Socioeconomic determinants of fertility in ethiopia


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Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI) Seminar Series, January 26, 2011

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Socioeconomic determinants of fertility in ethiopia

  1. 1. Socioeconomic Determinants of Fertility in Ethiopia Fanaye Tadesse ESSP II January 26, 2011
  2. 2. Background <ul><li>Ethiopia: </li></ul><ul><li>One of most populous countries in Africa </li></ul><ul><li>Characterized by high population growth rate of 2.6% </li></ul><ul><li>High population growth rate sustained by high fertility rate </li></ul><ul><li>Total fertility rate stands at 5.4 which is high by any standard </li></ul><ul><li>In 15 years the TFR showed only a one child drop </li></ul><ul><li>Ranked among African countries that have high fertility rate </li></ul><ul><li>Not considered to be among the countries at or near the start of the transition to low level of fertility (United Nations Population Fund, 2007) </li></ul>
  3. 3. Objective <ul><li>To identify the socioeconomic determinants of Fertility in urban and rural Ethiopia using the DHS data (2005) </li></ul>
  4. 4. Conceptual framework TFR Age at first marriage Postpartum infecundity Contraceptive use Induced abortion Socio-economic characteristics Proximate determinants
  5. 5. EDHS Data <ul><li>Sample size of women – 14,070 </li></ul><ul><ul><li>4,423 - urban </li></ul></ul><ul><ul><li>9,647 - rural </li></ul></ul><ul><li>Women with in the age range of 15 and 49 </li></ul><ul><li>Stark difference in the TFR of women in Urban and rural areas </li></ul><ul><ul><li>Rural women on average have two and a half times more than urban women </li></ul></ul><ul><ul><li>TFR of 6.0 Vs 2.4 </li></ul></ul>
  6. 6. Measuring fertility at household level: <ul><li>Dependent variable of interest is the number of children ever born by a woman - a non-negative integer or count. </li></ul><ul><ul><li>Result compared with other models with dependent variables given by: </li></ul></ul><ul><ul><li>Completed fertility – for women between ages 40 and 49 </li></ul></ul><ul><ul><li>Ideal number of children women desire to have–gives insight for younger women in the data who have not completed their fertility (though it could be very much hypothetical ) </li></ul></ul><ul><li>The variable is discrete and the distribution places probability mass at non negative integer values only </li></ul><ul><li>a Poisson model which is starting point of estimation of count data models </li></ul>
  7. 7. Variables <ul><li>Explanatory variables included in the model that are expected to have an effect on the fertility decision of women are : </li></ul><ul><ul><ul><li>Education </li></ul></ul></ul><ul><ul><ul><li>Child mortality - number of children that were born alive to the woman but died before the age of five; </li></ul></ul></ul><ul><ul><ul><li>The participation of the woman in income generating activity; </li></ul></ul></ul><ul><ul><ul><li>Access to information measured by access to radio (whether a woman listens to a radio at least once a week) </li></ul></ul></ul><ul><ul><ul><li>A variable to indicate the woman’s economic status (whether poor or rich) </li></ul></ul></ul><ul><ul><ul><li>control variables - Age, Age 2 , marital status, religion, region </li></ul></ul></ul>
  8. 8. Place of residence Education Description Residence Percentage Urban 17.8 Rural 82.2 Education Percentage No education 65.9 Primary 22.2 Secondary 10.5 Higher 1.4
  9. 9. Descriptive – variables of interest Variable Urban Rural Number of children born 3.43 1.53 Desired number of children 5.35 3.68 Number of children for women between ages 40-49 7.29 4.79 Age 27 28 Child mortality 0.56 0.16 Variable Percentage Religion Christian 65.76 Muslim 32.14 other 2.07 Labor force participation 39.83 Listens to the radio at least once a week 47.5
  10. 10. <ul><li>DHS doesn’t have an income data </li></ul><ul><li>Uses nationally constructed wealth index with each asset assigned to a weight (factor score) generated through principal components </li></ul><ul><li>However, the index is criticized of being urban biased </li></ul><ul><li>Separate indexes were constructed for rural and urban areas using separate asset composition (following Vyas and Kumaranayaka, 2006) </li></ul><ul><li>The separately constructed indexes are then mapped to the national index to adjust for distributions and a composite national index is constructed (methodology suggested by Rutstein, 2008) </li></ul>Measuring economic status
  11. 11. Endogeneity <ul><li>Wealth is likely to be endogenous in the model </li></ul><ul><li>Predicted value of wealth index </li></ul><ul><li>Instrument used – access to financial institution </li></ul><ul><li>Original wealth index and wealth quintiles used for regression on desired number of children – no possible endogeneity expected </li></ul>
  12. 12. Results – Whole sample   Children ever born Completed (or near completed) Fertility Desired children   age 0.2628*** 0.035 0.045*** age squared -0.003***     primary educ 0.030 -0.543 -0.429*** secondary educ -0.194*** -0.673 -0.306** higher educ -0.290*** -2.937 -0.375** Labor force participation -0.093*** -0.774*** -0.161** christian -0.012 -0.032 -0.669*** other religion -0.037 -0.356 0.149 child mortality 0.113*** 0.627***   listens to radio 0.077 0.737*** -0.126* urban -0.148*** -0.562*** -0.644*** poor -0.101*** -0.717** -0.060* middle -0.167*** -1.368*** -0.073 rich -0.197*** -1.518*** -0.085 richer -0.256** -1.708* -0.237 ever married 2.403***   0.848*** N 13769 1663 12411
  13. 13. Results – Urban   Children ever born Completed Fertility Desired children   age 0.083*** 0.103** 0.051*** age squared -0.001***     primary educ -0.005 -0.162 -0.242** secondary educ -0.051** 0.256 -0.336*** higher educ -0.087*** -1.592 -0.467 Labor force participation -0.052*** -0.554*** -0.074 christian -0.080*** -1.452*** -1.332*** other religion -0.104 -1.353 -2.380*** child mortality 0.068*** 0.714***   listens to radio 0.005 -0.027 0.068 poor -0.009 0.586 -0.318*** middle -0.030 0.685 -0.161 rich -0.025 0.268 -0.172 richer -0.026 0.119 -0.039 ever married 1.326***   0.282*** N 4253 464 4017
  14. 14. Results – Rural   Children ever born Completed Fertility Desired children   age 0.364*** 0.040 0.058*** age squared -0.004*** primary educ 0.078 -0.265 -0.404*** secondary educ -0.172** -2.647 -0.735*** higher educ -0.915*** -1.094** Labor force participation -0.119** -0.694*** -0.166** christian 0.036 0.197 -0.723*** other religion -0.011 -0.264 0.505* child mortality 0.151*** 0.619*** listens to radio 0.127*** 0.686** -0.233*** poor -0.145*** -0.463* -0.051 middle -0.234*** -0.879** -0.083 rich -0.305*** -1.545*** -0.345*** richer -0.364*** -1.575*** -0.241*** ever married 2.634***   0.884*** N 9516 1199 8405
  15. 15. Discussion of Results <ul><li>Education of women affects fertility negatively </li></ul><ul><ul><ul><li>Formal schooling for women is the single most consistent variable correlated with their low fertility (Bledsoe and Cohen, 1993) </li></ul></ul></ul><ul><ul><ul><li>Awareness about family planning </li></ul></ul></ul><ul><ul><ul><li>Empowers women in making their own decision – raises their status </li></ul></ul></ul><ul><ul><ul><li>The effect of education on fertility is not significant for women with completed fertility since the percentage of women with education is very small </li></ul></ul></ul><ul><ul><ul><li>However, Primary education is not significant in affecting the number of children ever born </li></ul></ul></ul>
  16. 16. <ul><ul><ul><li>Countries with lower levels of development and modernization and highly gender-stratified cultural settings, are likely to find that a higher level of education is required (Akmam, 2002) </li></ul></ul></ul><ul><li>Child mortality affects fertility positively </li></ul><ul><ul><ul><li>hoarding or replacement </li></ul></ul></ul><ul><ul><ul><li>with the death of an infant, duration of breast feeding and post-partum abstinence is curtailed, which promotes fertility </li></ul></ul></ul><ul><li>Participation of women in income generating activity affects fertility negatively </li></ul><ul><ul><ul><li>- Higher opportunity cost of childbearing and childrearing </li></ul></ul></ul>Discussion of Results
  17. 17. <ul><li>Economic status (wealth) </li></ul><ul><li>- Found to be negatively related to fertility for the whole sample and rural areas </li></ul><ul><li> - Quantity - quality tradeoff </li></ul><ul><ul><ul><li>- Higher status households would have lesser children not to sacrifice consumption of status goods </li></ul></ul></ul><ul><ul><ul><li>Women in higher quintiles may have better information and access to contraceptives </li></ul></ul></ul><ul><ul><ul><li>Higher fertility of the poor may or may not necessarily be a rational decision </li></ul></ul></ul><ul><li>The effect of economic status on fertility in urban areas is found to be insignificant </li></ul><ul><ul><li>Innovation diffusion approach – Fertility decline coming from spread of ideas values and tech to population regardless of economic status ( Casterline, 2001) </li></ul></ul>Discussion of Results
  18. 18. <ul><li>Urban dwellers are likely to have lower number of children than those living in rural areas </li></ul><ul><ul><li>Early stage of fertility transition accompanied by a widening gap in urban- rural fertility </li></ul></ul><ul><ul><li>Fertility decline advances jointly with increasing rates of urbanization </li></ul></ul><ul><li>Married woman have more children than single women, other things remaining constant </li></ul><ul><ul><ul><ul><li>Delay in marriage would possibly result in lower number of children </li></ul></ul></ul></ul><ul><li>Positive effect of access to information on actual fertility is a bit puzzling </li></ul><ul><ul><ul><ul><li>However, the negative and significant effect on desired number of children is as expected. </li></ul></ul></ul></ul>Discussion of Results
  19. 19. Conclusion <ul><li>Urbanization plays a key role in reducing fertility </li></ul><ul><li>Improving economic status of women leads to lower fertility </li></ul><ul><li>Education of women beyond primary level has a strong effect in reducing fertility </li></ul><ul><li>Lowering child mortality through better access to health services could reduces fertility </li></ul>
  20. 20. Further investigation <ul><li>Possibility of feedback effect from fertility to </li></ul><ul><ul><li>Child mortality </li></ul></ul><ul><ul><li>Labor force participation of women </li></ul></ul>