Coastlines & Unemployment


For our research, we looked at Coastlines (km) and Unemployment of countries. To mediate these two variables, we chose to measure the Public Debt, measured in percent of gross domestic product. Initially, we wanted to test random ecological and economic variables to find if they would have a relationship. is where we got the statistics for public debt, measurement of coastline, and unemployment rate. We hypothesized that there would be an inverse relationship between coastlines and unemployment. When we put all these variables together, however, we discovered that a relationship between these two factors. Our findings prompt us to further question the significance of a country’s natural resources in relation to economic benefit.


Coastline and Unemployment rate are inverse relationships of one another. The more access a country has to coastlines, then the more businesses are attracted to this area, which would lead to more jobs available to the residents, leading to lower unemployment rates. In addition, countries will have abundant resources available thus fueling the economy and reduces unemployment.


1. We opened Google chrome and typed in Nation Master.
2. Then, we looked through the database for interesting ecological and economic variables.
3. After deciding to use coastlines and unemployment, we begin grabbing our data. Our countries were selected based on coastline because there was a smaller pool of data compared to unemployment’s.
4. Once we gathered our data and place it in Microsoft Excel, we created scatter plots.
5. Then we went back to Nation Masters, and collected data for our intermediate variable.
6. We apply the same steps in 4 and 5 to our intermediate variable.
7.Then we began to analyze the information.

During our research, we depended on the computer, Internet, Nations Master data base, google docs, and excel. We searched for our demographic and ecological variables before we formed our hypothesis. We tried thinking of ways that a countries environment would influence not only its ecological systems but its economic stability as well. What we came up with was ways that a countries amount of coastline could influence the amount of people who are employed.

For our ecological variable, we went on Nation Master, clicked “Environment” and open the subfile to located: Total Country Coastlines (KM)

For our economic variable, we also went on Nation Master, click “Economics,” and searched for: Unemployment Rate (% by Country)

For our intermediate variable, once again we went on Nation Master, looked under Economics, and found Public Debt.
Intermediate Levels: Public Debt ( % of GDP)

While we were researching our economic and ecological variables, we felt the best connection would be economic. We reasoned that public debt and unemployment should be similar. Unemployment will cause an increase in public debt which means that there is a positive relationship between unemployment and public debt.


The Coastline variable measures how much access the country has to coasts. This is our ecological variable. The unemployment rate serves as our economical variable and this is measuring how well countries economic structure can handle higher populations. This is a dependent variable in our research because we think, higher amount of coastlines, the more people live there, creating more jobs, and creating less unemployment. % of GDP is our intermediate variable. The more people who have jobs, the better the economy is, which would lead to a higher GDP for that country.The amount of kilometers of coastline a country has can be used to measure the amount of biodiversity in the coastal regions. Here, the coastal variable was used as the dependent variable ad the variable that was independent was the biodiversity of the coasts. Unemployment was used in a study that showed the more economic freedom, the amount of unemployment is reduced. Here, unemployment is used as a dependent variable to economic freedom. That same study, what was also used, as an intermediate variable was the Gross Domestic Product. We chose our variables after going through several other topics, starting off with carbon credits to arable land. The coast has always been rich places in resources, and many cities have always been located near the coast. because the coast has always had this high population, we assumed that this would lead to a bigger job market, and have less of an unemployment rate than inland areas.


By the end of our data sampling, it appears that there is no relationship between our variables. Coastline and Unemployment, our main variables, have no correlation.


We could not include the linear regression line. However, our R^2 value is .0007 which numerically suggest that there is not relationship between Coastlines and Unemployment.

Our ecological and intermediate variable also have no correlation.


The R^2 value of this graph is 0.0115.

Lastly, our economic variable and and intermediate variable has no correlation.

external image DIA7JWblSK1SVUNXpp0s3Vl36AiKgwxw1CdSjQsJFTA0i4MpaaBan5jtpADMub06PripG-BWLM-2cTv6ZLf_Q9fD-AfTViHlUOJrIFFxxOZiGocEPQ

The R^2 value is .0023. This is suggest that we did not choose a very good intermediate value.

Since all our our variables have a R^2 value near zero, we can conclude they have no relationship with one another. Thus it disproves our hypothesis: There will be an inverse relationship between coastlines and unemployment. From our data, we concluded that there is no relationship between coastlines and unemployment.


Our research did not support our hypothesis though it did answer our question: there is no increase or decrease of unemployment with more coastal land available. Our hypothesis was stating that the more coastline a country has the more jobs that would be available in that area, leading to less unemployment, and this would all be measured in percent of GDP. Our research showed that the correlation between coastal regions and the amount of unemployment is nonexistent. Our r(2) values for the relation between coastline and unemployment is .00067, and the r(2) value for coastline and public debt is .01151.

A limitation for our research is the year available for data we were collecting. We had to use data from two different data points, the coastal region was taken from 2003 and the public debt was from 2007. Another limitation would be the way the coastline data was taken. There may be areas where coastline is present, but that place may not be inhabited. This would mess with our hypothesis because we were stating that there was a correlation between coastline and increased number of people there, and this situation would go against what we are trying to prove. Some countries that are prosperous despite their lack of coastline are Switzerland, Luxembourg, and Czech Republic.
A variable we could add to the hypothesis that would make some type of correlation would be the number of businesses located in that country. Our next hypothesis would be: The more coastline found in a country, the higher number of businesses located in that region, because of the rich, resources that area provides and also the quick access to imports from the ports. We still think that there is a strong relationship between ecological and economic variables, so we will test new variables, like amount of businesses per area, in the future.
Works Cited
"Coastline by Country. Definition, Graph and Map." NationMaster - World Statistics, Country Comparisons. 18 Dec. 2003. Web. 10 Dec. 2010. <>.

"Public Debt by Country. Definition, Graph and Map." NationMaster - World Statistics, Country Comparisons. 18 Dec. 2003. Web. 10 Dec. 2010. <>.

"Unemployment Rate (most Recent) by Country." NationMaster - World Statistics, Country Comparisons. 18 Dec. 2003. Web. 10 Dec. 2010. <>.

"Economic Freedom Helps Cut Unemployment, Study Shows." Science, Industry and Business: the Innovations-report. 06 Nov. 2007. Web. 10 Dec. 2010. <>.

“Coastline by Country. Definition, Graph and Map." NationMaster - World Statistics, Country Comparisons. 18 Dec. 2003. Web. 10 Dec. 2010. <>.