Parameter estimates and assumptions for multi-state population projections

Given that the proportion of the population with tertiary education is less than 1 percent (see Table 4), our multi-state projections involve three states: No education, primary, and secondary or higher. We also contemplated the possibility of disaggregating the primary education group into two categories (Primary 1-4 and Primary 5-8) as in Table 5. Though this approach is reasonable for Malawi, it would certainly weaken any international comparison of the results. On the other hand, given that the objective of the study is to assess the likelihood of Malawi meeting the education-related MDGs, we carried out the projections to 2015.

Census data provides the population by age, sex, and education in 1998. We applied the percentage distribution of education in each age and sex group to the Malawi National Statistics Office (NSO) estimates of population by age and sex for the year 2000.

The baseline for the age- and education-specific fertility rates were estimates from the 2000 Malawi DHS data, using the software developed by Macro for this purpose. As shown in Table 5, the TFR is 6.3, that is 7.3 among women with no formal education, 6.4 among those with some primary education, and 3.1 among secondary or higher educated women. On the other hand, the NSO estimate of TFR in 2002⁷ stands at 6.54. Applying the education differentials from the DHS data to the overall TFR above, and adjusting⁸ in the PDE software, yields the following TFR by educational group:

7.59 for women with no education; 6.17 for women with some primary education; and 3.84. According to the NSO, TFR is anticipated to reach 5.96 in 2012. This value was disaggregated with regard to education in a similar way as for the 2000 TFR.

In practical terms, we first inputted in PDE the age- and education-specific rates derived from the DHS data. After modifying the TFRs generated by PDE to those derived from the NSO estimates, the software readjusted the age-specific values accordingly. The same procedure was applied for the estimates during the period 2010-2015. Detailed results are shown in Table 6.

According to the NSO, life expectancy at birth (LEB) in 2002 is estimated to be 42.84 years for males and 45.75 for females. According to a family of the Coale and Demeny (1983) life table (West), these values of LEB almost correspond to level 11 of mortality 11. In the absence of information on differentials in LEB by education for males and females, we further assumed that the LEB of people with no formal education corresponds to level 10 (39.7 for men and 42.5 for women); that of people with some primary education corresponds to level 11 (42.1 for males and 45.0 for females); and

7 Where possible we use values related to 2002 for the projection over the period 2000-2005, and those related to 2012 for the projection over the period 2010-2015.

8 The overall TFR generated by the PDE software from the age- and education-specific fertility rates may be different from the overall TFR expected. Therefore, we had to adjust and obtain the targeted overall TFR. Indeed, PDE readjusts the age-specific rates to fit a given TFR.

that of people with some secondary or tertiary education corresponds to level 12 (44.5 for men and 47.5 for women). The NSO projections of LEB for 2012 (for the period 2010-2015) stand at 50.7 years for males and 53.3 years for females, which represent an increase of almost seven years between 2000 and 2012. These estimates seem unrealistic, given the previous trends of LEB derived from the census data (an increase of two years between 1977 and 1987, followed by a slight decline of almost one year between 1987 and 1998). Instead, we used the NSO estimates for 2007 (45.7 for males and 48.3 for females) as the anticipated LEB for the period 2010-2015.

Table 6. Age- and education-specific fertility rates for multi-state population projections, Malawi. Source: Author’s calculations and assumptions.

2000-2005 2010-1015

Age None Primary Secondary+ None Primary Secondary+

15-19 0.2516 0.1765 0.0911 0.2420 0.1702 0.0902

Given that Malawi is among the countries most affected by HIV/AIDS, and that the age patterns of AIDS deaths seem to deviate from those of non-AIDS deaths, applying a standard set of mortality rates from the life table would substantially bias our results. We first use the software SPECTRUM⁹ to estimate the age- and sex-specific deaths rates (including AIDS deaths) in 2000. To do so, we hypothesized that the prevalence of HIV/AIDS rose from 5 percent in 1990 to 15 percent in 2000, and will decline to 10 percent in 2015. These age- and sex-specific mortality rates were then inputted in PDE and adjusted to fit the levels of LEB estimated above.¹⁰ The age-, sex-, and education-specific mortality rates for the periods 2000-2005 and 2010-2015 are shown in Table 7.

9 SPECTRUM is a software prepared by the Policy Project, funded by the US Agency for International Development. It is accessible online at

http://www.futuresgroup.com/Resources.cfm?area=2a&get=Spectrum

10 Interestingly, while adjusting the mortality rates to fit a given level of LEB, PDE rigorously maintains the age patterns of the mortality rates.

Table 7. Age- and education-specific mortality rates for multi-state population projections, Malawi. Source: Author’s calculations and assumptions.

Age None Primary Secondary None Primary Secondary Males, 2000-2005 Females, 2000-2005

0-4 0.0625 0.0583 0.0540 0.0553 0.0519 0.0483

5-9 0.0061 0.0057 0.0053 0.0063 0.0059 0.0055

10-14 0.0042 0.0039 0.0036 0.0046 0.0043 0.0040 15-19 0.0057 0.0053 0.0049 0.0058 0.0054 0.0051 20-24 0.0073 0.0069 0.0063 0.0071 0.0067 0.0062 25-29 0.0084 0.0078 0.0073 0.0081 0.0076 0.0071 30-34 0.0097 0.0091 0.0084 0.0093 0.0087 0.0081 35-39 0.0117 0.0109 0.0101 0.0101 0.0095 0.0089 40-44 0.0142 0.0132 0.0123 0.0112 0.0105 0.0098 45-49 0.0176 0.0165 0.0153 0.0133 0.0125 0.0116 50-54 0.0236 0.0221 0.0204 0.0174 0.0163 0.0152 55-59 0.0296 0.0276 0.0256 0.0233 0.0219 0.0204 60-64 0.0436 0.0407 0.0377 0.0338 0.0318 0.0296 65-69 0.0566 0.0528 0.0490 0.0487 0.0457 0.0426 70-74 0.0774 0.0722 0.0669 0.0669 0.0628 0.0585 75-79 0.1190 0.1110 0.1029 0.0962 0.0903 0.0841

80+ 0.1355 0.1264 0.1172 0.1771 0.1663 0.1549

10-14 0.0038 0.0035 0.0033 0.0042 0.0039 0.0036 15-19 0.0051 0.0048 0.0044 0.0052 0.0049 0.0045 20-24 0.0066 0.0061 0.0057 0.0065 0.0060 0.0056 25-29 0.0075 0.0070 0.0065 0.0073 0.0068 0.0063 30-34 0.0087 0.0081 0.0075 0.0084 0.0078 0.0073 35-39 0.0105 0.0097 0.0090 0.0092 0.0085 0.0079 40-44 0.0127 0.0119 0.0109 0.0102 0.0094 0.0087 45-49 0.0158 0.0147 0.0136 0.0120 0.0112 0.0104 50-54 0.0212 0.0198 0.0183 0.0157 0.0146 0.0136 55-59 0.0265 0.0247 0.0228 0.0211 0.0196 0.0182 60-64 0.0392 0.0365 0.0337 0.0306 0.0284 0.0264 65-69 0.0508 0.0473 0.0437 0.0441 0.0410 0.0380 70-74 0.0695 0.0647 0.0597 0.0606 0.0563 0.0522 75-79 0.1069 0.0995 0.0919 0.0871 0.0809 0.0750

80+ 0.1217 0.1132 0.1046 0.1604 0.1490 0.1382

LEB 44.58 46.17 47.95 47.07 48.91 50.75

Overall LEB 45.75 48.30

aLife expectancy at birth

Age- and sex-specific transitions rates (from no education to primary, referred to as P12, and from primary to secondary or higher, referred to as P23) were estimated from the 2000 Malawi DHS. This dataset retrieves information on school enrolment, educational attainment, and highest grade of school for the previous and the current school year. It also provides information on repetition and dropout. More specifically, among children aged 0-4 years, P12 (the probability of being enrolled in primary school over a five-year period) is estimated as the proportion of people aged 5-9 who are currently (in 2000) enrolled in primary school (64 percent for boys and 67.4 percent for girls). Transition to primary school for the age groups 5-9 and 10-14 were estimated from the proportion of people not enrolled the previous school year who are currently in school. On the other hand, age- and sex-specific transition rates from primary to secondary and higher were estimated by using information on repetition and advance rates. Values are summarized in Table 8.

Table 8. Age- and sex-specific educational transition rates for multi-state population projections, Malawi. Source: Author’s calculations and assumptions.

2000-2005 2010-2015

Males Females Males Females Age P₁₂ P₂₃ P₁₂ P₂₃ P₁₂ P₂₃ P₁₂ P₂₃

0-4 0.640 0.000 0.674 0.000 0.749 0.000 0.775 0.000 5-9 0.679 0.016 0.655 0.018 0.794 0.016 0.753 0.018 10-14 0.426 0.199 0.368 0.174 0.499 0.199 0.423 0.174 15-19 0.000 0.223 0.000 0.133 0.000 0.223 0.000 0.133 20+ 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Notes:

P12 is the transition rate to primary school.

P23 is the transition rate from primary to secondary or higher education.

We assume an increase of 17 percent in P12 among males and 15 percent among females. P23 is constant over time.

In the absence of information on international migration, we assume that net migration is zero. The NSO estimates of population forwarded the same assumption.

Table 9. Estimated school attainment by sex, Malawi, 2000 and 2015. Source: Author’s calculations using the Demographic and Health Surveys.

2015 2000

Both sexes None Primary Secondary+ Total None Primary Secondary+ Total 5-14 15.5 83.9 0.6 100.0 29.0 70.7 0.3 100.0 15-19 5.1 77.1 17.8 100.0 15.5 71.6 12.8 100.0 20-24 8.2 61.7 30.1 100.0 25.5 54.7 19.8 100.0 25+ 25.8 54.8 19.4 100.0 41.2 48.3 10.5 100.0 Total 5+ 18.0 68.1 13.9 100.0 32.0 59.4 8.6 100.0 Cases 2,364,255 8,943,584 1,832,505 13,140,344 2,730,653 5,073,151 733,656 8,537,459

Males

5-14 16.1 83.4 0.5 100.0 29.9 69.8 0.3 100.0 15-19 4.7 76.4 18.9 100.0 12.3 73.9 13.8 100.0 20-24 7.7 57.9 34.4 100.0 17.7 54.8 27.5 100.0 25+ 18.1 56.8 25.0 100.0 28.5 55.8 15.7 100.0 Total 5+ 14.7 68.4 16.9 100.0 25.6 62.8 11.6 100.0 Cases 945,330 4,397,341 1,087,935 6,430,606 1,068,371 2,618,945 485,774 4,173,090

Females

5-14 15.0 84.4 0.6 100.0 28.1 71.6 0.3 100.0 15-19 5.5 77.7 16.8 100.0 18.7 69.4 11.9 100.0 20-24 8.7 65.4 25.9 100.0 31.9 54.7 13.4 100.0 25+ 32.9 53.0 14.1 100.0 53.5 41.0 5.5 100.0 Total 5+ 21.1 67.8 11.1 100.0 38.1 56.2 5.7 100.0 Cases 1,418,925 4,546,243 744,570 6,709,738 1,662,281 2,454,206 247,882 4,364,369