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The Business School, RMIT VN
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ECON1313/1327: BASIC ECONOMETRICS (S3-2025)
Assessment 2: What Explains Cross-Country Economic Growth?
(Individual Assignment)
Due: 18th December 2025, Week 8. 23:59 PM
Submission Instructions:
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You are required to do the analysis using R Studio only. Analysis done using another
software will not be accepted. Ensure that your code and data allow the marker to reproduce
all the results you report in the Word file.
Word limit: 2500 words (+-10%). Words in tables, figures, and pictures are counted in the
assigned word limit. The reference list is excluded from the word count limit. No hidden
texts or concealing hyphens are allowed. The font used should be Times New Roman 12
pt. Include page numbers in either the footer or the header.
If you wish to include graphs in your assignments/report, use the Export, Copy to Clipboard
function in R Studio, and then paste it into your Word document. If you want to report
results from R, please reproduce them and put the information in tables (e.g., Stargazer).
There is a penalty of 10% of the total mark (10% of 40 points in this case) for each
screenshot.
You need to submit your report as a Word file, NOT a PDF file. All texts in PDF format or
images (screenshots) will not be marked.
There is a penalty of 10% of the total mark per day for late submission (or 0.42% per hour
late). Hence, make sure you submit it on time.
There is a penalty of 1 mark for each 100 words over the limit. Note that it is compulsory
to specify the word count on the cover page. An additional penalty of 10% of the total mark
will be applied on a case-by-case basis for reports with no word counts or providing an
incorrect word count on the cover page.
Please note that RMIT treats plagiarism very strictly, so be sure to avoid plagiarizing your
report or enabling the plagiarism of another student’s report.
Please do not copy the question parts to avoid a high similarity rate in Turnitin.
The submitted R code file needs to be replicated. Any result in the Word file that cannot be
reproduced by the submitted R file will not be marked. You will need to submit two
documents: 1. Final Word report, 2. R code file (This will NOT be marked but used for
cross-checking and reference).
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You may use AI tools to assist with R coding (for example, syntax support, debugging, or
code explanation) and to help with reference suggestions or summaries. All analysis,
interpretation, and written discussion must be your own work. Using AI to generate full
text or analysis will be treated as a breach of academic integrity.
Assignment Overview:
You are working as an analyst in a global policy institute. Your task is to examine how key
macroeconomic factors: investment, inflation, trade openness, FDI inflows, national savings, and
population growth, are associated with economic growth across different groups of countries.
You will work with cleaned cross-sectional datasets from the World Bank (2022) and apply
econometric techniques from Weeks 4–7:
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Week 4: Simple regression, scatter plots, interpretation of OLS
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Week 5: Multiple regression and goodness of fit
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Week 6: Hypothesis testing, t-tests, p-values, F-tests, adjusted R²
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Week 7: Quadratic and interaction terms
Your assignment must reflect these concepts clearly and accurately.
Cluster Selection:
You will be provided with three clean datasets:
Income Group
Year
Sample Size
High Income
2022
49
Upper-Middle Income
2022
81
Low Income
2022
29
Each student must work with two clusters, assigned using the last three digits of your student ID:
Cluster Selection Rule:
1. Add the last three digits of your student ID.
2. Use the rule below:
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If the sum ≤ 10 → High Income + Low Income
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If the sum > 10 and even → Upper-Middle Income + High Income
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If the sum > 10 and odd → Low Income + Upper-Middle Income
Example:
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ID S12403 → 4 + 0 + 3 = 7 → High Income + Low Income
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ID S12462 → 4 + 6 + 2 = 12 → Upper-Middle + High Income
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ID S12461 → 4 + 6 + 1 = 11 → Low Income + Upper-Middle Income
Important:
You must:
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Load your two assigned cluster datasets in R Studio.
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Merge them row-wise in R (for example, using rbind()). Do not merge the data in Excel
or other software.
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All models in Parts 2–4 must use your combined dataset.
No cleaning or downloading is required.
Dataset Variables
Each dataset includes the following World Bank indicators (2022):
Variable
WDI Code
Definition
Unit
GDP Growth
NY.GDP.PCAP.KD.ZG
Real GDP per capita growth
%
Investment
NE.GDI.TOTL.ZS
Gross capital formation
% of GDP
Inflation
FP.CPI.TOTL.ZG
CPI inflation
%
Trade Openness
NE.TRD.GNFS.ZS
Trade (% of GDP)
% of GDP
FDI Inflows
BX.KLT.DINV.WD.GD.ZS
Net FDI inflows
% of GDP
National Savings
NY.GNS.ICTR.ZS
Gross national savings
% of GDP
Population Growth
SP.POP.GROW
Annual population growth
%
Part 1. Literature Review & Descriptive Statistics (20 points)
A. Literature Review (10 pts)
Review three empirical studies (2020–2025) on the determinants of economic growth.
Provide a detailed summary of one study, explaining:
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variables used
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methodology (brief)
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data and sample
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key findings
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Note: The literature review should be short and focus on variables, data, and findings
B. Descriptive Statistics (10 pts)
Using your combined two-cluster dataset, report:
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Summary statistics for all variables
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A short interpretation of key economic patterns
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A correlation matrix
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One short comment on an unexpected or interesting correlation
Part 2. Simple Linear Regression (10 points)
Estimate the simple regression model:
GDP_Growthi = β₀ + β1Investmenti + ui
Using your combined dataset:
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Interpret β₀, β₁, and R² in clear economic terms
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Provide one scatter plot with fitted regression line
Part 3. Multiple Regression & Interpretation (20 points)
Estimate the multiple regression model:
GDP_Growthi = β₀ + β1Investmenti + β2Inflationi + β3Tradei + β4FDIi + β5Savingsi + β6Populationi + ui
Requirements:
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Present a clean regression table
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Interpret each coefficient concisely
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Comment on multicollinearity using the correlation matrix or by comparing patterns in
the coefficients
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Compare simple vs. multiple models using R² and adjusted R²
Part 4. Hypothesis Testing, Nonlinearity & Interaction (30 points)
A. Hypothesis Test (10 pts)
Choose one key variable from the multiple regression.
State:
H0: βj = 0, H1: βj ≠ 0
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Report the t-statistic and p-value
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State whether you reject or fail to reject the null hypothesis at the 5% significance level,
and explain what this means in words
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Explain the economic implication of your result
B. Nonlinear Inflation Effect (10 pts)
Add a squared inflation term:
GDP_Growthi = β₀+⋯+β2Inflationi + β3Inflationi2 + ui
Answer the following:
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Use the signs of β₂ and β₃ to determine whether the inflation–growth relationship is Ushaped, inverted U-shaped, or approximately linear
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Explain what this shape means for economic growth.
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Briefly discuss one policy implication for inflation management.
C. Interaction Effect (10 pts)
Estimate:
GDP_Growthi = β₀ + β1Investmenti + β2Populationi + β3(Investmenti × Populationi) +ui
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Explain how the effect of investment changes with population growth
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Provide one interaction plot (marginal effects or predicted values)
Part 5. Conclusion & Policy Recommendations (10 points)
Summarize your main findings clearly:
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Identify the most influential variable (based on coefficient size and significance)
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Mention one limitation (e.g., omitted variables, cross-sectional data)
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Recommend two macroeconomic policies supported by your regression results
Personal Reflection (10 points)
1. What was the most important econometric concept you learned from this assignment?
2. What challenges did you face when interpreting the regression results, and how did you
resolve them?
This reflection should be written in your own words and should not contain technical equations.
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Submission format
Written format.
Referencing guidelines
Use RMIT Harvard referencing style for this assessment.
You must acknowledge all the sources of information that you have used in your assessments.
Refer to the RMIT Easy Cite referencing tool to see examples and tips on how to reference in
the appropriate style. You can also refer to the library referencing page for more tools such as
EndNote, referencing tutorials and referencing guides for printing.
Do you want to ask questions?
If you have questions, please consult with your workshop lecturer.
Extensions
If your assessment preparation has been impeded by unforeseen circumstances (such as illness,
injury, unexpected work commitments or carer responsibilities) you may be eligible for an
extension.
Requests for extensions must be emailed to the course coordinator Dr. Kashif Iqbal, Email:
Kashif.iqbal@rmit.edu.vn, at least one working day before the assessment deadline using the
extension request form (available from the RMIT Extensions website).
If you are seeking an extension on or after an assessment due date, or you require an extension of
more than 7 days, you must apply for special consideration. This is a confidential process.
Please see the RMIT extensions website for details regarding grounds for extensions and special
consideration and the required supporting documentation.
Academic integrity and plagiarism
Academic integrity is about honest presentation of your academic work. It means
acknowledging the work of others while developing your own insights, knowledge and ideas.
You should take extreme care that you have:
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Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you
have quoted (i.e., directly copied), summarised, paraphrased, discussed, or mentioned in
your assessment through the appropriate referencing methods.
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Provided a reference list of the publication details so your reader can locate the source if
necessary. This includes material taken from internet sites.
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If you do not acknowledge the sources of your material, you may be accused of plagiarism
because you have passed off the work and ideas of another person without appropriate
referencing, as if they were your own.
RMIT University treats plagiarism as a very serious offence constituting misconduct.
Plagiarism covers a variety of inappropriate behaviours, including:
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Failure to properly document a source.
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Copyright material from the internet or databases.
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Collusion between students.
For further information on our policies and procedures, please refer to the University website.
Assessment declaration
When you submit work electronically, you agree to the assessment declaration.
AI Acknowledgment
During the preparation of this assignment, I used AI tools only to assist with R coding (syntax
guidance, debugging, and code explanation) and reference organization. All data analysis,
interpretation, and written discussion are entirely my own work. I take full responsibility for the
content and results presented in this report.
Additional Note on AI Use and Academic Integrity
If your tutor or marker has reasonable concerns that your report relies excessively on AI-generated text or
does not reflect your own understanding, you may be required to attend a short viva-style clarification
meeting (online or on campus) before your final grade is released. The purpose of this meeting is to
confirm that you can explain your analysis independently and in your own words. If you are unable to do
so, academic integrity procedures may be initiated in accordance with RMIT policy.
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