Chilean Political Landscape Dataset

District-level electoral and party system indicators

Overview

The Chilean Political Landscape Dataset (CLPD) is a longitudinal dataframe of district-level electoral outcomes spanning across four decades of Chilean democracy. It contains standardized measures of party system fragmentation, ideological orientation, and institutional change across legislative, regional, and municipal contests. Version 1.0 contains 5,301 observations and will expand as new electoral data become available.

Data formats optimized for Python (pandas) and R (tidyverse) workflows.

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5,300+

Observations

Years

Variables

1.8MB

Dataset Size

Data Access

Download Dataset
Harvard Dataverse · DOI: 10.7910/DVN/NGRY3R
Codebook
Variable definitions and measurement protocols
Replication Files
R scripts and supplementary materials

Code Examples

# Load CPLD dataset
library(tidyverse)

cpld <- read_csv("cpld_1989_2024.csv")

# Explore key variables
glimpse(cpld)
# 5,301 × 42 variables including:
# enpv, enps, polarization_district, av_id, ethnic_frac
            

# Calculate effective number of parties over time
cpld %>%
  filter(type == "Diputados") %>%
  group_by(election) %>%
  summarise(
    mean_enpv = mean(enpv, na.rm = TRUE),
    mean_enps = mean(enps, na.rm = TRUE)
  )
            

# Analyze fragmentation trends pre/post-2015 reform
cpld %>%
  filter(type == "Diputados") %>%
  mutate(period = if_else(election >= 2017, 
                            "Post-Reform", 
                            "Pre-Reform")) %>%
  ggplot(aes(x = election, y = enpv, color = period)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = TRUE) +
  labs(title = "Party System Fragmentation")
            

# Examine ideological polarization by district
cpld %>%
  filter(type == "Diputados", election >= 2009) %>%
  ggplot(aes(x = factor(election), 
             y = polarization_district)) +
  geom_boxplot(fill = "steelblue", alpha = 0.7) +
  labs(x = "Election Year", 
       y = "Ideological Polarization (Dalton)")
            

# Disproportionality analysis using Gallagher index
cpld %>%
  filter(type == "Diputados") %>%
  group_by(election) %>%
  summarise(
    mean_gallagher = mean(Gallagher, na.rm = TRUE),
    mean_dm = mean(dm, na.rm = TRUE)
  ) %>%
  ggplot(aes(x = mean_dm, y = mean_gallagher)) +
  geom_point(size = 3) +
  geom_text(aes(label = election), vjust = -0.5)
            

Citation

When using the CPLD in your research, please cite both the dataset and the associated publication.

Dataset:
Bunker, Kenneth. 2025. "Chilean Political Landscape Dataset (CPLD)". Harvard Dataverse, V1.
doi: 10.7910/DVN/NGRY3R

Publication:
Bunker, Kenneth. 2025. "Decades of democracy: insights into the political landscape of Chile." Humanities and Social Sciences Communications 12, Article 1911.
doi: 10.1057/s41599-025-06180-1

% BibTeX - Dataset
@data{bunker2025cpld,
  author={Bunker, Kenneth},
  title={{Chilean Political Landscape Dataset (CPLD)}},
  year={2025},
  publisher={Harvard Dataverse},
  version={V1},
  doi={10.7910/DVN/NGRY3R},
  url={https://doi.org/10.7910/DVN/NGRY3R}
}

% BibTeX - Publication
@article{bunker2025decades,
  title={Decades of democracy: insights into the political landscape of Chile},
  author={Bunker, Kenneth},
  journal={Humanities and Social Sciences Communications},
  volume={12},
  number={1911},
  year={2025},
  publisher={Palgrave},
  doi={10.1057/s41599-025-06180-1}
}
            

Related Publications

Decades of Democracy
Humanities and Social Sciences Communications · doi: 10.1057/s41599-025-06180-1
Chile's Transformation Amidst Crisis
Palgrave Macmillan · doi: 10.1007/978-3-031-96463-3
Challenging Conventional Wisdom in New Democracies
Election Law Journal · doi: 10.1089/elj.2024.0064
Mapping Party Systems Over Time
Journal of Political Ideologies · doi: 10.1080/13569317.2025.2452179

Dataset Keywords & Coverage

Electoral Systems & Measures: Effective Number of Parties (ENP), ENPv (votes), ENPs (seats), Laakso-Taagepera index, party system fragmentation, district magnitude, assembly size, seat product model, proportional representation, electoral reform analysis

Disproportionality Indices: Gallagher index, Loosemore-Hanby index, Rae index, Lijphart indices, Cox-Shugart measures, electoral disproportionality, seats-votes relationship, representational equity

Party System Analysis: Party nationalization (Mainwaring-Jones, Chhibber-Kollman), ideological polarization (Dalton index), party competition, coalition dynamics, multiparty systems, party system evolution

Geographic & Demographic: District-level data, municipal elections, regional councils, ethnic fractionalization (Alesina index), indigenous populations, Chilean census data, spatial analysis, subnational politics

Technical Specifications: R programming (tidyverse, dplyr), Python pandas-ready, CSV format, longitudinal data analysis, time-series cross-sectional data, hierarchical modeling, comparative politics methodology

Election Types Covered: Chamber of Deputies (Diputados), Senate (Senadores), Mayoral elections (Alcaldes), Municipal councils (Concejales), Regional governors (Gobernadores), Regional councils (Cores), Constitutional conventions (Constituyentes)