Module Code: ECO00003I
UNIVERSITY OF YORK
DEPARTMENT OF ECONOMICS AND RELATED STUDIES
ECONOMETRICS 2
SUMMATIVE ASSIGNMENT – SPRING/SUMMER 2023
The submission deadline for the Econometrics 2 summative assignment (project) is Thursday
27 April 2023, by 2pm (UK time).
1. Introduction and Research Question
Research question:
The purpose of this project is to specify, estimate, interpret and evaluate an econometric
model of the wage equation with a specific focus on the association between wage and
education, in particular, whether individuals with a University degree have different
wage structures to other employees.
You are expected to conduct an econometric analysis to answer this research question and
submit a report of 2,500 words. Instructions on how to format, structure and submit your report
are presented in pages 2-5.
The project data is a sample of cross-section data from the Quarterly Labour Force Survey
(QLFS), September – November 2021 dataset. The dataset is called Project2023.dta.
Instructions on how to download the dataset and a description of the variables are presented in
section 3. You are advised to use STATA to conduct the econometric analysis (see section 4).
Support after the project is released will be limited to purely technical help with STATA (see
section 5).
A detailed marking grid, which will be used for the overall assessment of your project and
shared as individual feedback, is available at the VLE submission point. Markers will be
looking for strong evidence of a sound understanding of key concepts and methods of
econometrics, ability to conduct an econometric analysis as well as critical and original
thinking.
We would encourage you to view your project as a way to ‘showcase’ your econometric skills.
In particular, we encourage you to take the space and time in your project to fully interpret your
results and the implications for your econometric model and estimates (including the full
explanation of how any test(s) undertaken is constructed and carried out), relate your answer
to the research question as clearly as possible and discuss the limitations of your methods and
results.
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2. Instructions: word limit, format, submission and project report outline
2.1 Word limit and format
The project report should not be longer than 2,500 words of text and excessive length will be
penalised: only the first 2,500 words will be graded. Please note that the project title, exam
number, bibliography, figures, equations and tables are not included in the 2,500 word count.
The main results (regression outputs, tests) should be integrated into the main body of your
report. Additional tables of results, graphs and diagrams etc. can be presented in appendices
and will not be counted within the 2,500 words. (The appendices should not exceed eight
pages). For example, you might include the calculation of test statistics in the appendices and
the hypotheses, explanation of the test, results and interpretation in the main body of the report.
Please consider the readability of your project:
 Use a standard font (Calibri, Arial or Times New Roman), size 12, font colour Black.
 Your figures, tables and regression outputs should be legible and captioned.
 You can provide screenshots of relevant STATA outputs or export the results in tables.
All materials should be appropriately referenced, e.g. the Harvard referencing style is advised.
Your final report should be compiled in a single PDF document:
 It is your responsibility to make sure that the PDF document is legible.
 You do not have to submit your Stata logs or do file.
Your report will be marked anonymously. Do not include your name or student number. Only
include your exam number (starting with Y…).
2.2 Submission
As mentioned above, the submission deadline for the Econometrics 2 summative assignment
is Thursday 27 April 2023, by 2pm (UK time).
Your project should be submitted electronically through the VLE (see Econometrics 2 2023
Project submission point on the Econometrics 2 VLE page). Please follow the instructions on
the VLE page. You will also find important information on the exceptional circumstances
process.
This formally assessed project forms 100% of your final module mark for Econometrics 2
(ECO00003I).
Under no circumstances should you submit a project that you have worked on with another
student, this is an individual project for you to complete on your own.
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2.3 Project report outline
Your project report should include the following sections listed 1-9 below. The descriptions
below indicate the material that should be included in your report. Please note though that if
you have further tests, hypotheses or relevant discussion that you wish to include in your
project you should do so.
Section 1 – Introduction and description of the economic model
This should be a brief introduction to the wage equation in general, and how education might
have an impact on an employee’s wage and wage structure.
Please note:
 “Wage equation” refers to an equation where the dependent variable is “wage” (in some
form) and the components of the equation include independent variables or the factors
that determine the “wage”. The nature of the relationship between these independent
variables and the dependent variable is referred to as the “wage structure”.
 Although you need a clear introduction to the topic, please do not write an essay on
‘wages’. The purpose of the 2nd year Econometrics project is to show that you can
undertake an econometrics project, rather than write an essay. So please note that a
concise and focussed introduction drawing out the important variables for the analysis
of the wage equation at the micro level should be the aim.
 More generally, please note that a good project report should demonstrate: some
knowledge of the economics of the wage equation and its relation to the estimated
coefficients; a good understanding of the formulation of hypotheses and the appropriate
test statistics; and the flexibility to formulate and test new hypotheses of interest.
Reading:
 You will find on the VLE references to core undergraduate Labour Economics
textbooks and academic papers that should provide sufficient background for your
project but you will benefit from reading more widely. You must reference any material
you have used correctly and fully in the text and in your bibliography. If you are in any
doubt about the conventions of academic referencing, review the Academic Integrity
on-line tutorial that you completed in autumn term of 1st year on the VLE and access
the Academic Integrity website www.york.ac.uk/integrity. For further information
please see your DERS Student Handbook.
Section 2 – Description of the econometric model
Consider this in relation to the ideal econometric specification i.e. variables that you would
have liked to include in your model as well as the actual variables you are going to include.
For example, you may wish to formally specify the model you would have wished to estimate
(if you had the variables) as well as the model that you are actually going to estimate for your
project. This section should also note the functional form that you will be using.
Present your econometric model in the form of a population regression function.
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Please note:
 You are advised to choose a semi-log model specification where the dependent variable
is a logarithm. However, this should not discourage you from formally investigating
the appropriateness of this functional form in Section 5.
Section 3 – Statement of the hypotheses to be tested
State the hypotheses (both the null and alternative) that you will consider for investigating the
research question presented in page 1, along with the tests that you will use to test these
hypotheses. In addition, you should include a full description of how these tests will be
implemented. The tests presented could include, for example, tests of individual, joint or
overall significance, tests for structural equivalence etc. The actual testing of your hypotheses
and interpretation of results should be presented in Section 6 below.
You can also present further additional hypotheses that you consider of particular interest given
any preliminary analysis or consideration of initial findings.
Section 4 – Discussion of any data issues, limitations, concerns
Consider issues of measurement error on the dependent variable and the explanatory variables
related to education and discuss potential implications on your model and estimates.
Section 5 – Presentation of your estimated model(s) and specification tests
Present your estimated model in the form of a sample regression function and provide the
relevant STATA output.
Present your specification tests, explain why they are relevant to consider and how they have
been undertaken. Present the results of the specification tests and discuss the implications for
your model and estimates.
You can present more than one estimated model but should explain why you think this is
appropriate or relevant.
Section 6 – Interpretation and discussion of your results
Provide an interpretation of the sign, magnitude and statistical significance of all estimated
coefficients (based on appropriate standard errors given your specification tests undertaken in
section 5).
Provide and interpret the results of the tests you presented in Section 3.
Provide an answer to the research question presented in page 1 and discuss potential limitations
of your approach and results.
Please note:
 Make sure that you interpret your results appropriately and fully given the functional
form of the model. Consider each of the partial regression coefficients in relation to
whether the partial regression coefficient is, for example, attached to a dummy variable,
or whether there is a quadratic form in the explanatory variable of interest. Consider
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any ‘caveats’ to your results based on the diagnostic tests and any data issues/concerns
you highlighted in sections 4 and 5.
Section 7 – Further discussion
Discuss the following issue in relation to your results. 250 words maximum within the overall
2,500 word limit.
The Quarterly Labour Force Survey includes further information on individual characteristics,
and especially ethnicity. The survey question that could be used to construct an ethnicity
variable asks the employee:
“What is your ethnic group? Please choose one option that best describes your ethnic group
and background”
(1) White
(2) Mixed / Multiple ethnic groups
(3) Asian / Asian British
(4) Black / African / Caribbean / Black British
(5) Chinese
(6) Arab
(7) Other ethnic group

Do you think this would be a useful variable to include in your model? Explain your answer.
Would including this variable in your model present any specific issue or concern if you wanted
to estimate the extended model using Ordinary Least Squares (OLS)? Explain your answer.
Section 8 – Project extensions
Open section to outline additional variables, types of data and/or estimation techniques that
you might have liked to use in relation to this project. 200 words maximum within the overall
2,500 word limit.
Section 9 – Concluding remarks
100 words maximum within the overall 2,500 word limit.
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3. Data
As part of the project, you will be using real research data from the UK Data Service. This data
was collected from real people who agreed for their data to be used for research and learning
purposes. Before you can access this data, you need to agree to some important conditions of
use.
On the Econometrics 2 VLE page, under Econometrics 2 Project 2023, you will find a link to
a VLE test which outlines the access conditions and has the response options of “agree” or
“disagree”. To have access to the data, you will have to select “agree”. If you need to download
the data multiple times, you will need to agree to the conditions again.
These data are a sample from the Quarterly Labour Force Survey (QLFS) September –
November 2021 which were collected in September, October, November 2021. The QLFS is a
voluntary sample survey of private households in the UK. The basic unit of the survey is the
household and the data should be considered as a cross-section dataset.
The sample you have been given has employees with permanent jobs aged 21 to 60 (inclusive)
who have left full-time education. There is a total of 6,254 employees in the dataset you have
been provided with. Of these, 3,539 individuals report having a university degree
(undergraduate or postgraduate) or equivalent (degree=1). Education can also be measured
quantitatively with the continuous variable edage which indicates the age at which employees
left full-time education.
You are allowed to create additional variables based on the variables already provided in the
dataset (see list of variables p7, and summary statistics p8-10). For example, you can use a log
transformation or create additional dummy variables. Make sure that you explain clearly how
you built, named and interpret these additional variables in section 2 of your report.
4. Software
You are advised to use STATA. This package is the only one for which the course tutors will
provide support. The dataset has a .dta format and can be open directly with STATA.
You can download STATA on your own computer or laptop. You will find instructions on the
Econometrics 2 VLE page in Introduction to STATA. The software is also installed on all
computers on campus.
5. Support
A dedicated Discussion board is available on the Econometrics 2 VLE page, under
Econometrics 2 Project 2023. This will be the only communication channel available. Please
do not send emails to your course tutors, we will redirect you systematically to the discussion
board. We strongly recommend that you subscribe to the Discussion board to receive
notifications when a new message is published.
You can ask clarifying questions about the project outline and receive help on purely technical
issues with STATA. Your course tutors will not provide advice on how to conduct your
econometric analysis. This is to ensure fairness and consistency.
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Variables available in Project2023.dta :
hourwage the gross (before tax) hourly wage rate of the employee (£s) constructed
from their reported weekly equivalent gross wage divided by their usual
working (basic and overtime) hours per week.
lnhourwage the natural logarithm of “hourwage”.
age the age of the employee
edage the age at which the employee left full-time education
potexp the potential labour market experience of the individual. This is the length
of time (in years) that the employee could have spent working in the labour
market (i.e. current age minus the age left full-time education).
potexp2 the square of “potexp”
female a dummy variable taking the value 1 if the employee is female, zero
otherwise
male a dummy variable taking the value 1 if the employee is male, zero
otherwise
pt a dummy variable taking the value 1 if the employee works part-time, zero
otherwise
private a dummy variable taking the value 1 if the employee works in the private
sector, zero otherwise
england a dummy variable taking the value 1 if the employee works in England,
zero otherwise.
wales a dummy variable taking the value 1 if the employee works in Wales,
zero otherwise.
scotland a dummy variable taking the value 1 if the employee works in Scotland,
zero otherwise.
ni a dummy variable taking the value 1 if the employee works in Northern
Ireland (NI), zero otherwise.
none a dummy variable taking the value 1 if the employee has no formal
educational or vocational qualifications, zero otherwise.
gcse a dummy variable taking the value 1 if the employee’s highest education
qualification is a GCSE (or equivalent, one or more), zero otherwise.
alevel a dummy variable taking the value 1 if the employee’s highest education
qualification is an A level (one or more) or similar, zero otherwise.
degree a dummy variable taking the value 1 if the employee’s highest education
qualification is a degree (undergraduate or postgraduate) or similar, zero
otherwise.
small a dummy variable taking the value 1 if the number of employees at the
workplace is less than or equal to 25, zero otherwise.
manager a dummy variable taking the value 1 if the employee is a manager, foreman
or supervisor, zero otherwise
london a dummy variable taking the value 1 if the employee works in London,
zero otherwise.
healthy a dummy variable taking the value 1 if the employee did not report any
health issue, zero otherwise
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Summary statistics: full sample (all employees)
Variable Obs Mean Std. dev. Min Max
hourwage 6,254 19.25774 11.6687 7.7 144.23
lnhourwage 6,254 2.824925 .4898696 2.04122 4.971409
age 6,254 43.32395 10.50573 21 60
edage 6,254 19.30269 2.941076 14 30
potexp 6,254 24.02127 11.57092 0 45
potexp2 6,254 710.886 554.1029 0 2025
female 6,254 .5148705 .4998188 0 1
male 6,254 .4851295 .4998188 0 1
pt 6,254 .1909178 .3930559 0 1
private 6,254 .6800448 .4664962 0 1
england 6,254 .8049248 .3962902 0 1
wales 6,254 .0479693 .2137184 0 1
scotland 6,254 .0721138 .258697 0 1
ni 6,254 .074992 .2633995 0 1
none 6,254 .0689159 .2533313 0 1
gcse 6,254 .1653342 .3715116 0 1
alevel 6,254 .1998721 .399936 0 1
degree 6,254 .5658778 .4956807 0 1
small 6,254 .2892549 .4534527 0 1
london 6,254 .0930604 .2905403 0 1
manager 6,254 .4083786 .4915732 0 1
healthy 6,254 .6985929 .4589058 0 1
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Summary statistics: employees who report having a university degree (degree=1)
Variable Obs Mean Std. Dev. Min Max
hourwage 3,539 22.46041 12.59772 7.7 144.23
lnhourwage 3,539 2.988282 .4817457 2.04122 4.971409
age 3,539 42.24498 10.13647 21 60
edage 3,539 20.97966 2.631104 15 30
potexp 3,539 21.26533 11.0211 0 45
potexp2 3,539 573.6445 490.8962 0 2025
female 3,539 .535462 .4988113 0 1
male 3,539 .464538 .4988113 0 1
pt 3,539 .1715174 .3770137 0 1
private 3,539 .610907 .4876134 0 1
england 3,539 .7990958 .4007332 0 1
wales 3,539 .0463408 .2102517 0 1
scotland 3,539 .079401 .2704018 0 1
ni 3,539 .0751625 .2636906 0 1
none 3,539 0 0 0 0
gcse 3,539 0 0 0 0
alevel 3,539 0 0 0 0
degree 3,539 1 0 1 1
small 3,539 .2356598 .4244705 0 1
london 3,539 .1172648 .321781 0 1
manager 3,539 .4744278 .4994162 0 1
healthy 3,539 .7191297 .4494878 0 1
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Summary statistics: employees who do not report having a university degree (degree=0)
Variable Obs Mean Std. Dev. Min Max
hourwage 2,715 15.08307 8.718424 7.7 138.45
lnhourwage 2,715 2.61199 .4125471 2.04122 4.930509
age 2,715 44.73039 10.80975 21 60
edage 2,715 17.11676 1.567681 14 29
potexp 2,715 27.61363 11.28338 1 45
potexp2 2,715 889.7801 580.2347 1 2025
female 2,715 .4880295 .4999488 0 1
male 2,715 .5119705 .4999488 0 1
pt 2,715 .2162063 .4117324 0 1
private 2,715 .7701657 .4208036 0 1
england 2,715 .812523 .3903658 0 1
wales 2,715 .0500921 .2181752 0 1
scotland 2,715 .0626151 .242314 0 1
ni 2,715 .0747698 .263068 0 1
none 2,715 .1587477 .365508 0 1
gcse 2,715 .3808471 .4856845 0 1
alevel 2,715 .4604052 .4985216 0 1
degree 2,715 0 0 0 0
small 2,715 .359116 .4798297 0 1
london 2,715 .0615101 .2403079 0 1
manager 2,715 .3222836 .467437 0 1
healthy 2,715 .6718232 .4696361 0 1
MP, MAT, VS
March 2023