Does Cultural Distance Hinder Trade in Goods? A Comparative Study of Nine OECD Member Nations

We examine the effect of cultural distance, a proxy for the lack of a minimum reservoir of trust necessary to initiate and complete trade deals, on bilateral trade flows. Employing data for 67 countries that span the years 1996–2001, we estimate a series of modified gravity specifications and find that cultural dissimilarity between nations has an economically significant and consistently negative effect on aggregate and disaggregated trade flows; however, estimated effects vary in magnitude and economic significance across measures of trade and our cohort of OECD reference countries. The consistently negative influence of cultural distance indicates that policymakers may wish to consider mechanisms that enhance the build-up of trust and commitment when seeking to facilitate the initiation and completion of international trade deals. Our findings also imply that coefficient estimates from related studies that do not account for the trade-inhibiting effect of cultural distance may be biased.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Rent this article via DeepDyve

Similar content being viewed by others

Cultural Distance and International Trade

Article 07 August 2023

Does Cultural Distance Affect International Trade in the Eurozone?

Chapter © 2020

Does international trade favor proximity in cultural beliefs?

Article Open access 06 June 2022

Notes

“Appendix 1” provides a listing of the countries in our data set.

“Appendix 2” provides details regarding data sources and the construction of the immigrant stock series.

The effect of immigrants on trade between each OECD country–country j pairing can be computed similarly.

The number of trading partners in our analysis is determined by the availability of data on cultural distance. On average, the values surveys provide data for 1,121 residents of each nation in our sample.

“Appendix 3” lists the variables, corresponding data sources and additional notes.

We also estimate the relationship using the Random Effects Generalized Least Squares approach. The results do not differ from those presented here.

References

Author information

Authors and Affiliations

  1. Department of Economics, University of Minnesota—Duluth, Duluth, MN, 55812, USA Bedassa Tadesse
  2. Department of Economics, Franklin and Marshall College, 415 Harrisburg Pike, Lancaster, PA, 17603, USA Roger White
  1. Bedassa Tadesse
You can also search for this author in PubMed Google Scholar You can also search for this author in PubMed Google Scholar

Corresponding author

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

ESM 1

Appendices

Appendix 1: country listing

Albania, Algeria, Argentina, Armenia, Australia, Austria, Azerbaijan, Bangladesh, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Czech Republic, Denmark, Dominican Republic, Egypt, El Salvador, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Korea (Rep.), Latvia, Luxembourg, Macedonia, Mexico, Morocco, Netherlands, New Zealand, Nigeria, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, Tanzania, Turkey, Uganda, Ukraine, United Kingdom, United States, Uruguay, Venezuela, Vietnam, Zimbabwe.

Appendix 2: immigrant stock data and estimate construction

Data for Australia, Canada, Denmark, the Netherlands, Norway, Sweden and the US represent foreign-born populations by country of birth, while data for Germany and Italy represent foreign-born populations by country of nationality. Immigrant stock data are from national statistic agencies and have been compiled by the Migration Policy Institute (2007). For six of the nine host countries in our data set, Denmark (Danmarks Statistik), Germany (Statistiches Bundesamt), Italy (Istituto Nazionale di Statistica), Norway (Statistisk Sentralbyrå), the Netherlands (Centraal Bureau voor de Statistiek) and the US (US Census Bureau), immigrant stock data are complete to the extent that the statistical agency provides annual immigrant stock values for the years 1996–2001. Due to a lack of data, immigrant stock values are estimated for 1997–2000, for Australia (Australian Bureau of Statistics) and Canada (Statistics Canada), and for 1996–1998 for Sweden (Statistiska Centralbyrån). Available immigrant stock values are accepted as correct and are employed as benchmark values. Inflow data (reported along with available stock data by the noted statistical agencies) is used to estimate stocks for all other years. For example, immigrant stocks for Canada, for the years 1997–2000, are constructed as \(IM_ = IM_ + \sum\limits_^t \) . IN ijt is the immigrant inflow from country j to country i (in this case, Canada) during year t. is an adjustment factor accounting for return migration and deaths of immigrants during non-benchmark years. The adjustment factor is the immigrant stock from country j in Canada during 2001 less the sum of immigrants from country j in Canada in 1996 and the inflow from country j during the years 1997–2001 divided by five: \(\rho _j = \frac - \left( > \right)>>\) . Immigrant stock variables for Australia and Sweden are estimated similarly.