to increasingly large negative effects on earnings and employment in regions exposed to deeper tariff cuts (Kovak, 2013; Dix-Carneiro and Kovak, 2017). Since the more affluent regions, including the country’s leading areas such as São Paulo, Belo Horizonte, and Rio de Janeiro, experienced larger tariff cuts, liberalization contributed to a decline in inter-regional inequality. Costa et al. (2016) find that strong productivity growth in China led to the de-industrialization of Brazilian cities, but generated demand for Brazilian commodity exports. Hence, they also conclude that the “China shock” reduced regional inequality in Brazil. In Peru, Sotelo (2020) finds that increased global demand for grains and higher grain prices have had uneven rural-urban effects, benefiting farmers, but hurting urban consumers.

A collection of studies uses survey data from the late 1990s to the early 2000s to document territorial household income differences and dynamics in nine Latin American economies.<sup>4</sup> The analysis is conducted at the 2<sup>nd</sup> administrative level, except in Colombia, where it is at the 1<sup>st</sup> administrative level, and Ecuador, where the data are at the level of the parish, which is a sub-division of the 2<sup>nd</sup> administrative level. The synthesis of the results from these studies, provided by Modrego and Berdegué (2016), indicates slow, absolute convergence of mean household incomes in Brazil (Favareto and Abramovay, 2016), Colombia (Fernández et al. 2016), Ecuador (Larrea et al. 2016), Guatemala (Romero and Zapil Ajxup, 2009), and Mexico (Yúnez Naude et al., 2016),<sup>5</sup> but not in Chile (Modrego et al., 2016), and Peru (Escobal and Ponce, 2016). However, the results from these country studies are not directly comparable due to differences in the definitions used in each country for poverty lines, income, and other indicators, and differences in the level of aggregation of territorial units. In addition, none of these studies uses regional price indexes to adjust incomes and poverty lines for cost-of-living differences.

Related to this literature are studies focusing on the factors shaping territorial income differences and their evoluation. Among these factors are (i) physical geography and natural endowments (Olfert et al., 2014); (ii) human capital and the tendency of people to sort spatially in response to labor market, amenities, cost of living, and cultural considerations; (iii) place-based investments in physical and technological infrastructure and institutions of importance to economic activity and social life (e.g. financial services); and (iv) the spatial structure of economic activity, which is shaped by agglomeration forces, market access, learning externalities, and migration frictions, among other factors.

In Latin America, territorial differences in labor income have been explained by differences in fundamentals, including resource endowments (Mesquita Moreira et al., 2013), human capital (Skoufias and Lopez-Acevedo, 2009; Acemoglu and Dell, 2010; Modrego and Berdegué, 2016),<sup>6</sup> local

> 4 These countries include Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru. 5 Davalos et al. (2015) reach a similar conclusion of income convergence using municipality-level income data for Mexico from 1990 to 2010, while Lopez-Calva et al. (2021) find absolute convergence from 1992 to 2014 using a unique five wave panel dataset at the municipal level. The convergence process in both cases stemmed from a combination of positive developments in poor municipalities and stagnation or negative growth in richer ones.

> 6 While the synthesis in Modrego and Berdegué (2016) confirms the role of human capital in the convergence process in most Latin American countries, they conclude that the relevance of other factors is country specific.

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