STAT6077 Key Topics in Social Science: Measurement and Data Semester 2, 2021/2022
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STAT6077 Key Topics in Social Science: Measurement and Data
Semester 2, 2021/2022
Assignment: STAT6077 Key Topics in Social Science: Measurement and Data
TASK 1: POVERTY AND INEQUALITY
In this task you will use data from the 2006/07 Family Resource Survey (FRS_0607.dta). The data contain observations from 49,279 individuals (adults and children) living in 21,187 households in the UK. Note that, since weights are not included in the dataset, you do not need to use weights for this task. The table below provides a list of variables.
Analysis of FRS data (Note: please provide your Stata do-file in the Appendix.)
Variable name |
Description |
sernum |
Unique household identification number |
indinc |
Individual income gross |
nindinc |
Individual income net |
age80 |
Age (all adults aged 80 over are coded as 80) |
famsize |
Number of individuals in household |
empstati |
ILO employment status variable |
By analysing this dataset, answer the following questions:
a) How does the choice of gross or net household income influence our estimate of
the percentage of the population that are ‘poor’? To answer this question, calculate the percentage poor for gross and net household income separately and then compare the results. For each income type, use three relative poverty lines:
40%, 50% and 60% of the median household income. [14]
b) How does the composition of the poor (focusing on children and the elderly) vary according to the choice of equivalence scale? To answer this question, compare the percentage of children (defined as under 18) and the percentage of the elderly (defined those aged 65 and older) among the poor. Use net household income, and a relative poverty line that is 40% of the median. Use three different equivalence scales of your choice. [8]
c) Explain any assumptions that you are making in your analysis for a) and b). [3]
[TASK 1 total: 25]
TASK 2: EDUCATION
Task 2 asks you to analyse educational achievement data from PISA .
Analysis of PISA data (Note: please provide your Stata do-file in the Appendix.)
Your task is to examine the educational achievement of 15 year olds in Spain. The data set is called “PISA_2012_ESP”, a reduced version of the PISA 2012 data set. See the following table for a description of the variables in the data:
Variable name |
Description |
country |
=ESP for Spain |
schoolid |
Unique school identifier and Primary Sampling Unit |
stidstd |
Unique student identifier |
w_fstuwt |
Student weight variable – please use for all the calculations |
immig |
Immigration status |
st04q01 |
Gender |
hisei |
Ganzeboom index, continuous variable. Higher values indicate higher parental occupational status |
hisced |
Highest educational level of parents |
math |
PISA achievement score (mean of the 5 plausible values) – this is your dependent variable |
a) Estimate the population mean and population standard deviation of math and of hisei. [2]
b) Use regression to describe the relationship between PISA math achievement and three variables: hisei, immig and hisced
• First consider each of these three variables separately, in three different regression models.
• Then, in a fourth regression model, include each of these three variables together in the same model.
Present the results of the four models in one single table that includes all important information in a way that results of the regressions can be easily compared.
[6]
c) Interpret the results from your models in b).
[8]
[TASK 2 total: 16]
TASK 3: SOCIAL MOBILITY
Parts a)-c) do not require you to conduct your own data analysis but ask you to interpret existing analysis of Understanding Society data; part d) asks you to reflect on the literature on social mobility.
Interpretation of Understanding Society data Here we examine educational mobility, based on existing analysis of the Understanding Society (US) Wave 1 (2009) data set. The following page contains a log of Stata output from analysis of these US data. The data relate to individuals aged 30 and over. The table below contains a description of the relevant variables.
Variable name |
Description |
a_hidp |
household identifier |
a_pno |
person (individual) number |
a_indinus_xw |
weight |
a_psu |
Primary Sampling Unit |
a_hiqual_dv |
Highest qualification of individual (1- Degree; 2- Other higher; 3- A- level etc.; 4– GCSE etc.; 5- Other qualifications; 9 – No qualifications) |
a_paedqf |
Report of father’s educational qualifications (1- he did not go to school at all; 2– he left school with no qualifications or certificates; 3- he left school with some qualifications or certificates; 4– he gained post-school qualifications (e.g. city & guilds) 5- he gained a university degree or higher; -9: missing). |
a_panssec5_dv |
Report of father’s occupation when individual was aged 14: NSSEC 5 categories (1-Management & professional; 2 – Intermediate; 3 – Small employers & own account; 4 – lower supervisory & technical; 5 – Semi-routine, routine & never worked or long-term unemployed) |
a_sex |
Individual’s gender: 1-male; 2-female |
a_dvage |
Individual’s age |
Answer the following questions.
a) Interpret the results of the analysis on the following page. [6]
b) Describe the possible limitations of this empirical evidence as an assessment of educational mobility in the UK. [3]
c) Describe how you could conduct further analysis of this dataset to examine whether there is evidence of changes in educational mobility over time. [3]
2022-03-25