Proposal

We are interested in researching internet access on a country level and comparing the findings against various global economic indicators. For the purposes of this project, we will be examining data from the Organization for Economic Cooperation and Development (OECD), WikiLocation, the Internet Telecommunications Union (ITU), the World Bank, and the Big Mac Index.

We have outlined our questions, data sources, and folder structure below for those interested in replicating our work.

Questions

Are internet access and certain economic indicators correlated in the OECD?

$H_0$: Internet penetration rates are not correlated with the chosen economic indicator $H_A$: Internet penetrateion rates are correlated with the chosen economic indicator

Do these trends hold for a broader dataset?

$H_0$: The economic effect due to Internet access is the same in OECD and non-OECD countries

$H_A$: The economic effect due to Internet access is different amongst OECD and non-OECD countries

Data

Each folder will have it's own data-scraping/cleaning location, named after the indicator in question. Inside that folder, we will place a .R file (with plenty of comments!) that collects the data locally and uses as much automation as possible. Then, each file will be stored in the csvs section.

The following is an outline of the sources we plan on using:

OECD:

• OECD Broadband Statistics: This shows time series broadband data across 35 OECD countries
• OECD Economic Data: This shows time-series economics data across 35 OECD countries. Note the api query maker. It will make our life easy.

Global:

Internet Usage Indicators

• ITU data International Telephone Union

Economic Indicators

• World Bank Development Indicators: All kinds of other economics indicators, callable by ISO 3 letter country code.
• Big Mac Index: This is an indicator we can compare to the economic data.

We can run this analysis twice. Once for the OECD and once for all other countries.

CSVs

This folder contains cleaned versions of all the datasets used. This way, it will be easy to import each data set. We will be left with internet penetration rates, a Wikipedia density indicator, a World Bank indicator, and a Big Mac Indicator, all indexed by the ISO 3 letter country code.

Database

Each of these csvs will be imported and read into a database using R.

Analysis

Our final analysis will live in the root directory and will have all of the fancy features needed to get an A. This are being tracked in the issues page for this repository.