017 017 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. The author may be contacted at jbaez@worldbank.org. The Poverty & Equity Global Practice Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. ‒ Poverty & Equity Global Practice Knowledge Management & Learning Team This paper is co-published with the World Bank Policy Research Working Papers. Gone with the Storm: Rainfall Shocks and Household Well-Being in Guatemala* Javier E. Baez Leonardo Lucchetti Maria E. Genoni Mateo Salazar JEL Classification: I3, J2, O1 Keywords: Economic development, natural disasters, consumption, poverty, human capital. ___________ * Javier Baez is a Senior Economist with the Poverty Global Practice, World Bank and a Research Fellow at the Institute of the Study of Labor (IZA) (jbaez@worldbank.org). Maria E. Genoni is an Economist with the Poverty Global Practice, World Bank (mgenoni@worldbank.org). Leonardo Lucchetti is an Economist with the Poverty Global Practice, World Bank (llucchetti@worldbank.org). Mateo Salazar is a Research Assistant with the Poverty Global Practice, World Bank (mateosalazar@worldbank.org). Special thanks go to Claudio Castañón and Mauricio Garita for facilitating access to rainfall data from the Guatemalan Institute of Seismology, Volcanology, Meteorology and Hydrology (INSIVUMEH). We thank insightful comments from Emmanuel Skoufias, Alan Fuchs and Louise Cord. 1. Introduction Climate is often referred to as an important determinant of economic performance. More recently, largely motivated by the ongoing debate on global warming, the influence of climate factors on economically relevant outcomes has attracted even more attention. With temperatures expected to continue rising and scientists projecting an increase in the frequency and severity of extreme weather events, understanding the consequences of weather-related shocks on economic development, particularly on human welfare, is increasingly important. The aggregated first-order effects of natural disasters such as human deaths and injuries, destruction of critical infrastructure, and disruption of economic activities are evident. Yet, quantifying the direct and indirect (short- and long-term) effects of large shocks on the well- being of households and assessing how they cope with these risk factors is more challenging while at the same time it is central to more fully estimating their economic impacts and designing effective risk management strategies (World Bank, 2013). The last few years have seen a large body of empirical research that examines the effects of anomalous deviations in weather outcomes on a wide range of variables associated with household welfare as well as households’ capacity to protect their welfare when confronted by such shocks. The outcome variables analyzed range from consumption to income to asset ownerships to mortality to investments in education, health and nutrition to risk-coping actions, among others (See Dell et al. 2013 and Baez et al. 2010 for surveys of this literature). At least three clear patterns emerge from the existing literature. First, households possess numerous strategies for dealing with extreme weather but overall their mitigation capacity is insufficient for the task of maintaining –let alone improving– their welfare. For instance, excess rainfall and droughts have been found to reduce by half the crop income among affected households in Burkina Faso and more than half of this loss is directly reflected in consumption (Kazianga and Udry 2006). Similarly, rainfall shocks were found to force households in rural Ethiopia to deplete their productive assets between 8% and 62% (Dercon 2004). The second observation has to do with the persistence of the effects. There is a host of empirical results indicating that the immediate negative consequences of weather shocks often carry over the longer term. Alderman et al. (2006) show, for instance, that children who became stunted due to a drought in Zimbabwe never fully recovered later in life and exhibited lower school attainment and earnings in adulthood. Finally, there is remarkable impact heterogeneity. The 2 evidence consistently shows that the poorer populations often carry the heaviest burden. For example, stunting in children after the floods that hit Bangladesh in 1998 was substantially higher among households in the bottom 40 percentile of the consumption distribution (Del Ninno and Lundberg 2005). Seeking to contribute to that literature, this paper looks at the vulnerability of households to large rainfall shocks in a context where natural risks are prevalent and poverty is pervasive, with more than half of the population living in poverty. More specifically, employing a double- difference (D-D) analysis that exploits spatial and time variation in extreme (excessive) rainfall, we investigate whether households in the proximity of Agatha –a major tropical storm that hit Guatemala in 2010 and dropped the largest rainfall in the country since 1963– saw a fall in their consumption and were likely to fall further into poverty as result of the event, and whether they engaged in sub-optimal strategies to confront the shock. The paper also examines the variability of the impacts across different groups of people and postulates hypotheses about some of the potential mechanisms at play. The data used for this study come from two cross- sections of national representative household-level survey data collected before (2006) and almost one year after the storm occurred (2011) as well as administrative data with monthly rainfall and temperature data for the period 1963-2013 recorded by 73 meteorological stations scattered across Guatemala. The study finds that household welfare, measured by per capita consumption, fell on average by 8.2% of the median consumption at the baseline among affected households (i.e. located in areas where the windstorm brought rainfall substantially above the historical levels) relative to households less affected or not affected by Agatha. Further inspection of the data shows, however, that the losses in consumption attributed to the shock arose mostly among households from urban areas, which experienced a decline of 12.6% of their median consumption at baseline. Point estimates from a specification that captures variation in the intensity of the shock with categorical levels of precipitation anomalies yield similar results, with the impact on per capita consumption ranging from 9% to 14%. Nearly half the drop in consumption is explained by a reduction in food expenditures of 10% that correspond to 43-108 fewer calories per person per day. Affected households also cut back expenditures on durables, including basic items such as a stoves or refrigerators. 3 The fall in consumption due to Agatha increased the overall poverty rate by 3 percentage points or 7%. In line with the effects on consumption, this result is driven entirely by a higher incidence of poverty in urban areas, which saw a statistically significant increase of 5.5 percentage points (18%). Roughly speaking, these effects translate into 80,000 more families falling into poverty as a result of Agatha. The negative effects of the storm partly explain the increase in poverty observed in urban Guatemala (from 30% to 35%) between 2006 and 2011, which the national authorities and analysts previously attributed solely to the collateral effects of the global financial crisis. Unpacking the mechanisms of transmission, we find that Agatha reduced income per capita in affected areas on average by 10%, mostly among salaried workers. In an effort to cope with the shock, adults –particularly urban men– increased their labor supply (on the intensive margin) on average by almost 2.5 additional hours per week (5.3%). Similarly, the engagement of affected rural children in paid and unpaid work increased by 12.8%, which came at the cost of reducing their school participation (2.6%). Supplementary analyses confirm the robustness of the negative effects uncovered in this paper. The results hold after performing two placebo tests, including one that uses a “fake” treatment to test the central underlying assumption of parallel trends and another one to test for the possibility of endogenous compositional changes in the treatment and comparison groups over time. The results are also very robust to other issues such as migration, the overlap of Agatha with other precipitation anomalies, and different definitions of the shock using varying critical thresholds. There is substantial impact heterogeneity between urban and rural areas. A leading factor is the strength of the shock itself. Our evidence shows that Agatha dropped relatively much more rainfall over urban areas. We also observe some suggestive evidence that increases in food prices accelerated after the shock in some parts of the region that saw the largest precipitation anomalies. In contrast, the relatively low sensitivity of rural households may be partly explained by the timing of Agatha with respect to the local agricultural cycles. For the most part, the excessive precipitation fell in a period of the harvesting season that was not harmful for maize, beans, coffee and sugar cane, the main crops grown in affected areas. Finally, a large 4 CCT program targeted mostly to rural households could have also helped protect their basic welfare in the aftermath of the shock. The results of this paper are in line with evidence from previous studies that have investigated the vulnerability of households to natural disasters in Guatemala. In 2005, the country was hit by Stan, another tropical storm, which was found to increase child labor and reduce school participation for children aged 13 to 15 (Bustelo, 2012). Similarly, human capital formation was disrupted by a strong earthquake that struck Guatemala in 1976. An increase of a standard deviation in the intensity of the earthquake was associated with a reduction of 0.2 and 0.4 years of schooling among individuals exposed to the disaster in early childhood and school age, respectively (Hermida, 2010). Contrary to these pieces of evidence, our paper highlights that urban households carried the burden of the consequences of the shock. The rest of the paper is structured as follows. The next section provides background information on the natural disaster and the socioeconomic context in which it took place. Section 3 describes the data used in the analysis. Section 4 describes the identification strategy. Section 5 presents the empirical results, including discussion on robustness checks and interpretation of the findings. Finally, section 6 concludes. 2. Country Context and Tropical Storm Agatha Guatemala, a lower-middle-income country, is the third largest in terms of land area in Central America (after Nicaragua and Honduras). Poverty is pervasive across the country. As of 2006, four years before the shock examined in this paper, the per capita consumption of over half of the population (51%) was below the national poverty line. Poverty rates in rural areas have historically been on the order of 70% to 80%. The precarious socioeconomic environment is further compounded by high incidence of malnutrition and infant mortality rates and low coverage and quality of basic services such as electricity, water and sanitation. The large risk exposure of Guatemala to natural disasters poses a serious threat to the human welfare of its population. The geographic location of the country makes it prone to frequent and high-intensity geological and weather-related shocks such as earthquakes, volcanic eruptions, droughts, storms and hurricanes. In fact, Guatemala ranks 5th worldwide 5 based on its economic risk to natural hazards (CEPAL et al. 2011). 1 Similarly, the Global Climate Risk Index puts Guatemala in 12th place worldwide based on the number of extreme weather events recorded between 1991 and 2010 –and in 2nd place for events recorded only in 2010 (Harmeling 2011). Tropical Storm Agatha exemplifies the high vulnerability of Guatemala to natural risks. Triggered by a tropical wave that moved westward from the coast of Africa on May 8, 2010, Agatha originated as a tropical depression on May 29, 2010 in the eastern Pacific. A few hours later the tropical depression developed into a cyclone, making landfall in Champerico, southwest of Guatemala, near the border with Mexico, at 16:40. The surface circulation of Agatha weakened as it continued northeastward into the Sierra Madre Mountains and it began to dissipate on May 30 over northwestern Guatemala. Reaching top winds of nearly 80 kilometers/hour, Agatha produced torrential rains, widespread floods and landslides across several countries in Central America. Guatemala, however, was the hardest hit. Some parts of the country received more than 910 millimeters of rainfall, the highest levels recorded in over 60 years, making Agatha the strongest tropical cyclone to ever strike Guatemala in terms of amount of rain dropped since records have been kept. The human losses, the destruction of homes, crops and critical infrastructure –including schools and health centers– and their subsequent disruption of economic and institutional systems forced government officials to declare a state of emergency for the entire country. Assessments conducted jointly by national and international institutions estimated that nearly 400,000 people (around 3% of the total population) needed humanitarian assistance and the total damages attributed to the storm amounted to 2.2% of the GDP. Donations centers spread across the country started deploying relief aid started on May 31, but anecdotal evidence suggests that this assistance was far from sufficient to mitigate the immediate consequences of the disaster (CEPAL et al. 2011). 3. Data Two main sources of data underlie the empirical analysis.. The first source is the Living Standards Measurement Survey (Encovi for its acronym in Spanish) developed by the Guatemalan Statistics Bureau (INE). Encovi is a comprehensive, multi-purpos,e cross-sectional 1 It is estimated that 83% of Guatemala’s GDP is generated in areas expecially prone natural disasters. 6 household survey that collects information on a wide range of aspects covering the main demographic, social and economic characteristics of the population. The sample consists of approximately 13,500 households (equivalent to over 69,000 individuals) and is representative at the national, urban, rural, regional and state levels.2 The survey is collected every 4 to 5 years between March and August, which means that the post-shock survey (2011) was fielded between 10 and 15 months after Agatha hit Guatemala, allowing for identification of its short- to medium-term impacts. We pool the 2006 (pre-shock) and 2011 (after-shock) waves of Encovi to run a D-D analysis, which constitutes the basis for our research design (discussed in more detail in the next section). The two surveys used the same sampling frame drawn from the 2002 National Housing and Population Census 3 and collected data using the same field protocols and questionnaires. The same survey design for the two waves allows us to define a fully comparable set of variables before and after the shock. We construct outcome variables to measure household well-being (consumption and income per capita, 4 binary indicators to distinguish households below and above the national poverty threshold, 5 and measures to capture the depth and severity of poverty) as well as other dimensions through which households may have attempted to cope with the shock (adult and child labor supply, school participation and changes in asset ownership). The richness of the data also allows including as control variables a standard set of household-level socioeconomic and demographic characteristics. The climate data –the second main source of information– was compiled from a historical registry administered by the Guatemalan Institute of Seismology, Volcanology, Meteorology 2 Guatemala is administratively divided into eight regions and 22 states. 3 The 2002 Census is comprised of 15,511 primary sampling units (PSUs) corresponding to 2,127,915 occupied dwellings. The sample for the survey consists of 1,184 (2006) and 1,200 (2011) PSU's –selected from random clusters of the 2002 Census– and 14,400 dwelling or secondary sampling units (SSU's), selected randomly within the cluster. The PSU’s overlap in 2006 and 2011. 4 Household expenditures captured in the survey include expenses on food, rent, durable goods, payment of basic services and education, and health services. Unit prices to value the official consumption basket to measure poverty are obtained from the household questionnaire. A consumption price index is constructed to account for geographical differences across municipalities. In 2011, the Guatemalan Statistics Bureau (INE) modified the methodology to construct the consumption aggregate for households, making it incomparable with the consumption measure produced in 2006. To ensure full comparability, we applied the same methodology (2006 definition of the consumption aggregate) to both years. 5 Guatemala uses consumption as the welfare indicator to measure poverty based on two official poverty lines: 9 Quetzales/person/day for extreme poverty and 18 Quetzales/household/month for moderate poverty in 2006. The values for 2011 correspond to 12 and 25, respectively. The extreme poverty represents the cost of acquiring the minimum calories required to sustain life. The value of the moderate poverty line accounts for a minimum consumption of basic goods and services. 7 and Hydrology (Insivumeh). This system keeps records on daily and monthly rainfall and temperature from 1963 to 2013 for a grid of 73 weather stations scattered across the country.6 However, many stations operated only for a short period of time. Consequently, in order to gauge more reliable estimates of historical rainfall patterns across geographic areas, we used information from the 39 stations that recorded weather data uninterruptedly from 1980 to 2010 (see Figure 2 for a detailed description of the coverage of the climate data).7 Additionally, we constructed shock measures using a slightly larger subset of weather stations (42 stations with monthly rainfall data for the period 1990-2010) to check the consistency of both the treatment status assigned to each municipality (treated vs. comparison or alternatively high- vs. low- intensity rainfall due to Agatha) and the base empirical results resulting from a balanced panel of weather stations for the period 1980-2010. We complement the precipitation data from Insivumeh with weather records from 15 other stations owned by the Guatemala Sugarcane Association (Cengicaña). These stations are geographically located in southern Guatemala, the area most affected by Agatha. Overall, the density of weather stations is larger in this part of the country. This is expected to increase the accuracy of rainfall measurement, something important considering that the south is more mountainous.. The average distance from the municipalities to the closest weather station in our final sample of analysis is 19 kilometers (kms) (s.d. = 12 kms). Finally, 327 municipalities in Encovi were matched8 to the closest weather station to determine their historical rainfall in the month of May and allocate the treatment. 4. Identification Strategy The identification of the causal effects of the 2010 Agatha storm on household welfare exploits the time and spatial variation in the trajectory and intensity of the shock across the Guatemalan territory in a D-D analysis. More specifically, our empirical strategy relies on the comparison, before and after the 2010 Agatha storm, of the outcomes of interest (for instance, per capita consumption or poverty incidence) between the more-affected (treated) and less- or 6 Daily rainfall and temperature data are patchy across stations in the registry so we use records on monthly averages which are more complete in the dataset. 7 Only one out of the 73 stations has been recurrently active during the whole period. 8 The algorithm to match a station to a municipality calculates the centroid (i.e. the average position of all the points in a shape) of the polygon that represents a municipality and finds the nearest weather station (linear distance controlling for the earth’s curvature). The maximum distance is 85 km and the minimum is less that 1km. 8 non-affected (comparison) households. The standard assumption underlying the validity of our estimates is that differences between the treatment and comparison groups would have remained constant in the absence of Agatha. As discussed in more detail in the following section, we confirmed the validity of this assumption using two waves of household data spanning a period before the shock. Considering the nature of the event analyzed in the paper, a key element of our research design is the treatment (i.e. shock) allocation mechanism to classify the units of analysis between affected and less- or non-affected households. Following applications in the climatology literature, we construct measures of standardized precipitation anomalies recorded in May 2010 for each weather station to identify areas that experienced extreme rainfall shocks due to Agatha (Heim 2002; Keyantash and Dracup 2002). These measures capture the number of standard deviations away from the long-term (1980-2010) mean for each station. For the base empirical models we define excessive rainfall shocks as standardized precipitation anomalies that are 2 or more standard deviations above the historical mean, a typical threshold used in the literature. The treatment status of the households is thus coded by a binary variable (Rain Shock = 1 for affected households, = 0 otherwise) determined by the standardized precipitation anomaly of the closest weather station. In the robustness section we also discuss the sensitivity of the results to definitions of precipitation anomalies that take as a reference the long-run median rather than the mean. The base empirical models are estimated with the following specification: (1) Y = α + 2011 + β Rain Shock +X ′γ + ε t=2006, 2011 where Yimt denotes the outcome of interest (for instance, household consumption or poverty status) for household i living in municipality m in period t; 2011t is a year fixed effect that controls for the average changes in the welfare outcome of households across all municipalities between 2006 and 2011; αm are municipality fixed effects that control for time-invariant municipality characteristics and Rain Shockmt is a binary variable that identifies households located in the most affected municipalities in 2011. All regressions also control for a vector of household-level characteristics Ximt that are not expected to be affected by the shock but are likely to influence household consumption and include age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Finally, εimt is a 9 random, idiosyncratic error term. β1 is the (reduced-form) parameter of interest.9 In order to better fit the distribution of the precipitation generated by the disaster and improve the measurement of the shock, we also estimate models that take into account the varying strength of the event. To do so, affected households are classified into low-, medium- and high-intensity groups if the standardized precipitation anomaly in May, 2010 falls between 2 and 3, 3 and 5 and more than 5 standard deviations away from the long-term mean, respectively. For these models, however, the definition of the comparison group stays the same as that used in equation (1). The econometric specification for the treatment dose analysis is as follows: (2) Y = α + 2011 + β Low + β Medium + β High +X ′γ + ε where Lowmt, Mediummt, and Highmt are binary variables to capture the sub-treatment groups. In this case, the parameters of interest are β2, β3, and β4. Results from estimating (1) and (2) are presented in the next section. A threshold of two standard deviations may be an arbitrary cutoff to accurately capture excessive (damaging) rainfall. In addition to the level of precipitation, the occurrence, magnitude and duration of floods are also determined by geological, topological and hydrological characteristics of the area under analysis. Our empirical models control for municipality fixed effects and thus could partly account for these factors. Notwithstanding that, concerns of measurement error in the way that the precipitation anomalies are defined may remain. To explore this, we test the ability of the shock measure to predict the actual manifestation of floods in the aftermath of Agatha. Yet, the flood data available have two caveats. They include only those events reported by local authorities –possibly missing some floods in a nonrandom fashion– and do not say anything about the intensity of the floods10. We run models with the probability of a municipality reporting at least one flood as the dependent variable and the standardized rainfall recorded in May 2010 and surface area of the municipality as regressors. We observe a strong and statistically significant association between the continuous shock measure and the occurrence of at least one flood in a 9 In all regressions, the standard errors are clustered at the municipality level to allow for correlation across households within a municipality. 10 The information contains geo-referenced incidents recorded by the National Coordinator for Disaster Reduction (CONRED) and the Secretary of Planning (SEGEPLAN) for the period 2008-2011. It allows identifying the type of incident (e.g. flood) as well as whether the event was caused by Agatha. 10 municipality. An increase of a standard deviation above the historical rainfall mean due to Agatha is associated with an increase of 26 percentage points in the probability of a municipality reporting a flood (Table 1). Similarly, a map that crosses the geographical location of municipalities and the floods shows a larger concentration of events in treatment municipalities (Figure 1). Table 2 presents summary statistics of baseline key demographics and socioeconomic variables –including pre-shock means of the outcomes under analysis– for treated and control households. Balancing tests in the top panel of the table reveal that for a subset of variables, the differences between the two groups at baseline are statistically significant. The size of the differences is chiefly explained by the fact that a larger proportion of urban households are located in highly affected areas. Whereas some baseline statistical differences remain even after breaking down the sample by area, their economic significance is low and unlikely to confound the results. For instance, the average size of control households is 0.23 members smaller than treatment households (4.36 v. 4.59). Similarly, 92% of the control households have access to electricity, slightly less (95%) than the treatment group. Moreover, the central outcome variables analyzed in the paper (consumption per capita and poverty incidence) are fully balanced for the two groups at baseline. Notwithstanding that, we ran some specifications of the models with an array of cross-sectional time-invariant covariates to control for possible systematic differences between the two groups –including potential compositional changes over time– and to increase the precision of the estimates. 5. Empirical Analysis 5.1 Results Household consumption and poverty The analysis initially investigates the extent to which households that were severely hit by Agatha cut back their expenditures as a result of and/or to cope with the effects of the shock. In doing so, we first examine the evidence graphically looking at kernel estimates of the densities of consumption per capita (in logs) for affected (dashed lines) and non-affected households 11 (dotted lines) (Figure 3).11 The data (broken down by area) show that there were not large discrepancies in the densities of the treated and comparison groups at baseline (graphs at the top) and the graphs in the second row depict little differences between the estimated densities for the two groups. The story is rather different after the shock. As shown in the third row, there was a left-shifting of the entire density among households in urban areas affected by the shock compared to households in non-affected urban areas. In contrast, the densities for affected and non-affected households in rural areas behaved more or less similarly. Unconditional double differences of the densities for both groups over time (shown at the bottom of Figure 3) reveal that a greater share of treated urban households fell below the pre- shock median following the shock, providing suggestive evidence of negative impacts on consumption. We formally test the observations emerging from the visual inspection of the empirical densities. Table 3 presents fixed-effects model estimates of the D-D estimator ( ) from equation (1) following the binary treatment definition. The shock coefficient is statistically significant for the whole sample and for urban households (P-values of 0.014 and 0.001, respectively) but not for rural households. The point estimates indicate that consumption per capita fell on average by 69 quetzales (8.2% with respect to its pre-shock median value) among affected households (column 1, Table 3). The results in the whole sample are largely driven by the impacts observed among urban households. For these households, consumption per capita declined by 12.6% relative to the median consumption at baseline. Estimates from the treatment dose specification (equation 2) point to similar results. Household expenditures fell across the three categorical levels of precipitation anomalies (column 2, Table 3), again more strongly among urban households whose point estimates of the effect of Agatha are highly significant in a statistical and economic sense, showing a fall in consumption in the 9-14% range. Moreover, and giving credibility to the shock measure, the gradient between the intensity of the shock and the size of the impacts is evident in the whole sample and in the subsample of urban households. The fall in consumption attributed to Agatha pushed some households into poverty. Linear 11 Expenditures include the value of goods purchased, the estimated value of goods consumed from self- production, and the value of goods received as gifts from others. That is, the expenditure measure already reflects responses used by households to smooth consumption (such as receiving transfers, selling assets, or increasing labor supply). 12 probability models of the poverty headcount using the D-D model defined in equation (1) indicate that overall poverty increased by 3 percentage points or 7% (column 5, Table 3).12 In line with the heterogeneity of the impacts on consumption, the result is driven entirely by a higher incidence of poverty in urban areas. Indeed, households in urban centers were 5.5 percentage points or 18% more likely to be poor after being hit by Agatha (p-value = 0.026). Roughly speaking, these effects translate into roughly 80,000 more families falling into poverty. Results from the treatment dose specification (equation 2) are also indicative of negative effects on poverty, particularly among households exposed to rainfall intensity Low (2 2) -69.036** 0.030 -0.002 [27.814] [0.019] [0.018] t * ( 2 < rainfall z-score <= 3) -50.806 0.023 -0.004 [37.537] [0.024] [0.022] t * (3 < rainfall z-score<= 5) -71.968** 0.035 -0.011 [29.671] [0.024] [0.020] t * (rainfall z-score >= 5) -84.366 0.029 0.014 [53.776] [0.025] [0.021] Baseline Mean/Median 598.8 598.8 0.453 0.453 0.112 0.112 Panel B: Urban t * (rainfall z-score> 2) -181.140*** 0.055** 0.000 [44.103] [0.025] [0.010] t * ( 2 < rainfall z-score <= 3) -179.968** 0.085*** 0.020* [89.900] [0.030] [0.012] t * (3 < rainfall z-score<= 5) -167.649*** 0.025 -0.023 [48.588] [0.033] [0.014] t * (rainfall z-score >= 5) -195.220*** 0.070** 0.014 [62.435] [0.029] [0.012] Baseline Mean/Median 796.7 796.7 0.306 0.306 0.0469 0.0469 Panel C: Rural t * (rainfall z-score> 2) 8.584 0.015 -0.009 [34.086] [0.027] [0.028] t * ( 2 < rainfall z-score <= 3) 29.077 -0.015 -0.019 [36.608] [0.032] [0.032] t * (3 < rainfall z-score<= 5) -17.423 0.048 -0.004 [37.397] [0.034] [0.032] t * (rainfall z-score >= 5) 25.569 0.003 0.003 [72.903] [0.044] [0.046] Baseline Mean/Median 496.9 496.9 0.561 0.561 0.159 0.159 Observations: 26,587 Total; 11,225 Urban; 15,362 Rural. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Total Consumption is the monthly expenditure p.c. of a household. Quetzales of 2006. For Total consumption the baseline median is presented. Moderate poverty means that the p.c. expenditure is under the moderate poverty line. Extreme poverty means that the p.c. expenditure is under the extreme poverty line. For poverty the baseline mean is presented. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 26 Table 4. Impacts on Consumption Components Food Health Education Durables Measure of Shock (1) (2) (3) (4) Panel A: Total t * (rainfall z-score> 2) -16.054 0.828 -5.821** -16.554* [10.956] [2.683] [2.559] [9.231] Baseline Median 283.5 3.314 4.103 7.869 Panel B: Urban t * (rainfall z-score> 2) -40.622** -5.967 -8.830* -51.447*** [16.306] [4.775] [5.094] [16.087] Baseline Median 336.1 4.571 11.82 15.91 Panel C: Rural t * (rainfall z-score> 2) 7.954 5.190* -2.860 -3.366 [14.200] [2.986] [2.292] [8.135] Baseline Median 250.4 2.481 1.961 4.378 Observations: 26,587 Total; 11,225 Urban; 15,362 Rural. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Consumption on food, health services, education and durable goods are monthly p.c. terms in Quetzales of 2006. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 27 Table 5. Impacts on Income per Capita (Total and by Components) Non- Total Labor Labor Labor Private Public Other Income Per Income Per income from Non-wage Income Transfers Transfers non-labor Capita Capita salary work income Per Capita Per Capita Per Capita income Measure of Shock (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Total t * (rainfall z-score> 2) -44.4* -52.4** -33.6** -18.8 12.4 2.9 0.6 -2.9 [26. 2] [21.6] [14.4] [12.9] [9.0] [6.0] [0.9] [11.4] Baseline Mean/Median 566.6 391.7 198.4 46.3 20.50 69.9 10.8 78.3 Panel B: Urban t * (rainfall z-score> 2) -57.4 -51.9* -35.7** -16.2 28.4** 7.2 1.1 -9.6 [40.7] [30.4] [18.1] [23.1] [12.6] [9.5] [1.0] [23.6] Baseline Mean/Median 781.7 556.3 326.2 35.4 15.740 70.8 7.2 142.7 Panel C: Rural t * (rainfall z-score> 2) -13.7 -22.1 -12.5 -9.6 6.8 2.3 -0.2 1.5 [30.0] [23.4] [16.0] [14.7] [10.4] [7.6] [1.3] [6.9] Baseline Mean/Median 438.6 289 126.5 50.1 23.4 69.2 13.4 31.8 Observations: 26,163 Total; 10,905 Urban; 15,258 Rural. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. All quantities are monthly p.c in Quetzales of 2006. All median baseline values are presented in all cases except for private transfers, public transfers and other non-labor income that the mean baseline value is presented. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy.***p<0.01,**p<0.05,*p<0.1. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 28 Table 6. Impacts on Labor Income and Labor Supply Working Hours Worked Hourly Wage Sub-groups (1) (2) (3) Panel A: Total 0.000 0.876 -0.505** Total [0.007] [0.702] [0.249] -0.002 0.874 -0.707** Men [0.005] [0.694] [0.305] 0.010 1.049 -0.028 Women [0.013] [1.014] [0.314] Panel B: Urban 0.003 1.990** -0.886** Total [0.013] [0.864] [0.352] 0.002 2.514*** -1.031** Men [0.013] [0.945] [0.493] 0.005 1.471 -0.561 Women [0.017] [1.281] [0.554] Panel C: Rural -0.007 0.353 -0.112 Total [0.008] [0.959] [0.307] -0.004 0.394 -0.294 Men [0.005] [0.936] [0.352] -0.009 0.375 0.475 Women [0.019] [1.563] [0.426] Observations: 55,194 Total; 23,323 Urban; 31,871 Rural. 58% are men. 23% do not report wage. Notes: Results from diff-diff regression on the sample of all adults 17 to 65 years old controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Working represents the binary variable that identifies economically active individuals that were employed or are actively looking for a job during the four weeks preceding the survey. Hours worked per week. Hourly wage per week in Quetzales of 2006. *** p<0.01, ** p<0.05, * p<0.1. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 29 Table 7. Impacts on Children's Schooling and Labor Force Participation School Attendance Labor force participation 7 to 15 7 to 11 12 to 15 7 to 15 7 to 11 12 to 15 Measure of Shock (1) (2) (3) (4) (5) (6) Panel A: Total t * (rainfall z-score> 2) -0.022* -0.028** -0.017 0.031* 0.020 0.047* [0.012] [0.013] [0.017] [0.018] [0.016] [0.027] Observations 33,022 18,977 14,045 33,222 12,464 9,028 Baseline Mean 0.833 0.906 0.782 0.183 0.101 0.300 Panel B: Urban t * (rainfall z-score> 2) 0.006 0.002 0.010 -0.021 -0.023 -0.020 [0.018] [0.017] [0.031] [0.022] [0.019] [0.038] Observations 11,530 6,513 5,017 11,599 6,515 5,084 Baseline Mean 0.886 0.937 0.855 0.136 0.0644 0.233 Panel C: Rural t * (rainfall z-score> 2) -0.027* -0.035** -0.022 0.042* 0.030 0.061* [0.015] [0.015] [0.021] [0.023] [0.023] [0.033] Observations 21,492 12,464 9,028 21,623 12,461 9,162 Baseline Mean 0.804 0.890 0.742 0.236 0.120 0.374 Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Unit of observation are the children surveyed in ENCOVI 2006 and 2011. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 30 Table 8. “Fake” Treatment Effects (Panels A and B) and Migration Analysis (Panel C) Total Consumption Health Education Moderate Poverty Extreme Poverty Measure of Shock (1) (2) (3) (4) (5) Panel A: Results using Encovi 2000 t * (rainfall z-score> 2) -36.633 -9.020* 0.334 -0.023 0.017 [41.047] [4.797] [3.136] [0.030] [0.019] Baseline Mean 957.0 34.53 40.87 0.459 0.106 Panel B. Results on pre-determined variables Education Age Gender Area of residence Single-married Measure of Shock (1) (2) (3) (4) (5) t * (rainfall z-score> 2) -0.238 -0.086 0.014 0.013 0.009 [0.154] [0.378] [0.011] [0.024] [0.011] Baseline Mean 3.966 45.47 0.788 0.424 0.792 Panel C. Results on migration HH Head moved less than 1 year HH Head and spouse moved less than 1 year ago/Born in different municipality ago/Born in different municipality Measure of Shock (1) (3) t * (rainfall z-score> 2) 0.001 -0.002 [0.003] [0.002] Baseline Mean 0.0131 0.00690 Observations: 20,788 Panel A; 23,320 Panel B; 26,587 Panel C. Notes: Results from D-D regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Pre- treatment placebo refers to the D-D methodology applied to Encovi 2000 and Encovi 2006. The Z-scores indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 31 Table 9. Effects of Agatha on Consumption and Poverty (Subsample of Households Located Less than 50 Kilometers Away from the Closest Weather Station) Total Consumption Moderate Poverty Measure of Shock (1) (2) Panel A: Total t * (rainfall z-score> 2) -79.983*** 0.033* [28.177] [0.019] Baseline Mean/Median 603.3 0.448 Panel B. Urban t * (rainfall z-score> 2) -176.094*** 0.047* [44.263] [0.025] Baseline Mean/Median 799.4 0.303 Panel C. Rural t * (rainfall z-score> 2) -1.191 0.022 [35.389] [0.028] Baseline Mean/Median 499.1 0.558 Observations: 25,803 Panel A; 11,021 Panel B; 14,782 Panel C. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Baseline median for Total Consumption and baseline mean for Moderate Poverty. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 32 Table 10. Effects of Agatha on Consumption and Poverty (Subsample Household Exposed to Precipitation Anomalies of z-scores≥6 ) Coef CI 90% CI 90% (1) (2) (3) Panel A. Total Total Consumption t * ( 2 < rainfall z-score <= 6) -54.03* -100.09 -7.9 [27.9] t * (rainfall z-score> 6) -111.4** -199.05 -23.8 [53.1] Moderate Poverty t * ( 2 < rainfall z-score <= 6) 0.026 -0.007 0.06 [0.020] t * (rainfall z-score> 6) 0.034 -0.002 0.08 [0.025] Panel B. Urban Total Consumption - t * ( 2 < rainfall z-score <= 6) 164.4*** -245.03 -83.9 [48.8] - t * (rainfall z-score> 6) 206.6*** -308.7 -104.5 [61.8] Moderate Poverty t * ( 2 < rainfall z-score <= 6) 0.044 -0.0006 0.09 [0.027] t * (rainfall z-score> 6) 0.071** 0.0223 0.1196 [0.029] Observations: 26,587. Notes: Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. Table 11. Agricultural Cycle of Main Crops in Areas Affected by the Shock Agricultural land in affected areas Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec P H Maize p h 38% P H Beans p h Coffee 22% H H Sugar Cane 13% H and P H Note: H = first harvesting season; P = first planting season; h = second harvesting season; p = second planting season. Vertical gray bar corresponds to the timing of the Tropical Storm Source: Guatemalam Department of Food Security. 33 Figure 1. Map of Floods Reported after Agatha and Treatment Status of the Municipalities Notes: The red dots represent each flood reported to CONRED in the aftermath of the storm. The darker blue polygons represent affected municipalities. Source: calculations by the authors based on data from CONRED and Insivumeh. Figure 2. Coverage of Weather Stations in Guatemala 2010 2000 Years available 1990 1980 1970 1960 Weather stations Notes: The graph illustrates the years for which rainfall information is available in each weather station of the combined INSIVUMEH and CENGICAÑA grid. Source: calculations by the authors based on data from Insivumeh and Cengicaña. 34 Figure 3. Kernel Estimates of the Density Functions of Household Consumption per Capita Urban Rural Note: Epanechnikov kernel of total household consumption per capita. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. 35 Figure 4. Distribution of Rainfall Z-score in May 2010 Source: calculations by the authors based on data from Insivumeh. Figure 5. Food Price Index by Geographical Regions Notes: vertical line denotes the timing of the shock. Source: calculations by the authors based on price indices by INE. 36 Figure 6. Treatment Effects on the Prices of Selected Food Items Notes: Point estimates from econometric regressions specified as models in Table 1. Robust standard errors clustered at the municipality level. Source: calculations by the authors based on data from Encovi (INE) and Insivumeh. Figure 7. Annual Domestic Production (2006-2012) Notes: dotted line denotes the interval of time covered in the analysis Source: Calculations by the authors based on data from Faostats (FAO). 37 Poverty & Equity Global Practice Working Papers (Since July 2014) The Poverty & Equity Global Practice Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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