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Time differences, communication and trade: longitude matters II

Abstract

This paper uses a gravity model to examine the effect of time differences 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 is smaller 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.

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Fig. 1

Notes

  1. 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 Head 2008:37).

  2. 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).

  3. 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 Daude 2007: 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. 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 identify trading opportunities while they are away; thus greater ease of leisure travel can also lead to more trade (Kulendran and Wilson 2000).

  5. 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.

  6. 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.

  7. 7.

    Previous research (e.g. Stein and Daude 2007) 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. 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. 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).

  10. 10.

    Note that the maximum possible great circle distance in an E–W 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 E–W direction.

  11. 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 N–S distance is −0.43; the equivalent correlation for the difference in latitude is −0.32.

  12. 12.

    The marginal effects shown in Table 1 are converted into percentage equivalents using the formula [exp(b)−1]*100.

  13. 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.

  14. 14.

    The z-statistic is calculated using \( z = {{(b_{1} + b_{2} )} \mathord{\left/ {\vphantom {{(b_{1} + b_{2} )} {\sqrt {s.e.(b_{1} )^{2} + s.e.(b_{2} )^{2} } }}} \right. \kern-0pt} {\sqrt {s.e.(b_{1} )^{2} + s.e.(b_{2} )^{2} } }} \) where b 1 and b 2 are the estimated coefficients for the time difference variable for the two different years (2005 and 1950).

  15. 15.

    For evidence of such complementarity see Gaspar and Glaeser (1998).

  16. 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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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.

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Correspondence to Edward Anderson.

Appendix

Appendix

For data on bilateral trade, this paper uses the publicly available dataset assembled by Head et al. (Head et al. 2010), which reports bilateral merchandise trade (exports and imports reported separately) between 208 countries over the period 1948–2006. This dataset is available at www.cepii.fr/anglaisgraph/bdd/gravity.htm. The underlying source of the trade data is the IMF Direction of Trade Statistics database.

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, N–S distances, great-circle and cargo distances, are from www.travelmath.com. Official times and the times of solar noon in each principal city are taken from www.timeanddate.com. The most populous 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 at www.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 telephone 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
Table 7 List of countries included in sample
Table 8 Tobit estimation results

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Anderson, E. Time differences, communication and trade: longitude matters II. Rev World Econ 150, 337–369 (2014). https://doi.org/10.1007/s10290-013-0179-9

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Keywords

  • Trade
  • Gravity model
  • Time zones
  • Communication

JEL Classification

  • F10
  • F15
  • F20