This penultimate post on the 2026 Peruvian presidential election examines the relationship between the electoral results and socio-economic development at the departmental level. Not surprisingly, the rightwing candidate Keiko Fujimori generally supported the interests of the business community and the wealthier segments of society, whereas the leftwing candidate Roberto Sánchez focused more on marginalized communities and the poorer segments of society. One might therefore expect Fujimori to have done better in Peru’s more economically productive departments, as reflected by per capita GDP figures. But as it turned out, departmental GDP per capita was a poor predictor of election results. As can be seen on the paired maps posted below, Sánchez won the most economically productive department, Moquegua, as well as many of those in the second-highest tier. He also won the lowest-ranked department, San Martin, and many of those in the second-lowest tier. By the same token, Fujimori took a majority of votes in some of the most economically productive provinces (Ica) and some of the least (Ucayali).
There are several reasons why GDP per capita at the department level does not indicate voting behavior, both in general terms and in the 2026 Peruvian election. Most important, high figures do not necessarily mean high median wages and salaries, especially in resource-dependent economies. Peru’s Moquegua Department has an elevated per capita GDP figure because it has relatively few people but highly productive copper mines and processing facilities. The productivity of the mineral sector, in turn, spurs demand for higher wages and enhanced social services, boosting leftwing candidates. Similar dynamics are at play in other Peruvian departments, such as Pasco in the central Andean region. They were formerly common in the United States, where mining-dependent counties were once reliable strongholds of the Democratic Party.
Peruvian departments with relatively low per capita GDP figures generally have small mining sectors and are characterized by poor infrastructure and extensive subsistence-oriented agriculture. As would be expected, most departments in this category supported the leftwing candidate, Roberto Sánchez. But again, there are important exceptions. The eastern lowland province Ucayali, for example, has the fourth lowest per capita GDP in Peru (according to ChatGPT), yet it supported Keiko Fujimori. Ucayali has experienced rapid population growth in recent decades, concentrated in its capital city, Pucallpa, which grew from 170,000 residents in 2000 to 326,000 in 2017. The department’s economy is heavily dependent on natural resource extraction, particularly logging. Many people in the department are concerned that the environmental and indigenous-rights policies of the political left would threaten that economic foundation. Many others, of course, are more concerned about environmental degradation and cultural survival. The election results suggest that the former group slightly outnumbers the latter (the department gave 52.56 percent of its votes to Fujimori and 47.44 percent to Sánchez).
It would be interesting to map the departments of Peru by median income and compare the resulting map with that of the 2006 election. I was not, however, able to locate reliable data. Instead, I mapped the country’s departments by the UN’s Human Development Index (HDI), which combines several social and economic indicators. I turned to AI for the most recent HDI data, but again ran into reliability issues. At first glance, the best set of numbers seemed to come from Grok, which I mapped accordingly (see below). Grok put Ancash Department in first place, but Google AI (Gemini) claims that Lima (both the province and the separate metropolitan region) rank in the top position. ChatGPT, to the contrary, put Moquegua and Arequipa in the two highest slots. More significantly, it ranked Ancash in a significantly lower position, stating that:
Áncash is not among the top five [Peruvian departments by HDI]. In the most recent UNDP report, Áncash is highlighted for having one of the largest reductions in human development once inequality is taken into account (one of the country’s highest inequality-adjusted HDI losses), even though its overall HDI is around the national middle range.
This ChatGPT response made me want to map its Peruvian HDI rankings, both in conventional and inequality-adjusted terms. But although the chatbot kept telling me that the data that I wanted is readily available, it refused to provide it. Here is its first and third responses:
ChatGPT Response 1:
I can absolutely produce:
- A complete ranking of all 25 departments by HDI (2024 values).
- A complete ranking of all 25 departments by inequality-adjusted HDI (IHDI/IDH-D).
- A comparison table showing:
- HDI
- IHDI
- percentage loss due to inequality
- change in rank after adjusting for inequality.
ChatGPT Response 3:
I can’t accurately show the full ranking because I don’t have the actual 2024 department-level values. The official 2025 UNDP Peru Human Development Report makes the complete ranking available only in its downloadable Excel annex (“Anexo 1 – IDH 2017–2024 a nivel distrital“), not in the report text or any searchable web page. The web index confirms the annex exists, but it does not expose the rows of the spreadsheet
If I had unlimited time and patience, I would turn to the “Excel annex” in question for the data that I want. But my time and patience are limited, and I had hoped that AI would be able to do such work for me. Perhaps I should pay more money for the AI services that I us.
But despite such frustrations, the basic human development patterns of Peru are clear: HDI figures are quite high along the coast and are significantly lower in the highlands and in the eastern lowlands. Such patterns do not correlate well with those found on the recent electoral maps. The high-HDI coastal region, for example, is politically divided, with the northern segment leaning right and the southern segment leaning left.
Finally, I would also note that different AI providers also gave somewhat different figures for per capita GDP by province. These differences can be assessed by comparing the data provided by Gemini (Google), ChatGPT, and Grok, which can be seen in the maps posted below. Gemini (Google) provided an actual map (sourced from Reddit), whereas the other two provided data that I mapped myself. I have also supplied a map based on 2014 data provided by Wikipedia. These various sources in combination tell us that the per capita GDP of Lima Department and Lima Metropolitan area have dropped relative to the rest of the country since 2014. This relative decline probably stems from the growing significance of the mining sector in Peru’s national economy.
