Lab 3: Set Working Directory, Scatterplots, And Introduction

Lab 3 Set Working Directory Scatterplots And Introduction Tolinear R

In this assignment, you are required to analyze a dataset related to global income, population, and historical development using statistical commands in Stata. The core tasks include setting your working directory, importing data, creating scatterplots and regression models, calculating summary statistics by regions, and interpreting the results in relation to economic growth and development. You will also examine a specific dataset on North and South Korea to compare their economic trajectories over time. Your work should be presented clearly with complete sentences, numbered and titled figures and tables, and include all relevant commands and interpretation of findings.

Paper For Above instruction

The objective of this assignment is to explore the temporal and spatial growth patterns of countries' income and population metrics over several centuries, employing a series of statistical tools within Stata to analyze historical economic data.

First, the assignment requires setting the working directory in Stata by using the cd command, which involves copying the file path from the system and incorporating it into your script. This setup facilitates efficient data management and analysis, particularly when running multiple commands through a .do file. The next step involves importing the dataset HW2.dta using the use command and verifying the successful import. To streamline the process, include clear all at the top of your script to clear previous data before loading the new dataset.

Upon importing the data, your initial question pertains to the number of variables and observations in the dataset, which provides foundational context for subsequent analyses. The dataset includes variables tracking GDP per capita, population, and geographic indicators across different years and regions.

Next, you will generate scatterplots to visualize relationships between variables, such as GDP in different years. Utilizing the twoway scatter command along with options for labeling observations and customizing appearance with schemes (e.g., (s1mono)), you will illustrate how income levels differ across countries. To add a line of best fit, combine the scatterplot with the lfit option, which helps interpret the trend direction and strength. For example, plotting GDP per capita in 2000 against 1500 reveals the degree to which historical wealth influences contemporary economic standings.

Following this, linear regression models are employed to quantify the relationships observed in the scatterplots. Regressing GDP in one year on GDP in another year (e.g., 2000 on 1500) enables assessment of how wealth persistence or mobility has occurred over time. The regression output provides coefficients that indicate the average change in the dependent variable associated with a unit change in the independent variable, along with standard errors and R-squared values to evaluate model fit.

To contextualize regional differences, summary statistics such as mean populations or GDPs are calculated by continent. Creating a new variable for continent classification based on original regional indicators allows grouping data into broad geographic regions. Using the tabstat command with the by() option, you will report average population or income for each continent. Comparing these means over time reveals global development patterns and regional inequalities. For example, calculating the ratio of Western Europe's GDP per capita to the global average in different years highlights relative prosperity shifts.

Furthermore, the assignment incorporates analysis of historical population data. By transforming population variables with the natural logarithm function (log()), you can improve visualization and interpretability when examining the relationship between population size and wealth, both in the past and the present. Regression models allow quantification of how population size correlates with wealth, shedding light on the demographic-economic relationship over centuries.

Finally, the dataset on North and South Korea is analyzed to compare their economic trajectories. Sorting data by year, plotting their GDP per capita over time, and interpreting the trends can reveal the impact of political and institutional differences on economic development. The twoway line command facilitates visual comparison, with color and line pattern distinctions clarifying each country's trajectory. This analysis can inform discussions on how government institutions and policy choices influence economic growth, considering alternative explanations such as historical circumstances, external interventions, or geographic factors.

Throughout your report, ensure all figures and tables are numbered and titled appropriately, e.g., "Figure 1: Scatterplot of GDP per Capita in 1600 and 2001." Use complete, well-structured sentences to clearly explain your findings and interpret the coefficients, states, and ratios. For example, "On average, a one-unit increase in GDP per capita in 1600 is associated with a beta-unit increase in GDP per capita in 2001," providing units and context. Discuss the significance and implications of your results in relation to historical and contemporary development patterns, providing a comprehensive and insightful analysis based on the data.

References

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• Rodrik, D. (2011). The Globalization of Economics and the Limits of Policy. Harvard University. Harvard Institute for International Development.
• World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
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