Skip to main content

Time differences, communication and trade: longitude matters II


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.

This is a preview of subscription content, access via your institution.

Fig. 1


  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.


  1. Andersen, T., & Dalgaard, C.-J. (2009). Cross-border flows of people, technology diffusion and aggregate productivity. (Discussion paper 06–04), Department of economics, University of Copenhagen.

  2. Anderson, E. (2007). Travel and communication and international differences in GDP per capita. Journal of International Development, 19(3), 315–332.

    Article  Google Scholar 

  3. Anderson, J., & Marcouiller, D. (2002). Insecurity and the pattern of trade: An empirical investigation. Review of Economics and Statistics, 84(2), 342–352.

    Article  Google Scholar 

  4. Anderson, E., Tang, P., & Wood, A. (2006). Globalisation, co-operation costs and wage inequalities. Oxford Economic Papers, 58(4), 569–595.

    Article  Google Scholar 

  5. Anderson, J., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93(1), 170–192.

    Article  Google Scholar 

  6. Cookson, C. (2009). Hour power. The Financial Times, 23 December, p. 9.

  7. Dettmer, B. (2011). International service transactions: Is time a trade barrier in a connected world? JENA Economic Research Papers. Jena, Germany: Max Planck Institute of Economics.

    Google Scholar 

  8. Disdier, A.-C., & Head, K. (2008). The puzzling persistence of the distance effect on bilateral trade. Review of Economics and Statistics, 90(1), 37–48.

    Article  Google Scholar 

  9. Feenstra, R. (2004). Advanced international trade. Princeton: Princeton University Press.

    Google Scholar 

  10. Fink, C., Mattoo, A., & Neagu, I. (2005). Assessing the impact of communication costs on international trade. Journal of International Economics, 67(2), 428–445.

    Article  Google Scholar 

  11. Frankel, J., & Rose, A. (2002). An estimate of the effect of common currencies on trade and income. Quarterly Journal of Economics, 117(2), 437–466.

    Article  Google Scholar 

  12. Gaspar, J., & Glaeser, E. (1998). Information technology and the future of cities. Journal of Urban Economics, 43(1), 136–156.

    Article  Google Scholar 

  13. Gupta, A., & Seshasai, S. (2004). Toward the 24-hour knowledge factory. (Working Paper 4455-04), Sloan School of Management, MIT.

  14. Hamermesh, D. (1999). The timing of work over time. Economic Journal, 109, 37–66.

    Article  Google Scholar 

  15. Head, K., Mayer, T., & Ries, J. (2009). How remote is the offshoring threat? European Economic Review, 53(4), 429–444.

    Article  Google Scholar 

  16. Head, K., Mayer, T., & Ries, J. (2010). The erosion of colonial trade linkages after independence. Journal of International Economics, 81(1), 1–14.

    Article  Google Scholar 

  17. International Telecommunications Union (ITU). (1999). Direction of traffic 1999. Washington D.C: ITU, Geneva and TeleGeography Inc.

    Google Scholar 

  18. Kaufmann, D., Kraay, A., Mastruzzi, M. (2009). Governance matters VIII: Aggregate and individual governance indicators, 1996–2008. (Policy research working paper 4978), World Bank, Washington D.C.

  19. Keller, W. (2004). International technology diffusion. Journal of Economic Literature, 42(3), 752–782.

    Article  Google Scholar 

  20. Kikuchi, T., & Long, N. Van. (2010). A simple model of service offshoring with time zone differences. The North American Journal of Economics and Finance, 21(3), 217–227.

    Article  Google Scholar 

  21. Kulendran, N., & Wilson, K. (2000). Is there a relationship between international trade and international travel? Applied Economics, 32(8), 1001–1009.

    Article  Google Scholar 

  22. Leamer, E., & Storper, M. (2001). The economic geography of the internet age. Journal of International Business Studies, 32(4), 641–666.

    Article  Google Scholar 

  23. Loungani, P., Mody, A., & Razin, A. (2002). The global disconnect: The role of transactional distance and scale economies in gravity equations. Scottish Journal of Political Economy, 49(5), 526–543.

    Article  Google Scholar 

  24. Marjit, S. (2007). Trade theory and the role of time zones. International Review of Economics and Finance, 16(2), 153–160.

    Article  Google Scholar 

  25. Melitz, J. (2007). North, South and distance in the gravity model. European Economic Review, 51(4), 971–991.

    Article  Google Scholar 

  26. Melitz, J. (2008). Language and foreign trade. European Economic Review, 52(4), 667–699.

    Article  Google Scholar 

  27. Parsons, C., Skeldon, R., Walmsley, T., Winters, L. (2007). Quantifying international migration: A database of bilateral migrant stocks. (Policy research working paper 4165), World Bank, Washington D.C.

  28. Portes, R., & Rey, H. (2005). The determinants of cross-border equity flows. Journal of International Economics, 65(2), 269–296.

    Article  Google Scholar 

  29. Rajaratnam, S., & Arendt, J. (2001). Health in a 24 h society. The Lancet, 358(9286), 999–1005.

    Article  Google Scholar 

  30. Rauch, J. (1999a). The role of transportation costs for people. International Regional Science Review, 22(2), 173–178.

    Article  Google Scholar 

  31. Rauch, J. (1999b). Networks versus markets in international trade. Journal of International Economics, 48(1), 7–35.

    Article  Google Scholar 

  32. Rauch, J. (2001). Business and social networks in international trade. Journal of Economic Literature, 39(4), 1177–1203.

    Article  Google Scholar 

  33. Rauch, J., & Trindade, V. (2002). Ethnic Chinese networks in international trade. Review of Economics and Statistics, 84(1), 116–130.

    Article  Google Scholar 

  34. Santos Silva, J., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641–658.

    Article  Google Scholar 

  35. Stein, E., & Daude, C. (2007). Longitude matters: Time zones and the location of foreign direct investment. Journal of International Economics, 71(1), 96–112.

    Article  Google Scholar 

  36. Storper, M., & Venables, A. (2004). Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 4(4), 351–370.

    Article  Google Scholar 

  37. Tomasik, R. (2013). Time-zone related continuity and synchronization effects on bilateral trade flows. Review of World Economics/Weltwirtschaftliches Archiv, 149(2), 321–342.

    Article  Google Scholar 

  38. Waterhouse, J., Reilly, T., Atkinson, G., & Edwards, B. (2007). Jet lag: Trends and coping strategies. The Lancet, 369(9567), 1117–1129.

    Article  Google Scholar 

Download references


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.

Author information



Corresponding author

Correspondence to Edward Anderson.



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 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 Official times and the times of solar noon in each principal city are taken from 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 Data on sea distances between major ports were kindly provided by John Howe at AtoBviaC Plc ( The Kaufmann et al. (2009) rule of law index is available at; the Parsons et al. (2007) migrant database is available at: 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

About this article

Cite this article

Anderson, E. Time differences, communication and trade: longitude matters II. Rev World Econ 150, 337–369 (2014).

Download citation


  • Trade
  • Gravity model
  • Time zones
  • Communication

JEL Classification

  • F10
  • F15
  • F20