This section reports the results for the country-level measures of inequality from 1990 through 2012, using the HEIS surveys. First, the country-level measures are calculated and then the within-country measures across ethnic, gender, and regional groups are estimated.
Fractionalization and polarization indices are very stable measures over time unless there is a large change in the share of population of one group versus another.Footnote 22 The average fractionalization index measured over ethnic groups in the 1990–2012 period is 0.55. It is about 0.568 in 1990 and declines slowly to 0.532 in 2012 (Fig. 13 in the “Appendix” reports this index over time.) This means there is practically no change in this measure over time.
The polarization index across ethnic groups in the 1990–2012 period has an average of 0.83. It starts 0.846 in 1990 and slowly declines to 0.82 in 2012. In other words, there is little change, if any, in this measure over time (Fig. 13 in the “Appendix” reports this index over time.)
GINI coefficient for annual income at the country level is calculated over time and reported in Fig. 8.Footnote 23
We are also interested in the inequality in the type of occupations various groups have. Occupations can be categorized according to skill level to high skill, medium skill, and low skill. Legislators, senior officials and managers, professionals, and technicians and associate professionals are considered high-skilled workers. Medium-skilled workers include clerks, service workers and shop and market salespeople, skilled agricultural and fishery workers, crafts and related trades workers, machine operators and assembly-line workers, and different ranks in the armed forces. Elementary occupations are considered low skilled. Since the number of categories for occupational skills is small (here, three), the variable that represent occupational skill will only take three values and will not be continuous. Therefore, measuring Group-weighted Coefficient of Variation (GCOV), Group-weighted Gini coefficient (GGINI), and Group-weighted Theil index (GTHEIL) may not be very informative.Footnote 24 Instead, we measure cross-cuttingness and cross-fractionalization.
Cross-cuttingness and cross-fractionalization between gender and occupation are about 0.80 and 0.04 respectively. These measures between ethnicity and occupation are about 0.91 and 0.09 respectively. All these values are stable over time.
Group-Based Inequalities Over Time
To be consistent with studies of other countries, two samples were used to measure inequality of education, the 25+ and the 15+ samples. It is almost imperative that everyone has completed their education by age 25. If a lower age is chosen as a cut-off point (say 18), we do not know how many years of education a person who is a student may eventually achieve and therefore will under-estimate their education level. This under-estimation is more likely to happen for highly educated and as a result we may under-estimate inequality. On the other hand, there are many countries that experienced substantial increase in educational attainment in the last decades. Looking at the sample of age 25+ is not able to depict such a change. Moreover, many drop out of school before age 15 and finalize their educational attainment. Therefore, the sample of 15+ should be considered. Since the 15+ sample is more relevant for Iran, the result for the 15+ sample is reported here and the estimates for the 25+ sample are included in the “Appendix”. Figure 9 reports the 2-year moving average of the estimates for group-based inequality in years of education attained across gender, ethnicity, urban/rural, and capital/others (Fig. 9a–d respectively.) As the capital is only identifiable in data after 1997, the results for group-based inequality measures between capital and the rest of the country are estimated for the years after 1997 (Fig. 9d).
Figure 9a depicts the inequality across men and women from 1990 through 2012. As can be seen, all inequality measures (GCOV, GGINI, and GTHEIL) between men and women have been continuously decreasing in this period. This interesting result is consistent with the literature on education in Iran. The increase in educational attainment, particularly for girls, has started decades ago. But after the Islamic Revolution of 1979, this rise in educational attainment was faster especially for girls for several reasons. One of the aims of the revolutionaries was to improve the conditions of the poor and people who were consistently on the margins for policy makers, particularly, people in rural areas and poor neighbourhoods of urban areas. In the decades after the revolution and even during the Iran–Iraq war in the 1980s, there has been significant investment in infrastructure of rural areas and small towns. Many projects in electrification, access to clean water, telephone and telecommunication, road building and, as mentioned, school and health facility construction were implemented. The result of these projects were significant expansion of infrastructure across the country which is shown in Table 1 and explained in Sect. 2. Especially, access to schools at all levels (primary, middle school, high school, as well as college and universities) substantially increased since the revolution. As a result, girls who are less likely to be sent to distant schools were now more likely to attend schools which were nearby.
Another development after the revolution was that all private schools became public. For example, the number of public primary schools rose by 60% the year after the revolution (Majbouri 2010). This means that there was a sudden rise in the supply of free education after the revolution. Girls’ educational attainment is more responsive to price reductions, since demand for educating girls is more elastic than boys. This is because parents are more likely to take girls out of school than boys if the cost is high. As a result, after the revolution, parents sent their children, especially girls, to school more.
The third reason for the faster rise in female education compared to male is the gender based segregation of schools. Since schools were segregated after the revolution, conservative families were more likely to send their children to school as schools were considered ‘safer’ for their daughters (Majbouri 2010). The fourth reason is that the teachers’ gender, particularly in middle and high schools, was supposed to be the same as the students’. Therefore, female teachers taught female students and male teachers taught male students. The quality of female teachers was higher than male teachers, on average. This was because, as we saw in Sect. 2, female labour force participation in Iran was (and still is) low (about 20%), and there were only a few jobs, such as teaching, that were attractive to women for various reasons.Footnote 25 Therefore, these jobs attract the top 20% of women who could work and wanted to. On the other hand, many jobs were available as well as attractive to men, and teaching was not necessarily one of their top choices. Therefore, the quality of female teachers was likely higher than the quality of male teachers. As a result, female students received higher quality education than male students and therefore, could attain more years of education than their male counterparts. All these explain the consistent decline in inequality of education across men and women between 1990 and 2012, as depicted in Fig. 9a.Footnote 26
The general trends in group-based inequality measures in Fig. 9b–d are also downward. This means inequality across ethnicity, urban/rural and capital/others has been reduced since 1990. This reduction can be attributed to the continuous expansion of schooling infrastructure even at the tertiary level across the country. In 2012, all inequality measures across ethnic groups are at the same low level as inequality measures across gender. Regional inequalities (urban/rural and capital/others) have been almost halved in this period. Inequality among urban and rural areas is the largest compared to other categories and this is not surprising as return to education is different across rural and urban areas and individuals with the highest education migrate to urban areas. Therefore, rural areas will always have lower average education levels than urban areas not just because they have less access to education (especially tertiary level) but because of migration. As one can see the difference between the capital and the rest of the country has been always less than the difference between urban and rural areas, but at the end of 2012 the inequality between the capital and the rest of the country is still larger than inequality among ethnic groups and between genders.
Overall, one can argue that inequality in educational attainment has been on the decline across all groups and more opportunities are offered to people at the margin in the society. There is only a deviation from the trend in years 1992 through 1998 in Fig. 9b–d. Inequality decreased in 1992 through 1994, and then increased between 1995 and 1998. The potential reason behind the rise in inequality is the minor economic crisis of 1994 and 1995. The fast economic growth after the end of Iran–Iraq war and higher oil prices, encouraged the government to borrow more, mostly from international institutions and in the form of short-term debt. But the unexpected fall in oil prices in late 1993 made it very difficult to pay back these debts. This was because the share of short-term debt out of total public debt was larger in Iran compared to the countries affected in the East Asian currency crisis during 1997–98 (Pesaran 2000). As a result, the government, short in foreign currency, substantially increased the exchange rate, which raised the inflation to 50% in 1995; an unprecedented rate (see Fig. 4). Higher prices reduced purchasing power, especially for the poor, and had impacts on households for a few years after this crisis. Since the people on the margin and the poor are more likely to reduce their educational attainment due to crisis, the inequality increased during the crisis.
We do not observe the value of assets the households have in the data. What we observe is whether the household owns a particular durable good or not. Examples of these durable goods are autos, motorcycles, bikes, and household appliances. Since we only have the binary information of whether the household owns a particular durable good or not, we can combine these binary data into one index using the principal component method for every year. We call this index, asset index, from now on.
Note that the principal component method gives us positive and negative values. But the inequality measures can be calculated on variables that take non-negative values (zero and above; such as income or expenditure). To turn the asset index into a non-negative variable without changing its distribution (i.e. inequality), we add the absolute value of the minimum of the index to it. The result is the asset index distribution shifted to the right by the size of the absolute value of its minimum. The minimum of this shifted distribution is zero and all values lay on or above zero, but the shape of the distribution and its dispersion does not change. Hence, inequality remains the same.
Since assets are recorded at the household level, it is not possible to estimate inequality measures between men and women. Therefore, inequality measures across ethnic groups, urban/rural, and capital/others are calculated. Figure 10 contains the results.
As can be seen, inequality in assets has been decreasing. For the results to be comparable and consistent, the basket of assets used to calculate the asset index has remained unchanged over time. In other words, when newer assets like computers, access to internet, and cell phones are reported in the surveys, they are not included in the basket of assets used to calculate the asset index. So the inequality measures shown in Fig. 10 are inequalities in the more basic assets. Therefore, the reason behind the downward trend in inequality measures is that every year more households owned these basic assets and hence, the difference among households were reduced every year. Interestingly, there is a similar increase in inequality between 1994 and 1998 as those reported for education. The potential explanation for this increase is the same: the economic crisis of 1994–95 which affected the marginalized population: the poor, and the rural population more.
Some of the assets used to create asset index are public goods, such as access to electricity and clean water. The decreasing trend in inequality between ethnic groups supports the argument by Gisselquist (2014) that ethnic diversity by itself does not ‘lead to poor provision of public goods.’
In this section, we calculate the inequality measures for income at the individual level. All sources of income including wages and salaries, business profits, pensions, revenue from renting properties and assets, educational and charity grants are added up at the individual level. All incomes are annual.Footnote 27 Figure 11 reports the results.
We use two samples to study inequality of income between men and women. One is a sample of all men and women who earned income and the other consists of all men and women above age 25, regardless of whether they earned income or not. This is because female participation in the labour market was (and still is) low in Iran (around 20%) and not all working women earned income (some were unpaid family labour working in the family business without pay). Therefore, income for the majority of women was zero. This zero female income as opposed to the positive male income creates an unequal bargaining power within the household for women and affects household decisions. Therefore, any inequality measure that ignores women who do not work is ignoring this inequality and underestimates the inequality between genders. On the other hand, one may argue that some women do not work by their own choice and including their zero income in compiling inequality measures gives us an over-estimation of inequality. Here, we report the results including and excluding women with no income. Figure 11a reports inequality measures among men and women who earn income and Fig. 11b depicts them among all men and women who are 18 or older regardless of whether they work or not.Footnote 28
We learn three lessons from the results: first, as expected, inequality is substantially larger in the sample that includes women without income (compare Fig. 11a with b). The measures reported in Fig. 11b are the largest group-based measures of inequality reported in this study. The second lesson is that inequality of income between male and female earners slowly increased in the 1990s as the economy experienced a downturn from the 1994–95 crisis and the bust in the oil markets. But in the 2000s, with the significant increase in oil prices, inequality of gender decreased over time (Fig. 11a). Figure 11b, also shows some reduction in inequality among all men and women between 1990 and 2012. This can be attributed to two factors: (1) boom in the oil markets, and (2) a small rise in the share of women who worked for pay during this period, and hence a reduction in the share of women with no income (see Fig. 6). Inequality across ethnic groups was fluctuating but roughly stable in the 1990–2012 period (Fig. 11c), but it is substantially smaller than inequality between genders. This means that gender inequality is more important than ethnic inequality in this context. The trend in ethnic inequality of income roughly follows the rise and fall of the oil market. When oil markets boom inequality improves. Figure 11d, e show inequality across regions. Not surprisingly, inequality between urban and rural areas is larger than inequality between the capital and the rest of the country. They generally have a decreasing trend over time, except a sharp increase in inequality in 2008 and 2009. One reason which could have affected on this was that 2007 through 2009 were drought years in Iran, which could have particularly affected the inequality between urban and rural areas negatively.
Expenditure Per Capita
Total expenditure for the household is calculated based on consumption of durable and non- durable goods and services in 1 month. HEIS datasets provide a very disaggregated and detailed account of all types of expenditure at the household level. Expenditure on non-durable goods, such as food and clothing, is measured in the month prior to each survey. But the expenditure on durable goods is measured in the 12 months prior to the survey. Therefore, this expenditure is divided by 12 to get the monthly average. At the end, the total expenditure for the household is divided by the size of the household to get per capita expenditure in the household.
Since the expenditure for men and women is not distinguishable, we cannot estimate inequality between genders, but inequality across other groups can be calculated. Figure 12 shows these inequality measures over time.
The measures of inequality between ethnicities seem to be following the oil prices/oil revenue per capita. When the oil prices dipped in 1994 and the economy went into recession, inequality started to rise. It remained high until 2002 when oil prices started to rebound. With the rise of oil prices between 2003 and 2008, ethnic inequality declined. Due to the collapse of oil prices in 2011, there are signs of rise in inequality.
The inequality between urban and rural areas and between capital and the rest of the country seem to roughly follow the oil markets as well. As the oil markets boom, inequality declines. inequality between the capital and the rest of the country dropped significantly since 2002. This could be due to the rise in cost of living in all urban areas across the country during the 2000s. The capital city is already at full capacity and other cities are becoming more attractive than the capital. This has raised the cost of living and housing in those cities and raised per capita expenditure reducing the inequality between the capital and other cities in the country. More research is required to clearly understand the changes in inequality of expenditure between the capital and the rest of the country.