metadata: authors: 0: author: de Astarloa,Bernardo Díaz
1: author: Pkhikidze,Nino
count: Africa,Europe,Kenya,Madagascar,Nigeria,Rwanda,Tanzania,Georgia,World
docna: 0: docna: Intra-national Trade Costs in Low- and
            Middle-Income Countries
docty: Policy Research Working Paper
abstracts: cdata!: Casual observation suggests that
            intra-national trade costs remain high in low- and
            middle-income countries. Precisely estimating them is
            crucial for guiding policies aimed at optimizing economic
            efficiency within a country's borders. This paper
            estimates intra-national trade costs for six low- and
            middle-income countries in Africa and Eastern Europe: Kenya,
            Madagascar, Nigeria, Rwanda, Tanzania, and Georgia. The
            analysis exploits unit-level price data collected by
            countries&#x2019; national statistical offices for
            consumer price index calculation purposes. It applies the
            price differential methodology, which aims at estimating
            trade costs while accounting for the possibility of
            imperfect competition among intermediaries, controlling for
            spatial variation in markups. The findings show that the
            intra-national trade costs in the sample of countries are
            between 2.5 and 14 times larger than previous estimates for
            the United States using the same methodology.
display_title: Intra-national Trade Costs in Low-
            and Middle-Income Countries
listing_relative_url: /research/2024/06/34332635/intra-national-trade-costs-low--middle-income-countries
topicv3: Transport and Economic Geography m1326235 1813,Transport Economics m1326662 162,Trade Facilitation and Logistics m1327174 772,Road Transport m1327629 1801,Trade Linkages, Spillovers and
            Connectivity m1504235 2601
publishtoextweb_dt: 2024-06-03T00:00:00Z
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 36
author_keys: 0: author_key: NA
1: author_key: 1816486
model_extractions: Policy Research Working Paper 10789 Intra-national Trade Costs in Lowand Middle-Income Countries _Bernardo Díaz de Astarloa Nino Pkhikidze_ Transport Global Practice June 2024
Policy Research Working Paper 10789

# **Abstract**

Casual observation suggests that intra-national trade costs remain high in low- and middle-income countries. Precisely estimating them is crucial for guiding policies aimed at optimizing economic efficiency within a country’s borders. This paper estimates intra-national trade costs for six lowand middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania, and Georgia. The analysis exploits unit-level price data collected by countries’ national statistical offices for consumer price index calculation purposes. It applies the price differential methodology, which aims at estimating trade costs while accounting for the possibility of imperfect competition among intermediaries, controlling for spatial variation in markups. The findings show that the intra-national trade costs in the sample of countries are between 2.5 and 14 times larger than previous estimates for the United States using the same methodology.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bernardo. diazastarloa@economicas.uba.ar and npkhikidze@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Intra-national Trade Costs in Low- and Middle-Income Countries<sup>∗</sup>

Bernardo D ́ıaz de Astarloa<sup>†</sup> Nino Pkhikidze<sup>‡</sup>

**_Keywords:_** Trade costs, Price differential methodology, Travel distance, Kenya, Madagascar, Nigeria, Rwanda, Tanzania, Georgia.

**_JEL classi ication:_** R12, F1, O1, D02.

> ∗This paper was prepared as background research for the World Bank’s Shrinking Economic Distance Flagship Report. Ignacio Caro Sol ́ıs provided superb research assistance. For help and assistance in accessing price data, we thank the World Bank’s country teams and the national statistics offices of the research countries. We have benefited greatly from discussions with Matias Herrera Dappe, Roman Zarate, Theophile Bougna, Alejandro Molnar, Mathilde Lebrand, Aiga Stokenberga, and the participants of World Bank’s Workshop on Shrinking Economic Distance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.

> †Inter-disciplinary Institute of Political Economy, University of Buenos Aires. Email: `bernardo.diazastarloa@economicas.uba.ar` .

> ‡World Bank. Email: `npkhikidze@worldbank.org` .
# **1 Introduction**

International trade costs have fallen drastically in the last decades. However, intra-national (i.e. within-country) trade costs still remain high in low- and middle-income countries. Quantifying trade costs and their components is crucial since lower trade costs can increase efficiency, improve the welfare of households and firms, and drive structural transformation through increased trade opportunities (Sotelo, 2019; Fajgelbaum and Redding, 2014; Costinot and Donaldson, 2016). By understanding the costs associated with domestic trade, policy makers can identify barriers hindering market access and formulate targeted policies to reduce these obstacles.

Low- and middle-income countries are often characterized by many barriers affecting the free flow of goods within their borders, such as poor road conditions, old transport fleet, and poor logistics. These affect trade flows and limit the gains of reduced international trade costs and increased regional integration. Remote regions tend to be especially isolated often due to high travel costs, including high travel time. In turn, higher transportation costs are often passed on to consumers in the form of increased prices. Therefore, households and firms in these areas face higher spatial price gaps, which limits the variety of goods they can choose from and reduces disposable income (Aggarwal, 2018; Martin, Mayneris and Theophile, 2020).

Despite the recent isolated attempts to study the extent of intra-national trade costs,<sup>1</sup> there is a gap in the literature on the cross-country comparison of within-country trade costs.

In this paper we estimate intra-national trade costs for six low- and middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania and Georgia. We exploit microdata collected by countries’ national statistical offices (NSO) for consumer price index (CPI) calculation purposes.

There are two main estimation-based approaches in the literature to estimate trade costs, the gravity approach and the price differential approach (Co ̧sar, 2022). The gravity approach looks at the impact of varying travel distance or travel time on trade flows, which are inversely proportional to each other.<sup>2</sup> Using gravity estimation to estimate the impact of travel distance on trade between Colombian cities, Duranton (2015) estimates that increasing the travel distance between them by 10% decreases trade flows by 7% and weight by 6%.

The price differential method combines data on prices from different markets or cities with variables that are thought to affect the cost of shipping goods between locations. Using these data, one can compute price differences across space (spatial price gaps) and assess how different cost shifters affect these differences. Distance between where the

> 1See, for example, Atkin and Donaldson (2015) on the estimates for Ethiopia, Nigeria, and the U.S., and Bougna, Ewane, Jones and Kondylis (2020) on Rwanda.

> 2Head and Mayer (2014) summarize the literature on gravity equations and provide an extensive overview of best-practice methods and a step-by-step cookbook.

2
product is produced or sourced from (origin locations) and where it is finally sold by retailers (destination locations) typically summarizes the contribution of transport costs to overall trade costs. However, there may be other variables that can be correlated with distance which can also affect trade costs, such as the extent of competition between logistics operators, distributors or retailers (Atkin and Donaldson, 2015) or within-country “border-effects” (Borraz, Cavallo, Rigobon and Zipitria, 2016).

In this paper, we follow the price-differential approach and apply the methodology developed by Atkin and Donaldson (2015), which aims at estimating trade costs while taking into account the possibility of imperfect competition among intermediaries, controlling for spatial variation in markups. We find that intra-national trade costs in our sample of countries are between 2.5 and 14 times larger than previous estimates for the U.S. by Atkin and Donaldson (2015) using the same methodology.

This paper relates to a growing literature that attempts to estimate intra-national trade costs. Given its historically high transport costs, Africa, and in particular SubSaharan Africa, has been the focus of several papers studying trade and transport prices. Bougna et al. (2020) estimate intra-national trade costs in Rwanda using data on rural markets and find them to be 10 times higher than in the U.S. They also find that markups are higher in locations with fewer traders. Mayneris, Martin and Theophile (2020) document spatial differences in the cost of living across Ethiopian cities and find that more remote cities show significantly higher prices, less product availability, and a higher cost of living. Porteous (2019) takes a different methodological approach to Atkin and Donaldson (2015) to estimate intra-national trade costs in Sub-Saharan Africa. The paper focuses on staple cereal grains traded between 230 large regional hubs in 42 countries, and quantifies trade costs by estimating a structural model that features competitive traders (i.e., abstracting from markups), grain consumption, storage, and trade. The results in Porteous (2019) suggest trade costs that are qualitatively similar but higher in magnitude to those estimated in Atkin and Donaldson (2015), although the former potentially include nondistance dependent factors. Specifically, the median trade cost is over 5 times higher than international benchmark freight rates. In a counterfactual where trade costs on overland links are reduced to levels similar to the rest of the world, grain prices fall 30% on average and aggregate welfare increases by 1.6% of GDP. Teravaninthorn and Raballand (2009) estimate trade costs using trucker surveys in the U.S. and along several major transport corridors in Sub-Saharan Africa and find 1.88 to 3.28 times higher trade costs compared to the U.S., which are lower than the results of Atkin and Donaldson (2015). In terms of the trade costs in high-income countries, Agnosteva, Anderson and Yotov (2019) use gravity model techniques to understand the effects of intra-regional, inter-regional and international frictions on trade flows in Canadian provinces, and find that further and less-developed regions tend to have relatively low internal frictions and large border effects, while economically more developed and central regions show relatively low border barriers.

3
# **2 Data**

Our sample covers 6 low- and middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania and Georgia. In order to apply the methodology developed by Atkin and Donaldson (2015), we require two sets of data for each country. First, we need price data on narrowly-defined product varieties, with observations spanning multiple locations within each country at a monthly frequency over several years. Second, we need the locations in which product varieties were produced or from which they were imported. The latter allows for the identification of trading pairs and the corresponding direction of trade from origin to destination locations. In this section, we briefly describe how we construct these data. Appendix A includes additional details about the data preparation process.

## **2.1 Price data**

Our price data come from CPI microdata provided to us by NSOs in each country in our sample. NSOs collect price data from a predefined list of product varieties (including goods and services), designed to capture the consumption basket of a typical household, at specified locations. Apart from the monthly price quote, the information provided to us by NSOs includes a product description, in some cases including the brand and presentation (e.g., “Coca-Cola, canned, 500ml” or “Body lotion, Nivea, 200ml”), the name of the location where the price was collected, and, occasionally, the name of a producer or the country from where the product was imported.

We restrict the choice of products to narrowly defined varieties, i.e., those for which we can observe a detailed product description and a brand. For most countries in our sample, prices are at the town or city level, with the exception of Rwanda, where prices are collected at the (more aggregated) district level.

The analysis in section 4 uses clean samples of price data, obtained by applying a cleaning algorithm to the raw data. Moreover, as in Atkin and Donaldson (2015), we convert all prices to constant U.S. dollars by deflating them using inflation rates at origin locations and bilateral nominal exchange rates between local currencies and the U.S. dollar. The cleaning and deflating procedures are described in Appendix A.

## **2.2 Products’ origin locations**

The estimation strategy proposed by Atkin and Donaldson (2015) depends on precisely identifying locations where a product is produced or imported. To do this, we search over the Internet to determine which manufacturers produce each brand and where their factories are located. For imported goods, we identify the main ports of entry in each 4
country and their locations to assign imported products to them.<sup>3</sup> For landlocked countries, we identify ports at neighboring countries through which most imports come (e.g., in some cases, countries are in conflict with some bordering countries and imports do not come through them). Then, we identify the border crossings that are closest to those ports. Finally, we geocode all locations to determine their latitude and longitude. For each product, we define a “trading pair” as an origin and a destination location.

Conditional on being narrowly defined, we consider products that are broadly available across locations and periods in our sample. Specifically, we restrict samples to products for which prices are observed in the products’ source and destination locations in more than six months.<sup>4</sup> Table 1 describes the main features of the resulting samples we consider for each country. The number of products ranges from 9 in Madagascar to 43 in Rwanda. The full list of products for each country, including their brands, presentations, and manufacturers, is included in the Appendix A.2. The number of destination locations (markets, cities, or districts) ranges from 6 in Georgia to 38 in Nigeria. Figure 1 shows the locations for each country in our sample, as well as the major roads connecting them, and indicates the location where a product is manufactured or imported, on which we elaborate in section 2.2.<sup>5</sup> Table 1 also shows the periods covered by each country sample. With the exception of the Nigeria sample, which starts in January 2001, all samples start after January 2010. The longest panel corresponds to Madagascar and covers the period from January 2010 to April 2021. The shortest panel is the one for Kenya, covering from October 2018 to January 2022.

Average origin prices, price gaps between origin and destination locations, and distances, are reported in Table 2.

Table 1: Main features and coverage of the data

|Country|No. of products|Origins|Destinations|Type of locations|Start period|End period|
|---|---|---|---|---|---|---|
|Kenya|20|10|32|Cities|Oct 2018|Jan 2022|
|Madagascar|9|3|7|Major cities|Jan 2010|Apr 2021|
|Nigeria|25|5|38|Cities|Jan 2001|Jul 2010|
|Rwanda|43|4|13|Districts|Jan 2013|Dec 2020|
|Tanzania|32|5|20|Cities|Jan 2012|Apr 2021|
|Georgia|21|4|6|Cities|Jan 2012|Dec 2020|

Notes: this table describes the main features of the datasets for each country in our sample. Source: authors’ elaboration based on CPI data from countries’ NSO.

> 3Our main sources are Logcluster, a wiki with detailed logistics information about each country, and World Port Source.

> 4As discussed in section 3.2, estimation of pass-through rates relies on variation of prices over time for each product-destination pair.

> 5The data underlying the maps are taken from the Global Roads Inventory Project (GRIP), available at `https://www.globio.info/download-grip-dataset` .

5
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## **2.3 Distance between locations**

The baseline measure of distance we employ is geodesic distance, that is, the shortest distance between two locations. Table 2 reports the mean, minimum and maximum geodesic distance between origin and destination locations.

Alternatively, following Atkin and Donaldson (2015), we use two additional measures to control for quantity and quality differences in countries’ road networks. The first, which aims at capturing quantity differences, is the distance between two locations along the quickest route, as calculated by Google Maps. Figure 1 shows the road network in each country. The second, which should capture quality differences, is the time it takes to complete a trip between two locations following the quickest route, also from Google Maps.

Table 2: Descriptive statistics

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Mean origin price|2.306|0.922|0.902|2.328|0.898|2.577|
||(1.833)|(0.451)|(1.223)|(2.993)|(0.840)|(2.775)|
|Mean destination price|2.357|0.918|0.960|2.302|0.910|2.584|
||(1.984)|(0.520)|(1.386)|(2.980)|(1.001)|(2.729)|
|Mean price gap (trading pairs)|0.052|-0.004|0.058|-0.025|0.013|0.007|
||(0.454)|(0.264)|(0.433)|(0.480)|(0.662)|(0.454)|
|Geodesic distance (km)|279|414|597|80|493|170|
||(208)|(224)|(292)|(35)|(276)|(79)|
|Log distance to origin location|5.305|5.858|6.214|4.273|5.943|5.001|
||(0.870)|(0.605)|(0.691)|(0.499)|(0.927)|(0.551)|
|Min. distance (trading pairs, km)|20.6|114.3|34.2|7.9|2.2|50.9|
|Max. distance (trading pairs, km)|915.8|874.9|1,254.8|152.9|1,362.6|318.5|
|Number of trading pairs|134|18|153|30|85|20|
|Observations|9,280|5,997|48,379|26,215|50,583|6,173|

Notes: This table presents descriptive statistics for our sample of countries. The first and second rows report the mean price at the factory location (or location of the port of entry) and the mean price at destinations. The third row reports price gaps using data form trading pairs. All prices are deflated by inflation at the origin location and then converted to U.S. dollars using the exchange rate prevailing during the base period. The fourth row reports mean geodesic distance between trading pairs measured in kilometers. The fifth row reports the average distance to the origin location (in log kilometers). The fifth and sixth rows report the minimum and maximum distances between trading pairs, respectively. The seventh row reports the number of trading pairs. The last row reports the number of observations. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **3 Methodological framework**

The theoretical framework underlying our estimation strategy is based on the model developed by Atkin and Donaldson (2015). In this section, we outline the main elements 7
and intuition of the theory. Interested readers can find additional details and proofs in that paper.

# **3.1 Theory**

The economy is characterized by multiple markets (or locations) indexed by _d_ and a single consumption good. The inverse demand for this product in each market is given by _P_ ( _Qd, Dd_ ), where _Qd_ is quantity demanded and _Dd_ characterizes demand conditions in location _d_ . Production of the good can take place at home or abroad at a single factory. What matters for the model is the _domestic_ origin of the product, denoted _o_ . If produced at home, _o_ indicates the factory location; if imported, _o_ indicates the port or border crossing through which the product entered the country.

Identical domestic intermediaries buy the product in wholesale markets at the origin location at price _Po_ and sell it to consumers in _d_ , effectively acting as retailers as well. Intermediaries’ trading technology is characterized by their total costs, _Cd_ ( _qd_ ), where _qd_ is the quantity traded from _o_ to _d_ . In the extension of the model to many products, we further assume that each product is sourced from one location only, so that _o_ can be omitted from the subscript in _qd_ . Total costs include a fixed cost _Fd_ and a marginal cost _cd_ . The marginal cost includes the cost of buying the good at the origin, given by its price _Po_ , and the cost of trading the food from _o_ to _d_ , given by _τ_ ( **X** _d_ ), where **X** _d_ is a vector of cost shifters, including distance and, potentially, other factors such as road quality and destination-specific retail costs. Total costs are then given by An intermediary choose quantities _qd_ to maximize profits in an imperfectly competitive market, subject to the perceived response of other intermediaries. There is no entry of intermediaries and, under certain assumptions, the first-order conditions of the intermediary’s optimization problem imply that price gaps between the origin and the destination locations are given by: where _μd_ is the mark-up charged by the intermediary at location _d_ , which depends on trade costs, demand conditions at _d_ , and a competitiveness index _φd_ , which in turn depends on the number of intermediaries and the extent of strategic interactions among them.

From (1), variation in trade costs shifters implies: where _ρod ≡_ 1 + ( _∂μ/∂τod_ ) is the pass-through rate, that is, the effect of trade costs on 8
prices. It can be shown that, in this framework, the pass-through rate depends on the elasticity of the slope of the demand schedule and competitive conditions ( _φd_ ). In our empirical analysis in Section 4, the main trade cost shifter of interest _xod_ is distance. To simplify the exposition, in what follows we set **X** _od_ = _xod_ .

Equation (2) illustrates three sources of bias that can arise when aiming at identifying trade costs from spatial price gaps: (1) incomplete pass-through ( _ρod_ = 1), (2) variation in demand conditions across locations, and (3) variation in the extent of competition across locations. Under additional assumptions, the pass-through rate can control for this sources of bias. In particular, under the assumption that the pass-through rate is independent of quantities _qd_ , equation (2) simplifies to where _ad_ is a local demand shifter. Under this specification, _ρod_ and the local demand shifter _ad_ are sufficient to control for local competitive and demand conditions.

The following subsection explains how this theoretical framework can guide the specification of empirical models to estimate the effect of distance on trade costs exploiting origin-destination price gaps, controlling for other factors that correlate with distance and could affect price differentials through variation of markups across locations. Section 4 describes the methods used to estimate these models.

# **3.2 Estimation strategy**

The estimation strategy exploits price data on multiple products _k_ sourced from locations _o_ and selling at locations _d_ at different time periods _t_ . With variation across products, destinations, origin locations, and time, equation (3) implies the following relationship between product prices at the origin and the destination: where we have further assumed that the pass-through rate remains fixed over time.<sup>6</sup> The latter assumption holds if local demand elasticities and competitive conditions (e.g., entry of new intermediaries) are constant over time.

In equation (4), trade costs _τ_ ( _x_<sup>_k_</sup> _odt_<sup>),pass-throughrates</sup><sup>_ρk_</sup> _od_<sup>anddemandshifters</sup><sup>_ak_</sup> _dt_ are not observable. The strategy aims at identifying _τ_ ( _x_<sup>_k_</sup> _odt_<sup>)and</sup><sup>_ρk_</sup> _od_<sup>basedonvariation</sup> in prices over time and space, and distance between locations, while treating _a_<sup>_k_</sup> _dt_<sup>asun-</sup> observed heterogeneity and capturing it through a set of fixed effects. For this reason, precisely identifying origin locations is key for the accuracy of the estimates.

The estimation procedure involves two steps, as follows:

> 6It is infeasible to estimate separate pass-through rates for each period since the number of parameters to estimate would equal the number of observations.

9
1. _Pass-through rates_ . In the first step, we produce estimates of pass-through rates _ρ_ ˆ<sup>_k_</sup> _od_<sup>byregressingdestinationpricesonoriginprices,controllingfortradecostsand</sup> demand shifters. Specifically, we estimate the parameters of the following model:

where _γd_<sup>_k_areproduct-destinationfixedeffects,</sup><sup>_γ_</sup> _d_<sup>_kt_isaproduct-destinationlinear</sup> time trend, and _ε_<sup>_k_</sup> _odt_<sup>isanunobservederrortermthatcapturesshockstotradecosts</sup> and local demand shifters.

2. _Trade costs_ . In the second step, we recover estimates of trade costs _τ_ ˆ( _xodt_ ) using pass-through estimates from the first step. Given (unbiased) estimates _ρ_ ˆ<sup>_k_</sup> _od_<sup>,wecan</sup> manipulate equation (4) to derive and expression for an “adjusted price gap”: Computing this “adjusted price gap” allows for the estimation of _τ_ ( _x_<sup>_k_</sup> _odt_<sup>)when</sup> markups are positive and vary across locations, i.e., _ρ_<sup>_k_</sup> _od_<sup>=1.Localdemandcon-</sup> ditions are controlled for by including fixed effects interacted with the adjustment factor, and trade costs are decomposed as _τ_ ( _x_<sup>_k_</sup> _odt_<sup>) =</sup><sup>_f_(</sup><sup>_xod_)+</sup><sup>_χ_</sup> _odt_<sup>_k_, where</sup><sup>_χ_</sup> _odt_<sup>_k_captures</sup> unobservable time-varying factors that affect trade costs. The estimating model then becomes where _αt_<sup>_k_areproduct-timefixedeffects,</sup><sup>_αd_aredestinationfixedeffects,and</sup><sup>_ξ_</sup> _odt_<sup>_k_</sup> captures unobserved shocks to local demand and trade costs. In Section 4.3, we specify the function _f_ ( _xod_ ) that we take to the data to recover the effect of distance on trade costs.

# **4 Estimates of intra-national trade costs**

This section presents estimation results to quantify the relationship between distance and intra-national trade costs in our sample of low- and middle-income countries. Using the notation introduced in the previous section, we attempt to estimate _∂τ_ ( _xod_ ) _/∂xod_ , where _xod_ is one of the different measures of distance described in section 2.3. Ultimately, we apply the strategy described in section 3.2, which aims at estimating this relationship using data about price gaps across locations, controlling for variation in markups across locations. Before turning to that exercise, for benchmarking purposes, we first present estimates that abstract from variation in markups.

10
# **4.1 Spatial price gaps**

Under the theoretical framework described in section 3.1, if markups did not vary across locations the pass-through rate would be constant across trading pairs and equal to one, i.e. _ρ_<sup>_k_</sup> _od_<sup>= 1.Fromequation(3),thisrestrictionimplies</sup> We estimate equation (8) through OLS imposing a linear relationship between distance and trade costs, i.e. _τ_ ( _xodt_ ) = _βxod_ + _γt_<sup>_k_+</sup><sup>_χk_</sup> _odt_<sup>,where</sup><sup>_γ_</sup> _t_<sup>_k_areproduct-timefixedeffects</sup> that capture economy-wide product-specific shocks to trade costs and _χ_<sup>_k_</sup> _odt_<sup>capturesother</sup> components of trade costs that are unrelated to distance. With the assumption of perfect competition, differences in prices across locations should reflect trade costs and, therefore, we would expect _β_<sup>ˆ</sup> to be positive. A negative _β_<sup>ˆ</sup> could arise in the more general case of imperfect competition with variable markups. Yet another potential explanation for observing price gaps that decrease with distance is that local distribution or logistic costs in the destination are higher at destination locations that are closer to the origin. Results are presented in Table 3. Except for Rwanda, the results show a positive relationship between origin-destination price gaps and distance.

Table 3: Estimates of the relationship between distance and trade costs using origindestination price gaps

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Distance to origin (log km)|0_._070<sup>_∗∗∗_</sup>|0_._071<sup>_∗∗∗_</sup>|0_._030<sup>_∗∗∗_</sup>|_−_0_._082<sup>_∗∗∗_</sup>|0_._026<sup>_∗∗∗_</sup>|0_._028<sup>_∗∗_</sup>|
||(0_._006)|(0_._005)|(0_._003)|(0_._005)|(0_._002)|(0_._009)|
|Observations|9_,_280|5_,_997|48_,_379|26_,_215|50_,_583|6_,_173|
|Adj. _R_<sup>2</sup>|0_._258|0_._408|0_._249|0_._344|0_._238|0_._585|

_Notes_ : This table presents the relationship between distance and price gaps across regions, under the assumption that markups do not vary across locations. All regressions use the (unadjusted) price gap between trading pairs and include product-time fixed effects. Standard errors in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **4.2 Pass-through rates**

The first step of the procedure for estimating intra-national trade costs outlined in section 3.2 involves estimating pass-through rates. Pass-through rates are informative of how (exogenous) price changes at origin locations translate into (endogenous) price changes in destination locations, in the way described by equation (4), which we reproduce here: Recall that, in equation (9), _a_<sup>_k_</sup> _dt_<sup>arenon-observabledemandshifters.FollowingAtkin</sup> 11
and Donaldson (2015), we specify equation 9 including a set of fixed effects such that variation in trade costs and local demand shifters that are product-specific within a destination over time is orthogonal to price changes at the origin: where _γd_<sup>_k_areproduct-destinationfixedeffects,</sup><sup>_γ_</sup> _d_<sup>_kt_isaproduct-destinationlineartime</sup> trend, and _ε_<sup>_k_</sup> _odt_<sup>isanunobservederrortermthatcapturesshockstotradecostsandlocal</sup> demand shifters. In order to obtain unbiased OLS estimates of _ρ_<sup>_k_</sup> _od_<sup>, prices at the origin</sup><sup>_P k_</sup> _ot_ should not be correlated with destination-specific and time-varying shocks to trade costs or demand captured by _ε_<sup>_k_</sup> _odt_<sup>.AsnotedbyAtkinandDonaldson(2015),thisislikelyto</sup> hold for imported goods, whose prices are mostly determined abroad, or if prices at the origin are either pinned down by production costs at the factory gate or set on the basis of demand shocks at the origin. This condition would not hold, however, in the presence of economywide or spatially correlated demand shocks that are product-specific. In this case, estimates of _ρ_<sup>_k_</sup> _od_<sup>wouldbebiasedupwards.</sup> Figure 2 displays our results. For each country in our sample, the figure plots estimates of _ρ_<sup>_k_</sup> _od_<sup>forallproducts</sup><sup>_k_andlocations</sup><sup>_d_againstthecorrespondingorigin-to-destination</sup> distance _xod_ , together with a non-parametric estimate of the relationship between the pass-through rate and distance. The figure reveals substantial variation in the magnitude of pass-through rates, although there are some common patterns. First, few estimates violate the theoretical restriction that the pass-through rate be positive. As detailed in Table 4, between 75% and 85% of our estimates in any country are positive. Second, there is substantial evidence of incomplete pass-through, which suggests imperfect competition. The average pass-through rate is below one in all countries and the fraction of estimates below one ranges from 77% in Tanzania to 94% in Georgia and Nigeria. Third, in three out of six countries the pass-through rate shows a statistically significant negative relationship with distance, as the fourth row of Table 4 indicates. In these countries (Georgia and Nigeria), the pass-through rate is lower in locations that are farther away from the product’s origin location. In those countries where the negative relationship holds, it tends to be fairly monotonic, as Figure 2 shows.

12
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Table 4: Summary statistics of pass-through estimates

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Average pass-through|0.543|0.295|0.369|0.396|0.552|0.377|
||(0.751)|(0.646)|(2.568)|(0.882)|(1.247)|(0.426)|
|% of estimates below 1|83.2|82.4|94.2|84.0|76.7|94.3|
|% of estimates below 0|15.1|25.5|20.5|21.2|24.0|18.2|
|Distance to origin location|0.082|0.115|-0.012|-0,075|0.029|-0.099|
||(0.057)|(0.149)|(0.128)|(0.091)|(0.057)|(0.081)|
|Observations|232|51|832|368|562|88|

_Notes_ : This table presents summary statistics of pass-through estimates. The third row includes estimates of a linear relationship between the pass-through rate and (log) distance to origin. For average pass-through rates, standard deviations indicated in parenthesis. For the coefficient on distance to origin coefficient, standard errors indicated in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **4.3 Estimates of trade costs using pass-through-adjusted price gaps and distance**

We now turn to the estimation of trade costs under the more general case of imperfect competition. With imperfect competition, price gaps can vary across trading pairs not only because trade costs vary with distance, but also because intermediaries can charge different markups in different locations, i.e. there is spatial variation in markups. Atkin and Donaldson (2015) emphasize three mechanisms by which markups can vary with distance:

1. More remote locations face higher trade costs, which, all else equal (i.e. demand conditions), induces traders to choose lower markups since they face higher marginal costs.

2. More remote locations are characterized by fewer traders and less competition, which induces traders to choose higher markups.

3. Traders in more remote locations may face different demand conditions, which induces correlation between distance and markups.

In their results, Atkin and Donaldson (2015) find that ignoring variation in markups can considerably underestimate intra-national trade costs. Their methodology to account for spatial variation in markups in the estimation of trade costs is based on using the pass-through rates estimated above, _ρ_ ˆ<sup>_k_</sup> _od_<sup>,toadjustpricegapsacrosstradingpairs,as</sup> explained in section 3.2. Assuming a linear relationship between distance and trade costs, 14
the resulting model that we take to the data is: In this equation, _β_ captures the effect of distance on trade costs, _αt_<sup>_k_areproduct-time</sup> fixed effects, _αd_ are destination fixed effects, and _ξodt_<sup>_k_capturesunobservedshockstolocal</sup> demand and trade costs.<sup>7</sup> Panel A in Table 5 presents estimates of _β_ for all countries in our sample, the main results of the paper. All specifications include product-time and destination fixed effects, and standard errors are clustered at the product-time level. The table includes estimates using different measures of distance, but we start discussing the baseline estimates that consider geodesic distance. Except for Georgia and Madagascar, all coefficients are statistically significant. Once we take into account the possibility of imperfect competition, moreover, all coefficients are positive (with the exception of Georgia, where the coefficient is not statistically different from zero), indicating that trade costs increase with distance. Kenya shows the highest coefficient, Tanzania the lowest. Compared to the estimates in Table 3, adjusting for markups does not produce a common pattern across countries. For Kenya, and Nigeria, the coefficients are higher than those in the unadjusted regressions. For Rwanda, the coefficient turns positive. For Madagascar and Tanzania, the coefficients are now lower.

Our baseline estimates imply significant variation in the magnitude of trade costs across countries. To illustrate this, we compute the _ad valorem_ trade cost equivalent implied by _β_ ˆ, which is defined as where _x_<sup>min</sup> _od_ and _x_<sup>max</sup> _od_ are the (log) distances between the least and most remote locations away from the source location, respectively, and _P d_ is the average destination price in the sample. This measure quantifies the additional trade cost implied by trading the good in the most remote location compared to the least remote location, as a fraction of the average final consumer price. In our sample, the average minimum distance between origin and destination locations is 38 kilometers and the average maximum distance is 813 kilometers. Using these values, Figure 3 shows the _ad valorem_ equivalent trade cost of trading a good between two locations 775 kilometers apart implied by the coefficients in Table 5.<sup>8</sup> Excluding Georgia, for which we obtain a coefficient equal to zero, these _ad valorem_ equivalents range from 3.2% in Rwanda to 19.2% in Kenya.

> 7As in Atkin and Donaldson (2015), for our baseline estimates we winsorize pass-through estimates that are below 0.2.

> 8We use the geodesic distance coefficient for the results presented in the figure.

15
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Table 5: Estimates of the effect of distance on trade costs

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|_Panel A: All goods_|||||||
|Geodesic distance to origin (log km)|0_._148<sup>_∗∗∗_</sup>|0_._024|0_._052<sup>_∗∗∗_</sup>|0_._024<sup>_∗_</sup>|0_._016<sup>_∗∗∗_</sup>|0_._000|
||(0_._014)|(0_._028)|(0_._007)|(0_._011)|(0_._004)|(0_._029)|
|Adjusted _R_<sup>2</sup>|0_._980|0_._931|0_._849|0_._971|0_._831|0_._990|
|Driving distance to origin (log km)|0_._127<sup>_∗∗∗_</sup>|0_._028|0_._052<sup>_∗∗∗_</sup>|_−_0_._0010|0_._023<sup>_∗∗∗_</sup>|_−_0_._047|
||(0_._013)|(0_._026)|(0_._007)|(0_._009)|(0_._004)|(0_._035)|
|Adjusted _R_<sup>2</sup>|0_._98|0_._931|0_._849|0_._971|0_._832|0_._99|
|Time along best route (log hrs)|0_._134<sup>_∗∗∗_</sup>|0_._048|0_._060<sup>_∗∗∗_</sup>|0_._004|0_._029<sup>_∗∗∗_</sup>|_−_0_._04|
||(0_._013)|(0_._028)|(0_._007)|(0_._010)|(0_._005)|(0_._039)|
|Adjusted _R_<sup>2</sup>|0_._98|0_._931|0_._851|0_._971|0_._831|0_._99|
|Observations|9_,_280|5_,_997|48_,_378|26_,_215|50_,_583|6_,_173|
|_Panel B: Locally-produced goods only_|||||||
|Geodesic distance to origin (log km)|0_._148<sup>_∗∗∗_</sup>|0_._068<sup>_∗_</sup>|0_._041<sup>_∗∗∗_</sup>|0_._120<sup>_∗∗∗_</sup>|0_._028<sup>_∗∗∗_</sup>|0_._084<sup>_∗∗∗_</sup>|
||(0_._014)|(0_._031)|(0_._010)|(0_._015)|(0_._004)|(0_._021)|
|Adjusted _R_<sup>2</sup>|0_._980|0_._915|0_._846|0_._979|0_._825|0_._978|
|Driving distance to origin (log km)|0_._127<sup>_∗∗∗_</sup>|0_._05|0_._043<sup>_∗∗∗_</sup>|0_._100<sup>_∗∗∗_</sup>|0_._031<sup>_∗∗∗_</sup>|0_._133<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._029)|(0_._009)|(0_._012)|(0_._004)|(0_._028)|
|Adjusted _R_<sup>2</sup>|0_._980|0_._915|0_._846|0_._979|0_._825|0_._978|
|Time along best route (log hrs)|0_._134<sup>_∗∗∗_</sup>|0_._056|0_._050<sup>_∗∗∗_</sup>|0_._074<sup>_∗∗∗_</sup>|0_._037<sup>_∗∗∗_</sup>|0_._185<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._032)|(0_._010)|(0_._012)|(0_._005)|(0_._036)|
|Adjusted _R_<sup>2</sup>|0_._980|0_._915|0_._848|0_._979|0_._825|0_._978|
|Observations|9_,_280|5_,_271|43_,_541|9_,_999|36_,_541|3_,_406|
|Destination fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Product-time fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes_ : This table presents the effects of distance on intra-national trade costs. All regressions use the adjusted price gap between trading pairs and include product-time and destination fixed effects ˆ multiplied by (1 _− ρod_<sup>_k_)</sup><sup>_/ρ_ˆ</sup><sup>_k_</sup> _od_<sup>.PanelAincludesallgoods,includingimportedgoods.PanelBconsiders</sup> locally produced goods only. Product-time clustered standard errors in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **5 Discussion of results and robustness checks**

The baseline estimation presented above uses geodesic distance as the main exogenous variable shifting trade costs. However, this measure may mask cross-country differences in the quantity and quality of road infrastructure. For example, if country A has more roads connecting two locations than country B, then the cost-advantage of having more roads would be reflected in a lower coefficient in country A. Figure 1 suggests road infrastructure certainly varies across the countries in our sample. In the same vein, if there are crosscountry differences in road quality, adjusting for the quality of roads should also reduce cross-country differences in our baseline estimates.

Motivated by this, Panel A of Table 5 also includes coefficients when we replace geodesic 17
distance with two alternative measures, the driving distance along the best route (to adjust for quantity) and the time traveled along the best route (to adjust for quality), both computed using Google Maps. Using these alternative measures of distance changes the coefficients differently for different countries, without a clear pattern.

The methodology for recovering trade costs that we employ relies heavily on precisely identifying the source location of goods. One concern is that the source locations of imported goods included in our sample are not precisely identified for every period. That is, we may be incorrectly assigning the border or the port of entry. Alternatively, it may be that some of the distribution or logistic costs are already included in the origin price that we observe, because we may not be measuring these goods exactly at the border, so that trade costs for these goods may be underestimated.

For these reasons, we rerun steps 1 and 2 of our estimation procedure considering locally-produced goods only. Results are included in Panel B of Table 5, for all distance measures. It turns out that focusing on locally produced goods significantly changes the results. Coefficients estimated using geodesic distance increase markedly for Georgia, Rwanda, and Tanzania, where the share of imported products in the sample is above 30%, and for Madagascar, where only one product is imported, but for which we have only 9 products. In Georgia, the coefficient turns from zero to 0.084 and in Madagascar, Rwanda and Tanzania the coefficients increase by a factor of 2.8, 4.5 and 3, respectively. The coefficient for Kenya remains unchanged, since there are no imported goods, and the coefficients for Nigeria decrease 20%. The implied _ad valorem_ equivalents change in the direction of the change in the coefficients, and are included in Figure 3.<sup>9</sup> For example, in Georgia the _ad valorem_ transport cost is now 15.2%, and in Madagascar and Rwanda it is above 20%.

# **5.1 Robustness checks**

In this section, we perform a number of robustness checks to study the sensitivity of the results in section 4.3 to the assumptions regarding the estimation of pass-through rates and intra-national trade costs. The estimates of the effect of distance on trade costs obtained under these alternative specifications are presented in Table 6, which reproduced the baseline estimates in the first row. In all cases, we use our preferred sample of locallyproduced goods only. We explain each of the robustness checks in what follows.

We begin by exploring alternatives to the estimation of pass-through rates, which are important to control for the effect of imperfect competition, in rows 2-4. The baseline specification bottom-codes estimates of pass-through rates by setting _ρ_ ˆ<sup>_k_</sup> _od_<sup>= 0</sup><sup>_._2 if</sup><sup>_ρ_ˆ</sup><sup>_k_</sup> _od_<sup>_<_0</sup><sup>_._2,</sup> since the theoretical framework imposes that pass-through rates be positive. Results are sensitive to using the raw estimates, as can be seen from row 2 of Table 6. The effect

> 9To compute the _ad valorem_ equivalents, we now use the average destination price of locally-produced goods.

18
of distance on trade costs is lower in all countries but in Georgia and Nigeria, where it becomes significantly higher. For Madagascar and Rwanda, the coefficient becomes negative. Row 3 shows estimates under a specification that relaxes the assumption that the pass-through rate is constant over time. Instead, under this specification we split each sample period in halves and estimate a different pass-through rate for each period.<sup>10</sup> Allowing the pass-through rate to vary over time changes the results for all countries except for Tanzania. For Georgia, the coefficient is higher than in the baseline specification while it turns negative for the rest of the countries. In row 4, pass-through rates are estimated under a specification that adds two lagged origin price terms to equation (10), in order to capture long term pass-through effects. The pass-through rate is then the sum of the contemporaneous effect and the coefficients associated with the two lags. Using these estimates lowers the effect of distance (and the precision of the estimates) in all countries, turning it negative in all of them except in Nigeria and Tanzania.

In row 5, we control for potential correlation between spatial variation in locationspecific trade costs and distance between locations, which could bias our baseline estimates. To do this, we include fixed effects for each destination location _d_ , which were included in specification (11) but interacted with the adjustment term (1 _− ρ_ ˆ<sup>_k_</sup> _od_<sup>)</sup><sup>_/ρ_ˆ</sup> _od_<sup>_k_.For</sup> some countries, coefficient estimates change significantly after including destination fixed effects. For Georgia and Nigeria the coefficients increase markedly (by a factor of 2.9 and 15.3, respectively). For Kenya and Rwanda they also increase, but more moderately. For Madagascar and Tanzania, they turn negative. These results suggest that, in these countries, local components of trade costs, such as fulfilment, distribution and retail costs, may not be independent of distance to the source location. However, the results are ambiguous about the sign of the bias.

The last set of robustness checks explores changes in the estimation of both passthrough rates and trade costs altogether. First, specifications in rows 6 and 7 include additional fixed effects to control for more general demand and trade costs structures. Specifically, instead of including destination fixed effects, rows 6 and 7 show estimates from specifications with year-location and month-location fixed effects, respectively, in equations (10) and (11). These alternative specifications change coefficients significantly but without a clear pattern across countries. For some, the coefficient on distance increases and, for others, it decreases and even turns negative (for Nigeria, estimating the model with month-destination fixed effects is computationally infeasible). Finally, the specifications in rows 8 and 9 aim at evaluating the possibility that demand shocks in the destination and origin locations are spatially correlated, which would bias the baseline estimates. To this end, models (10) and (11) are re-estimated with samples restricted to locations that are at least 100km (row 8) and 200km (row 9) apart (the latter is not feasible for Rwanda, for which there are no locations more than 200km apart). Again, coefficients

> 10This is approximately 5 years for all countries except for Kenya, for which we only have 3.3 years of data.

19
change significantly for all countries, except possibly for Kenya, without a clear pattern, which suggests that the identifying assumption of spatial independence of demand shocks at origin and destination locations is not plausible for our sample of countries.

All in all, the conclusion from the robustness exercises is that our baseline estimates are not stable as we implement alternative specifications or samples, although changes in the estimates do not follow a clear pattern across countries. This suggests that there may be idiosyncratic institutional features in each country that should be taken into account more precisely in order to better understand the nature of the determinants of trade costs.

Table 6: Estimates of the effect of distance on trade costs: robustness checks

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Baseline|0_._148<sup>_∗∗∗_</sup><br>|0_._068<sup>_∗_</sup><br>|0_._039<sup>_∗∗∗_</sup><br>|0_._122<sup>_∗∗∗_</sup><br>|0_._028<sup>_∗∗∗_</sup><br>|0_._084<sup>_∗∗∗_</sup><br>|
||(0_._014)|(0_._031)|(0_._009)|(0_._015)|(0_._004)|(0_._021)|
|Not winsorizing|0_._097<sup>_∗∗∗_</sup>|_−_0_._258<sup>_∗∗∗_</sup>|0_._208<sup>_∗∗∗_</sup>|_−_0_._060<sup>_∗_</sup>|0_._006|0_._157<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._016)|(0_._054)|(0_._025)|(0_._004)|(0_._029)|
|_ρ_ por diferent time periods|_−_0_._178<sup>_∗∗∗_</sup>|_−_0_._063<sup>_∗∗_</sup>|_−_0_._005|_−_0_._063<sup>_∗_</sup>|0_._029<sup>_∗∗∗_</sup>|0_._141<sup>_∗∗∗_</sup>|
||(0_._046)|(0_._020)|(0_._016)|(0_._027)|(0_._006)|(0_._030)|
|_ρ_ with 2 lags|_−_0_._033<sup>_∗_</sup>|_−_0_._253<sup>_∗∗∗_</sup>|0_._010|_−_0_._027|0_._005<sup>_∗_</sup>|_−_0_._024|
||(0_._015)|(0_._016)|(0_._008)|(0_._021)|(0_._002)|(0_._023)|
|Destination fxed-efects|0_._233<sup>_∗∗∗_</sup>|_−_0_._498<sup>_∗∗∗_</sup>|0_._596<sup>_∗∗_</sup>|0_._165<sup>_∗∗∗_</sup>|_−_0_._015<sup>_∗∗_</sup>|0_._243<sup>_∗∗∗_</sup>|
||(0_._016)|(0_._048)|(0_._222)|(0_._038)|(0_._005)|(0_._047)|
|Year-destination fxed efects|0_._092<sup>_∗∗∗_</sup><br>|_−_0_._235<sup>_∗∗∗_</sup><br>|_−_0_._242<sup>_∗∗∗_</sup><br>|_−_0_._036<br>|_−_0_._009<br>|0_._183<sup>_∗∗∗_</sup><br>|
||(0_._013)|(0_._016)|(0_._072)|(0_._025)|(0_._004)|(0_._036)|
|Time-destination fxed efects|0_._097<sup>_∗∗∗_</sup>|0_._158<sup>_∗∗∗_</sup>|–|_−_0_._039|_−_0_._014<sup>_∗∗∗_</sup>|0_._336<sup>_∗∗∗_</sup>|
||(0_._014)|(0_._032)|–|(0_._027)|(0_._003)|(0_._054)|
|Locations _>_100km apart|0_._125<sup>_∗∗∗_</sup>|_−_0_._498<sup>_∗∗∗_</sup>|1_._152<sup>_∗_</sup>|3_._805<sup>_∗∗∗_</sup>|_−_0_._076<sup>_∗∗∗_</sup>|4_._051<sup>_∗∗∗_</sup>|
||(0_._017)|(0_._048)|(0_._469)|(0_._150)|(0_._011)|(0_._502)|
|Locations _>_200km apart|0_._349<sup>_∗∗∗_</sup>|_−_0_._420<sup>_∗∗∗_</sup>|_−_0_._303<sup>_∗_</sup>|–|_−_0_._082<sup>_∗∗∗_</sup>|6_._960<sup>_∗∗∗_</sup>|
||(0_._038)|(0_._106)|(0_._118)|–|(0_._015)|(1_._992)|

_Notes_ : Each cell reports the main coefficient from a regression of price gaps on (log) distance under alternative specifications, which are described in section 5.1. All regressions use the adjusted price gap between trading pairs and include time-product fixed effects multiplied by (1 _− ρ_ ˆ _od_<sup>_k_)</sup><sup>_/ρ_ˆ</sup><sup>_k_</sup> _od_<sup>.Standard errors</sup> in parentheses. _∗∗∗_ significant at 1%, _∗∗_ at 5%, and _∗_ at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **6 Comparison to previous literature**

To put our results in perspective, we start by comparing them to those in Atkin and Donaldson (2015), from whom we borrow the methodology to recover trade costs. Their baseline _β_<sup>ˆ</sup> is equal to 0.037 for Ethiopia, 0.056 for Nigeria, and 0.011 for the U.S. Our estimates are slightly lower. In terms of _ad valorem_ equivalents, their estimates imply transport costs of 25%, 15%, and 5% for Ethiopia, Nigeria, and the U.S., respectively.<sup>11</sup>

> 11To make their results comparable, we use the distance between the most and least remote locations that we use to compute the results in Figure 3.

20
Our results for Nigeria are very similar (16.5%) to their estimates. Moreover, with the exception of Kenya (19.2%), the recovered trade costs in the rest of the countries are close to those in the U.S.

When we consider the results based on the locally-produced goods samples, however, our results are more sensible and in line with Atkin and Donaldson (2015) for most countries. Coefficients are between 2.6 (Tanzania) and 14 (Kenya) times larger than in the U.S.

To try understand the sources of the differences in our estimates with those in Atkin and Donaldson (2015), we make a more precise comparison focusing on Nigeria, for which we have a similar sample covering the same time period, and re-estimate pass-through rates and trade costs restricting the sample to the goods that are included in Atkin and Donaldson (2015) (“AD-sample” from now on). The sample that we have access to includes 15 products out of the original 18 products in their paper and the total number of observations is lower, at 25,189, compared to 26,025 in Atkin and Donaldson (2015). Our results produce a mean pass-through rate of 0.484 (s.e. 3.2) compared to 0.390 (s.e. 0.66) in Atkin and Donaldson (2015).<sup>12</sup> Moreover, while Atkin and Donaldson (2015) estimate a significant negative relationship between the pass-through rate and distance (-0.101), we get positive relationship (0.01) which is not estimated with precision. These differences notwithstanding, the estimation of trade costs under the adjusted price gap specification produces identical results to Atkin and Donaldson (2015). That is, we get ˆ ˆ _β_ = 0 _._ 056 (0 _._ 007) compared to _β_ = 0 _._ 056 (0 _._ 008).

We can also compare our results to previous attempts to recover transport costs in Rwanda using this methodology, albeit with different data, by Bougna et al. (2020). Instead of using CPI data, Bougna et al. (2020) use market price data from November 2016 to July 2018 collected by the Government of Rwanda in collaboration with the World Bank in the context of an impact evaluation of a large feeder road rehabilitation program. They estimate a pass-through rate equal to 0.34, close to our estimate of 0.39, and _β_<sup>ˆ</sup> = 0 _._ 106, which is higher than our estimate using the full sample, but similar to our estimate using the sample of locally-produced goods only (0.120).

# **7 Conclusion**

We apply the methodology developed by Atkin and Donaldson (2015) to estimate intranational transport costs, accounting for spatial variation in markups across a sample of Eastern European and African countries. Our results suggest significant heterogeneity in the magnitude of intra-national transport costs across the countries in our sample. Moreover, we find that our estimates are sensitive to the choice of products, with most countries showing higher transport costs when we restrict the sample to locally produced goods, ex12The percentage of pass-through rates below one is slightly higher, 93.1%, and we obtain a lower fraction of estimates below zero, 12.6%, compared to 17%.

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cluding imported goods. We attribute these differences to a more accurate identification of production locations for locally produced goods, which is key for the methodology to produce reliable estimates of transport costs.

Our results that rely on the samples of locally produced goods are in line with those in the previous literature. Compared to estimates for the U.S. in Atkin and Donaldson (2015), we find that intra-national trade costs are between 2.5 (Tanzania) and 14 (Kenya) times larger in our sample of countries.

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