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How does UNESCO’s Convention on Cultural Diversity affect trade in cultural goods?


After a long and heated argument on whether international trade in cultural goods should be an exception to free trade, UNESCO’s Convention on Cultural Diversity (CCD) was adopted and entered into force in 2007 to protect and promote cultural diversity. This paper provides the first empirical assessment of the impact of CCD on trade in cultural goods. By using trade data for 2004–2010 and employing the first-differenced difference-in-differences method, we estimate the effects of ratifying CCD on the imports of cultural goods and on the extensive margin of cultural imports. Our estimation results provide little evidence that CCD is an instrument of disguised protectionism. Furthermore, we find that CCD contracting countries tend to increase the country margins of cultural imports for some subcategories of cultural goods more than CCD non-contracting countries. This change implies that CCD contributes to the promotion of cultural diversity.

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  1. The formal name is the Convention on the Protection and Promotion of the Diversity of Cultural Expressions.

  2. Article 20 of CCD explicitly states that the Convention does not modify the rights and obligations of the parties under any other treaty to which they are party.

  3. Moreover, for a number of countries that have ratified CCD, the Convention has actually changed their domestic policies and legislation (Baltà 2014).

  4. We acknowledge that many international transactions of cultural content are currently made online, which cannot be captured by trade data. According to IFPI (2018), the digital share of global revenue in the recorded music industry has been growing since the mid-2000s. The share was 28.9% in 2010, but it increased to 54.3% in 2017. This is mainly due to the rapid growth of streaming services in the recorded music industry. Thus, because the online cross-border transaction may still have not been so prevalent in our observation period and because bilateral data on e-commerce transactions are not publicly available, we focus on trade in goods. See Hellmanzik and Schmitz (2015) for the detail on the services trade in cultural goods.

  5. We have 110 countries’ trade data, but in the following estimation, one contracting country (Jamaica) and one non-contracting country (Myanmar) are excluded given the lack of data on their GDPs. Therefore, the number of observations in our sample becomes \(110-2=108\). In “Appendix,” Tables 18 and 19 list the sample countries by CCD contracting status. For two reasons, we restrict our analysis to WTO members’ imports. First, the primary political dispute is over the relation between CCD and GATT/WTO. Second, WTO non-members account for a small proportion of world trade.

  6. We take the log of the outcome variable plus one to keep the number of sample countries constant and the estimation results comparable rather than throwing away the observations with zero trade flow.

  7. Note that in our first-differenced panel specification, the time-invariant country-specific variables related to the country’s “cultural attitudes,” such as language, are eliminated from the estimation equation.

  8. See Cameron and Trivedi (2005) and Abadie and Imbens (2006) for a more detailed explanation of the PSM method, which is widely used in the trade literature. For instance, in the case of international agreements, Baier and Bergstrand (2009) employed the PSM method to examine the effects of free trade agreements.

  9. Baier and Bergstrand (2004) estimated the determinants of free trade agreements. To the best of our knowledge, our study is the first to econometrically examine the determinants of ratifying CCD.

  10. Those countries are Austria, Cyprus, Denmark, Finland, France, Germany, Iceland, Niger, Norway, Portugal, Spain, and Sweden. In addition to the 12 countries, one contracting country, Romania, does not have data on migrants. As a result, the number of contracting countries in our sample becomes \(68-1-12=55\), and the number of observations in our matched sample is \(55 \times 2=110\).

  11. Those 19 countries are Argentina, Australia, Burundi, Barbados, Switzerland (Liechtenstein), Congo, Dominican Republic, Georgia, Guinea, Grenada, Guyana, Hungary, Nigeria, Nicaragua, Netherlands, Qatar, Chad, Saint Vincent and the Grenadines, and Zimbabwe.

  12. We also examine whether CCD contracting countries increased their cultural imports from countries with a colonial link. However, we do not find any significant impacts.

  13. We appreciate the anonymous referee for suggesting this.

  14. For countries that confirmed CCD before CCD was enforced, we count k as the number of years after 2007.


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Part of this study was conducted as the project on law and economics of cultural trade undertaken by the Research Institute of Economy, Trade and Industry (RIETI). Jinji acknowledges financial support from the Japan Society for the Promotion of Science under the Grant-in-Aid for Challenging Exploratory Research No. 25590057. We are also grateful to Mina Sakamoto (Taniguchi) for her excellent research assistance. The authors are solely responsible for any remaining errors.

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Appendix 1: Gravity equation

In the previous sections of the main text, we employed the first-differenced DID specification to examine the impacts of the CCD on international trade in cultural goods. This approach is based on Aichele and Felbermayr (2012) and has advantages in addressing the endogeneity issue. However, this approach is not necessarily the most common in the trade literature. Many studies have employed a bilateral gravity equation to examine the effects of a policy change on international trade. In addition, previous studies examined international trade in cultural goods using gravity equations (e.g., Disdier et al. 2010, Hellmanzik and Schmitz 2015, and Schulze 1999).

The gravity equation has the advantage to increase the number of sample countries. We can utilize year-by-year information on the CCD ratification status in panel data and include all countries that ratified CCD during the sample period (2004–2010) in a panel gravity framework.

Therefore, this section employs the gravity equation and examines the effect of CCD on bilateral trade in cultural goods. Although the first-differenced DID approach allows us to control for the endogeneity in the previous sections, this endogeneity can also be controlled for in a gravity estimation with country–pair fixed effects, \(FE_{ij}\), in the panel setting (Baier and Bergstrand 2007). In particular, we conduct the fixed effect estimation of the following gravity equation:

$$\begin{aligned} \ln IMPORT_{ijt}= & {} \beta _{0}+\beta _{1}CCD_{it}+\beta _{2}CCD_{jt} \nonumber \\&+FE_{i}+FE_{j}+FE_{ij}+YEAR_{t}+\upsilon _{ijt}, \end{aligned}$$

where \(IMPORT_{ijt}\) is country j’s core cultural goods imports from country i in year t and \(\upsilon _{ijt}\) is an error term. \(CCD_{it}\) is a dummy variable that takes the value of one if exporting country i is a contracting country of CCD, whereas \(CCD_{jt}\) is a dummy variable that takes the value of one if importing country j is a contracting country of CCD. To address the cross-sectional biases given the multilateral trade resistance or the “omitted price” bias (Anderson and Van Wincoop 2003), we follow the approach of Redding and Venables (2004) and include exporter and importer fixed effects, \(FE_{i}\) and \(FE_{j}\), respectively. We cannot include time-varying country fixed effects, \(FE_{it}\) and \(FE_{jt}\), because they will eliminate the effects of CCD, \(CCD_{it}\), and \(CCD_{jt}\). Instead, we include year fixed effects, \(YEAR_{t}\), to control for time-series “omitted price” bias, although they cannot remove the time-series “omitted price” bias at the country level, as suggested by Baldwin and Taglioni (2006).

We employ the fixed effect model to estimate equation (7) as in Baier and Bergstrand (2007). This approach can control for the endogenous bias caused by the tendency that countries self-select into CCD because we include country fixed effects and country–pair fixed effects in the panel gravity. Column (1) of Table 17 shows the results of the gravity equation (7) of bilateral trade in cultural goods during 2004–2010. Both coefficients of \(CCD_{it}\) and \(CCD_{jt}\) on trade in cultural goods are significantly positive, indicating that CCD has positive impacts on both exports and imports in core cultural goods. In column (1) of Table 17, the coefficient of \(CCD_{jt}\) is quantitatively similar to those in Table 4, which implies that CCD contracting countries import 2.2% (\(={\exp }(0.022)-1\)) more core cultural goods than non-contracting countries. Similarly, the coefficient of \(CCD_{it}\) implies that CCD contracting countries export 2.3% (\(={\exp }(0.023)-1\)) more core cultural goods than non-contracting countries.

Column (2) of Table 17 shows the estimation results of the gravity equation that uses the number of core cultural products imported by country j from country i instead of the bilateral trade values of core cultural goods. The coefficient of \(CCD_{it}\) is negative but insignificant, whereas the coefficient of \(CCD_{jt}\) is positive but insignificant, as in Table 7. The result implies that the impacts of CCD on the number of imported cultural products are negligible, as shown in the first-differenced DID results.

To keep the number of sample countries constant and the estimation results comparable, we take the log of \(IMPORT_{ijt}+1\) and \(PRODUCT_{ijt}+1\) in the previous analysis, although Silva and Tenreyro (2006) revealed that this procedure leads to inconsistent parameter estimation. To address this issue, we also employ the Poisson pseudo-maximum-likelihood (PPML) estimator using the dependent variables in level, as proposed by Silva and Tenreyro (2006). The PPML method prevents us from including country–pair fixed effects, \(FE_{ij}\), into the estimation for computational limitations. Therefore, we follow previous studies such as Disdier et al. (2010) and Hellmanzik and Schmitz (2015) and conduct estimations without country–pair fixed effects but with standard bilateral gravity variables, such as the log of distance between exporting and importing countries (\(\ln Distance\)) and a dummy for common language (Language), as explanatory variables.

The estimation results using the PPML method are presented in columns (3) and (4) in Table 17. The magnitude of the coefficients becomes slightly larger and the coefficient of \(CCD_{j}\) becomes significant. The upward bias may be caused by the fact that we cannot control for the bias attributable to self-selection in CCD in PPML estimation without country–pair fixed effects. However, the estimated signs of the coefficients are the same as those in columns (1) and (2).

To summarize, our analysis again finds that CCD has not supported disguised protectionism. Rather, it suggests that the CCD has positive impacts on the bilateral international trade in core cultural goods in the standard gravity framework. The magnitude of the coefficients in the gravity framework are similar to those in the first-differenced DID specification. The effects of CCD on imports and exports of core cultural goods are around 2%. In addition, the gravity analysis reveals that CCD has not decreased the number of traded cultural goods, as shown by the first-differenced DID approach.

Table 17 Gravity equation of cultural goods (2004–2010)

Appendix 2

See Tables 18, 19, 20, 21 and 22.

Table 18 List of CCD contracting countries (68 countries)
Table 19 List of CCD non-contracting countries (40 countries)
Table 20 Descriptive statistics of cultural imports by FCS category
Table 21 UNESCO Framework for Cultural Statistics
Table 22 List of variables

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Jinji, N., Tanaka, A. How does UNESCO’s Convention on Cultural Diversity affect trade in cultural goods?. J Cult Econ 44, 625–660 (2020).

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  • Trade and culture
  • Cultural goods
  • UNESCO’s Convention on Cultural Diversity
  • Difference-in-differences

JEL Classification

  • F13
  • F14
  • Z10