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Gapminder Dataset Needed Variables Information

The sample

The sample is a portion of the GapMinder data which includes one year of numerous country-level indicators of health, wealth and development. Data on gapfinder are sourced from various governmental websites and reliable archives which are results of close observations of the countries for the period of time the study entails. Fifty(50) randomly selected countries are selected for the the analysis from which inference is drawn for all countries(population).

Procedure The countries' data are sourced from publicized data which are factored by various economic and scientific tools. They are monitored closely and announced by the bureau of statistics in each country. IMF and other reliable international bodies verify the credibility of this data before being archived and uploaded on Gapminder.

Measures Life expectancy was calculated by constructing a life table. A life table incorporates data on age-specific death rates for the population in question, which requires enumeration data for the number of people, and the number of deaths at each age for that population.

HIV rate is expressed as the estimated number of persons newly infected with HIV during a specified time period (e.g., a year), or as a rate calculated by dividing the estimated number of persons newly infected with HIV during a specified time period by the number of persons at risk for HIV infection.

Income per person is a statistic that measures the average income earned per person in a given area country in a specified year. It is calculated by dividing the country's total income by its total population.

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PART III - Charts

The code for constructing the charts

The histograms shows the variables to be used. CO2 emissions is seen to be skewed to the right likewise HIV rate also. Life expectancy, however, is a left-skewed distribution.

The scatter plot shows the relationship between HIV rate and Life expectancy

The Regression plot shows that there is a negative correlation between HIV rate and Life expectancy. This means that there is no relationship observed between the two.

From the chart above, it is seen that there s a fairly strong correlation between CO2 emission and life expectancy. The implication of this is a relationship between the two considered variables.

After segregating into percentiles, majority of the life expectancy records are seen to fall within 25% percentile.

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PART II - Coding with interpretation

Here is the code I ran with comments to properly define each step

Here are the outputs.

This describe output will be explained below

The histogram of the variables used

The data to be used for the project is analyzed using descriptive statistics to find out direct features of each variable. And from each variables, there are seen to be certain depictions that are peculiar to each data set.

From the descriptive output, it is seen that there is a mean of about 2% of the countries’ population that are HIV positive. This depiction is a summary of data between a minimum percent of 0.06% to a maximum value of 25.9%. The standard deviation of this variable is considerably large when compared with the data range with deviation of around 4%. The histogram shows that the distribution is skewed to the right and not normal.

The emission of carbon-dioxide is seen to have a mean of 5 metric tons with standard deviation from this mean being 2.6 which is quite large too considering the minimum and maximum observations being 1.3 and 3.3 metric tons respectively. It is also a non-normal distribution.

Finally, life expectancy of all the 213 countries observed has a mean of 69.75 years. According to this metric, this means that averagely, people are expected to live about 70 years until death. With the maximum life expectancy recorded from the countries being 83.4 years while the minimum is 47.8 years old. This data is also seen to be non-normal and is skewed to the left.

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The relationship between both HIV rate and carbon dioxide emission and life expectancy.

OVERVIEW

After downloading the datasets and observing the variables using their respective codebooks, I eventually got attracted to the information contained in the GapMinder codebook. The data are related to economic situations and I feel that working on this might create discoveries that will eventually create some realizations that will help the society.

With this in view, I consulted research expert who is specialized in handling demographic studies to pinpoint common economic concerns that might be reviewed using the dataset. After several deliberations, I decided to base my research on finding the relationship between both HIV rate and carbon dioxide emission, and life expectancy.

I have decided to use the CO2 emissions and life expectancy variables contained in the GapMinder dataset to meet the aim of this project. This would help give just the light needed to make this research quite explicit.

RESEARCH QUESTION

In order to extensively cover the aspect of this study, the question to be researched is “Is there a relationship between carbon dioxide emission and life expectancy?”.

RESEARCH HYPOTHESIS

Ho: There is a relationship between carbon dioxide emission and life expectancy. Ho2: There is a relationship between HIV rate and life expectancy.

LITERATURE REVIEW

There exists tremendous literature in the area of environmental hazards, but some studies on the area of CO2 effects on life expectancy in the recent decades are quite rare. Chigozie Nelson Nkalu, Richardson Kojo Edeme (2019) investigated the extent to which environmental hazards affect the life expectancy in Africa using Nigeria as a case study. From their research, it is evidenced in the estimation results that CO2 emission from solid fuel consumption reduces life expectancy, while population growth and income extend life expectancy in their varying degrees and magnitudes.

Rogers and Wofford (1989) examined the determinants of life expectancy in 95 developing countries using cross-sectional data. The study found that agriculture-related population, urbanization, access to drinking water, illiteracy rate, average number of doctors per population, and average calories per person play significant roles in determining life expectancy at birth in the 95 developing nations.

Also, Amjad and Khalil (2014) examined the impact of socioeconomic factors on life expectancy in Sultanate of Oman using autoregressive distributed lag (ARDL) model on a time series data ranging from 1970 to 2012. The result indicated that CO2 is negatively and significantly associated with life expectancy in the short run but positive and insignificant in the long run.

Mariani, Pérez-Barahona, and Raffin (2009) carried a study on life expectancy and the environmental quality dynamics in Germany using the OLG model. The study reveals a positive correlation between life expectancy or longevity and environmental quality in both the transition path and long run. Similar to this findings is Bayati, Akbarian, and Kavosi (2013) which investigated the determinants of health status proxied by life expectancy in East Mediterranean Region (EMR) using panel data econometrics technique with fixed effects after pre-evaluating the parameters using Hausman test. The study covers a time period between 1995 and 2007, and estimated output shows that life expectancy is influenced positively by the level of urbanization, food availability, income per capita, education index, and employment. The study concluded that life expectancy can be improved in the EMR if policymakers can concentrate on those factors exogenous to the health care system such as reduction in unemployment, increase in productivity, and economic growth.

 Therefore, this is an extensive review of related literature on the key indicators of environmental hazards and life expectancy, and with reference to the research question and study objective.

REFERENCES

Ali, Amjad, and Khalil Ahmad. The Impact of Socio-Economic Factors on Life Expectancy for Sultanate of Oman: An Empirical Analysis. 2014, https://mpra.ub.uni-muenchen.de/70871/.

Mariani, Fabio, et al. “Life Expectancy and the Environment.” Journal of Economic Dynamics and Control, vol. 34, no. 4, Apr. 2010, pp. 798–815. ScienceDirect, doi:10.1016/j.jedc.2009.11.007.

Nkalu, Chigozie Nelson, and Richardson Kojo Edeme. “Environmental Hazards and Life Expectancy in Africa: Evidence From GARCH Model.” SAGE Open, vol. 9, no. 1, Jan. 2019, p. 215824401983050. DOI.org (Crossref), doi:10.1177/2158244019830500.

Rogers, Richard G., and Sharon Wofford. “Life Expectancy in Less Developed Countries: Socioeconomic Development or Public Health?” Journal of Biosocial Science, vol. 21, no. 2, Apr. 1989, pp. 245–52. DOI.org (Crossref), doi:10.1017/S0021932000017934.