The density of social networks and fertility decisions: evidence from south nyanza district, kenya

Demographers have argued increasingly that social interaction is an important mechanism for understanding fertility behavior. Yet it is still quite uncertain whether social learning or social influence is the dominant mechanism through which social networks affect individuals’ contraceptive decisions. In this paper we argue that these mechanisms can be distinguished by analyzing the density of the social network and its interaction with the proportion of contraceptive users among network partners. Our analyses indicate that social learning is most relevant with high market activity; in regions with only modest market activity, however, social influence is the dominant means by which social networks affect women’s contraceptive use.

[1]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[2]  Kevin White,et al.  Accuracy, stability and reciprocity in informal conversational networks in rural Kenya , 2000, Soc. Networks.

[3]  S. Fiske,et al.  The Handbook of Social Psychology , 1935 .

[4]  S. Moscovici.  Social influence and conformity , 1985 .

[5]  Susan Cotts Watkins,et al.  Social interactions and contemporary fertility transitions. , 1996 .

[6]  J. Behrman,et al.  Empirical assessments of social networks, fertility and family planning programs: nonlinearities and their implications. , 2000, Demographic research.

[7]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[8]  Kenneth D. Bailey,et al.  Macro-micro linkages in sociology , 1992 .

[9]  Robert H. Bates,et al.  Markets and States in Tropical Africa. The Political Basis of Agricultural Policies , 1981 .

[10]  Eugene A. Hammel,et al.  A Theory of Culture for Demography , 1990 .

[11]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[12]  W. Chung,et al.  Social networks and the diffusion of fertility control in the Republic of Korea. , 1999 .

[13]  Thomas W. Valente.  Network models of the diffusion of innovations , 1996, Comput. Math. Organ. Theory.

[14]  T. Valente,et al.  Social network associations with contraceptive use among Cameroonian women in voluntary associations. , 1997, Social science & medicine.

[15]  M. Arends-Kuenning.  How do family planning workers’ visits affect women’s contraceptive behavior in bangladesh? , 2001, Demography.

[16]  Robert H. Bates,et al.  Markets and States in Tropical Africa: The Political Basis of Agricultural Policies , 1982 .

[17]  Angela Reynar.  Fertility decision -making by couples amongst the Luo of Kenya , 2000 .

[18]  H. Kohler.  Fertility decline as a coordination problem , 2000 .

[19]  Noah E. Friedkin,et al.  Network Studies of Social Influence , 1993 .

[20]  L. Freeman,et al.  Cognitive Structure and Informant Accuracy , 1987 .

[21]  R. Pollak.  A Transaction Cost Approach to Families and Households , 1985 .

[22]  S. Watkins,et al.  The buzz outside the clinics: conversations and contraception in Nyanza Province, Kenya. , 1997, Studies in family planning.

[23]  H. Kohler.  Social interactions and fluctuations in birth rates , 2000 .

[24]  R. Pollak,et al.  Cultural and Economic Approaches to Fertility : A Proper Marriage or a Mesalliance? , 1993 .

[25]  John B. Casterline,et al.  Social learning social influence and new models of fertility. , 1996 .

[26]  Barbara Entwisle,et al.  Community and contraceptive choice in rural Thailand: A case study of Nang Rong , 1996, Demography.

[27]  C. Manski.  Identification of Endogenous Social Effects: The Reflection Problem , 1993 .

[28]  Y. Ben-Porath,et al.  The F-connection: families friends and firms and the organization of exchange , 1980 .

[29]  H. Kohler,et al.  Learning in social networks and contraceptive choice , 1997, Demography.