Fertility preferences and the trade-off between quantity and quality of child-raising: insights from southern Ethiopia.

Eva Boonaert
Persbericht

Zorgt scholing van meisjes voor een daling in bevolkingsomvang? Evidentie uit zuidelijk Ethiopië

Een groeiende wereldbevolking

Weet u dat de wereldbevolking verwacht wordt tegen het einde van deze eeuw de kaap van 11 miljard (!) te bereiken, waarvan de grootste groei voorspeld wordt te komen uit Sub-Sahara Afrika voor 2050? Na 2050 wordt er zelfs voorspeld dat Afrika het enige continent is dat nog zal toenemen in bevolkingsomvang, voornamelijk in Sub-Sahara Afrika vanwege de jonge leeftijdsstructuur. Daarom is een analyse van de populatiedynamiek van Sub-Sahara Afrika nodig om het huidige gezinsplanningsbeleid te verbeteren. De populatiedynamiek wordt beïnvloed door twee factoren (indien migratie buiten beschouwing gelaten wordt), namelijk sterfte en vruchtbaarheid. Het sterftecijfer daalt als gevolg van wetenschappelijke en technologische vooruitgang, wat in het algemeen een teken van ontwikkeling is van een land. Het sterftecijfer is uiteraard niet iets wat men wil doen dalen om de bevolkingsgroei te beperken. Bijgevolg is de belangrijkste controle op de huidige bevolkingsgroei een daling van de vruchtbaarheidscijfers.

Oké, maar wat is nu het probleem?

Hoeveel kinderen denkt u dat een vrouw gemiddeld krijgt in Sub-Sahara Afrika? Het correcte antwoord is 4.8 kinderen, wat het hoogste ter wereld is (t.o.v. 2,4 geboorten per vrouw als wereldgemiddelde). Hoe kan hier nu iets aan gedaan worden? Vaak wordt er geopperd dat een focus op onderwijs de vruchtbaarheidscijfers kan doen dalen. Met andere woorden, er wordt vaak vanuit gegaan dat de bevolking een afweging maakt om minder kinderen te willen en meer te investeren in het onderwijs van de kinderen. Echter is hier weinig bewijs voor in Sub-Sahara Afrika, en voorgaande studies die gedaan werden geven geen eenduidig antwoord. Desalniettemin is het belangrijk om te onderzoeken in hoeverre mensen een afweging maken tussen het aantal kinderen en hun opleiding. De reden hiervoor is dat indien zou blijken dat hoge vruchtbaarheidscijfers en lage scholingsniveaus hand in hand gaan, toekomstige generaties opnieuw laag opgeleid zullen zijn en meer kinderen zullen krijgen wat dus leidt tot een zelfversterkend effect en het bereiken van een lagere bevolkingsgroei zal bemoeilijken.

Daarnaast kan zowel de voorkeur voor onderwijs als het aantal kinderen beïnvloed worden door gendervoorkeuren. Wat het onderwijs betreft is de kloof tussen mannen en vrouwen in Sub-Sahara Afrika de grootste ter wereld. In Sub-Sahara Afrika gaan kinderen gemiddeld 9,8 jaar naar school, met een genderkloof van jaar waarbij meisjes gemiddeld 9,3 jaar naar school gaan en jongens 10,3 jaar. Wat betreft de voorkeur voor het aantal kinderen blijkt er nauwelijks een genderpreferentie te zijn indien men kijkt naar de huidige geslachtsverhouding van vijfjarigen voor Sub-Sahara Afrika van 1,02 (van hoog naar laag op plaats 192 van de 241 landen wereldwijd). Recent onderzoek met betrekking tot de genderkloof in het onderwijs in Sub-Sahara Afrika toont aan dat er evidentie is voor een voorkeur voor jongens in het onderwijs. Studies naar de genderkloof in voorkeur voor zonen of dochters in Sub-Sahara Afrika zijn echter beperkt. Het is echter belangrijk om ook de mogelijke genderkloof te onderzoeken, aangezien, indien meisjes lager opgeleid zijn dan jongens, de empowerment van vrouwen in toekomstige generaties laag zal blijven, terwijl tegelijkertijd de mogelijke voordelen voor het kiezen van een andere tijdsbesteding dan het opvoeden van kinderen voor meisjes laag zullen blijven. Dit resulteert volgens eerdere studies in hogere vruchtbaarheidsniveaus en kan daarom tevens het bereiken van een lagere bevolkingsgroei bemoeilijken.

Meten is weten

Om deze vragen naar de vruchtbaarheidsvoorkeuren, het bestaan van een mogelijke afweging tussen het aantal kinderen en scholing, en een eventuele genderkloof te beantwoorden, werden 426 jongeren tussen de 18 en 25 geïnterviewd in 6 districten van de Southern Nations, Nationalities en People's Regio in Ethiopië. Het interview bestond uit een keuze-experiment gevolgd door een enquête.

Voorbeeld van een keuzekaart uit het keuze-experiment

Veldwerk in Ethiopië

De resultaten tonen drie elementen. Ten eerste laat het keuze-experiment zien dat de voorkeur voor de intrinsieke ideale gezinsgrootte gemiddeld 5,98 kinderen is voor mannen en 5,62 kinderen voor vrouwen. Dit is meer dan één kind hoger dan de aangegeven gewenste gezinsgrootte tijdens de survey. Onze studie toont aan dat de kloof verklaard wordt door een intrinsieke hogere ideale gezinsgrootte en niet door een onvervulde behoefte aan anticonceptie. Bovendien is er een grote heterogeniteit in het gewenste aantal kinderen. De belangrijkste factoren die verband houden met een voorkeur voor een grotere gezinsgrootte zijn: man zijn, getrouwd zijn, kinderen hebben en woonplaats. Aanbevelingen voor het huidige beleid rond gezingsplanning zijn om vooral te focussen op het verminderen van de gewenste gezinsgrootte van deze doelgroepen. Alleen dan zal de vraag naar voorbehoedsmiddelen toenemen. Ten tweede bevestigt onze studie het bestaan ​​van een afweging tussen het aantal gewenste kinderen en het scholingsniveau. In de meeste gevallen geldt dit echter alleen totdat respondenten een bepaalde gezinsgrootte hebben bereikt. Na het bereiken van een bepaalde gezinsgrootte willen respondenten enkel meer kinderen krijgen indien meer van deze kinderen een hoger opleidingsniveau kunnen behalen. Dit impliceert dat, hoewel hoge opleiding van ouders op lange termijn gecorreleerd is met lage vruchtbaarheidsniveaus, op korte termijn het stimuleren van onderwijs geen duidelijk effect heeft op het aantal kinderen, aangezien het de vruchtbaarheid zowel kan verlagen of verhogen. Implicaties voor het huidige beleid zijn om zich te concentreren op zowel het stimuleren van onderwijs als het verlagen van de ideale gezinsgrootte om een ​​lager bevolkingsniveau te bereiken. Ten derde blijken alleen mannen een geslachtsvoorkeur voor zonen te hebben wat betreft het geprefereerde aantal kinderen, terwijl zowel mannen als vrouwen een gendervoorkeur hebben voor jongens in het onderwijs. Om de ongelijkheid tussen mannen en vrouwen in het onderwijs te verminderen en de afname van de bevolkingsgroei te versnellen, toont dit onderzoek dus aan dat het beleid het onderwijs voor meisjes moet stimuleren.

Bibliografie

Aitchison, J., & Bacon-Shone, J. (1984). Log contrast models for experiments with mixtures. Biometrika, 71(2), 323–330. https://www.jstor.org/stable/2336249%0D
Aleksandrovs, L., Goos, P., Dens, N., & de Pelsmacker, P. (2015). Mixed-media modeling may help optimize campaign recognition and brand interest. Journal of Advertising Research, 55(4), 443–457. https://doi.org/10.2501/JAR-2015-025
Alemayehu, T., Haider, J., & Habte, D. (2010). Determinants of adolescent fertility in Ethiopia. Ethiopian Journal of Health Development, 24(1). https://doi.org/10.4314/ejhd.v24i1.62942
Ali, M. M., Cleland, J., & Shah, I. H. (2012). Causes and consequences of contraceptive discontinuation: evidence from 60 demographic and health surveys.
Alidou, S., & Verpoorten, M. (2019). Family size and schooling in sub-Saharan Africa: testing the quantity-quality trade-off. Journal of Population Economics, 32(4), 1353–1399. https://doi.org/10.1007/s00148-019-00730-z
Alvergne, A., Lawson, D. W., Clarke, P. M. R., Gurmu, E., & Mace, R. (2013). Fertility, parental investment, and the early adoption of modern contraception in rural Ethiopia. American Journal of Human Biology, 25(1), 107–115. https://doi.org/10.1002/ajhb.22348
Aragaw, K. A. (2015). Application of logistic regression in determining the factors influencing the use of modern contraceptive among married women in Ethiopia. American Journal of Theoretical and Applied Statistics, 4(3), 156–162. https://doi.org/10.11648/j.ajtas.20150403.21
Ariho, P., & Kabagenyi, A. (2020). Age at first marriage, age at first sex, family size preferences, contraception and change in fertility among women in Uganda: analysis of the 2006-2016 period. BMC Women’s Health, 20(1), 8. https://doi.org/10.1186/s12905-020-0881-4
Atake, E.-H., & Gnakou Ali, P. (2019). Women’s empowerment and fertility preferences in high fertility countries in Sub-Saharan Africa. BMC Women’s Health, 19(54). https://doi.org/10.1186/s12905-019-0747-9
Bank, T. W. (2019). Ethiopia gender diagnostic report.
Barbour, R., & Kitzinger, J. (1999). Developing focus group research: Politics, theory and practice (1st ed.). Sage Publications.
Baschieri, A., Cleland, J., Floyd, S., Dube, A., Msona, A., Molesworth, A., Glynn, J. R., & French, N. (2013). Reproductive preferences and contraceptive use: a comparison of monogamous and polygamous couples in Northern Malawi. Journal of Biosocial Science, 45, 145–166. https://doi.org/10.1017/S0021932012000569
Basu, D., & De Jong, R. (2010). Son targeting fertility behavior: some consequences and determinants. Demography, 47(2), 521–536. https://doi.org/10.4324/9780203939451-14
Becker, G. S. (1960). An economic analysis of fertility. In Demographic and economic change in developed countries (pp. 209–240). Columbia University Press.
Becker, G. S., Murphy, K. M., & Tamura, R. (1990). Human capital, fertility, and economic growth. Journal of Political Economy, 98(5), 12–37. https://doi.org/10.1086/261723
Becker, G. S., & Tomes, N. (1976). Child endowments and the quantity and quality of children. Journal of Political Economy, 84(4), 143–162. https://doi.org/10.2307/1831106
Becker, N. G. (1968). Models for the response of a mixture. Journal of the Royal Statistical Society. Series B (Methodological), 30(2), 349–358. https://doi.org/10.1111/j.2517-6161.1968.tb00735.x
Becker, N. G., & Lewis, H. G. (1973). On the interaction between the quantity and quality of children. Journal of Political Economy, 81, 279–288. https://doi.org/10.1086/260166
Becker, N. S. (1978). Models and designs for experiments with mixtures. Australian Journal of Statistics, 20(3), 195–208. https://doi.org/10.1111/j.1467-842X.1978.tb01102.x
Becker, S. O., Cinnirella, F., & Woessmann, L. (2010). The trade-off between fertility and education: evidence from before the demographic transition. Journal of Economic Growth, 15, 177–204. https://doi.org/10.1007/s10887-010-9054-x
Behrman, J. A. (2015). Does schooling affect women’s desired fertility? Evidence from Malawi, Uganda, and Ethiopia. Demography, 52(3), 787–809. https://doi.org/10.1007/s13524-015-0392-3
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 5(1), 289–300. https://www.jstor.org/stable/2346101
Bérenger, V., & Verdier-Chouchane, A. (2016). Child labour and schooling in South Sudan and Sudan: Is there a gender preference? African Development Review, 28(S2), 177–190. https://doi.org/10.1111/1467-8268.12200
Berlie, A., & Alamerew, Y. (2018). Determinants of fertility rate among reproductive age women (15-49) in Gonji-Kollela District of the Amhara National Regional State, Ethiopia. Ethiopian Journal of Health Development, 32(3), 1–12.
Bethlehem, J. (2009). The nonresponse problem. In Applied Survey Methods (pp. 209–248). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470494998
Bhalotra, S., & Clarke, D. (2016). The twin instrument (No. 10405). https://ssrn.com/abstract=2886776
Bhat, C. R. (1997). Endogenous segmentation mode choice model with an application to intercity travel. Transportation Science, 31(1), 34–48. https://doi.org/10.1287/trsc.31.1.34
Bielli, C., Berhanu, G., Isaias, A., & Orasi, A. (2001). Population growth and environment in Ethiopia.
Bingenheimer, J. B., & Raudenbush, S. W. (2004). Statistical and substantive inferences in public health: Issues in the application of multilevel models. Annual Review of Public Health, 25(1), 53–77. https://doi.org/10.1146/annurev.publhealth.25.050503.153925
Bloor, M., Frankland, J., Thomas, M., & Robson, K. (2001). Focus groups in social research (1st ed.). Sage Publications.
Bongaarts, J. (1978). A Framework for Analyzing the Proximate Determinants of Fertility. Population and Development Review, 4(1), 105–132. https://doi.org/10.2307/1972149
Bongaarts, J. (2011). Can family planning programs reduce high desired family size in Sub-Saharan Africa? International Perspectives on Sexual and Reproductive Health, 37(4), 209–216. https://doi.org/10.1363/3720911
Bongaarts, J. (2015). Modeling the fertility impact of the proximate determinants: Time for a
tune-up. Demographic Research, 33(19), 535–560. https://doi.org/10.4054/DemRes.2015.33.19
Bongaarts, J. (2017). The effect of contraception on fertility: Is sub-Saharan Africa different? Demographic Research, 37(6), 129–146. https://doi.org/10.4054/DemRes.2017.37.6
Bongaarts, J., & Casterline, J. (2013). Fertility transition: Is sub-Saharan Africa different? Population and Development Review, 38, 153–168. https://doi.org/10.1111/j.1728-4457.2013.00557.x
Bongaarts, J., & Potter, R. E. (1983). Fertility, biology, and behavior: An analysis of the proximate determinants (1st ed.). Academic Press.
Bougma, M., LeGrand, T. K., & Kobiané, J.-F. (2015). Fertility limitation and child schooling in Ouagadougou: Selective fertility or resource dilution? Studies in Family Planning, 46(2), 177–199. https://doi.org/10.1111/j.1728-4465.2015.00023.x
Burger, R. P., Burger, R., & Rossouw, L. (2012). The fertility transition in South Africa: a retrospective panel data analysis. Development Southern Africa, 29(5), 738–755. https://doi.org/10.1080/0376835X.2012.731779
Burks, J. J., Randolph, D. W., & Seida, J. A. (2019). Modeling and interpreting regressions with interactions. Journal of Accounting Literature, 42, 61–79. https://doi.org/10.1016/j.acclit.2018.08.001
Buyinza, F., & Hisali, E. (2014). Microeffects of women’s education on contraceptive use and fertility: The case of Uganda. Journal of International Development, 26(6), 763–778. https://doi.org/10.1002/jid.2915
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications (1st ed.). Cambridge University Press.
Campbell, D., Boeri, M., & Doherty, E. (2015). Learning, fatigue and preference formation in discrete choice experiments. Journal of Economic Behavior and Organization, 119, 345–363. https://doi.org/https://doi.org/10.1016/j.jebo.2015.08.018
Campbell, D., Hutchinson, W. G., & Scarpa, R. (2008). Incorporating discontinuous preferences into the analysis of discrete choice experiments. Environmental and Resource Economics, 41(3), 401–417. https://doi.org/10.1007/s10640-008-9198-8
Caputo, V., Van Loo, E. J., Scarpa, R., Nayga, R. M., & Verbeke, W. (2018). Comparing serial, and choice task stated and inferred attribute non-attendance methods in food choice experiments. Journal of Agricultural Economics, 69(1), 35–57. https://doi.org/10.1111/1477-9552.12246
Casterline, J. B., & Han, S. (2017). Unrealized fertility: Fertility desires at the end of the reproductive career. Demographic Research, 36(14), 427–454. https://doi.org/10.4054/DemRes.2017.36.14
Central Statistical Agency. (2007a). The 2007 population and housing census of Ethiopia: Statistical report for the Oromiya; Part I: Population size and characteristics.
Central Statistical Agency. (2007b). The 2007 population and housing census of Ethiopia: Statistical report for the SNNPR; Part I: Population size and characteristics.
Central Statistical Agency. (2012). Census 2007 Report.
Central Statistical Agency, & ICF International. (2011). Ethiopia demographic and health survey 2011.
Central Statistical Agency, & ICF International. (2014). Ethiopia mini demographic and health survey 2014.
Central Statistical Agency, & ICF International. (2016). Ethiopia demographic and health survey 2016.
Central Statistical Agency, & ICF International. (2019). Ethiopia mini demographic and health survey 2019.
Channon, M. D., & Harper, S. (2019). Educational differentials in the realisation of fertility intentions: Is sub-Saharan Africa different? PLoS ONE, 14(7). https://doi.org/10.1371/journal.pone.0219736
Chao, F., Gerland, P., Cook, A. R., & Alkema, L. (2019). Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels. Proceedings of The Royal Society B - Biological Sciences, 116(19), 9303–9311. https://doi.org/10.1073/pnas.1812593116
Chemhaka, G. B., & Odimegwu, C. O. (2019). The proximate determinants of fertility in Eswatini. African Journal of Reproductive Health, 23(2), 65–75. https://doi.org/10.29063/ajrh2019/v23i2.7
Cinnirella, F. (2019). Marital fertility and investment in children’s education. In Cliometrics of the Family. Studies in Economic History (pp. 33–54). Springer. https://doi.org/10.1007/978-3-319-99480-2_3
Cinnirella, F., & Streb, J. (2017). The role of human capital and innovation in economic development: evidence from post-Malthusian Prussia. Journal of Economic Growth, 22(2), 193–227. https://doi.org/10.1007/s10887-017-9141-3
Clarke, D. (2018). Children and their parents: a review of fertility and causality. Journal of Economic Surveys, 32(2), 518–540. https://doi.org/10.1111/joes.12202
Cleland, J., Bernstein, S., Ezeh, A., Faundes, A., Glasier, A., & Innis, J. (2006). Family planning: the unfinished agenda. The Lancet, 368, 1810–1837. https://doi.org/10.1016/S0140
Coale, A. (1973). The Demographic Transition Reconsidered. Proceedings of the International Population Conference, 53–72.
Colleran, H., & Snopkowski, K. (2018). Variation in wealth and educational drivers of fertility decline across 45 countries. Population Ecology, 60, 155–169. https://doi.org/10.1007/s10144-018-0626-5
Collins, A. T., Rose, J. M., & Hess, S. (2012). Interactive stated choice surveys: a study of air travel behaviour. Transportation, 39(1), 55–79. https://doi.org/10.1007/s11116-011-9327-z
Cornell, J. A. (1971). Process variables in the mixture problem for categorized components. Journal of the American Statistical Association, 66(333), 42–48. https://doi.org/10.1080/01621459.1971.10482215
Cornell, John A. (1981). Experiments with mixtures : designs, models, and the analysis of mixture data. Wiley-Blackwell.
Couper, M. P., Tourangeau, R., & Kenyon, K. (2004). Picture this: Exploring vsual effects in web surveys. Public Opinion Quarterly, 68(2), 255–266. https://doi.org/10.1093/poq/nfh013
Davis, K., & Blake, J. (1956). Social structure and fertility: An analytic framework. Economic
Development and Cultural Change, 4(3), 211–235. https://doi.org/10.1086/4497 14
de la Croix, D., & Perrin, F. (2016). French fertility and education transition: Rational choice vs. cultural diffusion (No. 2016–7).
de Leeuw, E. D., How, J. J., & Dillman, D. A. (2008). International Handbook of Survey Methodology (1st ed.). R. Taylor & Francis Group
Defo, B. K. (2011). The importance for the MDG4 and MDG5 of addressing reproductive health issues during the second secade of life: Review and analysis from times series data of 51 African countries. African Journal of Reproductive Health, 15(2), 9–30. www.jstor.org/stable/45120802
Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. https://doi.org/10.1080/00224065.1980.11980968
Desai, J., & Tarozzi, A. (2011). Microcredit, family planning programs, and contraceptive behavior: Evidence from a field experiment in Ethiopia. Demography, 48(2), 749–782. https://doi.org/10.1007/s13524-011-0029-0
Deshazo, J. R., & Fermo, G. (2002). Designing choice sets for stated preference methods: The effects of complexity on choice consistence. Journal of Environmental Economics and Management, 44, 123–143. https://doi.org/doi:10.1006/jeem.2001.1199
Diebolt, C., & Perrin, F. (2019). A cliometric model of unified growth: Family organization and economic growth in the long run of history. In Studies in Economic History (pp. 7–31). Springer. https://doi.org/10.1007/978-3-319-99480-2_2
Draper, N. R., & St John, R. C. (1977). A mixtures model with inverse terms. In Technometrics (Vol. 19, Issue 1). https://www.jstor.org/stable/1268252
Easterlin, R. (1975). An economic framework for fertility analysis. Studies in Family Planning, 6(3), 54–63.
Eastwood, R., & Lipton, M. (2011). Demographic transition in sub-Saharan Africa: How big will the economic dividend be? Population Studies, 65(1), 9–35. https://doi.org/10.1080/00324728.2010.547946
Education Policy Data Center. (2019). Ethiopia: Education overview. https://www.epdc.org/country/ethiopia
Eissler, S., Thiede, B. C., & Strube, J. (2019). Climatic variability and changing reproductive goals in Sub-Saharan Africa. Global Environmental Change, 57. https://doi.org/10.1016/j.gloenvcha.2019.03.011
Eliason, S., Baiden, F., Tuoyire, D. A., & Awusabo-Asare, K. (2018). Sex composition of living children in a matrilineal inheritance system and its association with pregnancy intendedness and postpartum family planning intentions in rural Ghana. Reproductive Health, 15(1), 187. https://doi.org/10.1186/s12978-018-0616-2
Eloundou-Enyegue, P. M., & Giroux, S. C. (2012). Fertility transitions and schooling: From micro- to macro-level associations. Demography, 49(4), 1407–1432. https://doi.org/10.1007/s13524-012-0131-y
Ethiopian Society of Population Studies. (2008). Levels, trends and determinants of lifetime and desired fertility in Ethiopia: Findings from EDHS 2005.
Farina, P., Gurmu, E., Hasen, A., & Maffioli, D. (2001). Fertility and family change in Ethiopia.
Federal Ministry of Education. (2015). Education Sector Development Program V.
Ferede, T. (2013). Multilevel modelling of modern contraceptive use among rural and urban population of Ethiopia. American Journal of Mathematics and Statistics, 2013(1), 1–16. https://doi.org/10.5923/j.ajms.20130301.01
Fern, E. F. (2001). Advanced focus group research (1st ed.). Sage Publications.
Fernihough, A. (2017). Human capital and the quantity–quality trade-off during the demographic transition. Journal of Economic Growth, 22(1), 35–65. https://doi.org/10.1007/s10887-016-9138-3
Feyisa, A. D. (2019). Household Survey on risk and resilience, soil and water conservation, land use and agricultural productivity in Southern Ethiopia.
Fiebig, D. G., Keane, M. P., Louviere, J. J., & Wasi, N. (2010). The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Science, 29(3), 393–421. https://doi.org/10.1287/mksc.1090.0508
Flato, M. (2018). The differential mortality of undesired infants in Sub-Saharan Africa. Demography, 55(1), 271–294. https://doi.org/10.1007/s13524-017-0638-3
Food and Agricultural Organization. (2018). Ethiopia: Small family farms country factsheet.
Fuse, K. (2010). Descriptive findings variations in attitudinal gender preferences for children across 50 less-developed countries. Demographic Research, 23(36), 1031–1048. https://doi.org/10.4054/DemRes.2010.23.36
Galor, O. (2012). The demographic transition: Causes and consequences. Cliometrica, 6(1), 1–28. https://doi.org/10.1007/s11698-011-0062-7
Galor, O., & Moav, O. (2002). Natural selection and the origin of rconomic growth. Quarterly Journal of Economics, 117(4), 1133–1191. https://doi.org/10.1162/003355302320935007
Galor, O., & Weil, D. N. (1999). From Malthusian stagnation to modern growth. The American Economic Review, 89(2), 150–154.
Galor, O., & Weil, D. N. (2000). Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. The American Economic Review, 90(4), 806–828.
Gastwirth, J. L., Gel, Y. R., Hui, W. L. W., Lyubchich, V., Miao, W., & Noguchi, K. (2019). lawstat: Tools for biostatistics, public policy, and law. R Package Version 3.3. https://cran.r-project.org/package=lawstat
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., Alkema, L., Fosdick, B. K., Chunn, J., Lalic, N., Bay, G., Buettner, T., Heilig, G. K., & Wilmoth, J. (2014). World population stabilization unlikely this century. Science, 346(6206), 234–237. https://doi.org/10.1126/science.1257469
Gibson, M. A., & Lawson, D. W. (2011). “Modernization” increases parental investment and sibling resource competition: evidence from a rural development initiative in Ethiopia. Evolution and Human Behavior, 32(2), 97–105. https://doi.org/10.1016/j.evolhumbehav.2010.10.002
Glaser, B. G., & Strauss, A. L. (1967). The discovery of Grounded Theory (1st ed.). Routledge. https://doi.org/10.4324/9780203793206
Goos, P., & Hamidouche, H. (2019). Choice models with mixtures: An application to a cocktail experiment. Food Quality and Preference, 77, 135–146.
https://doi.org/10.1016/j.foodqual.2019.04.006
Goos, P., & Jones, B. (2011). Optimal design of experiments: a case study approach (1st ed.). John Wiley & Sons.
Goos, P., Jones, B., & Syafitri, U. (2016). I-Optimal Design of Mixture Experiments. Journal of the American Statistical Association, 111(514), 899–911. https://doi.org/10.1080/01621459.2015.1136632
Gould, W. (2016). Stata. StataCorp.
Greenbaum, T. L. (1998). The handbook for focus group research (2nd ed.). Sage Publications.
Greene, W. H., & Hensher, D. A. (2003). A latent class model for discrete choice analysis: contrasts with mixed logit. In Transportation Research Part B: Methodological (ITS-WP-02-08).
Groves, R. M., Singer, E., & Corning, A. (2000). Leverage-Saliency theory of survey particiaption: description and an illustration. The Public Opinion Quarterly, 64(3), 299–308. https://doi.org/10.2307/3078721
Gu, Y., Hole, A. R., & Knox, S. (2013). Fitting the generalized multinomial logit model in Stata. The Stata Journal, 13(2), 382–397. https://doi.org/10.1177/1536867X1301300213
Günther, I., & Harttgen, K. (2016). Desired fertility and number of children born across time and space. Demography, 53(1), 55–83. https://doi.org/10.1007/s13524-015-0451-9
Hall, R. W. (1999). Transportation Science. In Handbook of transportation science (1st ed., pp. 1–4). Springer US. https://doi.org/10.1007/978-1-4615-5203-1_1
Hayford, S. R., & Agadjanian, V. (2011). Uncertain future, non-numeric preferences, and the fertility transition: A case study of rural Mozambique. Etude de La Population Africaine, 25(2), 419–439. https://doi.org/10.11564/25-2-239
Hensher, D. A. (2008). Joint estimation of process and outcome in choice experiments and implications for willingness to pay. Journal of Transport Economics and Policy, 42(2), 297–322.
Hensher, D. A., & Greene, W. H. (2010). Non-attendance and dual processing of common-metric attributes in choice analysis: a latent class specification. Empirical Economics, 39(2), 413–426. https://doi.org/10.1007/s00181-009-0310-x
Hensher, D. A., Rose, J., & Greene, W. H. (2005a). The implications on willingness to pay of respondents ignoring specific attributes. Transportation, 32(3), 203–222. https://doi.org/10.1007/s11116-004-7613-8
Hensher, D. A., Rose, J. M., & Greene, W. H. (2005b). Applied choice analysis: a primer (1st ed.). Cambridge University Press. https://doi.org/10.1007/9781316136232
Hess, S., & Hensher, D. A. (2010). Using conditioning on observed choices to retrieve individual-specific attribute processing strategies. Transportation Research Part B: Methodological, 44, 781–790. https://doi.org/10.1016/j.trb.2009.12.001
Hess, S., & Rose, J. M. (2012). Can scale and coefficient heterogeneity be separated in random coefficients models? Transportation, 39(6), 1225–1239. https://doi.org/10.1007/s11116-012-9394-9
Hess, S., & Stathopoulos, A. (2013). Linking response quality to survey engagement: a combined random scale and latent variable approach. Journal of Choice Modelling, 7, 1–12. https://doi.org/10.1016/j.jocm.2013.03.005
Hess, S., Stathopoulos, A., Campbell, D., O’Neill, V., & Caussade, S. (2012). It’s not that I don’t care, I just don’t care very much: Confounding between attribute non-attendance and taste heterogeneity. Transportation, 40(3), 583–607. https://doi.org/10.1007/s11116-012-9438-1
Hess, S., & Train, K. E. (2017). Correlation and scale in mixed logit models. Journal of Choice Modelling, 23, 1–8. https://doi.org/10.1016/j.jocm.2017.03.001
Hinde, A. (1998). Demographic methods (1st ed.). Taylor & Francis Ltd.
Hole, A. R. (2010). A discrete choice model with endogenous attribute attendance (No. 2010006; Sheffield Economic Research Paper Series).
Hole, A. R. (2011). Attribute non-attendance in patients’ choice of general practitioner appointment. International Choice Modelling Conference, 20.
Hong Il Yoo, L. (2019, November 18). LCLOGIT2: Stata module to estimate latent class conditional logit models. Boston College Department of Economics. https://econpapers.repec.org/RePEc:boc:bocode:s458616
Hossein, I., Saqib, N. U., & Haq, M. M. (2018). Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice. Economics Letters, 172, 85–88. https://doi.org/10.1016/J.ECONLET.2018.08.037
Hoyos, D. (2010). The state of the art of environmental valuation with discrete choice experiments. Ecological Economics, 69(8), 1595–1603. https://doi.org/10.1016/j.ecolecon.2010.04.011
Huber, J., & Train, K. E. (2001). On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters, 12(3), 259–269.
Huber, Joel, & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(3), 307. https://doi.org/10.2307/3152127
Ibrahim, F. M., & Arulogun, O. S. (2019). Posterity and population growth: fertility intention among a cohort of Nigerian adolescents. Journal of Population Research, 37, 25–52. https://doi.org/10.1007/s12546-019-09230-z
Ito, T., & Tanaka, S. (2017). Abolishing user fees, fertility choice, and educational attainment. Journal of Development Economics, 130, 33–44. https://doi.org/10.1016/j.jdeveco.2017.09.006
Kaplan, H. (1994). Evolutionary and wealth flows theories of fertility: Empirical tests and new models. Population and Development Review, 20, 753–791. https://doi.org/10.2307/2137661
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. https://doi.org/https://doi.org/10.2307/2291091
Kazeem, A., Jensen, L., & Stokes, C. S. (2010). School attendance in nigeria: Understanding the impact and intersection of gender, urban-rural residence, and socioeconomic status. Comparative Education Review, 54(2), 295–319. https://doi.org/10.1086/652139
KC, S., & Lutz, W. (2014). Demographic scenarios by age, sex and education corresponding to the SSP narratives. Population and Environment, 35(3), 243–260. https://doi.org/10.1007/s11111-014-0205-4
Kehlbacher, A., Balcombe, K., & Bennett, R. (2013). Stated attribute non-attendance in successive choice experiments. Journal of Agricultural Economics, 64(3), 693–706. https://doi.org/10.1111/1477-9552.12021
Kessels, R., Jones, B., & Goos, P. (2019). Using Firth’s method for model estimation and market segmentation based on choice data. Journal of Choice Modelling, 31, 1–21. https://doi.org/10.1016/j.jocm.2018.12.002
Kessels, R., Jones, B., Goos, P., & Vandebroek, M. (2008). Recommendations on the use of Bayesian optimal designs for choice experiments (KBI 0617).
Khademi, E., & Timmermans, H. (2012). Application of Mixture-Amount Choice Experiment for Accumulated Transport Charges. Procedia - Social and Behavioral Sciences, 54, 483–492. https://doi.org/10.1016/j.sbspro.2012.09.766
Khuri, A. I. (2006). Response surface methodology and related topics. World Scientific Publishing Co. https://doi.org/10.1142/5915
Kjaer, T. (2005). A review of the discrete choice experiment - with emphasis on its application in health care (No. 1). Health Economic Papers.
Kodzi, I. A., Johnson, D. R., & Casterline, J. B. (2012). To have or not to have another child: Life cycle, health and cost considerations of Ghanaian women. Social Science and Medicine, 74(7), 966–972. https://doi.org/10.1016/j.socscimed.2011.12.035
Kowalski, S. M., Cornell, J. A., & Vining, G. G. (2000). A new model and class of designs for mixture experiments with process variables. Communications in Statistics - Theory and Methods, 29(9–10), 2255–2280. https://doi.org/10.1080/03610920008832606
Kravdal, Ø., Kodzi, I., & Sigle-Rushton, W. (2013). Effects of the number and age of siblings on dducational transitions in Sub-Saharan Africa. Studies in Family Planning, 44(3), 275–297. https://doi.org/10.1111/j.1728-4465.2013.00358.x
Krueger, R. A. (1988). Focus groups: a practical guide for applied research (5th ed.). Sage Publications.
Kuépié, M., Shapiro, D., & Tenikue, M. (2015). Access to schooling and staying in school in selected Sub-Saharan African countries. African Development Review, 27(4), 403–414. https://doi.org/10.1111/1467-8268.12156
Lack, D. (1954). The Natural Regulation of Animal Numbers (1st ed.). Oxford University Press.
Laelago, T., Habtu, Y., & Yohannes, S. (2019). Proximate determinants of fertility in Ethiopia: An application of revised Bongaarts model. Reproductive Health, 16(1), 13. https://doi.org/10.1186/s12978-019-0677-x
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Policital Economy, 74(2), 132–157. https://www.jstor.org/stable/1828835
Lancsar, E., Fiebig, D. G., & Hole, A. R. (2017). Discrete choice experiments: a guide to model specification, estimation and software. PharmacoEconomics, 35(7), 697–716. https://doi.org/10.1007/s40273-017-0506-4
Lancsar, E., Louviere, J., & Flynn, T. (2007). Several methods to investigate relative attribute impact in stated preference experiments. Social Science and Medicine, 64(8), 1738–1753. https://doi.org/10.1016/j.socscimed.2006.12.007
Landry, A. (1987). Adolphe Landry on the demographic revolution. Population and Development Review, 13(4), 731–740. https://www.jstor.org/stable/1973031
Lawson, D. W., & Borgerhoff Mulder, M. (2016). The offspring quantity-quality trade-off and human fertility variation. Philosophical Transactions Series B - Biological Sciences, 371(1692), 20150145. https://doi.org/10.1098/rstb.2015.0145
Leroy-Beaulieu. (1896). Traité Théorique et Pratique d’Économie Politique (1st ed.). Librairie Guillaumin et Cie. Lesthaeghe.
Livi Bacci, M. (2017). A Concise History of World Population (6th ed.). Wiley Blackwell.
Loomis, J. B. (2014). Strategies for overcoming hypothetical bias in stated preference surveys. Journal of Agricultural and Resource Economics, 39(1), 34–46.
Louviere, J. J., Flynn, T. N., & Carson, R. T. (2010). Discrete Choice Experiments are not conjoint analysis. Journal of Choice Modelling, 3(3), 57–72. https://doi.org/10.1016/S1755-5345(13)70014-9
Louviere, J. J., Street, D., Burgess, L., Wasi, N., & Islam, T. (2008). Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information. Journal of Choice Modelling, 1(1), 128–164. https://doi.org/10.1016/S1755-5345(13)70025-3
Louviere, J. J., Street, D., Carson, R., Ainslie, A., Deshazo, J. R., Cameron, T., Hensher, D., Kohn, R., & Marley, T. (2002). Dissecting the random component of utility: a conceptual framework for understanding the issues. Marketing Letters, 13, 177–193. https://doi.org/10.1023/A:1020258402210
Mangham, L. J., Hanson, K., & Mcpake, B. (2009). How to do (or not to do): Designing a discrete choice experiment for application in a low-income country. Health Policy and Planning, 24, 151–158. https://doi.org/10.1093/heapol/czn047
Mangiafico, S. (2020). rcompanion: Functions to Support Extension Education Program Evaluation. R Package Version 2.3.25. https://cran.r-project.org/package=rcompanion
Mani, S., Hoddinott, J., & Strauss, J. (2013). Determinants of schooling: Empirical evidence from rural Ethiopia. Journal of African Economies, 22(5), 693–731. https://doi.org/10.1093/jae/ejt007
Manski, C. F. (1977). The structure of random utility models. Theory and Decision, 8(3), 229–254. https://search.proquest.com/docview/1303217712?accountid=17215
Matthijs, K. (2012). Bevolking : wie wat, waar, wanneer? (2nd ed.). Acco.
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (1st ed., pp. 105–142). Wiley.
McIntyre, D. M. (2014). Shared responsibilities for health: a coherent global framework for health financing. In Final report of the Centre on Global Health Security Working group on Health Financing. Chatham House.
Mekonnen, W., & Worku, A. (2011). Determinants of fertility in rural Ethiopia: the case of Butajira Demographic Surveillance System (DSS). Public Health, 11(1), 782. https://doi.org/10.1186/1471-2458-11-782
Melstrom, R. T., Jayasekera, D. H., Boyer, T. A., & Jager, C. (2017). Scale heterogeneity in recreationists’ decision making: Evidence from a site choice model of sport fishing. Journal of Outdoor Recreation and Tourism, 18, 81–87. https://doi.org/10.1016/j.jort.2017.02.007
Merton, R. K., & Kendall, P. L. (1946). The focused interview. American Journal of Sociology, 51(6), 541–557. https://doi.org/10.1086/219886
Microsoft. (2010). Microsoft Powerpoint (No. 2010). Microsoft.
Minas, G. (2008). A review of the National Population Policy of Ethiopia. In Digest of
Ethiopia’s National Policies, Strategies and Programs (pp. 23–45). Forum For Social Studies.
Ministry of Health [Ethiopia]. (2010). Health and Health-Related Indicators.
Ministry of Health [Ethiopia]. (2016a). Costed Implementation Plan for Family Planning in Ethiopia 2015-2020.
Ministry of Health [Ethiopia]. (2016b). National Adolescent and Youth Health Strategy.
Moav, O. (2005). Cheap children and the persistence of poverty. The Economic Journal, 115, 88–110. https://doi.org/10.1111/j.1468-0297.2004.00961.x
Mogstad, M., & Wiswall, M. (2009). How linear models can mask non-linear causal relationships: An application to family size and children’s education (No. 586).
Morgan, D. L. (1996). Focus groups. Annual Review of Sociology, 22, 129–152. https://doi.org/10.1146/annurev.soc.22.1.129
Morgan, D. L. (1998). Planning Focus Groups - Vol. 2 (1st ed.). Sage Publications.
Mortelmans, D. (2013). Handboek kwalitatieve onderzoeksmethoden (4th ed.). Acco.
Moya, C., Snopkowski, K., & Sear, R. (2016). What do men want? Re-examining whether men benefit from higher fertility than is optimal for women. Philosophical Transactions Series B - Biological Sciences, 371. https://doi.org/10.1098/rstb.2015.0149
Mühlbacher, A. C., Zweifel, P., Kaczynski, A., & Johnson, F. R. (2015). Experimental measurement of preferences in health care using best-worst scaling (BWS): Theoretical and statistical issues. Health Economics Review, 6(1), 1–12. https://doi.org/10.1186/s13561-015-0077-z
Ndagurwa, P., & Odimegwu, C. (2019). Decomposition of Zimbabwe’s stalled fertility change: a two-sex approach to estimating education and employment effects. Journal of Population Research, 36, 35–63. https://doi.org/10.1007/s12546-019-09219-8
Notestein, F. (1945). Population - The Long View. In T. W. Schultz (Ed.), Food for the World (1st ed., pp. 36–57). University Chicago Press.
Nylund-Gibson, K. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
ODK Development Team. (2019). Open Data Kit.
Office of the Prime Minister [Ethiopia]. (1993). National Population Policy of Ethiopia.
Ogle, D. H., Wheeler, P., & Dinno, A. (2020). FSA: Fisheries stock analysis. R Package Version 0.8.30. https://github.com/droglenc/FSA
Olson, D. J., & Piller, A. (2013). Ethiopia: An emerging family planning success story. Studies in Family Planning, 44(4), 445–459. https://doi.org/10.1111/j.1728-4465.2013.00369.x
Oumer, J. (2009). The challenges of free primary education in Ethiopia. University of Addis Ababa, International Institute for Educational Planning and UNESCO.
Pal, M., & Mandal, N. K. (2012). Optimum designs for estimation of parameters in a quadratic mixture-amount model. Communications in Statistics-Theory and Methods, 41(4), 665–673. https://doi.org/10.1080/03610926.2010.529538
Perman, R., Ma, Y., Mcgilvray, J., & Common, M. (2011). Natural Resource and
Environmental Economics (4th ed.). Addison Wesley.
Piepel, G. F., & Cornell, J. A. (1985). Models for mixture experiments when the response depends on the total amount. Technometrics, 27(3), 219. https://doi.org/10.2307/1269703
Population Reference Bureau. (2002). Making the link: Population, health and the environment. http://www.prb.org/Publications/Datasheets/2002/MakingTheLink.aspx
Powe, N. A., Garrod, G. D., & Mcmahon, P. L. (2005). Mixing methods within stated preference environmental valuation: choice experiments and post-questionnaire qualitative analysis. Ecological Economics, 52(4). https://doi.org/10.1016/j.ecolecon.2004.06.022
Pradhan, U. K., Lal, K., Dash, S., & Singh, K. N. (2017). Design and analysis of mixture experiments with process variable. Communications in Statistics - Theory and Methods, 46(1), 259–270. https://doi.org/10.1080/03610926.2014.990104
Prescott, P. (2004). Modelling in mixture experiments including interactions with process variables. Quality Technology & Quantitative Management, 1(1), 87–103. https://doi.org/10.1080/16843703.2004.11673066
Pritchett, L. H. (1994). Desired fertility and the impact of population policies. Population and Development Review, 20(1), 1–55. https://doi.org/10.2307/2137629
Proust, M. (2018). JMP 14 Consumer Research. SAS Institute Inc.
QSR International. (2018). Nvivo (12 Pro). QSR International.
R Core Team. (2020). R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature. Stata Journal, 2, 1–21. https://doi.org/10.1177/1536867X0200200101
Raghavarao, D., & Wiley, J. B. (2009). Conjoint measurement with constraints on attribute levels: a mixture-amount model approach. International Statistical Review, 77(2), 167–178. https://doi.org/10.1111/j.1751-5823.2009.00077.x
Revelt, D., & Train, K. E. (1998). Mixed logit with repeated choices: Households’ choices of appliance efficiency level. Review of Economics and Statistics, 80(4), 647–657. https://doi.org/10.1162/003465398557735
Ringheim, K., Teller, C., & Sines, E. (2009). Ethiopia at a crossroads: Demography, gender, and development.
Roose, H., & Meuleman, B. (2014). Methodologie van de sociale wetenschappen: een inleiding (12th ed.). Academia Press.
Root, L., & Johnson-Hanks, J. (2016). Gender, honor, and aggregate fertility. American Journal of Economics and Sociology, 75(4), 904–928. https://doi.org/10.1111/ajes.12159
Roser, M., Ritchie, H., & Ortiz-Ospina, E. (2019). World Population Growth. https://ourworldindata.org/world-population-growth
Rossi, P., & Rouanet, L. (2015). Gender Preferences in Africa: a Comparative Analysis of Fertility Choices. World Development, 72, 326–345. https://doi.org/10.1016/j.worlddev.2015.03.010
Rowland, D. T. (2010). Demographic methods and concepts (2nd ed.). Oxford University Press.
Ruseckaite, A., Goos, P., & Fok, D. (2017). Bayesian D-optimal choice designs for mixtures. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66(2), 363–386. https://doi.org/10.1111/rssc.12174
Rutaremwa, G., Galande, J., Nviiri, H. L., Akiror, E., & Jhamba, T. (2015). The contribution of contraception, marriage and postpartum insusceptibility to fertility levels in Uganda: an application of the aggregate fertility model. Fertility Research and Practice, 1(16). https://doi.org/10.1186/s40738-015-0009-y
Ruto, E., Garrod, G., & Scarpa, R. (2007). Valuing animal genetic resources: a choice modeling application to indigenous cattle in Kenya. Agricultural Economics, 38(1), 89–98. https://doi.org/10.1111/j.1574-0862.2007.00284.x
Ryan, M., Watson, V., & Entwistle, V. (2009). Rationalising the ‘irrational’: a think aloud study of discrete choice experiment responses. Health Economics, 18(3), 321–336. https://doi.org/10.1002/hec.1369
SAS Institue Inc. (2014). SAS/QC 13.2 User’s Guide: The OPTEX Procedure.
SAS Institute Inc. (2013). SAS 9.4 (9.4).
SAS Institute Inc. (2018). JMP Pro 14 (14.0.0).
Scarpa, R., Gilbride, T. J., Campbell, D., & Hensher, D. A. (2009). Modelling attribute non-attendance in choice experiments for rural landscape valuation. European Review of Agricultural Economics, 36(2), 151–174. https://doi.org/10.1093/erae/jbp012
Scarpa, R., Thiene, M., & Hensher, D. A. (2010). Monitoring choice task attribute attendance in nonmarket valuation of multiple park management services: does it matter? Land Economics, 86, 817–839. https://doi.org/10.3368/le.86.4.817
Schreiber, J. B., & Pekarik, A. J. (2014). Technical note: Using latent class analysis versus K-means or hierarchical clustering to understand museum visitors. Curator: The Museum Journal, 57(1), 45–59. https://doi.org/10.1111/cura.12050
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Shapiro, D., & Tenikue, M. (2017). Women’s education, infant and child mortality, and fertility decline in urban and rural sub-Saharan Africa. Demographic Research, 37(21), 670–699. https://doi.org/10.4054/DemRes.2017.37.21
Shenk, M. K., Towner, M. C., Kress, H. C., & Alam, N. (2013). A model comparison approach shows stronger support for economic models of fertility decline. Proceedings of the National Academy of Sciences, 110(20), 8045–8050. https://doi.org/10.1073/pnas.1217029110
Snee, R. D. (1973). Techniques for the Analysis of Mixture Data. Technometrics, 15(3), 517–528. https://doi.org/10.1080/00401706.1973.10489078
Street, D. J., Burgess, L., & Louviere, J. J. (2005). Quick and easy choice sets: Constructing optimal and nearly optimal stated choice experiments. International Journal of Research in Marketing, 22, 459–470. https://doi.org/10.1016/j.ijresmar.2005.09.003
Sugiura, N. (1978). Further analysis of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics - Theory and Methods, 7(1), 13–26. https://doi.org/10.1080/03610927808827599
Swait, J. (1994). A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data. Journal of Retailing and Consumer
Services, 1(2), 77–89. https://doi.org/10.1016/0969-6989(94)90002-7
Taş, E. O., Reimão, M. E., & Orlando, M. B. (2014). Gender, ethnicity, and cumulative disadvantage in education outcomes. World Development, 64, 538–553. https://doi.org/10.1016/j.worlddev.2014.06.036
Teklu, H., Sebhatu, A., & Gebreselassie, T. (2013). Components of fertility change in Ethiopia: Further analysis of the 2000, 2005, and 2011 demographic and health surveys.
Temel, T. (2013). Family size, human capital and growth: Structural path analysis of Rwanda. Journal of Economic Development, 38(4), 39–73.
Tesfu, S. T., & Gurmu, S. (2013). Mother’s Gender Preferences and Child Schooling in Ethiopia. Atlantic Economic Journal, 41, 265–277. https://doi.org/10.1007/s11293-013-9366-2
The Ethiopian National Meteorological Services Agency. (2019). Climatic and agroclimatic resources of Ethiopia.
The World Bank. (2017). World Population Prospects: The 2017 revision.
The World Bank. (2020). DataBank World Bank. https://data.worldbank.org/indicator/
Thompson, W. S. (1929). Population. American Journal of Sociology, 34, 959–975. https://doi.org/10.1086/214874
Thompson, W. S. (2003). The demographic transition model. In Encyclopedia of Population (1st ed., pp. 939–940). Macmillan Publishers.
Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 101(2), 266–270. https://doi.org/10.1037/h0070288
Timæus, I. M., & Moultrie, T. A. (2020). Pathways to low fertility: 50 years of limitation, curtailment, and postponement of childbearing. Demography, 57, 267–296. https://doi.org/10.1007/s13524-019-00848-5
Towriss, C. A., & Timaeus, I. M. (2017). Schooling and intervals following a birth in Eastern Africa. Population Studies, 72(1), 75–90. https://doi.org/10.1080/00324728.2017.1370121
Towriss, C. A., & Timaeus, I. M. (2018). Contraceptive use and lengthening birth intervals in rural and urban Eastern. Demographic Research, 38, 2027–2052. https://doi.org/10.4054/DemRes.2018.38.64
Train, K. E. (2001). A comparison of hierarchical bayes and maximum simulated likelihood for mixed logit.
Train, K. E. (2008). EM Algorithms for nonparametric estimation of mixing distributions. Journal of Choice Modelling, 1(1), 40–69. https://doi.org/10.1016/S1755-5345(13)70022-8
Trines, S. (2018). Education in Ethiopia. SAGE Open, 6(1). https://doi.org/10.1177/2158244015624950
Trinitapoli, J., & Yeatman, S. (2018). The flexibility of fertility preferences in a context of uncertainty. Population and Development Review, 44(1), 87–116. https://doi.org/10.1111/padr.12114
UNICEF. (2019). Birth, Marriage and Death Registration in Ethiopia. https://data.unicef.org/crvs/ethiopia/United Nations. (2019a). Human Development Reports. http://hdr.undp.org/en/countries/profiles/ETH
United Nations. (2019b). World Population Review. http://worldpopulationreview.com/countries/ethiopia-population/
Upadhyay, U. D., & Karasek, D. (2012). Women’s empowerment and ideal family size: an examination of DHS empowerment measures in Sub-Saharan Africa. International Perspectives on Sexual and Reproductive Health, 38(2), 78–89. https://doi.org/10.1363/380
Van Der Pol, M., Currie, G., Kromm, S., & Ryan, M. (2014). Specification of the utility function in discrete choice experiments. Value in Health, 17(2), 297–301. https://doi.org/10.1016/j.jval.2013.11.009
Van Hove, G., Bogdan, R. C., Biklen, S. K., Glesne, C., Neuman, W. L., & Howitt, D. (2014). Qualitative Research for Educational Sciences (1st ed.). Pearson Education Limited.
Van Lith, L. M., Yahner, M., & Bakamjian, L. (2013). Women’s growing desire to limit births in sub-Saharan Africa: Meeting the challenge. Global Health Science and Practice, 1(1), 97–107. https://doi.org/10.9745/GHSP-D-12-00036
Van Loo, E. J., Nayga, R. M., Campbell, D., Seo, H.-S., & Verbeke, W. (2018). Using eye tracking to account for attribute non-attendance in choice experiments. European Review of Agricultural Economics, 45(3), 333–365. https://doi.org/10.1093/erae/jbx035
Vimefall, E., Andrén, D., & Levin, J. (2017). Ethnolinguistic background and enrollment in primary education: Evidence from Kenya. African Development Review, 29(1), 81–91. https://doi.org/10.1111/1467-8268.12241
Vogl, T. S. (2016). Differential fertility, human capital, and development. The Review of Economic Studies, 83(1), 365–401. https://doi.org/10.1093/restud/rdv026
Wado, Y. D., Gurmu, E., Tilahun, T., & Bangha, M. (2019). Contextual influences on the choice of long-acting reversible and permanent contraception in Ethiopia: a multilevel analysis. PLoS ONE, 14(1). https://doi.org/10.1371/journal.pone.0209602
Werding, M., Editors Johannes Huinink, G., Ehrhardt, J., & Kohli, M. (2014). Children are costly, but raising them may pay: The economic approach to fertility. Demographic Research, 30, 253–276. https://doi.org/10.4054/DemRes.2014.30.8
Willis, R. J. (1973). A new approach to the economic theory of fertility behavior. Journal of Political Economy, 81(2), 14–64. https://doi.org/10.1086/260152
Wright, S. J., Vass, C. M., Sim, G., Burton, M., Fiebig, D. G., & Payne, K. (2018). Accounting for scale heterogeneity in healthcare-related discrete choice experiments when comparing stated preferences: a systematic review. The Patient - Patient-Centered Outcomes Research, 11, 475–488. https://doi.org/10.1007/s40271-018-0304-x
Wurpts, I. C., & Geiser, C. (2014). Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study. Frontiers in Psychology, 5(920). https://doi.org/10.3389/fpsyg.2014.00920
Yaya, S., Uthman, O. A., Ekholuenetale, M., & Bishwajit, G. (2018). Women empowerment as an enabling factor of contraceptive use in sub-Saharan Africa: a multilevel analysis of cross-sectional surveys of 32 countries. Reproductive Health, 15(1), 214. https://doi.org/10.1186/s12978-018-0658-5
Zijlstra, T., Goos, P., & Verhetsel, A. (2019). A mixture-amount stated preference study on the mobility budget. Transportation Research Part A - Policy, 126, 230–246. https://doi.org/10.1016/j.tra.2019.06.009
Znaniecki, F. (1934). The method of sociology (1st ed.). Farrar & Rinehart. https://doi.org/10.1177/000271623517700161

Universiteit of Hogeschool
Agro and Ecosystems Engineering
Publicatiejaar
2020
Promotor(en)
Prof. Miet Maertens en Prof. Peter Goos
Kernwoorden
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