Unraveling the causes of health inequalities
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Unraveling the causes of health inequalities. Adam Wagstaff. What’s it all about?. Having measured inequalities, natural next step is to seek to account for them TN#15 and TN#14 present methods aimed at decomposing causes of inequality

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Unraveling the causes of health inequalities

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Unraveling the causes of health inequalities

Adam Wagstaff


What’s it all about?

  • Having measured inequalities, natural next step is to seek to account for them

  • TN#15 and TN#14 present methods aimed at decomposing causes of inequality

  • Core idea is that outcome variable is caused by a set of determinants, which vary systematically with SES

  • E.g. poor have lower income but also less knowledge, worse access to drinking water, lack insurance coverage, etc.

  • Want to know extent to which inequalities in health status are due to (a) inequalities in income, (b) inequalities in knowledge, (c) inequalities in access to drinking water, etc.


Oaxaca

  • Oaxaca decomposes gap in outcome vbl between two groups

  • Attraction of Oaxaca over decomposition in TN#14 is that it allows for the possibility that inequalities caused in part by differences in effects of determinants

  • For example, health of the poor may be less responsive to changes in insurance coverage, or to changes in access to drinking water, etc.


equation for non-poor

y

ynon-poor

equation for poor

ypoor

xpoor

xnon-poor

x


equation for non-poor

y

ynon-poor

equation for poor

ypoor

xpoor

xnon-poor

x


But how far due to diffs in b’s rather than diffs in x’s?

equation for non-poor

y

ynon-poor

equation for poor

ypoor

xpoor

xnon-poor

x


Oaxaca #1: eqn (4)

equation for non-poor

y

ynon-poor

Dbxnon-poor

equation for poor

Dxb poor

ypoor

xpoor

xnon-poor

x


Oaxaca #2: eqn (5)

equation for non-poor

y

ynon-poor

Dxbnon-poor

Dbxnon-poor

equation for poor

Dbxpoor

Dxb poor

ypoor

xpoor

xnon-poor

x


Seeing how to do it …through an example from Vietnam

Av. HAZ z-score kids<10 yrs:

Poor = -1.86

Non-poor = -1.44

Diff = 0.42

U.S. reference group = 0.00


The regression equation

  • y is the HAZ malnutrition score

  • Same regression model as Wagstaff et al. [8]

  • x includes

    • log of the child’s age in months (lnage)

    • sex = 1 if male

    • safewtr = 1 if drinking water is safe

    • oksan = 1 if satisfactory sanitation,

    • years of schooling of the child’s mother (schmom)

    • log of HH per capita consumption (lnpcexp)

    • poor = 1 if child’s HH is poor (if pcexp<Dong 1,790,000


Differences in means between non-poor and poor


Testing for significant differences in b’s in Stata

xi: reg haz i.poor*lnage i.poor*sex i.poor*safwtr i.poor*oksan i.poor*schmom i.poor*lnpcexp [aw=wt]

testparm _I*


Stata regression output


Stata F-test output—sign. diffs.  use separate eqns

. testparm _I*

( 1) _Ipoor_1 = 0.0

( 2) _IpooXlnage_1 = 0.0

( 3) _IpooXsex_1 = 0.0

( 4) _IpooXsafwt_1 = 0.0

( 5) _IpooXoksan_1 = 0.0

( 6) _IpooXschmo_1 = 0.0

( 7) _IpooXlnpce_1 = 0.0

F( 7, 5154) = 2.03

Prob > F = 0.0472


Oaxaca in numbers


Oaxaca in a chart

Oaxaca #1 Oaxaca #2


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