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A New Dataset on Horizontal Structural Ethnic Inequalities in Thailand in Order to Address Sustainable Development Goal 10

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Abstract

Thailand is the world’s fourth most unequal country by wealth, but past research on this phenomenon has mainly been along individual or geographic lines as the census is insufficiently detailed to provide ethnic-based measures. This article introduces an innovative methodological solution to measure horizontal inequality in countries where the census excludes ethnicity. We investigate the roots of a structural ‘ethnic penalty’ by contextualising in some detail an ethnic gap in Thai public policies concerning poverty and inequality. Then, using data from the United Nations Development Programme’s ‘Human Achievement Index’ for Thailand, the Thai Office of the National Culture Commission’s Ethnolinguistic Maps of Thailand report, and Thai National Statistical Office population and poverty data, we compile the country’s first dataset on horizontal (between-group) inequality by ethnicity. Employing this novel approach, we then examine the eight sectors in the Human Achievement Index: health, education, employment, income, housing and living environment, transport and communication, family and community life, and participation. Describing how Thailand’s major ethnic groups fare in each sector and comparing inequality amongst sectors, this ethnolinguistics-based approach provides evidence of a significant ‘ethnic penalty’ in Thailand and makes initial recommendations for addressing the issue.

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Notes

  1. These were nationally-representative surveys carried out by the Comparative Study of Electoral Systems (CSES) dataset and the Democratization and Value Change in East Asia (DVCEA) dataset from the Asian Barometer. The questions were self-identification questions. Main categories for Ethnicity and Religion were provided, along with an option to provide a different response in an Other category.

  2. The Thai-Chinese constitute somewhere in the range of 11% (Selway 2007) to 14% (West 2012, p. 794). Of this number, 3–16% are Sino-Thai, almost completely assimilated into Thai culture. The remainder are also Thai speakers, but retain much stronger Chinese cultural practices, including some knowledge of a Chinese dialect. The reason for omitting Chinese identification on the census stems from the delicate position of the Chinese diaspora in Thailand since the 1890s and an official position of declining immigration quotas coupled with mandatory assimilation (Tejapira 2009).

  3. Ethnolinguistics is an inter-disciplinary science related to cultural linguistics and anthropological linguistics (Riley 2007).

  4. Regulation of the Central Registration Office on Consideration of Recording Persons’ Identification Status in the Civil Registration for Persons on the Highlands, B.E. 2543 (2000).

  5. The final report was published by the Office of the National Culture Commission (ONCC) in 2004 and endorsed by the Department of Rights and Liberties Promotion of the Ministry of Justice.

  6. We were unable to obtain the raw EMT dataset, despite numerous requests, so all of our information is derived from the written report. We note that there were some inconsistencies and some clear errors. For example, see footnote 10 on the Malay populations in the three Deep South provinces.

  7. We believe this level of aggregation is the best way to present the data in order to begin much-needed policy discussions. Moreover, most groups within a language family are at very similar levels along the various indices.

  8. This is both for ease of presentation, but also to aid initial policy discussions, since a move away from grouping all “Hill Tribes” together as a single category to considering the seven we present here (plus the Khmer and Malay) already represents a significant change for Thai policy makers. These groupings are based on discussions with two linguists of mainland Southeast Asia, Dr. Kirk Person of the Summer Institute of Linguistics and Professor John Hartmann of Northern Illinois University. We thank them for their help on this matter.

  9. The one exception is civic participation where rural areas tend to do better.

  10. In five provinces, we make minor methodological adjustments due to omissions in some of EMT’s publicly available data. For Pattani, Yala, and Narathiwat provinces, we rely on census data for number of Malay speakers as the EMT drastically underestimates these figures. For Nakhon Ratchasima, we increase the estimate of Thai Khorat, since the EMT figure does not accord with other sources. For Bangkok, the sizes of minority groups given in the EMT data are much smaller than estimates of various groups given in other sources. This data was likely computed inaccurately as it does not accord with regional-level percentages for each group, provided in another section of the report. We thus add all the ethnic group sizes in the Central region and take the difference between the reported group percentage at the regional level with the number we have from reported minority group sizes in each province. We add the difference to the size of ethnic groups in Bangkok.

  11. We treat the Thai Khorat as remote minorities in all provinces but Nakhon Ratchasima, where they are heavily concentrated in the provincial capital. The EMT underestimates the population of the Thai Khorat in Nakhon Ratchasima. We instead rely on the updated 2011 Thailand country submission to CERD.

  12. We thank Jay Goodliffe for a useful discussion on ecological inference. All final errors remain our own.

  13. We take the average of the three least urbanized provinces in the region and divide it by the average of the three most urbanized provinces in the region. We then average across the four regions. An alternative approach is to regress urbanicity on each measure and use the beta coefficient to calculate a ratio. The two methods produce almost identical results.

  14. This and the three other civil society participation measures confirm existing literature that finds civic participation to be higher in rural areas. See Albritton and Bureekul (2002).

  15. For a robustness check on this method, see the Supplementary Appendix where we compute provincial-level ratios for income, the only index for which finer-grained information is available. The national-level method we use here produces a slighter higher ratio (0.88) than the average of the provincial ratios (0).

  16. We take population sizes into consideration to maintain the provincial average after the weighting. Thus, in other provinces with very small minority groups, the scores for majorities do not rise so significantly.

  17. 1.43%, though this figure should be taken in context: Thailand has among the lowest unemployment rates in the world. Most other groups are clustered together in the 0.60% range.

  18. We can also compute ordinal rankings based on the sum of ranks within each separate index. With this method, which might be preferred due to the roughness of the data and estimation techniques, the Southern Thai take top spot. In the second tier, the Malay jump to 4th spot tied with the Khorat Thai, with the Thai Lao falling to 6th. The Phu Thai drop to last place. Three tiers are still clearly discernible.

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Acknowledgements

The authors wish to acknowledge a grant of 4400 USD via Grant No. 14/2560 from the Research Group on Local Affairs Administration, College of Local Administration, Khon Kaen University, Thailand, for the research and article writing, and the assistance of Dr. Jay Goodliffe of the Department of Political Science of the Faculty of Humanities and Social Sciences at Brigham Young University for advice on ecological inferencing methodology. All and any errors are the authors’ own.

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  1. Social Survey Center, Research Group on Local Affairs Administration and Smart City Development, College of Local Administration, Khon Kaen University, 123 Friendship Highway, Khon Kaen, 40002, Thailand

    John Draper

  2. Department of Political Science, Brigham Young University, Provo, USA

    Joel Sawat Selway

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Draper, J., Selway, J.S. A New Dataset on Horizontal Structural Ethnic Inequalities in Thailand in Order to Address Sustainable Development Goal 10. Soc Indic Res 141, 275–297 (2019). https://doi.org/10.1007/s11205-019-02065-4

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