ECON3173 – Cross Section and Panel Data Analysis
Individual Project: Guidelines and Questions
This document provides guidelines and questions for the Individual Project of ECON3173, which accounts for 40% of the total marks.
Honoring the precepts of academic integrity and applying its principles are fundamental responsibilities of all students and scholars at UIC. You are advised to read through the ‘UIC Guidelines for Handling Academic Dishonesty’ file on iSpace before you start your assignment. Any form. of plagiarism or cheating can result in various disciplinary and corrective activities. Using generative AI tools is not allowed.
Deadline: by 20/12/2024. Submission Method:
a) Please submit your typing assignment report in a single PDF file to Turnitin ‘Submission Link: Report’via iSpace. The filename of your PDF submissions should have the following format: ECON3173_Project_Student ID_Name in Pinyin (e.g., ECON3173_Project_190000001_Mi Lin).
b) Save your data and .do file(s) in a zip file. Name your zip file as ECON3173_Project_Student ID_Name in Pinyin. Then, upload your file to‘Submission Link: Stata Data and Program’via iSpace. You are expected to submit 2 .do files,
namely ParA.do and PartB.do, respectively, should be able to replicate each part of your submitted work.
c) Use the ‘ECON3173_Individual Project_Report Template’ file on the iSpace to input your report. Ensure you provide a question number for each part of your work.
Format Requirements
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Cover page:
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Please input your name and student ID on the top of the cover page of the report template, which is available on iSpace.
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Word limit:
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The required minimum word count is 1,500 words, with a maximum of 2,000 words in total, excluding tables, graphs, and appendices.
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Referencing:
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Your report should include appropriate references in APA format to avariety of necessary literature sources and a wide- ranging bibliography of academic aspects of economics.
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Font / Size:
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Cambria 12 or Times New Roman 12.
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Spacing / Sides:
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1.0 / Single-sided / Single-line spacing between two paragraphs.
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Pagination required:
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Yes
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Margins:
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At 2.50 to both left and right, and ‘justified’ .
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Part A: Imitate an existing research (30%)
Traffic crashes are the leading cause of death for Americans between the ages of 5 and 32. Through various spending policies, the federal government has encouraged states to institute mandatory seat belt laws to reduce the number of fatalities and serious injuries. In this exercise, following Einav and Cohen (2003), you will investigate how effectively these laws increase seat belt use and reduce fatalities using the “SeatBelts.dta” dataset posted on iSpace. The data file Seatbelts contains a panel of data from 50 U.S. states plus the District of Columbia from 1983 through 1997. The dataset is detailed in “Seatbelts_Description.pdf”. It was used in the Einav and Cohen (2003) paper, which serves as the background reading for this exercise. Both files are available on iSpace as well.
A1. Using OLS to estimate the effect of seat belt use on fatalities by regressing fatalityrate on sb_useage, speed65, speed70, ba08, drinkage21, ln(income), and age. Does the estimated result suggest that increased seat belt use reduces fatalities? Report, interpret, and comment on your results. (5%)
A2. Run a one-way fixed effect model with state-fixed effects. Do the results change when you add state-fixed effects? Run a two-way fixed effects model with both time and state-fixed effects. Do the results change when you add time-fixed effects plus state-fixed effects? Report, interpret, and comment on your results. (5%)
A3. Which regression specification you obtained from A1 and A2 is most reliable? Explain why. (5%)
A4. Using the results from the two-way fixed effects model with both time and state- fixed effects, discuss the magnitude of the coefficient on sb_useage. Is it large? Small? How many lives would be saved if seat belt use increased from 52% to 90%? Illustrate your calculation and comment on your result. (5%)
A5. There are two ways that mandatory seat belt laws are enforced: “Primary” enforcement means that a police officer can stop a car and ticket the driver if the officer observes an occupant not wearing a seat belt; “secondary” enforcement means that a police officer can write a ticket if an occupant is not wearing a seat belt, but must have another reason to stop the car. In the data set, primary is a binary variable for primary enforcement, and secondary is a binary variable for secondary enforcement. Run a regression of sb_useage on primary, secondary, speed65, speed70, ba08, drinkage21, ln(income), and age, including fixed state and time effects in the regression. Does primary enforcement lead to more seat belt use? What about secondary enforcement? Report, interpret, and comment on your results. (5%)
A6. In 2000, New Jersey changed from secondary enforcement to primary enforcement. Assume that data availability is not an issue, design a Differences-in-Differences estimation strategy to estimate the number of lives potentially saved per year by making this change. Explain your approach. (5%)
Part B: A small-scale research project – innovation (70%) Introduction:
In this project, you are invited to empirically investigate potential causality between firms’ business environment and economic performance using a firm-level dataset from economies included in the World Bank Enterprise Survey Data (WBESD). WBESD database collects information about an economy’s business environment, how individual firms experience it, how it changes over time, and the various constraints to firm performance and growth, etc. The entire database is available to researchers and includes all questions from the surveys at the firm level.
Guidelines to download and prepare data for this individual project:
a) Please visit https://login.enterprisesurveys.org/ to register your user account for the WBESD database (see the snapshot below). Registration is free.
b) There are a total of 97 economies represented in the World Bank Enterprise Surveys Database (WBESD). Among these, 61 economies have a time span of at least three years. For their individual projects, students are required to select data from a panel of random combinations of three different economies out of these 61 economies.
Data allocation protocol: Students are required to pick a lottery ticker number first. An “Individual Project Lottery Ticket Sign-up Sheet” will be available in iSpace from 9 p.m. on Tuesday, 26/11/2024. Please sign up for a lottery ticket number by 12 noon on Wednesday, 27/11/2024. We will operate on a 'first-come, first-served' basis.
A lucky draw will be conducted in class on Thursday, 28/11/2024, to assign specific economies to each lottery ticket number.
c) Once registration is completed, login and download the data following the steps below:
i. Login with yourusername and password. You will be directed to the ‘Full Survey Data’ page.
ii. Select ‘Panel data’ under ‘Survey Type’ on the left. Ensure you are on the ‘Data by Economy’ view instead of ‘Combined Data’ . See the snapshot below.
iii. Download your economies’ corresponding data and documentation for all the available years.
For example, Afghanistan has two panel data files, one for 2005 and 2009 and the other for 2008, 2010, and 2014. Then download both of them.
iv. Extract the data and survey documentation files into a working folder on your PC. Now the data file is ready to open in Stata.
Answer ALL of the Following Questions
Note that this is not an essay-type assignment. Please answer the questions one by one. For each question, the performance of the Stata do files accounts for 20% of the marks.
B1) Use the Stata command “append” to append data of all years and economies into a single Stata data file with panel data format. Select and rename variables in Table 1. ‘Old name’ refers to the variable name in the original dataset, while ‘New name’ is the new corresponding name to be defined. Generate a new variable exp_dum (export dummy) equals 1 if sales_exp is positive; otherwise, 0. Generate a new variable foreign_dum (foreign ownership dummy) equals 1 if foreign is positive; otherwise, 0. Generate a new variable soe_dum (state ownership dummy) equals 1 if soe is positive; otherwise, 0. (5%)
Table 1: Variable List
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Survey Questions
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Old name
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New name
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GENERAL INFORMATION
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Year the survey was conducted
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year
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year
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Panel ID (the same ID for each firm across different years)
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panelid
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panelid
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What percentage of this firm is owned by the Government/State %
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b2c
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soe
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What percentage of this firm is owned by Private foreign individuals, companies, or organizations %
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b2b
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foreign
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SALES
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During the past fiscal year, what was this establishment’s total annual sales?
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d2
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sales
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During the past fiscal year, what percentage of this establishment’s sales were: Direct exports %
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d3c
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sales_exp
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LABOUR and CAPITAL
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Total number of permanent, full-time workers at the end of last fiscal year
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l1
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employees
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During the past fiscal year, what was the net book value, i.e., the value of assets after depreciation, of the following: Machinery, vehicles, and equipment
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n6a
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capital
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Cost of raw materials and intermediate goods used in production in the last fiscal year
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n2e
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materials
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BUSINESS-GOVERNMENT RELATIONS
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In atypical week over the last 12 months, what percentage of total senior management’s time was spent dealing with requirements imposed by government regulations? %
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j2
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reg_time
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Over the last 12 months, has this establishment secured a government contract or attempted to secure a contract with the government? Yes/No
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j6a
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gov_contract
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B2) Conduct exploratory data analysis for variables listed in B1), i.e., using appropriate summary statistics (e.g., observation number, mean, standard deviation, minimum and maximum values, etc.) to explain your data and make necessary comments.
(10%)
Considering total output is measured by Sales, labour input is measured by the total number of employees, capital is measured by capital, and intermediate inputs are measured by materials, a production function can be written as:
sales = Acapitalα Employeesβ Materialsy (1)
where A is the total factor productivity (TFP). Based on panel data, taking logarithms on both sides, equation (1) is transformed to
ln(salesit ) = ln(A) + α ln(capitalit ) + β ln(Labourit ) + y ln(Materialsit ) + eit (2)
B3) Based on the variable ‘panelid’, set the dataset in a panel format, then obtain the estimated coefficients in equation (2) by running panel data regression. Comment on your results, including a) a discussion on the capital, labour, and materials elasticities of sales, respectively, and b) a comparison between the panel two-way fixed effect and random effect model. (10%)
B4) Include Export_dumit as a new variable of interest in equation (2) to test if a firm’s performance improves after entering export markets. Based on relevant economic theories or literature, explore the data set to add appropriate control variables to the production equation (2). Run a panel regression and interpret the results. (10%)
B5) To what extent could we use the estimated coefficient on Export_dumit obtained in B4) for causal inference? Explain. Illustrate an appropriate empirical strategy to make improvements if deemed required. Finally, reestimate the model based on your proposed empirical strategy, compare the results to what you have obtained in B4, and comment on the results. (10%)
B6) Explore the complete data set (i.e., do not have to be limited to the variables listed in B1) and design an empirical model to evaluate the impact of ownership structure on the export decision. Interpret and comment on the empirical results you obtained.
(10%)
B7) Predict firm-level productivity (i.e., ln(A) + eit ) to test the hypothesis that good business-government relations boost productivity. Explore the complete data set (i.e., do not have to be limited to the variables listed in B1) to propose an empirical model with an appropriate empirical strategy based on relevant economic theories or literature. Explain the variable(s) you select for this question. Interpret and comment on the empirical results you obtained. (15%)