# 1. Introduction

There is a growing body of academic literature highlighting the productivity enhancing agglomeration economies from living and working in dense cities (Ahlfedt and Pietrostefani, 2019). Duranton and Puga (2004) outline the micro-foundations of agglomeration economies based on sharing, matching, and learning mechanisms. Dense cities encourage _sharing_ of indivisible public goods, production facilities, and marketplaces, a greater variety of inputs and individual specialization, and the pooling of risk; improve the quality and possibility of _matching_ between firms and workers; and increase opportunities for _learning_ from the generation, diffusion, and accumulation of knowledge.

Economists have used the elasticity of wages with respect to urban density as a canonical measure of agglomeration economies. A recent meta-analysis of empirical work by Ahlfedt and Pietrostefani (2019) covering 347 estimates shows a of doubling urban density is associated with a wage premium of 4 percent. In the United States, the elasticity of wages with respect to city size is 0.043; in France it is 0.03-- implying that doubling density could increase productivity by 3-4 percent. (Combes and Gobillon 2015, Melo et al. 2009, and Rosenthal and Strange 2004).

While these estimates suggest strong productivity enhancing agglomeration economies in dense urban environments, a major knowledge gap arises from the limited knowledge of agglomeration economies in developing country settings. The empirical reviews (Melo et al., 2009 and Ahlfeldt and Pietrostefani, 2019) and a recent literature survey (Duranton and Puga, 2020) primarily draw on developed country estimates. Further, there has been no attempt to systematically quantify the differences in estimates between advanced and developing countries. Filling this knowledge gap is important as most of future urban growth is expected in developing country cities (United Nations 2018) and these cities are growing in people but without the commensurate investments in human and physical capital that enhance the returns from density (Lall, Henderson and Venables 2017; Ellis and Roberts 2016).

Avner and Lall (2016) find that jobs are often located far from where people live; heavy congestion and high rates of walking and informal transportation fragment the labor market and lead to low employment rates and the misallocation of labor in Nairobi. For instance, _Matatu_ (privately owned minibuses) users on average can access only 4 percent of jobs within 30 minutes, 10 percent within 45 minutes, and 20 percent within 60 minutes. In metropolitan Buenos Aires, equivalent accessibility figures using public transportation are only 7 percent, 18 percent, and 34 percent for the same time thresholds (PeraltaQuiros 2015). In Ugandan cities, 70 percent of work trips are on foot (Uganda Bureau of Statistics 2010), with only 19 percent of the city’s jobs being accessible within a one hour commute (Bernard 2016). Further, many cities particularly in Sub-Saharan Africa and South Asia produce non-tradable goods and services (Lall, Henderson and Venables 2017, Venables 2017). This is consistent with what has been called “pre-mature urbanization” (see for e.g. Gollin et al., 2016), that is, people are concentrating in developing country cities but not because industrial dynamism is attracting them.

To shed light on the productivity advantages of agglomeration in developing country cities, we make the following contributions. _First_ , we expand the sample size of existing meta-analyses to systematically examine 1,242 elasticity estimates, originating from 70 studies covering 21 developing and 12 advanced countries. _Second,_ we construct novel estimates of density elasticity on urban costs, with respect to crime, congestion and pollution by collecting data from hundreds of cities around the world, including several in developing countries. This fills a critical knowledge gap as relative to studies measuring the 2