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ECON32202 HEALTH ECONOMICS

Creation date：2024-05-08 17:57:08

Belonging category：经济Economics

Tag ECON32202

HEALTH ECONOMICS

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ECON32202

HEALTH ECONOMICS
MOCK EXAM 2022
ONLINE OPEN BOOK EXAMINATION
Questions 1 – 3 are the actual questions from the 2021 exam. Q4 is a mock
question, as the material in Topic 6 has changed. Please note that questions
in Section A can cover any material in Topics 1 – 3 and questions in Section
B can cover any material in Topics 4 – 6. That is, some questions may span
multiple topics.
INSTRUCTIONS SPECIFIC TO THIS EXAM
There are two sections in this exam; Section A and Section B. Each Section contains two
questions. Answer ONE question from Section A and ONE question from Section B.
Each question is marked out of a possible 100 marks. Marks and word limits for
component parts of questions are clearly shown in parentheses.

Your answers should be typed. You can include images of algebra or figures as long as the
quality is sufficient so that it can be easily read. Where relevant, questions include word
limits. These are limits, not targets. Excellent answers can be shorter than the word limit.
If you go beyond the word limit the additional text will be ignored. Where a question
includes a word limit you HAVE to include a word count for your answer (excluding
formulae).

This is an ‘open book’ exam, and you are allowed to consult your lecture materials.
Candidates are therefore reminded that it is important to show understanding and,
where appropriate, critique. Simply reproducing material from the lecture notes is not
enough to guarantee good marks.

GENERIC INSTRUCTIONS RELATING TO ONLINE EXAMS AND SUBMISSIONS

Please refer to instructions in the “May/June Exam” folder in the “Assessment” tab of
BlackBoard for further generic information and instructions relating to formatting, word
limits, timings, referencing, IT problems and submission.

THIS PAPER MUST NOT BE SHARED WITH ANYONE

© The University of Manchester, 2022
ECON32202 MOCK EXAM 2022
Page 2 of 3

Section A: Answer ONE question from this section
1. (a) You are asked to help the National Health Service (NHS) derive demand
functions for general practice (GP) in England. The NHS provide you with data
for 2020 on the number of GP appointments made in England. State, with
justification, three other pieces of information you would like from the NHS to
include in the derivation of the demand functions. (20 marks; 300 words)

(b) “Supplier induced demand is a major source of concern in health care”. Outline
the key assumptions of supplier induced demand and show it graphically.
Then, by summarising and critiquing the existing evidence, state if you believe
supplier induced demand does exist in the market for health care.
(80 marks; 1200 words)

2. (a) What is price discrimination and why is it more common in the markets for
health care than in other industries? (20 marks; 300 words)

(b) By introducing and describing relevant economic concepts, discuss whether
you think that hospitals should be big and encouraged to merge. You should
back-up your opinions by referencing appropriate studies and commentary
pieces. (80 marks; 1200 words)

Section B: Answer ONE question from this section
3. (a) In recent years, governments around the world have worried that individuals
are drinking too many sugary drinks.

(i) Explain why externalities of sugary drink consumption may lead to a
level of (private) consumption which is above the socially efficient level
of consumption. (10 marks; 150 words)

(ii) Via the use of graphs, explain how taxation could be used to correct for
market failure in the market for sugary drinks.
(10 marks; 100 words)

(iii) Discuss alternative ways in which the government could intervene to
generate social efficiency in this market. (30 marks; 500 words)

(b) Discuss two methods of retrospective payment to hospitals in health care.
What are the consequences in terms of incentives relating to costs and
quality? How do the incentives of retrospective payment differ from the
incentives of prospective payment to hospitals in health care?
(50 marks; 750 words)

4. (a) Define the concept of “catastrophic health expenditure”, describe why it is not
desirable, and describe at least two interventions to reduce this phenomenon
implemented in a LMIC of your choice.
(60 marks; 1,000 words)

(b) Critique or support the implementation of community-based health insurance
as an instrument to finance health care in an East African country. Motivate
your answer with at least two arguments (in favour or against).
(40 marks; 500 words)

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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