Industrialization and ethnic change in the modern world

ABSTRACT

Despite the large recent attention given to ethnicity within the social sciences, the sources of modern ethnic change have remained opaque. Drawing upon social theory from Marx and Gellner, I argue here that industrialization incentivizes ethnic homogenization by lowering the relative value of land. Using carbon emissions per capita as a proxy for industrialization, I show that cross-country changes in ethno-linguistic fractionalization between 1961 and 1985 are negatively correlated with industrialization, and that this result is robust to the use of a variety of control variables, sub-samples and alternative measures of industrialization such as cement production, urbanization and agriculture as a percentage of GDP. In particular, I find no evidence for the direct role of the state in promoting ethnic homogenization, which adds to other recent evidence on how economic incentives may trump political ones as regards identity change, at least in the short- to medium term.

KEYWORDS:

• Ethnic change
• industrialization
• ELF
• structural transformation
• urbanization
• ethnic identity

Acknowledgements

I would like to thank Catherine Boone, Sean Fox, Michael Hechter, Eric Kaufman, Matthias vom Hau, Steffen Hertog, David Laitin and seminar participants at Cornell University, the Graduate Institute of International and Development Studies, LSE, Oxford University, Princeton University, the University of Pennsylvania and the Annual Meeting of the American Political Science Association for comments and suggestions. I would also like to thank Cecilia Lanata-Briones and Ulas Karakoc for research assistance and Karina Perez Jvostova, Zhanna Kovaleva, Mélanie Loubet and Alexander Zuev for help with Russian-English translations. All errors remain my own.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. The data set does not include Czechoslovakia, the USSR and Yugoslavia inasmuch as data on both carbon emissions and international migration was only available for their various successor states.

2. Using World Bank data, the number of country observations for these variables dating back to 1961 are 34 and 47, respectively. The same problem of missing country-year observations afflicts a variety of other data as well.

3. The DFbeta tool calculates the difference in the regression coefficient for a particular variable with and without each individual observation. The rule of thumb is to exclude outliers that yield a DFbeta value greater than |1|; in this case Kuwait, Qatar and the UAE all had DFbeta values of |1.3| or greater for either change in carbon emissions or change in migrant stock, with no other observation above |0.6|. Cf. Belsley et al. (1980), 28.

4. It is immediately clear from Figure 1 that Saudi Arabia is far away from the trend line. If it is excluded, then the coefficient on the carbon emissions is considerably larger.

5. The problem of missing data is particularly egregious in the case of African states, which are often missing data on such measures as revenue as a percentage of GDP and infant mortality.

6. The same results hold when using changes in road density and doctors per capita as additional proxies for state capacity.