Upward mobility offered by agglomeration economies could be offset and hindered by climatic and environmental stressors. Therefore, we hypothesize that _large or dense cities that are more exposed to climatic and environmental shocks do not offer residents a higher chance to become or stay nonpoor, compared to cities of smaller size_ .

# **3. Methodology**

## **3.1 Data**

We selected Chile, Colombia, and Indonesia as the cases for this study to demonstrate the application of analytical approaches with and without panel datasets. Analyzing these countries also merits the test of the approaches in countries where poverty is measured by income (Chile and Colombia) and consumption expenditures (Indonesia). The setting of Indonesia—its rapid urbanization and heterogenous urban and climatic characteristics across subnational regions—is particularly suitable to our analysis. Highly urbanized countries like Chile and Colombia have useful density variations to explore as well.

To answer our research question and verify our hypotheses, we combined household surveys with climatic datasets. For Chile and Colombia, we constructed synthetic panel datasets out of repeated cross-sectional household surveys. Flood risk is estimated as a key climate factor for each town. For Indonesia, we relied on panel household surveys (IFLS), combined with two climate indicators: SPEI and the flood risk index.

## **Synthetic panel data for Chile and Colombia**

Following Dang et al. (2014) and Dang and Lanjouw (2013), we applied the synthetic panel method to the household surveys of Chile ( _Encuesta de Caracterización Sociooeconómica Nacional_ [CASEN]) 2011 and 2015 and Colombia ( _Gran Encuesta Integrada de Hogares_ [GEIH]) 2008 and 2010.<sup>3</sup> This method essentially exploits the time-invariant variables in the cross-sectional surveys and some cohort-based assumptions about the error terms to construct the synthetic panels. The methodology is described in detail in Annex B. Recent applications and further validations of the synthetic panel methods have been implemented using household survey data from various countries in Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, Latin America, South Asia, and the Middle East and North Africa (see Dang, Jolliffe, and Carletto (2019); Dang and Lanjouw (forthcoming); and Garcés‐Urzainqui, Lanjouw, and Rongen (2021) for recent reviews).

We begin by identifying the potential time-invariant variables available in the cross-sectional surveys at two time points, which include household heads’ gender, age, level of education, and residence area (that is, urban or rural). These variables can usually be assumed to be time invariant if the underlying population remains unchanged over time. One way to test this assumption is to use a t-test for the equality of the means of the same variables in the two survey rounds. We have provided test results that consider the complex survey design in Table A1 for Chile and Table A2 for Colombia. The assumption of the equality of means is satisfied for household heads’ gender and secondary level of education in Chile and heads’ secondary and tertiary levels of education in Colombia. The assumption is satisfied for residence areas in both countries. Although the difference in heads’ primary level of education is statistically significant in Colombia, it has similar means over time. The differences between surveys for heads’ primary and tertiary levels of education in Chile and heads’

> 3 The years for the household surveys for Chile and Colombia were selected based on the availability of the subnational location information that can be matched with climatic layers on the geographic information system (GIS) platform.

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