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on Economics of Human Migration |
| By: | Sarr, Ibrahima (United Nations High Commissioner for Refugees); Dang, Hai-Anh (World Bank); Gutierrez, Carlos Santiago Guzman (University of Oxford); Beltramo, Theresa (United Nations High Commissioner for Refugees); Verme, Paolo (World Bank) |
| Abstract: | Household consumption or income surveys do not typically cover refugee populations. In the rare cases where refuges are included, inconsistencies between different data sources could interfere with comparable poverty estimates. We test the performance of a recently developed cross-survey imputation method to estimate poverty for a sample of refugees in Colombia, combining household income surveys collected by the Government of Colombia and administrative data collected by the United Nations High Commissioner for Refugees. We find that certain variable transformation methods can help resolve these inconsistencies. Estimation results with our preferred variable standardization method are robust to different imputation methods, including the normal linear regression method, the empirical distribution of the errors method, and the probit and logit methods. We also employ several common machine learning techniques such as Random Forest, Lasso, Ridge, and elastic regressions for robustness checks, but these techniques generally perform worse than the imputation methods that we use. We also find that we can reasonably impute poverty rates using an older household income survey and a more recent ProGres dataset for most of the poverty lines. These results provide relevant inputs into designing better surveys and administrate datasets on refugees in various country settings. |
| Keywords: | refugees, poverty, imputation, Colombia |
| JEL: | C15 F22 I32 O15 O20 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17036&r= |
| By: | Manzoni, Elena (University of Bergamo); Murard, Elie (University of Trento); Quercia, Simone (University of Verona); Tonini, Sara (Universidad de Alicante) |
| Abstract: | How do emotions affect policy views on immigration? How do they influence the way people process and respond to factual information? We address these questions using a survey experiment in Italy, which randomly exposes around 7, 000 participants to (i) sensational news about immigrant crimes, (ii) statistical information about immigration, or to (iii) the combination of both. First, we find different effects of news depending on the severity of the reported crime: while the news of a rape against a young woman significantly increases the demand for anti-immigration policies, there is no impact of the news of a petty theft. Consistent with a causal role of emotions, we find that the rape news triggers a stronger emotional reaction than the theft news, while having a similar effect on factual beliefs. Second, we document that information provision corrects beliefs, irrespective of whether participants are also exposed to the rape news. Yet, the exposure to the rape news strongly influences whether belief updating translates into change in policy views: when presented in isolation, information tends to reduce anti-immigration views; when combined with the rape news, the impact of the latter dominates and participants increase their anti-immigration views to the same extent as when exposed to the rape news only. This evidence suggests that, once negative emotions are triggered, having more accurate factual knowledge no longer matters for forming policy views on immigration. |
| Keywords: | news, information, immigration, experiment, belief, emotions |
| JEL: | F22 C90 D91 D72 D83 J15 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17017&r= |