What drives the spatial wage premium in formal and informal labor markets? The case of Ecuador

Abstract

This article investigates the incidence of agglomeration externalities in Ecuador, a small‐sized, middle‐income developing country. In particular, we analyze the role of the informal sector within these relations, since informal employment accounts for a significant part of total employment in the developing countries. Using individual level data and instrumental variable techniques, we investigate the impact of spatial externalities, in terms of population density, local specialization and urban size, on the wages of workers in Ecuadorian cities. The results show that spatial externalities matter also for a small developing country. Moreover, analysis of the interaction between spatial externalities and informality shows that, on average, workers employed in the informal sector do not enjoy significant benefits from agglomeration externalities. Finally, by investigating the possible channels behind spatial agglomeration gains we show that the advantages from agglomeration for formal sector workers may well be accounted for by better job‐quality matches and, to a lesser extent, by learning externalities. For informal sector workers, our findings also suggest possible gains from job changes, which offset a penalty for remaining employed in the same occupation.

Unravelling urban advantages - A meta-analysis of agglomeration economies

“…Many estimates use micro-data directly (see, e.g., Börjesson et al, 2019;. Others first estimate aggregate productivity differences that are subsequently used to estimate agglomeration economies (see, e.g., Matano, Obaco et al, 2020;Spanos, 2019). We code the latter "Yes", even though the final step uses aggregate data.…”

“…Third are models that control for geographic characteristics, such as topography, climate, coordinates, and urban structure (see, e.g., Duranton, 2016). Fourth, are models that control for the area of the spatial unit (see, e.g., Matano, Obaco et al, 2020).…”

“…The first type measures the absolute size of an industry sector, for example the total number of workers or firms in the surrounding area (see, e.g., Rigby and Brown, 2015). The second type is often described as "specialisation" and measures the relative size of a sector, for example using a location quotient of sectoral employment (see, e.g., Matano, Obaco et al, 2020).…”

Ciudades y desigualdad: una mirada a las Áreas Urbanas Funcionales españolas

“…Entre las 20 aglomeraciones urbanas más grandes del mundo, 15 se encuentran ahora en países en desarrollo. Por lo tanto, si la desigualdad aumenta con el tamaño de la ciudad, como sugiere la literatura reciente, el problema se vuelve especialmente alarmante para las ciudades del mundo en desarrollo, las cuales además son mucho menos funcionales que las de los países desarrollados y se enfrentan a altos niveles de informalidad en sus mercados de trabajo (Castells-Quintana, 2019;Matano et al, 2020). Por lo tanto, es probable que otros factores, más allá de la dinámica del mercado, sean relevantes.…”

Determination of the urban wage premium in Ecuador

“…The different effects of agglomeration are confirmed in meta-analysis studies and literature reviews about agglomeration economies (Melo et al, 2009& Combes et al, 2011. Other separate studies also show higher magnitudes of the UWP in developing countries: between 0.04 and 0.054 for Colombia (García, 2019;Duranton, 2016); 0.10 (with respect to employment density) for China (Combes et al, 2013); 0.07 (population on firm TFP) for Chile (Saito & Gopinath, 2009); 0.042 (population on productivity) for Mexico (Ahrend, et al, 2014); and between 0.03 (market size on wages) and 0.05 (density of firms on manufacturing productivity) for Ecuador (Alvarado & Atienza, 2014, Matano et al, 2020Guevara-Rosero et al, 2018). The magnitudes of the estimated UWP for advanced economies are lower: between 0.034 and 0.04 for the United States (Glaeser & Resseger, 2010;Krashinsky, 2011), 0.01 for Italy (Di Addario & Patacchini, 2008), 0.03 for France (Combes et al, 2010), and 0.025 for Spain (De la Roca & Puga, 2016).…”