Migration and Fertility Behavior in Sub-Saharan Africa : The Case of Ghana*
Notice bibliographique
Résumé
(ProQuest Information and Learning: ... denotes obscured text omitted.)RESEARCH CONTEXTWhile there are many individual studies on migration and fertility in sub-Saharan Africa, the systematic interaction between them have been less explored. This contrasts with the considerable research on fertility-childhood mortality nexus in the sub-Saharan African demographic literature (e.g., Gyimah and Rajulton, 2003; Kuate Defo, 1998; LeGrand, Koppenhaver, Mondain and Randall, 2003; Nyarko, Madise, and Diamond, 1999). To our knowledge, only a handful of studies have empirically examined the relationship between migration and fertility in sub-Saharan using national level data (e.g., Brockerhoff, 1995; Brockerhoff and Yang, 1994; Lee 1992; Lee and Pol, 1994), but even these were mostly based on data that may not capture recent trends1'.Previous research on migration in sub-Saharan Africa has primarily focused on motives, determinants and consequences (e.g., Adepoju, 1994; 2000; Bilsborrow, 1993; Erzaand Kiros, 2001; Hakim and Hamid, 1982; Oberai, 1987; Oucho and Gould, 1993; Stark, 1991; Zacharia and Conde, 1981). Although studying migration per se brings attention to the spatio-temporal aspects of population redistribution, a better understanding of population dynamics in general may be gained if the links between migration and the other components of population change are examined in unison. Fertility and migration, for example, are generally thought to be affected by similar factors and as such, understanding their inter-connectedness may provide a setting for analyzing fertility response to social and economic change (Davis, 1963; Lindstrom, 2002)Also, the bulk of previous migration-fertility research in the developing world has exclusively focused on rural-urban migrants. While such studies, particularly in Asia and Latin America, seem justified given the overarching volume of the rural-urban stream, the same cannot be said of sub-Saharan Africa where other migration streams (rural-rural, urban-rural, urban-urban) are equally important (Oucho and Gould, 1993). In the context of sub-Saharan Africa thus, the multi-dimensionality of the migration-fertility relationship may not be adequately captured if the other migrant streams are ignored. Perhaps the contradictory findings on the effect of migration on fertility in sub-Saharan Africa (see, Brockerhoff, 1995; Brockerhoff and Yang, 1994; Lee, 1992; Lee and Pol, 1994) may be due to this failure.With the availability of data for much of sub-Saharan African through the United States Agency for International Development's funded Demographic and Health Survey (DHS) program, this study contributes to the discussion by exploring the impact of migration on individual women's fertility in Ghana. With an estimated population of 20.5 million (Population Reference Bureau, 2003), Ghana is among the few countries in the region currently undergoing fertility transition (Kirk and Pillet, 1998). Between 1988 and 1998, for example, its total fertility rate (TFR) declined from 6.4 to 4.5 children (Ghana Statistical Service and Macro International, 1999). Considerable rural and urban differentials were, however, noticeable. In 1998, the TFR for urban areas was 2.9 compared with 5.4 in rural areas. Considering the pattern of migratory trends in the country, understanding the fertility-migration link may provide insightful clues on future population trends.THEORETICAL FRAMEWORK AND HYPOTHESESExploring the fertility behavior of migrants requires an understanding of the underlying theoretical mechanisms. This study is guided by competing but often complementary theses on migrant fertility, focusing on the processes of socialization, adaptation, selectivity, and disruption (Goldstein and Goldstein, 1983; Hervitz, 1985). These hypotheses have received varied empirical support in the developing world (see, e.g., Bacal, 1988; Brockerhoff and Yang, 1994; Campbell, 1989; Farber and Lee, 1984; Goldstein and Goldstein, 1983; Goldstein, White and Goldstein, 1997; Hervitz, 1985; Lee, 1992; Lee and Farber, 1985; Lee and Pol, 1993; Lindstrom, 2003; Stephen and Bean, 1992; Trovato, 1987; White, Moreno and Guo, 1995)The socialization hypothesis is premised on the notion that fertility preferences are formed in childhood and deeply rooted in one's upbringing. …
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».