covariates for state i that varies according to year t,
E
2
is a vector of associated effects, E
it
is a time-varying dummy variable denoting whether a FEP law had passed in state i by
year t,
E
3
is a vector of associated effects, A
it
is a time-varying dummy variable denoting
whether an open accommodations had passed in state i by year t,
E
4
is a vector of
associated effects, and
∈
it
is an error term. Hence,
E
1
and
E
2
may be interpreted as vectors
of the net effects associated with covariates X
i
and Z
it
, respectively, controlling for the
factors leading to the passage of FEP and open accommodation laws.
DATA
This model requires a data set with a variety of annual information on the social,
political, economic, and institutional characteristics of thirty-seven “northern” states
during the period 1941-1968.
3
No such data set currently exists, so I constructed the
appropriate data set from cross-sectional data on the states that I collected from a wide
range of published and unpublished sources, including government reports, private
publications, and archival records. Whenever possible, I sought annual data, but in the
instances where they were not available, I collected as much data as possible and then
generated annual data through linear interpolation.
My data set is organized in the standard unit-time format required by discrete-
time, event-history models—in this case, state-year observations. The first year for which
I record observations on the states is 1941. I continue to record observations on all thirty-
3
I exclude thirteen states altogether, eleven states from the South as well as Alaska and
Hawaii. I follow V.O. Key (1949) in defining the South as the set of states once
belonging to the former Confederacy. This includes Alabama, Arkansas, Florida, Georgia,
Mississippi, North Carolina, Louisiana, South Carolina, Tennessee, Texas, Virginia. The
exclusion of Alaska and Hawaii is consistent with a widespread convention established in
studies of state economic performance (e.g., Brace 1993).
Convention