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Economic and Social Determinants of Infant Mortality in Developing Countries

There are quite a number of researchers who have carried out empirical studies in the determinants of mortality rate, most of whom have concentrated on the factors that influence infant mortality rates in the developing countries.  Among these empirical studies, social and economic conditions have been cited as the leading determinants of infant mortality rates. These are the specific economic and social pathways, which affect health of infant babies.  The study on this area has been opted because it is important to develop a better understanding on the social and economic determinants of infant mortality rate with the aim of reducing health disparities in the developing countries, and due to the fact that this is a very important indicator of economic development. Neonatal mortality rates are particularly responsive to procedures in the course of the pregnancy, delivery and the neonatal period, as well as the care given to infants and their mothers. Post neonatal mortality rates are contemplated to be determined to largely by parental circumstances such as the care provision and their socioeconomic position. This study will begin by a review of literature, to establish the evidence that other authors have found regarding this topic. An empirical study will follow, whereby two models will be analyzed using regression analysis statistical methods.  The results of the statistical analysis will be discussed and finally a conclusion drawn.
Literature review
As study focusing on Croydon’s infant mortality was conducted by Ghosh and Alves (2011), whereby they found that newborn death rates are frequently expressed as three year running averages to smooth the data and give a more vigorous assessment in the fullness of time. Between 2006 and 2008, the authors established that the mortality rate was 5.4 deaths per 1,000 live births.  Baird, Friedman, Schady (2009) have enlarged the observations of relationship between health expenditures and infant mortality with high earnings owing to female labor involvement. On average, they have established that there is a huge, inverse relationship between infant mortality and per capita GDP. Female infant mortality is more responsive to economic fluctuations, than male infant mortality, particularly during adverse distress to GDP.  In poor countries, about 30 % of all fatality happens to children under the age of five, while it is less than 1 % in wealthy countries. This explains why the fatality of the developed countries, which are studied in this paper, is very low compared to that of the developing and poor countries.
In their empirical study, Leigh and Jencks (2006) found that higher GDP is connected to lower mortality, an impact that decelerates as the GDP rises. They also established that more disparity is linked to higher mortality. Zakir and Wunnava (1997) established forceful and compelling evidence concerning the determinants of mortality rates; by use of a cross sectional model whereby they established that infant mortality can be used as an indicator of health within and across an economy. High infant mortality may result from the lack of proper childcare owing to lack of education, poverty, and societal inclinations. A society with unhealthy infants who mature to become part of sick adults hamper economic progression in many ways, including  reducing workers’ productivity; hampering usage of natural resources that would else be achievable  under good health conditions; and plunging the subsequent generation into many problems, for example by hampering enrolment of children in schools. They additionally provide that infant mortality can be affected by fertility rates. Moreover, of all the predictor variables, fertility rates and female literacy rates have the strongest impact on infant mortality rates. For this reason, these factors are critical when developing programs to control infant death.
The infant mortality is deemed to be one of the most important indicators for socioeconomic development, though the factors that influence it have remained unresolved. There are some suggestions that the determinants of infant mortality relies on whether the country under study is developed or developing. The impact on the mortality rate, in the case of the developing countries, could be affected by the stages of development (Rostow, 1971). A cross-national study on infant mortality by Hertz, Hebert, and Landon (1994) was focused on the manner in which economic factors influence health indicators. Using data from 66 countries with all income groups, they found that changes in life expectancy at birth, maternal mortality rate and infant mortality were the main indicators (Hertz, Hebert, and Landon, 1994, p. 105).
There are much more studies that have attributed infant mortality to demographic incidences and that take it to be highly responsive to socioeconomic factors (Pampel and Pillai, 1986, p. 526). The negative relationship between infant mortality and economic development is particularly given weight in the literature. Deaton (2003) argues that income inequality yields health risks. This is contrary to the absolute hypothesis, which postulates that it is only income that affects health, and not its distribution.  In addition, this study provides an analysis of the various connections between income and health, whereby his argument is that income has some impact on health, in which case, income inequality affects health following epistemological transition.
Data and variables
Dependent variable
Mortality rate, infant (per 1,000 live births): this is the number of newborns who pass on before attainting their first year birthday, for every 1,000 of children who are born alive in any given year.
Independent variables
School enrollment, secondary, female (% gross): this is the percentage of female enrollment in secondary schools, over the population of the age groups that matches the level of secondary education. According to the World Bank (2013), this level of education depends on at least four years of schooling at the secondary level. As discussed in the literature, female education is one of the social factors that determine child mortality.  The hypothesis set for this variable is that countries that have high level of female secondary school enrollment increases the awareness amongst them, regarding health issues, which translates in to a lower mortality rate.
GDP per capita:  GDP per capita is “domestic and foreign value added claimed by residents (World Bank, 2013).  This measure of economic development has been chosen, because it has been continually mentioned in the literature as one of the key determinants of mortality rate. Gross Domestic Product is a measure of the economic health of a certain country. It is also used to measure the standard of living of the citizens of a certain country. This variable is commonly associated with negative relationship with Fertility Rate. For example, when a country is enjoying high standards of living, then its citizens are well-exposed to facilities and education that is needed in family planning – this in return lowers the Fertility Rate.
Labor participation rate, female (% of female population ages 15+): this is the percentage of the female population, who are at least 15 years and who are acting in economic activities, such as production of labor for the purpose of production of goods and services during a particular year.
Fertility rate, total (births per woman): Fertility rate is the ratio of live births within a certain area compared to 1000 people from that area, per year. In other words, fertility rate “represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates” (Becker & Lewis, 1973).
This study will involve simple and multiple regression techniques, as well as generation of graphs to explain the relationship between the dependent variables and the various independent variables, which represents the determinants for infant mortality in developing countries. The raw data will be collected from the World Bank website, and then captured in an excel worksheet. Consequently, this data will be migrated to SPSS software for regression analysis and for graphical generation, where necessary.
Results and analysis
Model 1
Table 2: Model regression summary


Regression Statistics

Multiple R

R Square

Adjusted R Square

Standard Error


Standard Error
t Stat


GDP per capita (current US$)

School enrolment, secondary, female (% gross)

Fertility rate, total (births per woman)

Labour participation rate, per woman

a. Predictors: (Constant), Labor participation rate, female (% of female population ages 15+), Fertility rate, total (births per woman), GDP per capita (current US$), School enrollment, secondary, female (% gross)

b. Dependent Variable: Mortality rate, infant (per 1,000 live births)

              The strength of the model is 79.8% as revealed by the adjusted R Squared, which measures the goodness of a model in predicting the value of the dependent variable. Apparently, 79.8 % indicates that the model is very good because only 21.2% is attributable to sampling error. This is also evident from standard errors which are very small in all the variables.
This coefficients column shows that infant mortality has a negative relationship with GDP per capita and female secondary school enrollment.
On the other hand, infant mortality is positively related to female’s fertility rate and labor participation rate, meaning that the countries that report high fertility rate and high female labor participation experience a problem of high infant mortality rates. However, coefficient for female labor participation rate is almost zero, with a possible explanation that women who participate in labor are bread winners, and as such they have enough income to spend maintaining health of their babies, even though they at the same time lack enough time to take care of their babies hence a net off-effect. Female secondary school enrolment and fertility rate are statistically significant at 5% confidence level, which is evidence that the relationship between these two variables is significantly statistical. This is because their p-values are less than 0.01.
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