# All posts tagged: Economic Development

## Basic Regression on Urban Population Growth and GDP per Capita

Continuing with Regression Modelling in Practice… If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? Through the past two courses, Data Analysis Tools and Data Management and Visualization, I established that there was a correlation between urban population and GDP per capita. For this assignment, my primary explanatory variable is Urban Population Growth rate and response variable is GDP per capita, both figures are from 2010. This is my code in Python: import pandas import numpy import seaborn import matplotlib.pyplot as plt import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi gapminder = pandas.read_csv(‘Data1.csv’, low_memory=False) gapminder[‘GDP2010’] = gapminder[‘GDP2010’].replace(0,numpy.nan) gapminder[‘GDPGrowth2010’] = gapminder[‘GDPGrowth2010’].replace(0,numpy.nan) gapminder[‘UrbanPop2010’] = gapminder[‘UrbanPop2010’].replace(0,numpy.nan) gapminder[‘UrbanPopGrowth2010’] = gapminder[‘UrbanPopGrowth2010’].replace(0,numpy.nan) gapminder = gapminder[[‘Country’, ‘UrbanPop2010’, ‘UrbanPopGrowth2010’, ‘GDP2010’, ‘GDPGrowth2010’]] gapminder = gapminder.dropna() PopDes = gapminder[‘UrbanPopGrowth2010’].describe() print (PopDes) RegData = gapminder[[‘Country’, ‘UrbanPopGrowth2010’, ‘GDP2010’]] RegData[‘UrbanPopGrowth2010’] = RegData[‘UrbanPopGrowth2010’] – RegData[‘UrbanPopGrowth2010′].mean() print (RegData.describe()) UrbanReg = smf.ols(formula=’GDP2010 ~ UrbanPopGrowth2010′, data=RegData).fit() print (UrbanReg.summary()) seaborn.regplot(x=’UrbanPopGrowth2010′, y=’GDP2010’, fit_reg=True, data=RegData) plt.xlabel(‘Urban Population Growth …

## In Speaking of Data: Gapminder

This is the start of the third course, Regression Modeling in Practice, in the Data Analysis and Interpretations Specialization by Wesleyan University through Coursera. The first assignment is to provide a description of the data I have been working with – what is the sample, how the data is collected and how I managed the data. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? My sample consists of countries, territories, and other political entities such as disputed territories, dependent territories, or semi-autonomous city-states like Hong Kong. According to Gapminder, where my data was downloaded, this list consists of 193 UN Nations, 51 other entities, 4 French overseas territories, 10 former states, and 2 ad-hoc areas totaling 260 (or N=260). However, because not every entity has data in the indicators I am using, the number of entities in my work is reduced to 164 (or N=164). In the case …

## The Moderating Variable

Last Lesson in Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. This is the last lesson in the Data Analysis Tools course. After analyzing for correlations between variables, this assignment focuses on moderating variables. A moderating variable is one that influences the strength and direction of the association between the explanatory and response variables. Last time, I established that there were correlations between the amount of urbanization, as measured by percentage of total population in cities with over 1 million people, urban population growth, and GDP per capita. Additionally, I found that there was a correlation between total populations in cities and urban population growth. I suspect that one of these two variables might be a moderating variable. I first looked at total …

## Correlations! Urbanization and Economic Development in Rich and Poor Countries

Continuing with Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. Finally! Quantitative to quantitative variable analysis! This is the lesson I have been waiting for. With my interest in urbanization and economic development, the data I pulled from Gapminder are all quantitative. As I previously mentioned, I do not like categorizing quantitative data because I believe it introduces too much subjectivity. Unless the data is qualitative to begin with, it makes little sense to categorize data. Compared to the other types of correlation tests, Pearson’s Correlation was relatively easy to perform in both Python and SAS. I looked at the relationships between urbanization rate, as measured by both urban population growth rate and percentage of population in large cities with over 1 …

## Chi-Square Testing…*Warning: It’s Painful*

Continuing with Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. For this assignment, I had to run Chi-Square tests on my variables. As always, both my Python and SAS codes are posted. Since all my data are quantitative, I had to first categorize them. Since I found a relationship between the absolute measure of urbanization (population in cities with over 1 million people) and GDP Growth rate, I decided to categorize GDP growth rate. Additionally, I wanted to see if there is a relationship between urbanization with the absolute measure of GDP  (GDP per capita). To categorize GDP per capita, I used cut-offs of 5000, 10000, and 100000 to produce three distinctive ranks whereby a country is poor if its GDP per …

## A Revelation…Through ANOVA

Now that I finished the first course in the Data Analysis and Interpretations Specialization, this is the start of the second called Data Analysis Tools.  If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. Since I have been presenting my SAS work in the previous course, I will be presenting my Python work for this course. I actually enjoy working with Python, it seems to have more flexibility and I am more used to its language, having some experience with R during graduate school. As always, I am also including the other set of code for reference, so please see my SAS code at the very bottom of the post. There will be two parts to this presentation. The first part will be a discussion of …

## The Urban Question Continued…Macau Grew the Most in 2010?

This is the last assignment for the introductory course Data Management and Visualization with Coursera, after which I will be moving onto the second course Data Analysis Tools Again, for those who have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. Even though I am learning both SAS and Python, I will only be presenting my work in SAS here. (Python code included as reference).The reason is that at the moment, for my current abilities, SAS produces output that is easier to present. For this last assignment, I will be presenting my data in visual form – through graphing variables individuals and to relate my explanatory variable (urbanization rate) with my response variable (GDP growth rate). Before I present my work, I must admit to a mistake I …

## Data Management – Missing Values

Continuing my course in Data Management and Visualization with Coursera… For those that haven’t followed on my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background in this project, please visit this page. Though I am working in SAS and Python in an attempt to learn both, I will only be presenting my work in SAS here (though I will also include my Python code for reference). The output format in SAS is easier on the eyes in my opinion. For this third assignment, I was originally going to try to calculate the urban population growth rate for 2010. Instead, I found the data available on Gapminder so I did not have to create a secondary variable for population growth. After inserting the latest variable, I decided to code out missing data. Since my focus is on GDP, …

## Frequency Tables…The World is Mainly Poor…

I have always been hungry for knowledge and been ambitious about my goals. Having the chance to finally learn some programming, I decided I was going to learn both SAS and Python. Luckily, this isn’t my first exposure. While at ASU, I learned the basics of R programming and it really helped me to get an understanding of writing syntax in both SAS and Python. For my second assignment for the Data Management and Visualization course with Wesleyan University through Coursera, I am required to post the program I wrote and the frequency tables that it produced. Just as a reminder, I am working with Gapminder data on urbanization and economic growth. For the present analysis, I am using GDP per Capita, GDP Growth, Urban Population as a percentage of total population from 2010 and Urban Population in agglomerations over 1 million people from 2007. This is my code in SAS: FILENAME REFFILE “/home/wfhsu.taiwan0/my_courses/Data1.xlsx” TERMSTR=CR; PROC IMPORT DATAFILE=REFFILE DBMS=XLSX OUT=Gapminder2010; GETNAMES=YES; RUN; PROC CONTENTS DATA=Gapminder2010; RUN; /*to importat data from Excel file upload*/ LIBNAME mydata …

## Data Visualization Assignment 1 – That Urbanization Thing

“When you are that curious about the world, scholarship never ends.” – October 18, 2015 As I mentioned in my last post, I started the Data Analysis and Interpretations Specialization with Coursera in order to gain more skills relevant to my pursuit of urban studies and interest in urban planning and development. Our assignment for the first week is to develop a research question based on the data sets provided by the course or another data set of our own choice. With my background and interest in cities, I looked through the code books for each data set looking for relevant data. I decided that the Gapminder data set had the information I needed to look into the effects of urbanization globally. Though the scale is on a national level, which can obscure many relationships, particularly the distinction between rural and urban areas and their respective economies, I look forward to comparing the general trends and effects of urbanization. The increasing rate of urbanization has been accompanied by a corresponding rise in number of urban …