Migration and Fertility Behavior in Sub-Saharan Africa : The Case of Ghana*
Bibliographic record
Abstract
(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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".