Econ M134
UCLA Spring 2010
Prof. Kahn
Homework #2
Due in Class, Wednesday, April 14
Environmental Kuznets Curve
This assignment is intended to explore the "Environmental Kuznets Curve", (henceforth EKC)
we have learned about in class.
See Chapter 3 of Kahn’s Green Cities.
We are going to explore the
relationship between income, population, and pollution in cities. Pollution will be proxied for using a
city’s level of PM10.
PM10 represent small particulate matter. Public health research has documented
that exposure to PM10 raises morbidity and mortality risk (see
http://en.wikipedia.org/wiki/Particulate).
Additionally this assignment will serve as an introduction to statistical estimation using
STATA.
STATA is available in many computer labs around campus; to find the most convenient lab
for your schedule visit this webpage:
http://www.computerlabs.ucla.edu/Info.asp
Additionally you
may access STATA remotely from your home computer; visit this webpage for more information on
remote access:
http://computing.sscnet.ucla.edu/public/services/remoteaccess.aspx
For those of you
who may already have a copy of STATA on your home computer, any edition at or above STATA 7.0
will be acceptable for completing this assignment.
You can also use any other statistics program, such as R, which is available for free. Some of the
questions can be done in Microsoft Excel. If you need help be aware that Neil only knows how to use
STATA.
You may form a study group of up to four people per group. Each group will only need to hand
in one assignment. Please write all
students' names
and
students' ID numbers
on the homework. If
possible, please type up your answers. Longer is not better,
keep your answers brief and to the
point.
Read the entire assignment, and be sure to follow the directions.
1. Download the data file called pm10b.dta from the course website, and open the data file using
STATA.
If you are not using STATA the data is saved as comma separated values pmb10.txt or as an
excel spreadsheet in pmb10.xls. The data includes 5 variables:
Variable
Description
Country
Country of Origin
City
City Name
pop2000
City Population in 2000
pm10
City ambient particulate matter
Rgdpl
Country real percapita GDP
rgdpl2
(PerCapita GDP)
2
Pop20002
(Population)
2
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Answer the following questions:
(a)
Are there more rich or poor cities in the dataset?
(Hint: make a histogram of real gdp)
(b) Are there more dirty or clean cities in the dataset? (Hint: make a histogram of pm10)
(c)
How does pollution depend on GDP in the dataset? (Hint: plot pm10 versus GDP)
2. Estimate a regression of city ambient pollution on city population, city population squared, real
GDP, and real GDP squared, without a constant. (the command to do this in STATA is:
reg pm10
pop2000
rgdpl rgdpl2, nocon
).
Report the results in a nice table and answer the following questions.
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 Spring '08
 BRESNOCK
 Economics, Simon Kuznets, Kuznets Curve

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