#internetuserate

“There was a time when people felt the internet was another world, but now people realize it’s a tool that we use in this world.”

After looking through the codebook for the Gapminder, I have decided to work on these two variables. Life expectancy is about the average number of years a newborn child would live if current mortality patterns were to stay the same. The main source of this variable are Human Mortality Database,World Population Prospects, Publications and files by history prof. James C Riley, Human Lifetable Database.

Internetuserate is about the people with access to the worldwide network. The main source is World Bank.

Now here the question arises. Is internet userate associated with life expectancy?

The answer is Yes. People easily reach health information online. This reflects an imapct of high income level and urbanization level in the internet-developed countries.We can say that internet userate is highly associated with life expectancy. The correlation value of these two variables is 0.694041. According to a study conducted by the Institute for Longevity Sciences at Wonkwang University, developed countries with high Internet accessibility were found to have longer healthy life expectancies than developing countries with fewer Internet users. Ireland, with the longest life expectancy of 71.5 years, topped the ranking of Internet users with 70.34 out of 100 citizens. As internetuserate is increasing, the rate of life expectancy is also increasing. This is clearly shown in this scatter plot graph.

However, every internet users like personalization. All the health related issues are solved online which are effective. So these are some of the information which shows that internetuserate is highly associated with life expectancy.

Data Management and visualization - week 1 1 DATA SELECTION: THE GAPMINDER I have chosen to analyse the GapMinder data, more specifically the variable that says about employment rates. I performed a prior choice of variables to analyse during this project. These are they: • Internetuserate: this variable came from World Bank, and refers to 2010 internet users (per 100 people). • Income per Person: this variable came from world Bank Work Development Indicators, and refers to 2010 Gross domestic Product per capita in constant 2000 US$. • Alcohol consumption: this variable came from WHO (World Health Organization), and refers to 2008 alcohol consumption per adult (15+). • CO2 emissions: this variable came from CDIAC (carbon dioxide Information Analysis Center), and refers to 2006 cumulative CO2 emission • Female employment rates: this variable came from International Labour Organization, and refers to 2007 percentage of female population, age above 15, that has been employed during the given year. • Life Expectancy: this variable came from the human Lifetable Database and some other sources, and refers to 2011 life expectancy at birth (in years) • Urban Rate: this variable came from World Bank, and refers to 2008 urban population (% of total population Some of these variables are often correlated with the interested variable. Others, I suppose it.

2 LITERATURE REVIEW My primary objective is to analyse the correspondence between employment rates and the percentage of population that uses the internet in their countries. According to (Jayakar & Park, 2013), inside the US, the investment in broadband infrastructure reduces the employment. According to these authors, the relationship between telecommunications and economic growth has been long recognized in the economics literature, ever since Jipp’s pioneering work found a positive correlation between telephone density in a country and per capita Gross Domestic product (GDP).

3 QUESTIONS MADE ABOUT THE DATA In this study, we will consider the Internet Use rate as a approximation of the telecommunications infrastructure available in each country under analysis. Here, we will answer this question: “Are the internet use rates correlated with the employment rates? And more, how are they correlated?” Also, we will look for other correlations in the database, as hat can be seen in the next topic. 4 OTHER QUESTIONS MADE ABOUT THE DATA 4.1 INCOME PER PERSON If the employment rates are high, intuitively we think the income per person are high too. Is it true?  Why? 4.2 ALCOHOL CONSUMPTION For low employment rates, will the alcohol consumption increases, decreases or stay the same? 4.3 CO2 EMISSIONS CO2 emissions is an explanatory variable of the industrial production in a country. Taking this information with the employment rates, que should ask if for low employment rates, does the CO2 emissions became low too? 4.4 FEMALE EMPLOYMENT RATES Here we can do two questions. First, the general employment rates are the same as female employment rates? This answer can reflect gender equality and the policy about this. Also, we can ask if, in fact the tendency of employment is really linear. For example, if the employment rates is basically formed for industrial jobs, knowing mostly male jobs, any changes on this rate will not affect the female employment rates. And vice versa. Finally, we can combine these rates, making an indicator of gender equality for the countries under analysis.

4.5 LIFE EXPECTANCY Is the employment rates affect the life expectancy? In countries with high employment rates, does the life expectancy is bigger? Here, we also can construct as indicator of life quality and retirement.

Hi there,

Finally back and writing another post. The Gapminder includes a lot of data on variables related to development and MDG – just my cup of tea. But I of course had difficulties making a choice on what variables to work with. I have now decided to focus on employmentrate and morbidity (that is breatscancer, HIV and suicide). 

Employment is essential not only to maintain an income but also to have appropriate living conditions. Unemployment contributes to social health inequalities and is found to be an independent predictor of morbidity (Bartley M et.al., 2004) and mortality from suicide and external causes (Moser KA, 1984; Sorlie PD, 1990).

So I decided that my first research questions should be:

1. Is employment status associated with morbidity (breatscancer, HIV and suicide) 

My second research question is 

2. Is the Internetusage associated with morbidity (HIV and suicide)?