Creating a 2000 IES-LFS Database in Stata



PROVIDE Project Technical Paper 2005:1

February 2005


P2102Total |

26835

1152.961

4247.576

0

360000

P2102Total~K |

26835

1152.961

4247.576

0

360000

P2103Total |

26835

2603.309

27987.51

-20000

3000000

P2103Total~K |

26835

2603.309

27987.51

-20000

3000000

P2104Total |

26835

4470.326

39426.95

0

3105800

P2104Total~K |

26835

4470.326

39426.95

0

3105800

P2105Total |

26835

432.705

7858.462

0

874000

P2105Total~K |

26835

432.705

7858.462

0

874000

3.2.6. Annualising and creating control totals (annualise.do and totals.do)

Some of the income and expenditures reported in the IES 2000 questionnaire is weekly or
monthly, and should be converted to annual figures. This do-file simply changes all the
weekly or monthly figures to annual figures. Do-file
totals.do recalculates the income and
expenditure sub-totals and also creates variables
totincCHECK and totexpCHECK, which can
be used to make sure that the mapping of income and expenditure in do-files
mapinc.do and
mapexp.do is done correctly. At the end of do-file totals.do food expenditure values that are
missing or zero are imputed. A similar process if followed for missing or zero tax expenditure
values when the total income level of the household creates the expectation that the household
should have reported tax expenditure. A discussion of food and tax expenditure imputations
follows in section 3.2.7.

3.2.7. Imputing ‘missing’ food and tax expenditure values

The total food expenditure variable of all households reporting zero food expenditure was
changed to missing based on the assumption that each household has to at least report some
expenditure on food. These ‘missing’ food expenditure values were then imputed by
estimating a double-log Engel equation of the form

ln(Yi)=a+b.ln(Xi)+c.ln(Hi)+εi
where a, b and c are constants, Yi is the food expenditure (logfoodexp), Xi the total household
expenditure (
logtotexp), Hi the household size (logH) and εi the error term. This double-log
formulation ensures that the share of total expenditure spent on food declines as total
expenditure increases, while larger households benefit from scale economies (see Van der
Berg
et al., 2003b). The following regression results were obtained (sampling weights used):

Source |        SS        df        MS

Number of obs =   25944


F( 2, 25941) =30994.49

Prob > F      =  0.0000

R-squared =  0.7050

Adj R-squared =  0.7050

Root MSE      =  .47803


---------+------------------------------

Model |   14165.074     2    7082.537

Residual |   5927.7668 25941  .228509572

---------+------------------------------

Total |  20092.8408 25943   .77449951

48


---------------

logfoodexp |

Coef.

Std. Err.

t

P>|t|

[95% Conf.

----------

Interval]

logtotexp |

.5992633

.0025101

238.74

0.000

.5943434

.6041832

logH |

.2185103

.0041317

52.89

0.000

.210412

.2266087

_cons |
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

2.439185

.0247711

98.47

0.000

2.390632

2.487738

_ _ _ _ _ _ _ _ _ _

© PROVIDE Project



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