt)) and mark-ups charged by intermediaries;because mark-ups potentially vary with distance, the extent to which price gaps vary overspace does not identify the extent to which the marginal costs of trading vary over space.

By contrast, a key message of Result 3 here is that ‘adjusted price gaps’ (ie Pkdt−ρk

odtPkot

ρkodt

)

are equal to sum of the marginal costs of trade (ie τ(Xkodt)) and a pass-through adjusted

demand shifter (ie (1−ρkodt)

ρkodt

akdt). This suggests that, with suitable controls for this demand-

shifter, the extent to which adjusted price gaps—rather than simple price gaps—vary overspace does identify the extent to which the marginal costs of trading vary over space. InSection 4.3 below we apply this method directly in order to estimate how distance (ie avariable Xk

odt that shifts τ(Xkodt)) affects the marginal costs of trading.

3.3 Using the Model to Measure Division of Surplus

In any market setting where producers enjoy market power a natural question con-cerns the share of surplus accruing to these producers, as well as the share of surplusthat is destroyed (ie deadweight loss) due to their market power. The setting we considerhere—in which intranational trade between producers and consumers is carried out byintermediaries who potentially enjoy market power—is no exception. In this setting thesurplus in question is essentially the gains from trade. That is, consumers in destinationlocation d benefit from being able to consume product k sourced from the origin location o(be that origin a domestic factory or a port through which foreign producers’ goods enter)because there are gains from trade (ie the product would cost more to produce in locationd).

Naturally it is challenging to identify the share of surplus accruing to consumers, in-termediaries and deadweight loss. And this is especially challenging in settings like ourswhere researchers lack data on the quantities of narrowly defined products consumed(which could in principle be used to estimate demand curves, mark-ups and hence thedivision of surplus). Fortunately, based on an extension of the logic in Weyl and Fabinger(2011), in the theoretical framework we have outlined above there exists a simple connec-tion between pass-through and the division of surplus, which allows an answer to answerthis question without the need for data on quantities consumed.

To see this, consider first the calculation of the amount of consumer surplus generatedby any partial equilibrium market setting (that is, where the prices in all other marketsare held constant) for product k in destination market d at date t. Consumer surplus

when Qkdt is supplied to the market is defined as CSk

dt(Qkdt)

.=∫ Qk

dtψ=0[P

kdt(ψ)− Pk

dt(Qkdt)]dψ,

14

where Pkdt(ψ) is the consumers’ inverse demand curve evaluated at argument ψ and

Qkdt is the total amount consumed in equilibrium in the market. Since dCSk

dt(Qkdt(ψ))

dPkot

=

−Qkdt(ψ)

dPkdt(Q

kdt(ψ))

dQkdt

dQkdt

dPkot

consumer surplus can also be written in a way that stresses its

essential connection with pass-through:

CSkdt(Q

kdt) = −

∫ ∞

ψ=Pkot

Qkdt(ψ)ρ

kodt(ψ)dψ. (9)

Following similar steps we now calculate the amount of surplus captured by inter-mediaries in this setting. Intermediaries’ surplus when Qk

dt is supplied to the market isdefined as total variable profits among intermediaries, or ISk

dt(Qkdt)

.= mk

odtΠkodt(q

kodt) =

mkodt

∫ ∞ψ=Pk

ot

dΠkodt(ψ)

dPkot

dψ.8 Differentiating total profits we have:

dΠkodt(ψ)

dPkodt

=(mk

odt − θkdt)q

kodt(ψ)

(mkodt + θk

dt) + θkdtE

kdt(ψ)

− qkodt(ψ),

=

(φk

odt − 1

φkodt

)ρk

dt(ψ)qkodt(ψ)− qk

odt(ψ), (10)

where, recall, Ekdt(ψ) is the elasticity of the slope of demand and ρk

odt(ψ) =

[1 + (1+Ek

dt(ψ))

φkodt

]−1

when each is evaluated at the argument ψ, andφkodt

.=

mkodt

θkdt

is the ‘competitiveness index’

first introduced above. This result holds for any demand curve. Using this result, inter-mediaries’ surplus can be written as:

ISkdt(Q

kdt) = −

∫ ∞

ψ=Pkot

Qkdt(ψ)dψ +

(φk

odt − 1

φkodt

) ∫ ∞

ψ=Pkot

Qkdt(ψ)ρ

kodt(ψ)dψ. (11)

Similar logic implies that intermediaries’ surplus can also be written as:

ISkdt(Q

kdt) = −

∫ Pkdt+τ(Xk

odt)

ψ=Pkdt

µkdt(ψ)dψ +

(φk

odt − 1

φkodt

) ∫ Pkdt+τ(Xk

odt)

ψ=Pkdt

µkdt(ψ)ρ

kodt(ψ)dψ, (12)

where µkdt(ψ) is the mark-up of intermediaries evaluated at the argument ψ. As expected,

8We work with a notion of surplus defined on total variable profits in part because nothing in ourdataset can be used to estimate the fixed costs intermediaries pay. While this overstates intermediaries’total profits it does not overstate consumer surplus or deadweight loss since the fixed costs of productionconsume resources available to society.

15

under the perfectly competitive limit (ie where the competitiveness index φkodt → ∞ and

hence, by equation (2), ρkodt(ψ)→ 1 for any value of ψ) there is no intermediaries’ surplus

(ie ISkdt(Q

kdt)→ 0 for any value of Qk

dt).Finally, following similar steps we calculate the amount of deadweight loss—denoted

DWLkdt(Q

kdt) when Qk

dt is supplied to the market—created by market power in this setting.Using similar arguments to those above this can be written as

DWLkdt(Q

kdt) = −

∫ Pkdt+τ(Xk

odt)

ψ=Pkdt

µkdt(ψ)ρ

kodt(ψ)dψ, (13)

where again µkdt(ψ) is the mark-up of intermediaries evaluated at the argument ψ. Natu-

rally, under the perfectly competitive limit (where mark-ups are always zero) this tendsto zero.

Applying equations (9), (11), (12) and (12) it is then straightforward to show the fol-lowing result:

Result 4(a): Under Assumptions 1-3, the ratio of intermediaries’ surplus ISkdt(Q

kdt) to consumer

surplus CSkdt(Q

kdt) in the market at destination location d for product k on date t is given by

ISkdt

CSkdt

(Qk

dt

)=

1(ρQ)k

dt

+1− φk

odt

φkodt

, (14)

where(ρQ)k

dt is a quantity weighted average of the pass-through rate, defined as

(ρQ)k

dt.=

∫ ∞ψ=Pk

otQk

dt(ψ)ρkodt(ψ)dψ∫ ∞

ψ=Pkot

Qkdt(ψ)dψ

. (15)

Similarly, the ratio of deadweight loss DWLkdt(Q

kdt) to intermediaries’ surplus ISk

dt(Qkdt) in this

market is given byDWLk

dt

ISkdt

(Qk

dt

)=

(ρµ

)kdt

φkodt + (φk

odt + 1)(ρµ

)kdt

, (16)

where(ρµ

)kdtis a mark-up weighted average of the pass-through rate, defined as

(ρµ

)kdt

.=

∫ Pkdt+τ(Xk

odt)

ψ=Pkdt

µkdt(ψ)ρ

kodt(ψ)dψ∫ Pk

dt+τ(Xkodt)

ψ=Pkdt

µkdt(ψ)dψ

. (17)

16

These results, which are derived for a completely general demand structure, high-light the close connection between pass-through and the division of surplus in a generaloligopolistic setting. However, pass-through enters these formulae always as a weightedaverage of pass-through values between relative points (either between the equilibriumprice and an infinite price as in the case of

(ρQ)k

dt, or between the equilibrium price andmarginal cost as in the case of

(ρµ

)kdt). Unfortunately in our setting the weights in these

weighted average formulae (consumption quantities Qkdt(ψ) or mark-ups µk

dt(ψ) in(ρQ)k

dtand

(ρµ

)kdt, respectively) are not observed, nor is there any hope of credibly estimating the

demand structure so as to estimate these weights because consumption quantities are notobserved.

However, in the case of the constant pass-through class of demand (ie that describedin Assumption 4), pass-through (conditional on a fixed competitiveness index) is con-stant and hence weighted averages of this constant are equal to the constant; that is, theweights in Result 4(a) need not be observed. This statement is formalized in Result 4(b):

Result 4(b): Under Assumptions 1-4, the ratio of intermediaries’ surplus ISkdt(Q

kdt) to consumer

surplus CSkdt(Q

kdt) in the market at destination location d for product k on date t is given by

ISkdt

CSkdt

(Qk

dt

)=

1ρk

dt+

1− φkodt

φkodt

, (18)

where ρkdt is the constant equilibrium pass-through rate in this market and ρk

odt =

(1 + δk

dtφk

odt

)−1

.

Similarly, the ratio of deadweight loss DWLkdt(Q

kdt) to consumer surplus CSk

dt(Qkdt) in this market

is given byDWLk

dt

CSkdt

(Qk

dt

)=

φkodt + ρk

dt(φkodt − 1)

φkodt

[φk

odt − ρkdt(φ

kodt − 1)

] . (19)

Combining these two results, the share of total surplus accruing to consumers is given by

CSkdt

CSkdt + ISk

dt + DWLkdt

(Qk

dt

)=

[1

ρkdt+

1φk

odt+

φkodt + ρk

dt(φkodt − 1)

φkodt

[φk

odt − ρkdt(φ

kodt − 1)

]]−1

.

This result describes how, under Assumption 4 (ie a constant pass-through demandsystem), shares of surplus distributed in equilibrium among consumers, intermediaries’and deadweight loss in any market are all simple functions of just the equilibrium pass-

17

through rate ρkdt and the competitiveness index φk

odt prevailing in that market. Conditionalon obtaining estimates of ρk

dt and φkodt, therefore, Result 4(b) provides a direct estimate of

the division of surplus. We pursue this in Section 4.4 below.

3.4 Summary of Theoretical Framework

Using the methodology outlined above we can answer the question posed in the In-troduction: How large are intranational trade costs? To summarize, our answer to thisquestion is achieved in three steps:

1. Step 1: Use price gaps to measure total intranational trade costs. Among the pairs ofmarkets that are actually trading goods, that is between origin and destination mar-ket pairs, these trade costs (for any good, market pair and point in time) can beidentified simply as the price gap, Pk

dt − Pkot.

2. Step 2: Estimate pass-through rates. For each product k and destination market d weestimate a separate equilibrium pass-through rate ρk

od.

3. Step 3: Use pass-through adjusted price gaps to measure how distance affects the marginalcosts of intranational trade. The theory above has demonstrated how price gaps overspace consist of both marginal costs of trading and intermediaries’ mark-ups. How-ever, armed with estimates of pass-through ρk

od from Step 2 above, we have shownhow an ‘adjusted price gap’ formula does reveal how marginal costs of trade varywith shifters to thoses costs, such as distance. The intuition for this is straightfor-ward: pass-through embodies, by definition, how marginal costs affect equilibriumprices, so once estimates of pass-through are known (from time-series variation) thisinformation is vital for studying marginal costs through observations on prices.

In addition, our model highlights the close connection between pass-through rates andthe division of surplus in a partial equilibrium setting. That is, the pass-through estimatesobtained in Step 2 above provide sufficient statistics for the calculation of the relativeshares of social surplus accruing to intermediaries, to consumers, and to deadweight loss.

4 Empirical Results

4.1 Step 1: Using price gaps to measure total intranational trade costs

As described above, our first goal is to measure the magnitude of the total costs ofconducting intranational trade in developing countries. We define these total costs as

18

the price a consumer would have to pay to buy a good produced (or imported from) ata non-local source within her own country. Defined in this way, total intranational tradecosts reflect both the marginal costs of intranational trade and the potential mark-ups thatintermediaries’ charge on intranational trades. But regardless of their composition, thesetotal trade costs reflect the extent to which consumers pay additional costs to purchasenon-local goods.

Result 1 above described how, for a given commodity, total trade costs for any givendestination consumption location are then simply equal to the difference between theprice of that commodity at its source and the price of that same commodity at the desti-nation location. In the notation laid out above, we have

Pkdt − Pk

ot = τ(Xkodt) +µk

dt = Tkodt,

where Tkodt is the total cost of trading good k from location o to location d at time t. By

definition this is the difference in price between that at the destination (Pkdt) and that at the

origin (Pkot). The total cost of trading is the sum of the marginal cost of trading (τ(Xk

odt)),and the mark-up charged by intermediaries (µk

dt).It is important to note that the logic of the above result is only valid for inference

drawn product by product, and on data for which it is reasonable to assume that observa-tions on product k at different points in space are effectively exactly the same product. Forthis reason we work exclusively with products that are extremely narrowly defined, withprecision similar to barcode level identifiers. While this affects the representativeness ofour basket of goods (in terms of consumption weights) it is hard to imagine pursuing anyother approach without confronting serious issues of unobserved quality variation overspace.

A second important feature of Result 1 is that the result that Pkdt − Pk

ot = Tkod, is only

true for pairs of locations, o and d, that are actually trading product k. Our theory saysnothing about the relationship between price gaps and trade costs for pairs of locationsthat are not trading. More generally, for any pair of locations i and j all that a theory ofarbitrage can say (without knowledge of whether the two locations are trading) is that

Pkit − Pk

jt ≤ Tkijt.

Hence, for pairs of locations for which it is not known that trade is occurring, price gapssay nothing about the actual magnitude of total trade costs—both zero and infinite totaltrade costs are consistent with any data set. It is for this reason that we proceed in thissection using information only on those pairs of locations that are actually trading each

19

particular commodity; all other pairs of locations are effectively uninformative of thecosts of trading.

While in principle one could use trade data to infer whether two given locations aretrading product k at time t, there are two serious practical obstacles in doing so. First,trade data is rarely available within countries (especially developing countries), and evenmore rarely with the spatial precision required to study location-to-location trade. Sec-ond, trade data are rarely available at the product (ie brand-name) level so there is rarelya chance to know whether product k is traded or not.

Our approach utilizes, in lieu of trade data, a simple approach to infer which locationpairs are actually trading each commodity: we simply infer the production (or import)location of each good (in each year) in our sample. While trade may not literally occurfrom a given source location, separately, to each destination location (for example, trademay follow a hub-and-spoke arrangement where the hub is a location in the center of acountry) our approach still identifies the trade cost along the route actually followed fromlocation o to location d. An important limitation of our approach, however, is that thereare many pairs of locations that are not trading (the goods in our sample) and betweenwhich we therefore cannot infer the costs of trading. For this reason we seek to estimatethe fundamental underlying relationship between trade cost shifters (such as distance)and trade costs, rather than the particular costs of trading among every possible pair ofmarkets.

Tables 1 and 2 describe (for Ethiopia and Nigeria respectively), by commodity, theaverage price gap among trading pairs, as well as the standard deviation of these pricegaps, the average source price, and the average distance to the source location. In orderto account for inflation over the sample period and different currency units, all prices areconverted into 2001 US dollars (using the base period exchange rate).9 As can be seen,trade costs (ie from the variable, ‘price gaps among trading pairs’) can be substantial bothin absolute units (2001 US dollars) and in relative terms (ie as a percentage of the priceof a good at its source). The tables also reports the average price gap among all pairs (ieboth trading and non-trading pairs), for comparison.

The variable that we refer to as ‘distance’ (both here and throughout the paper) isactually the calculated minimum travel time (using calculations performed by GoogleMaps), so it can be thought of as a metric for distance that is adjusted for road quality andthat allows traders to take (what Google Maps believes is) the quickest route from source

9The price index for each period is obtained by calculating the average across all goods of the propor-tional price changes at the good’s origin location. The normalized prices are converted into 2001 US dollarsusing the prevailing exchange rate during the first month of the sample.

20

to destination. We can infer the approximate algorithm used by Google in these twocountries by sampling travel times along various road types at a random set of locations.The average minutes per mile to travel along primary, secondary and tertiary roads ineach country are shown below.

Approximate Minutes/Mile (Google Maps)Road Quality Ethiopia Nigeria

National highway 1.2 1.2Secondary road 1.4 1.4Tertiary road 1.9 2.4

Figures 3 and 4 (again, for Ethiopia and Nigeria respectively) plot the extent to whichthese price gaps, among trading pairs only, co-vary with the (log) distance separating thetrading pairs (that is the source and destination location). Additionally, the figures plotthe extent to which absolute price gaps co-vary with the (log) distance separating the loca-tions for all (unique, non-trivial) pairs of locations. These are semi-parametric regressionsthat include product-time fixed effects but allow distance to enter non parametrically viaa local polynomial. In what follows we will only be able to identify the marginal costsof distance. Hence the plots are normalized such that the cost of distance is zero for allspecifications at the bottom of the range of observed distances. As these figures illustrate,on average there is an upward sloping relationship, implying that distance raises the totalcosts of intranational trade. The slope is approximately twice as steep when we restrictour sample to only trading pairs, a finding we will return to shortly.

An important lesson from the theory outlined above is that price gaps over space,among trading pairs, reflect both the marginal costs that traders face and the mark-upsthat they may charge if they possess market power. Because of this, the relationship ofprice gaps with distance (as reported nonparametrically in Figures 3 and 4) is a mixtureof how marginal costs vary with distance (presumably positively and monotonically) andhow mark-ups vary with distance (either because the gap between the choke price andcosts is higher in inland destinations or because of intermediaries’ market power varyingover space at destination locations further and further away from the origin location). Animportant goal of Step 3 below is a separation of these two interacting relationships withdistance in order to recover the true effect of distance on the marginal costs of trading.

Finally, Tables 3 and 4 report coefficient estimates from regressions in which we regresslocal currency price gaps between two locations on the (log) distance between the two lo-cations. Column (2) estimates this relationship on the sample of trading location pairs

21

only, while column (1) estimates this relationship on all (unique, non-trivial) pairs of lo-cations. The results in column (2) show that there is a large and statistically significantrelationship between price gaps and (log) distance. The estimated coefficient in column(2) corresponds to a rise in the price gap of 2.99 cents and 3.40 cents cents to ship a goodfor each additional unit of log-distance (measured in minutes of travel time) in Ethiopiaand Nigeria respectively.10 Remarkably, these estimates are similar across the two Africancountries in our sample yet nothing in our analysis imposed this.

Notably these estimates in column (2), which are obtained from trading location pairsalone, are considerably different than those in column (1) which use all pairs of locations.The estimates obtained in column (1) draw on variation that is informative about the costsof distance (ie trading pairs) and variation from pairs of locations that are not tradingand whose variation is therefore completely uninformative about the (point-identified, asopposed to set-identified) costs of distance. The finding that the estimated cost of distanceare smaller when the uninformative pairs are included is not surprising. The price gapbetween any non-trading pair i and j is a function of the difference in trade costs incurredtransporting goods from the origin to locations i and form the origin to location j. Thetriangle inequality implies that the distance between i and j will be weakly larger thanthe difference between the distance from o to i and the distance from j to i. Therefore,a regression of uninformative price gaps on the distance between i and j will tend tounderestimate the marginal costs of distance.

While the results in column (2) speak to how distance raises total trade costs theydo not, for reasons discussed above, indicate that distance necessarily poses significantphysical costs of trading. It could simply be the case that intermediaries delivering to in-creasingly remote locations charge higher mark-ups than do intermediaries at proximatelocations. Our analysis in Step 3 below aims to understand how much characteristicssuch as distance raise actual physical costs of trading (ie intermediaries’ marginal costs)by separating the price gap-to-distance relationship into its two constituent parts: themarginal cost-to-distance relationship and the mark-up-to-distance relationship.

4.2 Step 2: Estimating pass-through

In Step 3 below we aim to measure how the marginal cost of intranational trade riseswith distance. Doing so requires a method for differentiating the extent to which distanceaffects the marginal costs of trading from the extent to which distance affects the mark-

10Recall all prices are normalized to base period prices (ie January 2001) and converted to US dollarsusing the January 2001 exchange rates.

22

up charged by traders. Pass-through, defined as the extent to which a marginal costshock raises the equilibrium price (and is hence equal to one minus the extent to whicha marginal cost shock raises mark-ups), is an essential ingredient for this analysis. Thecurrent section—Step 2— aims therefore to provide estimates of pass-through for eachlocation d and product k in our sample.

Before constructing these estimates it is worth emphasizing that pass-through is anobject of policy interest in its own right. This is for two reasons. First, pass-throughmeasures the extent to which households in developing countries are exposed to changesin economic conditions beyond their borders. In a market economy these effects workthrough price signals. It is therefore important to understand the extent to which theprices faced by households in developing countries—and especially those in remote seg-ments of developing countries—are actually affected by foreign price developments (suchas tariff changes, exchange rate changes, improvement in international shipping technolo-gies, or fluctuations in world demand and supply). If the prices paid by remote house-holds (for imported goods) are largely unaffected by foreign price developments then thequestion of whether integration with world markets via border policies (such as tradeliberalization or exchange rate policy) has been good or bad for these households is anon-starter. Second, as discussed in Result 2 above, the extent of pass-through is also ametric for the extent to which the market in a location is perfectly competitive: perfectcompetition implies complete pass-through, while incomplete pass-through is prima facieevidence for imperfect competition. This logic applies equally to domestically producedgoods (ie the extent to which a shock to the price at the factory gate location affects eachdestination location’s retail price) and to imported goods (ie the extent to which a for-eign price development, such as an exchange rate appreciation, affects each destinationlocation’s retail price).

Recall from Result 2 above that pass-through (ρkodt) relates to the extent to which ex-

ogenous origin prices (Pkot) affect endogenous destination prices (Pk

dt) in the followingmanner:

Pkdt = ρk

odtPkot + ρk

odtτ(Xkodt) + (1− ρk

odt)akdt. (20)

When using this equation to estimate pass-through (ρkodt) three identification challenges

arise.First, there is no hope of estimating a separate pass-through rate ρk

odt for each timedestination d, product k and time period t. We therefore focus on estimating the averagepass-through rate (which we denote ρk

od) for a destination location d and product k acrossall time periods in our sample. Because the pass-through rate is always equal to ρk

odt =

23

(1 + δk

dtφk

odt

)−1

under A1-A4, the assumption that the pass-through rate ρkod is constant over

time (within a product and location) amounts to assuming that the second-order propertyof demand δk

dt (for a product in a location) and the competitiveness parameter φkodt (for the

sale of a product in a location) are constant over time.Second, estimation of ρk

od here requires controls for the marginal cost of trading (ieτ(Xk

odt)) and for local demand shifters (ie akdt). Unfortunately, both the marginal cost of

trading and local demand shifters are unobservable to researchers—indeed, if these wereobservable then answers to the question posed in this paper would be immediately avail-able. In the absence of such controls we make the weakest possible assumption requiredto identify ρk

od, namely that the product-specific variation in the marginal cost of tradingand local demand shifters within destinations over time is orthogonal to the variation inthe origin price over time (or at least to an instrument for changes in the origin price overtime). That is, we assume that the marginal cost of trading, τ(Xk

odt), can be decomposedinto local but time-invariant (βk

1od), local but trending (βk2odt), macro but time-varying (β3t)

and residual (ζkodt) factors as follows: τ(Xk

odt) = βk1od + βk

2odt + β3t + ζkodt. Analogously, we

assume that destination market additive demand shocks, akdt, from Equation (3) above can

be decomposed as follows: akdt = αk

1d + αk2dt + α3t + νk

dt. Note that while this assumptionplaces certain restrictions on how the additive demand shifter, ak

dt, varies across locations,time and products, we place no restrictions on the multiplicative demand shifter, bk

dt, fromEquation (3) above. Conditional on these assumptions we estimate pass-through rates ρk

odby location and product by estimating the following specification,

Pkdt = ρk

odPkot + γk

od + γkodt + γt + εk

dt, (21)

where Pkdt is the destination price, Pk

ot is the origin price, γkod and γt are product-destination

and time fixed-effects, respectively, γkodt is a product-destination linear time trend, and

εkdt = ρk

odζkodt + (1− ρk

od)νkodt is an unobserved error term.

Third, estimation of equation (21) via OLS requires the additional assumption thatE[Pk

otζkodt]= 0 and E

[Pk

otνkdt]= 0, namely that the origin price Pk

ot is not correlated withthe time-varying and local shocks to local (destination location d-specific) trade costs ordemand shifter. If origin prices are set abroad (in the case of imported goods), or arepinned down by production costs at the factory gate (in the case of domestic goods), orare set on the basis of demand shocks at the origin location (which we omit from ouranalysis), then this orthogonality restriction seems plausible. But a nation-wide demandshock for product k (note that a nation-wide demand shock for all goods is controlled forwith the γt fixed effect) would violate this assumption. In future versions of this paper

24

we aim to explore the plausibilty of this assumption by estimating equation (21) via aninstrumental variables method in which the IV for the origin price is the price of a produc-tion input sourced from abroad (indeed some products k are produced entirely abroad)or the exchange rate of the country producing the input (or the product k). For now itis worth noting that the likely bias from violations of this assumption will be positive,leading us to over-state the rate of pass-through.

Figures 5 and 6 (for Ethiopia and Nigeria respectively) contain our estimates of thepass-through rate for all goods and all locations, with pass-through rates (within a des-tination location averaged across all products) again plotted nonparametrically againstlog source-to-destination distance (again in travel time units).11 It is important to stressthat the goal of Step 2 here is to estimate pass-through (ie ρk

od) separately for each des-tination d and product k. For expositional purposes only, Figures 5 and 6 plot averagesof these estimates, averaged across all products. A general tendency in these figures isfor the pass-through rate to be lower at destinations that are further distances from theproduct’s source. This is confirmed in column 1 of Tables 9 and 10 which show shows sig-nificant negative coefficients from the regression of pass-through estimates on log source-to-destination distance (again in travel time units). Another general tendency is for esti-mated pass-through to lie below one, often considerably below one; the average estimatedpass-through rate in our sample is approximately 0.5. The theory outlined above (indeedvirtually any oligopolstic model) places no restrictions on the pass-through rate exceptthat it be positive (a restriction that none of our estimates violate). But beyond this non-negativity restriction pass-through could be below or above one; our estimates suggestthat pass-through below one is a commonplace (and naturally our OLS estimates of thepass-through rate are likely to be, if anything, biased upwards).

While the primary goal of estimating pass-through rates is to feed into Step 3 of ouranalysis below, below we will also use our estimated pass-through rates (from Step 2here) to identify the competitiveness parameter φk

od prevailing in each location due to

the relationship between pass-through and competitiveness, ρkod =

(1 + δk

dφk

od

)−1

in our

model.

11For now we estimate only contemporaneous pass-through rates (though due to the high serial correla-tion in source prices these estimates are similar to those from lagged regressions). In future versions of thispaper we aim to estimated distributed lag specifications and hence trace out the entire impulse response inthe destination price of a change in the port price, that is both short-run and long-run pass-through.

25

4.3 Step 3: Using pass-through adjusted price gaps to measure how

distance affects the marginal costs of intranational trade.

In section 4.1, we detailed how the price gaps among trading pairs increased withthe (log) distance separating the trading pairs. However, this positive relationship is notdriven solely by the fact that the marginal costs of trading increase with distance. In addi-tion, intermediaries charge markups, and our model clarifies that the size of the markupmay be related to distance for two distinct reasons. The theoretical framework outlinedabove offers guidance here, and this is particularly easy to see in the case of constantdemand preferences (ie Assumption 4) for which, recall, Result 3 is

Pkdt − ρk

odtPkot

ρkodt

= τ(Xkodt) +

(1− ρkodt)

ρkodt

akdt. (22)

Recall that in Step 2 above we have obtained estimates of the time-constant (or averageover time) pass-through rate in each location d and product k, an estimate that we denoteby ρk

od. Using these estimates makes the estimation of τ(Xkodt) in equation (22) feasible.

Again, as in Step 2 above, an identification challenge is posed by the presence of theunobserved demand-shifter ak

dt on the right-hand side of this estimating equation. Wetherefore assume that ak

dt can be decomposed as follows: akdt = αk

t + αd + νkdt and, further,

that E[Xk

odtνkdt]= 0. This assumption requires that the variation in additive demand

shifters across destination locations (ie the variation, νkdt, that remains after macro-level

time-product effects, αkt , and destination effects, αd, are removed) is uncorrelated with

shifters to the marginal costs of trading across locations, Xkodt. Again, we require no re-

strictions at all on the multiplicative demand shifters, bkdt, from Equation (3) above.

With this assumption in place we can now state our main estimating equation foridentifying the extent to which distance affects the marginal costs of trading (ie how somemarginal cost shifter Xk

odt affects the marginal costs of trading, τ(Xkodt)):

Pkdt − ρk

odPkot

ρkod

= τ(Xkodt) + γk

t

1− ρkod

ρkod

+ γkt + γd

1− ρkod

ρkod

+ γd + εkdt, (23)

where ρkod is a consistent estimator of the pass-through rate ρk

od obtained in Step 2 above,

γkt is a product-time fixed-effect, γd is a destination fixed effect and εk

dt =(1−ρk

od)

ρkod

νkdt is an

error term for which E[Xk

odtνkdt]= 0.

26

The key attraction of this equation from an empirical perspective is that it describesa way in which price data across origin-destination pairs can be used, in conjunctionwith estimates ρk

od of the pass-through rate (obtained in Step 2 above), to estimate theimportance of shifters Xk

odt to the marginal costs of trading. Further, in principle the effectof Xk

odt on τ(Xkodt) can be estimated entirely non-parametrically. For example, a central

question in the study of trade costs concerns the extent to which distance increases themarginal costs of trading. Equation (23) implies that this relationship between distanceand the marginal costs of trade is revealed, despite the potential presence of market power

in the trading sector, by simply using ‘adjusted price gaps’ (ie Pkdt−ρk

odPkot

ρkod

) rather than price

gaps (ie Pkdt − Pk

ot) as the dependent variable.To gain intuition for this expression, consider the following. First, and dropping

the sub- and superscript notation for now without the risk of confusion, the size of themarkup, µ = (1− ρ)(a − τ − Po), is proportional to the gap between the choke price aand the total cost to the intermediary, τ + Po. For the case of incomplete pass through,ρ < 1, higher marginal costs of transportation raise prices and hence reduce the markupthat intermediaries choose to charge ( dµ

dXod|(m

θ )od= −(1− ρ) dτ

dXod). This channel implies

that the marginal costs of distance are understated in section 4.1 if ρ < 1 and overstated ifρ > 1. Second, the pass-through rate varies across space due to the competitiveness of the

distribution sector ( dµdXod|τ = −(a− τ − Po)

dρ

d(mθ )od

d(mθ )od

dXod). For the case of incomplete pass

through, if routes that reach interior locations far from major production/import loca-tions are less competitive, pass through rates will be smaller in the interior and markupswill be larger. This channel implies that the marginal costs of distance are overstated insection 4.1 if ρ < 1, but the direction of bias is ambiguous if ρ > 1.

Our aim in this section is to estimate the true marginal costs of distance by correctingfor these two biases using the adjusted price gap methodology described in equation(23). We obtain estimates of pass through rates, ρk

od, from section 4.2. Dividing the pricegap Pk

dt − Pkot by the pass-through rate purges the price gap of the first bias. Further

transforming the price gap by replacing Pkot with ρk

odPkot, as well as including two new sets

of independent variables, γkt

(1−ρk

od

ρkod

)and γd

(1−ρk

od

ρkod

), where γk

t and γd are product-time

and destination fixed effects, explicitly controls for the fact that markups may vary overspace due to different levels of competition.

Tables 5 and 6 (for Ethiopia and Nigeria respectively) present the results of these re-gressions, where we model the marginal costs of trading τ(Xk

odt) as a simple functionof (log) distance (from origin location o to destination location d), where distance is inunits of travel time as before. Column 1 reproduces the unadjusted price gap specifi-

27

cation shown in section 4.1 above (for the interpretation of the coefficients in this table,see below). Column 2 goes part-way towards adjusting these estimates for potentiallyvarying mark-ups over space by dividing the price gap through by the pass-throughrate. And column 3 estimates equation (23) in the manner suggested by our model—

that is, using the appropriate ‘adjusted price gap’ (ie Pkdt−ρk

odPkot

ρkod

) as the dependent variable

and controlling for estimates of pass-through adjusted demand shifters,γkt

(1−ρk

od

ρkod

)and

γd

(1−ρk

od

ρkod

).12 All specifications include product-time fixed effects. A consistent pattern

emerges across these three columns, in both Ethiopia and Nigeria, that is consistent withour model. First, the estimated coefficient on the (log) distance separating the tradingpairs at first rises (in column 2 relative to column 1) when only the adjusted price gap isused; but this coefficient then falls (in column 3 relative to column 2) when both correc-tions are applied. These results imply that the two biases discussed above are substantialin magnitude but, at least in the case of Nigeria and Ethiopia, cancel each other out tosome extent.

Columns 4-6 of Tables 5 and 6 allow for a more general specification of τ(Xkodt). We

allow the marginal costs of trading τ(Xkodt) to be functions of both the (log) distance be-

tween the source and destination, the log weight of a unit of the good in question andthe interactions of the two. In both Ethiopia and Nigeria, the marginal cots of distance issubstantially larger for heavier goods.

The estimated coefficients in column (3) of each table correspond, according to ourmethodology, to the estimated marginal costs of distance (in travel time units) along themean journey length in each country (approximately 6 and 8 hours respectively). Thesenumbers are substantially higher than the estimated total costs of distance. The estimatedcoefficients imply that the marginals costs of trade increase by 4.11 cents and 5.70 centsfor each additional unit of log-distance in Ethiopia and Nigeria respectively (comparedto 2.99 cents and 3.40 cents cents without the correction for spatial markup variation).

To interpret these estimates, consider the following. The least remote locations in oursample are approximately an hour of travel (ie 60 minutes or 4.1 log minutes) away fromthe source of production. (This travel time falls within the second percentile of the dis-tribution of route lengths in both samples). The most remote locations in our sample areapproximately twenty hours (ie 1200 minutes or 7.1 log minutes) of travel away from the

12As we are dividing by ρkod, results are very sensitive to estimated ρk

ods close to 1. Therefore, we win-sorize all the pass-through rates estimates that fall below 0.2. Our results are robust to this procedure andthe un-winsorized regressions are reported in tables 7 and 8.

28

source of production. (This travel time falls within the 99th percentile of route lengths inEthiopia and the 97th percentile in Nigeria). Therefore, the additional trade costs incurredby transporting goods to the most remote compared to the least remote locations (a differ-ence of 3 log minutes) is 12 cents in Ethiopia and 17 cents in Nigeria. The mean productobservation in our Ethiopia sample costs just over 40 cents. So the ad valorem equivalentof this relative cost of remoteness is 30 percent. The equivalent calculation for our Nige-ria sample (mean product cost of 1.17 dollars) suggests a relative cost of remoteness of 14percent. These are considerable costs of intranational trade.

To obtain a better sense of the magnitude, we can make rough comparisons to esti-mates of the marginal costs of distance from other sources. In a widely cited paper, Hum-mels (2001) uses freight cost data included in some countries customs records to estimatethe relationship between freight costs and distance for internationally traded goods. Ta-ble 3 of his paper reports estimates of the ad-valorem freight costs for imported goodsat relatively proximate and relatively far distances from the origin port. Taking simpledifferences of these ad valorem costs and dividing through by the change in log distanceprovides estimates comparable to the ad valorem numbers given above.13 The impliedincrease in ad valorem costs for an increase in distance of 3 log points are listed in thetable below for the seven countries for which Hummels’ could obtain data. The US cus-toms data is particularly rich, and allows separate estimates by mode of transport.

Implied ∆ad-valorem transport cost for ∆ lndistance of 3 units (percent)(by mode of transport for cargo of mean kg/$)

US (Truck from CAN) 2.0US (Rail from CAN) 2.7US (Ocean) 4.9US (Air) 14.6

Source: Authors’ calculation based on Table 3 in Hummels (2001).

The increase in ad valorem trade costs associated with an increase in distance of 3 logpoints was 30 percent in Ethiopia and 14 percent in Nigeria. Since almost all this tradetravels by road, the most easily comparable figures from Hummels are those for trucktraffic from Canadian provinces to the US border.14 The change in ad valorem trade costswith log distance in our two African samples is approximately 7 to 15 times larger thanthe change in ad valorem freight costs with log distance found for international tradeshipments from Canada to the US. Therefore, the Ethiopian and Nigerian numbers implyextremely large costs of intranational trade relative to international trade costs along the

13We take the difference between teh smallest and largest distance estimates reported by Hummels(2001). All of these estimates are for cargoes with the mean weight to value ratio.

14The import data record the province of origin in Canada and the district of entry into the US.

29

same mode of transport linking two developed countries. Interestingly, the estimates forNigeria are not too dissimilar to the estimates Hummels obtains for landlocked Paraguay,where imports must travel long distances over developing-country land routes prior toarriving at the dry port.

Figures 7 and 8 (for Ethiopia and Nigeria respectively) present our nonparametric esti-mates of the effect of distance (again, in travel time units) on the marginal costs of trading.By and large these plots confirm the parametric (linear) regression estimates referred toabove. Importantly, the ordering of slopes across three different methodologies (all pairs,trading pairs and markup-adjusted pairs) is the same nonparametrically (in these figures)as parametrically (in the regression coefficients reported above).

Tables 7 and 8 carry out a variety of important robustness checks. Columns 1-4 repeatthe estimation of the main specification shown in the previous table but with the inclu-sion of destination or destination-time fixed-effects. This does not substantially affect thecoefficient estimates. However, given the limited number of source locations, the destina-tion fixed effects are highly correlated with distance and hence our preferred specificationincludes only product-time fixed effects. Columns 5-6 control for destination-specific de-mand shocks in a more parsimonious manner by including controls for the log populationand log income per capita at the destination. Columns 7-8 do not winsorize the ρk

od esti-mates that fall below 0.2 as we do for the main specification to avoid dividing price gapsby numbers close to zero. Columns 9-10 use exchange rates deflated by the local inflationrate to instrument for origin prices in the estimation of ρk

od for the subsample of importgoods where bilateral deflated exchange rates explain origin price movements. Columns11-12 build on columns 9-10 but also include the deflated local currency oil price as anexplanatory variable in the pass through regression. The inclusion of the oil price ex-plicitly deals with the worry that shocks to oil prices (potentially due to exchange ratefluctuations) can alter both the origin prices and marginal costs of transport. Columns13-14 remove goods which show strong evidence of producer price setting behavior. Asall goods show price variation of space, we remove the four goods from the two samplesfor which nominal prices remain fixed for long periods of time (24 months or more). Fi-nally, Columns 15-16 remove price pairs where the destination location is less than 100minutes from the source location in case demand shocks are spatially correlated biasingour pass through estimates towards one for nearby locations.

30

4.4 Implications for the share surplus accruing to consumers, to inter-

mediaries, and to deadweight loss

Consider the following thought experiment. Due to tariff reductions or improvementsin international transportation and logistics, events often termed ‘globalization’, the portprice of a particular import falls by 20 percent. This price reduction creates an additionalamount of social surplus that will in part: (1) accrue as consumer surplus to consumerslocated at various points within the country, (2) accrue as profits to the intermediaries whoprovide consumers with the import goods, and (3) end up as deadweight loss associatedwith intermediaries using their market power to restrict supply. The model in section 3shows—in Result 4(c)—that under the assumption that demands are in the constant pass-through class, the pass through rate and the competitiveness index of any particular routeprovide sufficient statistics for estimating how the social surplus is distributed betweenconsumers, intermediaries and deadweight loss.

But where can estimates of these parameters be obtained?First, Result 2(b) above has already discussed a method for obtaining consistent esti-

mates of ρkd, namely equilibrium pass-through ρk

odt =

(1 + δk

dtφk

odt

)−1

under the additional

assumption (Assumption 5) that pass-through is constant over time within a product-destination market (because δk

dt and φkodt are constant).

Second, the formula

ρkod =

(1 +

δkd

φkod

)−1

(24)

suggests how the pass-through rate ρkod and the competitiveness index φk

od are connectedto one another and hence how estimates of ρk

od could be used to estimate φkod. Unfortu-

nately, in general there is no unique mapping between ρkod and φk

od. Indeed, as equation(24) above illustrates, in principle there are DT known values of ρk

od, but DT unknown val-ues of δk

d and another DT unknown values of φkod to be estimated. We therefore assume (in

Assumption 8) that the variation in the demand-side determinants of pass-through (ie theparameters δk

d) and the supply-side determinants of pass-through (ie the parameters φkod)

are sufficiently orthogonal over destination markets d and products k as to allow data onthe pass-through rate (ie an estimate of ρk

od) to identify φkod which is all that is required to

apply Result 4(b) and hence provide an answer to the question of how does the share oftotal surplus accruing to consumers vary with remoteness. However, as should be clear,the particular assumption made in Assumption 8 here is overly sufficient since it restrictsthere to be only D + T unknown parameters to be estimated from DT pass-through ρk

od

31

estimates.

Assumption 5. The demand parameter δkd is constant over destination locations d but can vary

freely across products k; that is, δkd=δk∀d. Similarly, the competitiveness index parameter φk

od isconstant over products k but is free to vary across destinations d; that is, φk

od=φd∀k.

This is a particularly stark assumption but one that is perhaps not an implausible first-pass. That is, because of possible economies of scale it seems plausible that the essentialvariation in the number of intermediaries and their competitive conduct (ie mk

od and θkd),

and hence the overall competiveness index φkod, across products and locations is primarily

across locations. We have in mind here a notion that large locations have many intermedi-aries supplying any given good. Likewise, while we allow the additive and multiplicativeshifters of demand (ie ak

dt and bkdt) to vary across locations, products and time, it seems

plausible that the second-order curvature parameter δkdt, the unique demand-side param-

eter that governs pass-through, is constant across locations and time. That said, becauseAssumption 8 is overly sufficient for identification, it is testable, and we will explore thisin future work.

We are finally ready to state the key result that describes an empirical procedure (andthe assumptions required for it to be accurate) to Question 4:

Result 4(c): Under Assumptions 1-8, a consistent estimator of the competitiveness index at adestination (ie φd), up to a scalar, can be obtained by estimating the following regression by OLS

Ξkod = γd + γk + γkζk

od + εkod, (25)

where Ξkod

.= ln( 1

ρkod

− 1) if ζkod = 1 and Ξk

od.= ln(1 − 1

ρkod

) if ζkod = 0, ρk

od is a consistent

estimator of the equilibrium pass-through rate obtained by applying Result 2(b), γd and γk aredestination- and product-specific fixed effects respectively, and εk

d is an error term. Normalizingsuch that the lowest value of φd = 1, a consistent estimator of φd is φd

.= eγd . Further, an

estimate of the ratio of intermediaries’ surplus ISkd(Q

kd) to consumer surplus CSk

d(Qkd) in the

market at destination location d for product k is given by

ISkd

CSkd

(Qk

d

)=

1

ρkod

+ e−γd − 1, (26)

and an estimate of the ratio of deadweight loss DWLkd(Q

kd) to consumer surplus CS(Qk

d) in this

32

market is given byDWLk

dt

CSkdt

(Qk

dt

)=

eγd + ρkod(e

γd − 1)

eγd

[eγd − ρk

od(eγd − 1)

] . (27)

Result 4(c) therefore describes a simple method for using estimated pass-through ratesρk

od, which we estimate separately by product k and destination location d, to infer how thedistibution of suprlus varies with remoteness. That is, despite the lack of access to con-sumption quantity data in this setting, we can use estimated pass-through rates, guidedby Result 4(c), to infer the share of total surplus—that is, the gains from trade—accruingto consumers, to intermediaries, and to deadweight loss in each market (ie product andlocation) in our sample.

Note also that, along the way to answering this question, Result 4(c) suggests a way inwhich the competitiveness index φd for each destination d can be identified. As outlinedin Result 4(c), the good and origin-destination specific pass through rates ρk

od providesufficient variation to estimate the unknown competitiveness indices for each destinationlocation d. Various methods can be used to obtain estimates of the competitiveness indexφd. Here, we follow Result 4(c) and transform the pass-through rates to linearize thisrelationship and then apply OLS to recover estimates of φd.

Figures 9 and 10 show non-parametric plots of how the competitiveness index varieswith (log) distance to the capital city, with higher values of the index representing greaterlevels of competition. If intermediaries compete a la Cournot, the competitiveness indexsimply corresponds to the number of middlemen serving a particular location. Column(2) of Tables 9 and 10 report descriptive regression of the competitiveness index at eachlocation against the log distance to the capital city (Addis Ababa or Lagos). The value ofthe competitiveness index clearly declines with distance form the capital in both Ethiopiaand Nigeria. More remote locations have a less competitive intermediary sector servingthem.

Result 4(c) above also described how estimates of the distribution of surplus followsimply from estimates of pass-through and competitiveness. Figures 11, 12 present non-parametric plots of the relationship between the ratio of intermediary profits to consumersurplus and distance for each good. Figures 13, 14 present similar plots for the otherratio of interest, the ratio of deadweight loss to consumer surplus. Finally, Figures 15, 16present similar plots for the share of consumer surplus in total suprlus. Columns (3) to (5)of Tables 9 and 10 report descriptive regressions of these ratios and shares against the logsource-to-destination distance. For both Ethiopia and Nigeria, the further a good must

33

travel to reach the consumer, the smaller share of the (partial equilibrium) surplus thataccrues to the consumer. The additional share of surplus going to consumers in the leastremote locations (1 hour away) compared to the most remote locations (20 hours away)is 7 percent in Ethiopia and 19 percent in Nigeria.

The normalization that the least competitive locations were served by monopolisttraders is not wholly innocuous (although necessary to avoid the dummy variable trapin our estimation strategy). Reassuringly, choosing other normalizations where the leastcompetitive location is more competitive than under monopoly changes the share goingto consumers but does not affect the slope of this share with respect to log distance (thekey comparison of interest).

The fact that consumers accrue only a fraction of the surplus generated by the abilityto purchase goods made in distant locations does not tell us how much surplus is createdin total. Our finding that the marginal costs of intranational trade are extremely highclearly implies that the total quantity of surplus will be smaller in more remote locations.In the extreme case, high marginal costs of trade may prevent goods from even reachingremote locations. In this scenario, a tariff cut at the border will not increase the socialsurplus of interior consumers at all.

Consumer price index enumerators record as missing a good that is unavailable ata particular location during a particular month. This information on product availabil-ity provides suggestive evidence that internal trade costs substantially reduce the totalquantity of social surplus generated by trade. If products cannot be found in locationsfar from the port or factory, neither consumers nor intermediaries in this location benefitfrom trade in this product. Figures 17 and 18 plot product availability (a binary variable)on the log source-to-destination distance for the subset of product-location pairs whereproduct is observed at least once in the sample. As expected, in both countries productavailability falls precipitously with distance from the factory or port.

5 Conclusion

This paper sets out to answer the question how large are intrnational trade costs in de-veloping countries? We find that the costs of distance appear to be under-estimated bystandard spatial price gap methods used to infer trade costs. The costs of distance ap-proximately double when we use discard uninformative price gaps, those price gaps forwhich neither of the pairs is a source location for the good in question. The costs of dis-tance approximately double again when spatial variation in mark-ups accounted for by

34

using a sufficient statistic (pass-through rates) to adjust price gaps�Our finding that the costs of intranational trade are extremely high (approximately

7 to 15 times larger than the freight costs for road transport between Canada and theUS), has obviously implications for consumer welfare. High intranational trade costsreduce the amount of potential surplus consumers can derive from purchasing goodsmade in distant locations. Of the surplus that remains once the costs of distance have beenaccounted for, it appears that a significant fraction does not actually accrue to consumers(and instead accrue to intermediaries and deadweight loss), and that this is especiallytrue in the most remote locations.

35

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PAKES, A. (2008): “Theory and empirical work on imperfectly competitive markets,” Dis-cussion paper, National Bureau of Economic Research.

PARSLEY, D. C., AND S.-J. WEI (2001): “Explaining the Border Effect: The Role og Ex-change Rate Variability, Shipping Costs, and Geography,” Journal of International Eco-nomics, 55(1), 87.

SEADE, J. (1980): “On the Effects of Entry,” Econometrica: Journal of the Econometric Society,pp. 479–489.

SIMONOVSKA, I. (2010): “Income Differences and Prices of Tradables,” Discussion paper,UC Davis Working Paper.

SIMONOVSKA, I., AND M. WAUGH (2011a): “The Elasticity of Trade: Estimates and Evi-dence,” Discussion paper, National Bureau of Economic Research.

WEYL, E. G., AND M. FABINGER (2011): “A Restatement of the Theory of Monopoly,”Discussion paper.

38

Figures

Figure 1: Map of sample locations in Ethiopia

!

!!

!

!

!

!

!!

!

!

!

!

!

!

!!

!!

!!

! !!

!

!

!

!

!

!!

!

!

!

!

!

!!

!

!

!

!

!!

!

!

!

!

!

!

!

!

!

! !

!!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!

!!

!

!

!

!!

!

!

! !!

! !!

!

!!

!

!

!

#*

#*#*#*#*

#*

#*#*

#*

#*

#*

#*#*

#*#*

#*

#*

#*

#*#*#*#*#*

#*

#*

#* Source locations! Market observations

Primary roadsSecondary roads

#*

ss! Market observations

Note: Red triangles denote market locations at which prices are observed and blue circlesdenote origin locations (factories in the case of domestically produced goods, ports-of-entry in the case of foreign goods).

39

Figure 2: Map of sample locations in Nigeria

!

! !

!

!

!

!

!

!

!

! !!

!

!

!

!

!

!

!

!

!!

!

!

!

!

!

! !!

!

!

!

!

!

!!

#*#*

#*

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

#*

#* Source locations! Market observations

Primary roadsSecondary roads

Note: Red triangles denote market locations at which prices are observed and blue circlesdenote origin locations (factories in the case of domestically produced goods, ports-of-entry in the case of foreign goods).

40

Figure 3: Intranational trade costs and distance: Ethiopia

0.0

5.1

Cos

ts o

f dis

tanc

e (2

001

US

$)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

all pairs price gaps trading pairs price gaps

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).All plots are semiparametric and include product−time fixed effects (Baltagi and Li, 2002).

Ethiopia: All Goods

Figure 4: Intranational trade costs and distance: Nigeria

0.0

5.1

Cos

ts o

f dis

tanc

e (2

001

US

$)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

all pairs price gaps trading pairs price gaps

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).All plots are semiparametric and include product−time fixed effects (Baltagi and Li, 2002).

Nigeria: All Goods

41

Figure 5: Estimated pass-through rates for all goods and distance: Ethiopia

.5.5

5.6

.65

.7.7

5P

ass−

thro

ugh

rate

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 6: Estimated pass-through rates for all goods and distance: Nigeria

0.2

.4.6

.8P

ass−

thro

ugh

rate

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

42

Figure 7: Marginal costs of distance: Ethiopia

0.1

.2C

ost o

f Dis

tanc

e (2

001

US

$)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

all pairs trading pairs µ−adjusted trading pairs

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).All plots are semiparametric and include product−time fixed effects. Adjusted gaps control for market power.

Ethiopia: All Goods

Figure 8: Marginal costs of distance: Nigeria

0.1

.2C

ost o

f Dis

tanc

e (2

001

US

$)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

all pairs trading pairs µ−adjusted trading pairs

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).All plots are semiparametric and include product−time fixed effects. Adjusted gaps control for market power.

Nigeria: All Goods

43

Figure 9: Competitiveness of intermediaries and distance: Ethiopia

1.8

22.

22.

42.

6

Rel

ativ

e co

mpe

titiv

enes

s in

dex

of in

term

edia

ries

at d

estin

atio

n m

arke

t

60 120 240 600 1200Distance from location to capital city (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 10: Competitiveness of intermediaries and distance: Nigeria

33.

54

4.5

5

Rel

ativ

e co

mpe

titiv

enes

s in

dex

of in

term

edia

ries

at d

estin

atio

n m

arke

t

60 120 240 600 1200Distance from location to capital city (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

44

Figure 11: Ratio of intermediary profit to consumer surplus and distance: Ethiopia

1.2

1.4

1.6

1.8

22.

2

Rel

ativ

e ra

tio o

f int

erm

edia

ry p

rofit

sto

con

sum

er s

urpl

us

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 12: Ratio of intermediary profit to consumer surplus and distance: Nigeria

11.

52

2.5

Rel

ativ

e ra

tio o

f int

erm

edia

ry p

rofit

sto

con

sum

er s

urpl

us

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

45

Figure 13: Ratio of deadweight loss to consumer surplus and distance: Ethiopia

.2.2

5.3

.35

.4

Rel

ativ

e ra

tio o

f dea

dwei

ght l

oss

to c

onsu

mer

sur

plus

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 14: Ratio of deadweight loss to consumer surplus and distance: Nigeria

.05

.1.1

5.2

Rel

ativ

e ra

tio o

f dea

dwei

ght l

oss

to c

onsu

mer

sur

plus

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

46

Figure 15: Share of consumer surplus in total surplus and distance: Ethiopia

.38

.4.4

2.4

4.4

6.4

8S

hare

of t

otal

sur

plus

acc

ruin

g to

con

sum

ers

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 16: Share of consumer surplus in total surplus and distance: Nigeria

.35

.4.4

5.5

.55

.6S

hare

of t

otal

sur

plus

acc

ruin

g to

con

sum

ers

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

47

Figure 17: Product availability: Ethiopia

.75

.8.8

5.9

.95

Pro

duct

Ava

ilabi

lity

(Bin

ary)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Ethiopia: All Goods

Figure 18: Product availability: Nigeria

.75

.8.8

5.9

.95

Pro

duct

Ava

ilabi

lity

(Bin

ary)

60 120 240 600 1200Distance from source location to destination market (minutes, log scale)

95% confidence intervals shown. Locally weighted polynomial (Epanechnikov kernel, bandwidth=0.5).

Nigeria: All Goods

48

Tables

49

Tabl

e1:

Tota

lint

rana

tion

altr

ade

cost

s,on

aver

age:

Ethi

opia

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Elec

tric

Bulb

Win

eD

rink

Det

erge

ntTo

iletS

oap

Dri

nkBa

tter

ies

-C

igar

ette

sPh

ilips

Sari

sM

eta

Beer

Zah

ira

Lux

Coc

aC

ola

Ever

eady

Rot

hman

s(4

0/60

w)

(750

cc)

(330

cc)

(50G

m)

(90G

m)

(300

cc)

Dry

cell

(pac

k)

Abs

olut

ePr

ice

Gap

0.03

40.

297

0.02

80.

006

0.01

80.

035

0.03

10.

113

(All

Pair

s)(0

.039

(0.2

80(0

.029

(0.0

05)

(0.0

19)

(0.0

35)

(0.0

48)

(0.2

38)

Pric

eG

ap0.

029

-0.0

330.

027

0.00

30.

005

0.05

80.

000

-0.0

19(T

radi

ngPa

irs)

(0.0

40)

(0.3

22)

(0.0

34)

(0.0

06)

(0.0

21)

(0.0

38)

(0.0

57)

(0.2

08)

Ori

gin

Pric

e0.

165

1.04

90.

283

0.05

50.

180

0.14

70.

171

0.89

2(0

.037

)(0

.107

)(0

.021

)(0

.004

)(0

.012

)(0

.019

)(0

.049

)(0

.121

)

Dis

tanc

eto

521.

9335

6.87

383.

1051

3.11

524.

0235

6.14

504.

0150

2.26

Sour

ce(m

inut

es)

(205

.85)

(177

.65)

(198

.66)

(199

.49)

(206

.85)

(197

.70)

(214

.74)

(208

.78)

Uni

tWei

ght(

Gm

)20

850

400

5090

350

4020

Obs

erva

tion

s44

5402

2434

0925

2238

1234

4246

4758

4630

4331

9126

2552

96

(9)

(10)

(11)

(12)

(13)

(14)

(15)

Wat

erBe

erH

air

Oil

Mot

orO

ilD

rink

Pen

Beer

Am

boH

arar

Zen

ith

Mob

ilPe

psi

Bic

Bede

le(5

00cc

)(3

30cc

)(3

30cc

)(1

Lt)

(300

cc)

(Bal

lPoi

nt)

(330

cc)

Abs

olut

ePr

ice

Gap

0.04

10.

030

0.06

70.

135

0.03

00.

011

0.03

0(A

llPa

irs)

(0.0

45)

(0.0

41)

(0.0

65)

(0.2

02)

(0.0

27)

(0.0

12)

(0.0

31)

Pric

eG

ap0.

070

0.03

80.

042

0.02

20.

036

0.00

40.

067

(Tra

ding

Pair

s)(0

.047

(0.0

39)

(0.0

79)

(0.1

94)

(0.0

33)

(0.0

14)

(0.0

31)

Ori

gin

Pric

e0.

133

0.26

30.

599

1.85

90.

152

0.09

50.

238

(0.0

17(0

.031

)(0

.060

)(0

.217

)(0

.023

)(0

.014

)(0

.025

)

Dis

tanc

eto

423.

1458

4.72

394.

8536

8.16

294.

4052

0.86

469.

55So

urce

(min

utes

)(1

83.1

6(2

40.3

3(1

84.1

5)(1

84.2

6)(1

57.0

9)(2

06.6

0)(2

14.6

0)

Uni

tWei

ght(

Gm

)10

0040

035

012

0032

020

400

Obs

erva

tion

s41

8829

2465

9142

9193

2216

7148

4802

4702

6624

6239

Not

es:

Row

1us

esda

tafr

omal

lloc

atio

npa

irs.

Row

3on

lyus

esda

tafr

om“t

radi

ngpa

irs”

,eg

pair

sw

here

one

ofth

elo

cati

ons

isei

ther

the

fact

ory

loca

tion

for

that

prod

ucto

rth

epo

rtof

entr

y.Pr

ices

are

defla

ted

byth

eav

erag

eof

the

prop

orti

onal

pric

ech

ange

for

each

good

atit

sor

igin

loca

tion

.R

ealp

rice

sar

eco

nver

ted

into

US

Dol

lars

usin

gth

epr

evai

ling

exch

ange

rate

duri

ngth

eba

sepe

riod

(Jan

uary

2001

).St

anda

rder

rors

inpa

rent

hese

s.

50

Tabl

e2:

Tota

lint

rana

tion

altr

ade

cost

s,on

aver

age:

Nig

eria

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dri

nkC

emen

tBa

byFo

odD

eter

gent

Tea

Mar

gari

neSa

rdin

esBe

erC

emen

tBo

urnv

ita

Elep

hant

Cer

elac

Om

oLi

pton

Blue

Band

Titu

sG

uine

ssN

iger

cem

Abs

olut

ePr

ice

Gap

0.10

60.

606

0.14

30.

054

0.04

10.

124

0.04

10.

072

0.71

1(A

llPa

irs)

(0.1

28)

(0.5

53)

(0.1

42)

(0.0

77)

(0.1

31)

(0.1

18)

(0.0

38)

(0.1

08)

(1.0

23)

Pric

eG

ap0.

039

0.49

00.

039

-0.0

210.

000

0.04

6-0

.007

0.03

10.

360

(Tra

ding

Pair

s)(0

.119

)(0

.672

)(0

.161

)(0

.073

)(0

.037

)(0

.155

)(0

.047

)(0

.103

)(0

.932

)

Ori

gin

Pric

e1.

231

4.53

41.

259

0.35

80.

272

0.83

00.

366

0.42

43.

983

(0.0

48)

(0.8

55)

(0.1

34)

(0.0

71)

(0.0

21)

(0.1

77)

(0.0

75)

(0.0

40)

(0.2

38)

Dis

tanc

eto

600.

9356

6.98

579.

4257

2.69

643.

6558

4.71

585.

3835

6.06

458.

85So

urce

(min

utes

)(2

89.2

7)(2

77.8

1)(2

79.1

2)(2

68.1

7)(2

89.9

3)(2

74.2

3)(2

71.9

2)(2

76.5

6)(2

72.9

5)

Uni

tWei

ght(

Gm

)45

050

000

400

100

226

250

125

300

5000

0

Obs

erva

tion

s16

852

4656

348

191

3066

812

182

5338

453

498

5876

1071

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

Dri

nkD

rink

Dri

nkSu

gar

Pow

der.

Milk

Evap

.Milk

Evap

.Milk

Cig

aret

tes

Cok

eBt

l.C

oke

Can

Milo

StLo

uis

Peak

Peak

Car

nati

onB&

H

Abs

olut

ePr

ice

Gap

0.00

90.

021

0.10

50.

048

0.09

10.

021

0.04

00.

039

(All

Pair

s)(0

.008

)(0

.030

)(0

.093

)(0

.052

)(0

.088

)(0

.020

)(0

.101

)(0

.037

)

Pric

eG

ap-0

.002

-0.0

020.

007

0.01

00.

010

-0.0

22-0

.003

0.02

8(T

radi

ngPa

irs)

(0.0

13)

(0.0

27)

(0.1

25)

(0.0

56)

(0.1

10)

(0.0

29)

(0.0

58)

(0.0

39)

Ori

gin

Pric

e0.

128

0.25

41.

375

0.38

61.

368

0.32

00.

206

0.44

0(0

.012

)(0

.012

)(0

.059

)(0

.044

)(0

.081

)(0

.064

)(0

.011

)(0

.019

)

Dis

tanc

eto

623.

5962

8.91

623.

5958

5.15

613.

3157

9.17

654.

6554

8.18

Sour

ce(m

inut

es)

(292

.96)

(297

.96)

(291

.06)

(291

.06)

(293

.37)

(272

.61)

(273

.41)

(283

.16)

Uni

tWei

ght(

Gm

)30

020

045

090

040

017

017

030

0

Obs

erva

tion

s83

4871

7880

5323

260

6946

5383

044

5281

94

Not

es:

Row

1us

esda

tafr

omal

llo

cati

onpa

irs.

Row

3on

lyus

esda

tafr

om“t

radi

ngpa

irs”

,eg

pair

sw

here

one

ofth

elo

cati

ons

isei

ther

the

fact

ory

loca

tion

for

that

prod

ucto

rth

epo

rtof

entr

y.Pr

ices

are

defla

ted

byth

eav

erag

eof

the

prop

orti

onal

pric

ech

ange

for

each

good

atit

sor

igin

loca

tion

.R

ealp

rice

sar

eco

nver

ted

into

US

Dol

lars

usin

gth

epr

evai

ling

exch

ange

rate

duri

ngth

eba

sepe

riod

(Jan

uary

2001

).St

anda

rder

rors

inpa

rent

hese

s.

51

Tabl

e3:

Esti

mat

ing

the

Tota

lEff

ecto

fDis

tanc

e:Et

hiop

ia

(1)

(2)

Abs

olut

ePr

ice

Gap

Pric

eG

ap(A

llPa

irs)

(Tra

ding

Pair

s)

Log

dist

ance

betw

een

0.01

30**

*0.

0299

***

loca

tion

s(m

inut

es)

(0.0

0049

1)(0

.001

54)

Obs

erva

tion

s4,

302,

532

100,

905

R-s

quar

ed0.

356

0.25

8

Not

es:

All

colu

mns

regr

ess

log

dist

ance

ona

mea

sure

ofth

epr

ice

gap

for

each

good

and

pair

oflo

cati

ons.

Col

umns

1us

esda

taon

the

abso

lute

pric

ega

pbe

twee

nal

lloc

atio

npa

irs.

Col

umn

2us

esda

taon

the

actu

alpr

ice

gap

be-

twee

n“t

radi

ngpa

irs”

,eg

dest

inat

ion

pric

em

inus

orig

inpr

ice

for

pair

sw

here

one

ofth

elo

cati

ons

isei

ther

the

fact

ory

loca

tion

for

that

prod

uct

orth

epo

rtof

entr

y.Pr

ices

are

defla

ted

byth

eav

erag

eof

the

prop

orti

onal

pric

ech

ange

for

each

good

atit

sor

igin

loca

tion

.Rea

lpri

ces

are

conv

erte

din

toU

SD

olla

rsus

ing

the

prev

ailin

gex

chan

gera

tedu

ring

the

base

peri

od(J

anua

ry20

01).

All

regr

essi

ons

incl

ude

tim

e-pr

oduc

tfix

edef

fect

s.Ti

me-

prod

uct

clus

tere

dst

an-

dard

erro

rsin

pare

nthe

ses.

*si

gnifi

cant

at10

perc

entl

evel

,**

at5

perc

enta

nd**

*at

1pe

rcen

t.

52

Tabl

e4:

Esti

mat

ing

the

Tota

lEff

ecto

fDis

tanc

e:N

iger

ia

(1)

(2)

Abs

olut

ePr

ice

Gap

Pric

eG

ap(A

llPa

irs)

(Tra

ding

Pair

s)

Log

dist

ance

betw

een

0.02

37**

*0.

0340

***

loca

tion

s(m

inut

es)

(0.0

0259

)(0

.005

07)

Obs

erva

tion

s38

5,13

623

,520

R-s

quar

ed0.

490

0.48

9

Not

es:

All

colu

mns

regr

ess

log

dist

ance

ona

mea

sure

ofth

epr

ice

gap

for

each

good

and

pair

oflo

cati

ons.

Col

umns

1us

esda

taon

the

abso

lute

pric

ega

pbe

twee

nal

lloc

atio

npa

irs.

Col

umn

2us

esda

taon

the

actu

alpr

ice

gap

be-

twee

n“t

radi

ngpa

irs”

,eg

dest

inat

ion

pric

em

inus

orig

inpr

ice

forp

airs

whe

reon

eof

the

loca

tion

sis

eith

erth

efa

ctor

ylo

cati

onfo

rth

atpr

oduc

tor

the

port

ofen

try.

Pric

esar

ede

flate

dby

the

aver

age

ofth

epr

opor

tion

alpr

ice

chan

gefo

rea

chgo

odat

its

orig

inlo

cati

on.R

ealp

rice

sar

eco

nver

ted

into

US

Dol

lars

usin

gth

epr

evai

ling

exch

ange

rate

duri

ngth

eba

sepe

riod

(Jan

uary

2001

).A

llre

gres

sion

sin

clud

eti

me-

prod

uct

fixed

effe

cts.

Tim

e-pr

oduc

tcl

uste

red

stan

-da

rder

rors

inpa

rent

hese

s.*s

igni

fican

tat1

0pe

rcen

tlev

el,*

*at5

perc

enta

nd**

*at

1pe

rcen

t.

53

Tabl

e5:

Esti

mat

ing

the

Prim

itiv

eEf

fect

ofD

ista

nce:

Ethi

opia

(1)

(2)

(3)

(4)

(5)

(6)

Pric

eG

apPr

ice

Gap

/ρ

k odA

djus

ted

Pric

eG

apPr

ice

Gap

Pric

eG

ap/

ρk od

Adj

uste

dPr

ice

Gap

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

Log

dist

ance

to0.

0289

***

0.05

48**

*0.

0411

***

-0.0

459*

**-0

.065

6***

-0.0

906*

**so

urce

(min

utes

)(0

.001

47)

(0.0

0256

)(0

.002

46)

(0.0

0430

)(0

.008

50)

(0.0

0949

)

Log

dist

ance×

0.01

51**

*0.

0242

***

0.02

50**

*Lo

gw

eigh

t(0

.001

09)

(0.0

0193

)(0

.002

13)

Tim

e-Pr

oduc

tFE

Yes

Yes

Yes

Yes

Yes

Yes

Tim

e-Pr

oduc

t×1−

ρk od

ρk od

No

No

Yes

No

No

Yes

Des

tina

tion×

1−ρ

k od

ρk od

No

No

Yes

No

No

Yes

Obs

erva

tion

s10

0762

1007

6210

0762

1007

6210

0762

1007

62R

-squ

ared

0.25

80.

283

0.93

30.

272

0.28

80.

933

Not

es:C

olum

n1

regr

esse

slo

gdi

stan

ceon

the

pric

ega

pbe

twee

ntr

adin

gpa

irs

asin

tabl

e3.

Col

umn

2re

gres

ses

log

dist

ance

onth

epr

ice

gap

divi

ded

byth

ees

tim

ated

pass

thro

ugh

rate

sfr

omse

ctio

n4.

2,P

k ds−

Pk os

ρk od

,in

orde

rto

acco

untf

orth

ech

ange

inm

arku

psin

duce

dby

tran

spor

tcos

ts.C

olum

n3

uses

the

tran

sfor

med

pric

ega

pP

k ds−

ρk od

Pk os

ρk od

and

addi

tion

ally

incl

udes

tim

e-pr

oduc

tan

dde

stin

atio

nfix

edef

fect

sm

ulti

plie

dby

1−ρ

k od

ρk od

inor

der

toco

ntro

lfo

r

omit

ted

vari

able

bias

due

toth

ele

velo

fmar

ketp

ower

cova

ryin

gw

ith

dist

ance

.Col

umns

4,5

and

6re

peat

the

anal

ysis

buta

lso

incl

udin

glo

gdi

stan

cein

tera

cted

wit

hlo

gw

eigh

tfor

each

good

.All

regr

essi

ons

incl

ude

tim

e-pr

oduc

tfixe

def

fect

s.Ti

me-

prod

uctc

lust

ered

stan

dard

erro

rsin

pare

nthe

ses.

*si

gnifi

cant

at10

perc

entl

evel

,**

at5

perc

enta

nd**

*at

1pe

rcen

t.

54

Tabl

e6:

Esti

mat

ing

the

Prim

itiv

eEf

fect

ofD

ista

nce:

Nig

eria

(1)

(2)

(3)

(4)

(5)

(6)

Pric

eG

apPr

ice

Gap

/ρ

k odA

djus

ted

Pric

eG

apPr

ice

Gap

Pric

eG

ap/

ρk od

Adj

uste

dPr

ice

Gap

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

(Tra

ding

Pair

s)(T

radi

ngPa

irs)

Log

dist

ance

to0.

0343

***

0.06

37**

*0.

0570

***

-0.2

71**

*-0

.541

***

-0.3

84**

*so

urce

(min

utes

)(0

.005

29)

(0.0

102)

(0.0

0862

)(0

.022

3)(0

.040

2)(0

.032

1)

Log

dist

ance×

0.04

85**

*0.

0960

***

0.06

62**

*Lo

gw

eigh

t(0

.004

07)

(0.0

0728

)(0

.005

61)

Tim

e-Pr

oduc

tFE

Yes

Yes

Yes

Yes

Yes

Yes

Tim

e-Pr

oduc

t×1−

ρk od

ρk od

No

No

Yes

No

No

Yes

Des

tina

tion×

1−ρ

k od

ρk od

No

No

Yes

No

No

Yes

Obs

erva

tion

s23

084

2308

423

084

2308

423

084

2308

4R

-squ

ared

0.50

40.

399

0.96

40.

544

0.44

50.

965

Not

es:C

olum

n1

regr

esse

slo

gdi

stan

ceon

the

pric

ega

pbe

twee

ntr

adin

gpa

irs

asin

tabl

e3.

Col

umn

2re

gres

ses

log

dist

ance

onth

epr

ice

gap

divi

ded

byth

ees

tim

ated

pass

thro

ugh

rate

sfr

omse

ctio

n4.

2,P

k ds−

Pk os

ρk od

,in

orde

rto

acco

untf

orth

ech

ange

inm

arku

psin

duce

dby

tran

spor

tcos

ts.C

olum

n3

uses

the

tran

sfor

med

pric

ega

pP

k ds−

ρk od

Pk os

ρk od

and

addi

tion

ally

incl

udes

tim

e-pr

oduc

tfix

edef

fect

sm

ulti

plie

dby

1−ρ

k od

ρk od

inor

der

toco

ntro

lfor

omit

ted

vari

able

bias

due

toth

ele

velo

fmar

ketp

ower

cova

ryin

gw

ith

dist

ance

.C

olum

ns4,

5an

d6

repe

atth

ean

alys

isbu

tals

oin

clud

ing

log

dist

ance

inte

ract

edw

ith

log

wei

ghtf

orea

chgo

od.A

llre

gres

sion

sin

clud

eti

me-

prod

uctfi

xed

effe

cts.

Tim

e-pr

oduc

tclu

ster

edst

anda

rder

rors

inpa

rent

hese

s.*

sign

ifica

ntat

10pe

rcen

tlev

el,*

*at

5pe

rcen

tand

***

at1

perc

ent.

55

Tabl

e7:

Esti

mat

ing

the

Prim

itiv

eEf

fect

ofD

ista

nce:

Rob

ustn

ess

Che

cks

Ethi

opia

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Des

tina

tion

Des

tina

tion

-Yea

rC

ontr

ols

For

Cit

yN

otW

inso

rizi

ngFi

xed

Effe

cts

Fixe

dEf

fect

sSi

zean

dIn

com

ePa

ssTh

roug

hR

ates

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Log

dist

ance

to0.

0175

***

0.02

91**

*0.

0176

***

0.03

060.

0289

***

0.03

79so

urce

(min

utes

)(0

.001

31)

(0.0

0180

)(0

.001

30)

(.)(0

.001

47)

(.)

Tim

e-Pr

od.F

EYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

s

Tim

e-Pr

od.×

1−ρ

k od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

tina

tion

FEYe

sYe

sN

oN

oN

oN

oN

oN

o

Des

t.×1−

ρk od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

t.-Ye

arFE

No

No

Yes

Yes

No

No

No

No

Des

t.-Ye

ar×

1−ρ

k od

ρk od

No

No

No

Yes

No

No

No

No

Obs

erva

tion

s10

0762

1007

6210

0762

1007

6210

0762

1007

62R

-squ

ared

0.29

40.

934

0.31

80.

941

0.25

80.

995

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

Usi

ngEx

chan

geR

ates

Con

trol

sFo

rO

ilR

emov

ing

Goo

dsw

ith

Rem

ovin

gLo

cati

ons

asIV

’sin

ρk od

Pric

ein

ρk od

Prod

ucer

Pric

eSe

ttin

g<1

00M

inut

esA

way

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Log

dist

ance

to0.

0289

***

0.03

98**

*0.

0289

***

0.04

08**

*0.

0298

***

0.04

03**

*0.

0423

***

0.05

70**

*so

urce

(min

utes

)(0

.001

47)

(0.0

0240

)(0

.001

47)

(0.0

0256

)(0

.001

77)

(0.0

0293

)(0

.002

29)

(0.0

0367

)

Tim

e-Pr

od.F

EYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

s

Tim

e-Pr

od.×

1−ρ

k od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

t.×1−

ρk od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Obs

erva

tion

s10

0762

1007

6210

0762

1007

6279

218

7921

896

059

9605

9R

-squ

ared

0.25

80.

934

0.25

80.

933

0.24

60.

933

0.25

40.

933

Not

es:

For

each

spec

ifica

tion

pair,

the

first

colu

mn

regr

esse

slo

gdi

stan

ceon

the

pric

ega

pbe

twee

ntr

adin

gpa

irs

and

the

seco

ndco

lum

n

regr

esse

sth

etr

ansf

orm

edpr

ice

gap(P

k ds−

ρk od

Pk os)/

ρk od

onlo

gdi

stan

cean

dad

diti

onal

lyin

clud

esti

me-

prod

ucta

ndde

stin

atio

nfix

edef

fect

s

mul

tipl

ied

by(1−

ρk od)/

ρk od

inor

der

toco

ntro

lfo

rom

itte

dva

riab

lebi

asdu

eto

the

leve

lof

mar

ket

pow

erco

vary

ing

wit

hdi

stan

ce.

All

regr

essi

ons

incl

ude

tim

e-pr

oduc

tfix

edef

fect

s.C

olum

ns1

to2

incl

ude

dest

inat

ion

fixed

effe

cts.

Col

umns

3-4

repl

ace

the

dest

inat

ion

fixed

effe

cts

and

inte

ract

ions

wit

hde

stin

atio

n-ye

arfix

edef

fect

san

din

tera

ctio

ns.

Col

umns

5-6

incl

ude

cont

rols

for

the

dest

inat

ion

loca

tion

log

popu

lati

onan

dlo

gin

com

epe

rca

pita

.C

olum

ns7-

8ut

ilize

raw

ρk od

esti

mat

esth

atha

veno

tbe

enw

inso

rize

dbe

low

0.2.

Col

umns

9-10

use

defla

ted

exch

ange

rate

sto

inst

rum

ent

for

orig

inpr

ices

inth

ees

tim

atio

nof

ρk od

for

the

subs

ampl

eof

impo

rtgo

ods

whe

rebi

late

rald

eflat

edex

chan

gera

tes

expl

ain

orig

inpr

ice

mov

emen

ts.C

olum

ns11

-12

build

onco

lum

ns9-

10bu

tals

oin

clud

eth

ede

flate

dlo

calc

urre

ncy

oilp

rice

asan

expl

anat

ory

vari

able

inth

epa

ssth

roug

hre

gres

sion

.Col

umns

13-1

4re

mov

ego

ods

whi

chsh

owst

rong

evid

ence

ofpr

oduc

erpr

ice

sett

ing

beha

vior

.Col

umns

15-1

6re

mov

epr

ice

pair

sw

here

the

dest

inat

ion

loca

tion

isle

ssth

an10

0m

inut

esfr

omth

eso

urce

loca

tion

.Tim

e-pr

oduc

tcl

uste

red

stan

dard

erro

rsin

pare

nthe

ses.

*si

gnifi

cant

at10

perc

entl

evel

,**

at5

perc

enta

nd**

*at

1pe

rcen

t.

56

Tabl

e8:

Esti

mat

ing

the

Prim

itiv

eEf

fect

ofD

ista

nce:

Rob

ustn

ess

Che

cks

Nig

eria

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Des

tina

tion

Des

tina

tion

-Tim

eC

ontr

ols

For

Cit

yN

otW

inso

rizi

ngFi

xed

Effe

cts

Fixe

dEf

fect

sSi

zean

dIn

com

ePa

ssTh

roug

hR

ates

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Log

dist

ance

to0.

0858

***

0.07

54*

0.03

31*

0.05

410.

0343

***

0.05

29**

*so

urce

(min

utes

)(0

.018

0)(0

.038

6)(0

.018

2)(0

.041

2)(0

.005

29)

(0.0

0711

)

Tim

e-Pr

od.F

EYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

s

Tim

e-Pr

od.×

1−ρ

k od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

tina

tion

FEYe

sYe

sN

oN

oN

oN

oN

oN

o

Des

t.×1−

ρk od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

t.-Ti

me

FEN

oN

oYe

sYe

sN

oN

oN

oN

o

Des

t.-Ti

me×

1−ρ

k od

ρk od

No

No

No

Yes

No

No

No

No

Obs

erva

tion

s23

084

2308

423

520

2308

423

084

2308

4R

-squ

ared

0.51

80.

964

0.57

90.

975

0.50

40.

999

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

Usi

ngEx

chan

geR

ates

Con

trol

sFo

rO

ilR

emov

ing

Goo

dsw

ith

Rem

ovin

gLo

cati

ons

asIV

’sin

ρk od

Pric

ein

ρk od

Prod

ucer

Pric

eSe

ttin

g<1

00M

inut

esA

way

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Pric

eG

apA

dj.G

apPr

ice

Gap

Adj

.Gap

Log

dist

ance

to0.

0343

***

0.06

26**

*0.

0351

***

0.05

89**

*0.

0274

***

0.04

35**

*so

urce

(min

utes

)(0

.005

29)

(0.0

0929

)(0

.005

41)

(0.0

0884

)(0

.005

08)

(0.0

0895

)

Tim

e-Pr

od.F

EYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

s

Tim

e-Pr

od.×

1−ρ

k od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Des

t.×1−

ρk od

ρk od

No

Yes

No

Yes

No

Yes

No

Yes

Obs

erva

tion

s23

084

2308

422

676

2267

622

212

2221

2R

-squ

ared

0.50

40.

967

0.50

40.

964

0.52

30.

964

Not

es:

For

each

spec

ifica

tion

pair,

the

first

colu

mn

regr

esse

slo

gdi

stan

ceon

the

pric

ega

pbe

twee

ntr

adin

gpa

irs

and

the

seco

ndco

lum

n

regr

esse

sth

etr

ansf

orm

edpr

ice

gap(P

k ds−

ρk od

Pk os)/

ρk od

onlo

gdi

stan

cean

dad

diti

onal

lyin

clud

esti

me-

prod

ucta

ndde

stin

atio

nfix

edef

fect

s

mul

tipl

ied

by(1−

ρk od)/

ρk od

inor

der

toco

ntro

lfo

rom

itte

dva

riab

lebi

asdu

eto

the

leve

lof

mar

ket

pow

erco

vary

ing

wit

hdi

stan

ce.

All

regr

essi

ons

incl

ude

tim

e-pr

oduc

tfix

edef

fect

s.C

olum

ns1

to2

incl

ude

dest

inat

ion

fixed

effe

cts.

Col

umns

3-4

repl

ace

the

dest

inat

ion

fixed

effe

cts

and

inte

ract

ions

wit

hde

stin

atio

n-ti

me

fixed

effe

cts

and

inte

ract

ions

.C

olum

ns5-

6in

clud

eco

ntro

lsfo

rth

ede

stin

atio

nlo

cati

onlo

g

popu

lati

onan

dlo

gin

com

epe

rca

pita

.C

olum

ns7-

8ut

ilize

raw

ρk od

esti

mat

esth

atha

veno

tbe

enw

inso

rize

dbe

low

0.2.

Col

umns

9-10

use

defla

ted

exch

ange

rate

sto

inst

rum

ent

for

orig

inpr

ices

inth

ees

tim

atio

nof

ρk od

for

the

subs

ampl

eof

impo

rtgo

ods

whe

rebi

late

rald

eflat

edex

chan

gera

tes

expl

ain

orig

inpr

ice

mov

emen

ts.C

olum

ns11

-12

build

onco

lum

ns9-

10bu

tals

oin

clud

eth

ede

flate

dlo

calc

urre

ncy

oilp

rice

asan

expl

anat

ory

vari

able

inth

epa

ssth

roug

hre

gres

sion

.Col

umns

13-1

4re

mov

ego

ods

whi

chsh

owst

rong

evid

ence

ofpr

oduc

erpr

ice

sett

ing

beha

vior

.Col

umns

15-1

6re

mov

epr

ice

pair

sw

here

the

dest

inat

ion

loca

tion

isle

ssth

an10

0m

inut

esfr

omth

eso

urce

loca

tion

.Tim

e-pr

oduc

tcl

uste

red

stan

dard

erro

rsin

pare

nthe

ses.

*si

gnifi

cant

at10

perc

entl

evel

,**

at5

perc

enta

nd**

*at

1pe

rcen

t.

57

Tabl

e9:

Reg

ress

ing

Pass

-Thr

ough

Rat

es,C

ompe

titi

vene

ssan

dSu

rplu

sM

easu

res

onD

ista

nce:

Ethi

opia

Pass

-Thr

ough

Com

peti

tive

ness

Rat

ioof

Rat

ioof

Con

sum

er’s

Shar

eR

ates

Inde

xof

Inte

rmed

iary

Profi

tsD

eadw

eigh

tLos

sof

Tota

lSur

plus

Inte

rmed

iari

esto

Con

sum

erSu

rplu

sto

Con

sum

erSu

rplu

s(G

ood-

Loca

tion

)(A

llLo

cati

ons)

(Goo

d-Lo

cati

on)

(Goo

d-Lo

cati

on)

(Goo

d-Lo

cati

on)

Log

dist

ance

betw

een

-0.0

557*

**0.

284*

**0.

0283

***

-0.0

244*

**so

urce

and

dest

inat

ion

(0.0

155)

(0.0

606)

(0.0

0888

)(0

.007

69)

Log

dist

ance

betw

een

-0.2

30**

loca

tion

and

capi

tal

(0.1

06)

Con

stan

t0.

907*

**3.

459*

**0.

161

0.15

6***

0.55

3***

(0.0

913)

(0.6

21)

(0.3

59)

(0.0

533)

(0.0

460)

Obs

erva

tion

s14

1810

014

1814

1814

18R

-squ

ared

0.00

70.

027

0.01

40.

007

0.00

6

Not

es:

Col

umns

regr

ess

log

dist

ance

ones

tim

ates

ofpa

ss-t

hrou

ghra

tes,

com

peti

tive

ness

,the

rati

oof

inte

rmed

iary

profi

tto

cons

umer

surp

lus,

the

rati

oof

dead

wei

ghtl

oss

toco

nsum

ersu

rplu

san

dth

esh

are

ofco

nsum

ersu

rplu

sin

tota

lsur

plus

.Sta

ndar

der

rors

inpa

rent

hese

s.*

sign

ifica

ntat

10pe

rcen

tlev

el,*

*at

5pe

rcen

tand

***

at1

perc

ent.

58

Tabl

e10

:Reg

ress

ing

Pass

-Thr

ough

Rat

es,C

ompe

titi

vene

ssan

dSu

rplu

sM

easu

res

onD

ista

nce:

Nig

eria

Pass

-Thr

ough

Com

peti

tive

ness

Rat

ioof

Rat

ioof

Con

sum

er’s

Shar

eR

ates

Inde

xof

Inte

rmed

iary

Profi

tsD

eadw

eigh

tLos

sof

Tota

lSur

plus

Inte

rmed

iari

esto

Con

sum

erSu

rplu

sto

Con

sum

erSu

rplu

s(G

ood-

Loca

tion

)(A

llLo

cati

ons)

(Goo

d-Lo

cati

on)

(Goo

d-Lo

cati

on)

(Goo

d-Lo

cati

on)

Log

dist

ance

betw

een

-0.1

06*

0.33

6***

0.05

56**

*-0

.065

7***

sour

cean

dde

stin

atio

n-0

.054

4-0

.116

-0.0

0654

-0.0

186

Log

dist

ance

betw

een

-0.7

07**

*lo

cati

onan

dca

pita

l-0

.169

Con

stan

t1.

065*

**8.

004*

**-0

.010

3-0

.188

***

0.84

1***

-0.3

39-1

.012

-0.7

3-0

.038

8-0

.119

Obs

erva

tion

s48

936

489

489

489

R-s

quar

ed0.

011

0.15

0.01

90.

085

0.02

3

Not

es:

Col

umns

regr

ess

log

dist

ance

ones

tim

ates

ofpa

ss-t

hrou

ghra

tes,

com

peti

tive

ness

,the

rati

oof

inte

rmed

iary

profi

tto

cons

umer

surp

lus,

the

rati

oof

dead

wei

ghtl

oss

toco

nsum

ersu

rplu

san

dth

esh

are

ofco

nsum

ersu

rplu

sin

tota

lsur

plus

.Sta

ndar

der

rors

inpa

rent

hese

s.*

sign

ifica

ntat

10pe

rcen

tlev

el,*

*at

5pe

rcen

tand

***

at1

perc

ent.

59

Tabl

e11

:Reg

ress

ing

Prod

uctA

vaila

bilit

yon

Dis

tanc

e:Et

hiop

ia

(1)

(2)

(3)

Prod

uctA

vaila

bilit

yat

Leve

lofT

ime-

Prod

uct-

Loca

tion

(1=P

rice

Rec

orde

d,2=

No

Pric

eR

ecor

ded)

Log

dist

ance

to-0

.097

0***

-0.0

941*

**-0

.100

***

sour

ce(m

inut

es)

(0.0

0287

)(0

.002

56)

(0.0

0409

)

Con

stan

t1.

404*

**1.

386*

**1.

429*

**(0

.015

4)(0

.015

3)(0

.024

0)

Tim

e-Pr

oduc

tFE

No

Yes

Yes

Des

tina

tion

FEN

oN

oYe

s

Obs

erva

tion

s14

1920

1419

2014

1920

R-s

quar

ed0.

027

0.12

00.

234

Not

es:

Col

umns

1th

roug

h3

regr

ess

log

dist

ance

onpr

oduc

tav

aila

bilit

yat

the

mon

thly

Tim

ePe

riod

-Pro

duct

-Loc

atio

nle

vel

byor

dina

ryle

ast

squa

res.

Sam

ple

rest

rict

edto

prod

uct-

loca

tion

pair

sfo

rw

hich

the

prod

ucti

sob

serv

edin

atle

asto

nem

onth

.C

olum

n2

incl

udes

prod

uct-

mon

thfix

edef

fect

s.C

olum

n3

incl

udes

both

prod

uct-

mon

thfix

edan

dde

stin

atio

nfix

edef

fect

s.Ti

me-

prod

uct

clus

tere

dst

anda

rder

rors

inpa

rent

hese

s.*

sign

ifica

ntat

10pe

rcen

tle

vel,

**at

5pe

rcen

tan

d**

*at

1pe

rcen

t.

60

Tabl

e12

:Reg

ress

ing

Prod

uctA

vaila

bilit

yon

Dis

tanc

e:N

iger

ia

(1)

(2)

(3)

Prod

uctA

vaila

bilit

yat

Leve

lofT

ime-

Prod

uct-

Loca

tion

(1=P

rice

Rec

orde

d,2=

No

Pric

eR

ecor

ded)

Log

dist

ance

to-0

.040

7***

-0.0

468*

**-0

.013

1so

urce

(min

utes

)(0

.004

84)

(0.0

0446

)(0

.020

7)

Con

stan

t1.

011*

**1.

049*

**0.

939*

**(0

.031

1)(0

.027

7)(0

.127

)

Tim

e-Pr

oduc

tFE

No

Yes

Yes

Des

tina

tion

FEN

oN

oYe

s

Obs

erva

tion

s31

436

3143

631

436

R-s

quar

ed0.

004

0.28

20.

384

Not

es:

Col

umns

1th

roug

h3

regr

ess

log

dist

ance

onpr

oduc

tav

aila

bilit

yat

the

mon

thly

Tim

ePe

riod

-Pro

duct

-Loc

atio

nle

vel

byor

dina

ryle

ast

squa

res.

Sam

ple

rest

rict

edto

prod

uct-

loca

tion

pair

sfo

rw

hich

the

prod

ucti

sob

serv

edin

atle

asto

nem

onth

.Col

umn

2in

clud

espr

oduc

t-m

onth

fixed

effe

cts.

Col

umn

3in

clud

esbo

thpr

oduc

t-m

onth

fixed

and

dest

inat

ion

fixed

effe

cts.

Tim

e-pr

oduc

tcl

uste

red

stan

dard

erro

rsin

pare

nthe

ses.

*si

gnifi

cant

at10

perc

ent

leve

l,**

at5

perc

ent

and

***

at1

perc

ent.

61