especially in Bolivia, Honduras, and Peru, and much larger differences in poverty rates between urban and rural areas within geographic regions in Ecuador, Guatemala, and Nicaragua.

Third, territorial income disparities pose serious policy challenges in both developing and developed countries. In the Middle East and North Africa, they have been associated with political instability and the Arab Spring uprisings (Ianchovichina, 2018; World Bank, 2020). In advanced economies, they have been linked to growing political and social polarization. In the UK, support for Brexit was strongest in the country’s lagging regions (Rodriguez-Pose, 2018), which were also hit harder than other areas by the austerity policies implemented in response to the financial crisis in 2009 (Dorling and Thomlison, 2019). Crafting policies that address such challenges requires a good understanding of the size and dynamics in territorial income differences.

The paper makes several contributions to the literature. Previous studies look at issues of territorial income differences and dynamics separately, cover one or limited number of countries, employ disparate data sources – either national accounts or household surveys – and rely on value added or expenditure data at the first administrative level up to the early 2000s. Therefore, such studies may not be able to provide a complete or consistent regional picture on the size and evolution of territorial income differences within the Latin American countries in more recent years. This paper explores labor and place productivity differences and convergence dynamics during the pre-pandemic period between the early 2000s and the late 2010s using labor income data from a harmonized set of micro surveys for most countries in the region. We employ household labor earnings, deflated to address price variations across time and space, in the estimation of location premia at the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels, following the approach of Li and Rama (2015). We then categorize 2<sup>nd</sup> level administrative units (i.e. municipalities) based on their population size and share of urban residents and thus gauge the magnitude and evolution of location premia while controlling for the size and type of the administrative area. This way the paper estimates the local productivity premia in both rural and urban 2<sup>nd</sup> level administrative areas, while Quintero and Roberts (2018) only provide a snapshot of the variation in urban location premia within countries in Latin America.

Finally, the paper estimates the labor income gaps with the leading metropolitan area<sup>3</sup> in each country and the gaps for different socioeconomic groups, differentiated based on income (i.e. the bottom 40%), skill levels, locality type (i.e. urban/rural), and gender. This type of analysis is important for two reasons. First, as the largest business centers, the leading metropolitan areas host a major share of the country’s labor force and attract the most productive migrants. A decomposition of the income gaps with the leading area into differences in endowments and differences in the returns to these endowments allows us to assess the extent to which the income gaps can be exploited through migration by different population groups. Skoufias and Lopez-Acevedo (2009) undertake a similar analysis across and within geographic regions. However, they do not focus on the leading metropolitan areas and do not estimate how the income gaps vary across population groups with different skills or by gender.

The paper provides evidence of absolute convergence in real per capita household labor incomes, labor and place productivity at the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels between the early 2000s and the late 2010s in most Latin American countries. Convergence reduced regional inequality and reflected the expansion of agribusiness and extractive activities during the Golden Decade (2003-2013), which

> 3 The leading area /city is the main pole(s) of economic activity in the country.

3