The role of intergenerational transfers,land, and education in fertility transition in rural Kenya: the case of Nyeri district

This qualitative study reveals how population pressures, land availability, inheritance norms, and educational opportunities intertwine to influence fertility decline in rural Kenya. Focus group discussions with men and women whose childbearing occurred both before and after the onset of rapid, unexpected fertility transition in Nyeri, Kenya allowed individuals who actually participated in, or witnessed, the fertility transition to “voice” their perceptions as to the mechanisms underlying the transition. Findings suggest that, since land inheritance is a cultural norm, land scarcity and diminishing farm size often influence fertility decision-making and behavior via preferences for fewer children. Further, education does not appear to be the driving cause of fertility behavior change, but rather is adopted as a substitute for land inheritance when land resources are scarce. These findings have implications for our understanding of fertility behavior as well as for improving predictions of fertility transition in other rural sub-Saharan African contexts.     

Social security and conflict within the family

A husband and wife, though benefitting from marriage, may yet misappropriate some of the spouse's assets rather than let all be saved. In a Nash equilibrium, family savings may therefore be lower than what each spouse would prefer. Social Security, which is a form of forced, secure saving, can therefore increase welfare.      

Happiness functions with preference interdependence and heterogeneity: the case of altruism within the family

This study investigates the prevalence and extent of altruism by examining the relationship between parents’ and their adult children’s subjective well-being in a data set extracted from the German Socio-Economic Panel. To segregate the share of parents with altruistic preferences from those who are selfish, we estimate a finite mixture regression model. We control for various sources of potential bias by taking advantage of the data’s panel structure. To validate our modeling approach, we show that predicted altruists indeed make higher average transfer payments.