Demographers tend to study most phenomena by age and sex. But there are other human characteristics that show great discriminatory power in explaining differential trends and for which good statistical information is available. Education is a prime candidate; it has been shown that explicitly considering education as a source of observed heterogeneity can add greatly to understanding the forces driving changes in the population. When limiting the scope to demographic phenomena, both fertility and mortality are greatly influenced by education. The discriminatory power of educational categories as well as the overall influence of education on demographic trends is so pervasive that in a contribution entitled
“Demographic dimensions in forecasting: Adding education to age and sex,” Lutz et al.
(1999) suggested to give education a status in demography that comes near to that of age and sex. As the following description will show, the often raised counterargument, that not enough systematic information was available for education as compared to age and sex, does not hold. For virtually every country in the world there is enough information from censuses and surveys to study the changing educational structure of the population by age and sex. In terms of vital events, surveys provide almost complete information on fertility by level of education (which is the most important for population dynamics) while the situation is more difficult for mortality. It is quite unsatisfactory for migration where often the information by age and sex is also lacking. But the collection of demographic information in the future will be driven by what demographic scholars and subsequently administrators in official statistics consider information that is worthwhile to be collected. In that sense, illustrating the models of population dynamics by level of education and demonstrating the overriding importance of education will pose an incentive for collecting still better information in the future.
Following this principle, the International Institute for Applied Systems Analysis (IIASA), in collaboration with the Vienna Institute of Demography (VID) of the Austrian Academy of Sciences, has recently produced a unique new dataset which applies demographic multi-state projection techniques to reconstruct the population by age, sex and level of educational attainment from empirical data from around 2000 back to 1970 in five-year steps.
This has been done for 120 countries, including Iran. While a comprehensive description of methods and results is given in Lutz et al. (2007), in the following section we will provide only a brief outline of the approach and highlight the results for Iran. The same multi-state approach has recently been applied to project the population by levels of educational attainment to 2030 for all countries in the world (KC et al. 2008), and the results for Iran are included here.
At any point in time the distribution of the population by age, sex and level of educational attainment reflects the history of changes in the proportions of a cohort that attended school and reached certain educational levels. Since formal education typically happens in childhood and youth, the current educational attainment distribution of 50-54 year old women, for instance, reflects education conditions and school enrolment of more than 30 years ago. This is clearly visible in Figure 1, which shows the educational age pyramid of Iran around 2000. The figure gives the usual age pyramid with men on the left and women on the right, where shadings indicate a further subdivision for each age group of men and women by highest level of educational attainment. The picture clearly indicates that younger Iranians are not only much more numerous (due to the history of very high fertility), but also on average much better educated than older ones. This recent improvement in educational attainment is particularly impressive for women. While more than two-thirds of the young women in Iran today have completed at least junior secondary education, among their mothers’ generation only a tiny fraction did so. Hence, the history of rapidly increasing education over time is well reflected in today’s age pattern of education.
Iran - Population by Age, Sex and Educational Attainment in 2000
6000 5000 4000 3000 2000 1000 0 1000 2000 3000 4000 5000
15-19 25-29 35-39 45-49 55-59 65-69 75-79 85-89 95-99
Males Population in Thousands Females
No Education Primary Secondary Tertiary
` Age
Figure 1. Age and education pyramid for Iran, 2000.
The back-projection exercise described here utilizes the fact that much of a population’s education history is still reflected in its current structure. It goes back along cohort lines in five-year steps by deriving, e.g., the proportion without any formal schooling among 50-54 year old women in 1995 from that of 55-59 year olds in 2000 (see Figure 2 for the pyramid corresponding to 1970). There are only three possible factors that can cause these two proportions to differ: differential mortality, differential migration, and women who still acquire formal education after the age of 55. While such late educational transitions are typically irrelevant, differential mortality is a major issue because there is strong evidence in virtually all countries where such data exist that higher educational groups have significantly lower levels of mortality, presumably through better access to information, healthier lifestyles and better economic standing. Although this issue is mentioned in the economic literature of education data, it is not explicitly dealt with in earlier attempts of reconstructing human capital data (Barro and Lee 1996; Benhabib and Spiegel 1994). Only demographic multi-state methods can appropriately deal with differential vital rates.
Iran - Population by Age, Sex and Educational Attainment in 1970
2000 1500 1000 500 0 500 1000 1500 2000
15-19
Figure 2. Reconstructed age and education pyramid for Iran, 1970.
Lutz et al. (2007) provide a detailed account of all the specific assumptions that had to be made as part of this reconstruction exercise, discuss their plausibility, and provide sensitivity analyses. The method can be summarized as follows: First, for every country, an empirical distribution of the population by age, sex and four categories of educational attainment (no formal education, some primary, completed lower secondary, completed first level of tertiary) was retrieved for a year close to 2000. In the case of Iran, the data were taken from the 1996 census and projected forward to 2000. Second, we drew on an existing United Nations (2005) dataset which provides estimates of the age and sex structure in five-year intervals since 1950 for every country in the world. For this reason our effort did not have to reconstruct the absolute sizes of the populations by individual age groups, but only the proportions with different education levels in each age group of men and women. This also made the necessary demographic assumptions a lot easier, since it was not necessary to estimate the overall level of mortality or the total volume of migration (which is implicit in the UN estimates), but only to consider to what degree these demographic forces differed by level of education. While for migration the default assumption was that it did not contribute to changing the educational composition of the population, for mortality we assumed a consistent pattern that life expectancy at age 15 differs by five years between the lowest and the highest educational category (with the difference between no education and some primary being one year and the other differences being two years each). This assumption was based on an assessment of a selection of countries from different parts of the world for which such data exist (see Lutz et al. 2007).
A further problem arose from the fact that in the empirical data, the oldest age group is typically an open-ended category such as 65+ or 70+. When going back along cohort lines, those aged 70+ in 2000 are 40+ in 1970. To get information for the closed intervals 40-44 to 60-64, we need to make assumptions about the distributions across age and education categories in these open intervals which were based (unless empirical information was available) on exponential trend extrapolation of the proportions in the adjacent closed age groups. This source of uncertainty is the reason why it was decided to stop the reconstruction in 1970 and not go back further into history, as the assumptions would have become progressively more restrictive. Another set of assumptions referred to the ages at which
transitions from one educational category to another were made. Since the reconstruction is only performed for the population above age 15, this only concerned transitions to tertiary and to a lesser extent to the secondary level. Lutz et al. (2007) provide further technical details on the reconstruction of the dataset.
Table 1 provides the numerical output for the reconstruction for Iran. It shows the absolute numbers of men and women by five-year age groups and the four levels of educational attainment for Iran for 1970, 2000 and 2030. The bottom line of each sub-table gives the distribution across educational categories for all adult age groups together; the right-hand margin gives the summary measure of the average level of education for individual age groups in the form of mean years of schooling. Although the estimation of this indicator requires additional assumptions in terms of the average years of schooling it takes to become a member of a certain educational attainment category, it was decided to provide it in order to facilitate a comparison to other datasets that only provide mean years of schooling. Finally, the number in the lower right corner of each matrix gives the mean year of schooling for the entire adult population above age 25. This is the number most frequently used in economic studies. Although most often used by economists, this single number has only limited information content. It not only obscures the distribution across educational categories but also hides inter-cohort changes such as the significant improvement in the educational level of younger Iranian women. Having the full matrix available significantly expands the possibility for analysis not only along the age dimension but also with respect to different mixes of primary, secondary and tertiary education in the population.
7 Table 1. Results of the reconstructions and projections of the population by age, sex and level of educational attainment plus mean years of schooling (MYS), Iran, 1970, 2000 and 2030 (in thousands). Sources: Lutz et al. (2007); KC et al. (2008).
Age groups Males Females
No education Primary Secondary Tertiary MYS No education Primary Secondary Tertiary MYS In 1970
15-19 360.9 585.3 627.8 0.0 6.1 703.3 478.1 303.9 0.0 3.7 20-24 380.5 492.6 368.2 28.3 5.3 732.7 328.9 163.0 8.2 2.8 25-29 375.3 358.9 180.6 51.2 4.7 648.5 202.4 76.4 12.3 2.1 30-34 407.7 268.6 84.2 41.7 3.6 612.8 124.5 34.2 8.0 1.4 35-39 446.4 191.1 44.6 22.6 2.5 586.5 75.0 16.9 3.7 0.9 40-44 416.2 152.1 30.3 14.5 2.1 520.8 54.5 12.5 2.0 0.7 45-49 384.7 119.2 20.1 9.0 1.7 461.3 39.4 8.9 1.0 0.6 50-54 344.8 89.9 12.8 5.3 1.4 401.5 27.8 6.2 0.5 0.5 55-59 295.7 64.5 7.7 3.0 1.2 343.3 19.3 4.1 0.3 0.4 60-64 252.5 45.9 4.6 1.6 0.9 293.2 13.3 2.8 0.1 0.3 65+ 440.9 58.3 4.4 1.4 0.7 521.2 16.4 3.1 0.1 0.2 15+ 4105.6 2426.6 1385.5 178.6 3.6 5825.1 1379.5 632.1 36.2 1.8 25+ 3364.2 1348.6 389.4 150.3 2.5 4389.1 572.5 165.1 28.1 1.0 In 2000
15-19 119.3 726.3 3702.1 0.0 8.7 209.6 1059.4 3073.0 0.0 8.0 20-24 133.1 835.0 2032.3 495.5 9.1 248.6 1166.8 1529.4 396.1 7.9 25-29 148.0 765.5 1283.5 369.2 8.6 317.8 972.6 979.6 236.2 7.0 30-34 181.6 643.6 1113.0 268.1 8.2 416.2 809.2 806.6 138.5 6.2 35-39 231.5 558.5 969.7 234.4 7.9 508.0 643.4 643.8 110.9 5.6
40-44 301.7 557.9 680.9 191.7 7.3 603.6 562.5 410.1 74.5 4.8 45-49 302.4 498.5 390.3 163.7 6.9 659.7 455.3 236.0 62.7 4.0 50-54 268.5 354.6 200.8 97.9 6.0 636.8 290.9 122.7 34.5 3.0 55-59 263.6 258.2 116.6 60.1 5.0 513.7 163.5 58.9 16.0 2.2 60-64 315.9 214.4 71.1 37.0 3.8 493.5 102.6 29.5 7.2 1.4 65+ 1009.8 386.9 86.3 45.4 2.3 1239.6 135.9 33.2 6.2 0.8 15+ 3275.3 5799.4 10646.6 1963.0 7.6 5846.9 6362.0 7922.7 1082.9 5.8 25+ 3022.9 4238.0 4912.2 1467.5 6.8 5388.7 4135.9 3320.3 686.8 4.6 In 2030
15-19 9.3 381.0 3373.8 0.0 9.3 5.9 422.6 3168.6 0.0 9.2 20-24 15.1 416.3 2330.8 777.3 10.8 13.6 488.2 2298.0 589.2 10.3 25-29 20.2 405.1 1956.9 615.0 10.5 23.0 498.3 1910.3 444.4 9.9 30-34 28.4 435.0 1821.5 540.9 10.3 37.9 556.4 1569.7 549.8 10.0 35-39 56.5 690.2 2513.9 708.4 10.0 85.1 911.4 2093.1 720.7 9.6 40-44 84.3 844.1 2681.8 720.1 9.9 139.5 1148.5 2144.3 735.3 9.4 45-49 108.5 913.5 2538.4 652.7 10.2 194.9 1283.9 1937.7 672.9 9.4 50-54 117.5 745.6 1848.8 458.5 10.1 226.9 1073.5 1324.8 476.5 9.0 55-59 124.0 652.8 1156.1 303.7 9.4 282.1 874.0 793.2 327.7 8.1 60-64 139.6 508.7 923.3 231.5 9.2 354.8 702.8 723.4 127.6 7.0 65+ 534.3 1053.3 1452.3 468.5 8.3 1543.6 1362.0 1113.5 236.2 5.3 15+ 1237.6 7045.7 22597.6 5476.6 9.9 2907.4 9321.6 19076.6 4880.1 8.9 25+ 1213.2 6248.3 16893.0 4699.3 9.8 2887.8 8410.7 13610.0 4291.0 8.6
The methods of multi-state population dynamics can also be used to simultaneously project a population by different levels of educational attainment.
Unlike for the case of multi-state reconstructions, where differential fertility need not be of concern because cohort sizes are empirically given, for the projections one needs to make assumptions about the future trends in education-specific fertility rates. In the context of the global education projections produced by IIASA (KC et al. 2008), this is done by assuming overall fertility levels together with constant relative fertility differentials among education groups. For these projections several alternative scenarios were defined both with respect to the future overall levels of fertility as well as for the future educational transition rates from lower to higher educational categories. In the following illustrations for Iran we will focus only on the so-called “UN Scenario” which defines education-specific fertility and mortality rates in such a way that it replicates the overall fertility and mortality rates of the medium variant of the UN population projections. This implies that the education pyramid given in Figure 3 has an identical overall shape to the one resulting from the UN medium variant, and only the age- and sex-specific proportions with different levels of educational attainment have been added as additional pieces of information. For the age- and sex-specific probabilities of transitions to higher educational categories, the projections presented here follow the so-called global education trend (GT) scenario. This scenario assumes that over the coming years every country follows the trend that has been observed by all 120 countries over the past two decades. This is done separately for primary, secondary and tertiary education with each country starting the future trend for its young cohorts from its current position and then following the average trends of the countries that are already somewhat more advanced in their education process.
Iran - Population by Age, Sex and Educational Attainment in 2030
5000 4000 3000 2000 1000 0 1000 2000 3000 4000 5000
15-19
Figure 3. Projected age and education pyramid for Iran, 2030.
Figure 3 conveys the educational structure of a highly developed industrialized country for Iran in 2030, with high proportions of the population having secondary or
higher education. Particularly among younger adults in 2030 there will be virtually no uneducated persons left and only a few will remain with primary school as their highest attainment (see also Table 1). The overall shape of the age pyramid is rather unusual as a consequence of the very large cohorts that were born in the 1980s and who will be 40-50 years old in 2030. This big cohort will dominate the age structure of Iran for the decades to come. Actually, the reduction in cohort size will make it more likely for the young generation in Iran to receive more education than implied by the GT scenario, which is only defined in terms of proportions and does not consider the changing relative size of the youngest cohorts.
But cohort size can be assumed to matter for school enrolment rates. Due to still very high fertility, some African countries will have serious problems in even maintaining current school enrolment sizes. As the school age population increases rapidly, it will be even more challenging to expand enrolment rates according to the GT scenario. For Iran, however, the picture looks very different. Once the larger absolute number of children of the huge cohorts born during the 1980s has gone through school, the number of school age children will decline rapidly as a consequence of low fertility together with declining numbers of potential mothers. In all likelihood, this will result in significant further improvements of the numbers of children with secondary and in particular tertiary education. Hence, the projection presented here is likely to be an underestimation of future educational attainment in Iran. Depending on the level of fertility in the near future this decline in the school age population will be followed by some echo effect of children born to the large cohorts.