Running head: INDEPENDENT SAMPLES T- TEST
Independent Samples T-Test: Exploring the Relationship between Infant Mortality and Income
Inequality
Anna DeRuyter, Sandhiya Ravichandran, Stephanie Skees, and ShaCoria Winston
Joe Steensma, MPH, EdD
Biostatistics
George Warren Brown School of Social Work
Washington University in St. Louis
10/6/2015
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Introduction
Over time, many studies have measured the relationships and differences between income
inequality and health and much of the literature substantiates that there is a significant
relationship between these two variables. Using GINI coefficients as a proxy for income
inequality, Dorling, Mitchell, and Pearce (2007) reported that income inequality had the greatest
impact on mortality for the age group 25-39 worldwide. This study establishes the relationship
between health and income inequality (Dorling et. al, 2007). As follows, income inequality can
affect children’s health outcomes, with children of lower socioeconomic status disproportionately
affected by high rates of chronic impairment and greater activity limitations (Chen, Martin and
Matthews, 2006).
While mortality and morbidity are associated with lower status in wealth, a systematic
review of data from developing countries reported a negative relationship between wealth and
specifically, infant mortality (O’Hare, Makuta, Chiwaula and Bar-zeev, 2013). Essentially, a 10%
increase in gross domestic product (GDP) per capita purchasing power parity (PPP) in one of
these countries would expect a 10% decrease in infant mortality (O’Hare et al., 2013).
Accounting for inequality across the socioeconomic distribution, Hajizadeh, Nandi, and Heymann
(2014) found that being a part of a poor household was a risk factor for infant mortality in low
and middle-income countries. There is also significant positive association between wage
inequality, measured using the Theil index, and infant mortality rate (IMR) (Macinkoa, Shib and
Starfield, 2004).
This disparity exists in both developing and developed countries. In wealthier nations,
Spencer (2014) reported a positive association between income inequality and infant mortality
rate. One study analyzed the declining annual rate of Sudden Infant Death Syndrome (SIDS) in
Scotland during the early 1990s, and demonstrated a sharp decline in the rate of SIDS for women
living with low socioeconomic deprivation, measured by the Carstairs deprivation score (Wood,
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Pasupathy, Pel, Fleming and Smith, 2012). As follows, the study reported a much slower decline
of the rate of SIDS in women with high deprivation (Wood et al., 2012).
Despite the number of studies establishing a positive relationship between income
inequality and infant mortality, Regidor, Martinez, Santos, Calle, Ortega, and Astasio (2012)
reported that this relationship disappeared midway through a 15-year time span, when analyzing
Western countries. This disappearance may be attributable to increased funding of public health
interventions leading to the idea that health outcomes can change even when income inequality
remains stable in a country (Regidor et al., 2012). Furthermore, there was a lack of variation in
the factors over time (Regidor et al., 2012).
Through a complex model, Siddiqi, Jones and Erwin (2015) examined the
relationship between income inequality and IMR in the United States from 1990-2007. The study
reported that there was a negative relationship between income inequality and IMR, which was
contradictory to the earlier reported positive relationship (Siddiqi et al., 2015). However, this
relationship became positive in 2004 onwards (Siddiqi et al., 2015). This suggests that the
association may not be applied across time points and may be “dependent on contemporaneous
societal conditions” such as welfare policies and government resources (Siddiqi et al., 2015).
Moreover, this study also examined how social and economic policy was associated with
IMR, and six studies reported significant, positive associations of IMR with indicators of lessredistributive social and economic policy (Spencer, 2004).
With trends showing an association between income inequality and infant mortality rates,
this study seeks to understand whether there is a difference in infant mortality rates between high
and low income inequality countries, as defined by the GINI index.
Null hypothesis: There is no statistically significant difference in the means of infant mortality
rates between countries of high and low income equality (GINI index)
Alternative hypothesis: There is a statistically significant difference in the means of infant
mortality rate between countries of high and low income equality (GINI index)
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Methodology
Data was procured from the World Bank datasets for both variables. Existing data for
GINI Index values in 2010 were matched with data for IMR in 2010 for each country in the GINI
dataset (n=64). The IMR data was divided into two variables: high-income inequality and low
income inequality. The IMR data for countries in the top 50th percentile of 2010 GINI indices
was assigned to high-income inequality. Correspondingly, the IMR data for countries in the
bottom 50th percentile of 2010 GINI indices was assigned to low-income inequality.
A independent-samples t-test was performed to test whether there is a difference in the
means of IMR between countries with high income inequality and low income inequality.
Results
After conducting an independent samples t-test, we failed to reject the null hypothesis,
thus we are 95% confident that there is no significant difference in the means of IMR between
countries with high-income inequality and countries with low-income inequality. Represented in
Table 3 (Independent Samples Test), the significance value is .299, which is greater than the .05
needed to reject the null hypothesis. At the.05 significance level for a two-tailed t-test we used the
degrees of freedom value (62) to determine the critical t-value (2.00). Our results produced an
absolute t-value of 1.047 thus this substantiates our p-value results that indicate we have failed to
reject the null hypothesis.
For countries with lower income inequality Table 1 shows the mean (16.56) is greater
than the median (6.00) leading us to infer that when plotted the distribution will be positively
skewed (see Appendix A). The same was true for countries with higher income inequality as
Table 1 shows the mean (21.87) is greater than the median (16.1) again leading us to infer the
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distribution will be positively skewed (see Appendix A). Both of these conclusions were
reinforced by their respective histograms (see Appendix B). Descriptive statistics also reveal
kurtosis values of 3.26 and 3.17 for countries with lower income inequality and higher income
inequality respectively. These values both indicate stronger than normal peaks in the data. The
standard deviation for countries with low-income inequality of 20.99 indicates that on average,
any data point deviates from the sample mean by 20.99, which is equal to one standard deviation.
Further, the standard deviation for countries with high-income inequality of 19.51 indicates that
on average, any data point deviates from the sample mean by 19.51, which is equal to one
standard deviation.
Discussion
The GINI Index measures the degree of inequality in the distribution of family income in
a country. When income distribution within a country is more equal, the lower the GINI index
will be, thus, the more equal the distribution, the higher the GINI index will be. The value ranges
from 0 to 100; 0 indicating perfect equality, 100 indicating perfect inequality.
Infant mortality is measured in terms of the number of deaths per 1,000 live births. This
rate is measured for infants under the age of 1, and has high implications of the level of health for
a country. According to the United Nations, the infant mortality rate (IMR) worldwide is 49.4.
Differences in infant mortality rate occur by age, race and ethnicity. There are many causes that
contribute to infant mortality such as birth defects, preterm birth, Sudden Infant Death syndrome
(SIDS), and maternal complications during pregnancy and injuries (CDC). Despite the impact of
infant mortality are multiple intervention techniques that can reduce the prevalence of infant
mortality.
By only examining the relationship between infant mortality and income inequality, other
independent variables are not taken into account; other interventions may reduce infant mortality
without changing income inequality. For example, Hajizadeh, Nandi, and Heymann (2014)
believed their research implicated that social inequalities could be effected through public health
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interventions aimed at teenage pregnancy. Interventions such as earlier marriages, family
planning, maternal health services, and increased completion of secondary education may
effectively decrease infant mortality. The influence of interventions aimed at reducing social
inequalities may account for the lack of a significant relationship found between infant mortality
and income inequality. This is important to note when using findings to discern future
interventions.
There are many related measures, like GINI index, income, GDP, and socioeconomic
status, that can affect infant mortality, as discussed in this paper. However, one limitation of the
GINI index value itself is that the relationship between income inequality and GDP. Countries
with low GDP, which is usually associated with high IMR (O’Hare et al., 2013), may also have
low income inequality. This would skew the results of our t-test, where low-resource countries
with high IMR are assigned to low inequality.
Another limitation of the particular dataset used is that it used the GINI index as the sole
measure of income inequality. While this is undoubtedly a commonly used, well-accepted
measure of income inequality, there are also others that could have been used with different
strengths such as the Theil measure of income inequality. A strength of the GINI coefficient is
that it allows for direct comparison. However, because of the way it is measured, it does not
indicate which aspects of income inequality are stronger or weaker in various countries. Another
weakness is that it cannot be divided to look at population subgroup inequality (e.g. the income
inequality among states that make up the U.S.).
The lack of GINI index data limited our study to 2010, which had the highest number of
collected GINI index values in the past 5 years. Due to few values, this study used a comparison
of the top and bottom percentiles, resulting in unequal scaling of the variables. Low-income
inequality countries had a GINI Index range from 24.82 to 33.87, and high-income inequality
countries had a range from 34.74 to 57.49. This arbitrary differentiation of low and high-income
inequality presents as a challenge when interpreting these results. It is possible that more
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extensive GINI index data and the following equal scaling of the variables could result in a
significant difference.
Conclusion
While this study shows no significant difference in IMR for low and high-income
inequality countries, there are various moderating variables that affect this relationship. Further
research should examine these factors as independent variables in order to understand their
influence on IMR.
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References
Chen, E., Martin, A.D., & Matthews, K.A. (2006) Understanding Health Disparities: The Role of
Race and Socioeconomic Status in Children’s Health. American Journal of Public
Health. 96(4), 702-708. doi: 10.2105/AJPH.2004.048124.
Dorling, D., Mitchelle, R., & Pearce, J. (2007). The global impact of income inequality on health
by age: An observational study. BMJ. 335 (7625). 873. Doi:
10.1136/bmj.39349.507315.DE
Hajizadeh, M., Nandi, A., & Heymann, J. (2014). Social inequality in infant mortality: What
explains variation across low and middle income countries? Social Science and Medicine.
101. 36-46. doi: 10.1016/j.socscimed.2013.11.019
Macinkoa, J. A., Shib, L., & Starfield, B. (2004). Wage inequality, the health system, and infant
mortality in wealthy industrialized countries, 1970-1996. Social Science and Medicine.
58(2). 279-292
O’Hare, B., Makuta, I., Chiwaula, L., & Bar-zeev, N. (2013). Income and child mortality in
developing countries: a systematic review and meta-analysis. Journal of the Royal
Society of Medicine. 106(10), 408-414. doi: 10.1177/0141076813489680.
Regidor, E., Martinez, D., Santos, J., Calle, M., Ortega, P., & Astasio, P. (2012) New finding do
no support the neomaterialist theory of the relation between income inequality and infant
mortality. Social Science and Medicine. 75 (4). 752-753.
Doi:10.1016/j.socscimed.2011.09.041
Siddiqi, A., Jones, M.K., & Erwin, P.C. (2015). Does higher income inequality adversely
influence infant mortality rates? Reconciling descriptive patterns and recent research
findings. Social Science & Medicine. 131, 82-88. Doi: 10.1016/j.socscimed.2015.03.010
Spencer, N. (2004). The effect of income inequality and macro-level social policy on infant
mortality and low birth weight in developed countries--a preliminary systematic review.
Child Care Health Development, 30(6), 699-709.
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Wood, A.M., Pasupathy, D., Pell, J.P., Fleming, M., & Smith, G.C.S. (2012) Trends in
socioeconomic inequalities in risk of sudden infant death syndrome, other causes of
infant mortality, and stillbirth in Scotland: population based study. BMJ. 344:e1552.
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Appendix A
Table 1. Descriptive Statistics for Infant Mortality Rate in Low and High Income Inequality
Countries
Table 2. Test for Normality for Infant Mortality Rate in Low and High Income Inequality
Countries
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Appendix B
Figure 1. Histogram for Infant Mortality Rate in Low Income Inequality Countries
Figure 2. Histogram for Infant Mortality Rate in High Income Inequality Countries
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Appendix C
Figure 3. Box Plots for Range of GINI coefficient by Infant Mortality Rate in Low and High
Income Inequality Countries