One way to reduce the computational burden might be to group covariates
describing related phenomena together and then adding or removing them by
group. For instance, we are interested in the contribution of the housing market
to segregation but we only have some characteristics of the dwellings such as the
number of rooms, the type of construction (hut, house brick, shacks...), some of
their equipments (light, water, toilets...).
Then, all these variables together are
going to tell us something about the housing market.
Thus, it does not make
sense to evaluate their contribution individually. We will use this alternative in
our analysis.
Thus, our final specification is:
Segregation
ij
(
t
) =
α
+
Segregation
i
(
t

1)
×
β
1
+
Demographics
ij
(
t

1)
×
β
2
+
Income
ij
(
t

1)
×
β
3
+
HousingCharacteristics
ij
(
t

1)
×
β
4
+
PublicGoods
ij
(
t

1)
×
β
5
+
ij
(10)
with
β
1
, β
2
, β
3
, β
4
, β
5
column vectors of coefficients associated to a particular
regressor in the corresponding subsets of regressors.
Segregation
i
(
t

1) is a row
vector composed of two regressors, the cumulative distributions of Whites and
Blacks of the subplace i at the previous period.
Demographics
ij
(
t

1) is a row
vector composed of six regressors, the mean age, the marriage to divorce ratio,
40
the sex ratio, the average number of years of schooling, the share of individuals
speaking ”White” languages,
41
, and the unemployment rate.
All these regres
sors are computed by subplace i and population group j at the previous period.
Income
ij
(
t

1) is the mean income of group j individuals living in subplace i at
the previous period.
HousingCharacteristics
ij
(
t

1) is a row vector composed of
five regressors, the mean number of rooms in the dwelling, the share of houses in
brick, the share of informal dwellings, the share of owners, and the rural to urban
ratio.
PublicGoods
ij
(
t

1) is a row vector composed of two regressors, the share of
households not having access to a refuse disposal, and the share of households not
having access to public water.
Finally, the dependent variable
Segregation
ij
(
t
)
is the cumulative distribution of group j individuals for subplace i a the current
period.
3.6
Practical issues
We will face some practical issues with the estimation of the segregation curves
and their inference. First, observed and counterfactual segregation curves might
40
We aggregate all types of marriage (civil monogamous union, polygamous, and traditional).