Econometrics 2 ECO00003I Do graduates have different wage structures to non-graduates?

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Do graduates have different wage structures to non-graduates?

 

Econometrics 2 VLE announcement Thursday 2nd April 2020

As noted in the email sent to you yesterday, Wednesday 1st April 2020 from the Department of Economics & Related Studies there will be NO final exam in ECO00003I Econometrics 2.

 

The exam has been replaced by an extension to the assessed project, which will now carry 100% of the final module mark. The extension to the project is an increase in the permitted project length by 500 words (new word limit 2,500 in total), and the deadline for submission will be extended by one week. So the new submission deadline for the project will be Thursday 23rd April, week 2.

 

This VLE announcement is to confirm the following information:

  1. The submission deadline for the Econometrics 2 project will now be 2pmon Thursday 23rdApril 2020, week 2 of summer term 2020.

 

  1. The project length has been extended to 2,500words in total. The previous length was 2,000 words. You may use the additional 500 words throughout your project subject to the following two restrictions of:

300 words maximum for section 7 within the overall 2,500 word limit

and

300 words maximum for section 8 within the overall 2,500 word limit

 

Although these maximum word limits for each of Sections 7 and 8 have been raised, from 200 to 300, you do not have to increase the length of either of these sections. If you choose not to do so then you can use the ‘unused’ words elsewhere in the project, so long as the whole project is no more than 2,500 words.

 

To confirm, the total project length is now 2,500 words in total with revised maximum word limits on Sections 7 and 8.

 

 

 

The Project

The purpose of this project is to specify, estimate and interpret econometric models of the wage equation for employees with a specific focus on investigating whether graduate employees have different wage structures to non-graduates. The project data is a sample of cross-section data from the Quarterly Labour Force Survey (QLFS), July – September 2019 dataset.

 

The Project Report

The project report should not be longer than 2,000 words of text and excessive length will be penalised: only the first 2,000 words will be graded. Please note that the project title, abstract (if included and with its own separate maximum word limit of 100 words), exam number, bibliography, footnotes, figures, equations and tables are not included in the 2,000 word count.

 

Additional tables of results, graphs and diagrams etc. can be presented in appendices and will not be counted within the 2,000 words, however the appendices should not exceed eight sides of A4 (which is 4 pages of A4 double sided or 8 pages A4 single sided). For example, you might include regression output and the calculation of test statistics in the appendices and the hypotheses, explanation of the test, results and interpretation in the main body of the project text.

 

Please note that projects that are easier to read are easier to mark. Please give consideration to the readability of your project e.g. the formatting of tables, figures, equations and any regression output included. We would encourage you to include the main figures/tables/equations etc. within the main body of the project with only the additional figures/tables placed in a separate appendix.

 

The project report should include the following sections listed 1-9 below. The descriptions below indicate the material that should be included in your report. Please note though that if you have further tests, hypotheses or relevant discussion that you wish to include in your project you should do so.

 

  1. Introduction and description of the economic model.

This should be a brief introduction to the wage equation in general, and on how being a graduate might affect an employee’s wage and wage structure.

 

[Please note: Although you need a clear introduction to the topic please do not write an essay on ‘wages’. The purpose of the 2nd year Econometrics project is to show that you can undertake an econometrics project, rather than write an essay. So please note that a concise and focussed introduction drawing out the important variables for the analysis of the wage equation at the micro level should be the aim.

 

More generally, please note that a good project report should demonstrate: some knowledge of the economics of the wage equation and its relation to the estimated coefficients; a good understanding of the formulation of hypotheses and the appropriate test statistics; and the flexibility to formulate and test new hypotheses of interest.

 

Reading: Core undergraduate Labour Economics textbooks should provide sufficient background for your project but you will benefit from reading more widely. You must reference any material you have used correctly and fully in the text and in your bibliography. If you are in any doubt about the conventions of academic referencing, review the Academic Integrity on-line tutorial that you completed in autumn term of 1st year on the VLE and access the Academic Integrity website www.york.ac.uk/integrity. For further information please see page 51 of your DERS Student Handbook]

 

  1. A description of the econometric model.

Consider this in relation to the ideal econometric specification i.e. variables that you would have liked to have included as well as the actual variables you are going to include. This section should also note the functional form that you will be using.

 

[Please note: You are advised to choose a semi-log model specification where the dependent variable is a logarithm]

 

  1. A discussion of any data issues (such as measurement error), limitations, concerns.

 

  1. A statement of the hypotheses to be tested.

[Please note: You should include standard individual tests of significance, overall significance, test(s) to investigate the research question you have been set as well as tests for further additional hypotheses that you consider of particular interest/relevance given any preliminary analysis/consideration of initial findings.]

 

  1. Presentation of your estimated models with additional specification test statistics that you consider appropriate to use.

 

Each test should be presented with appropriate accompanying description of how these specification tests have been undertaken and why they are relevant to consider.

 

[Please note: These specification tests should be presented and discussed before any subsidiary hypotheses testing as in Section 6 below is undertaken.]

 

  1. Clear interpretation of both the economic meaning of your results and of the sign, magnitude and statistical significance of estimated coefficients (based on appropriate standard errors given your specification tests undertaken in section 5).

 

Clear testing and reporting of results in relation to your stated hypotheses from section 4.

 

[Please make sure that you interpret your results appropriately given the functional form of the model. Consider each of the partial regression coefficients fully in relation to whether the partial regression coefficient is for example attached to a dummy variable, or whether there is a quadratic in the explanatory variable of interest, and consider any ‘caveats’ to your results based on the diagnostic tests and/or any data issues/concerns you highlighted in section 3]

 

  1. Discussion of the following two issues in relation to your results (200 words maximum for section 7 within the overall 2,000 word limit)

 

7.1 To what extent do you think your estimated wage ‘return’ for a graduate holding a degree is a ‘return’ to the productivity enhancing effect of education, or a ‘selection’ effect representing the possibility that more productive employees go on to study for a degree? Can you separate out these effects in your results, briefly explain your answer?

 

7.2 Do you consider the group of “non-graduates” as defined for this project a useful, or convincing comparison group to the ‘graduates’ e.g. where the non-graduates include employees with A’levels, GCSEs or no qualifications at all?

 

[Please note: The point in this section is to comment on whether you think either of these issues are a concern and if so the (potential) implications (or biases) for your presented results. This section is a discussion section and allows you to show that you understand the potential limitations of your results rather than for you to necessarily re-estimate any of your presented models.]

 

  1. Project extensions (200 words maximum within the overall 2,000 word limit)

Open section for students to outline additional variables, types of data and/or estimation techniques that they might have liked to use in relation to this project, with appropriate explanation.

 

  1. Concluding remarks

 

 

Data

The data for the project are in the file “Project2020” and this data file will be in the following sub-directory from the end of week 9 spring term 2020:

 

‘Teaching on csrv.ad.york.ac.uk’ApplicationsDepartmentsEconDataSwaffield

 

These data are a sample from the Quarterly Labour Force Survey (QLFS) July – September 2019 which were collected in July, August, and September 2019. The QLFS is a voluntary sample survey of private households in the UK. The basic unit of the survey is the household and the data should be considered as a cross-section data set. The sample you have been given has full-time employees with permanent jobs aged 21 to 60 (inclusive) who have left full-time education. There are a total of 5,710 employees in the data set you have been provided with, observations 1-3,197 are employees without a degree and observations 3,198-5,710 are graduate employees. Graduate employees are those employees whose highest education qualification is a degree (undergraduate or postgraduate) or similar. Please note that those employees without degrees may have other qualifications, e.g. A’levels, GCSEs or no qualifications at all.

 

Variables:

 

hourW             the gross (before tax) hourly wage rate of the employee (£s) constructed from their reported weekly equivalent gross wage divided by their usual working (basic and overtime) hours per week

 

lnhourW         the natural logarithm of “hourW”.

 

age                  age of employee at time of the survey interview (July – September 2019)

 

age2                square of “age”

 

female             a dummy variable taking the value 1 if the employee is female, zero otherwise

 

tenure              number of years the employee has been with their current employer/firm

 

married           a dummy variable taking the value 1 if the employee reports being currently married, zero otherwise

 

manager          a dummy variable taking the value 1 if the employee is a Manager, Foreman or supervisor, zero otherwise

 

public              a dummy variable taking the value 1 if the employee works in the public sector, zero otherwise

 

small               a dummy variable taking the value 1 if the number of employees at the workplace is less than or equal to 25, zero otherwise.

 

London            a dummy variable taking the value 1 if the employee works in London (inner or outer), zero otherwise.

 

Degree            a dummy variable taking the value 1 if the employee’s highest education qualification is a degree (undergraduate or postgraduate) or similar, zero otherwise.

 

 

Summary statistics: Full sample (all employees)

 

Variable |        Obs        Mean    Std. Dev.       Min        Max

————-+———————————————————

hourW |      5,710    17.82341    10.30611        7.7     146.18

lnhourW |      5,710    2.760716    .4642171    2.04122   4.984839

age |      5,710    41.55412    10.55052         21         60

age2 |      5,710    1838.039    877.2715        441       3600

female |      5,710    .4313485    .4953079          0          1

————-+———————————————————

tenure |      5,710    8.822417    8.704566          0         44

married |      5,710    .5334501    .4989235          0          1

manager |      5,710    .4781086    .4995643          0          1

public |      5,710    .2802102    .4491411          0          1

small |      5,710    .2500876    .4331012          0          1

————-+———————————————————

London |      5,710    .0915937    .2884768          0          1

Degree |      5,710    .4401051    .4964431          0          1

 

 

 

Summary statistics: Sample of graduate employees

 

Variable |        Obs        Mean    Std. Dev.       Min        Max

————-+———————————————————

hourW |      2,513    21.60197    11.84129        7.7     146.18

lnhourW |      2,513    2.957212    .4646684    2.04122   4.984839

age |      2,513     39.4608     9.81453         21         60

age2 |      2,513    1653.442    802.0526        441       3600

female |      2,513      .49423    .5000662          0          1

————-+———————————————————

tenure |      2,513    7.652209    7.753262          0         40

married |      2,513    .5571031    .4968274          0          1

manager |      2,513    .5578989    .4967352          0          1

public |      2,513     .379228    .4852915          0          1

small |      2,513    .1910068    .3931726          0          1

————-+———————————————————

London |      2,513    .1444489    .3516143          0          1

Degree |      2,513           1           0          1          1

 

 

 

Summary statistics: Sample of non-graduates (without a degree)

 

Variable |        Obs        Mean    Std. Dev.       Min        Max

————-+———————————————————

hourW |      3,197    14.85328    7.711999        7.7     115.38

lnhourW |      3,197    2.606261    .4012633    2.04122   4.748231

age |      3,197    43.19956    10.81551         21         60

age2 |      3,197    1983.141    906.2416        441       3600

female |      3,197    .3819206    .4859332          0          1

————-+———————————————————

tenure |      3,197    9.742258    9.283088          0         44

married |      3,197    .5148577    .4998574          0          1

manager |      3,197    .4153894    .4928661          0          1

public |      3,197    .2023772    .4018348          0          1

small |      3,197     .296528     .456798          0          1

————-+———————————————————

London |      3,197    .0500469    .2180759          0          1

Degree |      3,197           0           0          0          0

 

 

Computing

You are advised to use OxMetrics8 (7 or 6) [PC-Give]. This package is the only one for which the course tutors will provide advice.

 

Submission date

The project must be submitted electronically by 2pm on Thursday 16th April 2020. All work submitted late, without valid mitigating circumstances will be subject to late penalties and the information on the late penalties can be found here:

https://www.york.ac.uk/economics/current-students/ug-information/exampapers/#tab-4

 

 

Please note the following:

 

  1. Your project will be submitted electronically through the Yorkshare VLE (see “Econometrics 2 2020 Project submission point” on the “Econometrics 2” VLE page). Further details and instructions will be announced by the end of week 10 of the spring term 2020.

 

  1. This formally assessed project is marked as part of your Part II examinations and forms 30% of your final module mark for Econometrics 2 (ECO00003I). Under no circumstances should you submit a project that you have worked on with another student, this is an individual project for you to complete on your own.

 

  1. Jo Swaffield will be away from York and unable to reply to your emails on Friday 10th April – Monday 13th April (inclusive).