2013_ Impact of Time Zone on Trade

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O R I G I N A L P A P E R

Time differences, communication and trade: longitude

matters II

Edward Anderson

Kiel Institute 2013

Abstract This paper uses a gravity model to examine the effect of time differ-

ences between countries on international trade. It builds on previous studies of this

issue by including a wider set of control variables, focusing on a longer time period,

and testing a series of related hypotheses. The results show that time differences

have a negative impact on merchandise trade, with each hour of time difference

reducing trade by between 2 and 7 %, although the size of the effect has fallen in

recent decades. There is also evidence that the negative impact of time differences issmaller where mechanisms of formal contract enforcement are stronger, and where

co-ethnic networks are more prevalent, and that time differences reduce bilateral

telephone traffic as well as trade. These results are consistent with the hypothesis

that time differences reduce trade by raising the non-pecuniary costs of travel and

communication.

Keywords Trade Gravity model Time zones Communication

JEL Classification F10 F15 F20

1 Introduction

What impact do time differences between countries have on international trade? It is

well known that geographical distance reduces trade: this has been shown by the

results of nearly 1,500 gravity models from over 100 research papers (Disdier and

E. Anderson (&)

School of International Development, University of East Anglia, Norwich NR4 7TJ, UK

e-mail: [email protected]

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Rev World Econ

DOI 10.1007/s10290-013-0179-9


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Head2008).1 But in comparison with the large literature on the effects of distance, it

is only recently that economists have begun to explore the effects of time

differences on trade (e.g. Marjit 2007; Stein and Daude 2007; Head et al. 2009;

Kikuchi and Long2010; Tomasik 2013).

Time differences might affect trade for two reasons. On the one hand, timedifferences raise the non-pecuniary costs of travel and communication, by causing

jet lag among travellers and by reducing the amount of time in the normal working

day in which simultaneous communication (e.g. telephone conversations, video-

conferencing) can take place. If travel and simultaneous communication are

important for tradefor example, by helping to establish and maintain trust, by

spreading information about trading opportunities, or by facilitating the flow of

complex, tacit know-how among vertically disintegrated production networks

greater time differences should lead to less trade. On the other hand, time

differences promote opportunities for trade in business services (Gupta and Seshasai2004; Marjit 2007; Kikuchi and Long 2010). For example, by employing teams in

different time zones, firms can work on product design and development around the

clock, thereby reducing product turnaround times. Thus the impact of time

differences could be positive or negative, depending on the sector: for example,

positive for trade in business services, but negative for merchandise trade in sectors

where travel and simultaneous communication are important.

The empirical evidence on time differences includes studies which look at

merchandise trade (Stein and Daude2007), and those which look at trade in services

(e.g. Head et al. 2009; Dettmer 2011). Both use the well-known gravity modelframework; the basic approach is to augment the standard set of explanatory

variables included in the gravity model (e.g. geographical distance, a common

currency) to include the time difference between trading partners. The aim of this

paper is to extend the results of Stein and Daude (2007) for merchandise trade, in

four main ways. First, we control for certain variables not included by Stein and

Daude (2007) but which might bias the estimated impact of time differences on

trade. Second, our sample spans the period 19502006, in contrast to Stein and

Daude (2007) who focus on data for 1999 only. This makes it possible to determine

whether the impact of time differences has changed in recent decades. Third, the

paper tests a series of additional hypotheses. The first is that any negative effect of

time differences on trade is greater where formal mechanisms of contract

enforcement in each trading partner are weaker, and where co-ethnic networks in

each trading partner are less prevalent. The reasoning is that travel and simultaneous

communication are more important in such caseseither as a means of establishing

and maintaining trust, or of acquiring information about foreign marketsso that

trade becomes more sensitive to the costs of travel and communication. The paper

also examines the relationship between time differences and international commu-

nication flows, as measured by bilateral telephone traffic.

1 After using article search engines to construct a database of 1,467 estimates from 103 papers, we find

that the mean effect [of distance on trade] is about 0.9, with 90 % of estimates lying between 0.28 and

1.55. On average, then, a 10 % increase in distance lowers bilateral trade by about 9 %. (Disdier and

Head2008:37).

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To give a flavour of the main results, the paper finds that time differences have a

negative and statistically significant impact on bilateral merchandise trade, across a

range of samples and estimation methods. For pooled samples across years, each

hour of time difference is found to reduce trade by between 2 and 7 %. This is

smaller than the range of 711 % reported by Stein and Daude (2007) buteconomically significant nonetheless. For example, the effect of a 5 h time

difference (e.g. that between London and New York) is equivalent to a rise in

geographical distance of between 18 and 42 %. The negative impact of time

differences has however fallen in size over recent decades, which may be due to the

emergence of new communication technologies. There is also evidence that the

negative impact of time differences is smaller where mechanisms of formal contract

enforcement, as measured by the Kaufmann et al. (2009) rule of law index, are

stronger, and co-ethnic networks, as measured by the size of the bilateral migrant

population, are more prevalent. Time differences also have a negative andstatistically significant impact on bilateral telephone traffic, with each hour of time

difference reducing traffic by between 3 and 5 %.

The results in this paper are consistent with the hypothesis that time differences

reduce merchandise trade by raising the non-pecuniary costs of travel and

communication. This of course does not deny that time differences can promote

trade in business services, as suggested by Gupta and Seshasai (2004), Marjit (2007)

and Kikuchi and Long (2010). For example, Head et al. (2009) find that time

differences do have a positive effect on certain categories of services trade, in

particular financial services and miscellaneous business services; similar results arealso reported by Dettmer (2011). Nevertheless, the results do suggest that, when

considering overall bilateral trade, the positive effect of time differences on trade in

business services may be outweighed by their negative effect on merchandise trade.

The remainder of the paper proceeds as follows. Section2discusses the existing

literature relating to the possible effect of time differences on trade. Section 3then

outlines the methods and variables used in the econometric analysis, while Sect. 4

presents the main results. Section5then presents the estimates of the impact of time

differences on bilateral telephone traffic. Section6 concludes and discusses

potential implications for policy.

2 Relevant literature

The first paper to discuss the potential effects of time differences on international

trade was Stein and Daude (2007). They argued that time differences raise

transaction costs, and in particular the non-pecuniary costs of travel and

simultaneous communication (e.g. telephone conversations, video-conferencing).2

On the one hand, air travel across time zones causes jet lag, which for many people

is an unpleasant experience that diminishes well-being and productivity for

2 There are no obvious reasons for thinking that, controlling for geographical distance, time differences

affect the pecuniary costs of travel and communication, e.g. the price of a long-distance air ticket or an

international telephone call. There are also no obvious reasons for thinking that time differences affect the

cost of non-simultaneous communication (e.g. written letters).

Time differences, communication and trade

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significant periods of time. On the other hand, simultaneous communication

becomes harder as time differences get larger, because there are fewer hours in a

normal working day when such communication can take place. For example, a time

difference of 6 h implies that two people working a normal working day of 8 h can

have a simultaneous conversation during only two of those hours. Although themain focus of Stein and Daude (2007) was on foreign direct investment, they argued

that the higher transaction costs caused by time differences would reduce trade as

well as foreign direct investment (Stein and Daude 2007: 107).3

There is a large literature on the health effects of jet lag. Key findings (e.g.

Rajaratnam and Arendt2001; Waterhouse et al.2007) include: (a) around two-thirds

of travellers report getting jet lag; (b) there are various adverse symptoms of jet lag,

including tiredness, impaired alertness and performance, gastrointestinal problems

and decreased short-term memory; (c) jet lag is different to travel fatigue, which is

determined by the length of journey rather than the time difference, and whichnormally disappears after a good nights sleep; (d) jet lag can last for several days,

with a rough guide being about two-thirds of the number of time zones that have

been crossed (i.e. the time difference in hours); and (e) jet lag generally occurs when

at least three time zones are crossed rapidly. There is also evidence that working

irregular hours has adverse consequences for health, similar to those caused by jet

lag (Rajaratnam and Arendt 2001). Working irregular hours also reduces

opportunities for social interaction (Hamermesh 1999).

There is also a large literature on the importance of travel and simultaneous

communication for trade. On the one hand, face-to-face communication can helpbuyers and sellers to establish and maintain trust (Leamer and Storper2001; Storper

and Venables2004). Travel can also be important in terms of obtaining information

about trading opportunities in foreign markets (Kulendran and Wilson2000).4 There

are of course ways of enforcing contracts which avoid the need for face-to-face

communication, e.g. via the legal system, and other ways of obtaining information

about foreign markets which avoid or at least substantially reduce the need for long-

distance travel, e.g. social, business or co-ethnic networks (Rauch 1999a, b, 2001;

Rauch and Trindade2002). One would therefore expect that any negative effect of

time differences on trade is greater where formal mechanisms of contract enforcement

are weaker, or where social, business or co-ethnic networks are absent.5

There is also evidence that short-term movements of staff (e.g. training visits)

play an important role in facilitating the transfer of technological and managerial

knowledge transfer between firms engaged in vertically-disintegrated production

3 Stein and Daude (2007) did expect the negative effect of time differences to be smaller on trade than

foreign direct investment, on the grounds that trade transactions are not as demanding in terms of real-

time interaction (Stein and Daude2007: 107). They go on to find that time differences reduce bilateral

investment by between 17 and 26 %, compared to between 7 and 11 % for trade.4 Moreover, travel need not be undertaken specifically for the purpose of obtaining market information

for learning to take place. People who are travelling for non-business reasons may happen to identifytrading opportunities while they are away; thus greater ease of leisure travel can also lead to more trade

(Kulendran and Wilson2000).5 Anderson and Marcouiller (2002) show that imperfect contract enforcement is associated with less

trade. They do not however examine whether the negative impact of imperfect contract enforcement is

greater when travel and communication costs are higher.

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networks or value chains (Rauch 1999a, 2001; Anderson et al. 2006; Anderson

2007; Andersen and Dalgaard2009). Personal interaction is considered particularly

important in this case, on the grounds that the know-how being transferred is

generally tacit in nature, and that transfer of tacit knowledge requires person-to-

person demonstrations and instructions (Keller 2004; Storper and Venables 2004;Andersen and Dalgaard2009). The idea that face-to-face interaction becomes more

important as ideas become more complex is also well established in urban

economics (e.g. Gaspar and Glaeser 1998).

There is a large amount of empirical evidence that communication promotes

trade. For example, Loungani et al. (2002) and Portes and Rey (2005) include the

amount of bilateral telephone traffic in a gravity model, and show that this variable

has a large and statistically significant positive impact on trade. Adding a measure

of communication flows to a gravity model does however raise concerns about

endogeneity. For this reason, Fink et al. (2005) estimate a gravity model including ameasure of communication coststhe price of a bilateral telephone callwhich

they consider to be more plausibly exogenous to bilateral trade flows. They go on to

show that this variable has a large and statistically significant negative impact on

trade. Nevertheless, the price of a bilateral telephone call reflects only the pecuniary

costs of international communication, and the question remains as to whether non-

pecuniary costs, such as those caused by time differences, also affect trade.

Time differences can promote trade in services, as argued by Gupta and Seshasai

(2004), Marjit (2007) and Kikuchi and Long (2010). One example is customer call

centres and help-desk operations; customers who call a helpline out of normal officehours in their country of residence can be automatically transferred to a centre in

another time zone where the local time falls within normal office hours (Gupta and

Seshasai2004). Another example is software design and development, where teams

working in one time zone can e-mail programming problems at the end of their

working day to teams in another time zone at the start of their working day, without

much interruption (Marjit 2007). The advantage here lies in the potential to reduce the

length of time it takes to design and develop new products. Realising these gains

requires a sufficiently large time difference to make a significant difference to

turnaround times, and the ability to transfer large amounts of information quickly

between teams (Marjit2007). In terms of empirical evidence, Head et al. (2009) find

that time differences have a positive and statistically significant effect on trade in both

financial services and miscellaneous business services (including call centres).

Similarly, Dettmer (2011) finds that time differences have a positive and statistically

significant effect on both business and commercial services. In this paper however, the

focus is on merchandise trade, where the potentially positive effect of time differences

on trade does not apply.

3 Methods and data

To determine whether time differences affect trade, this paper uses the well-known

gravity model framework. Exports from countryi to countryj in yeart(denotedxijt)

are determined by an equation of the form:


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xijtAXitA

MjtD

hijexpcTijkLijteijt 1

where AXit and AMjt are indices of the attributes of countries i and j, Dij is the

geographical distance between them, Tij is the time difference between them, and

Lijt is a vector of other linkage indicators, such as a common language, a commonborder, or a shared colonial history. Equation (1) is written under the assumption

that time differences do not change over time; this is the case for our preferred

measure of time differences, the solar time difference, which is fixed by geography

(see below). Geographical distance also does not change over time, but some of the

other linkage indicators do, such as a common currency.

The key coefficient of interest in Eq. (1) isc, which is expected to be negative on

the grounds that time differences raise the costs of travel and simultaneous

communication, which is important for at least some (if not all) types of

merchandise trade. Two main measures of time differences are used. The first is theofficial time difference, given by:

Tijabshi hjifabshihj 12 2a

Tij 24abshihjifabshi hj[ 12 2b

wherehiand hjare the official times in the most populous cities of countries i and j,

in hours before (-) or ahead (?) of greenwich mean time (GMT). If one country

operates DST, the official time difference will tend to vary depending on the time of

year; in this case we simply take the average time difference during the year. Since

some countries in the world have multiple official time zones, we alsocalculate the

minimum and maximum official time differences between countries.6

The other main measure used is the solar time difference, which is given by

replacing official times in the above formulae with the times of solar nooni.e. the

time at which the sun reaches its highest point in the skyalso in hours before (-)

or ahead (?) of GMT. Solar time differences are fixed by geography: on average, it

takes the sun four minutes to transit one degree of longitude, so the solar time

difference is essentially a function of the difference in longitude between two

locations. The advantage of using solar time differences is that, being fixed by

geography, they avoid potential reverse causation; by contrast, official times can bechanged by government policy, which raises the possibility that governments try to

minimise official time differences with their larger trading partners. The disadvan-

tage, however, is that solar time differences differ depending on which particular

pair of locations (towns or cities) in each country are selected; it is therefore much

more difficult to calculate the minimum and maximum solar time differences (and

we do not attempt to do so). In what follows we use solar time differences as our

main measure but also test the robustness of the results to the other three measures.

Solar and official time differences are of course highly correlated, since

governments typically set their official times with regard to the timing of solarnoon in their principal city. However, we do not include both solar and official time

difference in the same regression; the issue of whether official and solar time

6 The countries with multiple official time zones are Australia, Brazil, Canada, Democratic Republic of

Congo, Indonesia, Kasakhstan, Mexico, Mongolia, Russia and the United States.

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differences have different effects on trade is instead left for further research (see

Sect.6).

The paper also tests for a possible non-linear relationship between time

differences and trade. One possible reason is the evidence that jet lag only becomes

significant for time differences exceeding 3 h (see Sect. 2). Another is that for timedifferences exceeding 8 h, the number of overlapping hours between two locations

in a normal working day reaches zero, so that additional time differences beyond

this level have no further adverse impact on the ease of communication between

workers in different countries. We test for these possible effects by estimating a

piecewise regression model with breakpoints at time differences of 3 and 8 h.7

Two further hypotheses discussed in Sect.2 are also tested: that time differences

have less of a negative impact on trade where formal mechanisms of contract

enforcement in each country are stronger, and when co-ethnic networks are more

prevalent. These hypotheses are tested by interacting the time difference withmeasures of contract enforcement and co-ethnic networks, the expectation being

that the coefficient on each interacted variable is positive. Contract enforcement is

measured by the combined value of the Kaufmann et al. (2009) rule of law index in

each country. This index is a composite indicator designed to capture perceptions

of the extent to which agents have confidence in and abide by the rules of society,

and in particular the quality of contract enforcement, property rights, the police, and

thecourts, as well as the likelihood of crime and violence (Kaufmann et al. 2009:

6).8 Co-ethnic networks are measured by the bilateral migrant population of each

country pair: i.e. the sum of persons from country i living in country j and personsfrom country j living in country i, taken from Parsons et al. (2007).9

In terms of control variables, all regressions include separate fixed effects for each

exporter and importer in each year, in place of the attribute indices AXit andAMjt. This

version of the gravity model was proposed by Feenstra (2004) as a way of dealing with

the drawbacks of the conventional gravity model noted by Anderson and van Wincoop

(2003). The conventional gravity model, by contrast, assumes that AXit YaX

1

it yaX

2

it and

AMjt YaM

1

jt yaM

2

jt , whereYis GDP andy is GDP per capita (Disdier and Head2008: 39).

Since separate fixed effects are included for each exported and importer in each year,this avoids the need to include GDP and GDP per capita as control variables; including

these variables makes no difference to the results. However, the samples used for

estimation are sometimes deliberately restricted in order to remain within

7 Previous research (e.g. Stein and Daude2007) has used the number of overlapping hours in a normal

working day (spanning from 9 am to 5 pm) as an alternative measure of the time difference between

countries; this variable varies between 0 and 8 and is expected to have a positive effect on trade. This is

equivalent to estimating a piecewise regression model with a single breakpoint at eight hours and the

restriction that the slope of the regression is zero after the structural break. The piecewise model therefore

avoids the need to include the number of overlapping hours as an alternative measure of the time

difference.8 The rule of law index varies between -2.6 and 2.0. To facilitate interpretation of results we rescale the

index so that all values are positive. The combined (re-scaled) index varies from 0.6 to 9.1, with

percentiles at 3.3 (10th), 5.1 (50th) and 7.0 (90th).9 This variable is measured in log units (one is added to avoid taking logs of zero values) and varies from

zero to 16.1, with percentiles at 1.4 (10th), 4.9 (50th) and 9.1 (90th).


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computational limits. As noted by Head et al. (2010), including separate fixed effects

for each exporter and importer in each year in a panel dataset of 200 exporters and 200

importers over 50 years requires 20,000 dummy variables, which is beyond the

capability of standard statistical software packages.

For the linkage indicators, seven indicators commonly used in gravity modelestimations are included, namely: a shared border, a common language, member-

ship of a common currency union, membership of a common preferential trade

agreement, a common colonial historyboth between a former colony and the

mother country, and between two former colonies of the same mother countryand

political union (e.g. an overseas dependency or territory and its controlling state).

For common language, the direct communication measure provided by Melitz

(2008) is used, given by:

Mij Xk

pikpjk 3

where pik and pjk are the shares of population in countries i and j speaking a

particular language k either as a first or second language. Up to 29 major world

languages are included in the calculation; a minimum threshold of 4 % is set for any

one language to be included in the calculation. Thus if 80 % of the population

speaks English in country i and 40 % speak English in country j, and no other

language is spoken by at least 4 % of the population in both countries, Mijis equal

to 0.32. The measure is designed to capture the ability for two people based in

different countries to communicate directly with one another in the same language;Melitz (2008) shows that it has a much larger positive impact on trade than other

language measures used in the literature, such as whether or not two countries have

the same official language (e.g. Frankel and Rose2002).

Two measures of geographical distance are included in the regressions. The first is

the great-circle distancei.e. the shortest distance between any two points on the

earths surfacebetween the most populous city in each country, denotedDGCij . Great-

circle distances are closely related to time differences, but the correlation is by no

means perfect. First, the relationship depends on the direction of travel between

locations: in particular, whether this is predominantly EastWest (EW) or NorthSouth (NS). For example, while the distance between London and Los Angeles

(8,700 km) is similar to that between London and Johannesburg (9,100 km), the

former involves a time difference of 8 h while the latter involves a difference of just

1 h (or 2 h between October and March). Second, the relationship between time

differences and distance in an EW direction differs by latitude. As already noted, it

takes the sun 4 min on average to transit one degree of longitude. But while one degree

of longitude is equivalent to around 111 km at the equator, it is equivalent to around

56 km at 60N. Thus any given EW great circle distance (in km) will be associated

with a larger solar time difference, the further one is from the equator.10 For our

10 Note that the maximum possible great circle distance in an EW direction occurs when two locations

are on opposite sides of the globe. In this case, the solar time difference will also be at the maximum of

12 h. Thus is not possible for time differences to fall with higher great-circle distances in an EW

direction.

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sample, the correlation coefficient between solar time differences and great circle

distances is (in logs) 0.80.

Although standard in the literature, the use of great-circle distances could

however bias the estimated effect of time differences on trade. Consider Africa and

America, two continents which are aligned in a predominantly northsouthdirection. Cargo shipments between countries in these continents aligned in a

predominantly northsouth direction (e.g. Argentina and Brazil, Kenya and

Tanzania) can follow a fairly direct route by sea, but shipments between two

countries aligned in a predominantly eastwest direction (e.g. Argentina and Chile,

Kenya and Nigeria) must follow a much more roundabout routeunless they travel

by land, which is of course much more expensive. One might therefore expect to see

more trade in these continents in a northsouth direction than in an eastwest

direction, but for reasons unrelated to time differences.

To avoid this problem, we also include a measure of the cargo distance betweencountries, denoted DACij : This is given by:

DACij DSijD

Li D

Lj 4

whereDSij is the sea distance between the main ports in countries i and j, andDLi and

DLj are the great-circle distances between the main port and the most populous city

in countriesi and j. For land-locked countries, the nearest major foreign port is used

as the main port. One possible drawback with this measure is that the sum ofDLi and

D

L

j could exceed D

GC

ij , and in these cases it seems reasonable to assume that thegreat-circle route (by land) will be cheaper than the sea route. Thus if DLi

DLj [DGCij we setD

ACij D

GCij .

The regressions also control for two other distance-related variables. The first is

the difference in latitude between trading partners, defined by

dlatij abs abslati abslatj

5

where lati and latj stand for the respective latitudes of each trading partner, with

Northern latitudes positive and Southern latitudes negative; the second is the north

south (NS) distance between trading partners, defined by

nsij abslatilatj 6

As argued by Melitz (2007), differences in latitude promote opportunities for trade,

by causing differences in climate and other geographic conditions (e.g. soil type)

which can in turn be a source of comparative advantage. Melitz (2007) also finds

that NS distance has a positive impact on trade, even when controlling for dif-

ferences in latitude; a possible explanation relates to the opposition of seasons

(Melitz 2007: 979). Controlling for overall (great-circle) distance however, differ-

ences in latitude and NS distances are both negatively correlated with time dif-ferences, which reflect distance in an eastwest direction.11 Thus failing to control

11 More specifically, the correlation coefficient between the residuals from a simple linear regression of

the great circle distance between two cities (in logs) on the solar time difference and the NS distance is

-0.43; the equivalent correlation for the difference in latitude is -0.32.


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for differences in latitude and NS distances could bias the estimated effect of time

differences on trade.

Finally, two main estimation methods are used. First, we take logs of Eq. (1) to

obtain:

lnxijtlnAXitlnA

Mjt h lnDijcTijkLijt ln eijt 7

we then estimate Eq. (7) using ordinary least squares (OLS), omitting observations

with zero trade from the sample. Second, we estimate (1) directly using the method

of Poisson psuedo maximum likelihood (PPML), as argued by Santos Silva and

Tenreyro (2006). As a robustness check, we also estimate Eq. (7) using the Tobit

method, with one added to the value of the dependent variable prior to taking logs,

so that the lower truncation point occurs at zero. The Tobit results are on the whole

very similar to the OLS results and are reported separately in the Appendix.

Descriptive statistics and all sources of data are also reported in the Appendix.

4 Main results: time differences and trade

4.1 Results, pooled by year

Table1 shows the results of estimating Eq. (1) for three different samples:

19501995, 19702000 and 19752005, in each case with data at 15-year intervals.

Ideally a single sample would be used, but this is not possible for computationalreasons (see Sect. 3). The three samples are chosen so that together they cover a

range of years from the period of available data, but do not overlap in terms of the

precise years covered.

Turning to the key results of interest, the coefficient for time differences is

negative in all six regressions; it is statistically significant at the 1 % level when

using OLS and at the 5 % level or below when using PPML, although in the latter

case for the first and second samples only. Depending on the sample and estimation

method, each hour of time difference reduces trade by between 2 and 7 %.12 To give

some idea of the magnitudes involved, the effect of a 5 h time difference (e.g. thatbetween London and New York) is equivalent to a rise in geographical distance of

between 18 and 42 %; the effect of the maximum time difference of 12 h is

equivalent to a rise in distance of between 48 and 133 %.13 The effect is slightly

larger for samples including earlier years of data; this suggests a possible time trend

in the effect of time differences on trade, which is explored further in Sect. 4.2

below. The effect is also larger when using OLS rather than PPML, which parallels

the results for geographical distance (see below).

The remaining coefficients in Table1 are on the whole signed according to

expectation and similar in size to those obtained by previous studies. The coefficients

12 The marginal effects shown in Table 1are converted into percentage equivalents using the formula

[exp(b)-1]*100.13 These figures are calculated by multiplying the coefficients in Table 1 by 5 (or 12), then dividing by

the sum of the two coefficients for distance in the respective column, and then converting into percentage

equivalents.

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Table1

Timedifferencesandtrade:pooledresultsacrossyears

Estimationmethod

OLS

OLS

OLS

PPML

PPML

PPML

Sample

19501995

19702000

19752005

1950199

5

19702000

19752005

(1)

(2)

(3)

(4)

(5)

(6)

Timedifference

-0.068**

-0.063**

-0.045**

-0.030**

-0.023*

-0.018

0.006

0.007

0.007

0.010

0.009

0.010

Distance(great-circle)

-0.535**

-0.703**

-0.900**

-0.370**

-0.331**

-0.471**

0.040

0.043

0.042

0.051

0.049

0.054

Distance(cargo)

-0.430**

-0.334**

-0.251**

-0.078*

-0.126**

-0.080*

0.031

0.034

0.034

0.034

0.032

0.032

NSdistance

0.084**

0.077**

0.097**

-0.038

-0.047

-0.045

0.019

0.020

0.019

0.033

0.031

0.035

Differenceinlatitude

0.008

-0.023

-0.027

0.126**

0.127**

0.102**

0.016

0.017

0.017

0.031

0.031

0.035

Commonlanguage

0.717**

0.798**

0.839**

0.184

0.168

0.050

0.054

0.055

0.055

0.111

0.105

0.098

Commonborder

0.359**

0.401**

0.460**

0.705**

0.682**

0.595**

0.065

0.070

0.073

0.062

0.062

0.056

Commoncurrency

0.688**

0.755**

0.714**

0.140

0.063

0.167*

0.071

0.085

0.090

0.176

0.068

0.066

Commonlegalorigin

0.140**

0.169**

0.130**

0.093*

0.087*

0.083*

0.024

0.025

0.024

0.045

0.041

0.036

Commoncolonise

r

0.633**

0.490**

0.573**

-0.062

-0.006

0.067

0.048

0.047

0.046

0.117

0.108

0.113

Politicalunion

0.959**

1.034**

0.965**

0.773*

1.369**

1.011**

0.132

0.214

0.239

0.312

0.319

0.370


1 3


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Table1

continued

Estimationmethod

OLS

OLS

OLS

PPML

PPML

PPML

Sample

19501995

19702000

19752005

1950199

5

19702000

19752005

(1)

(2)

(3)

(4)

(5)

(6)

Colonialhistory

1.020**

1.085**

1.058**

0.135

0.102

0.163*

0.053

0.057

0.054

0.071

0.065

0.071

Commontradeag

reement

0.505**

0.525**

0.394**

0.811**

0.962**

0.673**

0.054

0.050

0.047

0.058

0.054

0.072

N

31,733

31,286

34,065

52,264

48,900

50,523

R2

0.75

0.75

0.75

0.92

0.95

0.92

Ineachsampledataareusedat15-yearintervals;forOLSestimation,observationsofzerotradeareexcludedfromthesample.Allregressionsinclude

separatefixed

effectsforeachexporterandimporterforeachyear

**,*Statistically

significantatthe1and5%levelrespectively

E. Anderson

1 3


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for the distance measures are negative and statistically significant at the 5 % level or

below, with the coefficient for great-circle distance being larger in absolute terms than

that for cargo distance. When using OLS, the sum of the two coefficients ranges from

-0.97 to-1.15, which is comfortably within the range of estimates for single distance

measures reported by Disdier and Head (2008). Like Disdier and Head (2008) andSantos Silva and Tenreyro (2006), we find that the effects of geographical distance are

much smaller when using PPML rather than OLS. Furthermore, replicating the

regressions in Table1 with time differences excluded from the model yields coefficients

for distance which are between 10 and 20 % higher in combined value, in absolute terms

(details available on request). This is to be expected given the strong positive correlation

between time differences and geographical distances noted in Sect.3.

The coefficients for NS distance and differences in latitude vary according to

estimation method. When using OLS, we find that the effect of NS distance is positive

and statistically significant but the effect of differences in latitude is not statisticallysignificant. This is similar to Melitz (2007), who also uses OLS, although our

estimated coefficients for NS distance are smaller, suggesting that a proportion of the

positive effect of NS distance on trade reported by Melitz (2007) can be accounted for

by the negative effect of time differences on trade. When using PPML however, we

find that the coefficients for differences in latitude are positive and statistically

significant while the coefficients for NS distance are not statistically significant.

These results suggest that differences in latitude do promote opportunities for trade,

which is as expected given their association with differences in climate and other

geographic conditions between countries referred to earlier.The coefficients for the remaining linkage indicators are positive and significant

at the 1 % level when using OLS, but are sometimes much smaller when using

PPML: for example, the coefficients for a common language and a common

currency. This also parallels previous research to some extent: Santos Silva and

Tenreyro (2006) for example find very large differences between OLS and PPML

estimates of the effect of colonial ties on trade. These results are however of less

relevance to the central focus of the current paper.

4.2 Results by year

Table2and Fig. 1show the estimated coefficients on time differences obtained from

separate estimates of Eq. (1) by year. Table2shows estimates at five-year intervals,

beginning in 1950 and ending in 2005, while Fig.1 shows the same information on an

annual basis. The results clearly indicate that the negative impact of time differences

has changed over the period. According to the OLS estimates for example, each hour

of time difference reduced trade by 6 % in 1950; by 1980 this had increased to 9 %, but

by 2005 it had fallen to 2 %. Furthermore, az-test strongly rejects the hypothesis that

the coefficients for time differences are equal in 1950 and 1980, or in 1980 and 2005, atthe 1 % level, for both the OLS and PPML estimates.14

14 The z-statistic is calculated using z b1 b2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffis:e:b1

2 s:e:b22

q where b1 and b2 are the

estimated coefficients for the time difference variable for the two different years (2005 and 1950).


1 3


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One possible explanation for the apparent fall in the negative impact of time

differences on trade between 1980 and 2005 is that new communicationtechnologies have made communication across time zones easier. For example, it

is possible that communication which used to occur via telephone or business travel

now takes place via e-mail, and that e-mail traffic is less sensitive to time

differences than telephone traffic or business travel. Further research would be

required to test this hypothesis, but the decline is of note and stands in marked

contrast to the persistentperhaps even increasingnegative impact of distance on

trade (e.g. Melitz2007; Disdier and Head2008). Further research is also required to

investigate the apparent rise in the negative impact of time differences between

1950 and 1980.

4.3 Interaction effects: contract enforcement and co-ethnic networks

As discussed in Sect. 3, the negative impact of time differences on trade may be

greater where formal mechanisms of contract enforcement via the legal system are

weaker, and where co-ethnic networks are less prevalent. These hypotheses are

tested by including interaction terms in the gravity model: between time differences

and the combined value of the Kaufmann et al. (2009) rule of law index, and

between time differences and the (log) bilateral migrant population.

The results are shown in Table 3. Columns (1) and (2) show the interaction with

the rule of law index; the sample in this case includes data for 19962004, at 4 year

intervals. The Kaufmann et al. (2009) rule of law index is available for the period

19962008, at intervals of 1 or 2 years; the sample used here is deliberately limited

Table 2 Time differences and trade: results by year

Estimation method OLS PPML

Year N b SE N b SE

1950 3,111 -0.063** 0.017 6,227 -0.031 0.030

1955 3,864 -0.067** 0.015 7,053 -0.049* 0.025

1960 4,921 -0.059** 0.013 10,628 -0.062** 0.020

1965 6,177 -0.073** 0.011 12,789 -0.070** 0.018

1970 8,017 -0.082** 0.012 14,351 -0.079** 0.018

1975 9,181 -0.072** 0.012 14,679 -0.101** 0.016

1980 9,831 -0.093** 0.012 14,779 -0.091** 0.015

1985 10,039 -0.078** 0.012 16,154 -0.048** 0.015

1990 11,448 -0.033** 0.011 17,443 -0.010 0.013

1995 12,614 -0.028** 0.010 18,469 -0.003 0.012

2000 13,230 -0.021* 0.010 18,395 -0.014 0.011

2005 13,436 -0.021* 0.011 18,401 -0.013 0.013

All regressions include separate fixed effects for each exporter and importer, and the same set of control

variables shown in Table1

**, * Statistically significant at the 1 and 5 % level respectively

E. Anderson

1 3


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to four-year intervals for computational reasons (see Sect. 3). Columns (3) and (4)

show the results for the interaction with the bilateral migrant population. In this

case, the sample includes data for 2000 only, since the migrant population data

Fig. 1 Time differences and trade: results by year.Notes All regressions include separate fixed effects

for each exporter and importer, and the same set of control variables shown in Table 1. The solid line

shows the point estimate; the dashed lines show the upper and lower limits of the 99 % confidence

interval


1 3


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provided by Parsons et al. (2007) are not available for other years. Once again, only

the key coefficients of interest are reported; all other coefficients from each

regression are on the whole signed according to expectation and statistically

significant (details available on request). The interaction terms are all positive as

expected, and statistically significant at the 1 % level or below. In terms of

magnitudes, the results in column (2) indicate that each hour of time differencereduces trade by 3 % at the median combined rule of law index (5.1), but by 6 % at

the 10th % (3.3). Similarly, the results in column (4) imply that each hour of time

difference reduces trade by 6 % at the median bilateral migrant population (4.9), but

by 9 % at the 10th % (1.4). The effect of time differences is therefore weaker when

travel and communication are arguably less important for trade, which is consistent

with the hypothesis that time differences reduce trade by raising the cost of travel

and communication.

The results in Table 3 also imply that the effect of time differences becomes

positive at high levels of contract enforcement and migrant populations, which lacksan obvious explanation. However, the number of trading partners for which this

applies is quite small. In column (2) for example, the effect of time differences

becomes positive only for trading partners with a combined rule of law index of 7.5,

which is the 95th % of the distribution. In column (4), the effect becomes positive

only for trading partners with bilateral migrant populations above 13.8 (in log

points), which is above the 99th %.

4.4 Additional tests

The results of additional tests are shown in Table 4. The regressions in Panels AC

replace the solar time difference measure used in Table1 with the three other

measures of time difference described in Sect. 3: the official time difference,

between each countrys principal city (Panel A), the minimum official time

Table 3 Time differences and trade: interaction effects

Estimation method OLS

(1)

PPML

(2)

OLS

(3)

PPML

(4)

Time difference -0.253** -0.105** -0.073** -0.097**0.015 0.025 0.015 0.026

Time difference * rule of law index 0.037** 0.014**

0.002 0.004

Time difference * bilateral migrant population 0.008** 0.007**

0.002 0.002

N 37,551 51,156 13,022 17,766

R2 0.78 0.94 0.78 0.97

As Table1. The regressions also include the rule of law index for each trading partner (columns (1) and

(2)) and the bilateral migrant population (columns (3) and (4)) as separate explanatory variables. The19962004 sample includes data at 4-year intervals only

** Statistically significant at the 1 % level

E. Anderson

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Table4

Timedifferencesandtrade:additionalres

ults

Estimationmethod

OLS

OLS

OLS

PPML

PPML

PPML

Sample

19501995

(1)

19702

000

(2)

19752005

(3)

19501995

(4

)

19702000

(5)

19752005

(6)

A Officialtimediffe

rence(betweenprincipalcities)

-0.064**

-0.059

**

-0.046**

-

0.018

-0.012

-0.011

0.006

0.007

0.006

0.010

0.009

0.010

B Minimumofficial

timedifference

-0.075**

-0.072

**

-0.055**

-

0.110**

-0.095**

-0.079**

0.006

0.007

0.006

0.009

0.008

0.008

C Maximumofficialtimedifference

-0.080**

-0.076

**

-0.060**

-

0.106**

-0.088**

-0.078**

0.006

0.007

0.006

0.009

0.008

0.008

D Timedifference,03h

-0.100**

-0.067

**

-0.016

-

0.005

0.026

-0.023

0.018

0.019

0.018

0.029

0.029

0.029

Timedifference,38ha

0.050*

0.018

-0.043

-

0.173**

-0.187**

-0.103**

0.023

0.024

0.023

0.042

0.041

0.040

Timedifference,[8ha

-0.030

-0.033

0.030

0.427**

0.376**

0.314**

0.021

0.021

0.020

0.033

0.032

0.036

AsTable1.AllregressionsincludeallcontrolvariableslistedinTable1andseparatefixedeffectsforeachexporterandimporterforeachyear

a

Indicatesthechangeintheeffectoftimediffere

nces,comparedtothepreviousin

terval

**,*Statistically

significantatthe1and5%levelrespectively


1 3


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difference (Panel B), and the maximum official time difference (Panel C). In each

case, only the coefficients for time differences are reported; the other coefficients

change relatively little in comparison with the values shown in Table 1 (details

available on request). When using OLS, the results are very similar to those reported

previously in Table1: official time differences have a negative and statisticallysignificant impact on trade, with each hour of time difference reducing trade by

between 4 and 8 %. When using PPML however, the results are more different. On

the one hand, the impact of official time differences between principal cities is

smaller than in Table 1, and no longer statistically significant; on the other hand, the

impact of maximum and minimum official time differences is larger than in Table 1,

and statistically significant. As discussed in Sect. 3, the solar time difference is our

preferred measure of time differences, on the grounds that being fixed by

geography, it avoids potential reverse causation. The results in Table4 suggest

therefore that distinguishing between official and solar time differences isimportant. There is however no obvious reason why the impact of minimum and

maximum official time differences would differ from that of official time

differences between principal cities, as suggested by the PPML results (although

not the OLS results).

The regressions in Panel D show the results when estimating a piecewise

regression model with breakpoints at 3 and 8 h. The coefficients represent the

change in the effect of time differences in comparison with the preceding interval;

thus if time differences exceeding 3 h have a greater negative impact on trade,

because of jet lag, and time differences exceeding 8 h have a lesser impact, becausethe overlapping hours in a normal working day have already reached zero, we would

expect the coefficient on time differences of 38 h to be negative, and that on time

differences above 8 h to be positive. There is little evidence in support of these

predictions when using OLS, but when using PPML the results are in support: the

coefficient for time differences of 38 h is negative and statistically significant,

while that for time differences above 8 h is positive and statistically significant.

5 Time differences, communication and trade

The results in Sect.4show that time differences have had a negative and statistically

significant impact on merchandise trade, at least until recently. As discussed in

Sects. 1 and 2, a plausible explanation is that time differences raise the non-

pecuniary costs of travel and communication, which in turn leads to less trade. This

section provides more evidence in support of this explanation by looking at the

relationships between time differences, trade and communication flows. Previous

research (Anderson 2007) has shown that time differences reduce international

business travel. Here we examine the effect of time differences on bilateral

telephone traffic, which is a widely used measure of communication flows (e.g.

Loungani et al.2002; Portes and Rey2005). Time differences may reduce telephone

traffic for two reasons: first by reducing the amount of time in the day in which

telephone conversations can take place, and second by making travel more

unpleasant, which in turn reduces telephone traffic on the grounds that travel and

E. Anderson

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communication are complements.15 We also examine the effect of time differences

on trade, controlling for bilateral telephone traffic. If time differences do reduce

trade by raising the non-pecuniary costs of travel and communication, we would

expect the estimated coefficient on time differences to be smaller (in absolute terms)

in this case than when not controlling for telephone traffic.The data on telephone traffic are taken from the 1999 direction of traffic (DoT)

database (ITU 1999). This reports bilateral telephone traffic for 149 countries

between 1993 and 1997; the data include all traffic passing through international

public switched telephone circuits, including in most cases that generated by mobile

telephones as well as fixed-line traffic (ITU 1999: 183). For each country in the

database, outgoing and incoming traffic (in minutes) is reported for the 20 foreign

countries with which bilateral traffic volumes (outgoing plus incoming) were largest

during 1997.

We investigate the effect of time differences on telephone traffic using the samegravity model framework set out in Sect. 3. The dependent variable is outgoing

telephone traffic from country i to country j in year t. Focusing on outgoing traffic

reported by the source country yields 14,900 potential observations: 20 observations

for each country and year in the DoT database. For control variables, the same set of

variables described in Sect. 3 are used, with two additions. The first is the bilateral

migrant population, from Parsons et al. (2007), entered separately (not interacted),

measured as a proportion of the combined population of the two countries. Strictly

speaking, this measure is not directly compatible with the DoT data since it refers to

a single year (in most cases, the year 2000); no time series data are available. Anargument for its inclusion can be made however, on the grounds that migrant stocks

typically change only gradually over time. The second is the price of an outgoing

phone-call (un-weighted average of peak and off-peak rates), measured in US

dollars per minute at official exchange rates, which is taken from the DoT database,

although in this case the data are only available for 19951997.

The results are shown incolumns (1) and (2) of Table 5, for estimation by OLS

and PPML respectively.16 The impact of time differences is negative and

statistically significant at the 1 % level when using OLS, but not when using

PPML. The remaining coefficients are on the whole signed according to

expectation. The coefficient for the price of a phone-call is negative as expected

and statistically significant at the 1 % level; the implied price elasticities are 0.61

and 0.40 (columns (1) and (2) respectively). The bilateral migrant population has a

positive and statistically significant impact on traffic, also as expected. The great-

circle distance measure has a negative and statistically significant impact on

telephone traffic, with elasticities of 0.53 and 0.62 (columns (1) and (2)

respectively), but the effect of cargo distance is much smaller. This is not

unexpected given that cargo distance (controlling for great-circle distance) mainly

proxies for transport costs. Differences in latitude and NS distances are also

15 For evidence of such complementarity see Gaspar and Glaeser (1998).16 Since there are no observations in the sample with zero outgoing traffic, no observations are lost by

taking the logarithm of this variable; the Tobit estimates were for this reason almost identical to the OLS

estimates and are therefore not reported here.


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Table 5 Time differences, telephone traffic and trade

Dependent variable Outgoing

telephone traffic

Outgoing

telephone traffic

Total exports Total exports

Estimation method OLS

(1)

PPML

(2)

OLS

(3)

OLS

(4)

Time difference -0.063** -0.005 -0.035** -0.016*

0.008 0.009 0.007 0.007

Distance (great-circle) -0.534** -0.615** -0.530** -0.166**

0.046 0.036 0.041 0.040

Distance (cargo) -0.003 0.019 -0.139** -0.135**

0.028 0.025 0.026 0.024

NS distance -0.055* -0.002 -0.030 -0.041

0.023 0.032 0.026 0.023

Difference in latitude 0.074** 0.022 0.101** 0.100**

0.021 0.032 0.024 0.021

Phone-call price -0.609** -0.401**

0.054 0.070

Bilateral migrant population 0.846** 1.145** 0.169** -0.010

0.072 0.041 0.008 0.010

Common border 0.044 0.340** 0.491** 0.479**

0.049 0.038 0.047 0.043

Common language 0.693** 0.623** -0.100 -0.403**

0.066 0.074 0.052 0.051

Common currency 0.498** -0.255* 0.674** 0.169

0.129 0.116 0.131 0.118

Common legal origin 0.205** -0.006 0.264** 0.169**

0.028 0.038 0.025 0.024

Common coloniser 0.120* -0.310** 0.433** 0.270**

0.059 0.110 0.060 0.058

Political union 0.923** 0.171 0.698** 0.630**

0.323 0.138 0.192 0.177

Colonial history 0.907** 0.139** 0.500** 0.167**

0.062 0.044 0.041 0.038

Common trade agreement 0.124** 0.148** 0.335** 0.188**

0.044 0.054 0.041 0.039

Outgoing telephone traffic 0.192**

0.022

Incoming telephone traffic 0.347**

0.023

N 5,167 5,167 14,799 14,799

R2 0.94 0.98 0.86 0.88

Each regression includes separate fixed effects for each exporter and importer and for each year

**, * Statistically significant at the 1 and 5 % level respectively

E. Anderson

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insignificant when using PPML, which is also plausible on the grounds that these

variables mainly proxy for sources of comparative advantage: any impact on

communication flows would therefore be rather indirect. The main unexpected

result is that the effects of a common currency and a common coloniser are negative

and statistically significant, at least in column (2), since these variables are notobviously a cause of higher communication costs.

As a final test, columns (3) and (4) of Table 5show the results of estimating a

gravity model of trade with telephone traffic (both outgoing and incoming,

measured separately in log minutes) among the control variables. These estimates

should be interpreted with caution. On the one hand, only the OLS results are

reported, since as noted the PPML results show no statistically significant effect of

time differences on telephone traffic; on the other hand, adding a measure of

communication flows to a gravity model raises endogeneity concerns (Fink et al.

2005). Moreover, the sample is in this case restricted to country pairs with relativelylarge amounts of bilateral traffic, since the DoT database only reports traffic for each

countrys top 20 partner countries. Nevertheless, it is apparent that the negative

effect of time differences estimated by OLS is less than half the size when

controlling for bilateral telephone trafficeach hour reducing trade by 1.6 %,

compared to 3.6 %and no longer statistically significant at the 1 % level. In

summary, although by no means conclusive, the OLS results in Table5are at least

consistent with the hypothesis that time differences reduce trade by raising the non-

pecuniary costs of travel and communication.

6 Conclusion

Recently, international economists have begun to investigate the effect of time

differences on international trade, to complement the large literature on the effects

of geographical distance. Using a range of samples and estimation methods, this

paper finds that time differences have had a negative and statistically significant

impact on merchandise trade, at least until recently. For pooled samples across

years, each hour of time difference is found to reduce total exports by between 2 and

7 %. This is somewhat smaller than previous estimates (e.g. Stein and Daude2007),

but economically significant nonetheless. The explanation offered in this paper is

that time differences raise the non-pecuniary costs of travel and communication,

which in turn leads to less trade. This explanation is supported by the finding that

the negative impact of time differences is smaller where travel and communication

would be expected to be less important for trade: in particular, where mechanisms of

formal contract enforcement are stronger and where co-ethnic networks are more

prevalent. There is also some evidence, albeit not conclusive, that time differences

reduce communication flows, as measured by bilateral telephone traffic, as well as

trade.

One obvious extension to the results in this paper is to consider the impact of

time differences at a more disaggregated level. As noted in Sect. 1, recent studies

have begun to explore the effects of time differences on different types of trade in

services (e.g. Head et al.2009; Dettmer2011). However, it would also be of interest


1 3


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to distinguish between different types of merchandise trade. For example, Rauch

(1999b) finds that geographical distance and a common language have a larger

impact on trade in differentiated manufactured products than on trade in more

standardised primary commodities, the most likely explanation being that travel and

simultaneous communication are more important for these goods. One wouldtherefore also expect to see a similar pattern for the effects of time differences.

Another issue for further research is whether official and solar time differences

have different impacts on trade. One might expect official time differences to matter

more, on the grounds that people organise their work-day to fit the official time in

their location. However, if official times differ significantly from solar times, there

is evidence that people tend to follow unofficial solar time instead, which suggests

that solar time differences also matter. For example, in 1949 Mao Zedong replaced

Chinas five time zones with one time zone, based on solar time in Beijing; official

time differences within the country were therefore eradicated. However, certainparts of the countrye.g. Xinjiang, roughly 2,000 km east of Beijingcontinue to

remain on unofficial local time; at the regions airports, announcements are needed

to remind travellers that scheduled times are official times, not local times (Cookson

2009). In this paper, no attempt is made to separate out these impacts, since official

and solar time differences between countries are very highly correlated. However, it

may be possible to shed light on the issue by focusing on trade within countries,

such as China, that span large eastwest distance, but in which the government

remains averse to having multiple official time zones.

Finally, the issue of whether time differences affect trade is not only of academicinterest; it is also of relevance for policy. In Russia for example, plans were

announced in 2009 to reduce the number of official time zones in the country from

11 to 4, in the interests of economic efficiency (Cookson 2009); by 2011, the

number of time zones had been reduced to nine. This process will reduce official

time differences in the country, but will not affect solar time differences. Thus if it is

the latter which primarily affect trade, the anticipated gains in economic efficiency

may not be forthcoming. There is therefore clear potential for further research on an

emerging issue which is not only of academic interest but also of relevance for

policy.

Acknowledgments I am grateful to John Howe at AtoBviaC Plc for providing the data on sea distances.

I am also grateful to Keith Head, Thierry Mayer and John Ries for making their gravity model dataset

publicly available, and to two anonymous referees for comments on a previous version of the paper.

Excellent research assistance was provided by Carmen Leon Himmelstine, Katelyn McGehee, Camille

Morel, Rajiv Pudaruth and Fariha Tahanin.

Appendix

For data on bilateral trade, this paper uses the publicly available dataset assembledby Head et al. (Head et al. 2010), which reports bilateral merchandise trade (exports

and imports reported separately) between 208 countries over the period 19482006.

This dataset is available at www.cepii.fr/anglaisgraph/bdd/gravity.htm. The under-

lying source of the trade data is the IMF Direction of Trade Statistics database.

E. Anderson

1 3
http://www.cepii.fr/anglaisgraph/bdd/gravity.htmhttp://www.cepii.fr/anglaisgraph/bdd/gravity.htm


23/33

Data for all other explanatory variables are taken from this dataset, except in the

following cases. The data on latitude and longitude of the most populous city in

each country, used to calculate differences in latitude, NS distances, great-circle

and cargo distances, are fromwww.travelmath.com. Official times and the times of

solar noon in each principal city are taken fromwww.timeanddate.com. The mostpopulous cities in each country are from the CIA World Factbook; the nearest major

port to the most populous city is calculated using data and satellite imagery

available atwww.worldportsource.com. Data on sea distances between major ports

were kindly provided by John Howe at AtoBviaC Plc (www.a2bviaconline.com).

The Kaufmann et al. (2009) rule of law index is available at info.worldbank.org/

governance/wgi/index.asp; the Parsons et al. (2007) migrant database is available at:

www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.

html. The language measure was calculated using data in Melitz (2008). The tele-

phone traffic and price data are all from ITU (1999).Descriptive statistics for all variables used in the analysis are shown in Table 6.

A list of the countries included in the sample is provided in Table 7, and the Tobit

estimation results are shown in Table 8.

Table 6 Descriptive statistics

Variable N Mean Standard deviation Minimum Maximum

Bilateral trade, log exports 97,084 1.364 3.185 -5.298 12.715

Bilateral trade, log (1 ? exports) 155,976 1.404 2.067 0 12.715

Time difference, solar 155,976 4.454 3.271 0 12

Time difference, official 155,976 4.465 3.349 0 12

Distance (log), cargo 155,976 9.117 0.744 3.165 10.129

Distance (log), great-circle 155,976 8.729 0.786 3.165 9.900

NS distance (log) 155,976 2.937 1.108 -4.094 4.616

Difference in latitude (log) 155,976 2.534 1.145 -4.094 4.157

Common language 155,976 0.079 0.216 0 1

Common border 155,976 0.019 0.137 0 1

Common currency 155,976 0.018 0.135 0 1

Common legal origin 155,976 0.379 0.485 0 1

Common coloniser 155,976 0.117 0.322 0 1

Political union 155,976 0.002 0.048 0 1

Colonial history 155,976 0.017 0.131 0 1

Common trade agreement 155,976 0.036 0.186 0 1

Rule of law index, origin 51,156 2.641 1.037 0.287 4.546

Rule of law index, destination 51,156 2.643 1.035 0.287 4.546

Bilateral migrant population (log) 17,766 5.441 2.906 0 16.085

Bilateral phone-call price (log) 6,944 1.402 0.733 -1.609 3.649

Outgoing telephone traffic (log) 21,042 0.097 2.249 -4.605 8.274

Incoming telephone traffic (log) 21,053 0.088 2.249 -4.605 8.274

For variables used in Tables1 and 3, descriptive statistics are shown for the combination of the three

samples


1 3
http://www.travelmath.com/http://www.timeanddate.com/http://www.worldportsource.com/http://www.a2bviaconline.com/http://info.worldbank.org/governance/wgi/index.asphttp://info.worldbank.org/governance/wgi/index.asphttp://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.htmlhttp://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.htmlhttp://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.htmlhttp://www.migrationdrc.org/research/typesofmigration/global_migrant_origin_database.htmlhttp://info.worldbank.org/governance/wgi/index.asphttp://info.worldbank.org/governance/wgi/index.asphttp://www.a2bviaconline.com/http://www.worldportsource.com/http://www.timeanddate.com/http://www.travelmath.com/


24/33

Table7

Listofcountriesincludedinsample

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Algeria

0.1

1

0

36.7

3.2

Angola

0.8

1

0

-8.8

13.2

AntiguaandBarb

uda

-4.2

-4

0

17.1

-61.9

Argentina

-4.0

-3

0

-34.3

-58.5

Australia

11.0

10

1

-33.9

151.2

8,

9.5

Austria

1.0

1

1

48.2

16.4

Bahamas

-5.3

-5

1

25.1

-77.3

Bahrain

3.3

3

0

26.2

50.6

Bangladesh

5.9

6

0

23.9

90.4

Barbados

-4.1

-4

0

13.1

-59.6

Belgium

0.2

1

1

50.9

4.4

Belize

-6.0

-6

0

15.5

-88.3

Benin

0.1

1

0

6.4

2.4

Bermuda

-4.4

-4

1

32.4

-64.7

Bhutan

5.9

6

0

27.5

89.8

Bolivia

-4.3

-4

0

-17.8

-63.2

Brazil

-2.2

-3

1

-23.5

-46.6

-

4

Bulgaria

1.5

2

1

42.7

23.3

BurkinaFaso

-0.2

0

0

12.4

-1.5

Burundi

1.9

2

0

-3.4

29.4

Cameroon

0.7

1

0

4.0

9.7

Canada

-5.4

-5

1

43.7

-79.4

-

4,-6,-7,-8

CentralAfricanR

epublic

1.2

1

0

4.4

18.6

Chad

0.9

1

0

12.1

15.0

E. Anderson

1 3


25/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Chile

-3.8

-4

1

-33.4

-70.7

China

8.0

8

0

31.0

121.4

Colombia

-5.0

-5

0

4.6

-74.1

Comoros

2.8

3

0

-11.7

43.2

Congo

0.9

1

0

-4.3

15.3

Congo,DR

0.9

1

0

-4.4

15.5

2

CostaRica

-5.7

-6

0

10.0

-84.1

CotedIvoire

-0.4

0

0

5.3

-4.3

Cyprus

2.1

2

1

35.2

33.3

CzechRepublic

0.9

1

1

50.1

14.4

Denmark

0.8

1

1

55.7

12.5

Djibouti

2.8

3

0

11.6

43.0

Dominica

-4.2

-4

0

15.3

-61.4

DominicanRepub

lic

-4.8

-4

0

18.4

-69.7

Ecuador

-5.4

-5

0

-2.2

-79.9

Egypt

2.0

2

0

30.0

31.3

ElSalvador

-6.0

-6

0

13.7

-89.1

Ethiopia

2.5

3

0

9.0

38.7

Fiji

12.8

12

1

-18.1

178.5

Finland

1.6

2

1

60.2

24.9

France

0.1

1

1

48.9

2.3

Gabon

0.5

1

0

0.6

9.3

Gambia

-1.2

0

0

13.5

-16.7

Germany

0.8

1

1

52.5

13.3


1 3


26/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Ghana

-0.1

0

0

5.6

-0.2

Greece

1.5

2

1

38.0

23.7

Greenland

-3.5

-3

1

64.2

-51.8

Grenada

-4.2

-4

0

12.1

-61.8

Guadeloupe

-4.2

-4

0

16.0

-61.7

Guatemala

-6.1

-6

0

14.6

-90.5

Guinea

-1.0

0

0

9.5

-13.7

Guyana

-4.0

-4

0

6.5

-58.3

Haiti

-4.9

-5

0

18.6

-72.3

Honduras

-5.9

-6

0

14.1

-87.2

HongKong

7.5

8

0

21.8

115.0

Hungary

1.2

1

1

47.4

19.3

Iceland

-1.6

0

0

64.2

-21.9

India

4.8

5.5

0

19.0

72.8

Indonesia

7.0

7

0

-6.2

106.9

8

,9

Iran,IslamicRepublicof

3.4

3.5

1

35.8

51.5

Iraq

2.9

3

0

33.2

44.4

Ireland

-0.5

0

1

53.4

-6.3

Israel

2.2

2

1

32.1

34.8

Italy

0.5

1

1

45.5

9.2

Jamaica

-5.2

-5

0

18.0

-76.8

Japan

9.2

9

0

35.7

139.7

Jordan

2.3

2

1

32.0

36.0

Kenya

2.4

3

0

-1.3

36.8

E. Anderson

1 3


27/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Korea,Republico

f

8.4

9

0

37.6

127.1

Kuwait

3.1

3

0

29.1

48.0

LaoPDR

6.8

7

0

18.0

102.6

Liberia

-0.8

0

0

6.3

-10.4

Madagascar

3.1

3

0

-18.9

47.5

Malawi

2.2

2

0

-14.0

33.8

Malaysia

6.7

8

0

3.1

101.7

Mali

-0.6

0

0

12.7

-8.0

Malta

0.9

1

1

35.9

14.5

Martinique

-4.2

-4

0

14.7

-61.0

Mauritania

-1.2

0

0

18.1

-16.0

Mauritius

3.8

4

0

-20.2

57.5

Mexico

-6.7

-6

1

19.5

-99.2

-

7,-8

Mongolia

7.0

8

0

47.9

106.9

7

Morocco

0.4

0

1

33.6

-7.6

Mozambique

2.1

2

0

-26.0

32.6

Myanmar

6.3

6.5

0

16.8

96.2

Nepal

5.6

5.75

0

27.8

85.4

Netherlands

0.2

1

1

52.4

4.9

NewCaledonia

11.0

11

0

-22.3

166.8

NewZealand

12.6

12

1

-36.9

174.8

Nicaragua

-5.9

-6

0

12.2

-86.3

Niger

0.1

1

0

13.5

2.1

Nigeria

0.1

1

0

6.6

3.0


1 3


28/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Norway

0.6

1

1

59.9

10.7

Oman

3.8

4

0

23.6

58.6

Pakistan

4.4

5

0

24.9

67.1

Panama

-5.4

-5

0

9.1

-79.4

PapuaNewGuine

a

9.7

10

0

-9.5

147.2

Paraguay

-2.9

-4

1

-25.2

-57.5

Peru

-5.2

-5

0

-12.1

-76.9

Philippines

8.0

8

0

14.6

121.0

Poland

1.2

1

1

50.3

19.0

Portugal

-0.7

0

1

38.7

-9.1

Qatar

3.4

3

0

25.3

51.6

Reunion

3.6

4

0

-21.1

55.6

Romania

1.7

2

1

44.4

26.2

Rwanda

1.9

2

0

-2.3

30.0

SaintKittsandNevis

-4.3

-4

0

17.3

-62.7

SaintLucia

-4.2

-4

0

13.7

-61.0

StVincent&the

Gren.

-4.2

-4

0

13.1

-61.2

Samoa

-10.5

-11

1

-13.8

-171.7

SaudiArabia

3.0

3

0

24.5

46.8

Senegal

-1.3

0

0

14.8

-17.5

Seychelles

3.6

4

0

-4.6

55.5

SierraLeone

-1.0

0

0

8.5

-13.2

Singapore

6.8

8

0

1.4

103.8

E. Anderson

1 3


29/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

SolomonIslands

10.6

11

0

-9.3

159.8

SouthAfrica

1.8

2

0

-26.1

27.9

Spain

-0.3

1

1

40.4

3.7

SriLanka

5.2

5.5

0

7.0

79.9

Sudan

2.1

3

0

15.6

32.6

Suriname

-3.8

-3

0

5.8

-55.2

Sweden

1.1

1

1

59.4

18.0

Switzerland

0.3

1

1

47.4

8.6

SyrianArabRepu

blic

2.4

2

1

36.2

37.2

Taiwan

8.0

8

0

25.0

121.6

Tanzania

2.5

3

0

-6.8

39.3

Thailand

6.6

7

0

13.8

100.5

Togo

0.0

0

0

6.1

1.2

TrinidadandTobago

-4.2

-4

0

10.5

-61.4

Tunisia

0.6

1

0

36.9

10.2

Turkey

1.8

2

1

41.0

29.0

Uganda

2.1

3

0

0.3

32.6

UnitedArabEmirates

3.6

4

0

25.3

55.4

UnitedKingdom

-0.1

0

1

51.5

-0.1

UnitedStates

-5.0

-5

1

40.7

-74.0

-

6,-7,-8,

-9,-10

Uruguay

-2.8

-3

1

-34.8

-56.2

Venezuela

-4.6

-4.5

0

10.5

-67.0

VietNam

7.0

7

0

10.8

106.7


1 3


30/33

Table7

continued

Solartime,most

populouscitya

Officialtime,most

populouscitya

Daylight

savingtimeb

Latitudec

Longitudec

Otherofficial

timezones

Yemen

2.9

3

0

15.4

44.2

Yugoslavia

1.3

1

1

44.8

20.5

Zambia

1.8

2

0

-15.3

28.5

Zimbabwe

2.0

2

0

-17.8

31.1

a

Inhoursahead

(?)orbehind(-)GreenwichMeanTime

b

1=

yes;0=

no

c

Indegrees

E. Anderson

1 3


31/33

References

Andersen, T., & Dalgaard, C.-J. (2009). Cross-border flows of people, technology diffusion and aggregate

productivity. (Discussion paper 0604), Department of economics, University of Copenhagen.

Table 8 Tobit estimation results

Sample 19501995

(1)

19702000

(2)

19752005

(3)

19962004

(4)

2000

(5)

ASolar time difference (between

principal cities)

-0.053** -0.043** -0.030**

0.004 0.004 0.004

B

Official time difference (between

principal cities)

-0.052** -0.043** -0.035**

0.004 0.004 0.004

C

Minimum official time difference -0.061** -0.053** -0.043**

0.004 0.004 0.004

DMaximum official time difference -0.064** -0.057** -0.046**

0.004 0.004 0.004

E

Official time difference, 03 h -0.074** -0.053** -0.038**

0.011 0.011 0.011

Official time difference, 38 ha 0.023 0.011 0.001

0.013 0.014 0.014

Official time difference,[8 ha 0.009 0.002 0.006

0.013 0.013 0.013F

Solar time difference -0.167** -0.052**

0.009 0.009

Solar time difference * rule of law

index

0.025**

0.001

Solar time difference * bilateral

migrant population

0.004**

0.001

The results shown are the Tobit coefficients, i.e. the marginal effects of each explanatory variable on the

latent dependent variable. All regressions include all control variables listed in Table 1and separate fixed

effects for each exporter and importer for each year; Data used in columns (1) (2) and (3) are at 15-year

intervals, in column (4) at 4 year intervals. The dependent variable in each case is the natural logarithm of

exports from country i to countryj; one is added to observations of zero trade to avoid taking logarithms

of zeroa Indicates the change in the effect of time differences, compared to the previous interval

** Statistically significant at the 1 % level


1 3


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