Creating a 2000 IES-LFS Database in Stata



PROVIDE Project Technical Paper 2005:1

February 2005


Table 3: Cross-tabulating gender, location and race

Gender________

_____________Person-level_____________

__________Household-level__________

LFS^ IES

Male__________

Female___________

Male___________

Female__________

Male___________

________48799

_____________424

__________15785

_____________79

Female__________

__________414

___________53960

____________46

_________10196

Accuracy________

_______99.2%

__________99.2%

_________99.7%

_________99.2%

Location________

__________Household-level__________

LFS^ IES

Urban_________

Rural______________

Urban__________

________14466

_____________383

Rural____________

__________1415

____________9854

Accuracy________

_______91.1%

__________96.3%

Race___________

________________________________Person-level________________________________

LFS^ IES

African_________

Coloured__________

Asian___________

White___________

African__________

________83896

______________60

______________2_

______________12

Coloured________

_____________93

___________11462

______________12

_______________7

Asian___________

____________3_

_______________0_

__________2040

_______________4

White___________

____________36

_______________6_

_____________0_

__________5914

Accuracy________

_______99.8%

__________99.4%

_________99.3%

_________99.6%

_____________________________Household-level_____________________________

LFS^ IES

African_________

Coloured__________

Asian___________

White___________

African__________

________20719

________________15

______________1_

_________________5

Coloured________

_____________33

____________2689

_____________2_

______________1_

Asian___________

_____________1_

_______________0_

____________523

_______________2

White___________

____________6_

_______________2_

_____________0_

__________2098

Accuracy________

_______99.8%

__________99.4%

_________99.4%

_________99.6%

2.4. IES 2000 data problems

2.4.1. Literature review

Most of the data problems in the IES 2000 dataset can be ascribed to accounting and coding
errors (Poswell, 2003). There are also a few inconsistencies when compared to the IES 1995
dataset. This restricts the confidence with which conclusions can be drawn about changes in
income or expenditure over time. Van der Berg
et al. (2003a) point out some of the specific
problems that they have encountered in the IES 2000 dataset:

When compared to IES 1995 the 2000 results indicate that income in South Africa has
been declining strongly in real terms between 1995 and 2000. This contradicts national
accounts and demographic statistics also compiled by Statistics South Africa. Statistics
South Africa has since admitted that the surveys are incomparable.
15, 16 When building a

15 This inconsistency is also pointed out by Simkins (2003). Simkins looks at the components of income and
finds that there are large and inexplicable drops in some of these components, particularly net profits
from business (half the 1995-level in nominal terms), occupational perquisites (down 42%) and
16

© PROVIDE Project



More intriguing information

1. Spectral calibration of exponential Lévy Models [1]
2. Structure and objectives of Austria's foreign direct investment in the four adjacent Central and Eastern European countries Hungary, the Czech Republic, Slovenia and Slovakia
3. Gerontocracy in Motion? – European Cross-Country Evidence on the Labor Market Consequences of Population Ageing
4. Comparison of Optimal Control Solutions in a Labor Market Model
5. Opciones de política económica en el Perú 2011-2015
6. The name is absent
7. The name is absent
8. Are Japanese bureaucrats politically stronger than farmers?: The political economy of Japan's rice set-aside program
9. Skill and work experience in the European knowledge economy
10. Placenta ingestion by rats enhances y- and n-opioid antinociception, but suppresses A-opioid antinociception