INTRODUCTION

Futuristic vision is one of the innate capacities of human beings for speculation, modelling and choosing between alternatives. The human brain system is endowed with the capacity for primary consciousness (seeing what is here and now) and reflexive understanding in time. This high-capacity consciousness is reflected in the ability to remember and to learn, to roam consciously throughout a rich, complex, extended present, to understand responsibilities and consequences, and to speculate on futures yet to come (Slaughter, 1994). In demography, population estimates and projections provide a futuristic vision of the size and growth of the population and on its age structure and sex composition. Several international organizations such as the United Nations, the World Bank and the International Institute for Applied Systems Analysis (IIASA) have produced world population projections. The United Nations projections are based on low, medium and high variant assumptions on fertility and mortality. The low and high variants differ from the medium variant only in the assumed levels of fertility. On the other hand, the World Bank regularly prepares single variant population projections for the world. The World Bank projections have a much longer time horizon than those of the United Nations. Recently, the population project at IIASA produced alternative population scenarios for 12 regions of the world (Lutz, 1994).

This paper will present alternative population projections for Egypt, the Sudan and Tunisia using the scenario approach developed at IIASA. Its primary objective is to show how education attainment and policies influence the future population patterns in these countries. These projections are important for social development planning and for integrating population parameters into development projects and strategies. Moreover, many individuals and organizations need population projections to plan business and commercial activities as well as marketing strategies for their products; population projections are useful in real estate and several other activities. This demand has been heightened by the recent upsurge in environment and energy issues and changes in levels of consumption and styles of living. Section I gives a brief presentation of the scenario approach. Sections II and III show fertility and mortality conditions in Egypt, the Sudan and Tunisia. The main objective here is to provide an understanding of the past and current demographic situation and its implications for future population trends in these countries (section IV). Section V presents the stands of Governments on population policies and in shaping future population patterns. We present our assumptions, scenario setting and projection results in sections VI and VII.

The word "scenario" has originated from the world of theatre, and its usage has spread to natural and social sciences (Lutz, 1995). The Oxford English Dictionary defines the word scenario as "a sketch or outline of the plot of a play, giving particulars of the scenes, situations, etc." The 1982 supplement to the Oxford Dictionary gives a more general definition: "a sketch, outline, or description of an imagined situation or sequence of events". Several other definitions and more details on scenarios are discussed in a working paper on "Scenario Analysis in Population Projection" by Lutz (1995). Suffice it to mention here that natural sciences attach a non-quantitative meaning to the definition of scenarios: "a scenario is a narrative account of possible states of affairs, typically as these evolve over time. Scenario contents we typically based on expert knowledge...Many scenarios are written in relation to the exploration of the future...Narrative scenario descriptions are particularly useful where the future is highly uncertain" (Systems and Control Encyclopedia, pp. 5152-4153). By contrast, social sciences give quantitative scenarios.

In the field of demography, the quantitative and non-quantitative definitions of scenarios may be equally important. This is has been clearly reflected in the scenarios developed at IIASA. In addition to the well-known advantages of the traditional cohort component method used by several international agencies, the scenario approach tells us how to derive assumptions and what to do with them. Also, it considers several populations which are interacting with one another. For example, it allows for interaction between population subgroups (or states) such as rural/urban, access to water/no access to water, literate/illiterate. Therefore, assumptions have to be state-specific in addition to age- and sex-specific. Moreover, transition rates between states, for example from the state of no education to the state of primary education, have to be specified.

The scenario approach used at IIASA is quite different from the United Nations variant approach. The main differences lie in the fact that IIASA scenarios systematically vary all components of population dynamics (fertility, mortality and migration), whereas the United Nations variants only consider alternative fertility trends. Mortality makes little difference between the United Nations variants. Moreover, the scenario approach used at IIASA is based on assumptions that are substantively founded in demographic behaviour, whereas other approaches used by the United Nations and the World Bank seldom mention substantive demographic and social reasons. The scenario approach enables one to make statements about how far off the future population could be from the expected evolution. It focuses on the hypothetical effect rather than on likelihood. It assumes two extreme paths, one high and one low, for each of the three components of population change (mortality, fertility and migration). A third path, the central scenario, is obtained by averaging the high and low assumptions of the three components. Therefore, it offers reasonable analyses of the hypothetical structural changes of population dynamics. The IIASA results show that the differences due to possible alternative mortality trends can be very significant. The low fertility/low mortality scenario brings the world population size in 2050 more than half way towards the central scenario as compared with the high mortality and low fertility scenario (Lutz, 1995).

It is an extremely difficult task to provide numerical information on changes in demographic rates over future time. The scenario approach suggests operationalization of knowledge, views and ideas as one possible solution. The low, central and high scenarios give a notion of the pace at which the rates are assumed to change. In the case of fertility, for example, "low" implies rapid fertility decline, "high" shows a slow pace of fertility change and "central" is the average of high and low fertility levels. In the case of mortality, "high" means substantial improvements in life expectancy at birth and "central" means stagnation at the current level (Lutz, 1994).

The real challenge in producing a national population projection is not the method but the derivation of a set of assumptions on fertility, mortality and migration. Two main issues are involved: the first relates to the use of past experience to derive an appropriate set of assumptions; the second concerns the strategy for dealing with future uncertainties. A blind and most straightforward consideration of the first issue is to assume the future as a continuation of the past. This is inappropriate because such an assumption completely ignores the future dynamics of population change. A more sensible approach is to consider the substantive historical knowledge as a base for deriving appropriate assumptions. In this case, substantive understanding of demographic processes enters indirectly into the formulation of fertility and mortality assumptions.

During the 1960s, the total fertility rates (TFRs) in Egypt, the Sudan and Tunisia 11/ were high (table 1). Early marriage, extended family norms and the predominance of the patriarchal family provide the social and cultural environment for high fertility. Women were traditionally subordinate, and a teenager goes from being dominated by her parental family to being dominated by her husband's family. Another possible explanation for high fertility is improving standards of living and income levels. The level of income affects fertility in a number of indirect ways. It is more likely that a woman does not work outside the house if her husband's income is rising. A high income would therefore increase the likelihood of the wife staying at home and possibly strengthen the motivation to have more children. In addition, rising incomes often lead to improvement in nutrition and health, thus contributing to lower infant and child mortality, which, in turn, affects fertility. Rising incomes are also associated with bottle feeding, eventually leading to a decrease in breast-feeding, which may have substantial positive effects on fertility. Since the need for a dowry, the cost of marriage and the cost of setting up a new household are obstacles that tend to delay marriage, rising incomes may permit earlier marriage and earlier childbearing.

Country	1960-1969	1970-1979	1980-1992	Change during the years 1970-1979 to 1980-1992
Egypt	7.1 (1960)	5.6 (1976)	3.9 (1992)	1.7 (30%)
Sudan	7.6 (1960)	6.9 (1979)	4.9 (1990)	2.0 (29%)
Tunisia	7.2 (1966)	5.7 (1975)	3.4 (1988)	2.3 (40%)

Source: United Nations (1993), Khalifa (1994), Department of Statistics (1991)

Country/year	15-19	20-24	25-29	30-34	35-39	40-44	45-49
Egypt
1980 (WFS)	99	256	286	217	130	48	16
1988 (DHS)	72	220	243	182	118	41	6
1991 (EMCHS)	69	215	216	192	93	40	6
1992 (DHS)	63	208	222	155	89	43	6
1995*	73	205	222	151	86	31	9
Sudan
1979 (WFS)	114	264	283	251	149	108	35
1983 (Census)	115	294	348	268	196	79	74
1990 (DHS)	69	183	240	236	157	82	25
1995*	88	234	290	259	167	84	25
Tunisia
1978 (WFS)	34	225	304	260	199	112	36
1984 (UN)	35	173	248	238	140	54	18
1988 (DHS)	17	131	195	176	113	41	9
1995*	23	128	209	152	82	31	6

Source: United Nations (1987, 1993) cited in National Board of Statistics (1991).

* United Nations, Population Division, Estimates for 1990-1995 (The 1994 Revision) mediam variant projections.

The level of fertility during late 1980s and early 1990s has been different from what it was during the 1960s and 1970s. The largest drop (40 per cent or 2.3 children) is found in Tunisia (1975-1989). In Egypt, the total fertility rate, which was 5.6 in 1976, declined to 4.1 in 1991 and 3.9 in 1992, a drop of about 30 per cent in 17 years. Also, fertility in the Sudan declined by about 29 per cent. Declining fertility could be seen in the downward trend in age-specific fertility rates (table 2). The largest decline is in the peak childbearing period 25-34. Factors such as increasing use of contraceptives, women's education, rising age at marriage and social change operate together to bring down fertility even though traditional and religious factors may impede this process.

While fertility in urban areas is following an irreversible downward trend, it is still high in rural areas. Women in urban areas in Egypt, the Sudan and Tunisia have substantially lower fertility than in rural areas. According to the World Fertility Survey (WFS) results, women in Egypt (1980) have a TFR of 3.8 in urban areas and 6.1 in rural areas. The WFS results for the Sudan (1979) show a TFR of about 4.8 for urban women and 6.4 for rural women. In Tunisia the WFS results give TFRs of 4.8 for urban areas and 7.0 for rural areas. Recent findings from the Demographic and Health Survey (DHS) reveal similar large urban-rural differences. The DHS results for Egypt (1992) give TFRs of 2.7 for the urban governorates, 22/ 2.8 for urban lower Egypt and 3.6 for urban upper Egypt, in contrast to 4.1 for rural lower Egypt and 6.0 for rural upper Egypt. In upper Egypt, there is a considerable difference in fertility between rural and urban areas. The Sudan DHS (1990) shows lower fertility in urban areas (TFR = 4.1) than in rural areas (TFR = 5.6) and lower fertility in Khartoum (TFR = 3.7) than in other regions of the country. According to the DHS for Tunisia (1988), rural women have a total fertility rate of 5.7 compared with a rate of 3.4 for urban women.

Differing compositional factors such as use of modern family planning methods and age at marriage may explain some of the rural-urban fertility differences. The WFS and DHS surveys for Egypt, the Sudan and Tunisia show substantially lower use of modern contraceptive methods in rural areas, in contrast with urban areas, where women have greater access to family planning services. According to the WFS, the difference (in percentage points) between rural and urban use of contraceptive methods 33/ is 29 for Egypt, 14 for the Sudan and 22 for Tunisia. More recent results from the Egyptian DHS (1992) show the percentage of current users of modern contraceptive methods among currently married women to be 55.6 per cent for the urban governorates, 58.5 per cent and 54.4 per cent in urban lower and urban upper Egypt, respectively, and 48.2 per cent and 23.0 per cent in rural lower and rural upper Egypt, respectively. This same measure for the Sudanese DHS (1990) is 11.3 per cent in urban areas and 2.2 per cent in rural areas (Department of Statistics, 1991). In Tunisia, the DHS results show that 60.5 per cent of urban women use modern contraceptive methods compared with 34.6 per cent of rural women. It is clear that there is a widening urban-rural gap in the use of modern contraceptive methods. Therefore, it is most likely that modern contraceptive methods will have a greater role in lowering fertility in urban areas as opposed to rural areas.

Also, there are substantial rural-urban differences in the median age at marriage. Because of the persistence of traditional beliefs and values and of the strong cultural support for the reproductive role of women, rural women marry at an earlier age than urban women. According to the WFS, the difference in the singulate mean age at marriage (SMAM) between rural and urban women is 3.2 years for Egypt, 1.9 years for the Sudan and 1.4 years for Tunisia. More recent findings from the DHS indicate that the median age at first marriage is on the rise and that the rural-urban difference is widening. For women aged 25-29, the DHS results for the Sudan give a median age at first marriage of 23.2 years for urban women and 19.0 years for rural women, a difference of 4.2 years. The Egyptian demographic and health survey (1992) gives a median age of 22.0 for urban women and 18.4 for rural women in the age group 25-29. For Tunisia, women aged 25-29 have a median age at first marriage of 23.6 in urban areas and 21.8 in rural areas. With education spreading in urban areas more rapidly than in rural areas, the urban-rural gap in the median age at first marriage is likely to increase.

Women's education and fertility

Education is a powerful socio-economic factor that affects reproductive behaviour and leads to a higher social status for women (United Nations, 1987; Casterline, 1984; and Cochrane, 1979). Empirical evidence continues to accumulate in support of a strong negative relationship between women's education and fertility. Women's education acts on fertility through a set of mechanisms and variables such as the following:

(a) School attendance for several years increases age at marriage and raises aspirations regarding a marriage partner;

(b) Women's education changes traditional values, attitudes and norms and strengthens women's social status within households and in communities;

(c) Education facilitates women's employment in modern activities outside the home, making it likely that a women's family desires and health aspirations will change;

(d) Educated women are more likely to use modern contraceptive methods.

Different cohorts have different levels of access to educational services. Young generations in many countries are currently better educated than older generations; therefore, they may experience greater fertility decline. Countries differ from one another with regard to the level of education (or the number of years of schooling) at which fertility starts to decline (Timur, 1977). Access to educational services differs by residence. In addition, a particular level of education may affect fertility in urban and rural areas differently.

However, education could result in an increase in fertility. Empirical studies have shown that the negative effect of women's education may not appear at the lowest levels of education (Cochrane, 1979). Some primary education may increase rather than decrease fertility. The highest fertility is often found not among women without education but among those with a few years of schooling. The positive effect of a low level of education is especially marked in rural areas and in less urbanized and poorer developing countries where income is low. One possible mechanism is that a few years of education, under backward conditions, may lead to a decline in breast-feeding or an improvement in health which are not offset by such actions as delay of marriage. In a pre-demographic transition stage, women's education may not instigate a movement towards lower fertility (Caldwell, 1983). According to Caldwell, schooling many children is economically rewarding for parents; therefore, fertility remains high.

Studies done for Egypt, the Sudan and Tunisia during the 1960s and early 1970s document a large fertility decline in response to an increase of a few years in women's education. In Egypt during the 1960s, women aged 30 or more years who had completed their primary education had 1.3 fewer children than women in the same age group with no education (Timur, 1977). A survey of fertility and family planning carried out in the Sudan in 1975 by Caldwell (as part of the "Changing African Family" project) unravelled, for the first time, fertility differences by level of education in urban areas. Age-standardized mean children ever born showed a small difference between women with no education and women with a few years of education. Women with secondary education had two fewer children than women with no education (Richard, 1982).

Data from the WFS and DHS provide more comprehensive and convincing evidence in support of education-fertility interrelationships. On average over all WFS countries, seven or more years of schooling reduce fertility by about three children, while fewer years of schooling lead to slightly higher fertility, in contrast with women who have no education (United Nations, 1987). The results of the WFS for Egypt, the Sudan and Tunisia show large fertility differences by level of education (table 3). Women with no schooling have substantially higher fertility than women with seven or more years of education. The difference between these

Country/fertility measure	Years of education
	0	1-3	4-6	7+	Difference(0-7+)
Egypt
Children ever born	6.8	7.2	6.5	3.7	3.1 (45.6%)
Total marital fertility rate	6.5	6.4	6.2	3.8	2.7 (41.5%)
Contraceptive use	17	25	32	53	36
Sudan
Children ever born	6.1	6.9	5.8	3.9	2.2 (36.1%)
Total marital fertility rate	6.7	7.4	6.9	5.1	1.6 (23.9%)
Total fertility	6.5	5.6	5.0	3.1	3.4 (52.3%)
Contraceptive use	2	11	12	35	33
Tunisia
Children ever born	6.8	5.9	6.4	3.6	3.2 (47.1%)
Total marital fertility rate	7.3	5.9	6.0	3.9	3.4 (46.6%)
Contraceptive use	25	46	50	62	37

Source: United Nations, Fertility behaviour in the context of development. Evidence from the World Fertility Survey. New York, 1987 (tables 112, 115 and 122)

two levels of education amounts to two and three or more children when CEB (children ever born), TFR (total fertility rate) and TMFR (total maternal fertility rate) are taken into consideration. It is important to note that CEB is a cohort measure based on ever-married women aged 40-49 for Egypt and Tunisia and on all ever-married women for the Sudan. The TMFR and the TFR are age-standardized period measures.

The Maternal and Child Health Survey (MCHS) conducted in Egypt in 1991 provides more insights on education-fertility interactions. If age at first marriage is controlled, women with no education have systematically higher fertility than women with secondary or higher-level of education (Hosam Eldin, 1994). The same differential pattern prevails when use of contraceptive methods is controlled. Among those who have ever used family planning methods, women with no education have, on average, 5.7 children ever born, compared with 4.2 for women with primary education and 3.0 for women with secondary or higher level of education. The Sudan DHS documents similar large differences by level of education. Women who had secondary education have, on average, 3.3 children, compared with 5.9 children for women with no schooling (Department of Statistics, 1991). The DHS for Tunisia shows a total fertility rate of 5.1 for women with no education, 3.9 for women who completed primary schooling and 2.4 for women with secondary or more education.

Unlike fertility and family planning, mortality involves fewer controversial issues, probably because of its lower sensitivity to cultural and religious factors. However, in pure development terms health and mortality are part of social welfare programmes. They are closely linked to social development and the quality of life. Social development activities and services are often organized by place of residence. Rural residents have poor health and higher mortality levels primarily because of lack of modern medical services. In addition, mortality is directly linked to the disease environment. Many diseases, such as malaria and tuberculosis, are influenced by ecological zones and local climatic conditions. Environmental hazards at global and local levels, such as ozone layer depletion and air pollution may also directly affect health and mortality. Therefore, mortality and health are more likely to be sensitive to environmental degradation than fertility (Lutz, 1994). These aspects are very important when the impact of mortality on future population trends is considered.

Mortality in Egypt, the Sudan and Tunisia has improved quite rapidly in the last three decades. This is clearly reflected in the increasing life expectancy at birth and the declining infant mortality rate (table 4). Infants are a special group which is highly vulnerable to the risk of death, because newborn babies are fragile and totally dependent on others for their survival. Infant mortality is a very sensitive indicator of social development. As the standard of living rises, so does the average level of health, and the health of babies improves faster than that of adults. Infant mortality in Tunisia has dropped substantially to a record low of 43 deaths per 1,000 live births in 1995. Life expectancy at birth in Tunisia has increased by about 23.6 years for females and 22.8 for males in the period 1950 to 1995.

Infant mortality in Egypt has dropped from 200 in the 1950s to 67 in the 1990s. During the same period, life expectancy at birth has increased from 43.6

Period	Egypt			Sudan			Tunisia
	e0(F)	e0(M)	IMR	e0(F)	e0(M)	IMR	e0(F)	e0(M)	IMR
1950-1955	43.6	41.2	200	39.1	36.3	185	45.1	44.1	175
1955-1960	46.1	43.7	183	40.1	37.3	170	47.6	46.6	163
1960-1965	48.6	46.2	175	41.1	38.3	150	50.1	49.1	155
1965-1970	51.0	48.5	170	43.1	40.3	130	52.6	51.6	138
1970-1975	53.4	50.8	150	45.1	42.3	110	56.1	55.1	120
1975-1980	55.3	52.9	131	48.1	45.3	97	60.6	59.6	88
1980-1985	57.8	55.3	115	50.6	47.8	92	63.6	62.6	71
1985-1990	62.3	59.8	81	52.4	49.6	85	66.4	64.9	49
1990-1995*	64.8	62.4	67	54.4	51.6	78	68.7	66.9	43

Source: United Nations. 1993. Demographic yearbook: Special issue, tab. 7. New York; Economic Commission for Africa (ECA). 1992. Demographic handbook for Africa (ECA/POP/TP/92/5), tab. 13. Adis Ababa.

Note: * United Nations, Population Division. Estimates for 1990-1995 (The 1994 revision) mediam variant projection.

to 64.8 years for females and from 41.2 to 62.4 years for males. These improvements in mortality are impressive. However, health has not improved equally in all segments of the population. Life tables calculated separately (Yousif, 1995) indicate that life expectancy at birth has increased by 6.9 years for males and females for the period between 1976 and 1986. Since then (1986-1991), females have gained slightly more years of life than males. Mortality improvement in Egypt between 1976 and 1986 was greater for the rural than the urban population. Life expectancy at birth has increased by 8.2 and 7.4 years for rural males and females, and by 5.0 and 5.8 for urban men and women, respectively. The gain in years of life is obviously higher in rural areas as opposed to urban areas. It may be true that cities in Egypt are unhealthy to live in. On the other hand, the proximity of rural areas to urban centres in Egypt facilitates access of rural inhabitants to modern health facilities. It is possible that these improvements in mortality are distorted somewhat by misclassification of death events by place of occurrence and place of usual residence.

Mortality in the Sudan has declined slowly, but its level still is high. Life expectancy at birth has improved by about 7.3 years for males and females in the period 1970-1975 to 1985-1990. Infant mortality has declined to 78. More recent evidence from the 1993 census indicates that infant mortality in the Sudan has increased to more than 100 (Department of Statistics, 1995). In view of the fact that infectious diseases, particularly malaria and tuberculosis, are still widespread, the small decline in mortality in the Sudan is not unexpected. Health development in the Sudan is hampered by internal war, famine and drought. Political and social development efforts for a considerable period of time are needed to repair the damage caused by three decades of civil war and frequent famines and droughts.

An important aspect of the demographic pattern in Egypt, the Sudan and Tunisia is that fertility was high for about two decades. This will shape the demographic situation in these countries for several years to come. Firstly, high fertility has produced a young age structure and high population growth momentum. Potential mothers for several decades to come have already been born. Therefore, the full demographic impact of the recent decline in fertility in these countries will not be felt for several years. In addition, sustained high fertility will cause the age composition of the population to be dominated by the young, those who are 15 years old or younger. However, the social consequences of a young age structure, particularly the pressure on services and an increasing demand for education, are inevitable.

Another aspect of demographic change in these countries is the widening rural-urban differences. Basically, there are two demographic regimes in each country: a rural high-fertility regime and an urban low-fertility regime. In both regimes mortality is declining. The use of contraceptive methods is substantially higher in the urban compared with the rural regime. The greatest demographic challenge for Governments lies in rural areas. Egypt provides a good example whereby the Government has set a national goal of achieving two children per couple by the year 2015. Fertility has been projected to decline from 3.9 in 1992 to 2.9 in 2005 to 2.1 in 2015 (Khalifa 1994). To achieve replacement fertility, the use of contraceptive methods is targeted to reach a high level of about 74 per cent. However, the main challenge for realizing these national goals lies in rural areas, particularly in upper Egypt, where fertility is high and the potential for decline is the greatest.

Mortality decline in Egypt and Tunisia is impressive. Governments in these countries have successfully implemented maternal and child health programmes based on simple and low-cost health technology. Immunization and oral rehydration therapy have become widespread. However, the demographic impact of these health programmes has not yet been fully assessed. They are likely to lead to a substantial decline in infant and child mortality. Infant mortality may decline further to a level below 20 infant deaths per 1,000 live births in both countries.

With differences in environment, social and economic development, and varying political support to national population programmes and activities, demographic change in these countries is bound to take a more divergent course than ever before. There are two powerful forces of demographic change in these countries. On the one hand there is social development and increasing government support for women's education, population activities and family planning programmes. On the other hand there are political, religious and cultural barriers to fertility decline. These forces, when viewed together, produce considerable uncertainties about future population trends. The degree of uncertainty varies from country to country, and within each country, from rural to urban areas. The downward trend in fertility in urban areas is irreversible, particularly in Egypt and Tunisia. In the 1980s Tunisia's population targets were to reach an annual growth rate of 1.8 per cent by 2001 and 1.1 per cent by 2021, primarily through greater use of contraceptive methods and a social programme of better education and improving the status of women (Sayed, 1993). Fertility in Tunisia is likely to reach replacement level before the Sudan and Egypt.

Future population trends are associated with substantial uncertainties in the Sudan. With spreading pronatalistic views and programmes, fertility in the Sudan may increase, particularly in rural areas. This is one extreme scenario which is not unusual in the Arab countries. For example, fertility in Egypt increased in the late 1970s and mid-1980s before declining in the late 1980s. Another possible scenario for fertility in the Sudan is that it may follow a downward pattern in response to social development and the increasing education of women. However, replacement fertility in the Sudan is unlikely to be achieved in the foreseeable future.

Population policies are determined in part by the Government and its understanding of population issues. Government policies and activities are crucial in modulating fertility and shaping future health conditions. Population policies and social programmes and activities influence the reproductive decisions of couples in various ways. For example, the use of modern contraceptives, which is the most efficient method for reducing fertility, is determined to a large extent by the stand of the Government on population issues. Government social policies and programmes in many developing countries have helped couples to realize their fertility and health aspirations, primarily because these programmes have gained social acceptance and direct government support. In some other countries, couples have failed to realize these aspirations, not because they are helpless victims of their own behaviour, but because of lack of government and public support and because of social and religious obstacles. The Government's stand on population issues and its perception of social problems is crucial in shaping the future pattern of fertility.

At the International Population Conference held in Bucharest in 1974, delegates from Egypt, the Sudan and Tunisia argued for development, as opposed to family planning, as a means of resolving population problems. Ten years later, in 1984, the political stand of these Governments had changed, and in 1994 some countries were in an advanced stage of population policy development. Tunisia and Egypt were the first Arab countries to express concern about fertility, set fertility targets and specify measures and plans to achieve them. The Government of Tunisia restricted polygamy in 1956, introduced family planning in the early 1960s and adopted a flexible stand on abortion. These measures were strengthened further by improving women's education and increasing women's participation in modern activities. The official stand of the Egyptian Government progressed from scepticism to sureness. During the 1960s and 1970s, population growth and its ramifications were seen as long-term problems which were undeniably important but neither pressing nor urgent (Waterbury, 1975). Population issues were of secondary importance. This situation has changed considerably in recent years, and the Government of Egypt has begun supporting population policies and activities more actively than ever before.

By contrast, the Sudan attempted several times to formulate a national population policy. Efforts in this direction have not been successful because of administrative and political problems. Though there is a clear understanding of population issues in the Sudan, Governments do not consider population policy a priority activity. There is a need to narrow the gap between decision makers and research scholars. Some population issues relating to migration and population distribution are indirectly influenced by government programmes and activities. Mortality conditions are primarily an outcome of poor health services and environmental conditions. In addition, fertility in the Sudan declined primarily because of increasing age at marriage as a result of increasing school attendance and migration of males. However, the slow pace of fertility decline is mainly due to a low level of education among women.

The multistate population projections that appear in this paper for Egypt, the Sudan and Tunisia are structured by education categories. Explicit consideration of education makes sense because education is the most important determinant of demographic behaviour, particularly fertility. However, the future educational composition of the population also has an overriding importance for the future social development of the country. Because education tends to change along cohorts (the educational composition of the population changes as the younger, more educated age groups [cohorts] move up the age scale) only demographic projection models can adequately capture future changes in the education of the total population. Like population growth, the educational composition has great momentum. Even with high school enrolment, it takes a long time to educate the previously uneducated population. Similarly, a stagnation in school enrolment owing to socio-economic or political problems may still be associated with increasing average education as the younger, more educated age groups gradually take over.

The population has been divided, by age and sex in each country, into three educational categories (primary, secondary and tertiary), plus a fourth category for no schooling. These definitions are based on the International Standard Classification of Education (ISCED). For the sake of simplicity, these categories are referred to as states.

State 1 (No schooling): Those who have never attended school plus those who have completed less than one year of education in primary schools.

State 2 (Primary education): Those who have completed the final grade at the primary level of education plus those who have not completed primary education but at least completed one year of schooling.

State 3 (Secondary education): Those who have completed the final grade at the secondary level of education plus those who entered secondary schools but did not complete the final grade.

State 4 (Tertiary education): Post-secondary education; anyone who undertook third-level studies (ISCED 5,6 or 7), whether or not he/she completed the full course, would be counted in this category.

The calculation of transition rates is rather problematic because the five-year age intervals (0-4, 5-9...85+) usually used for grouping demographic data do not correspond exactly to schooling intervals. In addition, because of early entry, repetition and school dropout, the duration of each level of education is substantially shorter than the age range of students. For example, six years of primary education in Egypt should correspond to the 6-12 age group, but in reality students in this level of education are in the 5-15 age groups. This holds true for all other countries. There are three transition stages:

(a) Transition from state 1 (No schooling) to state 2 (Primary);

(b) Transition from state 2 (Primary) to state 3 (Secondary);

(c) Transition from state 3 (Secondary) to state 4 (Tertiary).

To estimate the population in age group x (x=1.....18) at the time t+5 for state i (i=1.....n):

For age group 0-4: (x=1): P[x,t+5]=1/2*sum (x=4,...,10){(F[x,t]+F[x+1,t]*S[x,t])*(L[1]/5+1)*5/4*P[x,t]}

For age groups 5-9...80-84: (x=2..17): P[x,t+5]=(P[x-1,t]*S[x-1,t]

For age group 85+ (x=18): P[x,t+5]=P[x-1,t]*S[x-1,t]+P[x,t]*S[x,t]

Where:

P[x,t] = Population in age group x at time t.
F[x,t] = Fertility rate in age group x at time t.
S[x,t] = Survival ratio in age group x at time t.
L[1] = Number of years lived by a newborn during the first projection step (=> L[1], 2.5).

Scenario-setting

The scenarios combine assumptions on fertility, mortality and education. Migration was not included owing to lack of reliable data. For each scenario there are assumptions about future rates of transition between the four states defined above, future education-specific fertility rates and mortality rates. Mortality was assumed to be equal for all education categories. For each country the last census for which age- and sex-specific population data were published (1986 for Egypt, 1983 for the Sudan and 1984 for Tunisia) was considered. The fertility and mortality levels were updated with more recent data whenever such data was available. Scenarios were defined for a period of 50 years in five-year time periods starting from the date of the last census.

Egypt

The 1986 census results for Egypt give the distribution of the population by age and sex and the four education categories defined above. In the same year, the total fertility rate was 5.6 (6.14 for women with no schooling, 5.05 for women with primary education, 4.45 for women with secondary education and 3.56 for women with tertiary education). Life expectancy at birth was 60.3 for males and 62.8 for females. In the same year, the transition from no schooling to primary education took place at a rate of 94.8 per cent for males and 82.8 per cent for females. Transition rates from primary to secondary and from secondary to tertiary were 62.2 per cent and 28.9 per cent, respectively, for males, and 43.7 per cent and 16.3 per cent, respectively, for females.

Low Scenario:

Low Fertility: TFR assumed to decrease to 1.8 by the year 2036.

Low Mortality: Life expectancy at birth is assumed to increase by 3.05 years every decade until 2036.

High Education:

Transition from no schooling to primary education: 100 per cent transition to primary education (full intake rate) for men and women by the year 1991. Transition rate to primary education remains constant to the year 2036.

Transition from primary to secondary education: Assumed to reach 94.8 per cent for males and 83.0 per cent for females by the year 2036. Here we assumed a steady increase in education for men and women, greater for females (7.4 per cent increase per time period compared to 4.8 per cent for men) to close the gender gap.

Transition from secondary to tertiary education: A constant increase of 5.9 per cent for males and females until 2036.

Central Scenario

Central Fertility: TFR assumed to decline to 2.6 by the year 2036.

Central Mortality: Life expectancy at birth assumed to increase by 1.9 years every decade until 2036 for men and women.

Central Education: Transition rates assumed to increase to the level of enrolment projected by the UNESCO.

Transition from no schooling to primary education: 100 per cent transition rate (full intake rate) in primary education by the year 1991 for males and females. This rate was assumed to remain constant to the year 2036.

Transition from primary to secondary education: Transition rate of 73.5 per cent for males by the year 1996 and 64.5 per cent for females by the year 2015. Thereafter, these rates were assumed to remain constant to the year 2036.

Transition from secondary to tertiary education: A transition rate of 30 per cent for males and 21.7 per cent for females by the year 2011. Thereafter, these rates were assumed to remain constant to the year 2036.

High Scenario

High Fertility: TFR assumed to decrease to 3.4 by the year 2036.

High Mortality: Life expectancy at birth assumed to increase slowly by 0.7 years per decade until 2036 for males and females.

Low Education:

Transition from no schooling to primary education: A transition rate of 89.6 per cent for men and 65.9 per cent for women by the year 2036 was assumed.

Transition from primary to secondary education: A transition rate of 54.7 per cent for males and 33.5 per cent for females by the year 2036 was assumed.

Transition from secondary to tertiary education: 24.0 per cent for males and 13.5 per cent for females by the year 2036.

Sudan

The 1993 census results are not available, therefore we have used results of the 1983 census for the Sudan. This census gives the distribution of the population by age and sex and four education categories defined above. In the same year the total fertility rate was 6.87:7.07 for women with no schooling, 6.13 for women with primary education, 5.41 for women with secondary education and 4.32 for women with tertiary education. Life expectancy at birth was 44.5 for males and 45.8 for females. Also, in the same year transition from no education to primary schools was at a rate of 43.2 per cent for males and 32.0 per cent for females. Transition rates from primary to secondary and from secondary to tertiary were 28.9 per cent and 10.1 per cent, respectively, for males, and 18.8 per cent and 3.7 per cent, respectively, for females.

Low Scenario

Low Fertility: TFR assumed to decrease to 2.55 by the year 2033.

Low Mortality: Life expectancy at birth for men and women is assumed to increase by 3.25 years every decade until 2033.

High Education:

Transition from no schooling to primary education: 57 per cent transition to primary education for men and 45 per cent for women by the year 2033 (4.2 per cent increase per decade for men and women).

Transition from primary to secondary education: Assumed to reach 57.8 per cent for males and 37.6 per cent for females by the year 2033 (transition to secondary education assumed to double between 1983 and 2033).

Transition from secondary to tertiary education: Assumed to reach 30.3 per cent for men and 11.1 per cent for women by the year 2033 (transition to tertiary education assumed to triple during the projection period).

Central Scenario

Central Fertility: TFR assumed to decline to 4.0 by the year 2033.

Central Mortality: Life expectancy at birth assumed to increase by 1.5 years per decade until 2033 for men and women.

Central Education:

Transition from no schooling to primary education: Assumed to increase to a rate of 41.7 per cent for males and 33.4 per cent for females by the year 1990 (as reported by UNESCO). Constant rates were assumed to the year 2033 for men and women.

Transition from primary to secondary education: Transition rates assumed to reach 29 per cent for males and 25 per cent for females by the year 1990. Thereafter, these rates were assumed to remain constant to the year 2033.

Transition from secondary to tertiary education: After the increase during the 1980s, the transition rate to tertiary education is assumed to continue unchanged at a rate of 13.5 per cent for males and 8 per cent for females to the year 2033.

High Scenario

High Fertility: TFR assumed to decrease to 5.45 by the year 2033.

High Mortality: Life expectancy at birth assumed to decrease slowly by 0.6 years per decade until the year 2033 for males and females.

Low Education:

Transition from no schooling to primary education: We assumed a transition rate of 31 per cent for men and 21 per cent for women by the year 2033.

Transition from primary to secondary education: We assumed a transition rate of 28 per cent for males and 21 per cent for females by year 2033.

Transition from secondary to tertiary education: 10 per cent for males and 3 per cent for females by the year 2033.

Tunisia

The results of the 1994 census of Tunisia have not been officially released; therefore, the population distribution by age, sex and education attainment has been obtained from the 1984 census. In 1984 the total fertility rate was 4.47 (4.96 for women with no schooling, 4.04 for women with primary education, 3.57 for women with secondary education and 2.86 for women with tertiary education). Life expectancy at birth was 62.6 years for males and 63.7 years for females. In the same year, the transition from no schooling to primary education took place at a rate of 96.7 per cent for males and 82.6 per cent for females. Transition rates from primary to secondary and from secondary to tertiary were 63.0 per cent and 18.1 per cent, respectively, for males, and 44.6 per cent and 11.1 per cent, respectively, for females.

Scenario 1 (LLH)

Low Fertility: TFR assumed to decrease to 1.6 by the year 2034.

Low Mortality: Life expectancy at birth for men and women is assumed to increase by 3.2 years every decade until 2034.

High Education:

Transition from no schooling to primary education: 100 per cent transition to primary for men by the year 1989 and for women by the year 1999. These rates were assumed to remain constant thereafter.

Transition from primary to secondary education: Assumed to increase steadily by 4.8 per cent for men and 11.5 per cent for women until 2009, when both rates undergo a 4.8 per cent increase. The transition rates reach 95.9 per cent for males and 92.6 per cent for females in the year 2034.

Transition from secondary to tertiary education: Assumed to increase steadily by 7.2 per cent for men and 13.5 per cent for women until 2009, when both rates undergo a 7.2 per cent increase. The transition rates reach 33.6 per cent for men and 27.5 per cent for women in the year 2034.

Scenario 2 (Central)

Central Fertility: TFR assumed to decline to 2.2 by the year 2034.

Central Mortality: Life expectancy at birth assumed to increase by 2 years every decade until 2034 for men and women.

Central Education:

Transition from no schooling to primary education: Assumed to reach 100 per cent for men in 1994 and for women in 2009. These rates are assumed to remain constant until 2034.

Transition from primary to secondary education: Transition rates assumed to increase by 2.6 per cent for men and 8.1 per cent for women until 2009, when both rates undergo a 2.6 per cent increase. Transition rates will reach 79.3 per cent for men and 76.8 per cent for women by the year 2034.

Transition from secondary to tertiary education: Transition rates assumed to increase by 3.9 per cent for men and 9.1 per cent for women until 2009, when both rates undergo a 3.9 per cent increase. Transition rates reach 25.5 per cent for males and 20.9 per cent for females by the year 2034.

Scenario 3 (HHL)

High Fertility: TFR assumed to decrease to 3.1 by the year 2034.

High Mortality: Life expectancy at birth assumed to decrease slowly by 0.9 years per decade until the year 2034 for males and females.

Low Education:

Transition from no schooling to primary education: The transition rate for males was assumed to remain constant at a level of 96 per cent until 2034. For women the transition was assumed to decrease to 72.8 per cent by 2034.

Transition from primary to secondary education: The transition was assumed to remain constant at a rate of 63 per cent for males and 44 per cent for females until 2034.

Transition from secondary to tertiary education: A slow decrease of 1 per cent for men and 3 per cent for women was assumed until 2034.

The results displayed in Appendixes A and B show low, central and high scenarios by educational attainment for Egypt, the Sudan and Tunisia. These projections are based on the educational attainment of the population and age and sex based on assumptions regarding fertility, mortality and education. Given past demographic and education trends, these projections show what the future education attainment of the population would look like. With low fertility, low mortality and high education assumptions (scenario 1 LLH), fertility decreases rapidly to the end of the projection period. For women with no schooling, fertility decreases by 8 per cent per decade in Egypt and Tunisia, resulting in TFRs of 2.3 and 1.9 respectively, by the end of the period. For women with primary education, fertility decreases by 7 per cent per decade in Egypt and Tunisia, resulting in a TFR of 2.1 for the former and 1.7 for the latter. Fertility declines for women with no schooling and primary education in the Sudan by 5 per cent until the year 2008 and then by 9 per cent until the year 2033. Consequently, the TFR reaches a level of 2.9 for women with no schooling and 2.4 for women with primary education by the year 2033. For women in the secondary level of education, fertility decreases by 8 per cent, 7 per cent and 6 per cent in the Sudan, Egypt and Tunisia, respectively. These rates of decline result in replacement fertility (TFR = 2) in the Sudan, and below-replacement fertility in Egypt (TFR = 1.8) and Tunisia (TFR = 1.6) by the end of the projection period. Fertility for women with tertiary education decreases to below-replacement level in the Sudan (TFR = 1.6), Egypt (TFR = 1.6) and Tunisia (TFR = 1.5) by the years 2033, 2036 and 2034 respectively.

In the central scenario (scenario 2 CCC) for Egypt and the Sudan, fertility decreases in all education categories more rapidly for women with tertiary (4 per cent) and secondary (2 per cent) education per time period to the end of the projection period. As for women in the primary and no schooling categories, fertility declines by 1 per cent until the year 2011 in Egypt and 2013 in the Sudan. Thereafter, fertility decreases at a rate of 5 per cent for the primary and no schooling categories in Egypt until 2036, and at a rate of 4 per cent for the primary category and 3 per cent for the no schooling categories in the Sudan until 2033. Consequently, the TFRs for tertiary, secondary, primary and no schooling reach 1.9, 2.8, 2.9 and 3.5, respectively, by the year 2036 in Egypt, and 2.3, 3.3, 3.7 and 4.4, respectively, by the year 2033 in the Sudan. In Tunisia (central scenario), fertility decreases more rapidly for women with tertiary (3 per cent) and secondary (2.5 per cent) education per time period, resulting in TFRs of 1.7 and 2.2, respectively, by the year 2034. For women with no schooling and primary education, fertility declines by 1 per cent until 2014. After 2014, fertility rates for women with no schooling or primary education decrease at a rate of 5 per cent per time period; as a result, the TFR reaches 3.1 for the former and 2.5 for the latter by the year 2034.

Contrary to scenarios 1 and 2, fertility in the high scenario (scenario 3 HHL) for the Sudan decreases by a rate of 2 per cent for women with no schooling and 1 per cent for women with primary education. Consequently, the TFRs reach 6 and 4.8 for the no schooling and primary education categories, respectively, by the year 2033. As for the secondary and tertiary education categories, the increase in the TFR is slower. For these two categories the TFR in 2033 reaches 4.2 and 3.1, respectively. For Tunisia, the high scenario shows fertility increasing at a rate of 1.5 per cent for the no schooling category, 1 per cent for the primary education category and 0.5 per cent for the secondary education category. For the tertiary education category, fertility remains constant at a level of 2.2. Therefore, by 2034 the TFRs reach 4.2 for the no schooling category, 3.3 for the primary education category, 2.8 for the secondary education category and 2.2 for the tertiary education category. The high scenario for Egypt shows constant fertility for all education categories. However, because of the transition of the population from one level of education to another, the overall TFR for Egypt decreases to 3.4.

The results on the educational aspects of these projection scenarios are astounding. First, the gender gap in education narrows rapidly in Egypt and Tunisia primarily because of increasing educational attainment for women more than men. This is very clear for Egypt, where the proportion of women with no schooling declines from 63 per cent in 1986 (base year) to 47 per cent in 1996, in contrast with 45 per cent and 34 per cent for men in the same years. Women's attainment of primary-level schooling increases by 8 percentage points between 1986 and 1996, enough to close the gender gap. Thereafter, the proportion of women with a primary level of education exceeds that of men until 2036. Educational attainment at the secondary level increases monotonically for both men and women, with a more rapid increase for the latter than the former. Therefore, the gender gap narrows rapidly. However, in the tertiary education category an increasing gender gap is evident. The same holds true for Tunisia with minor differences in the order of magnitudes and the speed of the decline.

The situation for the Sudan is different because of its initially higher proportions of both men and women with no schooling. Overall, the educational attainment of the population improves gradually: proportions of both men and women with no schooling decline, and proportions in the primary, secondary and tertiary levels of education increase. The gains in education for men and women in the Sudan are achieved at the primary level as compared with the secondary level for Egypt and Tunisia.

Year	Egypt-Low scenario (males)					Year	Sudan-Low scenario (males)
	No schooling	Primary	Secondary	Tertiary	Total		No schooling	Primary	Secondary	Tertiary	Total
1986	11025439	7965193	4831116	821614	24643362	1983	7749906	2053467	636751	55282	10495406
1991	11889590	8479614	6927755	1228250	28525209	1988	8792957	2660862	830782	77467	12362068
1996	10568942	10169060	8769226	1876576	31383804	1993	9376008	3188625	1097095	103458	13765186
2001	9978347	10400393	11121645	2789555	34289940	1998	9859006	4006637	1308948	140261	15314852
2006	9568520	10376411	13258055	3958891	37161877	2003	10446173	4552650	1726147	171767	16896737
2011	9252285	10623916	14804457	5425087	40105745	2008	10995360	5207045	2069543	246436	18518384
2016	8961336	10847283	16533226	6768161	43110006	2013	11507909	5853526	2485010	323420	20169865
2021	8455904	11009436	18242572	8233596	45941508	2018	11779371	6492488	2947479	427959	21647297
2026	7729334	10914188	19972536	9836297	48452355	2023	11846921	7054856	3462562	566534	22930873
2031	6889797	10452802	21472393	11621862	50436854	2028	11764754	7458580	4016690	750429	23990453
2036	6097183	9778912	22623292	13567711	52067098	2033	11551954	7720022	4545480	992414	24809870
Appendix-A (continued)
Year	Egypt-Low scenario (females)					Year	Sudan-Low scenario (females)
	No schooling	Primary	Secondary	Tertiary	Total		No schooling	Primary	Secondary	Tertiary	Total
1986	14656738	5916993	2604554	234434	23412719	1983	8304788	1403006	343356	20622	10071772
1991	15977569	7053500	4068063	390481	27489613	1988	9633861	1904178	443628	26137	12007804
1996	14278681	10014831	5610152	633481	30537145	1993	10436584	2422952	616883	31153	13507572
2001	13570264	11090209	7927850	1029945	33618268	1998	11164312	3176201	772082	39161	15151756
2006	12965939	11800624	10282310	1583113	36631986	2003	11992809	3744230	1058974	46653	16842666
2011	12456306	12701375	12256906	2339896	39754483	2008	12778220	4410569	1312062	64910	18565761
2016	11882714	13523417	14422967	3054220	42883318	2013	13529658	5101935	1607468	83536	20322597
2021	11077001	14240029	16699027	3857642	45873699	2018	14049680	5821298	1937746	108507	21917231
2026	10024254	14636038	19110749	4765530	48536579	2023	14349658	6519272	2306568	141167	23316665
2031	8812568	14593191	21511128	5803286	50720173	2028	14474306	7125906	2708074	184011	24492297
2036	7628134	14214458	23719969	6971775	52534336	2033	14441488	7647857	3107877	240002	25437224

Year	Egypt-Central scenario (males)					Year	Sudan-Central scenario (males)
	No schooling	Primary	Secondary	Tertiary	Total		No schooling	Primary	Secondary	Tertiary	Total
1986	11025439	7965193	4831116	821614	24643362	1983	7749906	2053467	636751	55282	10495406
1991	11889590	8479614	6927755	1228250	28525209	1988	8792957	2660862	830782	77467	12362068
1996	10568942	10069966	8868319	1876576	31383803	1993	9375918	3214050	1066255	108872	13765095
2001	10224046	10066903	11464281	2764493	34519723	1998	10030087	4029918	1239512	144668	15444185
2006	10094034	10242854	13618464	3878355	37833707	2003	10926842	4640874	1556556	169074	17293346
2011	10059342	10895604	15324249	5161053	41440248	2008	11927824	5409556	1794972	215471	19347823
2016	10060388	11656891	17369390	6252147	45338816	2013	13064755	6246633	2074054	252123	21637565
2021	9766742	12506308	19534658	7405105	49212813	2018	14275632	7170368	2375211	292867	24114078
2026	9282184	13185521	21876376	8617166	52961247	2023	15496840	8174602	2703156	337196	26711794
2031	8772240	13622513	24156425	9908401	56459579	2028	16777172	9226215	3057614	385653	29446654
2036	8403112	14006336	26279495	11243568	59932511	2033	18165957	10307541	3432530	438327	32344355
Appendix-A (continued)
Year	Egypt-Central scenario (females)					Sudan-Central scenario (females)
	No schooling	Primary	Secondary	Tertiary	Total	Year	No schooling	Primary	Secondary	Tertiary	Total
1986	14656738	5916993	2604554	234434	23412719	1983	8304788	1403006	343356	20622	10071772
1991	15977569	7053500	4068063	390481	27489613	1988	9627082	1910957	443628	26137	12007804
1996	14278681	10002872	5622111	633481	30537145	1993	10424915	2432845	617711	32012	13507483
2001	13812209	11037663	7969365	1031398	33850635	1998	11306185	3166917	762328	43020	15278450
2006	13479746	11883833	10358229	1589439	37311247	2003	12431747	3737153	1010320	54572	17233792
2011	13239700	13051494	12457940	2354896	41104030	2008	13661811	4437067	1208993	76665	19384536
2016	12945703	14161849	14943253	3090431	45141236	2013	15039970	5201674	1437402	94607	21773653
2021	12337416	15465837	17468211	3921729	49193193	2018	16522449	6045482	1686377	114619	24368927
2026	11512767	16612941	20181190	4812959	53119857	2023	18030747	6965278	1958907	136402	27091334
2031	10621481	17526906	22931247	5767842	56847476	2028	19607554	7931359	2254934	160214	29954061
2036	9859552	18349239	25572070	6770062	60550923	2033	21296910	8937417	2570524	186419	32991270

Year	Egypt-High scenario (males)					Year	Sudan-High scenario (males)
	No schooling	Primary	Secondary	Tertiary	Total		No schooling	Primary	Secondary	Tertiary	Total
1986	11025439	7965193	4831116	821614	24643362	1983	7749906	2053467	636751	55282	10495406
1991	11889590	8479614	6927755	1228250	28525209	1988	8792957	2660862	830782	77467	12362068
1996	10833625	10060881	8630792	1857475	31382773	1993	9376008	3188625	1100446	100107	13765186
2001	10583514	10704826	10619128	2638213	34545681	1998	10105322	3941673	1297053	128024	15472072
2006	10642271	11312904	12507242	3487369	37949786	2003	11135433	4465320	1629325	146342	17376420
2011	10885266	12485960	13978337	4426285	41775848	2008	12336298	5136101	1868965	180557	19521921
2016	11270274	13899217	15743555	5171773	46084819	2013	13757273	5851543	2135974	204901	21949691
2021	11507426	15579957	17636836	5933744	50657963	2018	15362837	6625633	2411298	230613	24630381
2026	11592664	17343477	19741706	6709488	55387335	2023	17216819	7450888	2695848	257003	27620558
2031	11719122	19053051	21909476	7518440	60200089	2028	19393148	8316751	2986919	284175	30980993
2036	12131632	20846997	24075132	8342218	65395979	2033	21986646	9235063	3278084	312105	34811898
Appendix-A (continued)
Year	Egypt (females)					Sudan (females)
	No schooling	Primary	Secondary	Tertiary	Total	Year	No schooling	Primary	Secondary	Tertiary	Total
1986	14656738	5916993	2604554	234434	23412719	1983	8304788	1403006	343356	20622	10071772
1991	15977569	7054708	4068587	390561	27491425	1988	9633861	1904178	443628	26137	12007804
1996	15217626	9225479	5447549	614208	30504862	1993	10495021	2364515	617641	30395	13507572
2001	15065501	10790448	7024262	929042	33809253	1998	11521978	3001271	753005	36203	15312457
2006	15370026	12161989	8636907	1259650	37428572	2003	12847480	3476906	969045	40121	17333552
2011	15729235	14030614	9912219	1631853	41303921	2008	14354100	4055468	1134407	47365	19591340
2016	16293691	16147398	11292050	1916645	45649784	2013	16101073	4673281	1311947	52302	22138603
2021	16822135	18562317	12709988	2196623	50291063	2018	18060970	5338520	1494327	57252	24951069
2026	17275576	21132928	14189279	2471205	55068988	2023	20285712	6045257	1680916	61957	28073842
2031	17826635	23718851	15658911	2740923	59945320	2028	22854865	6782011	1868836	66361	31572073
2036	18775974	26354728	17042528	2998479	65171709	2033	25875435	7558225	2053108	70504	35557272

Year	Tunisia-Low scenario (males)					Tunisia-Central scenario (males)
	No schooling	Primary	Secondary	Tertiary	Total	No schooling	Primary	Secondary	Tertiary	Total
1984	1498371	1391045	573801	82824	3546041	1498371	1391045	573801	82824	3546041
1989	1520523	1456315	884226	114137	3975201	1515301	1455753	884034	114098	3969186
1994	1367099	1626282	1172842	165819	4332042	1358932	1632848	1166719	164106	4322605
1999	1283832	1680196	1491233	241481	4696742	1303075	1700368	1474893	233952	4712288
2004	1207449	1711543	1807174	328263	5054429	1258005	1772896	1777872	309896	5118669
2009	1126335	1757287	2082474	432930	5399026	1204732	1882773	2049506	396910	5533921
2014	1042381	1775105	2374612	533519	5725617	1146511	1972421	2356231	476084	5951247
2019	949672	1766755	2656124	646577	6019128	1080483	2042703	2668277	565847	6357310
2024	849467	1727571	2923945	769597	6270580	1002485	2087423	2981146	663758	6734812
2029	755500	1652741	3166404	902037	6476682	933602	2094798	3285253	768852	7082505
2034	666105	1551143	3375620	1043080	6635948	876616	2072823	3568454	879734	7397627
Appendix-A (continued)
Year	Tunisia-Low scenario (females)					Tunisia-Central scenario (female)
	No schooling	Primary	Secondary	Tertiary	Total	No schooling	Primary	Secondary	Tertiary	Total
1984	1981849	1120532	298607	28422	3429410	1987849	1120532	298607	28422	3429410
1989	2059822	1270758	509819	40629	3881028	2055049	1270679	509780	40624	3876132
1994	1927026	1528076	743130	62737	4260969	1933552	1522519	735999	61850	4253920
1999	1820319	1702768	1022649	101756	4647492	1871266	1702403	996038	97069	4666776
2004	1707263	1816662	1349844	150956	5024725	1809300	1854388	1291361	138131	5093180
2009	1605133	1891168	1665066	223229	5384596	1721001	2032200	1576574	193975	5523750
2014	1493786	1925122	2001191	303770	5723869	1612542	2177074	1911923	251802	5953341
2019	1366544	1942048	2320087	399592	6028271	1505578	2262386	2276574	325489	6370027
2024	1226064	1926942	2630384	505033	6288423	1381174	2332521	2632466	411256	6757417
2029	1086315	1874765	2922160	619096	6502336	1261276	2363593	2985435	504763	7115067
2034	948576	1792752	3185223	741040	6667591	1149374	2362855	3323544	604351	7440124

Year	Tunisia-High scenario (males)
	No schooling	Primary	Secondary	Tertiary	Total
1984	1498371	1391045	573801	82824	3546041
1989	1520523	1456315	884226	114137	3975201
1994	1385645	1623953	1160653	161686	4331937
1999	1345639	1713290	1450217	223660	4732806
2004	1323918	1817248	1736494	285351	5163011
2009	1298862	1975867	1993656	350482	5618867
2014	1275711	2131973	2286820	404523	6099027
2019	1252221	2287692	2591830	462242	6593985
2024	1234138	2438836	2906987	521295	7101256
2029	1237117	2582712	3225433	580650	7625912
2034	1261246	2730543	3545278	639989	8177056
Appendix-A (continued)
Year	Tunisia-High scenario (females)
	No schooling	Primary	Secondary	Tertiary	Total
1984	1981849	1120532	298607	28422	3429410
1989	2059822	1270758	509819	40629	3881028
1994	1966672	1515330	719529	59438	4260969
1999	1949879	1709515	939408	85700	4684502
2004	1954331	1899859	1170551	110412	5135153
2009	1950706	2140427	1378770	137377	5607280
2014	1947427	2384827	1609774	159339	6101367
2019	1942977	2633606	1850919	182028	6609530
2024	1944464	2880835	2099973	204502	7129774
2029	1968816	3121744	2351905	226062	7668527
2034	2021244	3362828	2603103	246509	8233684

Year	Egypt-Low scenario for males (percentage)				Year	Sudan-Low scenario for males (percentage)				Year	Tunisia-Low scenario for males (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	45	32	20	3	1983	74	20	6	1	1984	42	39	16	2
1991	42	30	24	4	1988	71	22	7	1	1989	38	37	22	3
1996	34	32	28	6	1993	68	23	8	1	1994	32	38	27	4
2000	29	30	32	8	1998	64	26	9	1	1999	27	36	32	5
2006	26	28	36	11	2003	62	27	10	1	2004	24	34	36	6
2011	23	26	37	14	2008	59	28	11	1	2009	21	33	39	8
2016	21	25	38	16	2013	57	29	12	2	2014	18	31	41	9
2021	18	24	40	18	2018	54	30	14	2	2019	16	29	44	11
2026	16	23	41	20	2023	52	31	15	2	2024	14	28	47	12
2031	14	21	43	23	2028	49	31	17	3	2029	12	26	49	14
2036	12	19	43	26	2033	47	31	18	4	2034	10	23	51	16
Appendix-B (continued)
Year	Egypt-Low scenario for females (percentage)				Year	Sudan-Low scenario for females (percentage)				Year	Tunisia-Low scenario for females (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	63	25	11	1	1983	82	14	3	0	1984	58	33	9	1
1991	58	26	15	1	1988	80	16	4	0	1989	53	33	13	1
1996	47	33	18	2	1993	77	18	5	0	1994	45	36	17	1
2001	40	33	24	3	1998	74	21	5	0	1999	39	37	22	2
2006	35	32	28	4	2003	71	22	6	0	2004	34	36	27	3
2011	31	32	31	6	2008	69	24	7	0	2009	30	35	31	4
2016	28	32	34	7	2013	67	25	8	0	2014	26	34	35	5
2021	24	31	36	8	2018	64	27	9	0	2019	23	32	38	7
2026	21	30	39	10	2023	62	28	10	1	2024	19	31	42	8
2031	17	29	42	11	2028	59	29	11	1	2029	17	29	45	10
2036	15	27	45	13	2033	57	30	12	1	2034	14	27	48	11

Year	Egypt-Central scenario for males (percentage)				Year	Sudan-Central scenario for males (percentage)				Year	Tunisia-Central scenario for males (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	45	32	20	3	1983	74	20	6	1	1984	42	39	16	2
1991	42	30	24	4	1988	71	22	7	1	1989	38	37	22	3
1996	34	32	28	6	1993	68	23	8	1	1994	31	38	27	4
2000	30	29	33	8	1998	65	26	8	1	1999	28	36	31	5
2006	27	27	36	10	2003	63	27	9	1	2004	25	35	35	6
2011	24	26	37	12	2008	62	28	9	1	2009	22	34	37	7
2016	22	26	38	14	2013	60	29	10	1	2014	19	33	40	8
2021	20	25	40	15	2018	59	30	10	1	2019	17	32	42	9
2026	18	25	41	16	2023	58	31	10	1	2024	15	31	44	10
2031	16	24	43	18	2028	57	31	10	1	2029	13	30	46	11
2036	14	23	44	19	2033	56	32	11	1	2034	12	28	48	12

Year	Egypt-Central scenario for females (percentage)				Year	Sudan-Central scenario for females (percentage)				Year	Tunisia-Central scenario for females (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	63	25	11	1	1983	82	14	3	0	1984	58	33	9	1
1991	58	26	15	1	1988	80	16	4	0	1989	53	33	13	1
1996	47	33	18	2	1993	77	18	5	0	1994	45	36	17	1
2000	41	33	24	3	1998	74	21	5	0	1999	40	36	21	2
2006	36	32	28	4	2003	72	22	60	0	2004	36	36	25	3
2011	32	32	30	6	2008	70	23	6	0	2009	31	37	29	4
2016	29	31	33	7	2013	69	24	7	0	2014	27	37	32	4
2021	25	31	36	8	2018	68	25	7	0	2019	24	36	36	5
2026	22	31	38	9	2023	67	26	7	1	2024	20	35	39	6
2031	19	31	40	10	2028	65	26	8	1	2029	18	33	42	7
2036	16	30	42	11	2033	65	27	8	1	2034	15	32	45	8

Year	Egypt-High scenario for males (percentage)				Year	Sudan-High scenario for males (percentage)				Year	Tunisia-High scenario for males (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	45	32	20	3	1983	74	20	6	1	1984	42	39	16	2
1991	42	30	24	4	1988	71	22	7	1	1989	38	37	22	3
1996	35	32	28	6	1993	68	23	8	1	1994	32	37	27	4
2000	31	31	31	8	1998	65	25	8	1	1999	28	36	31	5
2006	28	30	33	9	2003	64	26	90	1	2004	26	35	34	6
2011	26	30	33	11	2008	63	26	10	1	2009	23	35	35	6
2016	24	30	34	11	2013	63	27	10	1	2014	21	35	37	7
2021	23	31	35	12	2018	62	27	10	1	2019	19	35	39	7
2026	21	31	36	12	2023	62	27	10	1	2024	17	34	41	7
2031	19	32	36	12	2028	63	27	10	1	2029	16	34	42	8
2036	19	32	37	13	2033	63	27	9	1	2034	15	33	43	8
Appendix-B (continued)
Year	Egypt-High scenario for females (percentage)				Year	Sudan-High scenario for females (percentage)				Year	Tunisia-High scenario for females (percentage)
	No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary		No schooling	Primary	Secondary	Tertiary
1986	63	25	11	1	1983	82	14	3	0	1984	58	33	9	1
1991	58	26	15	1	1988	80	16	4	0	1989	53	33	13	1
1996	50	30	18	2	1993	78	18	5	0	1994	46	36	17	1
2001	45	32	21	3	1998	75	20	5	0	1999	42	36	20	2
2006	41	32	23	3	2003	74	20	6	0	2004	38	37	23	2
2011	38	34	24	4	2008	73	21	6	0	2009	35	38	25	2
2016	36	35	25	4	2013	73	21	6	0	2014	32	39	26	3
2021	33	37	25	4	2018	72	21	6	0	2019	29	40	28	3
2026	31	38	26	4	2023	72	22	6	0	2024	27	40	29	3
2031	30	40	26	5	2028	72	21	6	0	2029	26	41	31	3
2036	29	40	26	5	2033	73	21	6	0	2034	25	41	32	3

1/The three countries in this study, Egypt, the Sudan and Tunisia, are in various stages of development, modernization and social change. Each country provides its own demographic laboratory. In the 10-year period from 1976 to 1986, the population of Egypt increased by about 11.6 million persons (from 36.6 million in 1976 to 48.2 million in 1986) and is estimated in 1995 at around 52 million. The population of the Sudan was 10.3 million in 1955/1956, 14.8 million in 1973 and 21.5 million in 1983; it is estimated at 29 million in 1995. By contrast, Tunisia has a small population base. The population of Tunisia, which is growing at the slowest rate in North Africa, increased from 4.5 million in 1966 to 6.9 million in 1984 and is expected to reach about 9 million in 1995. These rapid changes in population size and growth are due to high fertility and rapidly declining mortality.

2/ In Egypt, Cairo, Alexandria, Port Said and Suez are defined separately as "urban governorates".

3/ This refers to the percentage of currently married women aged 15-49 who were currently using contraception by current residence, adjusting for the effects of age difference between residence groups (United Nations, 1987).

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