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information at the legislator-period level. In other words, each observation of the new
samples is a legislator that ran for a gubernatorial or mayoral position at t+l. If she won
the election, the variable success equals "1", zero otherwise. It must be remembered that
the data used in chapters 5 and 6 were at the bill level, where individual legislators were
the units of analysis. There, information was used at the maximum level of
disaggregation. In this case, my equation tries to capture the accumulated activity of a
Deputy during her congressional tenure immediately before running for a subnational
spot. This makes it inaccurate to continue working with information at the bill level. As
a consequence, I also collapsed the covariates of the right-hand side of the equation at
the legislator-period level. Such a decision may have consequences on the structure of
the variables, and might also affect the results. In order to avoid any kind of artificial
effect due to measurement error, I prefer to estimate the models with different
indicators. This is particularly salient for my principal covariate of the estimation, the
legislative submission variable.
I chose to work with two different measures of targeted legislative activity. The
first is the number of bills with territorial content submitted during the legislative period
prior to the executive candidacy. This variable will always be positive and ranges
between zero to 48 for municipal bills, and 0 to 156 for provincial legislation. The
distribution is shown below in Figure 7.1, and it reflects a high degree of variation across
individuals. A valid reason to explain these differences is linked with ambition, as will
be further tested. However, other idiosyncratic factors (individual skills, professional
background, or more money to hire additional staff members) could affect the global
amount of local bills submitted. As an attempt to overcome these differences, I use the
ratio of bills with a territorial content over the whole sample of bills submitted by a