Stata 14 health stats work – finding percentages in raw data

Stata 14 health stats work – finding percentages in raw data

Im doing a pooled crossectional study of the obesity and overweight rates on productivity within the uk.

Controlling for gender, age, ethnicity, education level and NS SEC level.

I am using the active people survey from 2012-2013, 2013-2014 and 2014-2015. And if it lets me will attach the stata files for all these years if not it is availbale on the essex data archive https://discover.ukdataservice.ac.uk/.

My issue is that my productivity data is in nuts3 regions whilst the active people survey data is in local authority data.

First thing i need doing is someone to get the percentages for all my control variables out of the raw data at the nuts3 level. I have attached a document converting the local authority labels from within the data to their nuts3 groups.

I need the percentages for each category of the variables below within each of the english nuts3 regions:

– Pos. = 48 Variable = d1 Variable label = D1 – RESPONDENT GENDER

This variable is numeric, the SPSS measurement level is NOMINAL

SPSS user missing values = -1.0 thru None

Value label information for d1

Value = 1.0 Label = Male

Value = 2.0 Label = Female

– Pos. = 55 Variable = D3_bands_3age Variable label = D3 – Age Bands (3) – 16-34, 35-54, 55+

This variable is numeric, the SPSS measurement level is NOMINAL

SPSS user missing values = -1.0 thru None

Value label information for D3_bands_3age

Value = 1.0 Label = 16-34

Value = 2.0 Label = 35-54

Value = 3.0 Label = 55+

– Pos. = 73 Variable = D4_bands_6 Variable label = ETHNIC GROUP (6)

This variable is numeric, the SPSS measurement level is NOMINAL

SPSS user missing values = -1.0 thru None

Value label information for D4_bands_6

Value = 1.0 Label = White

Value = 2.0 Label = Mixed

Value = 3.0 Label = Asian

Value = 4.0 Label = Black

Value = 5.0 Label = Other

Value = 6.0 Label = Chinese

– Pos. = 80 Variable = d6 Variable label = D6 – HIGHEST QUALIFICATION OBTAINED

This variable is numeric, the SPSS measurement level is NOMINAL

Value label information for d6

Value = 1.0 Label = HIGHER EDUCATION & DEGREE OR DEGREE EQUIVALENT

Value = 2.0 Label = OTHER HIGHER EDUCATION BELOW DEGREE LEVEL

Value = 3.0 Label = A LEVELS & EQUIVALENTS

Value = 4.0 Label = TRADE APPRENTICESHIPS

Value = 5.0 Label = GCSE/O LEVEL GRADE A*-C (5 OR MORE)

Value = 6.0 Label = GCSE/O LEVEL GRADE (LESS THAN 5 A*-C)

Value = 7.0 Label = OTHER QUALIFICATIONS

Value = 8.0 Label = NO QUALIFICATIONS

Value = 9.0 Label = REFUSED

Value = 10.0 Label = DON’T KNOW

Value = 11.0 Label = RESPONDENT QUITS INTERVIEW

– Pos. = 156 Variable = NS_SEC_bands_3 Variable label = NS SEC (3 bands)

This variable is numeric, the SPSS measurement level is NOMINAL

SPSS user missing values = -1.0 thru None

Value label information for NS_SEC_bands_3

Value = 1.0 Label = NS SEC1 to 4

Value = 2.0 Label = NS SEC 5 to 8

Value = 3.0 Label = NS SEC 9

– and the percentage weight (ie percentage of respondents) of each local authority region within each of the nuts3 regions

The Second thing is to use the local authority weight within each of the nuts3 regions to get a weighted average of the obesity and overweight rates (which is attached in precentages for each local authority region) for each english nuts3 region.

Thirdly i would like you to do a pooled crossectional regression of the nuts3 regions obesity and overweight percentages and all the control variables precentages on the unsmoothed GVA (excluding rental income) per hour worked (£) nuts3 productivity data (also attached) for the years 2013/2012-2013(for the obese,overweight and control vars), 2014/2013-2014 and 2015/2014-2015.

i would also like the whole process screenshotted and pasted on a word document throughout so i know its legitimate and im working with correct data.

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