School of Economics
Unit: Applied Economics ECONM1008
Assessment’s Contribution to Unit: 100 percent
Release Date: 9 December 2022
Submission Date: 16 December 2022

Students are strongly advised to submit their work ahead of the deadline. Should you have a problem with submission
to Blackboard you should email econ-pgt@bristol.ac.uk for guidance immediately.

• Your answer should be no more than the number of words specified for each question. Answers much longer
than this are unlikely to be sufficiently concise, whilst answers shorter than this are likely to be missing key
details, and are likely to gain lower intellectual marks
• Assignments handed in after the deadline, without a pre-arranged extension, will be subject to late penalties.
Details relating to penalties are at the end of this document.
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name containing your student number.
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advise you to generate your own diagrams but if you include diagrams or pictures that you have not produced
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Figures and tables should normally be included inline in the text.
• Your answer will be assessed using the University Marking Criteria.

This is a piece of COURSEWORK that contributes to your Unit mark and you can:
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[Document title]


Applied Economics 2022/23 – Summative Assignment

Dear Applied Economics students,

The assignment consists of three parts and we ask you to complete all three parts and all tasks. Together, the three parts
add up to a total of 100 points and determine your final grade.

Please follow the submission guidelines above. Read them carefully. Importantly, you are not allowed to work in groups
and must make an individual submission. We take plagiarism extremely seriously and check every submission with
plagiarism software.

Best of success!
Toman and Hans


[Document title]


Part 1: Microenterprise Training (50 points)

Many people in low-income countries work in the informal sector, in which microenterprises are ubiquitous.
Microenterprises usually generate low profits and have only few if any employees. Making microenterprises more
profitable could potentially transform them into an engine of growth and employment. One idea is that better managerial
know-how and practices might increase their profitability and operational scale. In a study published in the American
Economic Journal: Applied Economics, the economists Wyatt Brooks, Kevin Donovan, and Terence R. Johnson test
whether this idea could work. The article is available on Blackboard. You do not need to read the full article, but you
might find that especially sections one, two, and three (and the introduction) are helpful for answering the tasks.

Task 1.1: What is the research question and hypothesis the authors are testing? (5 points) [at most 100 words]

Task 1.2: Using Stata, replicate Table 3 (OLS Estimates on Profit at Different Time Periods (ANCOVA), p. 207) with the data
provided on Blackboard. Section III.A provides more details on the specification. Use a global to specify the set of control
variables. Use esttab and export the regression table to Word. Your table should look like the original one, but you can
simply use numbers as column titles. Please copy and paste the relevant Stata code and the table it produces below. Do
your results differ from the results published in the original paper? If so, how? (15 points)

Task 1.3: Interpret column 1 of Table 3 (use the estimates published in the paper), with particular emphasis on (i) the
marginal effects, (ii) whether the effects can be interpreted in a causal way, and (iii) whether the coefficients allow us to
identify the effect of actually having attended the business class and having interacted with a mentor (or just an intend-
to-treat effect). (15 points) [at most 250 words]

Task 1.4: Using Stata, replicate Figure 3 (Profit Time Series, p. 206) with the data provided on Blackboard. Your figure
does not need to include the grey rectangle highlighting when the intervention took place. Your legend can also be below
the figure. Please copy and paste the relevant Stata code and the figure it produces below. How does your figure differ
from the figure published in the original paper? (10 points)

Task 1.5: A policymaker in Bangladesh sees the results and wants to introduce a mentorship scheme for male
microentrepreneurs in Bangladesh. What would be your advice? (5 points) [at most 150 words]


Part 2: Cycling to school (36 points)

Ensuring inclusive and equal access to education is high on the global policy agenda. While girls have caught up and even
overtaken boys in many countries, especially at the primary school level, girls still lack behind in many low- and middle-
income countries, especially in secondary schooling and higher education. Policymakers have tried different tools to
increase girls’ enrolment in schooling. Some of these policies aim at increasing demand for schooling by providing cash
transfers to families conditional on their offspring attending schools, and other policies have tried to increase the supply
of schools by constructing more schools. Building new schools is costly, and an alternative way to improve access is to
help students with transportation. In 2006 the Indian state of Bihar therefore gave all girls who enrolled in grade 9 means
to buy a bicycle, which could help them access the school. In a study published in the American Economic Journal: Applied
Economics, the economists Karthik Muralidharan and Nishith Prakash investigate whether this policy worked. The article
is available on Blackboard. You do not need to read the full article, but you might find that especially sections one and
two are helpful for answering the tasks.

Task 2.1: Formulate the evaluation problem using the potential outcome framework: (i) Define the unit of observation, (ii)
define the treatment D, (iii) define the outcome variable(s) of interest Y, (iv) explain which two potential outcomes this
variable can take on for each unit i, (v) explain which of the two potential outcomes is observed for each unit. (12 points)
[at most 150 words]

Task 2.2: The study uses the difference-in-differences approach to identify the causal effect of the cycle program. Using
your own words, explain why we cannot use the increase in female secondary school enrolment from before to after
2006 in Bihar to conclude that the cycling program had a positive effect. Explain how the difference-in-differences
approach is able to address some of the problems of the simple before-after comparison. (12 points) [at most 150 words]


[Document title]


Task 2.3: Muralidharan and Prakash use two control groups: boys and girls in other states of India. They do so, because
they believe that only using boys as a control group leads to a violation of an important assumption behind the
difference-in-differences approach. Explain what this assumption is and how the results in Panel A of Table 1 (Testing the
Parallel Trends Assumption, p. 330) suggest that this assumption would be violated if only using boys as the control
group. (12 points) [at most 150 words]


Part 3: Summer School and Test Scores (R script) (14 points)

The following script follows the example used in the R tutorial that we created for you (see
https://hhsievertsen.github.io/applied_econ_with_r/), where we load and analyze fictitious data on test scores and
summer school attendance and child background.

Task 3.1: In the code block below we run a regression of summer school attendance on controls for student background
and an indicator for receiving the reminder letter. Replace XYZ1, XYZ2, and XYZ3 in the code block below with the correct
code and interpret the results. Your response should start with a list of what you replaced the three elements with (i.e.,
XYZ1=…, XYZ2=…, XYZ3=…). Your response should include the R output. (5 points) [at most 100 words, not counting the R
output and the list of replaced terms]

# Estimate LPM (the first stage)
models<-list(
m1<- XYZ1(summerschool XYZ2 letter,cluster="school_id",data=regdata),
m2<- XYZ1 (summerschool XYZ2 letter+parental_schooling+parental_lincome XYZ3 female,cluster="school_i
d",data=regdata)
)
# Store the mean of dependent variable in a data frame
added_stats<-tibble("Mean of Dep. ",m1=mean(regdata$summerschool),m2=mean(regdata$summerschool))
# Generate table
modelsummary(models, stars = TRUE,statistic = 'std.error',
fmt= '%.4f',add_rows = added_stats,
coef_omit= '(Intercept)', output = 'flextable')

Task 3.2: A policymaker hears about your results in Task 3.1 and states that “The analysis by a Bristol economist shows a
clear positive significant treatment of the summer school.” Explain why this statement is not correct. Because you are a
helpful economist you provide some additional analysis to inform the policymaker. Replace XYZ1, XYZ2, and XYZ3 in the
code block below with the correct code and interpret the results and explain how these results provide insights into the
claim made by the policymaker. Your response should include the R output. (5 points) [at most 100 words, not counting
the R output and the list of replaced terms]
# Ordinary Least Squares regression
model1<-lm(test_score~parental_schooling+parental_lincome+letter+female, XYZ1= XYZ2 (analysisdata,year=
=6))
# Summary of model1
XYZ3 (model1)

Task 3.3: Replace XYZ1 and XYZ2 in the R code below and interpret the findings. Your answer should include the R output.
(4 points) [at most 100 words, not counting the R output and the list of replaced terms]
# Estimate IV specification with feols
m1<-feols(test_score~parental_lincome+female+parental_schooling | # Outcome eq.
0| # Fixed effects
XYZ1~ XYZ2 # First stage
,cluster="school_id" # Cluster var
,data=regdata)
# Summary of results
summary(m1)


[Document title]



Penalties for late work
Assignments handed in after the deadline, without a pre-arranged extension will be subject to the following penalty:
• A fixed absolute penalty of 10 marks is applied for each day work is submitted after the agreed submission
deadline. Please note, weekend days count towards the calculation of late penalties, bank holidays and
University closure days do not.
• A mark of zero is applied to work submitted five or more days after the agreed deadline if this threshold is not
already reached.
Plagiarism
In academic writing, plagiarism is the inclusion of any idea or any language from someone else without giving due credit
by citing and referencing that source in your work. This applies if the source is print or electronic, published or
unpublished, another student’s work, or any other person.
The University's Examination Regulations state that “Any thesis, dissertation, essay, or other course work must be the
student’s own work and must not contain plagiarised material. Any instance of plagiarism in such coursework will be
treated as an offence under these regulations.” (Section 3.1).
The Examination Regulations give information on the University's procedures for dealing with cases of plagiarism
(Section 4)
More information about plagiarism, and how to avoid it is available from the Library website.


Referencing

If you reference papers in your answers, you should reference them using a consistent referencing system, such as the
Harvard referencing system; you should normally cite sources in the text. As a general rule, you should avoid using
footnotes to reference.
• If you include a quote, it should be in quotation marks, and a page number included in the in-text reference.
• Whilst you should normally avoid larger quotes, if you include them, you should also indent the text.
If you cite a paper in your essay, you should also include a full reference to the paper in the reference list at the end of
the paper.
• Do not list papers in your reference list that you have not referenced in the paper
[Document title]


University marking criteria (postgraduate)

Fail (<50%) Pass (50%-59%) (60%-69%) (70%+)
Attainment of Learning
Outcomes
An inadequate level of knowledge
and understanding of the basic
concepts addressed by the
question.

A reasonable knowledge and
understanding of the topic, although
more basic than at merit level, and at
least of essential reading and
material covered in lectures.
A good knowledge and understanding of the
topic with clear indication of some reading
beyond essential recommendations or
material covered in lectures. A ‘good’
knowledge implies largely an absence of
outright mistakes with clear evidence that
you have understood the material and are
not merely repeating it.
An authoritative knowledge and
understanding of the topic gained
from wide-ranging reading that
goes beyond essential
recommendations or material
covered in lectures.
Application of Methods Able to use a proportion of the
basic methods and techniques
taught.
Able to use most of the methods and
techniques taught.
Able to use well a range of methods and
techniques to come to conclusions.
Mastery of a wide range of
methods and techniques.
Analysis,
Comprehension and
Synthesis
Inappropriate or irrelevant
selection of content.

An ability to use relevant material
which is directed to the question.

An ability to use relevant material which is
directed to the question.
An ability to manipulate material
intelligently and to direct it to the
question set.

Technical Mastery Limited technical competence with
major shortcomings in significant
areas of the subject.

Some technical competence but with
shortcomings in significant areas of
the subject.

A good general level of technical
competence perhaps with some
shortcomings.
A high level of technical
competence with very few
mistakes of any kind.

Evaluation/Critical
Analysis
Little evidence of a critical
perspective or of analysis so that
the whole remains largely
descriptive.

Some evidence of the ability to
engage critically with content,
although not fully developed.

A good ability to evaluate critically relevant
theory and research.
A well-developed ability to engage
critically with that material.
Quality of presentation Poor presentation Adequate presentation Very good presentation Excellent presentation

Note: Because the marking criteria consider a number of dimensions, it is unlikely that a single piece of work fits nicely into all of the descriptions above. For example, a piece of work
may have excellent presentation, but due to significant errors, and major deficiencies, the piece of work may still be awarded a fail mark.