Internet use and employment around the world: are they correlated?
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 Jippâs 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: âAre the internet use rates correlated with the employment rates? And more, how are they correlated?â 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? Â 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.
4.6 URBAN RATES Here, we can analyse how is the employment structured in each country. If we found low urban rates and high employment rates, we can conclude that this country have at most rural jobs. And vice versa. 5 BIBLIOGRAPHY Jayakar, K., & Park, E.-A. (2013). Broadband availability and employment: an analysis of count-level data from the national Broadband map. Journal of Information Policy, pp. 181-200.