A Dataset of Latin American Presidents, 1990-2019 (v2.0)

Creator: Manuel A. Meléndez-Sánchez (mmelend@g.harvard.edu)

Updated: July 24, 2019 (16:05 EDT)

A. Suggested Citation

Meléndez-Sánchez, Manuel. 2019. A Dataset of Latin American Presidents, 1990-2019 (Version 2). Retrieved from https://github.com/melendezsanchez/latam_presidents/.

B. Overview

This dataset includes information about the 162 presidential terms that lasted for more than 24 hours between January 1, 1990 and July 23, 2019 in 18 Latin American countries:

1. Argentina
2. Bolivia
3. Brazil
4. Chile
5. Colombia
6. Costa Rica
7. Dominican Republic
8. Ecuador
9. El Salvador
10. Guatemala
11. Honduras
12. Mexico
13. Nicaragua
14. Panama
15. Paraguay
16. Peru
17. Uruguay
18. Venezuela

C. Variables

The current version of the dataset includes the following variables:

• Observation ID (term_id): The unit of observation is the leader-term. So, for example, there are two separate observations to Argentinian president Carlos Menem: one for his first term (1989-1995) and another for his second term (1995-1999). Each observation has a unique term ID captured by this variable. Term IDs concatenate a country code, the first word of the leader’s last name, and the last two integers of the year in which the term began.

• Country Name (country_name): The name of the country corresponding to each observation.

• Country Code (country_code): A three-character code corresponding to the country for each observation.

• Leader’s Last Name (hog_last): The leader’s (“head of government”, or hog) last name.

• Leader's First Name (hog_first): The leader’s (“head of government”, or hog) first name.

• Number of Consecutive Terms (term_c): This counts the number of consecutive terms that the leader has served, including the current observation.

• Number of Total Terms (term_t): This counts the total number of consecutive AND non-consecutive terms the leader has served, including the current observation.

• Term Start Year (term_start_year): The year in which the current observation (term) began.

• Term End Year (term_end_year): The year in which the current observation (term) ended.

• Party Name (party_full): The name, in English, of the party on which the leader ran. Non-party candidates are listed as Independent.

• Party Abbreviation (party_short): A 2-4 letter acronym identifying the party upon which the leader ran. Most of these acronyms correspond to the acronyms used by the parties in their own literature.

• Party ID (party_id): A unique identifier for each party. It is generated by concatenating the party abbreviation (party_short) with the country code (country_code).

• Path In (path_in): The path through which a presidential term began, coded as follows: 0 = democratic elected; 1 = democratically reelected (immediate reelection only); 2 = constitutional succession following vacancy; 3 = coup or other irregular, extra-constitutional path; 4 = other.

• Path Out (path_out): The path through with a presidential term ended, coded as follows: 0 = regular end of term, not eligible for (immediate) reelection; 1 = regular end of term, won reelection; 2 = regular end of term, lost reelection; 3 = regular end of term, eligible for (immediate) reelection but did not seek it; 4 = death; 5 = resignation; 6 = impeachment; 7 = coup or other irregular, extra-constitutional path; 8 = other; 9 = leader still in office.

• Doyle’s Populism Indicator (dd_pop): Dummy indicating whether the leader was identified as populist (1) in David Doyle’s 2011 paper [1]. Observations that were not coded by Doyle are listed as NAs.

• Campello’s Campaign Score (dc_camp): Variable indicating whether the leader’s campaign advocated statist (0) or neoliberal (1) policies, as per Daniella Campello’s 2014 paper [2]. I add a third category (2) for observations that fall within Campello’s temporal scope conditions but where not coded in paper because they were not preceded by a campaign. All other observations that were not coded by Campello are listed as NAs.

• Campello’s Government Score (dc_gov): Variable indicating whether the first year of the leader’s government advocated statist (0) or neoliberal (1) policies, as per Daniella Campello’s 2014 paper [2]. Observations that were not coded by Campello are listed as NAs.

• Campello’s Policy Switch Score (dc_switch): Dummy variable indicating whether leaders carried out a “policy switch,” defined as switching from a statist campaign platform to a neoliberal government or vice versa. This variable is generated by comparing scores on the dc_camp and dc_gov variables. 1 indicates a policy switch, 0 indicates no switch, and all observations not coded by Campello are marked NA.

• Team Populism’s Left-Right Score (tp_lr): Variable indicating the leader’s ideological orientation, as coded by Team Populism [3]. Leftist are coded (-1), centrists are coded (0), and rightists are coded (1). Observations that were not coded by team populist are marked NA.

• Team Populism’s Populist Rhetoric Index (tp_score): Index indicating the prevalence of populist rhetoric for each leader, as calculated by Team Populism [3]. Higher values represent more populist rhetorics. Observations that were not coded by Team Populist are marked NA.

• Team Populism’s Populist Classification (tp_cat): Categorial variable indicating whether a leader is “not populist,” “somewhat populist,” “populist,” or “very populist,” as calculated by Team Populism [3] based on tp_score. Observations that were not coded by team populist are marked NA.

• Levitsky and Loxton’s Populism Score (ll_pop): Variable indicating leader types as coded by Levitsky and Loxton’s 2013 paper [4]. Coded as follows: 0 = non-populists; 1 = full populists; 2 = movement populists; 3 = maverick populists; 4 = radical opposition leaders. Observations that were not coded by Levitsky and Loxton are marked NA.

D. References

[1] Doyle, David. 2011. “The Legitimacy of Political Institutions: Explaining Contemporary Populism in Latin America.” Comparative Political Studies 44(11): 1447-1473.

[2] Campello, Daniela. 2014. “The Politics of Financial Booms and Crises: Evidence From Latin America.” Comparative Political Studies 47(2): 260-286.

[3] Team Populism. 2019. Global Populism Database (Guardian Version). Retrieved from https://populism.byu.edu/Pages/Data on July 24, 2019.

[4] Levitsky, Steven and James Loxton. 2013. “Populism and Competitive Authoritarianism in the Andes.” Democratization 20(1): 107-136.