Scholarly article on topic 'Administrative barriers to trade'

Administrative barriers to trade Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Cecília Hornok, Miklós Koren

Abstract We build a model of administrative barriers to trade to understand how they affect trade volumes, shipping decisions and welfare. Because administrative costs are incurred with every shipment, exporters have to decide how to break up total trade into individual shipments. Consumers value frequent shipments, because they enable them to consume close to their preferred dates. Hence per-shipment costs create a welfare loss. We derive a gravity equation in our model and show that administrative costs can be expressed as bilateral ad-valorem trade costs. We estimate the ad-valorem equivalent in Spanish shipment-level export data and find it to be large. A 50% reduction in per-shipment costs is equivalent to a 9 percentage point reduction in tariffs. Our model and estimates help explain why policy makers emphasize trade facilitation and why trade within customs unions is larger than trade within free trade areas.

Academic research paper on topic "Administrative barriers to trade"

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Journal of International Economics

journal homepage: www.elsevier.com/locate/jie

Administrative barriers to trade^ , », CrossMark

Cecilia Hornok b, Miklos Koren aÄc'*

a Central European University, Nädor u. 9., 1051 Budapest, Hungary

b Centre for Economic and Regional Studies of the Hungarian Academy of Sciences (MTA KRTK), Hungary c CEPR, United Kingdom

ARTICLE INFO

ABSTRACT

Article history:

Received 22 August 2014

Received in revised form 7 January 2015

Accepted 7 January 2015

Available online 17 January 2015

JEL classification:

Keywords:

Administrative barriers Trade facilitation Customs union Gravity equation

We build a model of administrative barriers to trade to understand how they affect trade volumes, shipping decisions and welfare. Because administrative costs are incurred with every shipment, exporters have to decide how to break up total trade into individual shipments. Consumers value frequent shipments, because they enable them to consume close to their preferred dates. Hence per-shipment costs create a welfare loss. We derive a gravity equation in our model and show that administrative costs can be expressed as bilateral ad-valorem trade costs. We estimate the ad-valorem equivalent in Spanish shipment-level export data and find it to be large. A 50% reduction in per-shipment costs is equivalent to a 9 percentage point reduction in tariffs. Our model and estimates help explain why policy makers emphasize trade facilitation and why trade within customs unions is larger than trade within free trade areas.

© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license

(http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Exporters and importers around the globe face many administrative barriers. They have to comply with complex regulations, deal with a large amount paperwork, subject their cargo to frequent inspections, and wait for lengthy customs clearance. Minimizing the burden of these procedures, "trade facilitation" has been a priority for policymakers from developed and developing countries alike. In December of 2013, all members of the World Trade Organization (WTO) have agreed to the Bali Package, the first comprehensive agreement of the Doha round of negotiations. The main component of the Bali Package is an agreement on trade facilitation, requiring WTO members

☆ Koren is grateful for the financial support from the "EFIGE" project funded by the European Commission's Seventh Framework Programme/Socio-economic Sciences and Humanities (FP7/2007-2013) under grant agreement no 225551 and from the European Research Council Starting Grant no 313164 ("KNOWLEDGEFLOWS"). Hornok is thankful for the financial support from the GIST, Marie Curie Initial Training Network (ITN)' funded by the European Commission under its Seventh Framework Programme (Contract No. FP7-PEOPLE-ITN-2008-211429) and the Hungarian Academy of Sciences for the "Firms, Strategy and Performance" Lendulet Grant (LP2013-56). We thank Costas Arkolakis, David Atkin, Konstantins Benkovskis, Thomas Chaney, Jan De Loecker, Jeffrey Frankel, Doireann Fitzgerald, Patrick Kehoe, Ina Simonovska, seminar participants at Yale, Princeton, the Federal Reserve Bank of Minneapolis, Toulouse and the International Seminar on Macroeconomics at the Bank of Latvia and three anonymous referees for the helpful comments.

* Corresponding author at: Nador u. 9., 1051 Budapest, Hungary.

E-mail address: korenm@ceu.hu (M. Koren).

to adopt a host of measures streamlining the customs process, such as pre-arrival processing of shipments, electronic documentation and payment, and the release of goods prior to the final determination of customs duties, "[w]ith a view to minimizing the incidence and complexity of import, export, and transit formalities and to decreasing and simplifying import, export, and transit documentation requirements

[...r1

Why do countries rush to facilitate trade? They hope to increase trade volumes without endangering government revenues by reducing inefficiencies. In fact, studies of various trade facilitation measures find that they are associated with larger trade volumes.2 Even among countries within free trade areas (FTAs), tighter economic integration and a reduction of administrative barriers often lead to higher trade.3

We build a model of administrative barriers to trade to understand how they affect trade volumes, shipping decisions and welfare. A large

1 World Trade Organization (2013), Article 10,1.1.

2 See Engman (2005) and Francois et al. (2005) for a survey of the empirical evidence. Moi'se et al. (2011) construct various trade facilitation indicators and find that, taken together, they can reduce trade costs by up to 10%.

3 Hornok (2012) finds that countries joining the European Union (EU) in 2004 have witnessed a 5% reduction in trade costs even though they already had FTAs with the EU

since the mid-1990s. Handley and Limäo (2012) find similar trade-creating effect of the EU accession for Portugal. Chen and Novy (2011) estimate that EU countries within the Schengen area, which are not subject to border control, enjoy 10% lower trade frictions than other EU countries.

http://dx.doi.org/10.1016/jjinteco.2015.01.002

0022-1996/© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

part of administrative trade barriers are costs that accrue per each shipment, such as filling in customs declaration and other forms, or having the cargo inspected by health and sanitary officials. Hornok and Koren (in press) document that countries with high administrative barriers to importing (as reported in the Doing Business survey of the World Bank) receive less frequent and larger shipments. The starting point of our model is hence a tradeoff between administrative costs and shipping frequency. In the presence of per-shipment administrative costs, exporters would want to send fewer and larger shipments. However, an exporter waiting to fill a container before sending it off or choosing a slower transport mode to accommodate a larger shipment sacrifices timely delivery of goods and risks losing orders to other, more flexible (e.g., local) suppliers. With infrequent shipments a supplier of such products can compete only for a fraction of consumers in a foreign market.

We first use our model to derive a modified gravity equation of trade flows, in which administrative barriers show up as an additional tax on imports. Intuitively, when administrative barriers are high and shipments are infrequent, customers suffer utility losses that can be quantified as an ad-valorem tax equivalent. They also substitute towards local products accordingly.

We then show how to measure the welfare losses from administrative barriers to trade by estimating two key elasticities from the data. First, we need to know the sensitivity of consumers to timely shipments. This can be recovered from the observed shipping choices of exporters: if customers are very sensitive to timeliness, firms send many small shipments. Second, we need to estimate shippers' reaction to administrative costs. Calculating deadweight losses from the elasticity of consumer and firm behavior to prices is in the spirit of semi-structural estimation of Harberger (1964), Chetty (2009) and Arkolakis et al. (2012).

In our empirical analysis, we first show that per-shipment trade costs are sizeable and important for trade flows. We use the Doing Business database to measure the cost of shipping. Across 161 countries, the average trade shipment was subject to $3000 shipping cost in 2009. High shipping costs are associated with low volumes of trade: country pairs at the 25th percentile of per-shipment costs trade 68% more than country pairs at the 75th percentile. This magnitude is comparable to the trade creating effect of sharing a common border.

Administrative barriers of trade are larger in poor countries than in rich ones. Doubling the income of an importing country is associated with a 6% decrease in per-shipment costs. This pattern is consistent with the fact reported by Waugh (2010) that poor countries have higher trade barriers than rich ones, without correspondingly higher consumer prices of tradables. In our model, administrative barriers affect the convenience of imports and hence trade volumes, but not consumer prices.

In addition, we find that administrative costs help explain trade flows among countries with no preferential trade agreements and even within FTAs, but not within customs unions. One potential reason for this is that customs unions are subject to much less administrative barriers than FTAs and our measured administrative costs do not apply. In fact, this could provide a new explanation for why trade within customs unions is higher than trade within FTAs.4 Traditionally, the analysis of FTAs relative to customs unions focused on tariff harmonization, rules of origin and political economy (Krueger, 1997 and Frankel et al., 1997,1998).

We then study how exporters break down trade into shipments by exploiting shipment-level data for Spain for the period 2006-2012. We find that countries facing higher administrative barriers receive fewer shipments. This is similar to the finding of Hornok and Koren (in press).

Using our estimated elasticity of the number of shipments with respect to per-shipment costs, we can calibrate the welfare effect of

4 See, for example, Roy (2010) and also Section 4 of this paper.

these costs. We conduct two counterfactual trade facilitation experiments in the model. In the first one we reduce per-shipment costs by half. In the model, this is equivalent to about a 9% reduction in tariffs and results in about a 31% increase in trade volumes. The second exercise harmonizes administrative barriers so that each country matches the per-shipment cost of the average country in the top decile of GDP per capita. Because richer countries have lower trade barriers, this typically involves a reduction in per shipment costs. The tariff-equivalent effects of this policy vary substantially with development, being 13% for the lowest income decile and 2% for the highest.

Our counterfactual exercises suggest large trade creating effects of trade facilitation and large distributional effects from harmonizing administrative barriers.

Our emphasis on shipments as a fundamental unit of trade follows Armenter and Koren (2014), who discuss the implications of the relatively low number of shipments on empirical models of the extensive margin of trade.

We relate to the recent literature that challenges the dominance of iceberg trade costs in trade theory, such as Hummels and Skiba (2004) and Irarrazabal et al. (2013) They argue that a considerable part of trade costs are per unit costs, which has important implications for trade theory. Per unit trade costs do not necessarily leave the within-market relative prices and relative demand unaltered, hence, welfare costs of per unit trade frictions can be larger than those of iceberg costs.5

The importance of per-shipment trade costs or, in other words, fixed transaction costs has recently been emphasized by Alessandria et al. (2010). They also argue that per-shipment costs lead to the lumpiness of trade transactions: firms economize on these costs by shipping products infrequently and in large shipments and maintaining large inventory holdings. Per-shipment costs cause frictions of a substantial magnitude (20% tariff equivalent) mostly due to inventory carrying expenses. We consider our paper complementary to Alessandria et al. (2010) in that we exploit the cross-country variation in administrative barriers to show that shippers indeed respond by increasing the lump-iness of trade. Relative to their work, our focus is on characterizing the welfare consequences of administrative barriers in a simple-to-calibrate framework. We can do this by leveraging the semi-structural approach.

Our work is most related to Kropf and Saure (2014), who build a heterogeneous-firm trade model to study how fixed costs per shipment affect shipment size. They characterize the size and frequency of shipments as a function of firm productivity, and also show that aggregate exports follow from firm-level trade patterns. They then recover shipment costs from the observed shipment sizes, showing that these imputed costs are large and correlate plausibly with geographic variables and trade agreements. Given our lack of firm-level data, our model is admittedly simpler, but our focus here is also different: we want to understand the welfare consequences of administrative trade costs in a tractable aggregate framework. For this purpose, we derive a standard gravity equation in our model, and show how our model can be calibrated using a limited set of aggregate moments. We also offer new evidence on the trade effects of administrative barriers within and outside customs unions and conduct various counterfactual experiments.

2. A model of the shipping frequency of trade

This section presents a model that determines the number and timing of shipments to be sent to a destination market. Sending shipments more frequently is beneficial, because consumers value timely shipments. Producers engage in monopolistic competition as

5 Hummels and Skiba (2004) obtain an interesting side result on a rich panel data set, which is consistent with the presence of per-shipment costs. The per unit freight cost depends negatively on total traded quantity. Hence, the larger the size of a shipment in terms of product units, the less the per-unit freight cost is.

consumers value the differentiated products they offer. Each producer can then send multiple shipments to better satisfy the demands of its consumers.

There are J countries, each hosting an exogenous number of sellers and consumers. A seller can sell to a domestic consumer at no shipping cost. It can also sell to a foreign destination j, in which case it has to pay iceberg shipping costs as well as the administrative cost of exporting to country j.

The difference between administrative barriers and other trade costs is that the former apply for every shipment. We hence model them as per-shipment costs that are pure waste.

We characterize the shipping problem of sellers, and derive a gravity equation for trade flows between countries. We show that administrative costs act as an ad-valorem tax on bilateral trade. We then discuss the welfare implications of administrative costs, deriving a semi-structural formula for consumer surplus in the spirit of Harberger (1964), Chetty (2009) and Arkolakis et al. (2012).

2.1. Consumers

There is a unit mass of consumers in every destination country j.6 Consumers are heterogeneous with respect to their preferred date of consumption: some need the good on January 1, some on January 2, etc. The preferred date is indexed by t e [0,1 ], and can be represented by points on a circle.7 The distribution of t across consumers is uniform, that is, there are no seasonal effects in demand.

Consumers are willing to consume at a date other than their preferred date, but they incur a cost doing so. In the spirit of the trade literature, we model the cost of substitution with an iceberg transaction cost.8 A consumer with preferred date t who consumes one unit of the good at date s only enjoys e-8|t - s| effective units. The parameter 8 > 0 captures the taste for timeliness.9 Consumers are more willing to purchase at dates that are closer to their preferred date and they suffer from early and late purchases symmetrically.

Other than the time cost, consumers value the shipments from the same producer as perfect substitutes. The utility of a type-t consumer purchasing from producer a is

Xj(t, a) = J2 e-b]c-s]xj(t, a, s)

s e S(a)

Clearly, because of perfect substitution, the consumer will only purchase the shipment(s) with the closest shipping dates, as adjusted by price, e-8|t - s|/ps.

The consumer has constant-elasticity-of-substitution (CES) preferences over the bundles Xj(t, a) offered by different firms.

22. Exporters

There is a fixed Mi measure of firms producing in each country i. Because there are no entry costs, each firm exports to each destination country j.10

Exporters decide how many shipments to send at what times. Sending a shipment incurs a per-shipment cost off j. They then decide how to price their product. Both decisions are done simultaneously by the firms.

The marginal cost of production of supplier a is constant at c(a).11 It takes gross iceberg costs Ц > 1 for goods to reach country j from country i. This involves the per-unit costs of shipping, such as freight charges and insurance (it does not include per-shipment costs.) The cost-insurance-freight value of a good in country j is hence c(a)tij. We abstract from capacity constraints in shipping, that is, any amount can be shipped to the country at this marginal costs.

Hence the total cost of getting a shipment with Xj(a) units of the good to consumers in country j from country i (the home country of firm a) is

c(a)tijxj(a) + fij ■

Because we do not study free entry, we abstract from entry costs for both production and market access.

Given this cost structure, we can write the profit function of a producer a from country i selling to country j as

nj(a)= / [Pj (t, a, s)-c(a)tjxj(t, a, s)dt-nj(a)fij. (3)

■'c s=s1, ■ . . j)

Net revenue is markup times the quantity sold to all different types of consumers at different shipping dates. The per-shipment costs have to be incurred based on the number of shipping dates, which we denote by nj(a).12

2.3. Equilibrium

An equilibrium of this economy is a product price Pj(t, a, s), the number of shipments per firm nj(a), and quantity Xj(t, a, s) such that (i) consumer demand maximizes utility, (ii) prices maximize firm profits given other firms' prices, (iii) shipping frequency maximizes firm profits conditional on the shipping choices of other firms, and (iv) goods markets clear.

To construct the equilibrium, we move backwards. We first solve the pricing decision of the firm at given shipping dates. We then show that shipments are going to be equally spaced throughout the year. Given the revenues the firm is collecting from n equally spaced and optimally priced shipments, we can solve for the optimal number of shipments.

Uj (t) = i Xj(t, a)1-1/a da,

2.3.1. Pricing

(2) The revenue function of firm ( for its shipment at time s, coming

from consumer t is

where a is the elasticity of substitution. Let Ej denote the total income and, in the absence of trade imbalances, total expenditure of consumers in country j. By our assumption of symmetry, all consumer types have the same income Ej(t) = Ej.

Rj(t, a, s) = maxEj(t)

,S |s-t|

6 Because preferences are homothetic, this is without loss of generality.

7 Note that this puts an upper bound of^on the distance between the firm and the consumer. We are following the "circular city" discrete choice model of Salop (1979).

8 This is different from the tradition of address models that feature linear or quadratic costs, but gives more tractable results.

9 As an alternative, but mathematically identical interpretation, we may say that the

consumer has to incur time costs of waiting or consuming too early (e.g., storage) so that

the total price paid by her is proportional to e8|t - s|.

10 The working paper version of Hornok and Koren (2012) endogenizes the measure of exporters via free entry into each destination j.

11 We will later assume symmetry across firms from the same country—a Krugman (1980) model. For now, however, we keep the dependence on a in notation to illustrate how our model can be extended in a Melitz (2003) framework.

12 Clearly, the firm would not send two shipments on the same date, as it would only reach the same type of consumers. More on the equilibrium shipping dates below.

where E (t) is the expenditure of consumer t, p is the price of the product, and

pj (t) =

pAa) e

1-a -(a-1)8\t-s(a)

1/(1-a)

is the ideal price index of consumer t.

Because there is a continuum of competitors, an individual firm does not affect the price index Pj(t) nor expenditure Ej(t). This implies that the firm's demand is isoelastic with elasticity a. As a consequence, the firm will follow the inverse elasticity rule in its optimal pricing,

Pj(a) —

ci(a)tij

Price is a constant markup over the constant marginal cost. Firms may be heterogeneous in their marginal cost because of differences in productivity or factor prices in their source country. Importantly, a given firm charges the same price for each shipment date.

2.3.2. Shipping dates

Clearly, revenue (4) is concave in |s — t|, the deviation of shipping times from optimal. Because of that, the firm would like to keep shipments equally distant from all consumers. This implies that shipments will be equally spaced, s2 — s-i = s3 — s2 = ... = 1/n. The date of the first shipment is indeterminate, and we assume that firms randomize across all possible dates uniformly.

Because all shipments have the same price, consumers will pick the one closest to their preferred date t (other shipments are strictly inferior.) The set of consumers purchasing from a particular shipment

s is teIs— jfc;s + i).

An equal-spaced shipping equilibrium is shown on Fig. 1.

2.3.3. Revenue

To obtain the revenue from a shipment s, we integrate across the set of buyers buying from that shipment,

Rj(a,s) — Ej(t)

■'t—s-1

Pj(a) Pj(t)

— Ej

7S+A, e

Jt-s-i

-(a-1)6\s-t\

where we have exploited the symmetry of consumers. The integral in the last term evaluates to

r+2n e-(a-1)S \s-t\d. ~ 1-e 1(a 1)6/n

tjdt = 2 ■

(a-1)0

Fig. 1. Symmetric equilibrium shipping dates.

Because of the symmetry of consumers, each shipment brings the same revenue. The revenue from all shipments is then

Rj(a) — ni(a)Rj(a, s) — Ej

r1-e-2(a-1)S/nj(a) 1 ■ 2(a -1 )8/nj(a)

Let rj(w) denote the revenue of the firm if it sends timely shipments

(n ^ «>),

rj(a)= Ej

and r(n) denote the ad-valorem equivalent of infrequent shipments,

1/(a—1)

T(n) —

1 (a-1)6/n 1- e-2(a-1)s/n

The function r(n) is independent of j or rn. We can write the revenue of a firm (a as

Rj(a) — rj(a)T nj(a)\

The revenue of a firm is the product of two components: one depending only on market size and relative price as in a Krugman model, the other solely a function of shipping frequency. The ad-valorem equivalent of infrequent shipping, T(n), has the following properties. It is decreasing in n: the more shipments the firm sends the more consumers it can reach at a low utility cost. Because they appreciate the close shipping dates, they will perceive this firm as relatively cheap. At the extreme, if n «>, T(n) converges to 1, and the firm sells r(a). From the firm's point of view, the demand for timely shipping is fully captured by the function T(n), which acts as an ad-valorem tax on the firm's product. Later we will show that this analogy also applies to welfare calculations.

With this notation, we can write the price index of consumers in country j as

pj —

Pj(a)1 aT [n.-(a)l1 a da

1/(1-a)

2.3.4. Number of shipments

The firm cares about the net revenue coming from its sales. Because markup is constant, net revenue is just a constant 1/a fraction of gross revenue. Choosing the profit-maximizing number of shipments involves maximizing

rj(a)T(n)

with respect to n. Net revenue is rj(a)T(n)1 - a/aand each shipment incurs the per-shipment costfj. Revenue Rj only depends on the number of shipments through T(n).

Proposition 1. The profit-maximizing number of shipments is implicitly given by

dRj/a 1-a . . . -a ,, , f

—¿nr — rj(a)T(n) T (n) — fij■

¡t increases in 8 (less patient consumers), increases in a (consumers willing to substitute to other firms), increases in Ej/Pj (bigger firms in equilibrium) and decreases infj (costly shipments).

The number of shipments only depends on the ratio of maximal firm size rj(a) and per-shipment cost fj. Everything that makes the firm

t=s—h

larger in a market (large market size, weak competition, low costs of production and shipping) increases the optimal frequency of shipments. Large firms lose more by not satisfying their customers' need for timeliness and they are willing to incur per-shipment costs more frequently. Intuitively, lower per-shipment costs also imply more frequent shipments. At the extreme, as fij tends to zero, the firm sends instantaneous shipments, nj(a) ^ ~ and t converges to one.

To anticipate the calculation of the welfare effect, we rewrite Eq. (8) as an expression of an elasticity,

-nj(a)T' \nj(a)] a n,{a)fl.

a -1 Rj(œ)

The left-hand side of this equation is the absolute value of the elasticity of t with respect to n. The right-hand side is a constant markup times total shipping costs paid by the firm (nf), divided by total revenue of the firm. The last fraction can hence be thought of as the ad-valorem amount of shipping costs.

The intuition for this result is that the more elastic t is with respect to the number of shipments, the less willing is the firm to sacrifice revenue with infrequent shipments. It will hence send many small shipments, making the ad-valorem amount of shipping costs large. We can use this formula to recover the elasticity of t from the data.

2.3.5. Trade flows

The analysis so far is conditional on firm-level unit costs. To derive aggregate trade flows, we need to take a stand on these costs. Because we do not have firm-level data, we take the simple view that firms within the same country are identical in their cost of production, ci(a) = ci. An alternative approach, pursued by Kropf and Saure (2014) would be to assume heterogeneous firms with Pareto-distributed unit costs. Chaney (2008) and Arkolakis et al. (2012) discuss under what conditions such a heterogeneous-firm model leads to a similar gravity equation to the one we derive below.

Given the symmetry in costs, firms charge the same price in a given destination country j,

This is exactly the gravity equation in Anderson and van Wincoop (2003) and Eaton and Kortum (2002), except for the additional term T(n,j). Infrequent shipment hence acts as a bilateral trade cost between countries. We can use this insight to calculate the magnitude of trade losses from administrative barriers.

3. Welfare

What is the welfare cost of administrative barriers? Here we calculate how welfare depends on the choice of shipping frequency. The utility of the representative consumer is a monotonic function of real income Ej/Pj. We hence need to calculate the income and the price index faced by the representative consumer.

Our gravity equation satisfies Restriction 3 (CES import demand) of Arkolakis et al. (2012), but not Restriction 2 (constant profit shares) and Restriction 3 (identical elasticity of trade flows to wages and trade costs). This is because profit net of shipping costs is a nonlinear function of revenue and trade policy hence also changes the profit to cost ratio of the economy. We thus cannot use the result of Arkolakis et al. (2012) to characterize welfare across all equilibria. We can still use a loglinear approximation around the equilibrium to show how the additional inconvenience from an infinitesimal increase in shipping costs maps into welfare losses for the consumer.

Because we only consider changes to fj when analyzing welfare, we can treat customer income Ej as fixed as long as j is a small country. In this case, changes in the profits of exporters in country i do not matter for consumer income in country j. We can focus on changes to the price index.13

Recall from Eq. (10) the price index

j CT — 1

1/(1—a)

As the constant markup formula shows, individual product prices do not depend on nij or fj. We are hence interested in how t changes. We begin by log differentiating the price index with respect to the number of shipments per firm nij,

Pj(u) =

a— ^

and the price index can be written as

d In P

d In n.j

t.. —ntjT 1 tj .

Ej T( ntj

j a—1

1/(1—a)

The source countries differ in the number of exporters Mi, the marginal cost of production ci, the iceberg trade cost tij, and the ad-valorem loss from infrequent shipments T(nij). All these enter the price index of consumers.

Proposition 2. The total value of exports from country i to country j is given by

Ttj= E "

a e1 -

Countries that receive more shipments have lower perceived prices and higher customer utility. The effect on the price index depends on the size of the trade flow (timely shipments from a small trade partner being less important) and on the elasticity of t with respect to n. We can use Eq. (9) to substitute in for this elasticity,

d In Pj d In ntj

a Ttjntjftj a—1 E: Rti

This leads to the following proposition.

Proposition 3. The elasticity of the price index with respect to the per-shipment cost is given by

d in pj = £ Ej *tjd 'n fj

n1—a "£ E jE

IjM nj1—a ~pa—1

p1—a = V na—V"

'j E nt hj

13 An alternative way to close the model would be to assume free entry ofexporters. In this case, profits would be zero, and consumer income would be simply wage income. This would also be unchanged if both production costs and per shipment costs were denominated in labor.

Table 1

Average per-shipment costs across countries.

a dInnijUjfij 1—a d Infij Rij

The welfare effect of a change in per-shipment costs is a weighted average across source countries. The contribution of country i to this welfare effect is We use this result in the counterfactual exercise in Section 5 when we estimate tpi¡.

4. Evidence on administrative barriers and trade

We study how administrative barriers affect trade flows. We first estimate a gravity equation for bilateral trade volumes, including cost of shipping as an additional bilateral trade cost. We then show how shipping costs affect the number of shipments going to a country.

4.1. Data and measurement

We identify administrative costs from the Doing Business survey of the World Bank, from 2006 to 2012 (World Bank, 2014a). Doing Business measures the costs of exporting and importing a standard containerized cargo, noting the various customs and administrative procedures, documents, and the time and money they take. Our measure of shipping costs is the total monetary cost per shipment incurred by the exporter and the importer. Although not all components of these costs are strictly administrative, these correspond to the per-shipment cost in our model.14

Table 1 reports the average shipping costs across countries, broken down by the type of procedure and the direction of trade (export or import). Taken together, the average trade transaction would be subject to a total of $3000 cost and a waiting time of 50 days.

To study the covariates (though not necessarily determinants) of administrative barriers, we regress the log total per shipment costs (including both exporting and importing costs) on a host of country and country-pair observables. Table 2 reports the results. Administrative barriers tend to be lower for larger and richer countries that are closer to one another and are members of an FTA.

By far the most variation in administrative barriers is due to the level of development of the exporter and the importer. Doubling the GDP per capita of an importer is associated with a 6% decline in per-shipment trade costs. Twice as rich exporters, in turn, have 4% lower shipping costs. Motivated by this observation, we will study the effects of the counterfactual policy of reducing administrative barriers to rich-country levels.15

Data on trade flows comes from the UN Comtrade database (United Nations, 2014). We use bilateral distance measures and geographical variables from CEPII (Mayer and Zignago 2011), and gross domestic product data from the World Bank (World Bank, 2014b). To estimate how shipping choices depend on administrative costs, we need information on shipments. We use the shipment-level export database of the Spanish Agencia Tributaria (Agencia Tributaria, 2014). This contains information on every single international shipment leaving Spain. It records the date of shipment, its product code, value and weight, destination and transit countries, and the specifics of shipping, such as the mode of transport, the flag of vessel and whether the cargo is containerized. Agencia Tributaria does not make firm identifiers available, so, even though each shipment is made by a single firm, we cannot conduct firmlevel analysis.

Table 3 reports some shipment-level statistics about Spanish export in 2009. It shows, for selected destinations, the shipment value of the

Exporting Importing

Monetary Time Monetary Time

Document preparation $275 12.0 $307 13.8

Customs clearance and inspection $160 3.0 $207 3.7

Port and terminal handling $282 4.1 $318 4.7

Transit from port to destination $670 5.0 $772 4.6

Total $1387 24.1 $1604 26.8

Note: Based on Doing Business survey from 2009. Time costs are in days, monetary costs in US dollars.

median product, the number of times it is shipped in a month, and the number of months it is shipped in a year. Our first observation is that shipments are relatively large and infrequent. The average shipment size across all importers is $13,234 and the typical product only ships twice a year to the typical destination. This observation, noted before by Armenter and Koren (2014) and Hornok and Koren (in press), motivated us to model shipments as infrequently spread through time. We also find that countries with lower per-shipment costs receive smaller and more frequent shipments.

In our model, a shipment can only contain a single type of product from a single firm. In practice, shipments may be consolidated. Multi-product firms may send different products or freight forwarders may send cargo of different firms in the same shipment. To check how

Table 2

Covariates of shipping costs.

Exporter GDP (log) — 0.017a

(0.001)

Importer GDP (log) — 0.020a

(0.001)

Exporter GDP per capita PPP (log) — 0.060a

(0.003)

Importer GDP per capita PPP (log) — 0.086a

(0.003)

Free trade area (dummy) — 0.131a

(0.010)

Customs union (dummy) 0.010

(0.015)

Exporter is island (dummy) — 0.099a

(0.007)

Importer is island (dummy) — 0.025a

(0.007)

Distance (log) — 0.048a

(0.004)

Adjacent country (dummy) — 0.028

(0.022)

Former colony (dummy) 0.119a

(0.018)

Common colonizer (dummy) — 0.093a

(0.010)

Same country ever (dummy) — 0.053b

(0.025)

Common language (dummy) 0.061a

(0.008)

Common currency (dummy) 0.166a

(0.021)

Common legal origin (dummy) — 0.027a

(0.006)

Bilateral tariff (log) — 0.128a

(0.035)

Constant 10.515a

(0.051)

Observations 22,479

R-squared 0.253

14 Hornok and Koren (in press) also discuss the various components of per-shipment costs separately. Documentation and customs take about the third of the monetary costs and two thirds of the time costs of shipping.

15 We thank a referee for suggesting this policy exercise.

Note: Dependent variable is log total export plus import cost per shipment. The full sample includes the pairs of 178 exporting and 148 importing countries in 2006. The regression includes dummies for the continent of both exporter and importer. Robust standard errors are in parentheses. See Section 4.2 for details on variable definitions. a Significant at 1%. b Significant at 5%.

Table 3

Shipping costs and frequency.

Median shipment Number of times good Number of months in

value (USS) shipped in a month year good shipped

Selected low per-shipment cost importers

France $14,203 1.5 9

Germany $14,217 1.3 7

Japan $9674 1.0 2

USA $15,592 1.0 3

Selected high per-shipment cost importers

Algeria $15,894 1.0 2

China $19,442 1.0 2

Russia $12,263 1.0 2

South Africa $11,725 1.0 2

All importers $13,234 1.0 2

Notes: Reproduced from Hornok and Koren (in press), Table 2. Spanish exports to 144 non-EU and 25 EU importers in 2009 in 8381 eight-digit product lines (N = 3,019,277). The median value of individual shipments is converted to U.S. dollars with monthly average USD/EUR exchange rates. Shipment frequency statistics are for the median product. Trade in fuels and low-value shipments (less than EUR 2000 for Spain) are excluded.

important this model restriction is, we explored the bundling of shipments. For this exercise, we define a shipment based on shipping characteristics alone (such as date, final and transit country, vessel, containerization), while ignoring information on the product or its value. The vast majority, close to 60%, of such shipments contains only a single product item. We hence view the single-product, single-firm approximation of our model as empirically relevant.

In order to differentiate customs unions from free trade areas, we use the May 2013 version of the database created by Baier and Bergstrand (2007) to measure economic integration agreements. Because that data ends in 2005 and Doing Business starts in 2006, we use the year 2006 in our estimation of trade volumes.

42. Trade volumes

We first estimate a gravity equation of bilateral imports. We are interested in the trade-creating effect of customs unions relative to free trade areas, as well as the effects of per-shipment costs.

Our main specification is derived from Proposition 2. We let the iceberg trade cost tij depend on geographic variables such as distance, landlocked status, adjacency, colonial history, and the economic integration of the countries, such as membership in FTA, tariff rates or the use of a common currency. The ad-valorem equivalent of shipping costs, т(Пу), in turn, depends on the per-shipment costs accrued by exporters from country i to country j. The estimating equation is

lnj £„ + frFTA^ ft>CUif+ ftlnfij + fogravityy + uy. (11)

Imports from country i to country j depend on an FTA and a customs union dummy, per-shipment costs, as well as standard gravity control variables. Note that fj is the sum of export-specific costs in country i and import-specific costs in country j.

The unilateral control variables include total nominal GDP, GDP per capita in PPP terms (World Bank, 2014b), an indicator for whether the country is an island, and an indicator for its continent (CEPII GeoDist16). Bilateral controls include distance, adjacency, former colonial status, indicators for common language, common currency and common legal origins (CEPII GeoDist and Gravity17) and average bilateral tariff rate (CEPII MacMap18). We also control for whether one or both country is in the European Union, because within-EU trade data is collected differently.19

16 For description see Mayer and Zignago (2011).

17 See Head etal. (2013).

18 See Guimbard et al. (2012).

19 See Appendix B for a discussion.

Table 4 reports the results. All specifications are estimated by ordinary least squares (OLS). Columns 1 through 2 are estimated on the full sample of 178 exporter countries and 148 importer countries. The specifications vary by the degree of economic integration of the country pairs. We include an FTA and a customs union dummy, as well as our measure of shipping costs. The omitted category of economic integration includes country pairs with no trade agreement, or only preferential trade agreements short of an FTA.

All standard gravity variables have the expected sign and magnitude. As column 1 shows, countries in FTAs trade much more with one another than countries outside. An FTA is associated with a more than two-fold increase in trade. We separate customs unions from FTAs. Since all customs unions are also FTAs, the estimated effect of a customs union is in addition to the effect of being in an FTA. That is, customs unions are associated with a 44% increase in trade relative to FTAs.20 This is consistent with the model and the fact that customs unions require much less administration than FTAs.

Column 2 reports the elasticity of trade volumes with respect to per-shipment cost to be strongly negative at —1.06. The interquartile range of per-shipment costs is $1900 to $3100. This implies that country pairs at the 25th percentile of shipping costs trade 68% more than country pairs at the 75th percentile.21

What is the relationship between administrative costs, FTAs and customs unions? To answer this question, we break the sample into three. Column 3 includes country pairs that are not members of an FTA. Column 4 includes country pairs that are in an FTA but not in a customs union. Column 5 includes members of customs unions. We are interested in how the effect of shipping costs varies across these samples. Since the Doing Business survey asks about a standard cargo, it does not allow for the special administrative provisions of FTAs and customs unions.

We find that the negative effect of per-shipment cost is strong among countries outside of FTAs. The effect is similar among FTA members. The negative effect of shipping costs disappears for customs union members. Indeed, for these country pairs, much of the shipping costs do not apply, as there is no customs clearance and documentation needs are much reduced.

Column 6 reports a regression in which we interact shipping cost with FTA and customs union indicators. This is different from the regressions on the three subsamples in that other variables are restricted to have the same coefficient. Again, we see large negative association of shipping costs with trade for non-FTA members, somewhat smaller effects for FTA members, and the effect disappears for customs unions. The differences between the three groups are highly significant.

4.3. Shipments

We then turn to see how exporters break down total trade into shipments. We use shipment-level export data from Spain for the period 2006-2012. We identify the number of shipments щ as the total number of shipments going from Spain to country j in given year. One drawback of the Spanish data is that it contains no firm identifiers. We thus cannot calculate the number of shipments per firm, so we use the total number instead. Although admittedly a limitation, this measure is consistent with the model, where all firms are symmetric, and the total number of shipments Nij = Mny is just a constant multiple of the number of shipments per firm.

Eq. (12) is our estimating equation. The log number of shipments depends on per-shipment costs as well as standard gravity variables, including importer size (GDP), GDP per capita, distance, an island importer indicator, a former colony dummy, common language and legal

20 This result is somewhat sensitive to how we treat intra-EU trade. Table B.9 in Appendix B provides additional robustness checks. Our preferred estimates range from 32% to 44%.

21 Although quite speculatively, we could explain the trade effects of customs unions if they corresponded to a exp(-0.44/1.06) • 100 — 100 = 34 percent decrease in per-shipment costs.

Table 4

Gravity equation estimates for bilateral imports.

FTA but no CU

(6) Full

Free trade area (dummy)

Customs union (dummy)

Total export + import cost per shipment (log)

FTA x shipping cost

CU x shipping cost

Exporter GDP (log)

Importer GDP (log)

Distance (log)

Exporter GDP per capita PPP (log)

Importer GDP per capita PPP (log)

Exporter is EU member (dummy)

Importer is EU member (dummy)

Both countries EU members (dummy)

Exporter is island (dummy)

Importer is island (dummy)

Adjacent country (dummy)

Former colony (dummy)

Common colonizer (dummy)

Same country ever (dummy)

Common language (dummy)

Common currency (dummy)

Common legal origin (dummy)

Bilateral tariff (log)

Constant

Observations R-squared

0.799a (0.066) 0.367a (0.104)

1.264a (0.011) 1.094a (0.011)

-1.216a (0.032)

-0.106a (0.025)

- 0.246a (0.025) 0.254a (0.061) 0.438a (0.072)

-1.152a (0.115) 0.287a (0.049) 0.231a (0.052) 0.359a (0.130) 0.625a (0.105) 1.005a (0.077) 0.755a (0.171) 0.708a (0.063)

- 0.011 (0.154) 0.159a (0.044)

-0.673c (0.354) - 30.723a (0.420) 19,125 0.676

0.649a (0.073) 0.523a (0.140) - 1.063a (0.065)

1.238a (0.012) 1.072a (0.012)

- 1.298a (0.036)

- 0.070b (0.030)

- 0.246a (0.030) 0.170b (0.072) 0.247a (0.080)

- 1.383a (0.151) 0.311a (0.055) 0.306a (0.058) 0.385a (0.141) 0.748a (0.114)

1.028a (0.091) 0.424b (0.186) 0.693a (0.071) 0.059 (0.159) 0.219a (0.050)

- 0.725c (0.421)

- 20.791a (0.791) 14,490 0.703

- 1.108a (0.069)

1.265a (0.013) 1.098a (0.013)

- 1.252a (0.042)

- 0.060c (0.031)

- 0.205a (0.031)

0.308a (0.060) 0.278a (0.063) 0.738a (0.221) 0.835a (0.133) 0.858a (0.099) 0.543c (0.323) 0.673a (0.077) 1.297a (0.401) 0.226a (0.055) - 0.218 (0.407) - 22.584a (0.865) 12,688 0.663

-1.171a

(0.269)

1.173a (0.050) 1.018a (0.047) -1.099a (0.081) 0.039 (0.121) -0.583a (0.142)

0.182 (0.211) 0.793a (0.234) 0.814a (0.250) 0.436c (0.223) 1.376a (0.274) 0.476 (0.304) 0.495b (0.220) -2.059a (0.678) 0.461a (0.147) - 6.439a (2.050) - 15.058a (2.778) 1,049 0.743

-0.364 (0.367)

0.962a (0.031) 0.831a (0.032) -1.193a (0.102) 0.102 (0.151) -0.311c (0.163)

-0.288b (0.144) 0.304c (0.155) 0.001 (0.196) 0.911a (0.283) 1.089a (0.363) - 0.491c (0.298) 0.171 (0.242) -0.071 (0.129) 0.312a (0.105) 5.262c (3.078) - 14.328a (3.866) 753 0.869

-6.813a (1.618)

- 5.820b (2.751)

-1.174a (0.069) 0.976a (0.214) 0.749b (0.365) 1.235a (0.012) 1.069a (0.012)

- 1.288a (0.036)

- 0.060b (0.030)

- 0.228a (0.030)

0.297a (0.056) 0.292a (0.058) 0.351b (0.140) 0.793a (0.114) 0.994a (0.091) 0.361c (0.186) 0.710a (0.071)

- 0.203 (0.156) 0.222a (0.050)

- 0.734c (0.420)

- 20.063a (0.808) 14,490 0.703

Note: Dependent variable is log import value. The full sample includes the pairs of 178 exporting and 148 importing countries in 2006. All regressions include dummies for the continent of both exporter and importer. Robust standard errors are in parentheses. a Significant at 1%. b Significant at 5%. c Significant at 10%.

origin indicators and bilateral tariffs. Country 0 is Spain. We omit Spain-specific variables because they are soaked up by time dummies vt. One specification also includes an importer country fixed effect Vj.

ln N0jt = 0ilnfOjt + #>gravityojt + Vj + vt + u0jt ■ (12)

Table 5 reports the estimates. Because reporting standards are different for intra-EU trade, we only include non-EU destinations.

Column 1 reports a simple OLS estimate for the 131 non-EU destinations. Countries with higher per-shipment cost receive significantly fewer shipments, with an elasticity of —1.34. The interquartile range of per-shipment costs for non-EU destinations is $2000 to $3000. A country with $2000 per-shipment costs receives 72% more shipments from Spain than a country with $3000 costs.

Column 2 reports a specification with destination fixed effects. Such fixed effects can soak up any time-invariant heterogeneity across countries and their relation to Spain. (This is why the gravity variables are omitted.) The coefficient of per-shipment costs is still negative but no longer significant, with a p-value of 0.235.22

The fixed effect estimate is very noisy because there is little time-series variation in administrative costs. An analysis of variance reveals that 91% of the variation in log shipping cost is soaked by up destination country dummies. An additional 5% can be attributed to common time dummies, leaving about 4% idiosyncratic time variation.

22 This p-value is calculated with standard errors clustered by destination. The White heteroskedasticity-corrected p-value is 0.065.

Table 5

Shipping costs and the number of shipments.

(1) (2) (3)

OLS FE RE

Total export + import cost per shipment (log) -1.342a - 0.451 -0.764a

(0.212) (0.378) (0.246)

Importer GDP (log) 0.890a 0.511b 0.874a

(0.048) (0.256) (0.049)

Importer GDP per capita PPP (log) 0.266a 0.945b 0.361a

(0.086) (0.476) (0.105)

Importer is island (dummy) 0.522b 0.534b

(0.246) (0.247)

Distance (log) -1.381a - 1.336a

(0.294) (0.291)

Former colony (dummy) - 0.082 - 0.025

(0.234) (0.244)

Common language (dummy) 1.803a 1.805a

(0.264) (0.255)

Common legal origin (dummy) 0.902a 0.963a

(0.165) (0.168)

Bilateral tariff (log) -2.274 - 2.442

(1.986) (2.021)

Constant 6.390b - 9.067c 0.974

(3.182) (5.080) (3.408)

Observations 892 892 892

R-squared 0.906 0.988

Number of countries 131 131 131

Note: Dependent variable is the log number of shipments. The sample includes exports from Spain to 124 non-EU countries between 2006 and 2012. All specifications have year fixed effects. Standard errors are clustered by importing country. a Significant at 1%. b Significant at 5%. c Significant at 10%.

In column 3 we report a random effect specification, which allows for time-invariant heterogeneity across countries, but restricts these error terms to be orthogonal to explanatory variables and to have a normal distribution. Given these restrictions, the random effect estimator uses both cross-section and time-series variation. The estimated elasticity of the number of shipments with respect to shipping costs is — 0.764. Our preferred estimate of this elasticity is the more conservative — 0.451. We will use this estimate in the baseline counterfactual exercise, and explore sensitivity to other values.

In Hornok and Koren (in press), we have estimated product-level regressions to determine the elasticity of the number of shipments and the average shipment size with respect to per-shipment costs. Countries with higher per-shipment import costs receive fewer and larger shipments from both the U.S. and Spain. The elasticity of the number of shipments is between — 0.262 and — 0.104.23 Our estimates are larger. One possible explanation is that there are many zero trade flows at the product level, which biases a log-linear estimation. Missing trade is not an issue at the country level with a large exporting country such as Spain.

Table 5 of Hornok and Koren (in press) also shows that shipments are spread throughout the year: countries with high per-shipment cost receive shipments in fewer months. These empirical patterns motivated our model.

We also conducted an empirical analysis of the margins through which exporters change their shipping frequency. Simply put, they may (i) send more of the same good in larger shipments, (ii) pick slower modes of transport that allow for larger shipments and (iii) send bulkier products instead of small products. We do an index-number analysis to decompose the aggregate response into these channels. The results are reported in Appendix C. The main results are that shipping frequency is negatively associated with administrative costs even after controlling for mode of shipping, and that the mode itself does not vary significantly with administrative barriers.

23 Hornok and Koren (in press), Tables 3-4.

5. The effects of a reduction in administrative costs

To quantify the effects of administrative costs in the model, we conduct two simple counterfactual exercises. In the first trade facilitation scenario, we reduce per-shipment costs f by half. The second exercise exploits the cross-country variation in administrative costs. We change the administrative cost of each country to that of the average country in the top income decile. Because poorer countries have higher shipping costs, this scenario affects them more. The average import cost in the top income decile is $942, whereas the average export cost is $913.

As seen from Propositions 2 and 3, both the trade volume and the welfare effects are as if bilateral tariffs changed. We hence only need to calculate the tariff equivalent changes, fcj. We can only do this for the trade relations of Spain, because we need shipment-level data to calibrate the ad-valorem equivalent of per-shipment costs. Because of this data limitation and because our semi-structural approach only applies locally, the counterfactual exercises below should be understood as partial equilibrium changes. More specifically, we do not study the effect of the trade facilitation reform on firm profits and the potential spillovers across countries. We can however, characterize changes in consumer surplus and changes in bilateral trade volumes as long as the trade policy changes are small.

To calculate fcj, we need to know a. Following Simonovska and Waugh (2014), we calibrate a =4.1. This means that a 1% increase in ad-valorem trade costs reduces trade by a — 1 = 3.1%. It also implies a 32% markup. We also report results with the estimates of Eaton and Kortum (2002), a = 8.2.

We set the value of dlnnj/dlnfj in Proposition 3 to — 0.451 from Table 3. The ad-valorem amount of per-shipment costs is calculated

for each destination j as

where f0 j is the dollar measure of shipping costs in Doing Business with the exporter being Spain, N0j is the total number of shipments from Spain to country j, and T0j is total imports of country j from Spain.

Table 6 reports the average tariff equivalent effects of reducing f across the ten income deciles. The first column reports the average per-shipment cost in the income decile. Consistent with the evidence presented in Table 2, poorer countries have higher shipping costs. The second column reports the effects of reducing shipping costs by 50%. For example, for the lowest income decile, such a policy would be equivalent to an 11.6% decline in tariffs, whereas for the 9th decile, this would be equivalent to a 5.2% tariff decline.

The effects are heterogeneous, because the effect of a shipping cost reduction on the import price index is not linear (recall Proposition 3).

Table 6

Effects of reducing per-shipment cost by income decile of importer.

Percentage tariff decline equivalent to reducing shipping costs

Income decile Average shipping cost By 50% To top decile level Average tariff

1 (lowest) $3558 11.6 13.4 11.7

2 $3134 8.9 6.4 11.8

3 $2972 10.3 8.6 13.5

4 $2434 8.7 6.1 11.4

5 $2351 7.4 2.0 8.0

6 $2246 10.4 1.4 9.6

7 $2817 12.1 5.9 8.7

8 $2332 8.5 2.1 7.6

9 $2394 5.2 1.5 10.3

10 (highest) $1992 10.8 1.9 5.3

Table7

Tariff-equivalent effects (percent).

Shipment elasticity

-0.451 - 0.764

Elasticity of substition (a)

Scenario 4.1 8.2 4.1 8.2

50 percent decline 9.0 7.7 15.8 13.4

Matching top decile 4.5 3.9 7.7 6.6

Countries with large per-shipment costs enjoy a bigger gain from a 50% reduction.

The third column reports the tariff equivalent effect of setting each administrative cost to the average of the top income decile. This effect has a clear tendency with income per capita. Countries in the poorest decile see an effect equivalent to a 13.4% tariff reduction, whereas the average effect for countries in the 9th decile is 1.5% (note that even importers in the top decile gain from Spain reducing its somewhat higher-than-average shipping costs.)

For comparison, the last column of Table 6 shows the average bilateral tariff rate with respect to Spain. Recall that only non-EU countries are included in this exercise, hence, even for rich countries, the tariffs are substantial. There is much less variation in statutory tariff rates across income groups than in the tariff-equivalent effects of administrative barriers. Hence a trade facilitation reform equalizing administrative barriers offers a stronger force for convergence than a tariff harmonization reform.

5.1. Alternative calibrations

Table 7 reports the average tariff-equivalent effects across non-EU countries for alternative calibrations for the shipment elasticity and the elasticity of substitution. Reducing per-shipment costs by half is equivalent to reducing tariffs by 7.7 to 15.8 percentage points. A larger shipment elasticity (which implies that timeliness is more important) corresponds to a larger gain from administrative barrier reduction. The gain does not depend heavily on a.

The average effects are smaller in the scenario where we match the shipping cost of rich countries, because this corresponds to a smaller than 50% reduction for most countries. The equivalent tariff reductions range between 3.9 and 7.7%.

Table 8 reports the average percentage increases in bilateral imports from Spain. Given that Spain is a small trade partner for most of the 131 countries, this exercise ignores third-country effects. Trade volumes go up dramatically after this reduction in per-shipment costs, especially for high a. With a = 8.2, the trade creating effect of trade facilitation reform ranges from 31.3 to 148.0%. Even with a = 4.1, we see a 14.6 to 57.5% trade increase. These magnitudes are comparable to the trade creating effects of customs unions (Table 4).

There is a wide distribution of the effects across countries, because they are subject to different per-shipment costs. Fig. 2 plots the tariff equivalent of the per-shipment cost reduction for the cross-section of non-EU countries. For the bulk of the countries, the counterfactual trade facilitation reform is equivalent to 0 to 20% age point reduction in tariffs.

Tables

Trade response (percent).

Shipment elasticity

- 0.451 - 0.764

Elasticity of substition (a) Scenario 4.1 8.2 4.1 8.2

50 percent decline 30.7 70.9 57.5 148.0

Matching top decile 14.6 31.3 25.9 58.6

o >, CO

CÔ • c o Q

o L___

20 0 20 40 60 Equivalent tariff reduction (percentage points)

Fig. 2. Tariff equivalent of changing shipping costs to that of the top income decile.

6. Conclusion

We built a model of administrative barriers to trade to understand how they affect trade volumes, shipping decisions and welfare. Because administrative costs are incurred with every shipment, exporters have to decide how to break up total trade into individual shipments. Consumers value frequent shipments, because they enable them to consume close to their preferred dates. Hence per-shipment costs create a welfare loss.

We derived a gravity equation in our model and showed that administrative costs can be expressed as bilateral ad-valorem trade costs. We estimated the ad-valorem equivalent in Spanish shipment-level export data and find it to be large. A 50% reduction in per-shipment costs is equivalent to a 9 percentage point reduction in tariffs. Our model and estimates help explain why policy makers emphasize trade facilitation and why trade within customs unions is larger than trade within free trade areas.

Appendix A. Proof of Proposition 2

We can write firm revenue as

Rj = rijT{ nJ 1-a

c,1-a t,1,-CT

r--=E- ij

'ij ~ Ei

E„ 1-CT.1-CT / \1-°

kMkjCk tkj nkj) ■ Total import from country i to country j is

Tu^MiTuT nu) = Ej

i-CT M^-0 t^rfa)1-CT

'il -"'i'irv'il J_ 1-0 1-0 / \1-CT

With Pl denoting (1 - 1/o)Pl,

Ta- EiMid-0 ti-or( nl1-0 p0-1.

Add up all the sales of country i,

XTil = Et - Mf1-0XEitli0r(nii] ^p0-1

Table B.9

FTAs, customs unions and trade flows.

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

FTAs FTAs and CUs Non-EU sample EU dummies Nonzero trade (probit) Import (Poisson)

Free trade area (dummy) 0.805a 0.811a 0.796a 0.799a 0.055a 0.188b

(0.059) (0.065) (0.066) (0.066) (0.015) (0.098)

Customs union (dummy) - 0.018 0.277a 0.367a 0.110a 0.228c

(0.082) (0.106) (0.104) (0.015) (0.106)

Exporter is EU member (dummy) 0.254a (0.061)

Importer is EU member (dummy) 0.438a (0.072)

Both countries EU members (dummy) -1.152a (0.115)

Exporter GDP (log) 1.263a 1.263 a 1.276a 1.264a 0.089a 0.862a

(0.011) (0.011) (0.011) (0.011) (0.002) (0.025)

Importer GDP (log) 1.099a 1.099a 1.110a 1.094a 0.083a 0.848a

(0.011) (0.011) (0.011) (0.011) (0.002) (0.020)

Distance (log) -1.192a - 1.192a -1.197a -1.216a -0.100a - 0.648a

(0.031) (0.031) (0.032) (0.032) (0.004) (0.051)

Exporter GDP per capita PPP (log) -0.101a - 0.101a -0.108a -0.106a -0.023a - 0.047

(0.025) (0.025) (0.025) (0.025) (0.003) (0.055)

Importer GDP per capita PPP (log) -0.233a - 0.233a - 0.238a - 0.246a -0.019a - 0.084b

(0.025) (0.025) (0.025) (0.025) (0.003) (0.046)

Exporter is island (dummy) 0.286a 0.286a 0.307a 0.287a 0.041a -0.125

(0.049) (0.049) (0.050) (0.049) (0.006) (0.084)

Importer is island (dummy) 0.232a 0.232a 0.248a 0.231a 0.043a -0.074

(0.052) (0.052) (0.053) (0.052) (0.006) (0.078)

Adjacent country (dummy) 0.358a 0.359a 0.465a 0.359a -0.184a 0.255a

(0.131) (0.131) (0.144) (0.130) (0.043) (0.098)

Former colony (dummy) 0.720a 0.720a 0.724a 0.625a -0.062 -0.093

(0.106) (0.106) (0.109) (0.105) (0.064) (0.123)

Common colonizer (dummy) 1.003a 1.004a 0.981a 1.005a 0.032a 0.307

(0.077) (0.077) (0.078) (0.077) (0.007) (0.198)

Same country ever (dummy) 0.855a 0.856a 0.765a 0.755a 0.127a 0.487b

(0.171) (0.171) (0.188) (0.171) (0.011) (0.270)

Common language (dummy) 0.721a 0.721a 0.711a 0.708a 0.084a 0.313a

(0.063) (0.063) (0.064) (0.063) (0.006) (0.102)

Common currency (dummy) -0.074 - 0.066 0.352 -0.011 -0.068c 0.042

(0.151) (0.155) (0.241) (0.154) (0.033) (0.085)

Common legal origin (dummy) 0.165a 0.165a 0.158a 0.159a 0.020a 0.029

(0.044) (0.044) (0.045) (0.044) (0.005) (0.070)

Bilateral tariff (log) -0.792c - 0.793c - 0.746c -0.673b 0.039 -6.611a

(0.357) (0.357) (0.356) (0.354) (0.029) (1.018)

Constant -31.133a - 31.132a - 31.525a - 30.723a -32.013a

(0.412) (0.412) (0.416) (0.420) (0.889)

Observations 19,125 19,125 18,663 19,125 30,764 30,764

R-squared 0.675 0.675 0.656 0.676

Note. Dependent variable is log import value. The full sample includes the pairs of 178 exporting and 148 importing countries in 2006. All regressions include dummies for the continent of both exporter and importer. Robust standard errors are in parentheses. a Significant at 1%. b Significant at 5%. c Significant at 10%.

M,c,1-CT = -

i-a / \1-CTea-, j/i; r{nij) pj ■

ni-a = X ¡L^rf nj1-a Pj-1

so that we can write the above more succinctly as

M,c,-a=^ n a-1.

Pi-a =£ Er n a-1tira^ n;

Appendix B. FTAs vs customs unions: robustness analysis

This section addresses a multicollinearity problem of indicators of economic integration.24 In 2006, there were 1090 country pairs in customs unions, 782 of which also formed a common market, and 208 an economic union. It may be difficult to separately identify the effect of each subgroup on trade.

Column 1 of Table B.9 regresses log total import value on an FTA indicator and the same set of controls as in Table 4. Country pairs in FTAs trade about twice as much as other comparable country pairs. Column 2 includes a separate indicator for customs unions. Since all customs unions are also FTAs, the estimated effect of a customs union is in addition to the effect of being in an FTA. The estimated coefficient is not significantly different from zero, hence, customs unions seem to trade about as much as FTAs.

Substituting in, we get the result.

We thank a referee for pointing out this problem.

Table C.10

Decomposing trade into margins.

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

total extensive within transport prodcomp

Total export + import - 0.677a — 1.334b 0.380b — 0.079 0.355a

cost per shipment (log) (0.328) (0.257) (0.141) (0.063) (0.173)

Importer GDP (log) 0.975b 0.884b 0.089b 0.007 — 0.005

(0.071) (0.045) (0.025) (0.011) (0.030)

Importer GDP per capita 0.294a 0.299b 0.011 0.009 — 0.026

PPP (log) (0.122) (0.088) (0.048) (0.021) (0.059)

Distance (log) - 1.379b — 1.347b 0.187a — 0.091a — 0.128

(0.200) (0.154) (0.084) (0.037) (0.104)

Importer is island 0.569c 0.450a 0.004 0.088 0.026

(dummy) (0.326) (0.227) (0.125) (0.055) (0.153)

Former colony (dummy) - 0.585 — 0.248 — 0.120 — 0.002 — 0.215

(0.418) (0.444) (0.244) (0.108) (0.299)

Common language 1.550b 1.487b — 0.308 0.153 0.218

(dummy) (0.457) (0.436) (0.240) (0.106) (0.294)

Common legal origin 0.923b 0.933b 0.274b — 0.070 — 0.214c

(dummy) (0.229) (0.181) (0.100) (0.044) (0.122)

Bilateral tariff (log) 0.970 — 1.396 0.720 — 0.083 1.730

(2.295) (1.705) (0.937) (0.416) (1.150)

Constant — 12.329b — 4.720 — 7.435b 1.062 — 1.237

(3.707) (3.075) (1.690) (0.750) (2.074)

Observations 124 124 124 124 124

R-squared 0.866 0.910 0.202 0.128 0.136

Note. Dependent variables are described in the text. The sample includes exports from Spain to 124 non-EU countries in 2006. Robust standard errors are in parentheses. a Significant at 5%. b Significant at 1%. c Significant at 10%.

However, customs unions include the European Union, where trade data is collected differently. Whereas the source of trade data for extra-EU trade is customs records, intra-EU trade flows are measured via the Intrastat firm survey. When we restrict the sample to country pairs for which at least one of the countries is outside the EU (and trade is hence measured via customs), we find thattrade within customs unions is larger by 32% than trade in FTAs (see column 3). Consistent with this explanation, trade is only lower for intra-EU trade, whereas the external trade of EU countries is on average larger than that of similar countries (column 4).

Because zero trade flows are quite prevalent in the data, especially for less integrated countries, we have also explored other econometric specifications for the gravity equation. For countries outside an FTA, 44% of trade flows are zero, so these observations are excluded from the loglinear specification. The fraction of zeros is only 5% for FTA members, 9% for customs union members, and 2% for common market members.

Column 5 reports the marginal effects of a probit specification, where the dependent variable is a dummy for nonzero trade flow. Even conditional on a rich set of covariates, FTA members are 5.5% more likely to trade positive amounts; the probability of nonzero trade is an additional 11% higher for customs union members.

Column 6 reports a Poisson specification, which includes positive as well as zero trade flows. Santos Silva and Tenreyro (2006) argue for this specification not only due to the presence of zeros, but also because it is more robust to heteroskedasticity in trade. In this specification, customs union members trade 26% more than FTA members.

Taken together, we are confident that, conditional on our rich set of covariates, expected trade volumes in customs unions are larger than in FTAs.

Appendix C. A decomposition of aggregate exports

In this appendix we develop a decomposition of aggregate exports to a country into four margins: the number of shipments, the shipment size for a given product and transport mode, the transport mode, and the product composition margins. The four margins separate four

possible ways of adjustment. In response to higher administrative barriers firms may reduce the number of shipments, pack larger quantities of goods in one shipment, switch to a transport mode that allows larger shipments (sea or ground), or change the export product mix towards products that are typically shipped in large shipments.

Let g index products, m modes of shipment (air, sea, ground), and j importer countries. Let country 0 be the benchmark importer (the average of all of the importers in the sample), for which the share of product-level zeros are the lowest. In fact, we want all products to have nonzero share, so that the share of different modes of transport are well defined for the benchmark country.25

Let njgm denote the number of shipments of good g through mode m going to country j. Similarly, qjgm denotes the average shipment size for this trade flow in quantity units, pjgm is the price per quantity unit. We introduce the notation

for the mode composition of good g in country j, and

s, = I2knjgk

for the product composition of country j. We define s0gm and s0g similarly for the benchmark (average) importer.

We decompose the ratio of total trade value (X) to country j and the benchmark country,

Xj ^2^2mnjgmPjgmqjgm nj^2gSj^2mSjgmPjgmqjgm

EgE mn0gmP0gtnq0gm n0 5ZgS°g 5ZmS°gmP°gmq°gm '

as follows,

mSJgmPJgmqJgm

X° J2gS}gJ2mSigmPogm%gm

XygSjgXymSjgmp0gmq0gm XygSjgXymS°gmp0gmq0gi

EgSJgEmS°gmp0gmq0g^ EgS0gEmS°gmp0gmq0gm

The first term is the shipment extensive margin. It shows how the number of shipments sent to j differs from the number of shipments sent to the average importer. The ratio is greater than 1 if more than average shipments are sent to j. The second term is the within shipment size margin. It tells how shipment sizes differ in the two countries for the same product and mode of transport. The third term is a mode of transportation margin. If it is greater than 1, transport modes that accommodate larger-sized shipments (sea, ground) are overrepresented in j relative to the benchmark country. The last term is the product composition effect. It shows to what extent physical shipment sizes differ in the two countries as a result of differences in the product compositions. If bulky items and/or items that typically travel in large shipments are overrepresented in the imports of j, the ratio gets larger than 1.

We express the same decomposition identity simply as

^j, total ^ j,extensive

j,within ' ^j,transport ,yvj,prodcomp ■

(C. 1)

If administrative trade barriers make firms send less and larger shipments, one should see the shipment extensive margin to respond

25 Note that the mode of transport will not be well defined for a product/country pair if there are no such shipments. This will not be a problem because this term will carry a zero weight in the index numbers below.

negatively and the within shipment size margin positively to larger administrative costs. If firms facing per shipment administrative costs choose to switch to a large-shipment transport mode, the transport margin should respond positively. If firms shift the composition of the traded product mix towards typically large shipment products, it should show up as a positive response on the product composition margin.

We run simple cross section regressions with elements of decomposition (B.1) (in logs) on the left-hand side and the log total shipping cost (fij) and other "gravity" regressors on the right-hand side. The estimating equation is

lnXj z = ¡3 ■ ln fjj + y ■ other regressorsj + v + nj, (C.2)

where z e [total, extensive, within, transport, prodcomp] denotes the different margins, v is a constant and n is the error term. Additional regressors include GDP, GDP per capita, distance, bilateral tariff rates, geographic and cultural variables. We estimate (C.2) with simple OLS and robust standard errors in the case of the total margin. In the case of the five margins, we exploit the correlatedness of the errors and apply Seemingly Unrelated Regressions Estimation (SURE). The Breusch-Pagan test always rejects the independence of errors.

Similarly to the results reported in Table 4, higher shipping costs are associated with lower trade volumes (column 1 ofTable C.10). The negative relation is even stronger for the number of shipments: a 1% increase in shipping costs is associated with a 1.3% decline in the number of shipments, holding the mode of transportation and product composition fixed (column 2). The shipments going to high administrative barrier countries tend to be larger, both for a given product (column 3), and because of a tendency to send bulkier products (column 5). However, there is no significant response of the mode of transportation (column 4).

References

Agencia Tributaria, 2014. Comercio Exterior: datos mensuales máxima desagregación.

www.agenciatributaria.es. Alessandria, G., Kaboski, J., Midrigan, V., 2010. Inventories, lumpy trade, and large

devaluations. Am. Econ. Rev. 100 (5), 2304-2339. Anderson, J.E., van Wincoop, E., 2003. Gravity with gravitas: a solution to the border

puzzle. Am. Econ. Rev. 93 (1), 170-192. Arkolakis, Costas, Costinot, Arnaud, Rodriguez-Clare, Andres, 2012. New Trade Models, Same Old Gains. Am. Econ. Rev. vol. 102. American Economic Association, pp. 94-130 (February) Armenter, Roc, Koren, Miklós, 2014. A balls-and-bins model of trade. Am. Econ. Rev. 104 (7), 2127-2151.

Baier, Scott L., Bergstrand, Jeffrey H., 2007. Do free trade agreements actually increase

members' international trade. J. Int. Econ. 71, 72-95. Chaney, Thomas, 2008. Distorted gravity: the intensive and extensive margins of international trade. Am. Econ. Rev. 98 (4), 1707-1721. Chen, Natalie, Novy, Dennis, 2011. Gravity, Trade Integration, and Heterogeneity across Industries. J. Int. Econ. 85 (2). Elsevier, pp. 206-221

Chetty, Raj, 2009. Sufficient statistics for welfare analysis: a bridge between structural and reduced-form methods. Ann. Rev. Econ. 1(1), 451-488.

Eaton, Jonathan, Kortum, Samuel, 2002. Technol. Geogr. Trade 70 (5).

Engman, M., 2005. The Economic Impact of Trade Facilitation. OECD Trade Policy Working Papers No. 21.

Francois, J., van Meijl, H., van Tongeren, F., 2005. Trade liberalization in the Doha development round. Econ. Policy 2005,349-391 (April).

Frankel, J.A., Stein, E., Wei, S.J., 1997. RegionalTrading Blocs in the World Economic System. Institute for International Economics (Institute for International Economics).

Frankel, Jeffrey A., Stein, Ernesto, Wei, Shang-Jin, 1998. Continental Trading Blocs: Are They Natural or Supernatural. In: Frankel (Ed.), The Regionalization of the World Economy. University of Chicago Press.

Guimbard, H., Jean, S., Mimouni, M., Pichot, X., 2012. MacMap-HS6 2007, an exhaustive and consistent measure of applied protection in 2007. Int. Econ. Q2, 99-122.

Handley, Kyle, Limäo, Nuno, 2012. Trade and Investment under Policy Uncertainty: Theory and Firm Evidence. w17790, National Bureau of Economic Research (http://www. nber.org/papers/w17790).

Harberger, Arnold C., 1964. The Measurement of Waste. Am. Econ. Rev. 54 (3). American Economic Association, pp. 58-76

Head, K., Mayer, T., Rogoff, 2013. Gravity Equations: Toolkit, Cookbook, Workhorse. In: Gopinath, Helpman (Eds.), Handbook of International Economics vol. 4. Elsevier.

Hornok, C., 2012. Need for Speed: Is Faster Trade in the EU Trade-creating. Ph.D. Dissertation. Central European University.

Hornok, C., Koren, M., 2012. Welfare effects of administrative barriers to trade. EFIGE Working Paper no. 51.

Hornok C., Koren, M., 2015. Per-shipment costs and the lumpiness of international trade. Rev. Econ. Stat. http://dx.doi.org/10.1162/REST_a_00468 (in press).

Hummels, D., Skiba, A., 2004. Shipping the good apples out? An empirical confirmation of the Alchian-Allen conjecture. J. Polit Econ. 112 (6), 1384-1402.

Irarrazabal, A., Moxnes, A., Opromolla, L.D., 2013. The Tip of the Iceberg: A Quantitative Framework for Estimating Trade Costs. NBER Working Papers No. 19236.

Kropf, Andreas, Saure, Philip, 2014. Fixed Costs per Shipment. J. Int. Econ. 92 (1). Elsevier, pp. 166-184

Krueger, Anne O., 1997. Free trade agreements versus customs unions. J. Dev. Econ. 54 (1), 169-187.

Krugman, Paul, 1980. Scale economies, product differentiation, and the pattern of trade. Am. Econ. Rev. 70 (5), 950-959.

Mayer, T., Zignago, S., 2011. Notes on CEPII's Distances Measures: The GeoDist Database. CEPII Working Paper 2011-25.

Melitz, M., 2003. The impact of trade on aggregate industry productivity and intra-industry reallocations. Econometrica 71 (6), 1695-1725.

Moi'se, Evdokia, Minor, Peter, Orliac, Thomas, 2011. Trade Facilitation Indicators. OECD Trade Policy Papers. vol. 118. http://dx.doi.org/10.1787/5kg6nk654hmr-en.

Roy,Jayjit, 2010. Do Customs Union Members Engage in More Bilateral Trade than Free-Trade Agreement Members. Int. Rev. Law Econ. 18 (4). Blackwell Publishing Ltd., pp. 663-681

Salop, S.C., 1979. Monopolistic competition with outside goods. Bell J. Econ. 10 (1), 141-156.

Santos Silva, J.M.C., Tenreyro, S., 2006. The log of gravity. Rev. Econ. Stat. 88 (4), 641-658.

Simonovska, Ina, Waugh, Michael E., 2014. The elasticity of trade: estimates and evidence. J. Int. Econ. 92 (1), 34-50.

United Nations, 2014. United Nations Commodity Trade Statistics Database (COMTRADE). http ://comtrade.un.org/.

Waugh, Michael E., 2010. International trade and income differences. Am. Econ. Rev. 100 (5), 2093-2124.

World Bank, 2014a. Doing Business 2014. doingbusiness.org.

World Bank 2014b. World Development Indicators 2014. data.worldbankorg.

World Trade Organization, 2013. Agreement on Trade Facilitation. Ministerial Decision of 7 December 2013. WT/MIN(13)/36.