ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS EMPIRICAL PROJECT 2019
Instructions
The mark for this essay is worth 5% of your total mark for the module.
You will be awarded a mark of 0% or Grade F if you (1) do not attempt the summative assess- ment component or (2) attempt so little of the summative assessment component that it cannot be assessed. Please check the UCL Academic Manual (Section 3.11) for information on the consequences of not submitting or engaging with any of your assessment components.
If you are a re-sitting student or taking deferred assessment the academic regulations for 2017/18 apply to you. In this case if you do not complete or take an assessment component that is worth more than 20% of the total assessment you will be considered incomplete. This means that you cannot pass the module. If this is your first attempt, you may be entitled to LSA in the component. Please dis- cuss with the Departmental Tutor (f.witte@ucl.ac.uk) if you are unsure of the consequences for you.
If you have extenuating circumstances that affect your ability to engage with any of the module assessment components, please apply for alternative arrangements to the Economics Department as soon as possible. See details in Section 6 of the Academic Manual and send your request to economics.ug@ucl.ac.uk.
If you have a disability or long-term medical condition, you may be entitled to adjustments for assessments. This may include an extension for this essay. Please see Section 5 of the Academic Manual for information on how to apply for adjustments. Contact the Departmental Tutor, Dr Frank Witte (f.witte@ucl.ac.uk) and the UG Admin team (economics.ug@ucl.ac.uk). Do not contact the course lecturer about this.
Please follow these instructions so that we can ensure anonymity in marking and ensure compli- ance with UCL assessment policies. We will only be able to give you credit for your project if you follow these instructions.
1. Read and follow all these instructions before your submission deadline.
2. This is a group assignment and should be submitted in groups of 3 or 4 students.
3. All answers must be uploaded via Turnitin by 1pm on Monday March 25th 2019.
4. All marking on Turnitin is anonymised. Do not put your name or student number anywhere on your submitted answer – either in the document or in the file name.
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5. Name your file as follows: Candidate Number1 Candidate Number 2 … (i.e., the candidate number for each group member separated by ‘underscore’).
6. You should include your Stata commands in the appendix. If you use a different software for the project, you should state which programme was used and present your code and results in the appendix. Your essay should be no more than 800 words in length, including footnotes but not including bibliographies, tables or figures, but excluding the appendix which should include your Stata commands. You must state the number of words at the top of the first page of your essay. If your essay is longer than 800 words the following Faculty guidelines on penalties for over-long work will be applied:
• For work that exceeds a specified maximum length by less than 10% the mark will be reduced by five percentage marks, but the penalised mark will not be reduced below the pass mark, assuming the work merited a Pass.
• For work that exceeds a specified maximum length by 10% or more the mark will be reduced by ten percentage marks, but the penalised mark will not be reduced below the pass mark, assuming the work merited a Pass.
7. Late work will be marked but will be subject to UCL rules as set out in Section 13.12 of the Aca- demic Manual: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment -framework-taught-programmes/section-3-module-assessment#3.12. For the avoidance of doubt, a working day means a 24-hour period from the 1pm deadline.
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9. If you are normally entitled to Reasonable Adjustments, such as extra time in exams, you may be entitled to extra time for this Assessed Essay. You will need to have a SORA in place for this to be taken into account. Please see Section 5 of the Academic Manual for the process to follow if you have not already done so: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4- assessment-framework-taught-programmes/section-5-reasonable-adjustments. Make sure to fol- low the process as early as possible in Term 1. The responsible marker will know which candidate numbers have a SORA and this will be taken into account when reviewing the timing of sub- missions. Contact the Departmental Tutor, Dr Frank Witte (f.witte@ucl.ac.uk) and the UG Admin team (economics.ug@ucl.ac.uk). Do not contact the course lecturer about this.
10. You can upload your document as a word document or pdf. PLEASE make sure to allow sufficient time should problems arise with Turnitin on the morning of Monday March 25th 2019.
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11. Check the submission inbox for confirmation that your essay has been submitted. Once your submission has been accepted you will return to the ’My Submissions’ tab where you will be able to see the details of your submission. If your submission is not confirmed for some reason, or you are having issues uploading the document, get in touch with ISD (servicedesk@ucl.ac.uk) as soon as possible to figure out what the problem might be.
Any matters affecting your ability to submit on time should be directed to the Departmental Tutor (f.witte@ucl.ac.uk) rather than the module lecturer to ensure anonymity is retained.
QUESTION:
In “Competition and Innovation: An Inverted-U Relationship” (Quarterly Journal of Economics, Vol.120, No.2, 2005), Philippe Aghion, Nick Bloom, Richard Blundell, Rachel Griffith and Peter Howitt study the relationship between competition and innovation. This question is based on the specifications in the article, which use measures for both variables and investigates their empirical association. The innovation measure used in the study is (essentially) the number of patents issued in an industry during a given year over the period 1973 to 1994. The unit of observation is the industry at each (available) year and there are 17 industries in the data. To register the degree of competition in an industry, the authors use one (1) minus a measure called the Lerner index. An industry in perfect competition would have the index equal one. Lower values for the index register deviations from perfect competition.
The main variables in the dataset empirical proj 2019.dta are:
Variable Code sic2
patcw
Lc
yr∗
Variable
Industry Code
Citation weighted patents Competition
Year Dummies
1. Provide summary statistics for the variables above and obtain OLS estimates for the following regression:
patcwjt = α0 + α1Lcjt + α2Lc2jt + year dummies + ujt, (1)
where patcwjt and Lcjt are the citation weighted patents and competition measure, respectively, in the jth industry in year t, j = 1, …, 17 and t = 1, …, 22. Are the coefficients on Lcjt and Lc2jt jointly significant? How do you interpret them? What is the Average Partial Effect (APE) of Lcjt on patcwjt? (Note that this is equal to the Partial Effect at the Average (PEA).) Based on the estimates, which level of competition produces the highest level of innovation? What is the
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marginal effect of Lcjt for an industry where Lcjt = 0.5? What is the marginal effect of Lcjt for an industry where Lcjt = 1 (i.e., perfect competition)?
2. Suppose you want to estimate the probability that an industry issues a positive number of patents in a given year. To do that, create a dummy variable Yjt that records whether industry j issued at least a positive number of patents in year t or not and run the following model:
Yjt = 1(β0 + β1Lcjt + β2Lc2jt + year dummies + ujt ≥ 0)
where ujt ∼ N(0,1). What are the PEA and APE for Lcjt? Why are they different while the
PEA and APE for equation (1) are equal?
3. Note that the patcwjt is zero for 12.99% of the observations. Estimate a Tobit model where
patcw∗ =γ +γ Lc +γ Lc2 +yeardummies+u (2) jt 0 1 jt 2 jt jt
and patcwjt = patcwj∗t if patcwj∗t > 0 and patcwjt = 0, otherwise. Are the coefficients on Lcjt and Lc2jt jointly significant? What is the (unconditional) Average Partial Effect (APE) of Lcjt
on patcwjt? How does this compare with the APE obtained in item (1)? What is the marginal effect of Lcjt for an industry where Lcjt = 0.5? What is the marginal effect of Lcjt for an industry where Lcjt = 1 (i.e., perfect competition)? What is the Average Partial Effect (APE) of Lcjt on the probability that patcwjt > 0? How does this compare with the APE obtained in item (2)?
4. Note that the dataset is a panel of industries. Write up a panel data version of (1) with (industry) fixed effects (and maintaining year dummies). Provide an interpretation of the fixed effects in this particular context. Estimate the fixed effects model; how do the coefficient estimates on Lcjt and Lc2jt compare with those from the OLS regression in Question 1? What is the Average Partial Effect (APE) of Lcjt on patcwjt? What is the marginal effect of Lcjt for an industry where Lcjt = 0.5? What is the marginal effect of Lcjt for an industry where Lcjt = 1 (i.e., perfect competition)?
5. The authors point out that high levels of innovation may reduce competition and this would make Lcjt endogenous. Does the panel data model you estimated in the previous question allow you to control for this type of endogeneity? Discuss.
To address this problem, the authors employ instrumental variables that reflect policy changes affecting competition. Those policies are the Thatcher era privatisations, the EU Single Market Programme and the Monopoly and Merger Commission investigations that resulted in structural changes in certain industries. Those variables in the data set are listed below:
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Variable Code SMPhighD SMPmedD car
per
brew
tele
phar
text
raz
steel
ord
rd yUSA
rd yFRA tfpUSA tfpFRA imp yUSA1 imp yFRA1 exp yUSA1 exp yFRA1 muUt liUSA1 muFt liFRA1
Variable
Single Market Programme high impact
Single Market Programme medium impact Car Industry
Periodicals Industry
Brewing Industry
Telecoms Industry
Pharmaceuticals Industry
Textiles Industry
Razor Industry
Steel Industry
Ordnance Industry
Industry R&D/Y USA
Industry R&D/Y France
Industry TFP USA
Industry TFP France
Industry Imports/Y USA
Industry Imports/Y France
Industry Exports/Y USA
Industry Exports/Y France
Markup USA
Output minus variable costs over output USA Markup France
Output minus variable costs over output USA
Provide the TSLS estimates for the coefficients in the following version of regression (1):1
patcwjt = α0 + α1Lcjt + year dummies + ujt. (3)
Are the instruments sufficiently strong? Test for endogeneity of Lcjt using regression-based test covered in class.
1Item (4) demonstrates that (industry) fixed effects are important. Ideally you would like to estimate the panel data model there accounting for endogeneity in Lcjt and Lc2jt. Two problems arise: ( i ) applying TSLS in the presence of (industry) fixed effects and (ii) the fact that the endogenous variable shows up in Lcjt and in Lc2jt. This is possible, but is a topic for more advanced courses.
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