metadata: authors: 0: author: D'Aoust,Olivia Severine
1: author: Galdo,Virgilio
2: author: Ianchovichina,Elena
count: Latin America & Caribbean,Latin America
docna: 0: docna: Territorial Productivity Differences and
            Dynamics within Latin American Countries
docty: Policy Research Working Paper
abstracts: cdata!: The paper documents the evolution of
            territorial disparities in labor and location productivity
            in 14 countries in Latin America, using millions of
            observations from harmonized household surveys and censuses.
            Between the early 2000s and the late 2010s, most countries
            in the region experienced significant reductions in regional
            inequality as real labor incomes and location productivity
            premia converged at the first and second administrative
            levels. The leveling up reflected both the slowdown in
            productivity growth in affluent predominantly urban
            municipalities and the catchup of relatively poor,
            predominantly rural municipalities. Absolute convergence
            narrowed the labor income gaps with leading metropolitan
            areas, including the disparitites exploitable through
            migration, especially among the bottom 40 percent of
            households, as cities de-industrialized, yet continued to
            attract migrants. On the eve of the Covid-19 pandemic,
            income disparities with leading metropolitan areas remained
            high in nearly all countries, largely due to differences in
            educational attainment, but in a few countries, large
            differences in returns to endowments indicate potentially
            significant returns to migration to the leading metropolitan
            areas, especially for residents of relatively poor, remote
            regions. Rather than a clear rural-urban-metropolitan
            divide, in most countries the paper documents substantial
            overlap between the location-premia distributions of
            different types of second-level administrative areas and
            small differences between the average urban and rural place
            productivity premia.
display_title: Territorial Productivity Differences
            and Dynamics within Latin American Countries
listing_relative_url: /research/2023/06/34078434/territorial-productivity-differences-dynamics-within-latin-american-countries
topicv3: Urbanization and Growth m1326437 796,Growth and Lagging Regions m1326921 462,Growth, Poverty and Inequality m1327633 1091,Inequality and Shared Prosperity m1327655 700
publishtoextweb_dt: 2023-06-12T00:00:00Z
dois: 10.1596/1813-9450-10480
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1392275
1: author_key: 1174529
2: author_key: 453205
model_extractions: Policy Research Working Paper 10480 Territorial Productivity Differences and Dynamics within Latin American Countries _Olivia D’Aoust Virgilio Galdo Elena Ianchovichina_ Latin America and the Caribbean Region Office of the Chief Economist June 2023
Policy Research Working Paper 10480

# **Abstract**

The paper documents the evolution of territorial disparities in labor and location productivity in 14 countries in Latin America, using millions of observations from harmonized household surveys and censuses. Between the early 2000s and the late 2010s, most countries in the region experienced significant reductions in regional inequality as real labor incomes and location productivity premia converged at the first and second administrative levels. The leveling up reflected both the slowdown in productivity growth in affluent predominantly urban municipalities and the catchup of relatively poor, predominantly rural municipalities. Absolute convergence narrowed the labor income gaps with leading metropolitan areas, including the disparitites exploitable through migration, especially among the bottom 40 percent of households, as cities de-industrialized, yet continued to attract migrants. On the eve of the Covid19 pandemic, income disparities with leading metropolitan areas remained high in nearly all countries, largely due to differences in educational attainment, but in a few countries, large differences in returns to endowments indicate potentially significant returns to migration to the leading metropolitan areas, especially for residents of relatively poor, remote regions. Rather than a clear rural-urban-metropolitan divide, in most countries the paper documents substantial overlap between the location-premia distributions of different types of second-level administrative areas and small differences between the average urban and rural place productivity premia.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at odaoust@worldbank.org, vgaldo@worldbank.org, or eianchovichina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Territorial Productivity Differences and Dynamics within Latin American Countries**

Olivia D’Aoust,<sup>*</sup> Virgilio Galdo,<sup>$</sup> and Elena Ianchovichina<sup>♦</sup>

**Keywords** : labor earnings, income convergence, territorial inequality, location effects, spatial analysis, urbanization

**JEL codes** : O18, J31, J61, R11, R12, R23, C21

> * Olivia D’Aoust is a senior urban economist in urban, distaster risk management, resilience and land, Latin America and the Caribbean Region at the World Bank..

> $ Virgilio Glado is a research analyst in the Chief Economist Office of Latin America and the Caribbean Region at the World Bank.

> ♦ Elena Ianchovichina is a lead economist and the deputy chief economist of Latin America and the Caribbean Region at the World Bank. The paper has been cleared for publication as a World Bank Policy Research Working Paper. We thank Carlos Rodríguez Castelan and Nancy Lozano Gracia for their comments. The findings, interpretations, and conclusions expressed in this paper are entirely ours. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors or the governments they represent.
# **1. Introduction**

Territorial differences in labor earnings are expected to diminish with economic development as barriers to capital mobility decline and technology diffuses within countries. However, they may persist due to poverty traps<sup>1</sup> reflecting delays in subnational institutional development (Galvis and Meisel Roca, 2021) and in economies where mobility frictions are high. By making it costly to trade goods and relocate workers, frictions generate spatial misallocation and slow down aggregate economic growth. Yet, a decline in mobility frictions may not result in measurable improvements in aggregate economic growth when territorial income differences are not exploitable through migration, but rather reflect difference due to the sorting of more productive workers into urban agglomerations. If people lack the endowments that could enable them to earn higher real wages after relocating to high-income areas, migration may contribute to congestion there, and ultimately lower the local productivity premium (Grover, Lall, and Maloney, 2022). Importantly, even in the absence of mobility barriers, territorial income differences may persist and even grow due to market-driven agglomeration forces (Duranton and Puga 2020), which may lead to labor shortages in fast-growing urban centers. Conversely, leveling up may occur as local productivity in leading regions deteriorates and conditions in smaller lagging areas improve. Hence, a priori, it is unclear how large within-country territorial income differences are, how much they have changed over time, and to what extent income differnecs can be exploited through migration.

This paper answers these questions by documenting the territorial differences in labor earnings in fourteen Latin American countries<sup>2</sup> and their evolution between the early 2000s and the late 2010s, using millions of observations from recently released, harmonized micro data from population censuses and household surveys. Exploring this topic in the Latin American context is important for several reasons. First, prior to the mid-2000s, large territorial income differences within the Latin American counties have been documented in the literature, including between Mexico’s North and South (Aroca, Bosch, and Maloney, 2005; Gonzalez Rivas, 2007), Colombia’s peripheral and core regions (Galvis and Maisel Roca, 2010 and 2012; Burger, Hendriks and Ianchovichina, 2022), and Peru’s coastal and internal areas (Escobal and Ponce, 2011). Many studies conducted in the first half of the 2000s also question the idea of territorial income convergence in the region (Aroca et al. 2005; Bosch, Aroca, Fernandez, and Azzoni, 2003) or show that convergence has occurred at a very slow pace (Serra, Pazmino, Lindow, Sutton and Ramirez, 2006; Soto and Torche, 2004).

Second, territorial differences in income may explain a sizable share of overall inequality in a country (Elbers, Lanjouw, Mistiaen, Özler and Simler, 2004). Using survey data for eleven Latin American countries for the 2000s, Acemoglu and Dell (2010) investigate the sources of labor income inequality within and between countries in the Americas. They show that the within-municipality differences in labor income are five times the between-municipality differences, which in turn are twice the size of the between-country differences in labor income in the region. Skoufias and Lopez-Acevedo (2009) also find large differences in poverty rates and household incomes across geographic regions,

> 1 In turn, poverty traps may influence territorial growth through increased social fragmentation, underinvestment, and political instability and conflict (Alesina and Perotti, 1996).

> 2 The countries are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Honduras, Mexico, Panama, Paraguay, Peru, and Uruguay.

2
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
brought investments and increased productivity in lagging, predominantly rural areas and the slowdown in productivity growth in more affluent, urban areas, where employment shifted toward less dynamic, low-productivity nontradables as cities de-industrialized (Jedwab et al., 2022). Convergence also narrowed the income gaps with leading metropolitan areas, including the gaps exploitable through migration to the top locations, especially among the bottom 40 percent. Still, at the end of the 2010s, there were sizable differences in per capita household labor earnings with the leading areas in nearly all countries, mainly reflecting differences in educational attainment, in line with the sorting of skilled workers in leading areas, but in a few countries large differences in the returns to endowments indicate potentially significant reutrns to migration to the leading areas, especially for residents of the poorest, remote regions. Rather than a clear rural-urban-metropolitan divide, the location-premia distributions of different locality types overlapped to a great degree and differences in the average location productivity premia in urban and rural areas were relatively small in most countries. While the places with the highest location premia were not necessarily the leading metropolitian areas, in all cases, they were among the top 25 percent of most productive localities.

The remainder of the paper is organized as follows. Section 2 reviews the literature. Section 3 discusses the methodology and the data, while Section 4 presents the empirical results. Section 5 summarizes the findings and offers conluding remarks.

# **2. The literature**

There is a large literature on the size and evolution of territorial differences in income and welfare in Latin America. Most studies that belong to this literature focus on one country or a limited set of countries, use data up to the early 2000s, and rely on per capita GDP data at the first administrative level (i.e. states, provinces, or departments). Most studies find evidence of absolute convergence at the first administrative level, albeit at a speed below the one observed in developed countries, in Brazil (Azzoni, 2001; Serra et al., 2006), Chile (Serra et al., 2006), Peru (Serra et al., 2006; Iacovone, SanchezBayardo and Sharma, 2015), and Colombia (Serra et al., 2006), but not in Argentina (Serra et al., 2006) and Mexico (Rodriguez-Pose and Sanchez-Reaza 2002; Chiquiar 2005; Serra et al., 2006). In the latter, convergence was observed up to the mid-1980s, but not afterwards. In spatially polarized countries, such as Brazil, Chile and Peru, convergence is found to be stronger within regional clubs.

A strand of this literature explores regional income dynamics by sector. Using sectoral value added data, Iacovone et al. (2015) find absolute convergence in the Peruvian manufacturing and mining sectors, but not in services and agriculture. Iacovone et al. (2015) attribute the lack of convergence in poverty rates across departments to limited reallocation of labor toward the converging sectors, while Sotelo (2020) suggests that this could also be due to substantial regional differences in farm income. He links these differences to spatial variations in trade costs and land quality and the practice of farmers to allocate land to many crops, which differ in land intensity. In Mexico, growth in tourism has also had uneven spatial effects. Faber and Gaubert (2019) show that the local economic effects of tourism are in part driven by significant positive spillovers from manufacturing. However, these local spillovers are mostly offset by reduced agglomeration econonomies in less touristic regions.

Another strand of this literature explores the effects of trade liberalization during the 1990s and early 2000s and increased demand for commodities on regional income dynamics. Chiquiar (2005) shows that NAFTA did not reverse the pattern of divergence in Mexico’s state per capita GDP between 1985 and 2001; it benefited states endowed with better human capital and infrastructure and hurt the states with less productive agriculture in the South. In Brazil, trade liberalization in the early 1990s led 4
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.

5
institutions that determine the provision of local public services (Acemoglu and Dell, 2010),<sup>7,8</sup> and migration frictions in the form of high trade costs (Acemoglue and Dell, 2010) and migration barriers (Skoufias and Lopez-Acevedo, 2009), Using household surveys from the early 2000s and Oaxaca’s (1973) decomposition, Skoufias and Lopez-Acevedo (2009) contribute a comprehensive study of spatial differences in welfare and their determinants within and across geographic regions above the first administrative level in 8 LAC countries.<sup>9</sup> Within any given region, they find that rural areas are poorer than urban areas primarily because of differences in education levels, and not differences in returns to education. Hence, they conclude that migration within regions has equalized these returns. By contrast, across geographic regions, differences in returns explain a larger share of the welfare gap, signaling that mobility barriers tend to grow with distance. Skoufias and Lopez-Acevedo (2009) also characterize the profile of domestic migrants, their incentives to migrate, and the characteristics of sending and receiving regions. Migrants are typically well-educated, young, and relatively well-off, come from regions with relatively low stock of skills, and therefore tend to drain the lagging regions from skilled workers, potentially exacerbating territorial inequality. Migrant remittances may have an offsetting effect (Acosta, Fajnzylber and Lopez, 2008), but research suggests that this effect is less likely to occur if most migrants are skilled workers. The latter tend to migrate on a permanent basis and are more likely to be affluent (Skoufias and Lopze-Acevedo, 2009). Kanbur and Rapoport (2005) also question the quantitative importance of remittances for reducing territorial welfare differences.

In sum, the existing literature relies on disparate data sources – either national accounts or household surveys – and looks at issues of territorial income differences and dynamics separately. Therefore, it provides an incomplete or inconsistent regional picture on the sources and evolution of territorial income differences in Latin America. By differentiating between households and localities and by combining analyses of territorial labor and local productivity differences and dynamics, this paper builds a region-wide assessment of the spatial patterns in local productivity levels and growth. It also identifies the main factors behind the territorial income differences with leading metropolitan areas and therefore opportunities to lift welfare through migration to these areas.

# **3. Analytical approach**

We first present the methodology employed to estimate labor and place productivity by location, the approach for assessing productivity dynamics, and the estimation technique for decomposing the income gap with leading areas into an endowment component and a rate-of-return component. The latter allows us to determine the extent to which households can increase their incomes by relocating to the leading metropolitan area.

> 7 In countries with federal systems (e.g. Mexico and Brazil), state and local governments have the authority to change laws, including taxes, and de jure and de facto institutions, e.g., the degree of enforcement of national laws, the functioning of the judiciary, and the degree of de facto control by local elites.

> 8 Dell (2010) links the poor outcomes for some communities to the long-run effect of the mita – an extensive forced mining labor system in effect in Peru and Bolivia during the colonial era – which lowered incomes and access to education and infrastructure. Acemoglu et al. (2007) emphasize the link between political inequality in 19<sup>th</sup> century Cundinamarca, Colombia and its current economic outcomes. Similar conclusions are drawn by Naritomi, Soares, and Assuncao (2007) for Brazil.

> 9 These countries are Bolivia, Brazil, Colombia, Ecuador, Guatemala, Mexico, Nicaragua, and Peru.

6
_3.1 Methodology_ We proxy labor productivity with the real average per capita household labor income<sup>10</sup> in locality , represented in logs, l ), and alternatively, with the location premia before sorting, b , which ll represents local labor productivity, net of the effect of any time-varient exogenous shocks, captured h,ll,tt ll,tt by the time-fixed effect, (yy : γγ l tt b + (1) θθ The location premium after sorting, h,ll,tt ll,tt, in locality tt h,ll,tt at time , also referred to as the place productivity (yy ) = γγ θθ + εε premium, is defined as the fraction of per capita household labor income that cannot be accounted aabb ll,tt for by the household _h_ ’s observable, non-geographic characteristics, γγ ll tt (Skoufias and LopezAcevedo, 2009; Quintero and Roberts, 2018; and Li and Rama, 2015). We can then differentiate the h,ll,tt XX contribution of individual, portable characteristics and place-specific effects to labor earnings as follows: l + (2) aabb h,ll,tt ll,tt h,ll,tt tt h,ll,tt The set of observable portable characteristics, (yy ) = γγ + ψψXX , are listed in Appendix Table A1 and include the θθ + εε gender of the household head, the household size, the age of the household head and household h,ll,tt XX members, along with their squared values, the household head’s level of education and employment status, and the highest educational attainment in the household. Time dummies capture the effect of exogenous shocks, such as commodity price fluctuations, while the inclusion of many observable characteristics addresses issues related to sorting and mitigates to some extent the omission of unobservable non-geographic characteristics such as entrepreneurial spirit and commitment to hard work.

The assessment of territorial productivity dynamics relies on the standard model: , (3) b ll,tt,tt+TT ll,tt ll,tt which correlates growth gg = αα+ ββyy in the respective productivity measure – + μμ , , or – in locality between period _t_ and _t+T_ with its corresponding level in the initial period _t_ . We estimate (3) at the aabb ll,tt,tt+TT ll,tt ll,tt ll,tt 1<sup>st</sup> administrative level, and in most cases at the 2gg<sup>nd</sup> administrative level and by settlement type. yy γγ γγ llDepending on the size of the country and data availability, we are also able to differentiate between large and small settlement types. In a few cases, we estimate models (1), (2), and (3) only at the 1<sup>st</sup> administrative level due to data limitations.

The decomposition of the income gap with the leading metropolitan area into an endowment component, reflecting sorting, and a returns-to-endowment component, capturing the portion of the gap that can be exploited through migration requires us to estimate per capita labor income as a linear function of the same portable endowment household characteristics included in (1) and a disturbance term at time _t_ in the leading metropolitan area, _A_ , and the rest of the country, _R_ , respectively: εεh,AA,tt l + (4) h,AA,tt h,AA,tt AA,tt h,AA,tt (yy ) = XX ′ψψ εε

> 10 Labor income is a better proxy for labor productivity than total income or expenditure and is reported more accurately than total income.

7
<!-- Start of picture text --> —Iny = -X yp +X 'G -d<br>¥ -~7 ~X (p -y ).<br>7<br>?<br><!-- End of picture text -->
International at the University of Minnesota. In both cases, the census questionnaires include a module on income in addition to the standard questions on household and individual characteristics. Furthermore, the harmonization protocol ensures the comparability of geographical units across census years. In all other country cases, we use the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), which includes country harmonized household surveys jointly constructed by the Center for Distributive, Labor and Social Studies (CEDLAS) at the Universidad National de La Plata and the World Bank’s Poverty Group for the Latin America and the Caribbean region.<sup>11</sup> Most countries have conducted the surveys on an annual basis since 2000, but the frequency varies by country.<sup>12</sup> Only in Colombia, the analysis at the 2<sup>nd</sup> administrative level relies on per capita value-added data for the last ten years from the National Administrative Department of Statistics (DANE). Since neither the value-added data source, nor SEDLAC provides household information, it is impossible to estimate place productivity premia after sorting in Colombia.

The criteria for selecting survey years included the availability of information to harmonize geo-codes across survey years at the lowest possible administrative level and maximize the number of surveyed locations across time. Two to three consecutive surveys were selected to have adequate coverage at three specific time periods in the past twenty years: the early 2000s, the late 2000s, and the late 2010s. Thus, the sample includes only countries with enough information to identify sub-national administrative units, ensure comparability across space and time, and minimize standard errors of point estimates at lower administrative levels.

In some instances a particular survey was not used for reasons not mentioned above. A decision was made to exclude the 2000 Panama survey to minimize location re-coding. From 2001 onwards, Panama’s administrative units remained stable up to 2017. El Salvador was also excluded from the analysis because of potential issues related to the switch in currencies in the early 2000s. Choosing 2005 as the initial period did not help because this resulted in a substantial decline in the number of surveyed locations available in 2005 and at the end of the 2010s, which biases the estimation of the location premia. Appendix Table A2 summarizes information on data sources, level of geographic detail, and time coverage.

The income gap analyses employ only household survey data, which, unlike the census data, extend all the way to the end of the 2010s. In most countries, we are able to match the first and last periods in the income gap analyses with the first and last periods in the estimation of location premia. By linking the two types of analyses, we are able to perform consistency check (7).

We deflate labor earnings to address price variations across time and space and to ensure that the results are comparable both within and across countries. First, we convert labor income into constant 2011 US$ PPP. We then adjust the converted income to reflect differences across subnational regions, and whenever possible different types of areas within subnational regions (i.e., rural versus urban). Appendix Table A3 provides information on the data used to calculate the deflators.<sup>13</sup> Descriptive

> 11 See https://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/

> 12 Appendix Table A5 provides information on the year and sample size by country. In the case of Argentina, the surveys cover only urban areas so the categories are reduced to all urban residents, the bottom 40% of urban residents, and skilled urban residents. In the case of Uruguay, the surveys cover primarily urban areas.

> 13 In the absence of a deflator for a particular year, the deflator for the next closest year was used (case of Ecuador 2004, Panama 2010 and 2012). https://statistics.cepal.org/portal/cepalstat/index.html?lang=es.

9
statistics are presented in Appendix Table A4, while Appendix Table A6 provides details on the definition of leading metropolitan areas. Given its large size, Brazil’s leading metropolitan area includes three of its largest urban agglomerations – Rio de Janeiro, São Paulo, and Belo Horizonte. In all other countries, except Ecuador and Panama, the largest city is the leading metropolitan area. In Ecuador, Quito is the leading area, while urban Panama province, which includes Panama City, is the leading metropolitan area in Panama.

**Table 1. The urban gradient: definitions of locality types**

|**Locality type**<br>**Population size**|**Share of urban**<br>**residents (%)**|**Share of metropolitan**<br>**residents (%)**|
|---|---|---|
|**Mexico**|||
|Urban-Metro (large)<br>≥ 300,000|> 50%|> 50%|
|Urban-Metro (small)<br>< 300,000|> 50%|> 50%|
|Urban (large)<br>≥ 20,000|> 50%|≤ 50%|
|Urban (small)<br>< 20,000|> 50%|≤ 50%|
|Rural (large)<br>≥ 10,000|≤ 50%|≤ 50%|
|Rural(small)<br>< 10,000|≤ 50%|≤ 50%|
|**Brazil**|||
|Urban-Metro (large)<br>≥ 300,000|> 50%|> 50%|
|Urban-Metro (small)<br>< 300,000|> 50%|> 50%|
|Urban (large)<br>≥ 50,000|> 50%|≤ 50%|
|Urban (small)<br>< 50,000|> 50%|≤ 50%|
|Rural (large)<br>≥ 30,000|≤ 50%|≤ 50%|
|Rural(small)<br><30,000|≤ 50%|≤ 50%|
|**Colombia**|||
|Urban-Metro<br> <br>|All designated as metro<br>|areas in 2011<br>|
|Urban (large)<br>≥ 20,000|> 50%|Nonmetro areas|
|Urban (small)<br>< 20,000|> 50%|Nonmetro areas|
|Rural (large)<br>≥ 10,000|≤ 50%|Nonmetro areas|
|Rural (small)<br><10,000|≤ 50%|Nonmetro areas|
|<br>**All other LAC countries**|||
|Urban|> 50%||
|Rural|≤ 50%||

We explore the variation in productivity across different types of 2<sup>nd</sup> level administrative units, defined using an urban gradient that varies with the size of the country and territorial coverage of the data. In the cases of Brazil and Mexico, using the rich information in the IPUMS population censuses, it is possible to distinguish between six types of municipalities: large metropolitan areas; small metropolitan areas; large urban areas; small urban areas; large rural areas; and small rural areas. Table 1 lists the criteria for defining the different types of localities in each country case. For example, in Mexico, large metropolitan areas are municipalities with 300,000 or more people of whom more than 50 percent reside in urban households (as of 2000) and more than 50% are metropolitan residents. In Brazil, metropolitan municipalities are defined as in the case of Mexico, but urban and rural municipalities are considerably larger (Table 1). In Colombia, official aggregates of value added at the second administrative level are used to define five types of municipalities. In this case, metropolitan municipalities are those designated as such as of 2011. In all other countries, municipalities where the 10
urban population share is above 50 percent (circa 2000) are considered urban while the rest are classified as rural.

**Table 2. Municipality types: definitions and descriptive statistics**

||N|Avg.<br>growth|Std.<br>Dev.<br>Growth|Density|Comple<br>seconda|te<br>ry<br>Self-<br>employment<br>share|Wage<br>earners|
|---|---|---|---|---|---|---|---|
|**Mexico**||**2000**|**-15**|||**2000**||
|Urban-Metro (large)|41|0.003|0.017|1820|0.19|0.22|0.74|
|Urban-Metro (small)|72|0.012|0.018|735|0.16|0.24|0.71|
|Urban (large)|358|0.017|0.021|369|0.11|0.27|0.64|
|<br>Urban (small)|440|0.030|0.027|168|0.08|0.33|0.56|
|Rural (large)|587|0.030|0.029|70|0.05|0.35|0.49|
|Rural(small)|832|0.049|0.044|41|0.04|0.43|0.36|
|**Brazil**||**2000**|**-10**|||**2000**||
|Urban-Metro (large)|46|0.011|0.009|3438|0.25|0.24|0.75|
|Urban-Metro (small)|176|0.013|0.014|691|0.17|0.24|0.74|
|Urban (large)|434|0.016|0.015|139|0.16|0.31|0.65|
|<br>Urban (small)|842|0.018|0.019|50|0.13|0.31|0.64|
|Rural (large)|317|0.029|0.017|34|0.07|0.44|0.43|
|Rural(small)|225|0.028|0.024|31|0.08|0.44|0.42|
|**Colombia**||**2011**|**-19**|||||
|Metro|59|0.028|0.033|||||
|Urban (large)|168|0.020|0.045|||||
|<br>Urban (small)|150|0.016|0.051|||||
|Rural (large)|341|0.021|0.040|||||
|Rural(small)|404|0.028|0.055|||||
|**Peru**||**2000/03-**|**2017/19**|||**2000/03**||
|Urban|71|0.030|0.016||0.237|<br>0.502|0.398|
|Rural|115|0.046|0.021||0.127|<br>0.756|0.183|
|**Ecuador**||**2003/04-**|**2017/19**|||**2003/04**||
|Urban|69|0.024|0.025||0.125|<br>0.396|0.524|
|Rural|122|0.036|0.029||0.077|<br>0.529|0.385|
|**Chile**||**2000/03-**|**2015/17**|||**2000/03**||
|Urban|201|0.030|0.016||0.238|<br>0.213|0.691|
|Rural|98|0.036|0.014||0.117|<br>0.291|0.569|
|**Dominican Republic**||**2000/02-**|**2014/16**|||**2000/02**||
|Urban|51|0.007|0.020||0.107|<br>0.383|0.445|
|Rural|64|0.015|0.028||0.049|<br>0.530|0.297|
|**Honduras**||**2004/06-**|**2017/19**|||**2004/06**||
|Urban|34|0.005|0.030||0.089|<br>0.444|0.423|
|Rural|210|0.004|0.038||0.042|<br>0.597|0.278|
|**Costa Rica**||**2001/03-**|**2008/09**|||**2001/03**||
|Urban|30|0.019|0.022||0.142|<br>0.277|0.634|
|Rural|49|0.031|0.025||0.079|<br>0.315|0.591|

Table 2 displayes the number of different types of localities, obtained based on the classification in Table 1, along with their socioeconomic characteristics. In most countries, there are more rural than urban or metropolitan municipalities. Brazil and Chile are exceptions. In the former, there are more 11
small urban municipalities than any other municipality type. In the latter, there are more urban than rural municipalities. In all countries, the shares of residents with secondary education and wage earners increase with the urban gradient.

# **4. Empirical results**

This section first presents the estimated location premia before and after sorting at the 1<sup>st</sup> administrative level, and whenever possible, at the 2<sup>nd</sup> administrative level. It then reports results on territorial productivity dynamics by administrative level and type of locality. Finally, the section presents the income gaps with leading metropolitan areas and the factors behind them, followed by a discussion of the decompositions by administrative region and socioeconomic group.

# _4.1 Estimated location premia by administrative region_

Figure 1 shows estimates of local productivity premia before and after sorting in the late 2010s at the 2<sup>nd</sup> administrative level (e.g., municipality) in Brazil, Chile, Costa Rica, Mexico, the Dominican Republic, Ecuador, El Salvador, Honduras, and Peru, and at the first administrative region (i.e., state/province/department) in Argentina, Bolivia, Colombia, Uruguay, and Panama. These results, obtained by estimating respectively (1) and (2), suggest that the location premia before sorting substantially vary across and within countries in Latin America (Figure 1A). The places with highest labor productivity premia in the region are resource-rich areas as well as urbanized regions, some of which are the leading metropolitan areas, identified with black dots on the maps. More productive workers are attracted to these places to advantage of strong positive learning externalities available in proximity to other skilled workers (Quintero and Roberts, 2018)<sup>14,15</sup> and better consumer amenities.<sup>16</sup> However, once sorting is taken into account, the place productivity premia in the leading metropolitan areas is much lower than the labor productivity premia (Figure 1B).

A comparison of location premia estimates in the early 2000s and the late 2010s reveals a reduction in the spatial variation of productivity measures within countries (Table 3). In all countries, the spatial variation in the location premia before sorting (Table 3, columns (4) and (5)) is larger than the spatial variation in location premia after sorting (Table 3, columns (6) and (7)). Yet, sorting does not completely reduce the variation in the location premia. In the early 2000s, for the set of countries with municipal-level information, the spatial variation in the location premia after sorting – measured with the coefficient of variation to ensure cross-country comparability – was highest in Peru, Mexico, Honduras, and Brazil, while for those with only state or provincial information, it was highest in Bolivia and Panama (Table 3, column (8)). By the end of the 2010s, the territorial variation in place productivity declined in all countries, but the change was relatively small in Honduras and Panama

> 14 Static productivity gains stem from agglomeration economies, while dynamic productivity gains come from learning by working. De La Roca and Puga (2017) provide evidence that the additional value of experience gained in bigger cities persists after leaving the city.

> 15 Diamond (2016) finds that local labor demand changes are the primary reason for the increased skill sorting, but amenities have also adjusted to reinforce this effect in the US from 1980 to 2000.

> 16 Glaeser, Kolko, and Saiz (2001) argue that consumption amenities, such as restaurants, stores, or public services, are important for attracting firms and skilled workers, who tend to earn higher incomes and place greater value on the quality and variety of amenities. Big cities supply a greater variety and quality of amenities as they are bigger markets.

12
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The OLS estimates of location premia after sorting may be biased if the error term in regression model (2) is spatially correlated with errors in neighboring locations (Aselin 2003). We assess the h,ll,tt εε extent of spatial autocorrelation by performing the following tests on the residuals. First, for each h,ll̃,tt εε country, we average the residuals across locations and evaluate whether these mean residuals are closely distributed around zero. This first test suggests that there is no clustering of residuals at location level. Second, we formally test for spatial autocorrelation of the mean residuals using the Moran’s I test for global spatial autocorrelation. The index for this test can be obtained by estimating the following regression: W , (8) where is the standardized variable of interest (e.g., residuals, location premia), = ρρWW+ εε W is the spatial lag of the variable and is the spatial weight matrix (Anselin 1995). The Moran’s I statistic takes WW values between [-1,1] and evaluates the extent to which locations are interdependent. A positive (negative) value of Moran’s I indicates that positive (negative) spatial autocorrelation exists across WW WW ρρ locations. Appendix Table A7 shows estimates of the Moran’s I statistic along with p-values testing the null hypothesis for randomization or absence of spatial autocorrelation. The results in columns 1 and 2 of this table indicate that we cannot reject the absence of spatial autocorrelation of residuals. ρρ� The absence of spatial autocorrelation of residuals implies that the OLS estimates of location premia after sorting are unbiased. Yet, the estimated location effects are spatially correlated as shown in columns 3 and 4 of Appendix Table A7.

# _4.2 Estimated location premia by municipality type_

In line with the steep rise of population density along the urban gradient (Table 2), the denser the municipality, the further to the right is its distribution (Figure 3) and the higher its average location premia (Table 4). This is expected as economic agents benefit from stronger agglomeration economies and better learning externalities and market access in municipalities with higher population density. However, the rural-urban differences in average location premia at the end of the 2010s are small in most cases (last two columns of Table 4). Rather than a clear rural-urban-metropolitan divide, there is a gradation in labor and place productivity premia by type of municipality.

The location premia density charts, displayed in Figure 3, overlap in all cases to different extents. In Mexico, the highest location premia are not observed in the largest metropolitan areas, but in a few smaller metropolitan areas, large urban centers, and even some rural municipalities. Still, because the variation of location premia in larger metropolitan/urban areas is smaller, on average the location premia in less dense rural municipalities are lower than those in denser metropolitan areas.

In Brazil, the overlap of the rural and urban density plots is much less pronounced. There is a clear double-hump rural-urban pattern of lotion effects, which shows that the majority of rural municipalities have lower productivity premia than the majority of urban or metropolitan municipalities. In Colombia, the overlap in location premia by type of municipality is akin to the one observed in Mexico.<sup>17</sup> In Chile and the Dominican Republic, except for a few urban municipalities

> 17 We do not have data to estimate location premia after sorting by type of settlement in Colombia because SEDLAC data do not offer household information below the 1<sup>st</sup> administrative region.

15
where the location premia are highest, the rural and urban distributions almost completely overlap, indicating relatively high place productivity in rural areas, as shown in Table 4. In the rest of the countries, the least productive localities are rural areas, while the most productive municipalities are urban areas in Peru and Ecuador.

**Table 4. Mean Estimated Location Premiums before and after Sorting by Type of Locality, Period, and Country in Latin America**

**Panel A. Rural versus Urban Average Location Premia**

|||Before<br>|Sorting<br>|||After<br>|Sorting<br>||
|---|---|---|---|---|---|---|---|---|
||Early|2000s|Late|2010s|Early20|00s|Late|2010s|
||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|Brazil|5.37|4.72|5.62|5.01|4.52|4.10|4.77|4.39|
|Mexico|4.79|4.45|4.97|4.68|4.54|4.30|4.75|4.58|
|Costa Rica|5.78|5.38|5.62|5.54|5.16|5.01|5.62|5.54|
|Dominican Republic|5.32|5.15|5.36|5.29|4.96|4.88|4.87|4.83|
|Ecuador|4.87|4.56|5.44|5.18|4.05|4.00|4.38|4.28|
|Honduras|4.91|4.47|5.02|4.61|4.29|4.11|4.42|4.18|
|Chile|5.22|4.91|5.91|5.74|4.52|4.42|5.38|5.31|
|Peru|4.71|3.97|5.26|4.69|3.58|3.17|4.09|3.78|

**Panel B. Average Location Premia by Type of Locality**

|||Befo<br>|re sortin<br>|g,early2<br>|000s<br>|||Afte<br>|r sorting<br>|,early20<br>|00s<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Metro-<br>large|Metro-<br>small|Urban-<br>large|Urban-<br>small|Rural-<br>large|Rural-<br>small|Urban-<br>Metro|Urban-<br>Metro|Urban-<br>large|Urban-<br>Small|Rural-<br>large|Rural-<br>small|
|Brazil|5.83|5.59|5.41|5.28|4.70|4.75|4.70|4.68|4.53|4.46|4.09|4.12|
|Mexico|5.43|5.17|4.91|4.58|4.54|4.38|4.89|4.76|4.63|4.39|4.40|4.23|
|||Befo|re sortin|g,late 2|010s|||Afte|r sorting|,late 20|10s||
||Urban-<br>Metro|Urban-<br>Metro|Urban-<br>large|Urban-<br>Small|Rural-<br>large|Rural-<br>small|Urban-<br>Metro|Urban-<br>Metro|Urban-<br>large|Urban-<br>Small|Rural-<br>large|Rural-<br>small|
|Brazil|5.98|5.79|5.65|5.55|4.99|5.04|4.93|4.91|4.78|4.73|4.37|4.41|
|Mexico|5.41|5.28|5.05|4.82|4.69|4.67|5.00|4.93|4.81|4.65|4.58|4.58|

Note: Table 1 defines the urban gradient and different types of localities.

16
<!-- Start of picture text --> 3<br>3<br>2GG 2 -\ 2 2 yo,,<br>aSS JT oN ®55 y \\\<br>\ a /, \<br>14.Le.LeLe<br>4.Le.LeLe Y \ 1 y “/ “/ ‘ i)<br>YLsflsfl N A \ \\\ 4, / PPS o<br>77 1IsIs Poa.. \* Yr},V4V4 \\\<br>oa S\N _v-” YX.<br>0 === a 0 == ae<br>2 3 . 4 ; 5 6 2 3 4 5 6<br>Location premium (before sorting), 2015 Location premium (after sorting), 2015<br>—— Urban-Metro large =—--——- Urban-Metro small — Urban-Metro large -—--——- Urban-Metro small<br>———_ Urban_ Urban Urban large —---- Urban small ——— Urban large —---- Urban small<br>— Rural large —---- Rural small — Rural large —---- Rural small<br>1.5 2 oN<br>~\ /<br>yto\to\\ -, tii<br>= f\\ 15 fs i<br>1 oY y' \\\ H \ \<br>2 \ 2 d \ | VN<br>5 jf \ \ g 1 \ AN<br>a 5 // />“AN\ w\\\\\ a A “ “> SOO \\\<br>Z / ‘ YT Ns 5 / ‘ Sat \\<br>7 / YS oor \ . 7 / JAS “7 “A NY<br>/ / t SR s. / , ) Ne a WAY<br>“ y 7 / arenNX6NX66 / Z Z 7aa ss. ~Sy XN<br>0 Z-- = 0<br>4 5 6 7 3.5 4 45 ; 5 55<br>Location premium (before sorting), 2010 Location premium (after sorting), 2010<br>— Urban-Metro large —--—- Urban-Metro small ——— Urban-Metro large ——-—-—- Urban-Metro small<br>— Uhban large ~---- Urban small ——— _ Urban large ----—- Urban small<br>— Rural large -—---- Rural small ——— Rural large —-—--—- Rural small<br>1 ny‘\‘\<br>Hoy<br>in|<br>8 iII \\\<br>7onon \\\<br>26o5o55 iy" 1 | SK<br>fa) 7<br>4 /!!<br>/ Y<br>.22 Zrp!rp!<br>Ad<br>Lc?<br>0 a ==<br>7 8 9 10 11 12<br>Location premium (before sorting), 2019)<br>— Metro ———_ Urban large<br>—---- Urban small ——— Rural large<br>—---- Rural small<br><!-- End of picture text -->

<!-- Start of picture text --> 3<br>3<br>2GG 2 -\ 2 2 yo,,<br>aSS JT oN ®55 y \\\<br>\ a /, \<br>14.Le.LeLe Y \ 1 y “/ “/ ‘ i)<br>YLsflsfl N A \ \\\ 4, / PPS o<br>77 1IsIs Poa.. \* Yr},V4V4 \\\<br>oa S\N _v-” YX.<br>0 === a 0 == ae<br>2 3 . 4 ; 5 6 2 3 4 5 6<br>Location premium (before sorting), 2015 Location premium (after sorting), 2015<br>—— Urban-Metro large =—--——- Urban-Metro small — Urban-Metro large -—--——- Urban-Metro small<br>———_ Urban_ Urban Urban large —---- Urban small ——— Urban large —---- Urban small<br>— Rural large —---- Rural small — Rural large —---- Rural small<br>1.5 2 oN<br>~\ /<br>yto\to\\ -, tii<br>= f\\ 15 fs i<br>1 oY y' \\\ H \ \<br>2 \ 2 d \ | VN<br>5 jf \ \ g 1 \ AN<br>a / “AN\\ a “><br>5 // />“AN\ w\\\\\ A “ SOO \\\<br>Z / ‘ YT Ns 5 / ‘ Sat \\<br>7 / YS oor \ . 7 / JAS “7 “A NY<br>/ / t SR s. / , ) Ne a WAY<br>“ y 7 / arenNX6NX66 / Z Z 7aa ss. ~Sy XN<br>0 Z-- = 0<br>4 5 6 7 3.5 4 45 ; 5 55<br>Location premium (before sorting), 2010 Location premium (after sorting), 2010<br>— Urban-Metro large —--—- Urban-Metro small ——— Urban-Metro large ——-—-—- Urban-Metro small<br>— Uhban large ~---- Urban small ——— _ Urban large ----—- Urban small<br>— Rural large -—---- Rural small ——— Rural large —-—--—- Rural small<br>1 ny‘\‘\<br>Hoy<br>in|<br>8 iII \\\<br>7onon \\\<br>iy" | SK<br>1<br>26o5o55<br>fa) 7<br>4 /!!<br>/ Y<br>.22 Zrp!rp!<br>Ad<br>Lc?<br>0 a ==<br>7 8 9 10 11 12<br>Location premium (before sorting), 2019)<br>— Metro ———_ Urban large<br>—---- Urban small ——— Rural large<br>—---- Rural small<br><!-- End of picture text -->
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<!-- Start of picture text --> 2<br>1.5<br>os<br>’ ‘<br>>rf éa \.<br>Par,é % ‘ ‘<br>“* ‘<br>5 aa S<br>Pia aa x ~<br>- Me *<br>0<br>45eS,5 55 a éSs<br>Location premium (before sorting), 2017-19<br>—— Urban ----- Rural<br><!-- End of picture text --> <!-- Start of picture text --> 2.5<br>2 “7 ‘\<br>yy \<br>y \<br>4 \<br>@> 1.5 /, \ \<br>a 1 7 / , \\<br>UZ \<br>ae \<br>5 an ue \ ~y<br>2 NS ~<br>0<br>3.8 4 4.2 4.4 4.6 48<br>Location premium (after sorting), 2017-19<br>—— Urban o> o> Rural<br><!-- End of picture text --> <!-- Start of picture text --> 2 !v4 \v4 \ \ 2.5<br>iHH \<br>! \ 2<br>1.5 y22 \\\ / aN \<br>7>> '!! \ > 1.5 4 \<br>coo 4 ! \ c /<br>a 1 o /<br>/ \\\ a 1 !//<br>/ \\ !<br>.5!i‘5!i‘!i‘i‘‘<br>/ \\.\. 5 /// ‘\.\.<br>, \ +S<br>SA ons ons = ~<br>0 0<br>5.5 6 . 65 | 7 75 4.5 5 5.5 6 6.5<br>Location premium (before sorting), 2015-17 Location premium (after sorting), 2015-17<br>——_ Urban. -~~~- Rural ———_ Urban -~~~—- Rural<br><!-- End of picture text -->

<!-- Start of picture text --> 2 !v4 \v4 \ \ 2.5<br>iHH \<br>! \ 2<br>1.5 y22 \\\ / aN \<br>7>> '!! \ > 1.5 4 \<br>coo 4 ! \ c /<br>a 1 o /<br>/ \\\ a 1 !//<br>/ \\ !<br>.5!i‘5!i‘!i‘i‘‘<br>/ \\.\. 5 /// ‘\.\.<br>, \ +S<br>SA ons ons = ~<br>0 0<br>5.5 6 . 65 | 7 75 4.5 5 5.5 6 6.5<br>Location premium (before sorting), 2015-17 Location premium (after sorting), 2015-17<br>——_ Urban. -~~~- Rural ———_ Urban -~~~—- Rural<br><!-- End of picture text -->
<!-- Start of picture text --> 2 25<br>oN 2 oN<br>1.5 i, \\ / \<br>! \ / \<br>y\\<br>> f \\\ > 1.5 / \\\\\<br>o 4 fl \ 2 a ‘\<br>8““<br>aea\a\\ 1 7 V<br>/ \\\ y7777 \NN<br>5//<br>7\\ 5 ‘\<br>vo / \Q \ Z 4<br>0 oO -o77 ‘ 0 mae ~ Yo<br>4 4.5. 5 5.5 6 4 45 5 5.5<br>Location premium (before sorting), 2014-16 Location premium (after sorting), 2014-16<br>——— Urban -~---- Rural ———_ Urban ----- Rural<br>2<br>1<br>8 ony 1.5<br>4 XN<br>26 / / SAOTTOTT<br>2 / \ = Cs<br>[3aa / /‘‘ \ \ \ [5a2a22 1 yo 7 “ON AL ~y<br>4<br>//\\/\\\\\ 5 yyy \ \<br>2//<br>a__ a ee<br>of of =---7 —<br>3.5 4 45 5 5.5 6 3 3.5 4 45 5<br>Location premium (before sorting), 2017-19 Location premium (after sorting), 2017-19<br>———_ Urban --~—-~-- Rural ———_ Urban ----- Rural<br>a 4<br>1.5 i \<br>1 ON<br>1 \<br>!<br>/\\3\\3\33<br>t\\<br>1 / \ \<br>2== 1 \‘\‘\ 2== “sy<br>a55 /ii \ 2 2 “] aN<br>5 /7‘\7‘\‘\\ / / NS ~2<br>7 e-s! \ \ 1 7 / a SQ<br>/ \ ve ‘NY<br>0 O / ~~ 0 — uo _c- < < NY<br>5 5.5 . 6 . 6.5 7 5 5.2 5.4 5.6 5.8 6<br>Location premium (before sorting), 2008-09 Location premium (after sorting), 2008-09<br>——— Urban ----- Rural ——— _ Urban. -~-~--~-- Rural<br><!-- End of picture text --> <!-- Start of picture text --> 2 25<br>oN 2 oN<br>1.5 i, \\ / \<br>! \ / \<br>y\\<br>> f \\\ > 1.5 / \\\\\<br>o 4 fl \ 2 a ‘\<br>8““<br>aea\a\\ 1 7 V<br>/ \\\ y7777 \NN<br>5//<br>7\\ 5 ‘\<br>vo / \Q \ Z 4<br>0 oO -o77 ‘ 0 mae ~ Yo<br>4 4.5. 5 5.5 6 4 45 5 5.5<br>Location premium (before sorting), 2014-16 Location premium (after sorting), 2014-16<br>——— Urban -~---- Rural ———_ Urban ----- Rural<br>2<br>1<br>8 ony 1.5<br>4 XN<br>26 / / SAOTTOTT<br>2 / \ = Cs<br>[3aa / /‘‘ \ \ \ [5a2a22 1 yo 7 “ON AL ~y<br>4<br>//\\/\\\\\ 5 yyy \ \<br>2//<br>a__ a ee<br>of of =---7 —<br>3.5 4 45 5 5.5 6 3 3.5 4 45 5<br>Location premium (before sorting), 2017-19 Location premium (after sorting), 2017-19<br>———_ Urban --~—-~-- Rural ———_ Urban ----- Rural<br>a 4<br>1.5 i \<br>1 ON<br>1 \<br>!<br>/\\3\\3\33<br>t\\<br>1 / \ \<br>2== 1 \‘\‘\ 2== “sy<br>a55 /ii \ 2 2 “] aN<br>5 /7‘\7‘\‘\\ / / NS ~2<br>7 e-s! \ \ 1 7 / a SQ<br>/ \ ve ‘NY<br>0 O / ~~ 0 — uo _c- < < NY<br>5 5.5 . 6 . 6.5 7 5 5.2 5.4 5.6 5.8 6<br>Location premium (before sorting), 2008-09 Location premium (after sorting), 2008-09<br>——— Urban ----- Rural ——— _ Urban. -~-~--~-- Rural<br><!-- End of picture text -->
# _4.3 Territorial productivity dynamics_

This section reports results on territorial productivity dynamics, obtained by estimating regression model (3). Absolute convergence in real per capita household labor incomes at the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels is observed in most Latin American countries (Table 5). At the 1<sup>st</sup> administrative level, which refers to states, provinces, and departments, income convergence was relatively fast only in Bolivia, Colombia, Ecuador, Dominican Republic, and Mexico (see column 1 of Table 5). In these countries, the rate of convergence was faster than the 2 percent benchmark recorded during past convergence episodes in advanced economies. In Argentina, Brazil, Honduras, Peru, and Uruguay, the rate of convergence was just below the benchmark of 2 percent per year. Absolute convergence occurred at a slower pace in Chile and Panama. Only in Costa Rica, absolute convergence was observed at the lower canton level, but not at the 1st administrative level. In all countries with information at both administrative levels, absolute convergence was faster at the 2<sup>nd</sup> administrative level (Table 5).

Convergence can be partly attributed to the commodity boom during the Golden Decade (2003-13). With the rise of commodity rents due to increased demand for resources and agricultural products in China and other fast-growing economies, there were increases in investments and productivity in rural areas (Rodrígues, 2011; Rowe 2014; Adão 2015).<sup>18</sup> At the same time, the Dutch Disease effects of the commodity windfall, and in some countries, remittances, along with steep foreign competition from China, and advances in labor-saving technologies (Beylis et al., 2020), eroded the competitiveness of the manufacturing sector (Venables, 2017) and shifted employment away from urban tradables (Jedwab et al., 2022), especially in the largest cities, where laid off workers switched mostly to informal, lower quality jobs in the non-tradable sectors (Dix-Carneiro and Kovak, 2017; Dix-Carneiro and Kovak, 2019; Ponczek and Ulyssea, 2022). Altogether, these developments improved productivity in predominantly rural areas relatively more than productivity in large urban areas, resulting in absolute convergence in labor and place productivity premia in most Latin American countries (Appendix Tables A8 and Table 6). These results are in line with the findings in Dix-Carneiro and Kovak (2017) and Costa et al. (2016) who respectively show that trade liberalization and the “China Shock” resulted in reduced regional inequality in Brazi, while López-Calva et al. (2021) find absolute convergence in total incomes and poverty in Mexico from 2000 to 2014. They attribute it to positive developments in the poorest municipalities and stagnant or deteriorating performance of affluent ones.

> 18 Adão (2015) documents an increase of 8%-16% in the commodity wage premium (relative to non-commodities) in Brazil due to the rise in world commodity prices from 1991 to 2010.

20
**Table 5. Absolute convergence in real per capita household labor income at different administrative levels by country and period**

||Ad|ministrative lev|Annual per c<br>el 1|apita growth<br>Adm|ininistrative le|vel 2|
|---|---|---|---|---|---|---|
|**Mexico**|1990-15|2000-10|2000-15|1990-15|2000-10|2000-15|
|Log Y0|-0.0142***<br>(0.00378)|-0.0285***<br>(0.00665)|-0.0265***<br>(0.00476)|-0.0243***<br>(0.000394)|-0.0262***<br>(0.00109)|-0.0310***<br>(0.000609)|
|Obs.|32|32|32|2,303|2,330|2,320|
|R-squared|0.319|0.380|0.507|0.624|0.197|0.528|
|**Brazil**|2002/04-<br>2014/15|2000-10|2009/11-<br>2014/15|2002/04-<br>2014/15|2000-10|2009/11-<br>2014/15|
|Log Y0|-0.0107**|-0.0189***|-0.00817|-0.0889***|-0.0202***|-0.106***|
||(0.00489)|(0.00522)|(0.0114)|(0.00221)|(0.000555)|(0.00921)|
|Obs.|27|27|27|817|2040|817|
|R-squared|0.161|0.345|0.020|0.665|0.394|0.140|
|**Colombia**|2001/03-<br>2017/19|2001/03-<br>2008/10|2011-19|||2011-19|
|Log Y0|-0.0238***<br>(0.00589)|-0.0376***<br>(0.0113)|-0.0244***<br>(0.00694)|||-0.0319***<br>(0.00187)|
|Obs.|24|24|33|||1122|
|R-squared|0.425|0.335|0.286|||0.206|
|**Peru**|2000/03-<br>2017/19|2000/03-<br>2008/10|2008/10-<br>2017/19|2000/03-<br>2017/19|2000/03-<br>2008/10|2008/10-<br>2017/19|
|Log Y0|-0.0195***|-0.0224***|-0.0240***|-0.0296***|-0.0308***|-0.0473***|
||(0.00370)|(0.00768)|(0.00646)|(0.00168)|(0.00392)|(0.00338)|
|Obs.|24|24|24|186|186|186|
|R-squared|0.558|0.278|0.384|0.627|0.252|0.516|
|**Ecuador**|2003/04-<br>2017/19|2003/04-<br>2009/11|2009/11-<br>2017/19|2003/04-<br>2017/19|2003/04-<br>2009/11|2009/11-<br>2017/19|
|Log Y0|-0.0428***<br>(0.00737)|-0.0556***<br>(0.00992)|-0.0423**<br>(0.0184)|-0.0464***<br>(0.00332)|-0.0638***<br>(0.00549)|-0.0660***<br>(0.00799)|
|Obs.|21|21|21|191|191|191|
|R-squared|0.639|0.623|0.217|0.508|0.417|0.266|
||2000/03-|2000/03-|2009/11-|2000/03-|2000/03-|2009/11-|
|**Chile**|2015/17|2009/11|2015/17|2015/17|2009/11|2015/17|
|Log Y0|-0.0111*|-0.0150|-0.0148|-0.0207***|-0.0276***|-0.0385***|
||(0.00590)|(0.0116)|(0.0109)|(0.00155)|(0.00259)|(0.00505)|
|Obs.|13|13|13|299|299|299|
|R-squared|0.243|0.132|0.142|0.375|0.278|0.164|

21
> **Table 5. Absolute convergence in real per capita household labor income at different administrative levels by country and period (contd.)**

||Adm|ininistrative le|Annualper c<br>vel 1|apitagrowth<br>Ad|ministrative lev|el 2|
|---|---|---|---|---|---|---|
|**Dominican**|2000/02-|2000/02-|2008/10-|2000/02-|2000/02-|2008/10-|
|**Republic**|2014/16|2008/10|2014/16|2014/16|2008/10|2014/16|
|Log Y0|-0.0293***<br>(0.00601)|-0.0313***<br>(0.00962)|-0.0370***<br>(0.0115)|-0.0430***<br>(0.00395)|-0.0647***<br>(0.00603)|-0.0721***<br>(0.0132)|
|Obs.|30|30|30|115|115|115|
|R-squared|0.459|0.274|0.269|0.511|0.505|0.210|
|**Hd**|2004/06-|2004/06-|2009/11-|2004/06-|2004/06-|2009/11-|
|**onuras**|2017/19|2009/11|2017/19|2017/19|2009/11|2017/19|
|Log Y0|-0.0191**|-0.0328|-0.0408*|-0.0439***|-0.0834***|-0.0872***|
||(0.00803)|(0.0193)|(0.0207)|(0.00368)|(0.00776)|(0.00751)|
|Obs.|<br>16|<br>16|<br>16|<br>243|<br>243|<br>243|
|R-squared|0.287|0.171|0.216|0.372|0.324|0.359|
|**Costa Rica**||2001/03-<br>2008/09|||2001/03-<br>2008/09||
|Log Y0||-0.00613<br>(0.0130)|||-0.0242***<br>(0.00642)||
|Obs.||<br>7|||<br>79||
|R-squared||0.043|||0.155||
|**Argentina**|2003/05-<br>2017/19|2003/05-<br>2009/11|2009/11-<br>2017/19||||
|Log Y0|-0.0181***<br>(0.00465)|-0.0102<br>(0.0108)|-0.0397***<br>(0.00965)||||
|Obs.|<br>29|<br>29|<br>29||||
|R-squared|0.358|0.032|0.385||||
|**Uruguay**|2000/02-<br>2017/19|2000/02-<br>2008/10|2008/10-<br>2017/19||||
|Log Y0|-0.0177**<br>(0.00733)|-0.0448***<br>(0.0130)|-0.00714<br>(0.0175)||||
|Obs.|19|19|19||||
|R-squared|0.255|0.410|0.010||||
||2001/02-|2001/02-|2011/13-||||
|**Bolivia**|2017/19|2011/13|2017/19||||
|Log Y0|-0.0360***<br>(0.00500)|-0.0421***<br>(0.0119)|-0.0590***<br>(0.0139)||||
|Obs.|<br>9|<br>9|<br>9||||
|R-squared|0.881|0.641|0.719||||
||2001/03-|2001/03-|2009/11-||||
|**Panama**|2017/19|2009/11|2017/19||||
|Log Y0|-0.0117**<br>(0.00395)|-0.0131<br>(0.00780)|-0.0218<br>(0.0151)||||
|Obs.|<br>12|<br>12|<br>12||||
|R-squared|0.465|0.220|0.173||||

22
**Table 6. Absolute convergence in location premiums after sorting at different administrative levels by country and time period**

|||Annual|locationpremiu|mgrowth(after|sorting)||
|---|---|---|---|---|---|---|
||Ad|ministrative lev|el 1|Ad|ministrative lev|el 2|
|**Mexico**<br><br>|1990-15|2000-10|2000-15|1990-15|2000-10|2000-15|
|γ0<br>ab|-0.0145***<br>(0.00384)|-0.0275***<br>(0.00626)|-0.0265***<br>(0.00540)|-0.0263***<br>(0.000372)|-0.0330***<br>(0.00109)|-0.0278***<br>(0.000687)|
|Obs.|32|32|32|2301|2,330|2,320|
|R-squared|0.323|0.392|0.445|0.686|0.283|0.413|
||2002/04-||2009/11-|2002/04-||2009/11-|
|**Brazil**<br><br>|2014/15|2000-10|2014/15|2014/15|2000-10|2014/15|
|γ0<br>ab|-0.0178***|-0.0101|-0.0207*|-0.0942***|-0.0142***|-0.129***|
||(0.00548)|(0.00661)|(0.0120)|(0.00213)|(0.000657)|(0.0117)|
|Obs.|<br>27|<br>27|<br>27|<br>817|<br>2,040|<br>817|
|R-squared|0.295|0.086|0.106|0.706|<br>0.186|0.129|
|<br><br>|2001/03-|2001/03-|2008/10-||||
|**Colombia**<br><br>|2017/19|2008/10|2017/19||||
|γ0<br>ab|-0.0443***<br>(0.00888)|-0.0775***<br>(0.0217)|-0.0385***<br>(0.0112)||||
|Obs.|<br>24|<br>24|<br>24||||
|R-squared|0.531|0.367|0.351||||
|<br><br>|2000/03-|2000/03-|2008/10-|2000/03-|2000/03-|2008/10-|
|**Peru**<br><br>|2017/19|2008/10|2017/19|2017/19|2008/10|2017/19|
|γ0<br>ab|-0.0186***|-0.0122|-0.0286***|-0.0268***|-0.0357***|-0.0317***|
||(0.00639)|(0.0124)|(0.00818)|(0.00208)|(0.00427)|(0.00362)|
|Obs.|<br>24|<br>24|<br>24|<br>186|<br>186|<br>186|
|R-squared|0.278|0.042|0.356|0.474|0.275|0.294|
|<br><br>|2003/04-|2003/04-|2009/11-|2003/04-|2003/04-|2009/11-|
|**Ecuador**<br><br>|2017/19|2009/11|2017/19|2017/19|2009/11|2017/19|
|γ0<br>ab|-0.0496***|-0.0504***|-0.0695***|-0.0504***|-0.0617***|-0.0809***|
||(0.0100)|(0.0162)|(0.0221)|(0.00309)|(0.00632)|(0.00677)|
|Obs.|<br>21|<br>21|<br>21|<br>191|<br>191|<br>191|
|R-squared|0.562|0.339|0.342|0.584|0.336|0.431|
|<br><br>|2000/03-|2000/03-|2009/11-|2000/03-|2000/03-|2009/11-|
|**Chile**<br><br>|2015/17|2009/11|2015/17|2015/17|2009/11|2015/17|
|γ0<br>ab|-0.0195***|-0.0138*|-0.0424***|-0.0173***|-0.0178***|-0.0371***|
||(0.00397)|(0.00747)|(0.0133)|(0.00162)|(0.00251)|(0.00458)|
|Obs.|<br>13|<br>13|<br>13|<br>299|<br>299|<br>299|
|R-squared|0.687|0.236|0.481|0.278|0.145|0.181|
|**Dominican**|2000/02-|2000/02-|2008/10-|2000/02-|2000/02-|2008/10-|
|**Republic**|2014/16|2008/10|2014/16|2014/16|2008/10|2014/16|
||-0.0239***|-0.0250**|-0.0214|-0.0413***|-0.0609***|-0.0713***|
||(0.00742)|(0.00979)|(0.0132)|(0.00396)|(0.00676)|(0.0113)|
|Obs.|30|30|30|115|115|115|
|R-squared|0.270|0.190|0.085|0.491|0.418|0.260|

23
**Table 6. Absolute convergence in location premiums after sorting at different administrative levels by country and time period (contd.)**

||Ad<br>|Annual<br>ministrative lev<br>|locationpremiu<br>el 1<br>|mgrowth(after<br>Ad<br>|sorting)<br>ministrative lev<br>|el 2<br>|
|---|---|---|---|---|---|---|
|**Honduras**|2004/06-<br>2017/19|2004/06-<br>2009/11|2009/11-<br>2017/19|2004/06-<br>2017/19|2004/06-<br>2009/11|2009/11-<br>2017/19|
||-0.0253**|-0.0326*|-0.0307*|-0.0413***|-0.0669***|-0.0694***|
||(0.00944)|(0.0181)|(0.0165)|(0.00364)|(0.00656)|(0.00760)|
|Obs.|16|16|16|243|243|243|
|R-squared|0.338|0.188|0.199|0.349|0.301|0.257|
|**Costa Rica**||2001/03-<br>2008/09|||2001/03-<br>2008/09||
|||-0.00782|||-0.0307***||
|||(0.0215)|||(0.00753)||
|Obs.||<br>7|||<br>79||
|R-squared||0.026|||0.177||
||2003/05-|2003/05-|2009/11-||||
|**Argentina**|2017/19|2009/11|2017/19||||
||-0.0125**|-0.00396|-0.0379***||||
||(0.00598)|(0.00994)|(0.0135)||||
|Obs.|<br>29|<br>29|<br>29||||
|R-squared|0.139|0.006|0.226||||
||2000/02-|2000/02-|2008/10-||||
|**Uruguay**|2017/19|2008/10|2017/19||||
||-0.0206***|-0.0416***|-0.00549||||
||(0.00658)|(0.0110)|(0.0138)||||
|Obs.|<br>19|<br>19|<br>19||||
|R-squared|0.364|0.457|0.009||||
||2001/02-|2001/02-|2011/13-||||
|**Bolivia**|2017/19|2011/13|2017/19||||
||-0.0327***|-0.0280**|-0.0704***||||
||(0.00423)|(0.0103)|(0.0142)||||
|Obs.|<br>9|<br>9|<br>9||||
|R-squared|0.895|0.516|0.779||||
||2001/03-|2001/03-|2009/11-||||
|**Panama**|2017/19|2009/11|2017/19||||
||-0.0190|0.00568|-0.0515*||||
||(0.0133)|(0.0123)|(0.0265)||||
|Obs.<br><br>|<br>12<br>|<br>12<br>|<br>12<br>||||
|R-squared<br><br>|0.168<br>|0.021<br>|0.274<br>||||
|Note:γ0<br>abrefers|to the initial loc|ation premium|after sorting, estim|ated with regressi|on model (2). Fo|r Mexico the analysis|
|is based on cens|us data for all p|eriods while in|Brazil this is the c|ase for period 20|00-2010. For the|rest of the countries|
|the analysis is ba|sed on SEDLA|C harmonized h|ousehold surveys.||||

24
**Table 7. Absolute convergence in location premia before and after sorting by municipality type**

||**Metro large**<br>**Metro small**<br>**Urban large**|**Urban small**|**Rural large**<br>**Rural small**|
|---|---|---|---|
|**Panel**<br><br>|**A. Mexico**|||
||Annual locationpremiumgro|wth 2000-15(befo|re sorting)|
|γ0<br>b|-0.0464***<br>-0.0313***<br>-0.0215***|-0.0197***|-0.0170***<br>-0.0284***|
||(0.00531)<br>(0.00412)<br>(0.00151)|(0.00137)|(0.00157)<br>(0.00127)|
|Obs.<br><br>|<br> <br> <br>41<br>72<br>357<br> <br> <br>|<br>437<br>|<br> <br>586<br>827<br> <br>|
|R<sup>2</sup><br><br>|0.663<br>0.451<br>0.362|0.322|0.166<br>0.378|
||Annual locationpremiumgr|owth 2000-15(aft|er sorting)|
|γ0<br>ab|-0.0476***<br>-0.0354***<br>-0.0254***|-0.0253***|-0.0203***<br>-0.0311***|
||(0.00384)<br>(0.00375)<br>(0.00165)|(0.00143)|(0.00154)<br>(0.00123)|
|Obs.|<br> <br> <br>41<br>72<br>357|<br>437|<br> <br>586<br>827|
|R<sup>2</sup>|0.798<br>0.560<br>0.399|0.417|0.229<br>0.437|
|**Panel**<br><br>|**B. Brazil**<br>Annual locationpremiumgro|wth 2000-10(befo|re sorting)|
|γ0<br>b|-0.0110***<br>-0.0110***<br>-0.0120***|-0.00951***|-0.00920***<br>-0.00325|
||(0.00352)<br>(0.00153)<br>(0.000962)|(0.000824)|(0.00194)<br>(0.00239)|
|Obs.|<br> <br> <br>46<br>176<br>434|<br>842|<br> <br>317<br>225|
|R<sup>2</sup><br><br>|0.181<br>0.229<br>0.264|0.137|0.067<br>0.008|
||Annual locationpremiumgr|owth 2000-10(aft|er sorting)|
|γ0<br>ab|-0.0110**<br>-0.00809***<br>-0.0151***|-0.0152***|-0.0227***<br>-0.0178***|
||(0.00533)<br>(0.00222)<br>(0.00137)|(0.00111)|(0.00224)<br>(0.00272)|
|Obs.|46<br>176<br>434|842|317<br>225|
|R<sup>2</sup>|0.088<br>0.071<br>0.219|0.183|0.246<br>0.162|
||Urban-Metro<br>Urban large|Urban small|Rural Large<br>Rural small|
|**Panel**<br><br>|**C. Colombia**|||
||Annual locationpremiumgro|wth 2011-19(befo|re sorting)|
|γ0<br>b|-0.0175**<br>-0.0305***|-0.0341***|-0.0228***<br>-0.0463***|
||(0.00698)<br>(0.00408)|(0.00529)|(0.00294)<br>(0.00371)|
|Obs.|<br> <br>59<br>168|<br>150|<br> <br>341<br>404|
|R<sup>2</sup>|0.099<br>0.252|0.220|0.151<br>0.279|
||**Urban**<br>**Rural**|**Urban**|**Rural**|
|**Panel**<br>|**D. Peru**<br>|||
||Annual location premium growth<br><br>|Annual lo|cation premium growth|
||<br>2000/03-2017/19(before sorting)<br><br>|<br>2000/03-|<br>2017/19(after sorting)|
|γ0<br>b|-0.0158***<br>-0.0329***<br> γ0<br>ab|-0.0196***|-0.0394***|
||(0.00273)<br>(0.00303)|(0.00311)|(0.00304)|
|Obs.|71<br>115|71|115|
|R<sup>2</sup>|0.326<br>0.510|0.366|0.597|
|**Panel**|**E. Ecuador**|||
||Annual location premium growth<br><br>|Annual lo|cation premium growth|
||<br>2003/04-2017/19(before sorting)<br><br>|<br>2003/04-|<br>2017/19(after sorting)|
|γ0<br>b|-0.0467***<br>-0.0448***<br> γ0<br>ab|-0.0501***|-0.0541***|
||(0.00513)<br>(0.00444)|(0.00489)|(0.00404)|
|Obs.|69<br>122|69|122|
|R<sup>2</sup>|0.553<br>0.495|0.610|0.599|

25
**Table 7. Absolute convergence in location premia before and after sorting by municipality type (contd.)**

||**Urban**<br>**Rural**||**Urban**|**Rural**|
|---|---|---|---|---|
|**Panel F.**|**Chile**||||
||Annual location premium growth<br>2000/03-2015/17(before sorting)||Annual location pre<br>2015/17|mium growth 2000/03-<br>(after sorting)|
|γ0<br>b|-0.0172***<br>-0.0260***<br>(0.00144)<br>(0.00238)|γ0<br>ab|-0.0140***<br>(0.00202)|-0.0215***<br>(0.00282)|
|Obs.|<br> <br>201<br>98||<br>201|<br>98|
|R<sup>2</sup>|0418<br>0553||0193|0378|
|**Panel G.**|.<br>.<br>**Dominican Republic**||.|.|
||Annual location premium growth<br>2000/02-2014/16(before sorting)||Annual location pre<br>2014/16|mium growth 2000/02-<br>(after sorting)|
|γ0<br>b|-0.0317***<br>-0.0434***<br>(0.00499)<br>(0.00646)|γ0<br>ab|-0.0349***<br>(0.00564)|-0.0451***<br>(0.00553)|
|Obs.|<br> <br>51<br>64||<br>51|<br>64|
|R<sup>2</sup>|0.452<br>0.421||0.438|0.517|
|**Panel I.**|**Honduras**||||
||Annual location premium growth<br>2004/06-2017/19(before sorting)||Annual location pre<br>2017/19|mium growth 2004/06-<br>(after sorting)|
|γ0<br>b|-0.0400***<br>-0.0341***|γ0<br>ab|-0.0556***|-0.0421***|
||(0.00963)<br>(0.00370)||(0.0106)|(0.00387)|
|Obs.|<br> <br>33<br>210||<br>33|<br>210|
|R<sup>2</sup>|0.357<br>0.291||0.470|0.363|
|**PanelJ. C**|**osta Rica**||||
||Annual location premium growth<br>2001/03-2008/09(before sorting)||Annual location pre<br>2008/09|mium growth 2001/03-<br>(after sorting)|
|γ0<br>b|-0.00904<br>-0.0245***|γ0<br>ab|-0.0421***|-0.0218**|
||(0.0107)<br>(0.00886)||(0.0129)|(0.0102)|
|Obs.<br>|30<br>49<br>||30<br>|49<br>|
|R<sup>2</sup><br>|0.025<br>0.140<br>||0.276<br>|0.088<br><br>|
|Source: D’A<br>initial locati<br>characteristi<br>sorting, esti<br>characteristi<br>characteristi<br>all periods.<br>periods. In<br>metropolita<br>surveys.|oust, Galdo, and Ianchovichina (2022). Note:<br>on premium before sorting. These are the es<br>cs but are net of survey-year fixed effects as<br>mated with model (2.1). These are the estimate<br>cs (age, age squared, gender, human capita<br>cs) and are also net of survey-year fixed effect<br>In the case of Brazil, census data is used for<br>the case of Colombia, we use official aggre<br>n municipalities. For the rest of the countri|The resu<br>timated<br>in mode<br>d locatio<br>l, marita<br>s. In the<br>the perio<br>gates of<br>es, the a|lts are estimated based o<br>location premiums with<br>l (2.2).γ0<br>abrefers to the<br>n premiums controlling f<br>l status, household de<br>case of Mexico, the analy<br>d 2000-2010, while hous<br>value-added data and o<br>nalysis is based on SED|n model (2.3).γ0<br>brefers to the<br>out controlling for household<br>initial location premium after<br>or sorting based on household<br>mographics, and employment<br>sis is based on census data for<br>ehold data is used in all other<br>nly include one category for<br>LAC harmonized household|

Convergence occurred at different speeds across different types of localities (Table 7). In Mexico, convergence was fastest across metropolitan municipalities and slowest across large rural municipalities. In Brazil, convergence in labor productivity premia (i.e. location premia _before_ sorting) was fastest across the large urban and metropolitan municipalities, while convergence in place 26
productivity premia (i.e. location premia _after_ sorting) was fastest across rural areas, especially larger ones. In Chile, Colombia, Dominican Republic, and Peru, convergence was faster across rural municipalities. By contrast, in Honduras and Costa Rica, it was faster across urban municipalities. Location premia converged at similar rates across rural and urban areas in Ecuador (Table 7). Thus, in Mexico, Honduras, and Costa Rica, convergence was mainly driven by the urbanization process, while in Brazil, Colombia, Chile, Dominican Republic, and Peru, the commodity boom rather than the urbanization process was behind it. In Ecuador, both forces appear to have played an important role. Convergence reflected both improvements in growth in lagging municipalities and to different extents the slowdown in growth in previously high productivity municipalities. In almost all countries the fastest-growing municipalities were rural, although growth was much more dispersed across rural than urban ones, indicating that some rural areas continued to lag behind.

# _4.4 Income gaps with leading metropolitan areas_

The incomes gaps with the leading Latin American metropolitan areas, identified in (6), were high in many LAC countries in the early 2000s (Figure 4). In Peru, the average per capita household labor income in Lima was almost three times the corresponding income in other parts of the country. Convergence narrowed down to different degrees the income gaps with the leading areas between the early 2000s and the late 2010s (Figure 4). Still, on the eve of the Covid-19 pandemic, sizable differences in per capita household labor earnings with the leading areas were observed in all countries, except in Mexico and Costa Rica. The gaps remained particularly high in Peru, Panama, and Paraguay. In most cases, except Brazil, Colombia, Honduras, Mexico, and Panama, the gaps exploitable through migration, i.e. those that can be attributed to differences in returns to endowments and captured by the second component on the right-hand-side of (6), also declined by the end of the 2010s (Figure 4).<sup>19</sup> As suggested by consistency check (7), we find that in all countries the difference between the place productivity premium (i.e. the location premium after sorting) in the leading area and the median place productivity premium approximately equals the rate-of-return component in the income gap decomposition, shown in Figure 4. The correlation between the right-hand side and left-hand side of (7) is high for the sample of countries included in the paper. It is 0.78 in the first period and 0.73 in the second period. At the country level, differences between the right-hand side and left-hand side of (7) are expected to be larger when the survey years in the location premia estimiation and the income gap analysis are not fully aligned, some municipalities are omitted from the estimation of the location premia after sorting due to missing information on household characteristics, and the households are not evenly distributed across municipalities. For example, if the share of households living in municipalities with below median premia locations is higher than those located in above median premia localities, then the right-hand side of (7) is expected to be higher than its left-hand side. This is the situation in Bolivia, where 62% of all households lived in locations with below median location premia in the last period (see Appendix Figure A1). The opposite is observed in Honduras, where 59% of all households lived in locations with above median location premia (Appendix Figure A1).

> 19 This is consistent with the increase in the difference between the location premium in the leading areas ( ) and the median premium ( ) in Colombia, Honduras, and Panama in Table 8. In the cases of Brazil and Mexico, the periods shown in Table 8 do not align with the periods shown for the two countries in Figure 4, because the table reflects γγ<sup>∗</sup> ll50 information from the IPUMS census data, while the figure shows data from the SEDLAC household surveys.    γγ

27
<!-- Start of picture text --> BOL BRA CHL COL CRI DOM ECU HND MEX PAN PER PRY<br>soo-<br><<br>& =<br>BFE to0-i<br>HG G8 os 06 oo Bs Os<br>oe afoeoe ofae oeoe aeae oeaf<br>(Incomegap (SM Endowments Mil Returnsto endowments<br><!-- End of picture text -->
**Table 8. Dispersion in the location premia after sorting around the mean/median/leading location premium**

|**Mexico**<br><br>|1990|2000|2015|**Honduras**<br><br>|2004/06|2009/11|2017/19|
|---|---|---|---|---|---|---|---|
|%CV(γl)|γl<br>���<br><br>|14.668|10.488|7.615|%CV(γl)|γl<br>���<br><br>|9.195|7.835|8.380|
|%CV(γl)|γ<sup>∗</sup><br><br>|13.558|9.750|7.142|%CV(γl)|γ<sup>∗</sup><br><br>|8.561|7.349|7.899|
|Avg. (γ<sup>∗</sup>−γl)<br>|0.349|0.333|0.308|Avg. (γ<sup>∗</sup>−γl)<br>|0.306|0.286|0.256|
|γ<sup>∗</sup>−γl50 <br>|0.207|0.294|0.226|γ<sup>∗</sup>−γl50 <br>|0.232|0.259|0.252|
|**Brazil**<br><br>|2000|2010||**Costa Rica**<br><br>|2001/03|2008/09||
|%CV(γl)|γl<br>���<br><br>|8.971|7.685||%CV(γl)|γl<br>���<br><br>|3.869|3.241||
|%CV(γl)|γ<sup>∗</sup><br><br>|8.241|7.137||%CV(γl)|γ<sup>∗</sup><br><br>|3.685|3.138||
|Avg. (γ<sup>∗</sup>−γl)<br>|0.390|0.359||Avg. (γ<sup>∗</sup>−γl)<br>|0.252|0.185||
|γ<sup>∗</sup>−γl50 <br>|0.318|0.280||γ<sup>∗</sup>−γl50 <br>|0.227|0.177||
|**Peru**<br><br>|2000/03|2008/10|2017/19|**Colombia**<sup>**1/**</sup><br><br>|2001/03|2008/10|2017/19|
|%CV(γl)|γl<br>���<br><br>|11.244|9.063|6.985|%CV(γl)|γl<br>���<br><br>|3.433|3.127|2.346|
|%CV(γl)|γ∗<br><br>|9.371|7.899|6.207|%CV(γl)|γ∗<br><br>|3.360|2.948|2.241|
|Avg. (γ<sup>∗</sup>−γl)<br>|0.665|0.527|0.489|Avg. (γ<sup>∗</sup>−γl)<br>|0.092|0.270|0.221|
|γ<sup>∗</sup>−γl50 <br>|0.650|0.522|0.513|γ<sup>∗</sup>−γl50 <br>|0.055|0.314|0.227|
|**Ecuador**<br><br>|2003/04|2009/11|2017/19|**Argentina**<sup>**1/**</sup><br><br>|2003/05|2009/11|2017/19|
|%CV(γl)|γl<br>���<br><br>|7.087|5.625|4.474|%CV(γl)|γl<br>���<br><br>|5.298|4.587|4.069|
|%CV(γl)|γ∗<br><br>|6.359|5.269|4.237|%CV(γl)|γ∗<br><br>|4.811|4.312|3.915|
|Avg. (γ<sup>∗</sup>−γl)<br>|0.441|0.282|0.241|Avg. (γ<sup>∗</sup>−γl)<br>|0.397|0.305|0.190|
|<br>γ<sup>∗</sup>−γl50 <br>|0.406|0.263|0.221|<br>γ<sup>∗</sup>−γl50 <br>|0.472|0.357|0.217|
|**Chile**<br><br>|2000/03|2009/11|2015/17|**Uruguay **<sup>**1/**</sup><br><br>|2000/02|2008/10|2017/19|
|%CV(γl)|γl<br>���<br><br>|5.303|4.327|3.780|%CV(γl)|γl<br>���<br><br>|3.668|2.834|2.783|
|%CV(γl)|γ∗<br><br>|5.052|4.202|3.671|%CV(γl)|γ∗<br><br>|3.402|2.736|2.664|
|Avg. (γ<sup>∗</sup>−γl)<br>|0.223|0.153|0.160|Avg. (γ<sup>∗</sup>−γl)<br>|0.351|0.153|0.210|
|γ<sup>∗</sup>−γl50|0.219|0.157|0.178|γ<sup>∗</sup>−γl50|0.389|0.129|0.186|
|**Dominican**<br>**Republic**<br><br>|2000/02|2008/10|2014/16|**Bolivia**<sup>**1/**</sup><br><br>|2001/02|2011/13|2017/19|
|%CV(γl)|γl<br>���<br><br>|5.543|4.531|4.237|%CV(γl)|γl<br>���<br><br>|9.256|5.869|3.585|
|%CV(γl)|γ∗<br><br>|5.145|4.287|4.048|%CV(γl)|γ∗<br><br>|8.743|5.597|3.429|
|Avg. (γ<sup>∗</sup>−γl)<br>|0.380|0.280|0.226|Avg. (γ<sup>∗</sup>−γl)<br>|0.245|0.237|0.226|
|γ<sup>∗</sup>−γl50 <br>|0.351|0.246|0.175|γ<sup>∗</sup>−γl50|0.336|0.205|0.246|
|**Panama**<sup>**1/**</sup><br><br>|2001/03|2009/11|2017/19|||||
|%CV(γl)|γl<br>���<br><br>|6.614|7.076|5.865|||||
|%CV(γl)|γ∗<br><br>|6.272|6.513|5.538|||||
|Avg. (γ<sup>∗</sup>−γl)<br>|0.222|0.371|0.272|||||
|<br>γ<sup>∗</sup>−γ|0.152|0.301|0.203|||||
|l50 <br>Notes: 1/Analysis co<br>to the first administr<br>leading areas in Braz<br>leading area.%CV (<br><br>|<br>nducted at th<br>ative-level u<br>il, Chile, Me<br>γl)|γl<br>���<sup>and </sup><br><br>|<br>e first admin<br>nit where the<br>xico, and Pe<br><sup>%CV (γ</sup>l<sup>)|</sup>γ<sup>∗</sup><br> <br>|<br>istrative leve<br>leading loca<br>ru are the av<br><sup>measures t</sup><br> <br>|l. Location premium<br>tion is located. The<br>erage location premi<br><sup>he coefficient of var</sup><br><br>|(after sorting<br>location pre<br>ums of the<br><sup>iation aroun</sup><br><br>|) of leading<br>mia of the m<br>municipalitie<br><sup>d the mean</sup><br><br>|areas correspond<br>ulti-municipality<br>s included in the<br><sup>and the location</sup><br><br>|
|<br>premium in the leadi<br>|<br> <br>ng area, resp<br>|<br>ectively.Avg<br>|. (γ<sup>∗</sup>−γl)<br>|measures the averag<br>|e gap in loca<br>|tion premiu<br>|m of leading area<br>|
|<br><br>(γ<sup>∗</sup>)and each one of<br>area and the median.|<br> <br><br>the other lo<br>|<br> <br>cations(γl).|<br> (γ<sup>∗</sup>−γl50)|measures the gap bet|ween the loc|ation premi|um in the leading|

29
**Table 9. Income gaps and the contribution of endowment differences at the end of the 2010s**

|Country|Per capi<br>income<br>PP<br>|ta labor<br>(US$ P)<br>|Income<br>gap|Potential<br>increase in<br>income if|education|Of which:<br>employment|demographics|
|---|---|---|---|---|---|---|---|
||Leading<br>Area|Rest of<br>Country||equal<br>endowments||||
|Argentina|596.6|341.8|75%|43%|67%|4%|30%|
|Bolivia|336.4|211.5|59%|8%|64%|40%|_-4%_|
|Brazil|306.2|184.7|66%|32%|88%|-2%|14%|
|Chile|552.8|381.0|45%|21%|96%|3%|_1%_|
|Colombia|392.3|231.7|69%|38%|64%|11%|25%|
|Costa Rica|455.4|328.9|38%|29%|81%|_1%_|18%|
|Dominican Republic|371.3|255.0|46%|25%|96%|_4%_|_0%_|
|Ecuador|316.4|205.4|54%|33%|55%|10%|36%|
|Honduras|233.6|149.1|57%|40%|77%|_3%_|20%|
|Mexico|212.4|160.1|33%|22%|65%|_-1%_|36%|
|Panama|608.4|290.2|110%|50%|59%|18%|23%|
|Paraguay|532.1|298.3|78%|56%|66%|15%|19%|
|Peru|354.2|170.6|108%|26%|67%|12%|21%|
|Uruguay|589.7|388.5|52%|30%|79%|7%|13%|

Notes: Results are shown as geometric means of income, i.e. the logarithmic differences shown in Appendix Table A9 is used to compute the exponential difference: exp(ln(x) − ln(y ))-1 = x/y -1. Results in italics are not significant. Only urban households included in the Argentina’s and Uruguay’s samples. The last period in most countries is 2017-19, except in Chile, 2015, 2017; Costa Rica, 2008-09; Dominican Republic, 2014-16 and Mexico, 2016, 2018. The leading areas are the City of Buenos Aires in Argentina; Belo Horizonte, Sao Paulo, and Rio de Janeiro in Brazil; Santiago in Chile; Bogotá in Colombia; San Jose in Costa Rica; City of Santo Domingo in Dominican Republic; Quito (Urban Pichincha) in Ecuador; urban Tegucigalpa (Francisco Morazan) in Honduras; Mexico City metropolitan area in Mexico; urban Panama province in Panama; Ascunsion in Paraguay; Lima-Callao region in Peru; Montevideo in Uruguay.

# 4.4.1 Income gaps with leading areas by administrative region

In the largest countries, the income gaps with the leading area by 1<sup>st</sup> level administrative region, which depending on the country could be state, province, or department, considerably vary in size (Figure 5). They are largest for the residents of some of the poorest and often remote regions. Importantly, within countries, the variation in income gaps across states and provinces mostly reflects variation in the differences in returns to endowments, rather than variation in the endowment differences with the leading metropolitan areas (Figure 5), signaling that barriers to migration tend to grow with distance, as shown by Skoufias and Lopez-Acevedo (2009) and Bryan and Morten (2018).

Distance matters because the cost of travel rises with the distance traveled by migrants. Travel costs tend to be high in the large Latin American countries where traveling by air is the only way to get to many parts of these countries. The migrants’ social capital also tends to decline with distance. The strength of the migrant networks, which provide potential migrants with information, job opportunities, and even housing upon arrival in the destination city, tends to diminish with distance from the location of the migrant. Support through social networks is particularly important for the poorest residents, who have limited budgets and information through other sources. In addition, location preferences and discrimination may also serve as barriers to migration. Afro-descendants and indigenous workers in Brazil’s North and Northeast are significantly less likely to migrate out of their states than white residents, regardless of their level of education (Figure 6).

30
<!-- Start of picture text -->
[Income gap | Endowments Mil Returns to endowments<br>Corrientesso -J<br>.<br>Santiago delMisionesEstero —<br>Formosa — LL _ Ea<br>Salta — BGS<br>CatamarcaJujuy ——<br>Tucuman —<br>Buenos2 Aires - ———— —s)<br>Cordoba —<br>San Juan —<br>Entre Rios —<br>Mendoza —<br>La Rioja ~ — a<br>SantaSan LuisFe — — —<br>La Pampa - L__s-<br>Rio Negro — Le<br>Nowauen - - —<br>Santa 7 _—<br>Tierra del Fuego Cruz — ae<br>1 1 ' ' ' '<br>-50 0 50 100 150 200<br>Per capita Labor Income Gap (%)<br>Relative to the City of Buenos Aires<br><!-- End of picture text -->

<!-- Start of picture text -->
[4 Income gap ') Endowments Mil Returns to endowments<br>aul 7|<br>Bahia-Paa- E Cl EE_ S<br>Alagoas — (i<br>Sergipe- (EEE<br>AmazonasPaaiba- — LEC_ E<br>Pemambuco- -(__<br>Rio; Grande do Nortewe—<br>Amapa —<br>Tocantins —<br>Roraima —<br>Minas Gerais —<br>roo<br>EspiritoMato Grosso Santo — —<br>Mato Grosso do Sul —<br>Rio de Janeiro —<br>Rio Grande doParana Sul — —<br>Sao Paulo— [Hi<br>Santa Catarina- iH<br>Distrito Federal — Hill<br>1 i 1 1<br>0 100 200 300<br>Per Capita Labor Income Gap (%)<br>Relative to BH, RJ, SP metros<br><!-- End of picture text -->

<!-- Start of picture text --> C2 Income gap © Endowments MM Returns to endowments<br>Choca<br>La Guajira —=(—<br>CaucaSucre ——<br>cicctajagdalena —<br>Norte de Santander —<br>Cesarre —<br>bts<br>BolivarHuila — —<br>Caqueta —<br>BoyacaTolima — —<br>AtiC a nticoldas —<br>SantanderRisaraldaMeta - —— [oImEH<br>Valle —<br>CundinamarcaAntioquia ——<br>' t 1 1 '<br>0 50 100 150 200<br>Per Capita Labor Income Gap (%)<br>Relative to Bogota<br><!-- End of picture text --> <!-- Start of picture text --> (= Income gap TM! Endowments MMM Returns to endowments<br>baatChiapas~<br>rural sonataferacruz-Llave Eeceral =~<br>nage aa<br>San ena t S<br>MighoacanNtcala I=<br>Yucatan g<br>Durange aNevet =<br>Guanajuato —<br>campecheana a—|<br>cofaulaeCO = |<br>NENGChihuahuaem’Sindioa Tasel i——<br>Baja California —<br>Baja QuintansCalifornia Suree —<br>' ' ' i) 1<br>-50 0 . 50 100 150<br>Per Capita Labor Income Gap (%)<br>Relative to Metro Mexico<br><!-- End of picture text -->

<!-- Start of picture text -->
[2 Income gap §) Endowments Mill Returns to endowments<br>‘—_<br>Cajamarca -<br>AyacuchoHudnuco -~ (a—<br>Loreto -<br>Puno —<br>Pasco -<br>Amazonas ~<br>San Martin ~<br>Cusco —<br>Ancash —<br>Piura —<br>Junin -<br>Ucayali —<br>La Libertad —<br>Lambayeque —<br>Tumbes ~<br>Lima rural —<br>Moqueguaa~<br>Madre de Dios —<br>Ica -<br>Arequipa —<br>1 I i) i<br>0 100 200 300<br>Per Capita Labor Income Gap (%)<br>Relative to Metro Lima<br><!-- End of picture text -->
<!-- Start of picture text --> — White — Afro-descendant/lndigenous<br>15 -<br>E<br>mo lo<br>:<br>E<br>3<br>2<br>8 05 -<br>ai<br>0<br>a<br>| i | i<br>Less than primary Primary Secondary Tertiary<br>Educational attainnment<br><!-- End of picture text -->
Chile, Colombia, Dominican Republic, Panama, and Peru can be attributed mainly to differences in returns to endowments, possibly reflecting barriers to migration due to shortages of affordable formal housing in these countries’ leading cities.<sup>21</sup> Only in Paraguay, the skilled income gaps can be attributed to differences in the endowments of the skilled households in the leading area and the rest of the country.

The rural income gaps with leading areas (Figure 7, Panel C) are much larger than the average (Figure 4) and the urban income gaps (Figure 7, Panels A). In most countries, these gaps can be primarily explained with deficits in portable endowments, especially education. Only in Bolivia and Peru, the gaps mainly reflect differences in returns to endowments rather than differences in portable household characteristics. In Brazil, the gaps are large and can be explained both by differences in endowments and differences in the returns to these endowments. Consequently, only in these three countries, there is still some scope for lifting incomes through rural-urban migration.

In most countries, the income gaps with the leading areas are insignificant or very small among the bottom 40% (B40) of households (Figure 7, Panel D). The gaps are negligible in Argentina, Chile, Costa Rica, Dominican Republic, and Uruguay and close to or below 25% in Colombia, Ecuador, Honduras, Mexico, and Paraguay. Only in Bolivia, Brazil, Panama, and Peru, the income gaps among the B40 are around 50% and can be explained mostly with differences in returns to endowments, not differences in endowments, indicating potential benefits from migration to the leading areas.

4.4.3 Income gaps with leading areas by gender One way to explore whether there are systematic differences in the income gaps with the leading areas between men and women is to estimate regression models (4) and (5) at the individual level for men and women, estimate (6) by gender, and compare the female and male income gaps in each country.<sup>22</sup> The results suggest that the differences between the male and female urban income gaps were relatively muted in the early 2000s (Figure 8, Panel A), except in Brazil, Mexico, and Uruguay. By the end of the 2010s, female urban income gaps widened in Bolivia, Colombia, Ecuador, Peru, and Paraguay, but only in Colombia, they deteriorated relative to the early 2000s.

The growing gender disparities in the income gaps with the leading areas cannot be explained with growing gender disparities among the skilled workers (Figure 8, Panel B). Among the B40, female income gaps became significantly larger than male income gaps only in Colombia, Mexico, and Uruguay (Figure 8, Panel D). In Colombia and Mexico, where we have information on individuals residing in rural areas, this development can be attributed to the increase in the female rural income gaps with the leading area. Female rural income gaps also opened in Paraguay and grew in Ecuador, Panama, and Peru. These results are consistent with evidence from the migration literature, which documents no change in the preferences of young migrants for large cities (Rodríguez and Busso 2009; Rowe, 2013), but a shift in the sex and education composition of domestic migrants to leading areas towards migrants who are male and educated (Rodríguez, 2004).

> 21 See Bastos (2017), who shows that in Brazil the deficit in formal housing has been growing, and potentially deterring skilled workers who might be more reluctant than unskilled workers to live in poor quality informal housing.

> 22 While the SEDLAC data are representative only at the household level, it is possible to conduct the analysis at the individual level for the purpose of comparing the income gaps by gender.

33
<!-- Start of picture text --> ARG BOL BRA CHL COL CRI DOM ECU HND MEX PAN PER PRY URY<br>150-<br>z<br>é<br>e 100 -<br>g<br>iO mA. mF li " is<br>© em FS OO ee BS cs<br>ec otoS SF£€ OF€S OSSS OSSS EFCSS ESCS SSCS 8SGSS 88SS FSSS ESSS SS€$& SSS$<br>(> Income gap 1) Endowments Mill Returnsto endowments<br><!-- End of picture text --> <!-- Start of picture text --> ARG BOL BRA CHL coL CRI DOM ECU HND MEX PAN PER PRY URY<br>= 100 ~<br>|.i me. 2s "a "on<br>a) A pec FB Fes gach a<br>Ey CPee LEoo LFod LSoo EFoo EE8 BPSS OFPo BFoo OFOS OSPS OSPo OSoo EFOe<br>[5 Income gap Endowments lll Returnsto endowments<br><!-- End of picture text -->

<!-- Start of picture text --> BOL BRA CHL coL CRI DOM ECU HND MEX PAN PER PRY<br>2°<br>z<br>8 200 -<br>a oeo & aeoe osoeaeoe oe & oe aeoe oeoeoe oe oeoe & etoeoe 2 aeoe<br>[> Income gap lll Endowments Mill Returnsto endowments<br><!-- End of picture text --> <!-- Start of picture text --> ARG BOL BRA CHL COL cRI DOM ECU HND MEX PAN PER PRY URY<br>150—<br>=<br>E s0-<br>Ez<br>8LAFLPFLPLPELLPLPLELELELELFLF<br>[Income gap lll Endowments fll Returnsto endowments<br><!-- End of picture text -->
<!-- Start of picture text --> ARG BOL BRA CHL COL CRI DOM ECU HND MEX PAN PER PRY URY<br>100<br>a<br>oFE s0- ' bo al me ><br>8 L | t | ] | I<br>5; i I ] |<br>3|<br>£<br>3 a| I oO j i [ i if<br>e¢ ¢¢ ££ § € § SS FS SS ES FES SS ES SS SES ES<br>gf ee ue ee PE PE PE Ee att<br>& F & eS LF eS e F eS SF ee & & & F & &<br>== Women Men<br><!-- End of picture text -->

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# **5. Concluding remarks**

Between the early 2000s and the late 2010s, most Latin American countries experienced significant reductions in regional inequality as real labor incomes and local productivity premia converged at the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels. The decline in regional inequality reflected the expansion of agribusiness and mining, which brought investments to lagging, predominantly rural areas, and to different extents, the slowdown in productivity growth in large urban municipalities, which occurred as de-industrialization shifted urban employment toward less dynamic, low-productivity non-tradables (Jedwab et al., 2022).

Convergence diminished the income gaps with leading metropolitan areas in most countries. At the end of the 2010s, income disparities with leading metropolitan areas remained high in nearly all countries largely due to differences in educational attainment, but in a few countries large differences in returns to endowments indicate potentially significant returns to migration to the leading metropolitan areas, especially for residents of relatively poor, remote regions. Rather than a clear ruralurban-metropolitan divide, in most countries the paper documents substantial overlap between the location-premia distributions of different types of second-level administrative areas and small differences between the average urban and rural place productivity premia.

The estimated differences in returns to portable endowments in the leading and other areas capture the static effects of any barriers to migration. However, the differences in returns may also reflect differences due to measurement errors in the non-geographic characteristics, which may bias the estimated differences in returns and therefore affect the interpretation of the results for policy purposes. For example, since the quality of education in lagging regions or secondary cities is likely lower than the quality of education in the leading cities, the returns to education in the lagging region or secondary city may appear lower than the returns to education in the leading areas, although the returns to the quality-adjusted years of education may be the same. Hence, future work should focus on refining the treatment of portable endowments, especially education, and ways to capture currently unobservable portable characteristics.

Other promising directions for future research include analyses of the barriers to migration and their relative importance for different socioeconomic groups. While migration to leading areas may enable workers to capture a significant share of the income gaps in some countries, it may not be a solution for everybody due to preferences and cultural factors. Hence, the influence of different regional policies and institutions on local productivity premia, including those that boost the growth of secondary cities, merits further attention in the Latin American context.

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