Keith Head
email: keith.head@ubc.ca

Thierry Mayer
email: tmayer@univ-paris1.fr

John Ries
email: john.ries@ubc.ca

How Remote is the Offshoring Threat ?

Keywords: outsourcing, offshoring, services, distance effects, trade JEL Codes: F14, F15, F16

Advances in communication technology make it possible for workers in India to supply business services to head offices located anywhere. This has the potential to put high-wage workers in direct competition with much lower paid Indian workers. Service trade, however, like goods trade, is subject to strong distance effects, implying that the remote supply of services remains limited. We investigate this proposition by deriving a gravity-like equation for service trade and estimating it for a large sample of countries and different categories of service trade. We find that distance costs are high but are declining over time. Our estimates suggest that delivery costs create a significant advantage for local workers relative to competing workers in distant countries.

Introduction

In 1995, the title of a Richard Freeman paper asked "Are your wages set in Beijing?" He motivated the paper in part by referring to the large increase in "manufacturing imports from third world countries." A decade later the terms of the debate have shifted.

A more up-to-date title would be "Are your wages set in Bangalore?" Promoting his bestseller The World is Flat, Thomas [START_REF] Friedman | It's a flat world, after all[END_REF] wrote of how he had "interviewed Indian entrepreneurs who wanted to prepare my taxes from Bangalore, read my X-rays from Bangalore, trace my lost luggage from Bangalore and write my new software from Bangalore." The earlier focus was on China as a major exporter of goods to the United States but now attention has turned to India as a supplier of services. In either case, workers in high-wage countries are concerned about maintaining living standards in the face of competition with foreigners who are willing to work for much lower wages.

Imports of services from low-wage nations merit special attention for three main reasons. First, the service sector employs about three times as many workers as the goodsproducing industries. Second, the service sector contains a relatively large share of highly educated workers. These two facts imply a widening range of workers potentially facing competition from their counterparts in poor countries. The third special feature of services is that recent technological progress has been much more revolutionary with respect to moving ideas than it has with respect to moving objects. Since many services involve idea transmission, improved communication technologies can-in principle-place third-world service providers in direct competition with service workers in the developed world.

This paper investigates the extent to which service trade has managed to overcome the impediments created by geographic distance and institutional differences. We model the "international market for services" and generate a gravity-like model of service trade.

We posit that physical distance, differences in time zones, languages, and legal systems, all raise the costs of employing foreign service workers. These costs may vary across service sectors and may change over time. We estimate the model using data for 65 countries over the period [1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006]. The theoretical model and estimates of distance effects allow us to calculate the wage premium a firm would be willing to pay to avoid the costs associated with remote provision of services.

Two studies estimate gravity models for total services using 1999-2000 OECD data. [START_REF] Kimura | The gravity equation in international trade in services[END_REF] use data for ten OECD countries and 47 partners to compare gravity estimates for aggregate services and trade. They estimate distance elasticities that are smaller (in absolute value) than those typically found in the gravity literature of goods trade (see [START_REF] Disdier | The puzzling persistence of the distance effect on bilateral trade[END_REF]. [START_REF] Mirza | What is so special about trade in services? University of Nottingham Research Paper[END_REF] use 20 OECD reporting countries and 27 partners to test their theory that labour market characteristics in home and host countries interact in determining service trade. They also find relatively small trade-impeding effects of distance.

Our analysis makes a number of contributions to the literature. We examine disaggregated service trade categories, allowing us to separate services that are the subject of the offshoring debate-professional services such as financial, computer, and communication services-from those that are not such as transportation, tourism, and government services. We are also able to utilize a long time-series to evaluate changes in distance effects since 1992. In addition, our model provides theoretical underpinnings for a service gravity equation and the structure for evaluating the protection that distance affords local service workers in terms of wage premia.

The paper is organized as follows. Section 2 discusses international service trade statistics and provides an overview of the growth of different subcategories of service trade. In Section 3, we derive a gravity-like specification for service trade based on the notion of an international market for services. We explain how the model is implemented in section 4 and display and discuss the econometric results. In section 5, we make use of our estimates to calculate the wage premium a firm would be willing to pay to avoid the costs associated with remote provision of services. We conclude in section 6.

Data on service trade

The source of international service trade data is the Balance of Payments (BoP) that measures service transactions between resident and non-resident entities. Thus, these data cover three of the four modes of international service supply defined in the General Agreement on Trade in Services-cross-border supply (mode 1), consumption abroad (mode 2), and the presence of natural persons (mode 4). The first mode reflects remote provision of services whereas the latter two refer to consumers or sellers travelling abroad to make transactions. The BoP excludes mode 3-commercial presence-representing foreign affiliates sales to host-country consumers.

If the focus is on domestic workers, excluding commercial presence may be sensible.

Remote provision of services from foreign countries may pose a direct threat to domestic workers. Likewise a foreign service provider traveling to provide its services arguably takes a job that otherwise would be provided domestically. However, a foreign company that creates a local affiliate and employs local workers (commercial presence) may create jobs for domestic workers rather than destroy them. Jobs may be lost to the extent that the local affiliate imports upstream services from the home country, but these transactions are captured in the BoP as a service import. The Eurostat data is based on reports of the 27 EU countries plus Croatia, Japan, Norway, Turkey, and the United States) and 33 partner countries. Our regression analysis uses Eurostat data because it offers the longest time series information. Eurostat compiles information on service debits and credits (imports and exports) so bilateral service export information is available for many more countries than the reporting countries. Countries of interest such as India and China appear as "partners" in the Eurostat data through their transactions with reporting Eurostat countries ("reporters").

Trade flows between two partner countries are unavailable in this data set. to WDI exports ranges between 0.10 (India) and 0.31 (China). Our preferred econometric specification incorporates time-varying importer and exporter fixed effects. Countryspecific under reporting of trade will be captured by these effects. Under-reporting of partner trade will only introduce bias in the estimated distance effect if under-reporting is correlated with distance between trading partners.

The Eurostat data contains both zeros and missing values. We anticipate that trade costs of services will vary across the type of service and using disaggregated data allows us to measure different trade costs across subcategories. We also investigate how distance effects change over time, an exercise that is feasible given the 1992-2006 times series information available in the data set.

A model of bilateral services trade

To give our statistical analysis some formal foundations, we now develop a model of the determination of bilateral service offshoring flows. The derivation draws heavily on the [START_REF] Eaton | Technology, geography, and trade[END_REF] model of trade in goods. The exposition follows the [START_REF] Head | FDI as an outcome of the market for corporate control: theory and evidence[END_REF] model of bilateral FDI stocks.

In the Heckscher-Ohlin-Vanek model of trade in goods, workers are immobile between nations. However, they can export their labour services embodied in the form of goods.

In contrast, the key idea of service offshoring is that a firm can replace the services of domestic workers directly with the services of workers residing in foreign countries ("offshore"). Foreign workers can supply their services via communication technologies or via temporary visits to the domestic producer's facility.

Let there be S d service "positions" in the destination country d and N o "candidates" in the origin country. Let π od denote the fraction of positions in country d that are filled by candidates from country o. The number of jobs offshored to each origin country is therefore given by

S od = π od S d , where o S od = S d . (1)
To specify π od , we assume that each position is filled by the candidate who offers her services at the lowest unit labour costs inclusive of the costs of "delivering" a unit of

U od = -ln[(w o /z o )(1 + τ od )] = ln z o -ln w o -ln(1 + τ od ). ( 2
)
To maintain tractability, one must impose very specific functional forms.

U od = ln κ o + (1/θ) ln N o -ln w o -ln(1 + τ od ) + od , (3)
where od is a zero-mode, independent, identically distributed Gumbel variable with CDF exp(-exp(-θ )).

The Gumbel distribution assumption is extremely useful because the distribution of the probability that a given draw of od is the maximum draw takes the tractable form of the multinomial logit. The law of large numbers implies that the fraction of jobs going to origin o will converge on that probability as S d becomes large. Using these results we obtain

π od = Prob(U od > U o d | o = o) = exp[ln N o + θ(ln κ o -ln w o -ln(1 + τ od ))] i exp[ln N i + θ(ln κ i -ln w i -ln(1 + τ id ))]
.

The value of the service flows created by offshoring, denoted V od , is given by the number of jobs offshored multiplied by the price paid to the offshore service providers. In the model, the service provider receives w o . Hence, V od = w o S od . This formulation is equivalent to FOB pricing for trade in goods. Substituting (4) into (1), we can express expected bilateral exports of services as

V od = w o S od = w o π od S d = N o S d κ θ o w 1-θ o (1 + τ od ) -θ P θ d , (5)
where

P d ≡ [ i N i (w i (1 + τ id )/κ i ) -θ ] -1/θ
. This expression resembles the gravity equation for trade in goods in that expected bilateral flows are increasing in the product of origin and destination size variables (N o and S d ) and decreasing in measures of bilateral delivery costs, τ od . Better access to a larger set of low-wage, high-productivity workers, i.e. a low P d , implies that a higher fraction of the positions in country d will be taken by workers from other countries, thereby reducing bilateral offshoring to country o.

Additional insight into how the parameters of the model might be estimated emerges by re-expressing the right-hand side as

V od = exp[ln N o + θ ln κ o -(θ -1) ln w o Exporter effect + ln S d + θ ln P d Importer effect -θ ln(1 + τ od ) Bilateral delivery cost ]. (6)
This equation shows that bilateral service flows can be separated into a origin o-specific term, a destination d-specific term, and a bilateral (od) delivery cost term. Compressing the exporter and importer effects into one term each, we obtain a more compact expression for expected bilateral service flows:

V od = exp[FX o + FM d -θ ln(1 + τ od )]. (7)
This formulation closely resembles the FDI equation of [START_REF] Head | FDI as an outcome of the market for corporate control: theory and evidence[END_REF]. Aside from the exponential form, these equations are also close to the trade equations estimated by [START_REF] Eaton | Technology, geography, and trade[END_REF]. An equation observationally equivalent to (7) could be developed by assuming that firms demand differentiated inputs as in [START_REF] Ethier | National and international returns to scale in the modern theory of international trade[END_REF]. Suppose that firms have production functions in which varieties of business services enter with a constant elasticity of substitution, σ. This will lead to a version of equation ( 7) in which the fixed effects have different structural interpretations and σ -1 takes the place of θ.5

We find the model of differentiated candidates competing for a single position to be more appealing because it adheres more closely to the public discussion of offshoring.

Results

We begin by specifying the estimation equation and our measures of delivery costs. In order to compare distance effects for services to those that have been estimated for goods, subsection 4.2 provides regression results for goods and services under standard gravity and fixed effects specifications. Subsection 4.3 displays estimates of distance effects for different subcategories of service trade. In the final part of this section, we examine the robustness of the results to 1) a restricted sample that excludes trade between highincome countries and 2) estimation methods that impose fewer restrictions on the error term and the evolution of the coefficients.

Model implementation

We fit the model to 1992-2006 Eurostat data. In order to implement the model, we need to choose variables that proxy for the delivery costs, τ od , impeding service exports from country o to country d. We follow standard practice in assuming that ln(1 + τ od ) is linear in log geographic distance, ln D od , and a vector of indicator variables designed to measure the trade-fostering linkages, L od , between the origin and destination country. We augment this specification by including the difference in time zones between origin and destination, denoted ∆ od , which anecdotal accounts suggest to be especially important for service trade. Adding an error term, u od , to represent a potentially large set of additional between the aggregate predictions of a model with discrete choice of the best variety versus a model where expenditures are spread over all varieties was initially demonstrated in [START_REF] Anderson | Discrete Choice Theory of Product Differentiation[END_REF].

omitted determinants of bilateral delivery costs, yields

ln(1 + τ od ) = δ ln D od + ν∆ od -λL od + u od . (8)
The mean of u od is likely to change over time due to advances in technology that facilitate trade over all dyads. Hence, it is important to allow for time-varying means for u od which we accomplish with a full set of year dummies.

We posit that geographic distance, D od , raises delivery costs for services by increasing time devoted to travel, training, and translation. It is measured as the populationweighted average of the great-circle distances between cities in the origin and destination countries. In order to explore how distance costs have changed over time, we also interact distance with a time trend.

To the extent that electronic communication is a good substitute for face-to-face interaction, travel becomes unnecessary and geographic distance becomes less relevant.

However, even with email and teleconferencing, East-West distance can matter because of time zone differences (∆ od ). There will be a negative effect due to difficulties in coordinating with sleeping colleagues during one's working day. On the other hand, having wide time zone differences can make it possible for a company to operate over a 24hour business day. We can think of the former benefit of proximity as the "synchronization effect." The latter benefit of differences in time zones is the "continuity effect." As the effects oppose each other, the expected sign of ν is ambiguous.

Standard components of L od include colonial relationships and a shared language. We add one more variable, shared legal origins, that we suspect might matter particularly for service trade. Thus, the linkages vector comprises

L od = {Colony od , Language od , Legal od }.
The common legal system dummy variable should account for the bilateral ease of signing commercial contracts between the two countries. A common legal system makes it less costly to adapt national contracts or to seek information about the rules prevailing in the foreign partner country. We therefore expect this dummy to enter positively. Finally, a common language and a colonial relationship have been shown in many studies to promote bilateral trade in goods and FDI. The sources and construction of all the components of τ are described in the Data Appendix. The linkage variables are not highly correlated in our sample: the correlation between language and common legal system is 0.23, between language and colonial relationship is 0.38, and between colonial relationship and common legal system is 0.20.

To obtain the estimating equation, we substitute equation ( 8) into ( 7), yielding

V od = exp[FX o + FM d -θδ ln D od -θν∆ od + θλL od + θu od ]. (9)
We will refer to minus the coefficient on log distance, θδ in the model, as the "distance effect." If u od is homoskedastic and normally distributed, then the maximum likelihood estimates of the parameters can be obtained via a standard linear-in-logs regression:

ln V od = FX o + FM d -θδ ln D od -θν∆ od + θλL od + θu od . (10)
Equation ( 10) is our baseline specification. In the robustness section, we employ two alternative specifications that provide consistent estimates even if u od is heteroskedastic, and/or non-normal.

Before presenting regression results it is useful to examine the data graphically in Figures 4 and5, where we take the United Kingdom and France's imports of Other Commercial Services imports as examples. We control for differences in economic size across origins by dividing imports by the origin country's GDP. The scatter plots clearly exhibit downward slopes and the lines in the figures depict univariate regression lines fitted to the data. The OLS distance effects are 0.98 and 0.96. Given the log scale, these slopes imply that a 10% increase in distance decreases imports by about 10%.

Figure 4 also illustrates the influence of three components of the delivery cost vector τ od : sharing a common language, sharing the same legal origins, and having ever been in a colonial relationship. For the UK, these indicators mainly lie above the regression line, suggesting that, for a given distance, the UK imports more from countries with whom it has linguistic, legal, or historical ties. Francophone countries and former colonies are above the regression line for France but countries using the French legal system appear scattered around the line.6

Distance effects for goods and services

The first set of regression results are shown in Table 4 where we compare distance effects for goods and Other Commercial Services under different specifications. 7 We focus on OCS rather than all services because it excludes government, transportation, and travel, categories of service trade that are not represented in our model nor the subject of the offshoring debate. Since prior studies did not estimate a trade equation with the importer and exporter fixed effects shown in equation ( 10), the first four columns reporting "standard" gravity estimates where the fixed effects are replaced with the logs of population and income per capita of each country. We report only the coefficients on the trade cost proxies. The first two columns display results for OCS and goods using all available observations. Since the distance effect for goods is known to depend on the sample used for estimation, the last four columns confine the sample to observations where both OCS and goods flows are non-missing and non-zero. The final two columns portray results for q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 500 1000 2000 5000 10000 20000

Distance in KM (log scale) OCS Imports/Origin GDP (%,log scale) O L S C o e ff = -0 .9 6 q q q q q q q q q q q q q q Colony Shared Language Share Legal Origins Figure 4: The impact of distance on British imports of Other Commercial Services (OCS), 2000-2006 averages q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 500 1000 2000 5000 10000 20000

Distance in KM (log scale) OCS Imports/Origin GDP (%,log scale) O L S C o e ff = -0 .9 8 q q q q q q Colony Shared Language Share Legal Origins We refer to minus the coefficient on distance as the "distance effect." It comprises a base effect corresponding to 1992 and a time trend. When we confine the sample to be the same (columns 3-6), the base effect is higher for OCS than goods, particularly in the specifications that incorporate fixed effects. Estimates of the trend are positive and significant for OCS across samples and specifications, indicating that the tradediminishing effect of distance is becoming less pronounced over time. In the preferred fixed-effect specification shown in column (5), the 1992 estimate of the distance effect is 2.216 and the trend term is 0.072. These estimates imply that by 2006, the distance effect for OCS trade had fallen to 1.208 (= 2.216 -14 × 0.072).

The trend for the distance coefficient for goods in the full sample (274,700 observations) shown in column (2), is negative (-0.010) and statistically significant. This implies the distance effect for goods is growing over time. Combes et al. (2008, figure 5.1) graph the upward trend in distance effects estimated in cross-section data from 1870 to 2003 using worldwide bilateral goods trade data. [START_REF] Berthelon | On the conservation of distance in international trade[END_REF] examine industry-level trends and find that most are insignificant but about a quarter show significantly stronger distance effects. [START_REF] Disdier | The puzzling persistence of the distance effect on bilateral trade[END_REF] explained by other covariates such as population and income, then they are reflected in the distance effects. The fixed effects, however, account for trading propensities of entrants and produces trends in distance effects for goods that are consistent with the proposition of falling trade costs.

Table 4 reveals that colonial relationships, shared language, and legal systems generally exert positive and significant effects on bilateral trade.9 In the fixed effects regressions shown in the last two columns, the effects of colonial relations and shared legal origin are somewhat stronger for OCS than goods. When we use fixed effects, shared language enters insignificantly for OCS and has a small positive effect on goods trade.

Across specifications, time zone differences have a positive, significant, and fairly stable effect on goods trade, a result in contrast to the negative effects reported in [START_REF] Stein | Longitude matters: time zones and the location of foreign direct investment[END_REF] who state that "an additional hour of time difference reduces bilateral FDI by between 17% and 26%, the impact on trade ranges between 7% and 11%" (page 107). They consider the log of imports plus exports for 17 OECD countries and 58 partners in 1999 (988 observations), incorporate importer and exporter fixed effects, and use slightly different covariates than us. Our attempts to replicate their results using 1999 data for Eurostat reporting countries and their trading partners (1119 observations), the same specification, and the same covariates, produce positive and significant effects of time zone differences. 10 Time zone differences exert insignificant effects on OCS trade. These results suggest that the continuity effect (the ability to operate around the clock) offsets the synchronization effect (the need to coordinate during business hours). However, the continuity effect should be absent for goods and yet we obtain positive estimates, which suggests to us that time difference effects should be interpreted with caution.

The full-sample, standard gravity specification in column (2) imply that the distance effects estimates for goods range from 1.510 in 1992 to 1.650 in 2006. These are higher than what have been estimated in the literature: [START_REF] Disdier | The puzzling persistence of the distance effect on bilateral trade[END_REF] quantitative survey reports the mean estimate of the distance effect to be 0.9. The high distance effect we find is largely attributable to inclusion of time differences: when we estimate the column (2) specification without this variable, the coefficient on the base effect falls to -1.296 and the trend is unchanged. Both time zone differences and distance reflect geographic separation and are highly correlated (around 0.83 in cross-section). In the estimates in the goods regression reported in column (2), the promotion of trade associated 10 An issue arises in programming importer and exporter fixed effects when the dependent variable is the sum of imports plus exports-the trading pair corresponding the each observation are both importers and exporters. Stein and Daude do not explain their procedure but our finding of positive time zone differences is robust to different ways of handling the fixed effects and emerges in specifications without fixed effects.

with time zone differences is "offset" by a large distance effect.

The results for the common sample reveal that the specifications fit OCS trade nearly as well as goods trade as reflected in the comparable R 2 s in the last four columns of Table 4. In the preferred fixed effect specification shown in the final two columns, we observe that the distance effects for services were initially higher than those for goods but are falling more rapidly. OCS and goods distance effects are 2.216 and 1.559 in 1992 and fall to 1.208 and 1.447 in 2006. The similarity in the estimated magnitudes of goods and OCS distance effects suggests that there might be a common source that accounts for the majority of the distance effect observed for both types of trade. [START_REF] Grossman | Comment on determinants of bilateral trade: does gravity work in a neoclassical world[END_REF] argues that transport costs are unlikely to explain the distance effects estimated for goods. Instead he suggests, "I suspect [we need a] model with imperfect information where familiarity declines rapidly with distance. Perhaps it is a model with very localized tastes (as in Trefler's 'home bias ', 1995), which are historically determined and change only slowly with experience." These two mechanisms could work equally well to explain distance effects for services. Interestingly, [START_REF] Blum | Does the internet defy the law of gravity[END_REF] find an OLS distance effect of 1.2 for "digital goods" consumed over the internet. They attribute the finding to cultural differences that are increasing in geographic distance.

Distance effects for different categories of service trade

We now turn attention to how the distance effects for Other Commercial Services compare to other categories of service trade-Total, Finance, IT, and Miscellaneous Business Services. We employ origin-year and destination-year fixed effects and report results for each category in Table 5.

Column (2) displays results for OCS. They are slightly different than the fixed effects results in column (5) of Table 2 because we use the full sample as opposed to the sample common to positive goods observations. The coefficients on distance and the distance trend are quite comparable for Total Services and OCS. The estimated distance effect for Total Service trade in 2000 is much higher (1.629 = 2.141 -8 × 0.064) than the 0.6 and 0.2 found for Total Tervices in 1999-2000 by [START_REF] Kimura | The gravity equation in international trade in services[END_REF] and [START_REF] Mirza | What is so special about trade in services? University of Nottingham Research Paper[END_REF] respectively. Possible explanations for this difference include our use of fixed effects and the inclusion of time zone differences (that enter positively). Our results are closer to the 1.0 obtained by Schwellnus (2007, Table 3), who uses Eurostat OCS data and fixed-effects estimation.

Columns ( 3) and ( 4) show that trends in the distance effects for Finance and IT are also positive and significant. The estimate for MBS is comparable to that for OCS but insignificant (t-statistic of 1.42). Shared language exhibits perverse negative signs whereas estimates of the effect of colonial relationships and shared legal origins are positive, significant, and generally comparable across categories.

Recall that Miscellaneous Business Services includes legal, accounting, advertising, and management consulting services as well as call centres. Time zone differences for this service category are estimated to be positive and significant, a result consistent with the need to establish international call centre networks that operate around the clock.

Time zone differences are also positive and significant for Finance, suggesting that the continuity effect dominates in this industry as well.

Robustness

We investigate the robustness of our results across three dimensions. First, since the outsourcing debate focuses on service exports from low-wage countries to high-wage countries, we estimate distance effects for the sample that excludes trade between 25 high-income countries as defined by the World Bank. 11 Second, we estimate using two quasi maximum likelihood estimation (QMLE) methods that impose much weaker conditions on the error term than were implicitly imposed in the linear-in-logs specification.

Third, we allow the coefficients on distance (and other trade cost determinants) to vary freely from year to year.

The specification employed in the previous subsection follows the theory in allowing for origin-year and destination-year fixed effects. The specification assumes constant coefficients on the dyadic trade cost measures except for distance, which has a linear trend. In this subsection, we estimate equation ( 7) on year-by-year basis. One advantage of the year-by-year approach is that it allows for non-linear and even non-monotonic paths for the distance effect over time.

The year-by-year approach reduces the number of country effects in each regression by a factor of 1/15. This makes it feasible to estimate two quasi maximum likelihood estimation (QMLE) methods that require iterative techniques that do not converge with the large set of ot and dt dummy variables. There are two important motivations for QMLE regressions. 12 First, as emphasized in the recent paper by Santos Silva and Tenreyro (2007), least squares estimation of equation ( 10) only yields consistent estimates of the parameters of ( 7) if the error term u od is homoskedastic and normally distributed. We would like to obtain consistent estimates of the elasticity of trade with respect to distance without imposing such strong requirements on the error term. The QMLE methods have the second advantage of incorporating the zero trade flow observations that the linearin-logs regressions exclude (7.7% percent of the sample for OCS from 1992-2006). 13

The QMLE methods yield consistent and asymptotically normal coefficients as long 12 The application of these models to count data was pioneered by [START_REF] Gourieroux | Pseudo maximum likelihood methods: applications to poisson models[END_REF], who use the term "pseudo" instead of the "quasi" term used by [START_REF] Wooldridge | Quasi-likelihood methods for count data[END_REF][START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF].

13 [START_REF] Helpman | Estimating trade flows: trading partners and trading volumes[END_REF] develop an alternative method of incorporating zeroes, based on an underlying model of firm heterogeneity. Their method involves a first-stage selection equation into positive trade explained by i) a set of importer and exporter country dummies, ii) bilateral trade barriers that also explain trade volumes, and iii) variables that only explain trade partner selection but not trade volumes. Because our data comprises a larger share of missing observations and a smaller share of "true" zeroes than their paper, many of the variables in i) and ii) are perfect predictors of positive trade. Consequently, we can only estimate very incomplete and therefore unreliable versions of the specification.

as the conditional mean assumption for trade is correctly specified, i.e. if

E(V od ) = exp[FX o + FM d -θδ ln D od -θν∆ od + θλL od ]. (11)
The first method, Poisson QMLE, is efficient in its class when the variance of trade is proportional to its expected value. The second QMLE, the two-step negative binomial

(2-step NB), is efficient when the variance of trade is quadratic in the expected value.

Denoting the expected level of trade from equation ( 11) as m od , the variance assumptions underlying the two QMLEs can be represented as

Var(V od ) = σ 2 m od Poisson , and
Var(V od ) = m od + η 2 m 2 od Negative Binomial ,
The Poisson QMLE can be estimated using the standard poisson procedure provided in statistical packages as long as the option for robust standard errors is specified. The 2step NB involves the following procedure:14 The first step assumes η 2 = 1 to obtain obtain estimates of m which in turn can give an estimate of the variance as e 2 od ≡ (V od -m od ) 2 .

A regression of e 2 od -m od on m 2 od can then be used to obtain a consistent estimate of η 2 . The second stage uses this estimate in a negative binomial estimation. When the NB variance assumption holds and η 2 > 0, the 2-step NB QMLE is more efficient than Poisson QMLE.15 with an alternative that excludes time zone differences. The motivation for these latter regressions comes from the high correlation between time zone differences and distances (0.83) that we commented on earlier. When both effects are allowed to vary over time, the total change in the impact of geographic separation cannot be seen by inspecting distance coefficients alone. We neutralize this issue by making distance the sole measure of physical separation in the specification.

The results depicted in Figure 6 reinforce two of the main features of the pooled linear method reported in Tables 4 and5 q q q q q q q q q q q q q q q 1992 1994 1996 Number of non-negative observations q q q q q q q q q q q q q q q 1992 1994 1996 q q q q q q q q q q q q q q q 1992 1994 1996 Number of non-negative observations q q q q q q q q q q q q q q q 1992 1994 1996 The reduced sample yields similar results concerning the downward trend in distance effects. The main difference is that the "rebound" in the impact of distance that is observed around the year 2000 for the full sample is more pronounced when trade between rich countries is eliminated. In 2006, all estimation methods yield larger distance effects in the restricted sample, indicating that distance effects between rich countries are lower than those between rich and lower income countries.17 This could arise because communication costs are lower between rich countries, either because travel and communication infrastructure is better or cultural differences are more easily circumvented. The fig-

ure certainly does not support the proposition that the world is "flat" in the sense that countries like India are no longer subject to distance effects in their service trade with high-income countries.

The results displayed in this figure are comforting in that they confirm the view that distance effects for commercial services start very large and end up in the vicinity of distance effects for goods. One interpretation is that reductions in communication costs have facilitated service trade greatly relative to a situation in which frequent travel was essential. However, to the extent that familiarity and trust are still declining in distance, geography remains as an important inhibitor of trade in both goods and services. The hypothesis that changes in distance effects might reflect improvements in the speed of international flow of information receives some corroboration in recent work by [START_REF] Griffith | Is distance dying at last? Falling home bias in fixed effects models of patent citations[END_REF]. They find that the home bias in time to first citation for patents has declined substantially since 1990.

The 2-step NB and Poisson QMLE both provide consistent estimates of the underlying parameters under the conditional mean assumption shown in equation ( 11). Therefore the results should only differ because of sampling error. Yet we observe in Figure 6 that the confidence intervals for the two methods generally do not overlap. [START_REF] Gourieroux | Pseudo maximum likelihood methods: applications to poisson models[END_REF] show that the methods differ in the weights they place on each observation. If distance effects vary across sets of trading partners, the weighting of observations will affect the distance estimate. Thus, the observed differences in the 2-step NB and Poisson QMLE estimates suggest heterogeneity in distance effects, a phenomenon that is not predicted by existing models of bilateral trade.

The general picture that emerges from our regression results is that the standard gravity equation and the more sophisticated FE specifications explain service trade just as well as they explain trade in goods. We find strong distance effects, especially for the service categories that are the subject of the offshoring debate. These distance effects have evolved to become similar in magnitude to distance effects estimated for goods. Unlike goods, however, OCS distance effects exhibit a downward trend in all the econometric specifications.

Calculating the proximity premium

To assess the economic significance of our results, we use our theory to calculate the "wedge" between productivity-adjusted wages that protects domestic workers from foreign competition. Recall that we assume firms minimize delivered unit labour costs, (w o /z o )(1 + τ od ) and that bilateral delivery costs depend on distance with an elasticity of δ. We can use these assumptions to investigate how much higher unit labour costs a firm would be willing to pay to avoid the delivery costs associated with remote suppliers.

Suppose a supplier located at a nearby origin denoted o = n is being compared to a more remote supplier from o = r. We will consider the case where the only determinant of service delivery costs that differs between supply origins n and r is distance to the destination market. The service importer in the destination country is indifferent between the two suppliers when (w n /z n )D δ nd = (w r /z r )D δ rd . Rearranging and assuming D rd > D nd ,, we have the proximity premium as

PP = w n /z n w r /z r = D rd D nd δ ≥ 1.
The firm is willing to pay nearby workers higher wages even if productivity were the same in countries n and r. To obtain an idea of the magnitude of the PP at various distances, we require plausible values of δ.

Direct calculation of δ is difficult since the delivery costs associated with services are not readily observable. If all service provision required face-to-face interaction, one could estimate δ by regressing business travel costs on bilateral distance. [START_REF] Brueckner | International airfares in the age of alliances: The effects of codesharing and antitrust immunity[END_REF] finds a distance elasticity of 0.34 for international airfares in 1999. This could be an under-estimate of δ since it does not include the time-costs of travel. However, to the extent that electronic communication can substitute for travel, the 0.34 could also be an over-estimate. We can obtain an estimate of δ based on electronic communication using Hummels' (2001) regression of telephone call rates on distance using 1993 OECD data. His elasticity of 0.27 is probably an overestimate since increased competition in telecommunications-and the growth of the internet-have almost certainly lowered the elasticity of electronic communication costs with respect to distance.

The problem with direct measures of δ is that they do not capture information costs. If one accepts the Grossman argument that distance is a proxy for familiarity then we need a measure of δ that incorporates the costs of imperfect information. For this we should use our estimates of the effect of distance on service trade. Recall from equation ( 9) that the coefficient on distance in a trade equation is a compound parameter given by -θδ.

Dividing by an estimate of θ, the inverse measure of the dispersion of productivity, we could infer δ from the distance coefficient using the formula δ = θδ/ θ. We calculate the wedge for 2006 that applies to workers producing Other Commercial Services. The pooled regressions shown in Tables 4 and5 and the annual coefficients shown in Figure 6 provide a range of possible estimates of the distance effect, θδ. We select the 2006 linear-in-logs specification in which θδ = 1.10 because that value lies between the 2-step NB and Poisson estimates. [START_REF] Eaton | Technology, geography, and trade[END_REF] use the relationship between trade and prices to estimate θ = 8.28. [START_REF] Bernard | Plants and productivity in international trade[END_REF] use firm-level export information from the US to estimate θ = 3.6. Since estimates σ -1, derived from regressions of trade on tariffs or freight costs, can be interpreted as estimates of θ, the studies surveyed by [START_REF] Anderson | Trade costs[END_REF] are also relevant. They write that "the literature leads us to conclude that σ is likely to be in the range of five to ten." This corresponds to a θ range of four to nine, quite close to the range of θ derived from the Eaton and Kortum and Bernard et al. studies.

Using [START_REF] Eaton | Technology, geography, and trade[END_REF] to obtain the upper bound for θ and [START_REF] Bernard | Plants and productivity in international trade[END_REF] for the lower bound, δ ranges between 1.10/8.28 = 0.13 and 1.10/3.6 = 0.31. The figure highlights examples of relative distances from London using dotted vertical lines. As a short domestic distance, we use the 83km from London to Oxford, whereas the obvious long international distance is the 8,027km to Bangalore. Hence, a reasonable range for relative distance would be one to 100. The estimates based on our bilateral service trade equations imply London service purchasers are willing to pay 25% (assuming θ = 8.28) to 71% (assuming θ = 3.60) more for service suppliers in Oxford rather than Dublin. The relative proximity of Dublin versus Bangalore is worth 45% to 141% higher labour costs. Finally, workers in Oxford can be paid 81% to 313% more than workers in Bangalore in productivity-adjusted wages and yet still be attractive to a London service purchaser.

These calculations establish the economic significance of the estimated distance effects. However, there are three caveats worth mentioning. First, we rely upon a model that makes specific parametric assumptions on productivity dispersion and service delivery costs. Second, our estimates of relative delivery costs exclude costs other than distance.

Finally, even though we have attempted to bound the size of the distance-created wedge by combining optimistic and pessimistic estimates, we derived our range for θ from a small number of studies of trade in goods. Obtaining estimates of θ for services should be a research priority.

Concluding remarks

The service sector is becoming more important and service trade is growing relative to service output. The globalization of services creates opportunities for service exporters but challenges for those domestic workers whose productivity-adjusted wages are higher than foreign providers. Many discussions of services-see in particular Blinder ( 2006)imagine a dichotomy in which some services, such as family doctors, are inherently nontradeable, whereas others, such as call centres, are costlessly tradeable over very large distances. According to this dichotomy, large shares of service jobs are now "at risk" of being offshored to low-wage nations. A key empirical prediction of the dichotomy is that we should find no marginal effects of distance on international trade in services.

We hypothesize instead that the cost of utilizing foreign services is a continuous increasing function of distance. We provide a model of the market for international services that generates a gravity-like equation for service trade. We estimate the model for different service categories. Distance effects for the categories that include offshoreable services are statistically and economically significant throughout the sample period. In calculations based on plausible parameter values, service purchasers are willing to pay four times more for nearby (≈ 100km) than for remote (≈ 10,000km) service providers. Thus, distance shields workers to a significant extent from the threat of offshoring. However, distance effects for most services have declined substantially. If these trends continue, local service workers will increasingly find themselves in closer competition with foreign suppliers. The "theoretical" maximum number of flows is 64 (import and exports with reporting countries). "Maximum" shows the actual maximum across all partner countries for each year.

  Bilateral service trade flows are compiled by the OECD and Eurostat, the European Union's (EU) statistical agency. The World Bank's World Development Indicators (WDI) provides service trade data on a multilateral basis. WDI provides the most time and country coverage, 1976-2006 for 192 countries. These data are useful for summarizing world trends but cannot be used for bilateral flow estimation. Of the sources of bilateral trade, Eurostat has longer time coverage: 1992-2006 versus 2000-2006 for the OECD. 1

Figure 1 :Figure 1

 11 Figure 1: Service classifications in the Extended Balance of Payments

Figure 2 :Figure 3 :

 23 Figure 2: The growth of trade and production of goods and services

  the service from o to d. Unit labour costs of origin o are wages divided by productivity, w o /z o . Delivering services from o to d consumes additional labour time in order to produce a service suitable to the preferences of consumers in the destination market. This might involve travel, training, or translation time so we use T od to represent the hours required per unit of service output. If the origin workers incur the delivery costs, then delivery labour costs are given by w o T od . Assuming that the same productivity adjustment applies to both producing and delivering services, let T od = τ od /z o , where τ od is a parameter increasing in the distance between o and d. Combining production and delivery costs yields the delivered unit labour costs (w o /z o )(1 + τ od ). 4 We can model the objective function of the firm as maximizing the negative of the log of the delivered unit labour costs.

  First let candidate-worker productivity, z o , be distributed Fréchet. The cumulative distribution function (CDF) of z o is exp{-(z/κ) -θ }, where θ is an inverse measure of productivity variation and κ is a location parameter. Then the distribution of ln z o takes the Gumbel form with CDF exp(-exp(-θ(ln z -ln κ))), where ln κ is the mode of the distribution of ln z o . The maximum of N Gumbel draws retains the Gumbel form with the mode increased to ln κ + (1/θ) ln N . Assuming that each service position goes to the most qualified candidate in country o, and that countries differ in terms of the size of their candidate pool (N o ) and their modal productivity (ln κ o ), the objective function can be re-expressed as

Figure 5 :

 5 Figure 5: The impact of distance on French OCS Imports, 2000-2006 averages

  conduct a meta-analysis of 1467 distance effects estimated in 103 papers and find rising distance effects since the 1960s. We corroborate the rise in estimated distance effects here using the standard gravity specification on the sample of all positive trade flows.Trends in the distance coefficient for goods vary by specification, however. The trend in the other two specifications (standard gravity and fixed effects, columns 4 and 6) are positive but insignificant. Contrasting results of trends in distance effects under standard gravity and fixed effects specifications might be explained by entry into the sample of distant countries with low trading propensities. If low trading propensities are not fully

Figure 6

 6 Figure6displays the key results for the annual estimates of the distance effect for Other Commercial Services. Since our focus is on distance, and how it evolves over time, we display only the annual distance effects (coefficient on log distance times minus one) and their 95% confidence intervals. The lines marked with circles are linear-in-logs

:Figure 6 :

 6 Figure 6: Evolution of distance effects over time

  Time Zone Diff., full sample (d) No Time Zone Diff., No "Rich-Rich" obs.

  -negative observations outsourcing debate. Restricting the sample has the disadvantage, however, of losing information in the estimation of the distance effect-eliminating trade between rich countries reduces the sample of Other Commercial Services trade flows by 4220 observations (26%), 4162 of which are non-zero trade flows. 16 The size of the sample for each year is given at the top of each panel in the figure.

Figure 7 :

 7 Figure 7: Illustrative values of the proximity premium

  Figure 7 displays the proximity premia as a function of relative distance using four estimates of δ: the lower and upper bounds based on estimated distance effects, the air transport elasticity, and the telephone elasticity.

Table 1 :

 1 Worldwide Service Trade Note: Value, expressed in billions of US dollars, reflects world exports as recorded in World Development Indicators. Growth is the annual percent change from 1980-2006. The Eurostat share represents the trade of Eurostat reporting countries as a percentage of world trade in 2006.

		Value Growth %	Eurostat share
	Period:	2006 1980-2006	2006
	Total Services	2830.3	7.8	0.75
	Other Commercial Services 1360.7	9.5	0.85
	Travel	764.1	8.0	0.66
	Transportation	642.4	5.9	0.81
	Government	63.1	2.9	0.21

Table 1

 1

provides information on the coverage of the Eurostat data. The first two columns lists the 2006 value of service trade and its subcategories as well as their 1980-2006 growth rates. Service trade was $2.8 trillion in 2006. We observe that Other Commercial Services was the fastest growing subcategory, increasing at an annual rate of 9.5%. The third column lists the ratio of 2006 Eurostat data to WDI data. Eurostatcountry trade accounts for three-quarters of the aggregate service trade reported in WDI and 85% of Other Commercial Services.

Table 2

 2

	displays information for the top six reporters and the top six partners in
	terms of average 2000-2006 Other Commercial Service exports (data for all 65 countries
	are reported in the data appendix). The United States is the leading exporter of both
	Total Services and Other Commercial Services followed by the United Kingdom. The ratio
	of exports based on Eurostat information to the more complete WDI data is close to one
	for reporters, indicating the Eurostat information is complete for reporters. Among the
	Eurostat partners, Hong Kong and India have the highest OCS exports in 2006 according
	to WDI. China, which placed fifth in OCS that year, is the largest exporter of Total
	Services among partner countries. The last column reveals that Eurostat data is quite
	incomplete for partner countries. With the exception of Switzerland, the ratio of Eurostat

Table 2 :

 2 Average annual service exports, 2000-2006, as reported by World Development Indicators and Eurostat

	Reporters
			OCS		Total services
	Country	WDI	ES Ratio WDI	ES Ratio
	United States	164.8 151.5	0.92 331.2 249.7	0.75
	United Kingdom 114.6 104.8	0.91 167.4 156.1	0.93
	Germany	65	67.9	1.04 124.4 121.9	0.98
	Japan	47.7	44.1	0.92	86 76.2	0.89
	France	38.2	44.3	1.16	99.4 95.6	0.96
	Ireland	36	26.5	0.74	42.3 31.1	0.74
	Partners
	Hong Kong	26	5.4	0.21	52 13.7	0.26
	India	25.7	2.5	0.1	35.3	5.1	0.14
	Canada	25.5	6.5	0.25	46.9 14.6	0.31
	Switzerland	23.4	23.6	1.01	37.4 40.6	1.08
	China	20.7	6.5	0.31	54.1 16.8	0.31
	Singapore	19.5	5.2	0.27	40 11.8	0.3
	Note: Exports expressed in billions of US dollars. "Ratio" is the ratio of ex-
	ports reported in Eurostat and exports reported in World Development
	Indicators (WDI).

Table 3

 3

	lists the number of

Table 4 :

 4 OCS vs Goods in Gravity and FE Specifications

		(1)	(2)	(3)	(4)	(5)	(6)
		OCS	goods	OCS	goods	OCS	goods
	ln avg dist	-1.251 a -1.510 a -1.259 a -1.148 a -2.216 a -1.559 a
		(0.073) (0.026) (0.074) (0.053) (0.129) (0.086)
	ln avg dist × trend	0.012 b -0.010 a 0.009 c	0.004	0.072 a	0.008
		(0.005) (0.001) (0.005) (0.003) (0.013) (0.009)
	Time zone diff.	0.002	0.069 a	0.006	0.065 a -0.021 0.046 a
		(0.016) (0.006) (0.017) (0.013) (0.014) (0.009)
	Shared Language	0.712 a 0.745 a 0.734 a 0.227 b -0.099 0.119 c
		(0.136) (0.040) (0.155) (0.113) (0.073) (0.062)
	Colonial Relation	0.689 a 1.023 a 0.722 a 0.420 a 0.594 a 0.357 a
		(0.135) (0.082) (0.148) (0.115) (0.062) (0.047)
	Shared Legal origins 0.474 a 0.351 a 0.432 a 0.440 a 0.646 a 0.458 a
		(0.070) (0.030) (0.074) (0.056) (0.041) (0.023)
	Observations	15601 274700 14769	14769	15108	15108
	R 2	0.772	0.640	0.777	0.820	0.803	0.855
	Fixed effects	t	t	t	t	ot, dt	ot, dt
	Clustering	od	od	od	od	dt	dt

Note: Standard errors in parentheses with a , b and c respectively. Columns (1)-(

4

) include origin and destination log population and log per capita income. The R 2 in columns (

5

) and (

6

) include explanatory power of all FEs.

Table 5 :

 5 Fixed effects linear model for different types of servicesNote: Standard errors in parentheses with a , b and c respectively denoting significance at the 1%, 5% and 10% levels. Specifications employ originyear and destination-year fixed effects and standard errors are clustered by destination-year.

		(1)	(2)	(3)	(4)	(5)
		Total	OCS Finance	IT	MBS
	ln avg dist	-2.141 a -2.243 a -1.913 a -1.882 a -2.281 a
		(0.109) (0.130) (0.152) (0.178) (0.575)
	ln avg dist × trend	0.064 a 0.077 a	0.035 b	0.058 a	0.068
		(0.011) (0.013) (0.014) (0.016) (0.048)
	Time zone diff.	0.012	-0.018	0.145 a	0.011	0.228 a
		(0.012) (0.014) (0.019) (0.021) (0.047)
	Shared Language	-0.148 a -0.127 b -0.079 -0.326 a -0.714 a
		(0.055) (0.063) (0.086) (0.117) (0.139)
	Colonial Relation	0.642 a 0.552 a	0.241 a	0.670 a	0.471 a
		(0.053) (0.058) (0.092) (0.101) (0.122)
	Shared Legal origins 0.661 a 0.650 a	0.491 a	0.389 a	0.575 a
		(0.032) (0.039) (0.047) (0.059) (0.064)
	Observations	17607	16002	7778	6008	5642
	R 2	0.803	0.802	0.789	0.729	0.801

Table 6 :

 6 Average annual service exports, 2000-2006, as reported by World Development Indicators and Eurostat Note: Exports expressed in billions of US dollars. "Ratio" is the ratio of exports reported in Eurostat and exports reported in World Development Indicators (WDI).

	Reporters
		Total services		OCS
	Country	WDI	ES Ratio WDI	ES Ratio
	Austria	41.6	35.8	0.86	20.7 11.4	0.55
	Belgium	50.1	38.9	0.78	28.2 22.6	0.8
	Bulgaria	3.3	2.1	0.63	0.6	0.2	0.4
	Croatia	7.6	4.5	0.59	1.3	0.6	0.45
	Cyprus	5.5	4.4	0.8	1.8	1.5	0.81
	Czech Republic	9.1	8.3	0.91	2.9	2.2	0.78
	Denmark	34.3	29.2	0.85		5
	Estonia	2.4	1.6	0.7	0.6	0.3	0.47
	Finland	12.4	7.3	0.59	8.4	3.9	0.46
	France	99.4	95.6	0.96	38.2 44.3	1.16
	Germany	124.4 121.9	0.98	65 67.9	1.04
	Greece	26.6	24.2	0.91	2.7	2.1	0.77
	Hungary	9.5	7.1	0.75	4.2	3.5	0.84
	Ireland	42.3	31.1	0.74	36 26.5	0.74
	Italy	74.1	64.9	0.88	29.8 27.3	0.92
	Japan	86	76.2	0.89	47.7 44.1	0.92
	Latvia	1.7	1.2	0.74	0.4	0.1	0.28
	Lithuania	2.1	1.6	0.74	0.4	0.1	0.32
	Luxembourg	30.2	24.3	0.81	25.3 20.5	0.81
	Malta	1.6	1.2	0.75	0.5	0.2	0.32
	Netherlands	65.1	63.7	0.98	36 38.2	1.06
	Norway	23.5	19.4	0.82	8	6.5	0.81
	Poland	13.1	9.9	0.75	3.5	2.8	0.79
	Portugal	12.7	10.8	0.85	3.4	2.9	0.86
	Romania	3.5	2.5	0.7	1.7	1.1	0.64
	Slovak Republic 2.8	3	1.09	0.9	0.9	0.99
	Slovenia	3	2.4	0.81	0.7	0.5	0.63
	Spain	75.7	64	0.85	23.1	15	0.65
	Sweden	32.8	29.4	0.9	19.8 17.1	0.86
	Turkey	20.1	9.4	0.47	4	1.1	0.26
	United Kingdom 167.4 156.1	0.93 114.6 104.8	0.91
	United States	331.2 249.7	0.75 164.8 151.5	0.92

Table 7 :

 7 Average annual service exports, 2000-2006, as reported by World Development Indicators and Eurostat Note: Exports expressed in billions of US dollars. "Ratio" is the ratio of exports reported in Eurostat and exports reported in World Development Indicators (WDI).

	Partners
		Total services		OCS
	Country	WDI ES Ratio WDI ES	Ratio
	Albania	0.8	0.2	0.27	0.1	0	0
	Argentina	5.3	1.4	0.27	1.6 0.4	0.28
	Australia	24.8	10.6	0.43	6.2 3.7	0.59
	Belarus	1.6	0	0.03	0.4	0	0
	Brazil	12.4	4.5	0.36	6.6 1.9	0.29
	Canada	46.9	14.6	0.31	25.5 6.5	0.25
	Chile	5.5	1.1	0.2	1.3 0.4	0.27
	China	54.1	16.8	0.31	20.7 6.5	0.31
	Colombia	2.3	0.2	0.09	0.5	0	0.03
	Egypt	12	4.1	0.34	2.9 0.4	0.12
	Hong Kong	52	13.7	0.26	26 5.4	0.21
	Iceland	1.4	0.3	0.22	0.3	0	0
	India	35.3	5.1	0.14	25.7 2.5	0.1
	Indonesia	8.4	2.3	0.27	3	1	0.33
	Iran	1.4	0.3	0.21	0.1 0.2	1.86
	Israel	15.1	3	0.2	9.5 1.8	0.19
	Korea	37.1	12.8	0.34	11.5 4.6	0.4
	Malaysia	16.5	2.7	0.17	5.6 0.9	0.16
	Mexico	14	15.9	1.13	2.6 7.5	2.91
	Morocco	5.9	2.6	0.44	1.1 0.4	0.39
	New Zealand 6.4	2.8	0.44	1.3 0.9	0.68
	Nigeria	2.8	0.3	0.1	2.3 0.1	0.06
	Philippines	4	1.9	0.47	1 0.7	0.67
	Russia	18.2	5.7	0.31	6.4 2.3	0.36
	Singapore	40	11.8	0.3	19.5 5.2	0.27
	South Africa 8	3.3	0.41	1.5 1.1	0.74
	Switzerland	37.4	40.6	1.08	23.4 23.6	1.01
	Taiwan		10.8			4.9
	Thailand	17.3	5.3	0.3	4.4 1.3	0.3
	Ukraine	6.6	0.3	0.05	0.8	0	0.04
	Uruguay	1.1	0	0.01	0.2	0	0.13
	Venezuela	1.2	1	0.79	0.2 0.3	1.38
	Yugoslavia		0.2			0

Table 8 :

 8 Non-missing Eurostat data on other commercial services Note: Values represent the number of non-missing Other Commercial Services trade flows.The "theoretical" maximum number of flows is 128 (import and exports with 64 potential partners). "Maximum" shows the actual maximum across all reporters for each year.

	Reporters
	Country	1992 1994 1996 1998 2000 2002 2004 2006
	Austria	27	27	36	36	78	126	126	116
	Belgium	0	0	0	0	2	124	124	103
	Bulgaria	0	2	4	8	26	35	43	116
	Croatia	0	2	4	6	18	36	43	114
	Cyprus	0	2	2	4	20	40	114	116
	Czech Republic	9	16	20	20	116	124	114	116
	Denmark	0	0	0	0	18	30	46	116
	Estonia	0	2	8	12	32	40	114	104
	Finland	0	30	31	31	80	118	110	93
	France	29	31	31	29	74	118	108	113
	Germany	32	32	32	82	98	113	114	116
	Greece	0	4	32	0	38	119	114	67
	Hungary	9	16	20	20	112	124	114	116
	Ireland	0	0	0	0	8	126	124	89
	Italy	32	32	31	32	82	126	116	116
	Japan	17	24	41	44	58	78	88	91
	Latvia	0	2	4	6	126	116	114	114
	Lithuania	0	2	4	10	31	39	114	116
	Luxembourg	0	0	0	0	2	114	114	116
	Malta	0	2	2	4	20	34	45	88
	Netherlands	32	32	32	32	81	123	122	114
	Norway	0	2	8	12	44	36	68	82
	Poland	10	16	19	20	27	35	113	116
	Portugal	4	32	36	36	98	114	114	85
	Romania	0	2	4	8	26	36	44	116
	Slovak Republic	0	2	4	84	26	64	110	116
	Slovenia	0	95	98	100	113	126	126	116
	Spain	4	12	12	12	18	29	49	116
	Sweden	0	0	0	0	98	116	116	116
	Turkey	10	16	20	20	30	36	46	50
	United Kingdom	6	6	94	94	94	118	118	116
	United States	18	24	26	28	40	44	92	89
	Maximum	32	95	98	100	126	126	126	116

Table 9 :

 9 Non-missing Eurostat data on other commercial services Note: Values represent the number of non-missing Other Commercial Services trade flows.

	Partners
	Country	1992 1994 1996 1998 2000 2002 2004
	Albania	0	2	4	6	18	24	12
	Argentina	10	17	22	22	29	33	44
	Australia	9	16	22	21	32	36	50
	Belarus	0	2	4	6	18	24	12
	Brazil	9	17	24	23	31	36	48
	Canada	12	19	24	26	40	44	50
	Chile	0	2	4	8	26	33	44
	China	10	15	22	22	30	35	47
	Colombia	0	2	4	8	26	24	14
	Egypt	0	2	4	6	26	34	42
	Hong Kong	0	2	4	8	26	36	48
	Iceland	0	2	4	6	18	36	46
	India	10	16	22	21	30	36	48
	Indonesia	0	2	4	8	25	35	47
	Iran	0	2	4	6	18	28	21
	Israel	0	2	4	8	26	34	46
	Korea	0	2	4	8	26	36	46
	Malaysia	0	2	4	6	25	35	45
	Mexico	8	16	24	24	31	36	45
	Morocco	10	16	20	20	28	34	42
	New Zealand	9	16	21	22	31	36	49
	Nigeria	0	0	0	0	0	0	0
	Philippines	0	2	4	8	25	35	47
	Russia	10	14	20	21	30	38	46
	Singapore	0	2	4	8	26	35	47
	South Africa	0	2	4	8	28	34	48
	Switzerland	0	2	8	12	34	38	49
	Taiwan	0	2	4	8	25	36	45
	Thailand	0	1	4	8	26	36	46
	Ukraine	0	2	4	6	18	20	10
	Uruguay	0	2	4	6	18	32	43
	Venezuela	0	2	4	8	26	34	46
	Yugoslavia	0	2	4	4	18	20	12
	Maximum	12	19	24	26	40	44	50

A smattering of Eurostat data is available starting in 1985, but the data set is not complete enough to be useful until 1992.

We list only information for even years. Data for all 65 countries are reported in the data appendix.

[START_REF] Francois | Institutions, infrastructure and trade[END_REF] and[START_REF] Felbermayr | Exploring the intensive and extensive margins of world trade[END_REF] argue this is a reasonable assumption for data on goods trade.

This specification, in which delivery costs magnify unit labour costs, is chosen primarily for analytic tractability. It mirrors the "iceberg" assumption conventionally made for trade in goods.

See footnote 20 of[START_REF] Eaton | Technology, geography, and trade[END_REF] for a comparison of heterogeneous productivity and differentiated products derivations of the gravity equation and[START_REF] Anderson | Gravity with gravitas: a solution to the border puzzle[END_REF] for analysis of the structural interpretation of the importer and exporter fixed effects. The equivalence

Note that the Eurostat data do not report France's service imports from most of its former colonies in Africa.

Data on merchandise trade comes from data from the IMF's Direction of Trade Statistics (DoTS). The regressions are estimated using 1992-2006 data.

The number of observations in the "standard" gravity specification is slightly smaller than those using fixed effects because of missing GDP data forIsrael and Malta in 2006 and Cyprus in 2005 and 2006.

[START_REF] Stein | Longitude matters: time zones and the location of foreign direct investment[END_REF] find that a common legal system has a positive effect on bilateral FDI.

The countries are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.

The rationale for this method is given by[START_REF] Gourieroux | Pseudo maximum likelihood methods: applications to poisson models[END_REF]. More detail about the practical implementation can be obtained from[START_REF] Wooldridge | Quasi-likelihood methods for count data[END_REF]. We provide our Stata code at http://strategy.sauder.ubc.ca/head/sup/.

Our estimates of η 2 range from 0.20 in 1992 to 0.87 in 2005.

The restricted sample includes trade between high-income and lower income countries in both directions. We include exports from high-income to low-income countries because they should be subject to the same set of distance costs as trade in the other direction and provide useful information for estimating distance effects.

 17 Schwellnus (2007) obtains a relatively low estimate of the distance effect for OCS in 2000 for trade between OECD countries, 1.0, using a "Log" specification and fixed effects.

Data Appendix

Service trade

The Eurostat data is available online at http://epp.eurostat.ec.europa.eu/ under the "Economy and Finance" Theme as Balance of Payments-International Transactions, International Trade in Services (since 1985). The 1992-2006 data we employ comes from two downloads. We executed the first one in 2006 that provides data through 2004 and the second in 2008 (when we revised the paper). Data for the 1992-2004 period was more complete in the first download, so we just appended 2005 and 2006 data from the 2008 download to the data from the initial download. We construct the OCS category of trade by subtracting the sum of education (895), health (896), and government (291) services from "Other Services" (code 981, total services minus travel and transport). When reports on a bilateral flow from both the importer and the exporter exist, we take the larger of the two. The logic of this procedure is that service trade data are based on surveys and a larger flow should correspond to a more complete survey. We downloaded multilateral service trade data from the World Bank's World Development Indicators (WDI) at http://devdata.worldbank.org/dataonline/.

Trade cost proxies

Distance, D, common language, and colonial relationships come from the CEPII bilateral database (http://www.cepii.fr/anglaisgraph/bdd/distances.htm). For distance we use "distw," a population-weighted average of the great-circle distances between the 20 largest cities in the origin and destination countries. We use the Ethnologue-based version of common language that equals one if a language is spoken by at least 9% of the population in both countries. Legal is from Andrei Schleifer's Data Sets web page (http: //post.economics.harvard.edu/faculty/shleifer/Data/qgov_web.xls). We calculate time differences as the average number of hours-between 0 and 12-separating two countries. Denoting hours after GMT with H,

Time zones were obtained from Wikipedia.

Population and GDP

World Development Indicators Online (http://devdata.worldbank.org/dataonline/), provides population and GDP (in current USD) for all countries in our study except Taiwan, for which we obtained the data from a Taiwanese government website (http: //eng.stat.gov.tw/ct.asp?xItem=15062&ctNode=3567).