Creating a 2000 IES-LFS Database in Stata



PROVIDE Project Technical Paper 2005:1
this assumption various households now earn income from labour although the person-
level labour income variables for that household are all zero.

February 2005


In do-file inclabscaling.do a scaling factor is created that either leaves the person-
level
inclabp_new variable in tact or scales it up or down so that the sum of the
inclabp_new variables in each household equal the household-level inclab variable. For
4,946 households
inclab remained zero before and after the adjustments. Variable
inclab remained positive and unchanged for a further 7,428 households. For all these
households the
inclabp_new variables remained unchanged. For 11,399 households the
inclabp_new variables were scaled upwards by an average factor of 1.43 due to changes
made to
inclab. A further 2,410 households initially reported zero income from labour
but now had positive income figures. For these households the head of the household
was assumed to have earned that income. The original
inclabp_new variable was saved
as
inclabp_old and inclabp_new was scaled up to its new levels.

Figure 1 compares the new and old versions of person-level labour income. As
expected the average income is now slightly higher for all of the occupation groups,
except for farmers and unspecified workers. Many households that previously reported
zero income from labour were now added to these two groups due to the adjustments
made to
inclab in fixing.do. Specifically the addition of income from home production
to
inclab explains the increase in the number of agricultural workers. The new workers
added to this group obviously had a lower average wage than the rest of the agricultural
workers, which explains why the average wage drops. Most of the other ‘new’ additions
were allocated to the unspecified category, because these workers did not previously
report income and never specified an occupation category. The average wage of
unspecified workers drops for the same reason as the drop in agricultural wages.

4.2.5. Forming factor groups (newfact.do and newfact_old.do)

Do-file newfact.do creates a province-level occupation code variable called newfact.
This variable is similar to
mergefact but disaggregates workers further by race and
province. It contains 88 different types of labour. The original occupation groups
mapped from the LFS 2000:2 are (1) legislators, senior officials and managers; (2)
professionals; (3) technical and associate professionals; (4) clerks; (5) service workers
and shop and market sales workers; (6) skilled agricultural and fishery workers; (7)
craft and related trades workers; (8) plant and machine operators and assemblers; (9)
elementary occupations; (10) domestic workers; and (11) not adequately or elsewhere
defined, unspecified. In some provinces certain of these province-race-labour sub-
categories are not well represented, in which case aggregate groups are formed by
merging an occupation group with another of similar skills level. Thus, high skilled are

61

© PROVIDE Project



More intriguing information

1. Federal Tax-Transfer Policy and Intergovernmental Pre-Commitment
2. The name is absent
3. The name is absent
4. Transport system as an element of sustainable economic growth in the tourist region
5. The name is absent
6. Proceedings of the Fourth International Workshop on Epigenetic Robotics
7. The name is absent
8. Climate change, mitigation and adaptation: the case of the Murray–Darling Basin in Australia
9. Gender and aquaculture: sharing the benefits equitably
10. A Rare Case Of Fallopian Tube Cancer
11. The Composition of Government Spending and the Real Exchange Rate
12. Integrating the Structural Auction Approach and Traditional Measures of Market Power
13. The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?
14. Cardiac Arrhythmia and Geomagnetic Activity
15. Educational Inequalities Among School Leavers in Ireland 1979-1994
16. Revisiting The Bell Curve Debate Regarding the Effects of Cognitive Ability on Wages
17. An Estimated DSGE Model of the Indian Economy.
18. European Integration: Some stylised facts
19. Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages
20. The fundamental determinants of financial integration in the European Union