Impact of extreme rainfall shocks on the educational performance of vulnerable urban students: evidence from Brazil

de Lima, Francisca Letícia Ferreira; Barbosa, Rafael Barros; Benevides, Alesandra de Araújo; Mayorga, Fernando Daniel de Oliveira Article Impact of extreme rainfall shocks on the educational performance of vulnerable urban students: evidence from Brazil EconomiA Provided in Cooperation with: The Brazilian Association of Postgraduate Programs in Economics (ANPEC), Rio de Janeiro Suggested Citation: de Lima, Francisca Letícia Ferreira; Barbosa, Rafael Barros; Benevides, Alesandra de Araújo; Mayorga, Fernando Daniel de Oliveira (2024) : Impact of extreme rainfall shocks on the educational performance of vulnerable urban students: evidence from Brazil, EconomiA, ISSN 2358-2820, Emerald, Bingley, Vol. 25, Iss. 2, pp. 247-263, https://doi.org/10.1108/ECON-11-2023-0200 This Version is available at: https://hdl.handle.net/10419/329567 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Impact of extreme rainfall shocks on the educational performance of vulnerable urban students: evidence from Brazil Francisca Let ıcia Ferreira de Lima P os Graduaç~ ao em Economia –CAEN, Federal University of Cear a, Fortaleza, Brazil Rafael Barros Barbosa Federal University of Cear a, Fortaleza, Brazil, and Alesandra Benevides and Fernando Daniel de Oliveira Mayorga Sobral Campus, Federal University of Cear a, Fortaleza, Brazil Abstract Purpose –This paper examines the impact of extreme rainfall shocks on the performance in test scores of students living near at-risk urban areas in Brazil. Design/methodology/approach –To identify the causal effect, we consider the exogenous variation of rainfall at the municipal level conditioned on the distance from the school to risk areas and the rainfall intensity in the school months. Findings –The results suggest that extreme precipitation shocks, defined as a shock of at least three months of high-intensity rainfall, have an adverse impact on both math and language performance. Through a heterogeneous effects analysis, we find that the impact varies by student gender, with girls being more affected. In addition, among students who study near at-risk areas, those with better previous school performance and higher socioeconomic status are more negatively affected. Originality/value –Our results suggest that extreme weather events can increase the differences in human capital accumulation between the population living near risk areas and those living more distant from these areas. Keywords Extreme rainfall shocks, Risk areas, Educational performance Paper type Research paper 1. Introduction According to recent evidence from climate change literature, the expected number and magnitude of extreme weather events tend to increase in the coming years (IPCC, 2022). In urban areas, vulnerability to such events is geographically delineated by risk areas for natural disasters, i.e. regions of the cities where the occurrence of climatic shocks tends to produce more social damage. Nowadays, approximately 1.47 billion people, 19% of the world’s population, live in those areas (WB, 2020). Although there is a considerable amount of literature documenting the social costs of extreme climate episodes [1], there is little evidence of their educational costs. In urban areas, weather shocks are less related to economic losses than in rural areas, where such events reduce agricultural productivity, affecting local Evidence from Brazil 247 JEL Classification —I20, I24, I25 © Francisca Let ıcia Ferreira de Lima, Rafael Barros Barbosa, Alesandra Benevides and Fernando Daniel de Oliveira Mayorga. Published in EconomiA. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1517-7580.htm Received 30 November 2023 Revised 15 January 2024 29 January 2024 Accepted 29 January 2024 EconomiA Vol. 25 No. 2, 2024 pp. 247-263 Emerald Publishing Limited e-ISSN: 2358-2820 p-ISSN: 1517-7580 DOI 10.1108/ECON-11-2023-0200 economic opportunities. Despite the expected lower impact on educational outcomes in urban areas, students living near at-risk areas may be more exposed to climate shocks, potentially affecting human capital accumulation. If climate shocks negatively affect students living in at-risk areas, but not in other areas, this could partly explain why there is greater income inequality in urban areas (Glaeser, Resseger, & Tobio, 2009;Baum-Snow & Pavan, 2013; Baum-Snow, Freedman, & Pavan, 2018). This paper aims to understand the impact of extreme precipitation shocks on students’ performance living near risk areas in Brazil. According to the Brazilian government, there are approximately 2.47 million families (9.8 million individuals) living in such risk areas, mainly in highly urbanized cities (IBGE, 2018). Dwelling location near risk areas follows a process similar to slum formation and is more intensive in developing countries, such as Brazil (Cavalcanti, Da Mata, & Santos, 2019;Alves, 2021;Marx, Stoker, & Suri, 2013). Since central regions of cities become more expensive, individuals who seek to benefit from agglomeration effects (Combes, Duranton, & Gobillon, 2019;Bryan, Glaeser, & Tsivanidis, 2020;Duranton & Puga, 2004), but cannot afford housing in central areas, have no choice but to live in subhousing conditions, such as slums or areas subject to climate hazards. In this paper, we use a georeferenced database of risk areas in 826 municipalities in Brazil. The data are made available by the National Center for Natural Disaster Monitoring and Alert (Centro Nacional de Monitoramento e Alerta de Desastres Naturais - CEMADEM), an official Brazilian government agency that collects and monitors data on natural disasters. We linked the georeferenced risk areas to approximately 45 thousand georeferenced schools that participated in the National Educational Assessment (Sistema de Avaliaç~ ao da Educaç~ ao B asica [SAEB]) from 2007 to 2015 [2]. SAEB measures students’proficiency in mathematics and languages in the 5th and 9th grades (11 school years) of primary education and the 3rd grade of high school. However, we focus our analysis on the 9th grade because this is a critical period for Brazilian public school students. The 9th grade represents the transition from middle school to high school (first year of high school) and is the period with the highest dropout rate during the whole school cycle. We measure students’vulnerability to climate shock using the distance from the school to risk areas. Since transportation is costly for students, especially in developing countries, enrollment in a specific school has a strong geographic element. Then, we posit that students who attend schools near risk areas are likely to be more vulnerable to extreme weather events. To derive the causal impact of an extreme weather event on vulnerable students, we use a difference-in-difference empirical strategy. We define the precipitation shock as a variable that depends on three key factors: student vulnerability, measured by the distance from the student’s school to the risk area; the duration of the shock, defined by the number of school months [3] in which students are exposed to a rainfall shock, and the intensity, measured by the standard deviations of rainfall in a given year in a given municipality relative to the historical average rainfall in that municipality. The literature on the impact of natural disasters points out that these three elements are determinants in measuring the effect of climate shocks (Chen, Mueller, Jia, & Tseng, 2017;Guiteras, Jina, & Mobarak, 2015;Krichene et al., 2021). In addition, this is a straightforward and a flexible way of measuring precipitation shocks because we can easily compute such shocks at different values for the key factors, which enables us to better characterize the effect on educational outcomes and perform robustness checks. We report four main results. First, comparing only vulnerable students whose school is at least 200 m from a risk area, the occurrence of an extreme precipitation shock negatively affects performance in math and language. We refer to extreme shocks as precipitation whose intensity is greater than 1.5 σ relative to the average historical precipitation [4]. The effect size represents a reduction in test scores close to 0.05 σ in math and 0.03 σ in language, which corresponds to a small effect size relative to other educational interventions (Kraft, 2020). ECON 25,2 248 Second, by using variations in the three key factors that compose the precipitation shock variable, we document that the magnitude of the impact changes according to shock intensity, duration of the event and the degree of student exposure. Furthermore, low-intensity shocks (intensity 1.0 σ relative to the historical precipitation) have small and no significant effects on performance, and very extreme events (intensity 2.0 σ relative to the historical precipitation) have a large effect on student performance. To examine these findings in more detail, we focus the rest of the analysis on extreme precipitation shocks (intensity 1.5 σ relative to the historical precipitation). Third, we find relevant heterogeneous effects. Girls are much more affected by extreme weather shocks than boys, suggesting that these types of shocks have a gender effect. In addition, students with better prior educational attainment and higher socioeconomic status (SES) are also more sensitive to extreme shocks. All results are valid for a battery of robustness checks. This paper contributes to two main areas. First, recent literature investigates the effect of rainfall shocks on student outcomes in rural areas. In these areas, rainfall shocks represent an exogenous variation in the economic context (Zimmermann, 2020;Shah & Steinberg, 2017). However, this interpretation is misleading in urban areas because of the reduced importance of the agricultural sector in urban areas. Thus, this paper contributes to the literature that studies the impact of extreme climate shocks on urban areas (Gu, 2019;Sarmiento & Miller, 2006;Kumar, 2021;Gallagher, 2014). In these areas, vulnerability to climate shocks is much more related to the proximity of risk areas. Second, this paper contributes to measuring the social costs of climate change. There is extensive literature documenting the costs of climate change, such as Barrage, 2020;Diffenbaugh and Burke (2019) and Carleton and Hsiang (2016). We contribute to show that climate change shocks, especially to vulnerable individuals, have a large impact on the accumulation of human capital. In addition to this introduction, this work is divided into three sections. The next section discusses the backgrounds, the data and the econometric strategy. The next section reports the main results, and section four discusses the concluding remarks. 2. Data and empirical strategy 2.1 Data 2.1.1 Educational data. The data on education come from the SAEB (Sistema Nacional de Avaliaç~ ao da Educaç~ ao B asica), a nationwide standardized exam conducted by INEP [5] every two years since 2007 for all 5th and 9th graders in public schools that have at least 20 students enrolled in that particular grade level. This is a low-stakes assessment administered by the federal government to assess the progress of students’cognitive abilities across the country. It uses Item Response Theory (ITR), which allows comparability of test scores over time. It has no direct implications for student progress in school, student grades, teacher promotion or removal. Students are not informed about their individual performance on this assessment. SAEB data were collected from 2007, 2009, 2011, 2013 and 2015 to measure student performance, focusing on students enrolled in 9th grade of primary education. To facilitate the interpretation of the estimates, we standardized student test scores according to the individual-level distribution of test scores for students in municipalities that did not experience a precipitation shock. In addition, INEP applies, along with SAEB, a set of surveys among students, teachers and principals. We extract from this survey information on student socioeconomic backgrounds, such as gender, mother’s education, age and racial status. INEP also provides the addresses of all public elementary schools in Brazil. We use these addresses to georeference the schools [6]. Evidence from Brazil 249 2.1.2 Risk areas data. The location of risk areas is provided by CEMADEN (Centro Nacional de Monitoramento e Alerta de Desastres Naturais). These data inform the location of risk areas in 826 Brazilian municipalities. Risk areas are defined as areas within municipalities that are vulnerable to the occurrence of natural phenomena or situations that cause accidents. Such areas are delimited based on the occurrence of indications and evidence of earth movements observed on site, such as cracks in the soil, landslide steps, leaning trees, landslide scars and flood marks, among others. The regions that present a high risk are grouped and represented by polygons in the geographic space. The polygons were created by the federal agency IBGE (Instituto Brasileiro de Geografia e Estat ıstica)[7] to characterize the risk areas according to the socioeconomic information of the residents. This information has been used to subsidize public policies in those areas whose population is more vulnerable. We extracted only the georeferenced data from these polygons. In the online appendix [8], we compare the socioeconomic characteristics of municipalities with documented risk areas by CEMADEM and municipalities that are not in the sample. The municipalities in the sample are more urban and have a higher number of poor, as measured by the proportion of poor and the proportion of individuals with an income 1/4 of the minimum wage. They also have a higher per capita income and lower illiteracy rates. Indeed, the municipalities in the sample represent more populated municipalities in Brazil, nearly 47% of the Brazilian population. Figure 1 shows the location where CEMADEN has identified risk areas. Note that the risk areas are concentrated in coastal municipalities, which also gather the largest share of the Brazilian population. We relate Brazilian public schools to polygons of risk areas. Thus, we can measure the distance from each school to each risk area in the 826 municipalities. Our final sample contains approximately 15,506 elementary schools, serving approximately 864,000 9th-grade students each year, representing 36% of the total Brazilian students in that grade. 2.1.3 Other data. We supplement our core risk area and education data with municipal characteristics from IBGE. We use this source to gather information on municipal population, municipal income, inequality across municipalities and municipal Human Development Index (HDI). We use this information to address potential prior differences between municipalities. 2.2 Empirical strategy 2.2.1 Measuring extreme precipitation shock. The effect of precipitation shocks on social and economic outcomes depends on three factors: the intensity of rainfall, the duration of such an event and the degree of vulnerability of individuals exposed to the shock. We define precipitation shock in municipality min state ein school year t,T met , as follows: Tmet ¼1if 1dsm <Bfg31shockmt ≥nfg (1) Where, shock mt refers to the number of monthly precipitation shocks in the municipality min the school year tand 1shockmt ≥nfgis an indicator function that assigns the value 1 to municipalities that were exposed to at least nprecipitation shocks in the same school year. The term 1dsm <Bfgis an indicator function that assigns the value 1 to schools located at a distance (d sm )ofBmeters from the border of a risk area. The parameter nindicates the number of occurrences of precipitation shocks in schools near risk areas. We assume that n≥3, implying the variable T met is equal to 1 if occurs at least three precipitation shocks during the school year and the student’s school is located within Bmeters of some risk area. This parameter nallows us to measure the duration of precipitation events in the months that the students are at school. Assuming n≥3 also allows us to control rainfall events that may ECON 25,2 250 occur sporadically in just one or two months. In addition, natural disasters caused by excessive rainfall are strongly associated with the accumulation of water on the surface that occurs just in longer periods of exposure. In our main specification, we assume B5200 m (218,723 yards). Therefore, treated students are enrolled in schools within 200 m of a risk area in municipalities that were exposed to at least three precipitation shocks during the school year. In turn, students in the control group are enrolled in schools more than 200 m away from a risk area and those never exposed to a precipitation shock. We consider that students who attend school near a risk area are more likely to live near risk areas as well. This assumption is suitable for some reasons. First, in general, students enrolled in Brazilian public schools are poor [9]. For poor students the cost of attending a school far from their residence is not negligible, implying that they likely are also highly exposed to precipitation shocks. Second, there is a large literature documenting that the distance of student residence to school is an important factor of school choice (Carneiro, Das, & Reis, 2022). Approximately 200,000 students per year studying in a school within 200 m of a Figure 1. Location of risk areas in Brazil Evidence from Brazil 251 risk area in the 826 Brazilian municipalities considered in the analysis. This represents approximately 1 4of the students in these municipalities. Since there is no appropriate criterion for defining exposure to risk areas by distance from the student’s school in the presentation of the results, we change the value of the parameter B for the distances: B5500 and B5800 m. This variation prevents the conclusions of this article from being considered arbitrary and associated with a specific parameterization. We define the occurrence of a rainfall shock in municipality min the school year tas follows: shockmt ¼1precipmjt >k σ mj  (2) where, precip mjt is the amount of precipitation in municipality min the school year tin month j. In general, the SAEB exam is applied in the months of October and November, then jrefers to January through September. In turn, σ mj is the standard deviation of the historical mean rainfall in municipality min month j. The historical average was calculated from the last 30 years in each municipality (1976–2006). Finally, kis a parameter that measures the intensity of the precipitation. In presenting the results, we varied the parameter kby k51, 1.5, 2. Thus, k51 implies that the shock variable measures the incidence of a rainfall shock for which the intensity was greater than one standard deviation above the historical average. This definition of extreme precipitation shock has the advantage of being very flexible, allowing a more complete characterization of the impact of precipitation shocks on student performance. That is, it is possible to vary different parameters associated with the precipitation shock and thereby understand in more detail how such shocks affect student performances. 2.2.2 Econometric specification. This paper aims to identify the causal effect of extreme precipitation shocks on the 9th-grade performance of vulnerable students, i.e. those who attend schools near risk areas. We assume that these students have a high probability of living near risk areas as well [10]. The control group are those who live within 1,000 m of the border of a risk area and students who live near risk areas (within 200 m of their border) but were not affected by the precipitation shock. We estimate the following econometric specification: Yismet ¼β0þγTmet þβ0Xismt þθsþδet þ ε ismet (3) This kind of specification is known as the ring method. The ring method is motivated by the fact that since the treated and control units are all very close in spatial location, then shocks over time should be common across units in the neighborhood (Butts, 2022). The variable of interest (Y ismet ) is the performance, in math or language, of 9th graders of student iin school s in municipality min state ein year t.T met represents the rainfall shock in municipality min state ein school year t.X ismt is a vector of student characteristics, such as gender (girls), racial status (black and brown), student age and mother’s education [11].θ s represents school fixed effects that absorb idiosyncratic factors related to school characteristics, such as number of students, quality of teachers, etc. Note that this fixed effect also absorbs factors related to school location, such as socioeconomic conditions, violence, urban amenities around the school, etc. The inclusion of school fixed effects allows us to obtain the relevant counterfactual for a school’s nearly to risk areas: a school of the same type which may or may not be affected by an extreme precipitation shock. At last, δ et represents the year-fixed effect interacted with the state, which aims to absorb time-varying factors across the states, such as state educational policies, economic activity, etc. The parameter of interest is γwhich measures the occurrence of an extreme precipitation shock on student performance. Our main identification assumption is that precipitation shock is exogenous to student performance when controlled by the student factors, school and state-by-year fixed effects, ECON 25,2 252 and considering that the treated and control schools are all very close in spatial location, that is: E½YismetjXismt;θs;δet;Tmet¼E½YismetjXismt;θs;δet(4) The main threat to the identification is if the students predict the occurrence of the extreme weather event and migrate to a different school. This may affect their performance and is correlated with unobservable factors. However, this is a very difficult possibility. First, we test in the robustness section that the precipitation shock does not change significantly the class size of the treated school in comparison with the control schools. This suggests that students do not migrate to a different school in response to a precipitation shock, implying that students do not predict the occurrences of precipitation shocks. Second, predicting the occurrence of extreme weather events is hard even for experts, thus is not expected that students, or their parents, predicted adequately the occurrence of such events. 3. Results 3.1 Main results In this section, we present the main results. Table 1 reports the treatment effect of an extreme precipitation shock on the performance of students studying close (less than 200 m) to a risk area. To analyze the sensitivity of the estimates, we vary the specifications of the econometric model. In column 1, only school and year-fixed effects are added. In column 2, we add some controls at the student level, such as gender (female as a reference), self-reported racial status (black and brown as a reference), the student’s age and the education of the student’s mother or father. In column 3, time-varying state fixed effects are included. This specification, in column 3, represents our preferred model. Finally, in columns 4 and 5, the same specification (1) (2) (3) (4) (5) k51.5 k51.5 k51.5 k51.0 k52.0 Panel A: math Treatment 0.0663*** 0.0915*** 0.0559** 0.0218 0.130* (0.0147) (0.0119) (0.0225) (0.0218) (0.0751) Obs 10,35,266 967,338 967,338 967,338 967,338 R-2 0.076 0.112 0.113 0.113 0.113 School fixed effect Y Y Y Y Y Year-fixed effect Y Y N N N Student control N Y Y Y Y State-year fixed effect N N Y Y Y Panel B: language Treatment 0.0688*** 0.0947*** 0.0396* 0.0138 0.113* (0.012) (0.0119) (0.0218) (0.0236) (0.0613) Obs 10,35,266 967,338 967,338 967,338 967,338 R-2 0.068 0.127 0.128 0.128 0.128 School fixed effect Y Y Y Y Y Year-fixed effect Y Y N N N Student control N Y Y Y Y State-year fixed effect N N Y Y Y Note(s): Table 1 shows the estimates of the impact of a precipitation shock in the performance of students in math and language. Significance: ***1, **5 and *10%. Standard errors clustering at municipality level. Own elaboration Table 1. Effect of extreme precipitation shock on the performance of the students at-risk areas Evidence from Brazil 253 from column 3 is replicated, with the only difference being the intensity of the shocks, set to 1 (k51) and 2 (k52) standard deviations of precipitation above the municipality’s historical average. Since Brazil has large population differences in its municipalities, we weighted the estimates by the municipality’s population size. In addition, the standard errors are estimated by clustering at the municipality level, following recommendations from Abadie, Athey, Imbens, and Wooldridge (2017). Panel A presents the estimates for 9th-grade math performance. The occurrence of an extreme rainfall shock reduces mathematics performance by 0.055 σ (column 3). In turn, performance in language, presented in panel B, indicates that an extreme precipitation shock negatively impacts by 0.04 σ (column 3). Modifications in the econometric specification do not affect the findings, marginally changing the effect size. The estimates are sensitive to the introduction of time-varying state fixed effects, suggesting that local state actions may contribute to moderating the impact of extreme precipitation on student performance in math and language. Putting the estimates in perspective, we also calculated the effect of an extreme precipitation shock in terms of months of effective learning. The impact of an extreme precipitation shock corresponds to a loss of 1.48 and 2 months of effective learning during the school year for language and mathematics, respectively [12]. This implies that approximately 20% of the school year is lost due to extreme precipitation shocks. The estimates suggest that extreme rainfall events affect learning. Literature focused on rural areas, rainfall shocks may increase student dropout rates (Shah & Steinberg, 2017;Baez, De la Fuente, & Santos, 2010;Ferreira & Schady, 2009). Due to the composition change in schools, these studies do not identify the effect on learning adequately. In the robustness tests, we show that our estimates do not affect the class composition. Then, our results suggest that the rainfall shock causes a learning loss and not a school attendance reduction. This result provides evidence that a different type of policy mitigation is required, focusing on learning recovery. Given the trends of climate change and the resulting increase in the occurrence and intensification of weather events, students from municipalities affected by extreme precipitation shocks tend to widen the gap in terms of skill accumulation compared to students from municipalities that are less affected and also in comparison with students from municipalities that live further away from risk areas. Columns 4 and 5 show that the intensity of the shock matters for the size of the average effect. Precipitation shocks of low magnitudes, such as 1 standard deviation above the average precipitation (column 4), have no significant effects on student performance. On the other hand, if a high-intensity shock is considered, such as 2 standard deviations above average rainfall (column 5), the negative impact on performance is significant and has a high impact, 0.13 σ and 0.11 σ in mathematics and language, respectively. Considering the main specification, the magnitude of the impact is small, according to Kraft (2020)’s classification. Kraft (2020) classifies effect sizes according to a meta-analysis containing 750 Randomized Control Trials (RCTs) for developed countries. However, some additional aspects are important. First, the effect size depends on the magnitude of the extreme event. In Table 1, the very extreme events, k52, have a medium effect size according to Kraft’s classification. Second, the size of the effect can be sensitive to the stage of education. We focused only on 9th-grade students. Thirdly, although small, the frequency of events during the school year can increase the size of the impact. Given this variation according to the intensity of the precipitation shock, we will focus specifically on the results for k51.5. Table 2 presents the results by varying the minimum distance of schools from risk areas. As the minimum distance between schools and risk areas increases, the less likely the student is to live near such a risk area, and the impact of an ECON 25,2 254 16. This specification includes school-level fixed effects, time-varying county fixed effects, and control for students. The model is weighted by county population size and standard errors are estimated by clustering at the county level. References Abadie, A., Athey, S., Imbens, G. 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