ECM607A: Microeconometrics 1

Dear Madam/Sir,
May I request a quote for the assignment attached?
Word count: 2,500
Deadline: 29/03/2022
Software to be used: Stata
Thank you very much.
Best regards,
Massimo
ECM607A: Microeconometrics 1
Assignment Information (2022)
Credit level: 7
Credits: 10
Module convenor: Chenyang Wang (c.wang@reading.ac.uk)
Deadline: 31st March 2022 12:00 at noon
This assignment is worth 100% of the final module marks for ECM607A
Project Background
This project is intended to give you practice of: using a panel dataset and Stata; selecting a suitable empirical strategy (econometric estimators) to answer the set research question and to test hypotheses; interpreting output and drawing conclusions in relation to the research question and hypotheses. The provided dataset is an extract from the Understanding Society survey (USS), a UK survey which follows households over time (roughly 40,000 households and their members) and collects data on range of personal, socio-economic and attitude variables. The extract contains data from the first nine waves (2009-2018), and includes the general population and Northern Ireland sample, excluding ethnic minority/immigration boost and BHPS samples.
The USS is intended to be representative of the UK, and the provided extract provides a selection of the variables available from the full USS dataset. The data only includes individuals who completed a full interview in a given wave, and like other longitudinal surveys suffers from attrition, therefore the sample you will use for your estimations will not be fully representative. You are not expected to deal with issues of a non-random sample in this project but should be aware of any potential issues.
The dataset has been transformed into panel (long) form ordered by individual with a separate row for each wave they have a full interview; the dataset is, therefore, not a balanced panel. There may be various reasons individuals are missing from a wave. Firstly, they may not have been part of the target sample at that point e.g. because they were under 16 or were not living in the sample household; secondly, they may not have provided full (or any) information (you may also find individuals cannot be used in a given wave if they have information missing on variables of interest). In total there are 240,860 person-years in the provided dataset. The following documents have been uploaded to Blackboard under the set exercise area:
• US_data9W.dta – the Stata data file of the extract Understanding Society Survey data from the first nine waves (as mentioned this is already in panel long form), however, in order to access this dataset, you need to agree to the data access agreements.
• 6614_wave_1_to_9_ user_guide – a user guide for the Understanding Society which reports information about the design of the survey which is quite complex. However, this guide does not provide much information about the variables, but you can find out more details about the variables provided in the extract from the on-line dataset documentation You can also find the questionnaires for each wave on the online documentation which, where relevant, will tell you the source of the questions. The selection of variables you have been provided are taken from the individual interview(data file indresp); the household variables from the household interview (data file hhresp) and the fixed wave variables from the stable characteristics of individuals file (data file xwavedat).
• US_varlist_202021.xlsx – an Excel file with the variable list (sorted alphabetically and by theme: labels and which data file the variable come from is provided on the alphabetical tab) provided in the data, and waves the variables are in. The handlers of the USS, have provided the data in Stata form, labelled variables and values of the variable (where relevant); again, for further details about variables see the on-line dataset documentation (to find out value labels of a variable also use the labellist command):
Submission
All coursework has to be submitted on-line in PDF file through the ‘Assignment Submission’ tab in the main menu on the left of the module Blackboard page. It is important that you are officially registered to take the module for credit (as opposed to auditing it). You must submit your work as only one file. Name your file using your university username. Instructions on electronic submission of coursework can be found under the ‘Assignment Submission’ tab on Blackboard.
You can write your work in any software. Please make sure that your work is anonymised. Do not put your own name in the header or footer or the front page of your work. Blackboard will identify your work by your username, so your coursework will not get lost in the system.
1. Project Topic: Life Satisfaction
Life satisfaction – the dataset includes satisfaction with life overall but also includes other satisfaction variables such as satisfaction with income, health: these are measured on a scale of Completely dissatisfied (1) to Completely Satisfied (7)

Note the dataset also contains an alternative well-being measure of subjective Well-Being (GHQ).
2. Research Question
How do parenthood and marriage affect life satisfaction?
3. Exercise Structure
Undertake the following tasks included in each section (you are not limited to the suggested section headings, and you can use sub sections to help with the flow of your project):
Introduction
From the set research question (How do job and marriage affect life satisfaction?), list at least two hypotheses which you can test with the provided data. You can number your chosen hypotheses so that you can easily refer back to them. You should focus on one or two important factors for which you can make hypotheses and predictions about, and then include other factors as a set of controls. You need to set up any prior expectations in your hypotheses. Following each of your hypotheses, you should clearly justify what you are doing. Although a formal literature review is not required, do make reference to any literature, economic theory and other sources you used to develop your resulting hypotheses.
Data
You should discuss the dependent variable and your chosen key explanatory variables (e.g., what your dependent and independent variables are, and why you have selected them). You should provide relevant descriptive statistics (which may include graphics if you wish) of your dependent and independent variables (such as means, standard deviations, max, min) as this may aid your interpretation (especially if results are unexpected). Demonstrate your understanding of the data and variables through the use of descriptive statistics.
Methodology
Choose one or more estimation method (estimator) from four methods: pooled OLS, random effects, fixed effects, and ordered probit model or random effect ordered probit model. Discuss why you are using this/these estimation method (e.g. do tests for fixed versus random effects), how the estimator can be used to test your hypotheses, and how your chosen variables are measured. You should use regression equations to demonstrate the specification of your population model (i.e. include variables in your regression specification)
Reference should be made to any literature which helped make these decisions.
It is important to demonstrate your understanding of the estimators (estimation methods) applied and justify their use. I expect to see the specification of your population model(s) and the application of the estimator. You should provide enough information about the chosen methods so that the reader could replicate your analysis.
Results and discussion
You should provide well-presented tables of your statistics and regression output; I don’t want to see raw Stata output. Tips for formatting your project, especially tables, are given in the appendix.
Interpret and discuss your results (including any specification tests) to help answer the research question and provide the results of your hypothesis tests. Remember the aim is to use the appropriate estimator(s) to help answer the research question and test your hypotheses, so this is a very important section. It is important to discuss both the statistical and economic significance of the results – i.e., look at the magnitude, does the size of the effect matter, are the results meaningful, what do they mean in reality, are they important, would policy makers care about the results?
A key part of research is to be able to critically analyse your results. Therefore, do link results back to the research question and hypotheses, and more widely discuss them in relation to economic theory (do they support or contradict what economic theory would predict?), findings from past literature (do they back up, contradict or develop further what others have found?) and potentially the intuition behind the results (do they make sense, do they fit with your prior expectations?). Given you are using a sample to make inferences about a population of interest you also need to convince the reader that your results are good estimates of the true population parameters. Therefore, you should discuss any specification tests (fixed vs random effect, test of the assumptions in ordered models, joint significant test, etc), assumptions and any measures to judge the goodness of fit. It is also common to undertake a number of sensitivity/robustness checks to check your results are unchanged – this may involve changing the specification of your model, using a different measure of your variables of interest or using different estimators (e.g. you may show there is little difference between pooled OLS and ordered probit model but much more substantial differences between, say, random and fixed effects).
Conclusions
The conclusions summarise main results and provide answers to the research question and the results of your hypothesis tests. You can discuss any policy implications on the research question. You should discuss any caveats to your results e.g. potential problems with the estimators used, data limitations – these may appear in the conclusions or be discussed at other relevant points (such as in the discussion of the methods or in the results section). You can comment on potential avenues for future research.
References and appendices
Your reference list usually comes after the conclusions and before any appendices. You need to ensure you have cited any references within the text correctly and provided all references in your reference list. I do not mind which referencing system is used (e.g. the Harvard system) so long as you are consistent. Please see the library guide on citing references http://libguides.reading.ac.uk/citing-references. In particular these resources provide advice on different reference systems and how to reference a variety of sources such as journal articles, books, websites etc. There is also some advice on avoiding accidental plagiarism as if you are directly quoting or paraphrasing someone else’s ideas (which need to be written in your own words) as supporting evidence you need to acknowledge the source properly.
You can provide additional information in an appendix. You can report some results in the appendix to demonstrate you have done them if you don’t want to report them (fully) in the main body of the text; all appendices should be referred to at some point in the main text. You must paste a copy of your do-file into the appendix (you do not need to refer to) as discussed below.
Stata commands
You do not need to provide details of the Stata commands used within your text but you must include a copy of a do-file to show what commands you utilised to produce your output and to help assess whether you implemented these commands correctly (which you can copy and paste into your document) in your appendix. It is recommended you leave in any comments you have added so it is clear which of the reported output the reported commands has led to.
Excluding references, appendices and your do-file the recommended word count for the exercise is around 2,500 (should between 2000 to 3000) words. The Introduction to Stata course, Stata booklet and the PDF help files, along with the class exercises, should contain all the information you need if you are not familiar with certain Stata commands you want to use.
4. Example journal articles
You should select hypotheses which you can test with the provided data using some of the econometric methods that you have learnt in the module. On the on-line reading list you will find a few articles to get you thinking:
Two review articles on life satisfaction (relatively old but these should give you an idea of some of the factors that have been looked at in the life satisfaction literature):
Dolan, P., Peasgood, T. and White, M. (2008) Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. Journal of Economic Psychology, 29: 94–122.
Mackerron, G. (2012) Happiness economics from 35,000 feet. Journal of Economic Surveys, 26(4), 705-735.
And an example journal articles that used the BHPS (the predecessor to the USS, with the BHPS subsumed into the USS dataset but these respondents not included in your data):
Della Giusta, M., Jewell, S., and Kambhampati, U. (2011) Gender and Life Satisfaction in the UK, Feminist Economics, 17 (3), 1-34
And a few more example journal articles on life satisfaction:
Helliwell, J. F. (2003). Hows life? Combining individual and national variables to explain subjective well-being. Economic modelling, 20(2), 331-360
Margolis, R., & Myrskylä, M. (2012). Happiness: Before and After the Kids (pp. 2012-013). MPIDR Working Paper
Nomaguchi, K. M. (2012). Parenthood and psychological well-being: Clarifying the role of child age and parent–child relationship quality. Social science research, 41(2), 489-498
The above journal articles are just a few examples, and you should not simply try to replicate work these papers and other papers have done but come up with your own resulting hypotheses. You can search for papers that have used the USS data using their search facility: https://www.understandingsociety.ac.uk/research/publications
5. Further Project Considerations
Things to consider (also refer to the steps for empirical research in the Introduction to Econometrics for Research document):
How to Exploit the Panel Nature of the data?
How can you exploit the fact you have panel data? Note not all variables are available in all waves so this may limit the waves you can use or you may decide that a variable can be treated as fixed over time (common with personality variables, say). You have learnt how to estimate both linear and non-linear (discrete) models in the module, that allow for unobserved heterogeneity using panel data (linear models are much easier to estimate).
Should you treat satisfaction as an ordered or continuous variable?
Do you want to treat satisfaction as an ordered or continuous variable – or compare methods treating the variable as ordered and continuous? The follow article is commonly cited as it argues that whether you treat life satisfaction as a cardinal or ordinal variable (i.e., continuous versus an ordered/discrete variable) does not tend to make much difference to results rather it is more important to allow for unobserved heterogeneity.
Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness. The Economic Journal, 114, 641–659
Several studies have cited this article to justify their treatment of satisfaction as a continuous dependent variable (strictly speaking satisfaction is a discrete (ordered) variable): it is up to you to determine how you want to treat your dependent variable and also what methods you use (you do not necessarily have to follow what past studies have done, although past studies may help and guide you).
Data Tips
• The USS uses negative numbers to denote missing values with the following codes: -9 – missing; -8 – inapplicable; -2 – refused; -1 – don’t know. You can make use of the command mvdecode to change missing values to Stata’s missing value of a “.”.
• Some questions are only asked conditional on response to another question, for example, questions relating to the labour market are only asked to respondents currently participating in the labour market (the on-line dataset documentation provides an idea of who is asked the question) – with the rest coded as inapplicable (-8) – so it is important you understand which respondents are asked a question. I have tried to provide a brief overview of who the question is asked to in the variable list Excel file; note you may find occasionally some discrepancies between who was supposed to be asked the question and who was, possibly as a result of interviewer/coder error.
• Due to the fact that some of the income variables have imputed values some of the income values (for personal and household income) could be negative, in which case you may want to exclude these (some researchers set negative values to zero but this would pose an issue if you wanted to take the log of income)
• Note that hourly wage (hourpay) is estimated through dividing usual pay by usual hours (taking into account pay is measured on a monthy basis and hours on a weekly basis), using the following formula: (paygu_dv/jbhrs+jbotpd)*(12/52). Hourly pay is only derived for current employees (as the self-employed and those not in the labour do not report all the required information). For the self-employed individuals, you can use “total monthly personal gross income” minus “pay in second job derived” to estimate the income earned by them in their main business. Then, you can divide this difference by “hours normally worked per week” to estimate the usual pay by usual hours for the self-employed individuals. The overall formula is (fimngrs_dv-j2pay_dv)/jshrs.
Deadline
Monday 31st Mar 2022, before 12 Noon
Marking Criteria
The following 4 marking criteria will be used when marking the projects.
1. 35% – Development of hypotheses and clear outline of methodology:
Clear and well-developed hypotheses, with strong links to economic theory and/or previous evidence/literature. Chosen methodology is fully developed, described, fully understood and appropriate, with effective use of equations. The justification of methodology makes critical reference to relevant literature. Limitations to the data and chosen methods are well understood and discussed.
2. 35% – Application and analysis:
Effectively and correctly interpreted results, both in terms of economic and statistical significance. Critical analysis of produced evidence and findings which has both depth and breadth. Discussion of produced results rigorously linked to the research question and hypothesis tests, and economic theory/evidence from the literature. Construction of logical/convincing argument with conclusions supported by the project findings; with implications discussed. 4
3. 20% – Use of Stata and the dataset:
Effectively and correctly use Data and Stata commands to manage and clean data, to produce statistics, regression results and other results. It is desirable to have usage of Stata beyond that taught.
4. 10% – Presentation and Style:
Very well organised and structured work. Written expression is eloquent and flows flawlessly. Results, tables and other figures very well and clearly presented with detailed and concise information. References are all listed and cited correctly and consistently.
Students are reminded of the University’s penalty for late submission of work:
• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
Extensions and penalty remissions must be applied for on the Extenuating Circumstances form.
Appendix: Project Tips
Finding Literature and Using Literature
You will likely undertake further reading. The majority of your references should be academic sources. To help find journal articles and other academic sources refer to the University library guide on resources in economics https://libguides.reading.ac.uk/economics (in particular see the journal articles and E-resources tabs). You can search literature databases (e.g. econlit, a database specifically for economics articles) or source a reference from the reference list of another paper (how to do these is provided in the link above). Another commonly used tool is google scholar (https://scholar.google.com/) which leads to a wider range of search results than standard databases but includes only scholarly (as the name suggests) sources (unlike a basic google search which is not recommended).
A formal literature review is not required but do get into the habit of critical analysing/reviewing everything you read. More on critical analysis is provided below. The following library guide https://libguides.reading.ac.uk/literaturereview gives an idea on how to use and critical analyse literature in a research project, since the project contains elements of a literature review. In particular you should make critical reference to relevant literature in the introduction to help motivate your topic and research question (in your later research) and develop your hypotheses; in the methodology to justify the chosen methods; and critically link your discussion of results (and potentially the conclusions) and explanations of your results to past literature. It is important to cite sources properly and provide a reference list.
Guide to Academic Writing
Your project should be written in an academic way but if you are unsure how to do this, please refer to the library guides (especially if English is not your first language):
https://libguides.reading.ac.uk/?b=g&d=a. In particular look at the academic integrity toolkit guide: https://libguides.reading.ac.uk/academicintegrity and the academic writing guide: https://libguides.reading.ac.uk/writing. Academic writing should be critical i.e., demonstrate critical analysis and critical thinking, all important parts of the marking criteria for this project. See the academic writing guide link above for information on writing critically as well as http://www.phrasebank.manchester.ac.uk/being-critical/
When you are reading other’s research do not immediately take the findings, the arguments etc. at face value. You should question what you read, you may agree with it, but you should consider: Are there any issues with the sample, data or methods chosen, the arguments made? Are there limitations to the work? Have other researchers criticised the work? What contribution does the work make? You should then be able to state the reasons why you agree or disagree with the findings or arguments of past studies. This should help you to make use of and reference to past literature throughout your project.
More broadly critical writing within this project should involve making critical reference to your own findings and other appropriate evidence (such as findings of past studies); in particular demonstrating how you reached a given conclusion and providing implications and explanations for your findings, arguments and ideas, backed with appropriate evidence. Critical writing gets you to go beyond just providing a description of something (say a finding or idea) and to consider, why? how? why is something important? The key thing is to avoid being overly descriptive.
Project Formatting Tips
We have seen some useful commands such as outreg2 to export regression results in the introductory computer class (a video on how to export results can be found under the ‘Stata’ tab). Your work should be formatted in a professional way, so I do not want to see raw Stata output (unless in an appendix). You should present results succinctly and any results presented should add value to your project and be presented in a way the reader can understand what the table is reporting. If you are not sure what output and statistics to include in tables, or how to present results, have a look at some example journal articles that use the econometric techniques you are using. You can put some results into an appendix if you want to demonstrate you have done them but don’t want to report them (fully) in the main bod; but all appendices should be referred to at some point in the main text (with the exception of your do-file). Make sure you report specification tests statistics you perform – these may be entered directly in tables or added to the text/a footnote. Here are some tips on how you can present results.
1. Variable descriptions
If you have a lot of variables (control variables) you may not want to describe each in detail, in which case including a table of variable descriptions can be a useful way to present this information succinctly. There are several ways to do this, and I have given an example below. Firstly, you may want to separate variables into your dependent variable, independent variables and control variables. If you have categorical variables, you may simply want to list the categories or you may prefer to list each dummy variable you will include (including the base category which you can indicate is the base category). If you use shortened names elsewhere, here is a good place to define them. If you have generated any new variables or made any adjustments to variables you can also include notes on how they were generated/adjusted here if you wish.
Table 1 Dependent variable, independent variable, and control variable
Variable Name Variable description Notes/categories
Dependent variable
Life_satisfaction Life satisfaction Only includes values between 1 to 7
Independent variable
lwage Log of hourly wage …
whr Weekly working hours …

Married =1 if this individual is married, 0 otherwise …
Numkids Number of dependent Children …
etc … …
Control variable
Age Age of individual …
Age2 Squared term of individual age …
etc … …
Notes:…
If you have quite a bit of detail in this table, it is probably best to put it in the appendix (if you want to put it in the main text, it is best placed in the data or methodology section) but refer readers to the table in the text for more information.
2. Formatting Descriptive Statistics
The descriptive statistics reported by Stata are not well formatted plus you may want to combine statistics from different output provided by different commands. Therefore, I recommend copying tables into Excel where you can re-arrange statistics and format tables nicely. When creating tables ensure the reader has all the information needed to understand the table, so include an informative table title, relevant information within the table (such as column/row headings) and add any relevant notes to the bottom of the table. In this assignment, you can divide the individuals into two groups who have high and neutral or low life satisfaction, for example:
Table 2 Descriptive table of high life satisfaction (life_satisfaction = 5, 6 or 7) and neutral or low life satisfaction individuals (life_satisfaction = 1, 2, 3 or 4)
High life satisfaction neutral or
low life satisfaction Difference
(1) vs (2)

Variables (1) (2) (3)

Job relevant variables
Lwage 2.362 2.159 0.203***
Total working hours per week 35.517 43.529 -8.012**
etc … … …

Marriage relevant variables
Married (0 = no; 1 = yes) 0.627 0.452 0.175**
Numkids 0.642 0.751 -0.109**
etc … … …

Demographic characteristics
Male (female = 0, male = 1) 0.489 0.435 0.054**
Age 39.721 39.886 -0.165**
Age square/1000 13.752 13.927 -0.175*
Self-rated health level 0.376 0.394 -0.018***
BMI 25.97 26.031 -0.061
Year of Education 17.54 17.475 0.065

Family information
Own house (0 = no; 1 = yes) 0.905 0.905 0
Household income (10,000 pounds) 0.420 0.360 0.06**

Area
Living in London (0 = no; 1 = yes) 0.192 0.215 -0.023***

etc…
Observations XXX XXX
*p 0.1; **p 0.05; ***p 0.01
Note: Self-rated health level is a dummy variable equal 1 if individual think him/herself has good or very good health and equal 0 if has normal or poor or very poor health. Household income household total income per month
Source: Understanding Society, wave 1 to 9.
A few things to note about the above example table (there’s a fine balance between providing enough information but not overloading a table so as it makes it difficult to read); you do not need to do things exactly as I have done (so long as you heed the advice):
• Titles should be informative and clear but not too long, you can add additional notes at the bottom of the table
• Numbers should be rounded to make the table easier to read and for numbers to fit! I have used 2 decimal places; you should typically round to 1 or 2 decimal places in tables presenting descriptive statistics. However, for regression output commonly 3 decimal places are used.
• You should provide the source of data in notes to the table.
• You should provide additional information about the sample: in this case the sample used were of working age– the information may be contained in notes to tables if detailed or even in the title if short. Where the sample size is not included in the table (say because you are using percentages across several variables) indicate this in notes to the table. In this case I listed the number of men and women underlying the estimates in the table, as all estimates were separate by gender. Note the number of observations were different for the % distribution of the average wages.
• Ensure the reader understands the numbers in the table e.g. if they are percentages (make sure it is clear whether and how they add up to 100), frequencies or in other units (such as £ for average wage) and what is contained in a given cell, row or column.
• If you have undertaken any statistical tests you can add any test statistics to the (notes to the) table or if this is too messy (as the case for the individual t-tests in this example) add a note to say differences are statistically significantly different (and at what level: usually people report at the 10, 5 or 1% level – in this case all significant differences are significant at the 1% level): you’ll see a succinct way to do this in the regression output. You can also add any test statistics to footnotes if you want to discuss test results within the text but not report them in a table.
• Note I often paste in tables and Stata output into documents in quite small font to make them fit and to save space. You should make sure for your projects that the font is a reasonable size for the reader and, if need be, present results in several tables. At least 10 is a good font size and I would not go below a font size of 8.
3. Formatting Regression Output
You will have already seen outreg2 as a means to export regression. As an example, I exported a result here:
(1) (2) (3) (4)
VARIABLES POLS RE FE xtoprobit

self -0.238*** -0.164*** -0.058* -0.142***
[0.024] [0.021] [0.032] [0.022]
lw_main 0.189*** 0.122*** 0.038*** 0.114***
[0.011] [0.009] [0.012] [0.009]
jwhrs -0.000 0.000 -0.001 -0.001*
[0.001] [0.000] [0.001] [0.000]
1.degree 0.081*** 0.111*** 0.007 0.079***
[0.015] [0.014] [0.047] [0.015]
female -0.052*** -0.034** -0.050***
[0.014] [0.013] [0.014]
age_dv -0.060*** -0.058*** 0.000 -0.068***
[0.004] [0.003] [0.019] [0.004]
c.age_dv#c.age_dv 0.001*** 0.001*** 0.000*** 0.001***
[0.000] [0.000] [0.000] [0.000]
1.married 0.306*** 0.229*** 0.045** 0.248***
[0.016] [0.014] [0.023] [0.014]
ndepchl_dv -0.019** -0.006 -0.008 -0.014**
[0.007] [0.006] [0.010] [0.007]
1.white 0.243*** 0.233*** 0.216***
[0.026] [0.024] [0.026]
1.london_se -0.049*** -0.036** 0.074 -0.045***
[0.016] [0.015] [0.057] [0.016]
2.wave -0.046*** -0.050*** -0.096*** -0.052***
[0.013] [0.012] [0.022] [0.013]
3.wave -0.136*** -0.144*** -0.227*** -0.142***
[0.014] [0.013] [0.038] [0.014]
4.wave -0.193*** -0.205*** -0.336*** -0.207***
[0.015] [0.014] [0.056] [0.014]
5.wave -0.185*** -0.203*** -0.377*** -0.212***
[0.015] [0.014] [0.074] [0.014]
6.wave -0.055*** -0.071*** -0

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