**Table 9. Linear probability model, regression with SPEI (dep var: neither poor nor vulnerable)**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|City: Core|0.137<sup>***</sup><br>(0.0193)<br>|0.134<sup>***</sup><br>(0.0193)<br>|0.0646<sup>**</sup><br>(0.0284)|0.0637<sup>**</sup><br>(0.0283)|
|City: Periphery urban|−0.0504<sup>***</sup><br>(0.0192)<br>|−0.0502<sup>***</sup><br>(0.0190)<br>|−0.0207<br>(0.0256)|−0.0199<br>(0.0255)|
|City: Other urban|0.0581<sup>***</sup><br>(0.0210)|0.0556<sup>***</sup><br>(0.0214)|0.0285<br>(0.0265)|0.0281<br>(0.0264)|
|City: Rural (reference)|||||
|SPEI: Dry|0.0145|0.0235|0.0273<sup>*</sup>|0.00452|
||(0.0181)|(0.0316)|(0.0155)|(0.0271)|
|SPEI: Normal (reference)|||||
|SPEI: Rainy|−0.0610<sup>**</sup>|−0.0900<sup>**</sup>|−0.0598<sup>***</sup>|−0.0229|
||(0.0243)|(0.0419)|(0.0186)|(0.0204)|
|City: Core # SPEI: Dry||0.0195<br>(0.0348)||0.0433<br>(0.0293)<br>|
|City: Core # SPEI: Rainy||0.0266<br>(0.0690)||−0.131<sup>***</sup><br>(0.0362)|
|City: Periphery urban # SPEI: Dry||−0.0369<br>(0.0407)||0.0150<br>(0.0354)|
|City: Periphery urban # SPEI: Rainy||0.0359<br>(0.0495)||−0.0434<br>(0.0314)|
|City: Other urban # SPEI: Dry||0.0132<br>(0.0384)||0.0373<br>(0.0423)|
|City: Other urban # SPEI: Rainy||0.0558<br>(0.0768)||0.0541<br>(0.0370)|
|Household FE|No|No|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Adjusted R2|0.0312|0.0313|0.0097|0.0099|
|# of observations|47,795|47,795|47,795|47,795|
|# of households|18,490|18,490|18,490|18,490|

_Note_ : The table summarizes the estimation results of panel regression models in Equation (3) for households in the five waves of IFLS (1993, 1997/8, 2000, 2007/8, and 2014/15). The dependent variable is a binary indicator about household’s vulnerability status (1=not vulnerable; 0=vulnerable). Cluster robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **5. Discussion and conclusion**

This paper examines the effects of the climatic and environmental shocks on a key function of urban agglomerations to facilitate poverty reduction. Our study also showcases different empirical approaches depending on the availability of panel datasets. We constructed synthetic panel datasets for Colombia and Chile from repeated cross-sectional household surveys and examined the association between poverty changes over time and the city population size as well as the heterogeneity of such association by flood risks. By estimating two-way FE models with five waves of IFLS spanning from 1993 to 2015, we analyzed the probabilities of households escaping poverty in different locations—metro core, urban periphery, other urban areas, and rural areas—and flooding risks in Indonesia.

The results from the three case countries show similar patterns. The synthetic panel analyses for Colombia and Chile indicate a reduction in urban poverty rates measured by the upper-middle-income international poverty line (US$5.5 per day in 2011 PPP). The analysis finds that the probabilities of households’ transition from poor to nonpoor status were positively correlated with the city population size in both countries. More importantly, such upward mobility was observed only in larger cities with low flood risk. The results of our two-way FE regression analyses for Indonesia suggest that dense metropolitan areas have provided good opportunities for those migrants to escape poverty in 17