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ECOS3997 assessment
创建日期：2024-05-25 14:49:47
所属分类：经济Economics
标签： ECOS3997
assessment

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ECOS3997 assessment

Weight of final grade: 60%
Due on 5th of May (Friday) 11.59pm (online submission via Canvas)
Must be submitted as a word document (no PDFs) and include the word count
1. Background
According to the Australian Government Department of Education, students from
disadvantaged socioeconomic background have low participation in STEM (Science,
Technology, Engineering, and Mathematics) education. The average 15-year-old adolescent
from a low socioeconomic background is 3 years behind their high socioeconomic
background peers in math and science, is less likely to aspire to a STEM career, and is less
likely to end up working in a STEM occupation as an adult.
Studying STEM subjects is important for one’s future career trajectory. It is estimated that
75% of future occupations require STEM-related skills and wages tend to be higher in STEM
occupations (Panizzon et al., 2018). The socioeconomic gap in STEM in school is
contributing to the persistent earnings gap between individuals who grew up in advantaged
versus disadvantaged socioeconomic environments.
To address the problem, the University of Sydney in partnership with the NSW Department
of Education, implemented a program to lift students’ interest and confidence in studying
math and science, and increase the number of socioeconomically disadvantaged students
pursuing STEM-related careers in the future.
References
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
Panizzon, D. L., Geer, R., Paige, K., O'Keeffe, L., Schultz, L., Zeegers, Y., & Brown, L. (2018). Exploring the ‘hard facts’ around STEM in
Australia: Females, low socioeconomic status and absenteeism. International Journal of Innovation in Science and Mathematics Education,
26(8).
2. Program features
The program was conducted in 2018 and targeted students enrolled in Year 10. Through the
program, 3rd year University of Sydney students in STEM fields visited Year 10 classrooms
each month for the entire school year. During each visit (which lasted 3 hours), they
conducted one or more of the following activities:
1. Science workshops: demonstration of scientific concepts through experiments.
Workshops were educational, fun and sparked Year 10 students’ curiosity in learning
more about science.
2. Future studies and career mentoring sessions: sharing their lived experience and advice
on how to prepare for post-Year 12 studies in a STEM field.
3. One-on-one tutoring sessions: supporting Year 10 students with homework and preparing
for assessments in STEM-related subjects.
The program was designed to help students build confidence in their ability to succeed in
STEM, encourage them to consider a STEM career and support them in taking the required
steps to succeed in a STEM career. The university students involved in the program delivery
were role models to Year 10 students, as they were highly motivated to improve outcomes of
socioeconomically disadvantaged students and shared with them a similar socioeconomic
background.
3. Program implementation
Due to limited resources and considering the importance of rigorously evaluating the impact
of the program, it was implemented as a randomised controlled trial. First, the Department
of Education asked several principals of low SES schools in Greater Sydney if they would be
interested in implementing the program in their school, to which 30 school principals
responded positively. Then, of these 30 schools, 15 were randomly assigned to the treatment
group and received the program. The remaining 15 schools were assigned to the control
group and did not receive the program.
4. Outcomes for the evaluation & dataset
Immediately prior to the start of the program, a survey was conducted with Year 10 students
in all 30 schools that were interested in taking part in the program. The survey asked students
about their intentions with regards to future studies and jobs. At the end of the program, the
same survey was conducted with the same students. A final survey was also conducted 4 years
after the program to find out, if after leaving high school, the students ended up undertaking
further education in STEM (to encourage responses, everyone responding received a $20 gift
card).
The dataset for the evaluation of the program includes the following variables:
a) Treatment assignment and school characteristics
treatment: takes value 1 (program) if the student is in a school that received the program and
0 (control) otherwise
icsea: the school’s Index of Community Socio-Educational Advantage (ICSEA) (see
https://docs.acara.edu.au/resources/About_icsea_2014.pdf). The higher the value of the index, the higher the
level of socioeconomic advantage at the school level. The reference value (national average)
is 1000; a school with a ICSEA above 1000 is a school that scores above the national average
in terms of socioeconomic advantage; a school with a ICSEA below 1000 is a school that
scores below the national average in terms of socioeconomic advantage.
b) Pre-program outcomes (data collected immediately before the start of the program)
b.1) School outcomes
y9mathgrade: Math grade in Year 9 (scale: A-F, with A being the highest grade and F the
lowest grade)
y9sciencegrade: Science grade in Year 9 (scale: A-F, with A being the highest grade and F
the lowest grade)
b.2) Intentions and aspirations
preprog_y12intention: takes values 1 (yes) if the student answered they intended to
complete Year 12 and 0 (no) otherwise
preprog_furtheredu: takes values 1 (yes) if the student answered they intended to pursue
further education and 0 (no) otherwise
preprog_furtheredustem: takes values 1 (yes) if the student answered they intended to
pursue further education in a STEM field and 0 (no) otherwise
preprog_occupationstem: takes values 1 (yes) if the student answered they aspired to have
an occupation in STEM in 10 years and 0 (no) otherwise
c) Post-program outcomes (data collected after the completion of the program)
c.1) School outcomes
y10mathgrade: Math grade in Year 10 (scale: A-F, with A being the highest grade and F the
lowest grade)
y10sciencegrade: Science grade in Year 10 (scale: A-F, with A being the highest grade and F
the lowest grade)
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)
c.2) Intentions and aspiration
postprog_y12intention: takes values 1 (yes) if the student answered they intended to
complete Year 12 and 0 (no) otherwise.
postprog_furtheredu: takes values 1 (yes) if the student answered they intended to complete
further education and 0 (no) otherwise.
postprog_furtheredustem: takes values 1 (yes) if the student answered they intended to
complete further education in a STEM field and 0 (no) otherwise.
postprog_occupationstem: takes values 1 (yes) if the student answered they aspired to have
an occupation in STEM in 10 years and 0 (no) otherwise.
c.3) Long-run (post-Year 12) outcomes
posty12_edu: takes value 1 (yes) if the adolescent is pursuing further education after Year 12
and 0 (no) otherwise
posty12_edustem: takes value 1 (yes) if the adolescent is pursuing further education in
STEM after Year 12 and 0 (no) otherwise
3. 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.
Your 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)
Writing concisely is a key skill that develops with practice. The maximum number of
words for the report is 3000 (excluding references). If the report exceeds 3000 words, I
will only read the first 3000 words.
Guidelines for each section:
II. Introduction [Total: 35 out of 100 points]
a. Background information [20 points]
Explain why it’s urgent to address socioeconomic inequality among youth in
Australia, by:
• presenting statistics on the degree of socioeconomic inequality in adolescent
outcomes (can be in relation to participation and achievement in STEM but does
not necessarily have to),
• discussing past or current policies / programs that relate to the one being evaluated
(can be large programs ran in many schools, or local programs implemented by
one or few schools) and what we know about their effectiveness,
• discussing implications of the problem if left unaddressed. It must include a
discussion of the implications for individual well-being, the society, economic
growth and social policy.
Examples of valid sources: ABS data, government reports, journalistic articles,
commentaries from experts, etc.
b. Literature review [12 points]
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 literature review, for each article refer to the population being studied, the key
research questions, methods and findings. It is very important that you carefully
explain how each research article relates to your report.
Sources: research articles (from economics or other disciplines like psychology,
education, health, etc). Do your search on google scholar
(https://scholar.google.com.au/) using keywords.
How many articles? There is no minimum or maximum number, but quality is better
than quantity. You can aim referring to 3 highly relevant articles.
c. Brief statement about what you will do in your report [3 points]
• Briefly summarise the program you are reporting on.
• Give a roadmap for what comes next.
Should not be longer than 2-3 sentences.
III. Data analysis & results [20 points]
This section 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.
In each subsection a-b, you need to:
• Report on the statistical and economic significance of the gaps
o Statistical significance: is the gap statistically different from zero?
o Economic significance: is the gap large enough so that it is meaningful?
Remember statistical significance ≠ economic significance!
• Report on your analysis using figures and briefly comment on the statistical
significance of the differences using t-test
• Number and label each figure (hint: you can use command “graphcombine” in Stata
to combine all figures about pre-program outcomes into one single figure, and all
figures about post-program outcomes into one single figure)
IV. Discussion of the results [20 points]
In this section, you need to:
• Interpret and discuss the results: explain your findings and their implications, stressing
the economic significance of your results (that means, comment on whether the effects
small or large)
• Discuss strengths and weaknesses of your analysis. Examples of aspects to discuss:
o what aspects of the program implementation and data give credibility to your
results/conclusions?
o 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
V. Policy recommendations [10 points]
• Make a maximum of 3 brief policy recommendations based on your results
• Present the recommendations in bullet point format
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MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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