Econometric models of child mortality dynamics in rural Bangladesh

The studies distinguish the differences in child mortality dynamics in rural Bangladesh between two areas ICDDR,B and comparison with and without extensive health services.

Birth Spacing, Child Survival and Fertility Decisions: Analysis of Causal Mechanismsa

Abstract: We jointly analyze infant mortality, birth spacing, and total fertility of children in a rural area in Bangladesh, using longitudinal data from the Health and Demographic Surveillance System (HDSS) in Matlab. To distinguish causal mechanisms from unobserved heterogeneity and reverse causality, we use dynamic panel data techniques. We compare the results in a treatment area with extensive health services and a comparison area with standard health services. Simulations using the estimated models show how fertility and mortality can be reduced by, for example, breaking the causal link that leads to a short interval after a child has died. Eliminating this effect would reduce fertility and increase birth intervals, resulting in a fall in mortality by 0.14 and 2.45 per 1000 live births in treatment and comparison area, respectively. The effects of the numbers of (surviving) boys and girls on birth spacing provide evidence of son preference: having more boys has a stronger effect on the birth interval than having more girls, though both effects are significantly positive. A simulation suggests that if families would behave as if their all children were sons, fertility levels would be reduced by 3.5% and 5.7% in the ICDDR,B and comparison areas, respectively.child mortality;birth spacing;fertility;dynamic panel data models;Bangladesh

Infant Mortality in Rural Bangladesh: State Dependence vs. Unobserved Heterogeneity

Using longitudinal data of the Health and Demographic Surveillance System (HDSS) in Matlab, Bangladesh, covering the time period 1982 – 2005, and exploiting dynamic panel data models, we analyze siblings’ death at infancy, controlling for unobserved heterogeneity and a causal effect of death of one child on survival chances of the next child. Matlab is a rural area split into two: a “treatment” area where along with standard government services extensive maternal and child health interventions are available, and a “comparison” area where only the standard government services are available. The observed infant mortality rates are 50 per 1,000 live births in the treatment area and 67.4/1,000 in the comparison area. We use separate models for the two areas and analyze the differences in infant mortality between the two areas using several decompositions. Our model predicts that in the comparison area, the likelihood of infant death is about 30% larger if the previous sibling died at infancy than if it did not, and the estimates suggest that, in the absence of this “scarring” effect, the infant mortality rate among the second and higher order births would fall by 6.2%. There is no evidence of such a scarring effect in the treatment area, perhaps because learning effects play a larger role with the available extensive health interventions. We find that distance to the nearest health clinic can explain a substantial part of the gap in infant mortality between the two areas.childhood mortality;millennium goals;death clustering;dynamic panel data models

Does Family Planning Reduce Infant Mortality? Evidence from Surveillance Data in Matlab, Bangladesh

Abstract: Analyzing the effect of family planning on child survival remains an important issue but is not straightforward because of several mechanisms linking family planning, birth intervals, total fertility, and child survival. This study uses a dynamic model jointly explaining infant mortality, whether contraceptives are used after each birth, and birth intervals. Infant mortality is determined by the preceding birth interval and other covariates (such as socio-economic status). The decisions about using contraceptives after each birth are driven by similar covariates, survival status of the previous child, and the family’s gender composition. Birth spacing is driven by contraceptive use and other factors. We find favourable effects of contraceptive use, reducing infant deaths in second and higher order births. Because the mortality risks for first-borns is higher than for later births and contraceptive use reduces the number of higher order births, the net effect on the total infant mortality rate is small.child mortality;family planning;contraceptive use;demography;dynamic panel data models;Bangladesh

Econometric models of child mortality dynamics in rural Bangladesh.

The studies distinguish the differences in child mortality dynamics in rural Bangladesh between two areas ICDDR,B and comparison with and without extensive health services.

Does Family Planning Reduce Infant Mortality? Evidence from Surveillance Data in Matlab, Bangladesh

Abstract: Analyzing the effect of family planning on child survival remains an important issue but is not straightforward because of several mechanisms linking family planning, birth intervals, total fertility, and child survival. This study uses a dynamic model jointly explaining infant mortality, whether contraceptives are used after each birth, and birth intervals. Infant mortality is determined by the preceding birth interval and other covariates (such as socio-economic status). The decisions about using contraceptives after each birth are driven by similar covariates, survival status of the previous child, and the family’s gender composition. Birth spacing is driven by contraceptive use and other factors. We find favourable effects of contraceptive use, reducing infant deaths in second and higher order births. Because the mortality risks for first-borns is higher than for later births and contraceptive use reduces the number of higher order births, the net effect on the total infant mortality rate is small.

Infant Mortality in Rural Bangladesh:State Dependence vs. Unobserved Heterogeneity

Using longitudinal data of the Health and Demographic Surveillance System (HDSS) in Matlab, Bangladesh, covering the time period 1982 – 2005, and exploiting dynamic panel data models, we analyze siblings’ death at infancy, controlling for unobserved heterogeneity and a causal effect of death of one child on survival chances of the next child. Matlab is a rural area split into two: a “treatment” area where along with standard government services extensive maternal and child health interventions are available, and a “comparison” area where only the standard government services are available. The observed infant mortality rates are 50 per 1,000 live births in the treatment area and 67.4/1,000 in the comparison area. We use separate models for the two areas and analyze the differences in infant mortality between the two areas using several decompositions. Our model predicts that in the comparison area, the likelihood of infant death is about 30% larger if the previous sibling died at infancy than if it did not, and the estimates suggest that, in the absence of this “scarring” effect, the infant mortality rate among the second and higher order births would fall by 6.2%. There is no evidence of such a scarring effect in the treatment area, perhaps because learning effects play a larger role with the available extensive health interventions. We find that distance to the nearest health clinic can explain a substantial part of the gap in infant mortality between the two areas.

Infant Mortality in Rural Bangladesh:State Dependence vs. Unobserved Heterogeneity

Using longitudinal data of the Health and Demographic Surveillance System (HDSS) in Matlab, Bangladesh, covering the time period 1982 – 2005, and exploiting dynamic panel data models, we analyze siblings’ death at infancy, controlling for unobserved heterogeneity and a causal effect of death of one child on survival chances of the next child. Matlab is a rural area split into two: a “treatment” area where along with standard government services extensive maternal and child health interventions are available, and a “comparison” area where only the standard government services are available. The observed infant mortality rates are 50 per 1,000 live births in the treatment area and 67.4/1,000 in the comparison area. We use separate models for the two areas and analyze the differences in infant mortality between the two areas using several decompositions. Our model predicts that in the comparison area, the likelihood of infant death is about 30% larger if the previous sibling died at infancy than if it did not, and the estimates suggest that, in the absence of this “scarring” effect, the infant mortality rate among the second and higher order births would fall by 6.2%. There is no evidence of such a scarring effect in the treatment area, perhaps because learning effects play a larger role with the available extensive health interventions. We find that distance to the nearest health clinic can explain a substantial part of the gap in infant mortality between the two areas

Cause-specific Neonatal Deaths:Levels, Trend and Determinants in Rural Bangladesh, 1987-2005

Abstract: Reducing neonatal mortality is a particularly important issue in Bangladesh. We employ a competing risks model incorporating both observed and unobserved heterogeneity and allowing the heterogeneity terms for various causes to be correlated. Data come from the Health and Demographic Surveillance System (HDSS), Matlab. The results confirm the general conclusion on levels, trends and patterns of causes of neonatal deaths in the existing literature, but also reveal some remarkable socioeconomic differences in the risks of causespecific deaths. Deaths due to low birth weight and other causes (sudden infant death, unspecified or specified) are better explained from the socio- economic covariates than deaths due to neonatal infections or obstetric complications. The analysis highlights the role of maternal and child health interventions (particularly tetanus toxoid immunization for pregnant women, nutrition programs, and high coverage health services: distance to nearest health centre). Policies that increase quality and equity in child births may help to further reduce neonatal mortality.