Rotated component matrix for the variables included in the factor analysis for census sector.

In addition to the individual values for 2001 and 2014, the average value, percentage variation, standard deviation and the parameters estimated in the autoregressive (AR) models are also shown.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

In Brazil, each region has its own cultural identity regarding accent, gastronomy, traditions, all of which may reflect its literature. Specially, we believe that country's background and contextual features are directly related to what people read. Hence, we present an enhanced dataset that comprises cultural, geographic, and socioeconomic information to explore Brazilian cultural identity through reading preference.

As our main data source, we chose the Goodreads website due to the sheer volume of data available and its organized and easily accessible API. We collect data from Brazilian readers through the goodreads library, which provides a Python interface to the Goodreads API. Specifically, we collect members of two of the largest Brazilian reading groups: the "Clube de Leitores em Português" (4,229 members) and the "Goodreads Brasil" (3,222 members). For all members of both groups, we also collect data from their friends. Then, we filter only those containing Brazil as location information from the final users' set. Finally, with the same library, we gather users' bookshelves to assess their reading preferences.

To investigate the Brazilian reading identity, we consider a medley of demographic and socioeconomic data from the Brazilian Institute of Geography and Statistics (IBGE): including territorial area, population estimate, demographic density, Human Development Index (HDI), Gross Domestic Product (GDP), and monthly household income per capita. All indicators refer to the year 2020, except the HDI that refers to the year 2017. The data collection was carried out from February 23 to March 04, 2021.

Our final dataset, named as BraCID, comprises:

• 38,231 Brazilian Goodreads users
• 75,093 Distinct books
• 80 Literary genres
• 6 IBGE indicators regarding the 27 federative units of Brazil

Based on the degree of inequality in income distribution measured by the Gini coefficient, Brazil was the most unequal country in Latin America as of 2022. Brazil's Gini coefficient amounted to 52.9. Dominican Republic recorded the lowest Gini coefficient at 38.5, even below Uruguay and Chile, which are some of the countries with the highest human development indexes in Latin America.

The Gini coefficient explained The Gini coefficient measures the deviation of the distribution of income among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality. This measurement reflects the degree of wealth inequality at a certain moment in time, though it may fail to capture how average levels of income improve or worsen over time.

What affects the Gini coefficient in Latin America? Latin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 38 and 54 points according to the latest available data from the reporting period 2010-2021. According to the Human Development Report, wealth redistribution by means of tax transfers improves Latin America's Gini coefficient to a lesser degree than it does in advanced economies. Wider access to education and health services, on the other hand, have been proven to have a greater direct effect in improving Gini coefficient measurements in the region.

Descriptive characteristics of the sample (n = 12,220).

Regression results: Coefficients of adjusted kernel-weighted logistic regressions within subgroups of municipal quintiles of per capita income (Municipal Human Development Index–Renda [MHDI-R]).