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Social Indicators Research

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Open Access 24.11.2017

Iran’s Multi-ethnic Mosaic: A 23-Year Perspective

verfasst von: Mahdi Majbouri, Sanaz Fesharaki

Erschienen in: Social Indicators Research | Ausgabe 3/2019

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Abstract

In this study, we embark on measuring inequality in Iran. We compute three measures of group-based inequality (Group-weighted Coefficient of Variation, Group-weighted Gini, and Group-weighted Theil) for the following outcomes: education, assets, income, and expenditure per capita. The groups are defined based on gender, ethnicity/language (Persian, Azeri, and other ethnic minorities), and region (urban versus rural and capital city versus other places) using 23 years of annual Household Expenditure and Income Surveys from 1990 through 2012. Inequality between groups based on religion (Muslim, non-Muslim), citizenship (Iranian, non-Iranian) are also studied using the 2006 census. Our analysis of the trend of horizontal inequality reveals substantial reduction in between-group inequalities over the 1990–2012 period. Yet, gender based income inequality remains high. The implications and underlying reasons for these results are discussed.

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Such as the Podemos in Spain and Syriza (the coalition of the radical left) in Greece.

The region has been in turmoil recently and ethnic divisions were in the centre of the conflict.

Fractionalization is the most widely used measure of diversity. It is the probability that two individuals who are randomly chosen from the society belong to different groups.

Another advantage of studying group-wise inequality measures is that some indices of horizontal inequality can be used to derive indices of vertical inequality (Jayaraj and Subramanian 2006).

They show that three groups are more likely to be chronically poor: people who live in urban areas, younger individuals, and those living in female headed families.

The groups and variables are discussed in detail in Sects. 3.3 and 4 respectively.

Socio-economic Characteristics of Households 1992.

See Hakimian (2014) for a discussion of macroeconomic performance in post-revolutionary Iran.

Note that those born after 1974 went to school for the first time after the revolution.

For instance, women could not become agricultural engineers in the 1980s and early 1990s (Keddie 2000).

Many studies have been done on this issue. See for example Salehi-Isfahani (2005b), Chamlou and Karshenas (2016), Karshenas et al. (2016), Karshenas (2001), Moghadam (2000, 2011, 2013), Esfahani and Shajari (2012), Bahramitash and Esfahani (2011), Bahramitash and Olmsted (2014) and Majbouri (2010, 2015, 2016a, b, 2017).

The 2011 census is also available but we did not include the results for that census as they were similar to the 2006 census and did not generate any value added for this analysis. Instead, we use Household Expenditure and Income Surveys which, as described, are significantly more comprehensive than censuses and cover 23 consecutive years. They offer a wealth of data that censuses do not.

Amanolahi (2005) focuses on ethnicity as a cultural phenomenon; one which ‘encompasses the non-biological aspect of man’.

Gisselquist (2013) argues that ethnic politics are different in societies in which ethnic groups are ranked versus unranked.

A vast amount of literature is in Persian and every ethnic group have more or less adopted the language. It has been spoken from Tajikistan and Uzbekistan in Central Asia to Afghanistan, and Iran; even in the Ottoman Empire, where Persian was the language of the court. It is not a surprise that Persian is and has been chosen as the formal language, even by Turkic origin Iranian governments.

Iran’s constitution defined Persian as ‘the official language and script of Iran’ while recognizes the minorities’ right to use their language. Article 15: The official language and script of Iran, the lingua franca of its people, is Persian. Official documents, correspondence, and texts, as well as text-books, must be in this language and script. However, the use of regional and tribal languages in the press and mass media, as well as for teaching of their literature in schools, is allowed in addition to Persian. Article 19: All people of Iran, whatever the ethnic group or tribe to which they belong, enjoy equal rights; and colour, race, language, and the like, do not bestow any privilege.

Safavid dynasty, originally from Azeri parts of Iran, created the first modern Iranian nation-state in 1501, and declared Shi’ite Islam as the state religion. It was a way of distinguishing itself from the Sunni dominated Ottoman empire. Isfahan, a major Persian city in central Iran, was selected as capital, and Persian was the formal language. Turkic origin Qajar dynasty, gained power in 1785, and chose the Persian city of Tehran as the capital. This trend is continued and now Azeris are well integrated into the economic and political system of Iran.

Accessible at https://icr.ethz.ch/data/greg/.

We use Iran’s Cultural Council (2010) to identify the major ethnic group in each province.

SECH datasets are nationally representative panels. Three sets of SECH surveys were collected: 1987–1989, 1992–1995, and 2001–2003. Waves are collected annually. We had the last two panels and used them to compare results with those this study (see Table 2). Attrition is a major issue in these datasets as about 30% of the sample is lost by the third year of these surveys. Therefore, data for the first years of these panels, i.e. 1992 and 2001 are used.

There are multiple ethnic cleavages that are salient and might be studied in Iran. One we are unable to analyze in this paper is the Shi’a/Sunni distinction because we do not have any way to identify Shi’as and Sunnis. The religious sect is not collected in publically available data sets. Using regions to identify Shia’s and Sunnis will lead to similar results as those found for ethnicity, because we used regions to identify ethnicities too. The ethnicities measured as “Others” are located in Sunni dominated areas. The two other ethnicities, Persians and Azeris, are Shi’as. Therefore, dividing the sample into Shi’as and Sunnis is like grouping Persians and Azeris into Shi’as and calling “Others” Sunnis. Hence, this seems to provide little value added on top of the analysis for ethnicities which is already offered.

Fractionalization index is described in footnote #3. Polarization index is defined based on Montalvo and Reynal-Querol (2005). It is \(1 - \sum\nolimits_{i = 1}^{n} {\left( {\frac{{0.5 - p_{i} }}{0.5}} \right)}^{2} p_{i}\), in which n is the number of groups and p_i is the proportion of group i in the population .

These measures are calculated using ‘fastgini’ command in Stata.

Frances Stewart’s project on horizontal inequalities and conflict draws on these three measures of horizontal inequality: Group-weighted Coefficient of Variation (GCOV), Group-weighted Gini coefficient (GGINI), and Group-weighted Theil index (GTHEIL). In summary, these are extended versions of Coefficient of Variation, Gini coefficient, and Theil index that incorporate between group inequalities beyond the inequality in the population. Stewart et al. (2005) explore each of these measures in detail. The introduction to this special issue discusses these measures in more detail.

For example, according to law, husbands should agree with their wives working. Husbands are more likely to give permission to their wives to work when all of their wives colleagues are women. One of the jobs that offered (and still offers) such a working environment was teaching. All employees in girl’s schools were female and hence female teachers had all-female colleagues.

For more information on reasons behind the rise of female education in Iran see Keddie (2006) and Salehi-Isfahani (2005a: 286).

Since sometimes business profits are negative, income can be a negative variable. But only a very small fraction of the sample has negative incomes (around 1%). Those negative incomes are replaced with zero in the sample so that the measures are correctly correlated.

Samples used in all sub-figures include people aged 18 and above.

For example, in fear of deportation.

In our 2% census sample, there are 23,309 women in high skilled jobs. These the high skilled women are most frequently reported as managers (514 women), scientists and engineers (782 women), doctors and surgeons (499 women), pharmacists (52 women), professors (651 women), accountants and financial experts (1182 women), school principals (1062 women), college educated teachers and educators (9456 women), college educated nurses (1507 women), lawyers (152 women), paralegals (180 women), other college graduated professionals (1500 women), technicians (2444 women), and Clerks (2239 women).

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Titel: Iran’s Multi-ethnic Mosaic: A 23-Year Perspective
verfasst von: Mahdi Majbouri
Sanaz Fesharaki
Publikationsdatum: 24.11.2017
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 3/2019
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-017-1800-4

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