Economic and Social Determinants of Infant Mortality - Essay Example

Economic and Social Determinants of Infant Mortality in Developing Countries Contents Introduction and objective 2 Statement of the research problem 2 Brief review of relevant literature 3 Analytic regression methods, including statistical tests and/or corrections 4 Description of the regression model 5 What are the dependent and independent variables? 5 Dependent variable 5 Mortality rate, infant (per 1,000 births): this is the number of newborns who pass on before attaining their first year birthday, for every 1,000 of children who are born alive in any given year. 5 Independent variables 5 What does theory/literature review say 6 Hypothesis 7 Data and methods 7 Data sources 7 Summary statistics of the data 7 Model 1: Table 7 Model 2: Table 9 9 Model 2 9 Model 3: Table 10 Model 4: table 11 Interpretation of the results 12 Does the results support or not your original hypothesis? 14 Discussion 14 Works Cited 15 Economic and Social Determinants of Infant Mortality in Developing Countries Introduction and objective Statement of the research problem There are a couple of scholars 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 in the development of a better understanding on the social and economic determinants of infant mortality rate. It also aims at reducing health disparities in the developing countries, and 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. Postneonatal mortality rates are contemplated to be determined to largely by parental circumstances such as the care provision and their socioeconomic position. Studying mortality rates will help policy makers to come up with ways of reducing the infant mortality rates. For example, if one of the leading causes of infant mortality is lack of healthcare to women, policy makers can come up with methods of providing healthcare for pregnant women. Every day, millions of lives of infants are lost around the world. However, 80% of these deaths can be avoided if the right measures are taken. Studying the infant mortality rates helps the government, hospitals and other relevant agencies come up with new ways of preventing reducing the infant mortality rate. It will also help to improve the current prevention methods. Infant mortality is not only caused by biological factors but also social and economic factors. Studying the causes of infant mortality will help doctors and scientist to come up with new ways of controlling the biological causes of infant mortality. This will also provide them with ideas on how they can improve the quality of medicine. 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 reviewed, and a conclusion will be made. Brief review of relevant literature 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 years 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 the female literacy rates have the strongest effects on infant mortality rates. For this reason, these factors are critical when developing programs to control infant death. In this study, we use cross section data. The graphs used in this study include figures derived from 83 countries from all over the world. The variables taken included the mortality rate in the countries, Gross Domestic Product, school enrollment for female students in secondary schools, the fertility rate, and the labor participation rate per woman. The unit of observation in the data used is various countries. The data used in this research was gathered by the World bank through trained research specialists. The results and obtained from this research will be close to real life events. Therefore, the conclusions and recommendations that will be made from this research will be effective in addressing the issue of infant mortality. Analytic regression methods, including statistical tests and/or corrections The 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. Consequently, this data will be migrated to SPSS software for regression analysis and for graphical generation, where necessary. Description of the regression model What are the dependent and independent variables? Dependent variable Mortality rate, infant (per 1,000 births): this is the number of newborns who pass on before attaining 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 match 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 which have a large female secondary school enrollment level increases the awareness amongst them, regarding health issues, which translates into 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 an estimate 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 the female population ages 15 and above): 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 in a year. Fertility rate (births per woman): this is the ratio of live births in a certain location compared to 1000 people from that location, per year. In other words, fertility rate is a representation of the children who would be born by one woman if she were to live past her childbearing years and give birth according to the age-specific fertility rates currently” (Becker & Lewis, 1973). What does theory/literature review say 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. Hypothesis It is hypothesized that the mortality rate is positively related to female fertility rate on one hand and negatively related to GDP per capita, female enrolment to secondary school, and female participation in income generating activities. Data and methods Data sources The raw data will be collected from the World Bank website, and then captured in an excel worksheet. Summary statistics of the data Model 1: Table Model regression summary SUMMARY OUTPUT Regression Statistics Multiple R 0.898984 R Square 0.808172 Adjusted R Square 0.798206 Standard Error 13.14054 Observations 82   Coefficients Standard Error t Stat P-value Intercept 49.76903 13.80688 3.604655 0.000552 GDP per capita (current US$) -0.00018 0.00012 -1.53359 0.129228 School enrolment, secondary, female (% gross) -0.49011 0.09901 -4.95009 4.27E-06 Fertility rate, total (births per woman) 7.016719 1.874337 3.743575 0.000348 Labour participation rate, per woman 0.010153 0.093524 0.108562 0.913832 a. Predictors: (Constant), Labor participation rate, female (% of the female population ages 15+), Fertility rate, total (births per woman), GDP per capita (current US$), School enrollment, secondary, female (% gross) b. Dependent Variable: Mortality rate, infant (per 1,000 live births) Model 2: Table SUMMARY OUTPUT Regression Statistics Multiple R 0.573447 R Square 0.328841 Adjusted R Square 0.320451 Standard Error 24.11403 Observations 82   Coefficients Standard Error t Stat P-value Intercept 46.29626 3.401386 13.611 1.59E-22 GDP per capita (current US$) -0.00112 0.000178 -6.26073 1.79E-08 Model 2: graph Model 2 In the second model, GDP per capita is retained while others will be controlled. The aim is to compare the strength of this model with the first one. The following are the results for this model. The coefficient is negative meaning that countries with high GDP per capita also experience lower mortality rates, possibly because their living standards are better and hence they can afford to take good care of their infants. The p-value is less than 0.01, meaning that the model is statistically relevant at 5% confidence level. The adjusted R squared is 32%, which means that this model is not very good as the relationship is mostly attributable to sampling error. Model 3: Table SUMMARY OUTPUT Regression Statistics Multiple R 0.875714 R Square 0.766874 Adjusted R Square 0.76396 Standard Error 14.2119 Observations 82   Coefficients Standard Error t Stat P-value Intercept 94.4321 4.096528 23.05174 4.46E-37 Female school enrolment -0.83242 0.051313 -16.2223 5.11E-27 Model 3: graph Model 4: table Model 4: graph Model 5: table SUMMARY OUTPUT Regression Statistics Multiple R 0.242438 R Square 0.058776 Adjusted R Square 0.047011 Standard Error 28.5564 Observations 82   Coefficients Standard Error t Stat P-value Intercept 10.62839 10.51448 1.010833 0.315144 Labour participation rate, per woman 0.430482 0.192599 2.235114 0.028198 Model 5: graph Interpretation of the results 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 is negatively related to 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. It has a possible explanation that women who participate in labor are bread winners. Therefore, they have enough income to spend maintaining health of their babies. However, 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 proof that these two variables have a significantly statistical relationship. This is because their p-values are less than 0.01. In model 2, the GDP per capita is negatively related to Mortality rate. This is because as explained earlier, the countries with higher GDP per capita have citizens who can afford better health facilities. However, Adjusted R Squared of 32% shows that the model is not very effective – it is highly attributable to sampling error. The relationship is also statistically significant at 5% confidence level. In model 3, the female secondary enrolment is negatively related to the mortality rate. The p-value is less than 0.01, hence the relationship is statistically significant at 5% confidence level. Although the relationship between mortality rate and female participation in labor is positive, this relationship is very low as revealed by the low slanting shape of figure 4. Adjusted R Square of 4.7 shows that the model is largely bad hence the relationship is mostly attributable to sampling error. The model is also not statistically significant and hence this model is largely unreliable. In model 4, the relationship between mortality rate and fertility rate is strongly positive. It is also statistically significant. Adjusted R Square of 70.8% shows that the model is very good. Does the results support or not your original hypothesis? The results support the hypothesized that the mortality rate is positively related to female fertility rate on one hand and negatively related to GDP per capita, female enrolment to secondary school, and female participation in income generating activities. This is because having many children makes it hard for mothers to provide good care for their infants while GDP, level of education and female participation in income generation empower them. Therefore, they are able to provide good care for their infants, so they do not die. Discussion There is an interesting positive relationship between women fertility rate and infant mortality rate. A possible explanation for this observation is that women who bare many children are not capable of taking proper care of them, which results to their high rate of death. High Fertility rate may be as a result of lack of knowledge regarding family planning or traditional cultural practices. From the literature, it is also evident that high fertility rate can be attributable to low education, and possibly that is why female secondary enrolment has a positive relationship to infant mortality. Also, the results indicate that there is a positive relationship between infant mortality rate on one hand and Gross Domestic Product per capita on the other. A possible explanation for this is that economic development goes hand in hand with better health care provision while better education of the women helps them in taking better care of their infant babies, hence preventing early death. Infant mortality is a major indicator of the development of any country; therefore, its prevention is an essential policy issue. The major causes of infant mortality, from the literature, are the health related issues, which are indirectly influenced by social and economic factors. This study has clearly established the relationship between infant mortality and factors such as GDP per capita, women fertility rate, female enrolment in secondary schools, and female labor participation. The study shows that per capita income and female secondary school enrolment are negatively related to infant mortality rate. However, the fertility rate is positively related to the infant mortality rate. Female participation in does not establish any statistical relevance in this study, though it reveals slightly positive relationship with the mortality rate. Ideally, all the predictors that are negatively related to the mortality rate are usually linked to economic development. Female fertility rate which is positively related to the mortality rate is usually associated with lack of economic development, with factors such as lack of family planning knowledge and lack of health care facilities, among other being responsible for this situation. The main hindrance to this study is that not all nations consistently keep data that are analyzed in this research, and also there are other intervening factors that make it hard to generalize the results of this study. The finds of the study can be used by policy makers to plan, as well as give insights to researchers who have an interest in issues of macroeconomic nature. Works Cited Baird, G., Friedman, I., & Schady, K. (2009). Aggregate income shocks and infant mortality in the developing world. Nursing Journal, 4(5), 5-9. Becker, G., & Lewis, H.G. (1973). On the Interaction between the Quantity and Quality of Children. Journal of Political Economy, 82, 279-288. Deaton, A. (2003). Health, inequality, and economic development. Journal of Economic Literature, 41(1), 113–58 Ghosh, A., & Alves, B. (2011), Infant Mortality. Working paper Hertz, E., Hebert, J.R., & Landon, J. (1986). Social and environmental factors and life expectancy, infant mortality, and maternal mortality rates: results of a cross-national comparison. Soc Sci Med,. 9(1), 105-14. Leigh, G., & Jencks, T. (2006), Inequality and mortality: Long-run evidence from a Panel of countries. Working paper. Pampel, F.C., & Pillai, V.K. (1986). Patterns and determinants of infant mortality in developed nations, Demography, 23, 525-42. Rostow, W. W. (1971). The Stages of Economic Growth, 2nd. ed. Cambridge, MA: Cambridge University Press. How I did it I first visited Word bank database and collected all the relevant data in an excel worksheet. I then went to library and reviewed the internet for relevant literature review, which I used to write the introduction and the literature review sections of this study. To do the statistical analysis, I imported al the data in an excel worksheet, where I treated mortality rate as the dependent variable and others as the dependent variables. To get the regression outputs, I went to “data” then “data analysis” tab on the far right of the window. After pressing “data analysis” another small pop-up window appeared, with a list of functions where I clicked “regression” On the regression window, I heighted all the dependent variables as “Y” and all the independent variables as “X” – and then clicked output section where the output is displayed. I repeated the same for simple regression analysis, with each independent variable at a time. To insert graphs, I went to “insert” tab, then “scatter”. I highlighted the independent variable first followed by the dependent variable, and then finally clicking on one of the scatter diagrams displayed. I repeated the same for all the independent variables. Read More