• https://publications.iadb.org/es/evolucion-del-pib-departamental-y-analisis-de-la-desigualdad-regional-en-el-peru-1795-2017

Seminario, Bruno, et al. (2020). Departmental GDP Data and Regional Inequality Analysis in Peru (1795-2017). IDB Open Data. https://doi.org/10.60966/a79b-xz30

1. Spanish

What does this Peru dataset show?

This dataset reconstructs over 200 years of Peru’s macroeconomic history, including regional GDP, population, inequality metrics, and geographic characteristics across all 24 departments. It enables long‑run analysis of Peru’s economic structure, regional disparities, and demographic change.

What is the Gini coefficient, and how is it used here?

The Gini coefficient measures income inequality. This dataset tracks regional inequality from 1795 to 2017, rising from 0.26 to 0.36 and peaking at 0.43 in 1934. These values support comparisons of inequality trends within Peru and across Latin America.

Which regions and geographic units are included?

• All 24 departments of Peru
• Natural regions: Costa, Sierra, Selva
• Ecological floors: Quechua, Puna, Yunga, Janca, Rupa‑Rupa, Omagua, and others
• Area, populated area, altitude, and environmental characteristics for each region

What indicators are included?

• GDP (1990 Geary‑Khamis dollars)
• Population by department and year
• Gini coefficient and other inequality metrics
• Mobility and convergence indicators
• Area, altitude, and ecological classifications

What economic indicators can analysts derive?

• GDP per capita
• Historical GDP and population growth
• Regional GDP shares
• GDP per km²
• Long‑run structural change
• Coastal vs. Andean vs. Amazonian divergence

What are the key findings about inequality in Peru?

The dataset shows persistent regional disparities. A poor region in 1795 has a 94% chance of remaining poor in 2017; a rich region has a 95% chance of remaining rich. Middle‑income regions show more mobility and convergence.

How can this dataset be used for research or policy?

• Long‑term inequality and convergence analysis
• Regional development and territorial planning
• Understanding persistent regional gaps
• Designing region‑specific economic policies
• Comparing Peru’s inequality trends with those of other Latin American economies

What can data analysts use this dataset for?

• Time‑series modeling
• Spatial and regional clustering
• Historical GDP reconstruction
• Linking geography and ecological floors to economic outcomes

Does the dataset include subnational economic data for Peru?

Yes, the dataset provides department-level GDP data, which supports analysis of regional economic disparities. However, it does not include employment or poverty indicators.

Can this dataset be used to understand Peru’s long-term economic history?

Yes, the dataset spans from 1795 to 2017, allowing analysis of long-term economic trends and regional inequality.

How has Peru’s economy evolved over time?

The dataset supports analysis of long-term GDP changes across regions, showing structural shifts and persistent regional disparities over more than two centuries.

What role has mining played in Peru’s economic growth?

The dataset does not include sector-specific data, so the role of mining cannot be directly analyzed.

Can the dataset show how political instability affects economic growth?

The dataset allows observation of GDP fluctuations over time, but it does not include political variables needed to establish a direct relationship.

What insights can investors gain from this dataset?

The dataset provides long-term GDP trends and regional inequality patterns, which can help investors understand historical economic volatility and structural differences across regions.