All tables and figures should be numbered and fully titled, e.g. Figure 1: Histogram of average per capita income, 1975 and 2009.
All work must be concise and presented clearly. Write in complete and correct English sentences. Do not just give a number as an answer. Students lose points for unclear or incomplete presentation of data and findings.
If the person reading and grading your assignment cannot understand what you are trying to say, they cannot give you full credit for your ideas.
For full credit, please note which Stata command you used to obtain each part of the answer to each question
One example how to interpret the slope (beta) coefficient is the following :
On average, a (unit) increase in X (your independent variable) will be associated with SLOPE (unit) (increase/decrease) in Y (your dependent variable).
Please fill in the terms in parentheses and in capital letters.
Of course maybe you learned a slightly different language in another class and you are welcome to use that language. But do not forget to use the units of the variables and by all means include a version of the language “on average..”
do not forget that the intercept (alpha or constant) is the the average of Y (or the predicted Y) when X is 0.
Furthermore if you are comfortable with it you can provide a substantive interpretation of the coefficients, you can interpret the standard errors and the R^2-red.
I know some of you may have forgotten some of this but we will have a review of the OLS model in this week’s section and of course you can look through your notes from your intro to stats classes as well.
The goal of this assignment is to describe the historical growth of population and income around the world. All tables and figures should be numbered and titled, e.g. Figure 1: Scatterplot of GDP per capita in 1500 and 2000.
I. The first set of questions can be answered using the dataset hw2.dta.
1.Compare the wealth of countries in 1600 to the wealth of countries in 2001.
Are the wealthy countries in 1600 the same as the wealthy countries in 2001?
In order to explore this question, first generate and report a scatter plot by country of gdp per capita in 1600 and gdp per capita in 2001. [Hint: Label the values on the scatter plot with country names using the mlabel() option.]
To investigate the strength of the relationship, report a regression with gdp per capita in 2001 as the dependent variable and gdp per capita 1600 as the regressor.
Interpret the results.
2. Report the mean gdp per capita in 1820 and 2001 by world region. [Hint: Use the “tabstat” command with the by() option.]
What is the ratio of western Europe’s per capita gdp to the global average per capita gdp in 1820? And In 2001?
What is western Europe’s income ratio in those years?
Which regions were the poorest in 1820 and in 2001?
3. Compare the populations of countries in 1600 to their populations in 2001.
Report a graph and regression that demonstrates the relationship.
How strong is the relationship? [Hint: You may want to create new variables that are the logarithms of populations in the two periods to improve the graphic relationship. The Stata command log() takes the log of a value.]
4. What is the relationship of population to wealth in 1600? [Hint: This question is not asking you about the relations to per capita income.] In 2001?
Generate a graphic and a statistical comparison in both periods and summarize the comparative relationship. [Hint: For graphical purposes, you again may want to use logarithmic values.]
Create a third graph and regression to answer the following question: Have countries with larger populations in 1600 done better over the subsequent 401 years than countries which started with small populations?
II. The final question can be answered using the dataset korea.dta.
5. Sort the data by year and report a table with the population and gdp per capita of South and North Korea for the dates that have data for both countries.
Create a scatter plot using the following command:
twoway (line gdppc year if country==”North Korea”, clcolor(green) clpat(dash) legend(label(1 “North Korea”) label(2 “South Korea”))) (line gdppc year if country==”South Korea”, clcolor(red) clpat(dot) clwidth(medthick))
How would you interpret the data?
What do they say about the importance of government institutions for economic growth?
What alternative explanations can you think of? [Hint: Korea was a single country for most of the time period of this data set, was occupied by Japan beginning in 1905, and split into North (communist) and South (capitalist) at the end of WWII in 1945. See the CIA factbook for more information: https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html.]
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