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321_09_slides11

# 321_09_slides11 - Qualitative Information Chapter 7 Econ...

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Qualitative Information Chapter 7 Econ 321 Introduction to Econometrics Econ 321-Stéphanie Lluis 1

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Outline Qualitative variables Dummy variables Categorical variables Interaction effects Chow test Test for differences between categories When y is = 0 or 1 Program Evaluation Econ 321-Stéphanie Lluis 2
Qualitative data Person wage educ exper female Married 1 3.10 11 2 1 0 2 3.24 12 22 1 1 3 3.00 11 2 0 0 4 6.00 8 44 0 1 5 5.30 12 7 0 1 . . . . . . . . . . . . 525 11.56 16 5 0 1 526 3.50 14 5 1 0

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Using a single dummy independent variable u female educ wage 0 1 0 Female=1 for women, 0 for men For men: wage m = 0 + 1 educ For women: wage w = 0 + 1 educ + 0 = wage m + 0 So 0 is difference in hourly wage between men and women for the same level of education. H 0 : 0 =0 H A : 0 0 Here benchmark regression is the male one (reference group) Intercept shift in wage regression for women
Graph of wage = 0 + 0 female + 1 educ, 0 <0 wage educ women wage=( 0 + 0 ) + 1 educ men wage= 0 + 1 educ 0 + 0 0 slope 1 0 = E(wage| female=1, educ) - E(wage| female=0, educ) 0

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Other Example : effects of training grants on hours of training ) log( 07 . 6 ) log( 98 . 0 25 . 26 67 . 46 employ sales grant hrsemp Grant =1 if firm receives a job training grant, 0 if not. Hours of training per employee is higher by 26.25 hours for firms receiving a grant, holding sales and employ fixed. t-test on grant coefficient
Example: marriage and gender 2 2 00053 . 029 . r .00054expe - .027exper educ .079 singfem 110 . 198 . 213 . 321 . ) log( tenure tenure marrfem marrmale wage Categories: married men married women single men single female select a base/reference group: e.g. single men

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Log(wage) educ Married women lwage=(.321-.198) + 1 educ Single men lwage=.321 + 1 educ .321 single women lwage=(.321-.11) + 1 educ married men lwage=(.321+.213) + 1 educ .088
Multiple categories Categorical variables often take more than 2 values industry codes agriculture, forestry and fishing; manufacturing; services; transport and communication etc occupation codes professional, managerial, technical, skilled manual, etc regions north west, north east, midlands, east, south west, south east, Wales etc Ordinal variables Job satisfaction: “strongly disagree, ..., indifferent,..., strongly agree” 1 3 5

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Categorical variables Example Performance as a function of job satisfaction Performance = β 0 + β 1 JS + u Define JS1=1 if JS=1 and 0 otherwise,
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321_09_slides11 - Qualitative Information Chapter 7 Econ...

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