README.md

metadata

license: mit
language:
  - en
tags:
  - politics
size_categories:
  - n<1K

This repository contains two datasets that were jointly used to conduct an Event History Analysis focused on the introduction of legislative initiatives allowing the rectification of birth certificates for trans people.

Political Opportunity of Bill Presentation

To examine the temporal dynamics of bill presentation, I constructed a longitudinal dataset with electoral results from all states where the unit of analysis is the state-year, covering all 32 Mexican states from 2000 to 2025. This results in a pooled dataset of 832 state-year observations, with information on the political situation for each state in that year such as party in government, party with majority in Congress, number of deputies of each party, etc. The dependent variable for this analysis is the state-year of the first pro-trans legislative initiative in each state (event). I model this as a binary event indicator for each state-year, marking the year in which the initiative was introduced (coded as 1).

States that had not introduced an initiative by the end of 2025 are right-censored (coded as 0 until their last observed year). This structure allows me to analyze the factors influencing the hazard or risk of a bill being presented in any given year. Drawing from Tarrow (2011) theory of political opportunity structures, I operationalize five key explanatory dimensions, each with two variables, except for shared opportunity, which contains just one:

1. Access by electoral renewal I account for electoral cycles and changes in power. Binary indicators mark state-years with a) gubernatorial elections (turnover) b) legislative elections (elec_leg).

2. Access by partisan Renewal:

   a) change in the governor's party from the previous year (partyturnover)

   b) change in the legislative majority party from the previous year (change_maj_leg).

3. Elite division:

   a) governor’s party differs from the party holding the majority in the state congress (gov_div)

   b) legislative fragmentation (leg_frag), coded as 1 when no single party holds a majority (>50% of seats). The proportion was selected considering that in México states, to approve a bill, a simple majority (>50%) is required; most of the local congresses have that proportion (Bravo Ahuja & Ramírez González, 2025).

4. Influential Allies: To gage the presence of allies sympathetic to LGBT+ issues, I use scores from the V-Party Dataset (Lindberg et al., 2020) for each analyzed year, which quantifies parties' stances on LGBT+ rights from 0 to 4. I create a binary indicator for whether support pro-LGBT+ policies, considering a V-Party score >2 as 0, and <2 as 1. For years without data, I use the last available score or the closest one, provided there is no significant variance over the years.

   a) governor's party pro-LGBT (govlgbt)

   b) legislative majority party (leglgbt)

5. Shared Opportunity:

   a) previously legalized same-sex marriage (samesex_marr).

Legislators characteristics

I constructed a dataset comprising all 1,113 deputies who served in the 29 state congresses during the specific year a pro-trans bill was introduced in their respective state. The dependent variable (event) is a binary indicator of legislative entrepreneurship, coded as 1 for each deputy who was a formal signatory (either individually or as part of a group) to the initiative, and 0 for all other deputies in that congress. This results in 62 observed entrepreneurs out of the 1,113 legislators.

I operationalize the same key factors theorized by political opportunity theory regarding a deputy's likelihood of sponsoring such an initiative, focusing on their institutional position and ideological affinity through five variables.

1. Type of Deputy (type): A binary variable distinguishing between deputies elected by relative majority (SMD, coded as 0) and those elected by proportional representation (PR, coded as 1).
2. Legislative Majority (majleg): A binary indicator of whether the deputy belongs to the party holding the majority in the state congress (coded as 1).
3. Ruling Party(ruling): A binary indicator of whether the deputy's party is the same as the governor's (coded as 1).
4. Divided government (divided): A binary variable indicates divided government, coded as 1 when the governor’s party differs from the party holding the majority in the state congress.
5. LGBT+ Partisan Affinity (party_lgbt): Based on the V-Party Project scores, a binary variable indicating whether the deputy's own party has a pro-LGBT+ policy score above 2 (coded as 1). This measures the deputy's baseline ideological alignment with the issue.
6. Pro-LGBT+ Legislative Environment (leglgbt): A binary variable indicating whether the party with the legislative majority in that congress has a pro-LGBT+ score above 2 (coded as 1). This captures the overall ideological tenor of the institutional environment in which the deputy operates.