ECOS 3997 Interdisciplinary Impact in Economics
Inequality and social policy (Stream 2)
Lecturer: Dr Juliana Silva-Goncalves
Semester 1 2023
Week 6: 30th of March
Topic: Guidance for final report
Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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Socioeconomic gap in STEM
▶ Large socioeconomic gaps in the decision to study and pursue a
career in STEM (Science, Technology, Engineering, Math)
▶ Average 15-year-old adolescent from a low socioeconomic background
is:
▶ 3 years behind their high socioeconomic background peers in math and
science
▶ less likely to aspire to a STEM career
▶ less likely to end up working in a STEM occupation as an adult
See Australian Government Department of Education.
https://www.education.gov.au/australian-curriculum/national-stem-education-resources-toolkit/i-want-know-about-stem-
education/which-school-students-need-stem-education/students-low-socio-economic-areas
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Why is it a problem?
▶ 75% of future occupations require STEM skills (Panizzon et al., 2018)
▶ Wages are higher in STEM occupations (high economic returns from
studying STEM)
▶ SES gaps in STEM in school contribute to intergenerational persistence of
disadvantage
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Possible explanations
1. Segregated educational system
▶ Australia has one of the most segregated educational systems of all
OECD countries −→ schools mainly enrol students from low or high
SES backgrounds
▶ Schools with high numbers of low SES students have fewer educational
resources (e.g. laboratories), are more likely to experience disruptions
due to behavioural problems in class which negatively affects learning,
are less likely to report supportive and engaging relationships with
teachers
−→ Important issue, but quite difficult to solve in the short-term
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Possible explanations
1. Segregated educational system
▶ Australia has one of the most segregated educational systems of all
OECD countries −→ schools mainly enrol students from low or high
SES backgrounds
▶ Schools with high numbers of low SES students have fewer educational
resources (e.g. laboratories), are more likely to experience disruptions
due to behavioural problems in class which negatively affects learning,
are less likely to report supportive and engaging relationships with
teachers
−→ Important issue, but quite difficult to solve in the short-term
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Possible explanations
2. Family & Behavioural factors
▶ Lack of role models in the family
▶ Low confidence in one’s ability to do well in STEM subjects
▶ Low future aspirations
▶ Lack of encouragement / biased advice from others (e.g. teachers)
−→ Also important issues, which can possibly be addressed through policy
/ programs
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Program
▶ To address the problem, USYD in partnership with the NSW
Department of Education, implemented a program aiming at
increasing the number of socioeconomically disadvantaged students
pursuing STEM-related careers in the future
▶ Focus of the program: development of interest in studying STEM
subjects at school and beyond, increase students’ confidence in their
ability to be successful in STEM subjects, and raise aspirations
through role models
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Program
▶ Program focussed on Year 10 students (14-15 years old) in low SES
schools
▶ 3rd year University students in STEM fields visited classrooms once a
month
▶ During those visits, they conducted the following activities:
1. Science workshops
2. Future studies and career mentoring
3. 1-on-1 tutoring sessions
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Program implementation
▶ Implemented as a Randomised Controlled Trial
▶ 30 low SES schools in Greater Sydney were interested in taking part
in the program
▶ Of these 30 schools, 15 were randomly assigned to the treatment
group and received the program, remaining 15 schools were assigned
to the control group and did not receive the program
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Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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Pre-program outcomes
1. School outcomes
▶ Y9 math grade (A-F, with A being the highest grade and F the lowest
grade)
▶ Y9 science grade (A-F, with A being the highest grade and F the
lowest grade)
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Pre-program outcomes
2. Intentions & aspirations
▶ Whether the student intends to graduate from Year 12
▶ Whether the student intends to undertake further education (post-Year
12)
▶ Whether the student intends to undertake further education in a STEM
field (post-Year 12)
▶ Whether the student aspires to have a STEM job in 10 years
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Post-program outcomes
1. School outcomes
▶ Y10 math grade (A-F, with A being the highest grade and F the lowest
grade)
▶ Y10 science grade (A-F, with A being the highest grade and F the
lowest grade)
▶ Whether the student graduated from Year 12
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Post-program outcomes
2. Intentions & aspirations
▶ Whether the student intends to graduate from Year 12
▶ Whether the student intends to undertake further education (post-Year
12)
▶ Whether the student intends to undertake further education in a STEM
field (post-Year 12)
▶ Whether the student aspires to have a STEM job in 10 years
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Post-program outcomes
3. Long-run outcomes
▶ Whether the student is undertaking further education (after Year 12)
▶ Whether the student is undertaking further education in a STEM field
(after Year 12)
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Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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Sample
N %
Received the program (program) 2,116 47.02
Didn’t receive the program (control) 2,384 52.98
Total 4,500 100
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Variable list
Treatment assignment & school characteristics
treatment takes value 1 (program) if the student is in a school
that received the program and 0 (control) otherwise
icsea school’s Index of Community Socio-Educational Ad-
vantage (ICSEA)
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Variable list
Pre-program outcomes
y9mathgrade math grade in Year 9. Takes values 1 to 5, with 1
corresponding to the highest grade (A) and 5 corre-
sponding to the lowest grade (E or F)
y9sciencegrade science grade in Year 9. As above
preprog y12intention takes value 1 (yes) if the student answered they in-
tended to complete Year 12 and 0 (no) otherwise
preprog furtheredu takes value 1 (yes) if the student answered they in-
tended to pursue further education and 0 (no) oth-
erwise
preprog furtheredustem takes value 1 (yes) if the student answered they in-
tended to pursue further education in a STEM field
and 0 (no) otherwise
preprog occupationstem takes value 1 (yes) if the student answered they as-
pired to have an occupation in STEM in 10 years
and 0 (no) otherwise
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Variable list
Post-program outcomes
y10mathgrade math grade in Year 10. Takes values 1 (A) to 5 (E
or F)
y10sciencegrade science grade in Year 10. Takes values 1 (A) to 5
(E or F)
y12graduation takes value 1 (yes) if the adolescent graduated from
high school and 0 (no) otherwise (dropout out before
Year 12 or failed Year 12)
postprog y12intention takes value 1 (yes) if the student answered they in-
tended to complete Year 12 and 0 (no) otherwise
postprog furtheredu takes value 1 (yes) if the student answered they in-
tended to complete further education and 0 (no)
otherwise
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Variable list
Post-program outcomes (cont.)
postprog furtheredustem takes value 1 (yes) if the student answered they in-
tended to complete further education in a STEM
field and 0 (no) otherwise
postprog occupationstem takes value 1 (yes) if the student answered they as-
pired to have an occupation in STEM in 10 years
and 0 (no) otherwise
posty12 edu takes value 1 (yes) if the adolescent is pursuing fur-
ther education after Year 12 and 0 (no) otherwise
posty12 edustem takes value 1 (yes) if the adolescent is pursuing fur-
ther education in STEM after Year 12 and 0 (no)
otherwise
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Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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Your task
▶ You are an economist at the Australian Department of Prime Minister
and Cabinet, in charge of advising government about social policy to
reduce socioeconomic inequality in youth
▶ Your task is to write a report to the Minister for Education and
Youth, whose top priority is to fund effective programs across
Australia to decrease inequality in adolescent outcomes between high
and low SES schools
▶ Your report will be based on your analysis of the impact of the STEM
program
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Your task
Report must include the following sections:
I. Executive summary (≈ 300 words)
II. Introduction (≈ 1400 words)
III. Data analysis and results (≈ 500 words)
IV. Discussion of the results (≈ 600 words)
V. Policy recommendations (≈ 200 words)
VI. References (not included in word count)
Maximum number of words is 3000
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II. Introduction
Must include:
a. Background information
▶ Explain why it’s urgent to address socioeconomic inequality among
youth in Australia
▶ present statistics on the degree of socioeconomic inequality in
adolescent outcomes,
▶ discuss past or current policies / programs (can be large programs ran
in many schools, or local programs implemented by one or few schools)
and what we know about their effectiveness
▶ discuss implications of the problem if left unaddressed. For example,
discuss what might happen as a result of poor decision-making in
adolescence (e.g. dropping out of school) and the implications for
individual well-being, the society, economic growth and social policy
▶ Sources: ABS data, government reports, journalistic articles,
commentaries from experts, . . .
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II. Introdution
b. Literature review
▶ present research findings on the impact of programs related to the
one you are analysing (from Australia or other countries) to address
socioeconomic inequality in youth
▶ in your lit. review, for each article refer to the population being
studied, the key research questions, methods and findings
▶ important: explain how each research article relates to your report
▶ sources: research articles (from economics or other disciplines like
psychology, education, . . . ). Do your search on google scholar using
keywords
▶ how many articles? Quality is better than quantity. There is no
min or max number, but 3 relevant articles is fine
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II. Introduction
c. Brief statement about what you will do in your report
▶ briefly summarise the program you will be reporting on
▶ give a roadmap for what comes next
▶ should not be longer than a few sentences
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III. Data analysis & results
Must include:
a. Analysis of adolescent outcomes and intentions / aspirations, prior to
the program. This must include a comparison of adolescent outcomes
and intentions / aspirations, prior to the program, across schools that
received the program and schools that didn’t receive the program.
b. Analysis of the impact of the program on school outcomes, intentions
/ aspirations and long-run outcomes.
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III. Data analysis & results
▶ Report on the statistical and economic significance of the gaps
▶ Statistical significance: is the gap statistically different from zero?
▶ Economic significance: is the gap large enough so that it is
meaningful?
▶ Remember statistical significance ̸= economic significance
▶ You should report on your analysis using figures and comment on the
statistical significance of the differences using t-test
▶ Number and label each figure
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IV. Discussion of the results
Must include:
▶ Interpretation and discussion of the results —explain your findings
and their implications
▶ Stress the economic significance of the gaps between the different
groups under analysis (are the gaps small or large?)
▶ Discuss strengths and weaknesses of your analysis
▶ Examples of aspects to discuss:
▶ what aspects of the program implementation give credibility to your
results?
▶ what additional analysis would you like to conduct to learn in more
detail about the impact of the program but would require data that is
not available to you?
▶ Discuss the implications of your results for social policy
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V. Policy recommendations
▶ Maximum of 3 brief policy recommendations based on your results
▶ Present the recommendations in bullet point format
▶ Remember, this is what the Minister reads! Must be accurate, clear
and concise
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VI. References
▶ Reference all your sources of information
▶ Follow the guidelines for adequate referencing style, as shown here:
library referencing guidelines
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Week 6 Outline
Context
Outcomes
Dataset
Report
General instructions & Marking criteria
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General instructions
▶ Individual assessment
▶ Weight of final grade: 60%
▶ Due in Week 10 (Friday, 5/5, 11.59pm)
▶ Online submission via the Canvas site
▶ Must be submitted as a word document (no PDFs) and include the
word count
▶ You do not have to submit any Stata files
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Marking criteria
Criteria Weight
Clear, coherent and relevant exposition of background. Compelling
argument for why the problem is relevant from an economic and
policy point of view; Literature review is relevant, accurate and
well-articulated
35
Data analysis is well-executed 20
Results are adequately interpreted and discussed 20
Policy recommendations are thoughtful, aligned with the results,
clear and concise
10
Good writing style, free of spelling and grammatical errors. Appro-
priate formatting of the report. Correct referencing style
15
Total: 100
HD=85+, D=75-84, CR= 65-74, P=50-64, F=0-49
Detailed marking criteria will be posted on Canvas.
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Next tutorials
▶ This and next week’s tutorial sessions aim to support you with your
data analysis
▶ You can ask questions and get support with getting started with the
analysis
▶ Kieran will not guide you through the analysis. Instead, he will
answer your questions
▶ So, come prepared for the tutorials! Get started with your data
analysis now and try as much as possible to get most of it done by
end of Week 7
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Questions
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End of lecture 6!
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