Rivalry and recovery: The social consequences of climatic hazards in rural India

International Journal of Disaster Risk Reduction 46 (2020) 101488

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Rivalry and recovery: The social consequences of climatic hazards in rural India Brandon Behlendorf a, *, Amira Jadoon b, Samantha Penta c a

College of Emergency Preparedness, Homeland Security, and Cybersecurity, University at Albany (State University of New York), Richardson Hall 287, 135 Western Avenue, 12203, Albany, NY, USA b Department of Social Sciences & Combating Terrorism Center, United States Military Academy, 607 Cullum Road, 10996, West Point, NY, USA c College of Emergency Preparedness, Homeland Security, and Cybersecurity, University at Albany (State University of New York), Draper Hall, Room 304B, 135 Western Avenue, 12203, Albany, NY, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Social capital Social cohesion Disasters India

Although damaging, the economic and physical consequences of disasters triggered by natural hazards can be mitigated by community recovery effects facilitated by strong social capital. How disasters affect social capital itself, though, is less known; they can serve to both coalesce and cleave communities in their aftermath. Using panel data from 23,000 households and 1,250 villages in rural India, this study aims to answer the following question: how do natural hazards shape social capital? Focusing on four different climatic hazards (droughts, floods, hailstorms, and cyclones) and four measures of social capital (social cohesion, collective efficacy, formal networks, and associational membership), we find divergent effects of hazards, depending on type and recency. Droughts inhibit new access to formal sources of social capital and encourage negative perceptions of social cohesion, but only in the short-term. In contrast, hailstorms encourage short-term building and long-term strengthening of formal networks for all, at the long-term expense of membership in communal organizations. In short, our results suggest that climatic hazards encourage short-term contention within communities while building infrastructure for long-term access to formal sources of authority and resources, although these effects vary by hazard type.

1. Introduction Disasters triggered by natural hazards can inflict severe physical, economic and social damage upon societies, disrupting human lives and the daily functioning of communities. While the level of damage de­ pends on the nature and severity of disasters, they can be especially debilitating for economically challenged regions with limited mitiga­ tion, preparedness and response structures. Still, the resilient nature of some communities to recover post-disaster varies considerably, even in comparable environments and contexts. Using their embedded strengths and resources to cope and survive [1], a community’s ability to recover often extends beyond the severity of disasters or government assistance. In many cases, the constellation of social capital within communities –the capabilities and resources found within social networks – account for considerable differences in communities’ resilience [2,3,90]. Scholars generally agree that the level and quality of social capital in a community before and after disasters, generated across relational net­ works between households, are influential in shaping recovery efforts in

the aftermath of disasters [2]. In short, social capital facilitates collec­ tive action post-disaster recovery, helping households and communities to recover. How disasters affect social capital itself, though, is less known. Social capital is activated through cohesive relationships, and the role of di­ sasters in shaping communal cohesion is understudied. Since disasters can cause significant physical and psychological damage to communities [4–6], the relationship between social capital, social cohesion and the effects of disasters is unlikely to be unidirectional. In addition, social capital has been measured in dissimilar ways, ranging from trust and generosity to participation in civic organizations, where only some studies have the data to account for pre-disaster levels of social capital. As such, there is a need to look beyond their role in mitigating the effects of disasters by understanding the extent to which disasters shape social capital and cohesion. This study aims to answer the following question: how do natural hazards shape social capital? In answering this question, we look at the effects on different measures of social capital, including formal

* Corresponding author. E-mail addresses: [email protected] (B. Behlendorf), [email protected] (A. Jadoon), [email protected] (S. Penta). https://doi.org/10.1016/j.ijdrr.2020.101488 Received 6 September 2019; Received in revised form 12 January 2020; Accepted 15 January 2020 Available online 21 January 2020 2212-4209/© 2020 Elsevier Ltd. All rights reserved.

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communities, especially in times where effective cooperation is needed. Thus, disasters generate interactive opportunities among community/ neighborhood members, which increases cohesion, interpersonal or/and generalized trust. As a proxy for social capital, Dussaillant & Guzman [83] find a significant increase in interpersonal trust among severely affected urban residents in Chile following the 2010 Maule earthquake. Using a panel design, they find disasters may initiate a broader trust building process, especially where pre-earthquake trust levels were stronger. Combining experimental methods and survey data, Cassar, Healy and von Kessler [24] find that individuals in Thailand affected by the 2004 tsunami became more trusting of others, despite becoming more risk-averse. Similar increases in mutual trust were found in Japanese communities after the Tohoku earthquake in 2011 [25], although the effect was greater in more sparsely populated areas. In a separate study of several European disasters, Albrecht [26] found that trust was lower in areas with at least nine hazard-related deaths, a shift more likely associated with national rather than regional-level disasters. Akbar and Aldrich [27] provide a more nuanced examination of trust, finding that although interpersonal trust was higher than institutional trust after a flood in Pakistan, trust in general was lower with negative flood experience and material losses. Unsurprisingly, there was increased social trust where government per­ formance in the aftermath of the flood was comparatively better [27]. Shifting away from measures of trust, disasters have also increased voluntary participation in some communities. For example, following the Japanese Kobe earthquake, voluntary and non-governmental activities among communities increased, promoting post-disaster recovery [28]. Okuyama and Inaba [29] examined two kinds of social engage­ ment—formal and informal—following the 2011 Japan earthquake and tsunami, and found that disaster consequences on social engagement depend on the type of social engagement under examination. They write “that daily interactions with neighbors and friends and acquaintances were lower among those affected by the disaster” while “relatively formal participation in local collective group activities was higher among those affected in more devastated areas” ([29]:11). Although sense of commu­ nity and connectedness may be strengthened months after a disaster [30], others have found only short-term increases in social cohesion, dis­ appearing after a month [31]. Likewise, while crises present opportunities for social solidarity, some post-event activities are better at promoting and sustaining it than others [32]. In particular, activities promoting and sustaining social cohesion must not only offer emotional rewards, but also provide collective experiences with others [32]. In many cases, the positive effects of disasters are either reinforced by or dependent upon the type of pre-disaster networks in communities. For example, after the 1999 Izmit earthquake in Turkey, local leaders facili­ tated stronger recovery through pre-existing civic and political networks that bridged multiple communities [16]. In contrast, Mukherji [33] shows that strong bonding networks prior to the 2001 Gujarat earthquake in India were not associated with greater collective action for housing re­ covery post-disaster. While most studies have focused on the role of bonding networks post-disaster [34], recovery phases may draw on mul­ tiple types for different purposes. In addition, characteristics like network density and geographic proximity can affect recovery by providing increased options for resource sharing among tightly knit neighborhoods [35]. Taken together, these works suggest that disasters may alter social capital in different ways, depending on the type of pre-existing social structures in the community. In many cases, disasters can generate ther­ apeutic communities [22,23], dislodging existing norms and expectations guiding behavior. Alongside the potential for supportive communities, disasters are also occasions that can intensify social conflict and undermine social capital [36]. Among rural populations in Chile after the 2010 Maule earthquake, Fleming and colleagues [47] find considerable reductions in reciprocity among affected villages. In contrast to Dussaillant & Guzman [83], they highlight several conditions whereby disasters can reduce social capital. Disasters may 1) increase disputes due to scarce relief and recovery re­ sources; 2) decrease reciprocity due to information asymmetries within

networks, associational memberships, social cohesion and collective efficacy. Focusing on climatic hazards in rural India (those whose origin is hydrological, climatological, or meteorological), we assess whether the overall effects of disasters vary by their type or recency. There is only one study to our knowledge [50] that compares the effect of different types of natural hazards on social capital (at the country level). Our study is the first comprehensive study that accounts for pre-existing social capital at the outset of the period of analysis, focuses on subna­ tional variation in hazard exposure, and measures impact on different components and measurements of social capital. 2. Theory 2.1. Social capital and natural hazards Consisting of social networks, norms of reciprocity and trustwor­ thiness [7], social capital allows individuals to cooperate through shared norms for mutual benefit. Although serving different functions in many forms, social capital essentially consists of social structures and norms that facilitate efficient and productive social exchanges of actors within those structures [8]. It can be a source of enforceable trust and rules, provide economic opportunities and entrepreneurial success [9], facili­ tate accurate dissemination of information, and resolve coordination problems by reducing fears of free-riding [10]. Disadvantaged commu­ nities in less-industrialized countries can especially benefit from voluntary contributions by community members and delivery of important public goods through such collective action. Yet how exactly do networks, trust, social norms and community leadership mitigate the wide-ranging repercussions of natural hazards? Social ties, support, and capital can mediate a disaster’s negative effects on mental health [6], while strong social networks are linked to increased recovery [11]. Local community groups facilitate a more organized response in various stages of disaster management, from rescue to rehabilitation to preparedness [2,12]. Earthquake victims in China [91] and Nepal [13] relied on their social networks to obtain critical support and rebuild their lives and communities. Comparative studies from Kobe, Japan and Gujarat, India found that trust, social norms, participation and networks played a key role in enabling and enhancing collective action and recovery in two culturally and socio-economically different contexts [14]. Shared norms, values and trust can also coordinate collective action through rapid information dissemination, preventing migration out of the area [2,15,80]. In Turkey after the 1999 earthquake, social capital filled critical gaps in state services [16,17]; local leaders played an important role in mobilizing disaster victims to participate in collective recovery efforts and seeking donor funding. After the 3/11 disaster in Japan, the relocation of entire neighborhoods or villages encouraged greater resilience through its preservation of existing networks of social capital [18]. Communities can find short-term relief through bonding networks and devise long-term strategies to survive and cope through bridging to other resourceful social networks [19]. These findings are relatively consistent across disaster types, from floods [20] to cyclones [21] to tsunamis and hurricanes [2]. 2.2. The effects of natural disasters on social capital In contrast to the restorative nature of social capital post-disaster, less is known about the effects of disasters on social capital and cohesion. Some argue that disasters generate opportunities for cooperation amongst community members to cope with the aftermath [22,23], increasing overall social cohesion. Others contend that the destructive nature of disasters fosters subsequent conflict, diminishing productive exchanges of capital between community members [84]. In short, the impact of disasters on social capital and cohesion could foster either a rivalry between neighbors or the collective recovery of the community. Promoting recovery, disasters could increase trust within 2

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International Journal of Disaster Risk Reduction 46 (2020) 101488

communities; or 3) cause displacement or forced migration. Opportunities for communal unification can also provide ample sources of communal conflict resulting from disaster-related trauma and loss [37–39,84]. Therapeutic communities may form [23], yet those opportunities and resources are often limited to dominant social groups, excluding more vulnerable communities [40–42]. In many cases, disasters “force open whatever fault lines once ran silently through the structure of the larger community, dividing it into divisive fragments” ([38]:236).

3. Current study & methods While analytical variations across previous studies produce rich in­ sights, they make it difficult to draw generalizable conclusions about when and how we can expect disasters to shape social capital. Devel­ oping a more nuanced and consistent understanding of disasters’ impact on social capital requires an empirical approach that allows comparison across multiple disaster types and indicators of social capital and cohesion. Using a large panel sample of households in rural India, we evaluate whether the communal experience of multiple types of natural hazards encourages lasting impacts on social capital and cohesion. Our analysis not only explores how disasters in general affect distinct com­ ponents of social capital and cohesion, but also how these effects are conditioned by the type of disaster. In addition, we compare these effects over the short term (three years) as well as the long term (seven years) and exploit the rich data on rural Indian households to control for preexisting levels of social capital. India has consistently ranked as one of the top ten countries in the world in terms of absolute monetary losses due to natural hazards [51]. India’s vulnerability to natural hazards, such as hailstorms, droughts and floods, lies not only in its geo-climatic factors, but also due to densely populated regions and strong dependence on the agricultural sector (16% of the GDP and 49% of its labor force employment in 2018) [52]. Floods are closely tied to monsoon patterns between June and September, where nearly 75% of annual rain amounts fall, inundating roughly 7.5 million hectares annually [53]. Similarly, 68% of all agri­ cultural land is vulnerable to drought [52], affecting approximately 50 million people annually [53]. India’s long coastline is also highly prone to cyclones; on average, roughly five to six cyclones hit India’s coast every year, exposing approximately 370 million people [54]. Finally, as a pre-cursor to the monsoon season, strong hailstorms felt across northern India substantially impact agricultural production [55]. All told, between 1980 and 2010, India experienced over 400 major di­ sasters, killing over 140,000 people and affecting the lives of millions [56].

2.3. The differing experience of disaster The consequences for social capital could also be amplified by the frequency of disasters, leaving conflicting short- and long-term effects. For example, Said et al. [43] examine the effect of the flood exposure in Pakistan on individuals’ voluntary contributions to a common pool via experiments and surveys conducted three years later. Individuals who regularly experienced flooding contributed more on average across the sample, as did those who experienced the devastating 2010 floods. However, for those who were affected by the 2010 floods and subse­ quently experienced more flooding in later years, their contributions were lower. Similarly, others found that generosity in Sri Lanka among those who survived the 2004 tsunami was lower than the average respondent, even seven years out [44]. In a separate study, Chang [45] finds that the 2005 Carlisle flood in the UK led to changes in community cohesion (shared values, social interactions and participation in clubs and organi­ zations) that varied by the intensity of one’s exposure. While cohesion increased initially at the onset of the flood, residents shifted their focus subsequently towards individual interests, resulting in decreased cohesion overall as intensity increased. In some cases, the severity of disasters can have divergent effects on social processes. Four years after Hurricane Mitch in Honduras, Carter and Castillo [46] find that severity of the hurricane had opposing effects on trust and participation. Individuals residing in the more severely affected communities demonstrated more pro-social behavior, higher interper­ sonal trust and wider social networks, but they became less involved in formal organizations such as producers’ associations and local councils. This latter finding is confirmed by Wang and Ganapati [89], who find that even when accounting for pre-existing levels, hurricane Katrina slowed down growth of social capital post-disaster, measured as the number of memberships in local organizations (civic, sports, political, labor, business and professional). It is possible that the sheer destruction of infrastructure and displacement of over one million people due to the hurricane out­ weighed any potential membership opportunities. Thus, substantially devastating disasters may create negative externalities for social capital in both the short- and long-term, while the relative frequency of some haz­ ards could encourage cohesive recovery. Moreover, hazard types are not equivalent; different disasters can shape subsequent social capital in different ways. Research on the links between natural hazards and social capital have examined a diverse array of disasters, ranging from earthquakes [25,28,47–49,83], to tsu­ namis [24,44], to hurricanes [46,89]; and floods [43,45,49]. In some cases, different disasters in the same country can produce dissimilar effects on social capital. For example, although the Pakistani case of the 2005 earthquake shows no correlation between disaster exposure and trust [48], Said et al. [43] find that exposure to frequent flooding in Pakistan was correlated with greater potential for cooperation. Moving away from single case studies, Toya and Skidmore [50] analyze the ef­ fect of natural hazards (storms, floods, earthquakes, slides and volcanic activity) on societal trust across 146 countries, between 1985 and 2009 using five-year intervals. Although the total number of disasters in a country increases societal trust, only storms have a positive effect on trust when the analysis accounts for the type of disaster, whereas earthquakes have a negative effect. The authors suggest that the ease of predictability due to improvements in weather forecasting technology may allow communities to better prepare for disasters such as storms.

3.1. Data: India human development survey (IHDS) panel To understand the social consequences of natural hazards in rural households across India, we use data from the India Human Develop­ ment Survey (IHDS), a nationally representative survey of households across 33 states and union territories of India [57]. Households are drawn from urban blocks or villages selected via a stratified random sampling design, with a sample response rate of 92% [57]. For our purposes, household-level data from two waves of interviews conducted in 2004/05 (wave 1) and then in 2011/12 (wave 2) from rural villages were selected. The survey is a joint effort by research teams at the University of Maryland and the National Council of Applied Economic Research in New Delhi [82]. Both waves of the survey consist of information at the individual, household and village levels, with detailed information regarding in­ come, caste and community structures, employment, government sub­ sidies, social and cultural capital and village infrastructure. The two waves of surveys were conducted approximately seven years apart, and we limit our analysis to households that were surveyed in both waves.1 By matching and merging household and village information from the 1

Of the 31,300 rural households interviewed in wave 1, panel attrition resulted in 2857 households (9.1%) not surveyed in wave 2. Compared to those in both waves, respondents not surveyed in wave 2 were less poor yet less educated, held greater perceptions of social cohesion, greater participation in local associations, and more likely to be Muslim or Scheduled Tribe (see Ap­ pendix Table 1). Thus, the panel that remains has a slightly lower level of initial social capital, and potentially biases our estimates towards positive improve­ ments in later social capital resulting from disasters. 3

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wave 1 survey with the same information from the wave 2 survey, we are able to control for pre-existing levels of social capital and social structures at the household- and village levels. Our primary unit of analysis is the household, nested within villages with specific economic and social structures. Overall, our initial sample consists of 28,443 households and 1,362 villages across 31 states and territories.

variable capturing whether the respondent perceives that the commu­ nity generally comes together to solve collective action problems (1) or if families tend to resolve problems on their own (0).3 Both of these household perceptions may collectively form communal narratives that are generally sticky in the face of disaster [81], although our research design is focused on household variation in these perceptions within villages rather than between villages. Regarding structural social capital, we use previous work on social capital in India [64] to capture formal networks as a binary variable whether the household has any acquaintances or relatives who work in key institutions (education, health, or government sectors).4 These re­ lationships can serve as a form of linking social capital [65] to in­ stitutions that could give households greater access to physical or financial resources post-disaster. Finally, associational membership is a binary indicator of whether any household members belong to any communal organization, such as local mahila mandals (informal social service clubs), social club, employee/business unions, credit groups, or agricultural cooperatives. Although factor analysis was considered to create a single measure of social capital, polychoric correlations be­ tween the measures were less than 0.27 in wave 1 and 0.22 in wave 2, suggesting weak correlation and the need for separate models of each measure. We use wave 2 measures of social capital to capture potential social consequences, and include wave 1 values for each measure to control for pre-disaster levels of structural and cognitive social capital.

3.2. Measures 3.2.1. Social consequences Social capital constitutes enduring interactions, norms and rules that generate coordination [58], which may be rooted in general social interaction among community members or facilitated through formal organizations. Social capital in different communities across the globe can take varying forms, where a certain measure of social capital may be irrelevant or inconsequential in a given culture or society [59]. In the context of measuring social capital in less developed societies such as India, it is prudent to distinguish formal and informal forms of social capital; formal social capital encapsulates institutionalized norms, rules and networks whereas relations between families, neighbors and com­ munity members constitute informal social capital. In less developed communities, social capital is more likely to be embodied in informal networks due to the uneven distribution of institutional presence across rural villages. For example, the density of formal organizations in rural Rajasthan, India may not be an appropriate measure of social capital, if only a minority of the local population participates in such organizations [60]. In India, where approximately 66% of the total population resides in rural areas [61], structural (networks and rules) and cognitive (norms and values) social capital are likely to exist outside of any visible or formal organizations [62]. In Krishna’s [60] study of social capital in rural Rajasthan, structural and cognitive components of social capital are measured as the ability of a community to collectively manage common village land, resolve disputes, and trust other members of the community. Similarly, other research on recovery following the 2001 Gujrat Earthquake in India measures social capital by examining net­ works, trust levels, social norms and collective action [14]. Mosse’s [85] examination of communities in South India examines village reliance on community members’ collective action to effectively manage their water supply, operating outside of any formal organization. In such commu­ nities, formal organizations and regular meetings are generally avoided as they entail transaction costs and risk conflict. Overall, studies analyzing social capital in the context of India do not necessarily emphasize institutionalized social capital but tend to mea­ sure it via an examination of community members interactions with each other, their trust in government institutions, trust in relatives and neighbors, general feelings of community cohesion, and participation in various communal activities [28]. Thus, our outcomes comprise four binary dimensions of social capital and cohesion at-risk from the disruptive nature of disasters. Social cohesion and collective efficacy measure perceived norms of reciprocity and shared expectations (otherwise known as cognitive social capital), whereas formal networks and associational membership measure potential social avenues of capital within a community (otherwise known as structural social capital). So­ cial cohesion is captured by a single binary variable created from two questions in the survey regarding the perceived level of conflict in communities. Specifically, respondents were asked whether they perceive the existence of 1) general conflict in the village and 2) specific conflict between communities and jatis (subcastes). We code social cohesion as 1 only if there is an absence of perceived conflict in both categories, and 0 otherwise.2 Collective efficacy [63,92] is a binary

3.2.2. Disaster experience Disasters are inherently social phenomena [93], defining situations when the resources of an individual or community are not capable of responding to the occurrence of a natural or technological hazard [40, 66]. Given the considerable variation of disasters that affect India, we deviate from previous studies that focus on singular major events (such as a large-scale cyclones or widespread flooding) and focus on community-defined indicators of climatic disaster. Data on disaster ex­ periences at the village level come from the wave 2 village question­ naire, which surveyed a village leader or headperson for information on village-level capabilities and experiences.5 Respondents used a recall approach to determine whether any of four disaster types (droughts, floods, hailstorms, and cyclones) occurred in each of the years between the waves 1 and 2 surveys. Using this information, we create several indicators of disaster experience at the village level, including binary measures for each disaster type between the two waves, as well as any disaster during that period. In addition, we created additional binary

3

Specific question wording is “In some villages or neighbourhoods, when there is a community problem such as water supply problem, people bond together to solve the problem. In other communities, people take care of their own families individually. What is your community like? Bond together to solve problem (1) or each family solves individually (0).” 4 It is important to note that while the coding of formal networks for our purposes is consistent across the two waves of the survey, the ability to restrict networks to those beyond familial connections is available only for wave 1. While an important limitation, the majority of formal connections occur with someone outside of the family or household. In wave 1 (2005), familial con­ nections represent 15% of all formal medical connections, 24% of formal educational connections to the education sector, and 33% of formal connections to the government sector, suggesting that the majority were extra-familial. 5 Early scholars restrictively defined disasters, focusing on events “concen­ trated in time and space, in which a society, or a relatively self-sufficient sub­ division of a society, undergoes severe danger and incurs such losses to its members and physical appurtenances that the social structure is disrupted and the fulfillment of all or some of the essential functions of the society is pre­ vented” [22]:655). Given the familiarity of these hazards to communities in rural India dependent on agricultural [88], and the localized nature of original disaster definitions, we view the village headperson as a valid, alternative source of information regarding natural hazards that exceed locally-defined capacities.

2 Only 7% of respondents in 2005 and 2012 reported conflict among jatis with no general conflict reported in the community, suggesting high corre­ spondence between the two measures. Additional sensitivity tests are included in the Appendix.

4

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variables to capture whether a village experienced any disasters and specific disaster types in the three years prior to the wave 2 survey (to capture recent events).

all suggesting households are more similar to each other within villages than a one-level model would consider. 3.4. Analytical plan

3.2.3. Contextual features We include several covariates, at both the household- and villagelevel, which may be related to a household’s overall level of social capital. At the household level, we include covariates for the total number of persons, the highest level of adult education, and whether the household falls below the nationally-defined rural poverty line of roughly 450 Indian Rupees (INR) per capita per month [67]. We also account for whether the household had a member belonging to partic­ ular castes or religions that are likely to experience societal discrimi­ nation in India. Specifically, we code for whether the household belongs to an Other Backward Caste, Scheduled Caste, Scheduled Tribe, or is Muslim. Finally, to address demographic variation related to our perceptual outcomes, we include the age and gender of the individual responding to wave 2 of the survey. At the village level, we account for current social structures at wave 2 (2011/12) relevant to the opportunity for social capital to develop, including percentage of the village population which is Hindu, Other Backward Castes, Scheduled Castes and Scheduled Tribes. Social capital may also be a function of local economic opportunities [68,69]; thus, we include the percentage of village population who were eligible for and employed under the National Rural Employment Guarantee Act (NREGA). Finally, we include an indicator whether different castes/jatis physically live together in the village, as geographic separation can in­ fluence the ability to bridge social capital across communities.

Our research design consists of several components to estimate the effect of disasters between waves 1 and 2 on social capital and social cohesion in wave 2, while controlling for pre-existing levels in wave 1. First, we assess the effect of any disaster (drought, flood, cyclone, or hailstorm) experienced by a household between the two surveys (approximately seven years), as well as in the previous three years. Adopting this approach allows us to compare whether the long-term effect of climatic disasters on our outcomes of interest is distinct from the effect of disasters suffered more recently. We control for pre-disaster levels for all our outcome variables derived from wave 1, matched at the household level, to test the robustness of the relationship between di­ sasters and levels of social capital and cohesion in wave 2. Second, we repeat the same approach above disaggregated by disaster type. Finally, we test the sensitivity of our findings against a number of variations in measurement, including 1) those households where the same individual was surveyed during both waves and 2) disaggregated measures of social cohesion. For brevity and clarity, we restrict our discussion below to our key variables of interest and relationships that are statistically significant. 4. Results 4.1. Descriptive results Our initial sample contained 28,443 wave 1 rural households within India also surveyed in wave 2. To reduce potential confounders and ensure the households maintained the same exposure opportunity to the village-specific disasters, we removed households that moved between the two waves (n ¼ 1,355 households) and were missing a village-level questionnaire in wave 2 (n ¼ 866 households). While it is not ideal to exclude populations moving in and out of communities, we do not expect this exclusion to impact our results in any meaningful way for various reasons. First, the number of households that fall into this category are relatively small compared to the total number of house­ holds included in the analysis, and as such any associated effects would be limited. Second, excluding migrating populations from our sample allows our sample to remain consistent across waves and ensure that households experienced all of the disasters between the two waves. Finally, migration may result from reasons completely unrelated to di­ sasters that may exert an independent effect on perceptual measures of social capital not captured within our research design. Due to potential errors in matching individuals within households to the respondent answering the survey, we also removed any households where the respondent answering the survey was less than 14 years old (22 households), resulting in a final qualifying sample of 26,200 households. Regarding missing data, we used listwise deletion for our key level-1 and level-2 variables under the assumption that the data are missing not at random (MNAR) and other strategies, like multiple imputation, can generate results that are less efficient and more biased than listwise deletion [70]. Ultimately, we drop 270 households due to missing data at level-1, 1,911 households due to missing data at level-2, and 56 households due to missing data across both levels, for a total of 8.5% of our qualifying households. Our final sample size is 23,963 households nested within 1,252 rural villages within India. We report our variables and descriptive statistics in Table 1. Regarding our measure of interest, in wave 2 rural villagers within India report high levels of formal networks (72%) and relatively low participation in local associations (29%). While aggregate associa­ tional membership did not change much between wave 1 and 2 (31%– 29%), aggregate formal networks increased considerably between waves (50%–72%). In contrast, aggregate perceptions of general social

3.3. Modeling strategy: random intercept models Given the nested nature of households within villages, our research design employs multilevel random-intercept models to account for in­ dividual (level-1) and village-level (level-2) contextual variation in so­ cial capital and cohesion. Since all our outcome variables are binary, we " # use logistic regression, which calculates the log odds (log

pij 1 pij

) for the

ith household in the jth village. We cluster our standard errors at the state-level6 and grand-mean center all but our key independent variables. Our use of multilevel models is important for understanding the social consequences of disasters, especially within rural villages in India. First, climatic disasters under study here are larger than the size of a single village, suggesting that each household within the village has an equivalent level of possible exposure. Although the final effects may be mediated by household wealth or type of employment, the likelihood of exposure is consistent across the village. Thus, our standard errors are interdependent for households within the same village. Second, our key predictors (disaster experiences) are at the village-level, but social re­ lationships and perceptions of social cohesion are measured at the household-level. By structuring the model as multilevel, we are able to identify key differences between households within the same village as well as between villages. Third, our outcome measures exhibit consid­ erable correlation of responses within villages, with social cohesion (Intraclass Correlation Coefficient [ICC] ¼ 0.65), formal networks (0.41), associational membership (0.44), and collective efficacy (0.51) 6

While a host of potential factors at the state-level could influence the for­ mation of social capital and the potential exposure to disasters, the primary focus of this analysis is the influence of local disaster experience on household social capital, accounting for the heterogeneity of village social and economic structures. Clustering our standard errors by state across all of our models al­ lows us to control for differences in state-level characteristics, such as varying susceptibility of particular states to natural disasters, while focusing on key heterogeneity at the village and household levels. 5

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International Journal of Disaster Risk Reduction 46 (2020) 101488

cohesion did not change much (44%–47%) between the two waves, while perceptions of collective efficacy substantially increased (59%– 74%). Regarding disaster experience, 70% of the villages surveyed experi­ enced at least one of the four disaster types (drought, flood, cyclone, or hailstorm) between the two waves, and 51% of the villages experienced one of these disasters in the three years prior to wave 2. Drought was the most common disaster (51% of villages), followed by floods (26%), hailstorms (23%), and cyclones (12%).

relationships between our household-level and village-level control variables are largely in the expected directions. Since levels of social capital in wave 2 are likely to be conditioned by pre-existing levels of social capital, structural measures of social capital (formal networks and associational memberships) in wave 1 have a significant and positive effect on their wave 2 levels. In contrast, perceptual measures of social cohesion and collective efficacy were not significantly correlated be­ tween waves 1 and 2, suggesting variability in these measures of social capital across multiple-year periods. It is possible that the effects on social capital have a temporal char­ acteristic, since disasters can have distinct long- and short-term conse­ quences. To test if the effects are influenced by recency of events, we replicate the above analysis for disasters experienced in the previous three years only. In contrast to the prior results, we find that recent disasters were significantly correlated with reductions in the perception of social cohesion (β ¼ 0.564). This is equivalent to a 43% reduction in the odds of perceiving a cohesive community. Similar to the results above, experiencing any disaster in the previous three years is not significantly correlated with any other measure of social capital (Ap­ pendix Table 2). Disaggregating our disaster experience into specific types (droughts, floods, cyclones and hailstorms), we find that those who have experi­ enced any hailstorms have greater odds of connecting with formal sec­ tors (β ¼ 0.631), yet are less likely to associate with a communal organization (β ¼ 0.543) (Table 3). Other measures of social capital/ cohesion were not significantly correlated with the general experience of any drought, flood, or cyclone. When considering the effects of only recent disasters (Table 4) we find key differences by disaster type. Those experiencing recent droughts are more likely to report conflict in their local communities (β ¼ 0.563) and less likely to know someone in the health, education or governmental sectors of India (β ¼ 0.377). For those experiencing recent hailstorms, respondents are more likely to know individuals in the formal sector (β ¼ 0.676), a similar effect for experiencing any hailstorm between waves. For both short- and longterm, none of the other disaster types are significantly correlated with our measures of social capital. Thus, our results suggest that in the im­ mediate aftermath of hailstorms, individuals in our sample build stronger formal networks that persist for years, and ultimately reduce their involvement in local associational memberships. Additionally, drought experience creates immediate perceptions of social conflict and weakens formal networks, although this effect is only in the short term as the community (potentially) recovers. We can also examine the differential social consequences of disasters in rural India through the competing effects of droughts and hailstorms on formal networks. As Table 4 highlights, the odds of connection to formal networks in wave 2 were lower for those experiencing a recent drought and higher for those experiencing a recent hailstorm. With the binary nature of our two measures (wave 1 and wave 2) of formal net­ works, questions remain regarding whether disasters inhibit those with no formal networks from forming those relationships, or whether they weaken fragile formal networks and sever access to the resources and capital present in those connections. Rather than interaction effects [71, 72], we opt to conduct separate multilevel logit models regressing wave 2 formal networks on disaster experience for those without (0) and with (1) formal network connections in wave 1, and compare marginal pre­ dicted probabilities of wave 2 formal networks across the models. We use marginal predicted probabilities since we are interested in the fixed effects of specific disasters on household social outcomes across villages rather than conditional probabilities for each village. Results from the subgroup analysis (Appendix Table 3) highlight that recent hailstorm exposure is associated with increased odds of formal networks regardless of previous network connection, but recent droughts only inhibit those with no formal networks from forming them. Comparing predicted probabilities in Fig. 1, we can visually see not only the difference in effect across recent disasters, but also how this magnitude is different depending on the presence of formal networks at

4.2. Analytical results Considering the effects of experiencing any disaster, in Table 2 we find no significant relationship between general disaster experience and social capital/cohesion in wave 2. Individual perceptions of social cap­ ital/cohesion in these rural communities could be derived from enduring collective narratives [81], and thus personal disaster experi­ ences wouldn’t necessarily influence cognitive social capital. The

Table 1 Descriptive statistics. Variable

Mean / Prop

Level 1 (Household) Measures (n ¼ 23,963) Dependent Variables (Wave 2) No Perceived Social Conflict – Neither .47 Village nor Jatis No Perceived Social Conflict – Village .55 No Perceived Social Conflict – Jatis .55 Collective Efficacy .74 Formal Networks (Teacher, Doctor, Gov .72 Official) Associational Membership .29 Independent Variables (Wave 2, except where noted) Social Capital/Cohesion Measures – Wave 1 No Perceived Social Conflict – Village & .44 Jatis No Perceived Social Conflict – Village .52 No Perceived Social Conflict – Jatis .68 Collective Efficacy .59 Connected to Teacher, Doctor, Gov .50 Official Associational Membership .31 Respondent Measures Age 44.29 Female .34 Household Measures # of Household Members 4.92 Highest level of adult education 1.09 Below Poverty Line .20 Other Backward Caste .41 Scheduled Caste .24 Scheduled Tribe .11 Muslim .08 Level 2 (Village) Measures (n ¼ 1252) % Working under MNREGA 63.83 % Hindu 84.24 % Other Backward Caste 41.45 % Scheduled Caste 20.81 % Scheduled Tribe 11.62 Jatis Live Together .37 Village Disaster Experience (between waves) Any Disaster (w/in 7 yrs) .70 Within 3 Years .51 Any Drought .51 Within 3 Years .32 Any Flood .26 Within 3 Years .16 Any Cyclone .12 Within 3 Years .06 Any Hailstorm .23 Within 3 Years .13

Std Dev

Min

Max

0

1

0 0 0 0

1 1 1 1

0

1

0

1

0 0 0 0

1 1 1 1

0

1

14.87

14 0

99 1

2.40 .89

1 0 0 0 0 0 0

33 3 1 1 1 1 1

28.64 26.82 29.13 17.48 24.07

0 0 0 0 0 0

100 100 100 99.5 100 1

0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1

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International Journal of Disaster Risk Reduction 46 (2020) 101488

Table 2 Multilevel logistic regression of social capital on disaster experience between waves.

Any Disaster w/in 7 yrs Respondent-Level Age Female Household-Level Lagged Outcome (Wave 1) # Persons in HH Highest Education < Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste % Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(1)

(2)

(3)

(4)

Social Cohesion

Collective Efficacy

Formal Networks

Assoc. Member

0.596 (0.319)

0.052 (0.152)

0.210 (0.216)

0.210 (0.196)

0.002 (0.002) 0.026 (0.046)

0.001 (0.002) 0.055 (0.035)

0.002 (0.002) 0.263*** (0.045)

0.003 (0.003) 0.216*** (0.049)

0.046 (0.057) 0.002 (0.012) 0.032 (0.024) 0.039 (0.094) 0.199 (0.113) 0.043 (0.092) 0.147 (0.087) 0.266** (0.099)

0.048 (0.060) 0.018 (0.010) 0.045 (0.028) 0.185 (0.136) 0.002 (0.114) 0.072 (0.085) 0.049 (0.100) 0.074 (0.106)

0.444*** (0.051) 0.087*** (0.010) 0.558*** (0.026) 0.769*** (0.063) 0.396*** (0.103) 0.172* (0.073) 0.527*** (0.064) 0.602*** (0.103)

0.492*** (0.075) 0.087*** (0.015) 0.305*** (0.042) 0.523*** (0.093) 0.252* (0.114) 0.179* (0.080) 0.295** (0.112) 0.441*** (0.111)

0.001 (0.003) 0.008* (0.004) 0.003 (0.005) 0.010* (0.004) 0.003 (0.004) 0.225 (0.272) 23,963 11,677

0.008** (0.003) 0.004 (0.003) 0.003 (0.003) 0.002 (0.003) 0.002 (0.005) 0.061 (0.140) 23,963 11,076

0.002 (0.002) 0.004 (0.003) 0.002 (0.003) 0.002 (0.004) 0.002 (0.006) 0.069 (0.186) 23,963 11,264

0.003 (0.003) 0.003 (0.004) 0.001 (0.003) 0.004 (0.004) 0.009 (0.006) 0.128 (0.263) 23,963 11,478

Table 4 Multilevel logistic regression of different types of social capital on different recent disaster experiences.

Drought w/in 3 yrs Floods w/in 3 yrs Cyclones w/in 3 yrs Hailstorms w/in 3 yrs

Drought w/in 7 yrs Floods w/in 7 yrs Cyclones w/in 7 yrs Hailstorms w/in 7 yrs

(7)

(8)

Collective Efficacy

Formal Networks

Assoc. Member

0.371 (0.239) 0.373 (0.229) 0.003 (0.299) 0.044 (0.276)

0.149 (0.210) 0.238 (0.189) 0.114 (0.201) 0.342 (0.255)

0.146 (0.184) 0.105 (0.181) 0.254 (0.201) 0.631** (0.202)

0.138 (0.231) 0.081 (0.226) 0.226 (0.198) 0.543** (0.186)

(12)

Formal Networks

Assoc. Member

0.563** (0.212) 0.393 (0.251) 0.009 (0.341) 0.248 (0.287)

0.260 (0.214) 0.178 (0.227) 0.115 (0.268) 0.071 (0.226)

0.377* (0.164) 0.060 (0.152) 0.329 (0.218) 0.676** (0.250)

0.307 (0.256) 0.079 (0.210) 0.081 (0.209) 0.287 (0.276)

One of our key findings from Table 4 is the negative impact of recent droughts on individual perceptions of social cohesion within one’s village. Given that perceived cohesion was predicated on no perceived conflict in both the general community as well as the subcastes (jatis) within communities, the results may differ when considering these different perceptions separately. Replicating the models in Table 4 for these two perspectives separately (jati cohesion and general community cohesion), results (Appendix Table 4) show that the relationship be­ tween droughts on our pooled social cohesion measure is primarily driven by general perception of village cohesion rather than perceived cohesion between specific subgroups (or jatis). Similarly, although households experience disasters together, their influence on individual social capital measures may depend on the in­ dividual completing the survey. While memberships in associations or formal network connections can aggregate across a household, indi­ vidual perceptions of conflict and cooperation could be individualized within households. To test whether our findings are sensitive to this potential confounder, we conduct subgroup analyses of social conse­ quences for households with respondent match between the two waves (43% of sample) and without (57%). Across all four potential measures of social capital, results hold that recent disasters have differential social consequences, replicating the findings for droughts and hailstorms in the pooled sample (see Appendix Tables 5 and 6). One notable exception is the non-significant relationship between recent droughts and formal networks among those households with the same respondent for both waves (Appendix Table 5). However, the effect size is roughly similar to the pooled model ( 0.377 vs 0.314), replicated and significant in the sample of households with different respondents (β ¼ 0.460), and in the same direction for both subgroups.

Table 3 Multilevel logistic regression of different types of social capital on different disaster experiences between waves. (6)

(11)

Collective Efficacy

4.3. Sensitivity analyses

wave 1. Although we cannot verify whether the differential effect is statistically significant in our existing modeling approach, we can conclude that recent disaster experience shapes formal network con­ nections in wave 2 at a greater magnitude for those without formal networks in wave 1.

Social Cohesion

(10)

Social Cohesion

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. NOTE: Models control for individual respondent (age, female), household (Lagged outcome, # Persons, Highest education level, < Poverty Line, Muslim, Other Backward Caste, Scheduled Caste, and Scheduled Tribe identification), and village (% Employed [NREGA], % Hindu, % Other Backward Caste, % Scheduled Caste, % Scheduled Tribe, and whether the Jatis live together).

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001.

(5)

(9)

5. Discussion Overall, the results from this study highlight the complex social consequences for households exposed to climatic disasters [43,47,48]. Although an asset for households in recovery from disaster [2], it is less certain whether those disasters attenuate available social capital for affected communities. While some suggest that social capital improves after a disaster [24,25,28,83], others find disasters to have a negative effect [26,44]. The present study contributes to this literature by finding evidence that climatic disasters exert a differential influence on house­ hold social capital, depending on the specific type of disaster, its

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. 7

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International Journal of Disaster Risk Reduction 46 (2020) 101488

Fig. 1. Predicted Probabilities of Wave 2 Formal Networks, by Disaster Experience w/in 3 yrs.

recency, and the measure of social capital. In rural India, hailstorms and droughts exert the strongest social consequences. Hailstorms are particularly striking for their deviation from the other episodic disasters in our study. There are two possible explanations for this. First, in their discussions of disasters compared to other crises, Fritz [22] and Quarantelli and Dynes [73] highlight other distinguishing characteristics between the two which can foster either peaceful consensus or conflictual dissensus within communities. Among these are the location or threat origin, and the ability to clearly define the cause, needs/priorities, and actions to address those needs. Di­ sasters, which they argue are followed by consensus, have clearly identifiable causes, needs, and solutions, and the threats which caused the disaster originate outside of the community itself. Conversely, con­ flict emerges in situations in which the threats emerge from within the community, and when the causes, needs, and solutions are not clearly identifiable or there is disagreement as to what they are [22,73,74]. The latter is consistent with findings from subsequent work studying chronic technical disasters [84]. These factors may be at play in our study, shaping the consequences for social capital. In hailstorms, while social factors may create vulnerable infrastruc­ ture (like the quality of housing), the storm itself is exogenous to the community. In other words, in addition to being sudden onset and short in duration, hailstorms could easily be interpreted as a negative event originating outside of the community. Neighbors and community leaders do not contribute to the incidence of hailstorms, and therefore hailstorms can have a positive effect on developing additional social capital. In contrast, droughts can be exacerbated by human behavior. Even if droughts initiate from natural causes such as unusually low rainfall, water resource management can both increase the severity of

the consequences and/or create diverging effects within the community. The strain and losses associated with droughts could be attributed in part or entirety to the actions of others within the community, fostering conflict, and thereby decreasing perceptions of social cohesion. Second, disasters operate across a spectrum of onset, from slow to quick. Quick-onset disasters (like hailstorms) create urgent threats to survival, but also immediate opportunities for recovery that diffuse quickly through the community. In contrast, slow-onset disasters like droughts, emerging after many days/weeks/months with little rainfall, create prolonged scarcity leading to resource competition. While both require some level of cooperation for immediate relief, slow-onset di­ sasters threaten the long-term livelihood of individuals and may trigger more individualistic behavior and competitive coping strategies. This variation in the spectrum of onset could move disasters from consensus to dissensus events, and coupled with local post-disaster recovery ca­ pabilities, can shape their potential social consequences. Moreover, our non-significant findings related to floods and cyclones might be indic­ ative of competing social consequences depending on variation in onset and duration, although we cannot test this within the current research design. The divergent findings between hailstorms and droughts also suggest that broader study of the social impacts of disasters should move beyond historical limitations to include slow-onset events like famines, epi­ demics, and droughts [3,75,76]. The historical focus on earthquakes, tornadoes, hurricanes, and floods draws from the U.S. military’s original interest in using rapid-onset disasters as a way to better anticipate human behavior in an attack [36,77,86]. Research on droughts, epi­ demics, and famines typically lay in the domain of scholars from fields such as international development, among others [75,78]. While the

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International Journal of Disaster Risk Reduction 46 (2020) 101488

study of disaster has gradually expanded to some of these events, namely Chronic Technical Disasters [CTDs] [84], the field has generally remained focused on more ‘typical’ disasters. This study suggests that these kinds of events should be included under the umbrella of disrup­ tion that disaster scholars examine, particularly for disaster recovery scholars and those with an interest in social capital. This study also demonstrates the importance of using diverse mea­ sures of social capital in analysis, particularly quantitative analyses, because of the unequal effects some social forces have across social capital’s components. Highlighted earlier, previous work has focused solely on one or two measures of social capital, whether that be trust [24–27,83], collective action [16,28], giving [43,44] or other measures. By examining multiple measures of social capital, we are able to demonstrate more nuanced social consequences, and begin to explain some of the variation that exists in the literature on the nature of the effect of disasters on social capital. In particular, this study shows that while disasters can affect social capital, they do not affect all compo­ nents of social capital equally. Droughts have generally negative con­ sequences across both cognitive (social cohesion) and structural (formal networks) social capital, while hailstorms can both support (formal networks) and reduce structural social capital (associational member­ ship). This helps to explain the diversity of findings in the disasters and social capital literature. Studies using different measures of social cap­ ital may inherently find different consequences of disasters on social capital. Finally, the use of multiple periods in this study further demonstrates that these changes are not equally apparent at all points in time after the disaster. This change in impact over time has implications for practice. As crisis managers consider implementing initiatives to improve disaster social capital (before or after a disaster has occurred) this study offers insight into when responders may expect to see those changes emerge, and when it is therefore most appropriate to evaluate or assess the success of such programs. While this study does address many gaps in the literature, there are limitations to this research. The foremost of these is the distribution of types of disaster experiences among the villages included in this study. Within our sample of villages, there are not equal numbers of commu­ nities that have experienced each of the four categories of events. That the majority of the villages in the study had the same kind of disaster experience may influence the patterns apparent in the study. Second, disaster definitions were derived from individual assessment, rather than meteorological measurements. As village leaders were surveyed regarding their village’s experience with different disaster types, there were no exact definitions provided for different disasters. This creates some difficulty in applying a consistent definition of disasters across villages, although events such as hailstorms, floods, drought, and cy­ clones are severe enough to outweigh concerns about perceptual vari­ ation in measurement. Third, our measures of social consequences were limited, using binary indicators to capture complex constructs such as cohesion and efficacy. We view our work as an initial comparative effort across disasters and social consequences, and in much need of additional research using more robust measures of social consequences. Finally, our measures of disaster occurrence do not include their individual

severity. More severe disasters can strain local communities, creating opportunities for resource competition and social conflict to emerge among neighbors, and our research design is unable to address these important areas for future research. 6. Conclusion Overall, our analyses shed light on the debate whether natural haz­ ards generate a positive effect or a negative effect on social capital. Our analysis clearly suggests that the answer is not so straightforward; ef­ fects vary by types of disasters and have a strong temporal dimension, all of which depend on the specific component of social capital under question. By employing multiple measures of social capital - social cohesion, formal networks, associational members and collective effi­ cacy - our study demonstrates that disasters’ effects vary on different measures social capital, which may explain why extant literature on disasters and social capital remains inconclusive. Considering disaster type is also important; as our analyses demonstrates, disasters can have distinct effects on different measures of social capital, which are in turn influenced by temporality. Finally, our results also suggest that over­ lapping disaster types can have meaningful interactions in the short and long term. Data availability The data used in this study came from the India Human Development Survey (Waves 1 & 2), archived at the Interuniversity Consortium for Political and Social Research. Wave 1 can be accessed at https://doi. org/10.3886/ICPSR22626.v12 and Wave 2 can be accessed at https:// doi.org/10.3886/ICPSR36151.v6. Files used were the DS1 (Individ­ ual), DS2 (household), and DS12 (Village) files. In addition, a linking file between Wave 1 and Wave 2 respondents was downloaded from https ://www.ihds.umd.edu/faq/can-i-link-ihds-ii-households-and-individu als-ihds-i-files, which requires registration. Funding source Funding for this research was provided by the National Science Foundation (#1343123), who had no involvement in the preparation and submission of this manuscript. Declarations of competing interest None. Acknowledgements The authors would like to thank Theodore Wilson for his excellent statistical advice and the National Science Foundation for their support. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Appendix Appendix Table 1 Household Panel Attrition Difference in Means/Proportions, Wave 1 Wave 1 Only

Both Waves

t-test

Variable

N

Mean / (S.E.)

N

Mean / (S.E.)

# in HH

2847

28,422

Highest HH Edu

2842

4.130 (2.145) 0.972

28,383

6.025 (3.165) 1.019

N

(0.873) Prop.

N

(0.848) Prop.

χ2

2814 2826 2818 2830 2847 2847 2847 2847 2847 2847 2847

0.516 0.597 0.713 0.586 0.494 0.377 0.158 0.117 0.385 0.200 0.131

28,273 28,304 28,317 28,332 28,422 28,422 28,422 28,422 28,422 28,422 28,422

0.453 0.529 0.683 0.596 0.500 0.331 0.223 0.096 0.405 0.229 0.105

40.909*** 47.759*** 10.703** – – 24.572*** 64.328*** 12.920*** 4.302* 12.427*** 18.252***

Social Cohesion Village Cohesion Jatis Cohesion Collective Efficacy Formal Networks Assoc. Member < Poverty Line Muslim Oth. Back. Caste Scheduled Caste Scheduled Tribe

31.236*** 2.630**

Appendix Table 2 Multilevel Logistic Regression of Social Capital on Recent Disaster Experience

Any Disaster w/in 3 yrs Respondent-Level Age Female Household-Level Wave 1 Lagged DV # Persons in HH Highest Education < Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste % Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(A1)

(A2)

(A3)

(A4)

Social Cohesion

Collective Efficacy

Formal Networks

Assoc Member

0.564* (0.244)

0.225 (0.150)

0.008 (0.203)

0.141 (0.226)

0.002 (0.002) 0.027 (0.045)

0.001 (0.002) 0.056 (0.035)

0.002 (0.002) 0.264*** (0.045)

0.003 (0.003) 0.215*** (0.049)

0.049 (0.057) 0.002 (0.012) 0.032 (0.024) 0.038 (0.094) 0.198 (0.112) 0.044 (0.092) 0.148 (0.087) 0.267** (0.099)

0.048 (0.059) 0.018 (0.010) 0.044 (0.028) 0.185 (0.137) 0.006 (0.115) 0.071 (0.085) 0.048 (0.100) 0.075 (0.106)

0.445*** (0.050) 0.087*** (0.010) 0.557*** (0.025) 0.768*** (0.063) 0.390*** (0.102) 0.173* (0.074) 0.527*** (0.065) 0.603*** (0.103)

0.492*** (0.075) 0.087*** (0.015) 0.307*** (0.042) 0.524*** (0.093) 0.260* (0.113) 0.177* (0.080) 0.294** (0.112) 0.440*** (0.111)

0.001 (0.003) 0.008* (0.004) 0.002 (0.005) 0.010* (0.004) 0.003 (0.004) 0.247 (0.267) 23,963 11,676

0.008** (0.003) 0.004 (0.003) 0.003 (0.003) 0.002 (0.003) 0.002 (0.005) 0.054 (0.144) 23,963 11,075

0.002 (0.002) 0.004 (0.003) 0.002 (0.003) 0.002 (0.005) 0.002 (0.006) 0.069 (0.188) 23,963 11,266

0.003 (0.003) 0.002 (0.004) 0.001 (0.003) 0.004 (0.004) 0.010 (0.006) 0.131 (0.260) 23,963 11,479

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001.

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International Journal of Disaster Risk Reduction 46 (2020) 101488

Appendix Table 3 Multilevel Logistic Regression of Formal Networks on Recent Disaster Experience, by Previous Formal Networks

Drought w/in 3 yrs Floods w/in 3 yrs Cyclones w/in 3 yrs Hailstorms w/in 3 yrs Respondent-Level Age Female Household-Level # Persons in HH Highest Education < Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste % Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(A5)

(A6)

No Previous Formal Network (Wave 1)

Previous Formal Network (Wave 1)

0.492** (0.178) 0.060 (0.180) 0.360 (0.294) 0.724** (0.277)

0.212 (0.176) 0.058 (0.150) 0.256 (0.204) 0.698** (0.249)

0.003* (0.002) 0.273*** (0.055)

0.001 (0.003) 0.282*** (0.068)

0.090*** (0.009) 0.496*** (0.036) 0.772*** (0.079) 0.300* (0.120) 0.047 (0.118) 0.409*** (0.081) 0.670*** (0.165)

0.084*** (0.016) 0.609*** (0.038) 0.765*** (0.086) 0.562*** (0.137) 0.281*** (0.082) 0.608*** (0.097) 0.314* (0.134)

0.001 (0.003) 0.002 (0.003) 0.002 (0.003) 0.004 (0.005) 0.002 (0.006) 0.094 (0.184) 11,960 6298

0.000 (0.002) 0.003 (0.003) 0.000 (0.003) 0.003 (0.004) 0.000 (0.005) 0.093 (0.210) 12,003 5168

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. Appendix Table 4 Multilevel Logistic Regression of Cohesion on Recent Disaster Experience, by Different Measures of Cohesion

Drought w/in 3 yrs Floods w/in 3 yrs Cyclones w/in 3 yrs Hailstorms w/in 3 yrs Respondent-Level Age Female Household-Level Wave 1 Lagged DV # Persons in HH Highest Education

(A7)

(A8)

(A9)

Social Cohesion

General Cohesion

Jati Cohesion

0.563** (0.212) 0.393 (0.251) 0.009 (0.341) 0.248 (0.287)

0.581** (0.189) 0.287 (0.235) 0.052 (0.387) 0.317 (0.296)

0.424 (0.245) 0.438 (0.253) 0.097 (0.328) 0.180 (0.363)

0.002 (0.002) 0.027 (0.045)

0.002 (0.001) 0.031 (0.044)

0.003* (0.001) 0.045 (0.056)

0.048 (0.057) 0.002 (0.012) 0.032

0.064 (0.045) 0.005 (0.011) 0.052*

0.025 (0.061) 0.014 (0.015) 0.035 (continued on next page)

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Appendix Table 4 (continued )

< Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste % Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(A7)

(A8)

(A9)

Social Cohesion

General Cohesion

Jati Cohesion

(0.024) 0.039 (0.093) 0.200 (0.112) 0.045 (0.092) 0.148 (0.087) 0.268** (0.098)

(0.024) 0.024 (0.091) 0.069 (0.062) 0.065 (0.085) 0.120 (0.079) 0.202 (0.108)

(0.021) 0.076 (0.103) 0.106 (0.128) 0.034 (0.085) 0.128 (0.088) 0.258*** (0.072)

0.001 (0.003) 0.008* (0.004) 0.002 (0.005) 0.010* (0.005) 0.003 (0.004) 0.245 (0.259) 23,963 11,674

0.006 (0.004) 0.006 (0.004) 0.001 (0.005) 0.008 (0.005) 0.004 (0.005) 0.371 (0.293) 23,963 11,381

0.000 (0.003) 0.009* (0.004) 0.003 (0.005) 0.011* (0.005) 0.003 (0.004) 0.087 (0.220) 23,963 11,581

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. Appendix Table 5 Multilevel Logistic Regression of Social Capital on Recent Disaster Experience, for Households with Respondent Match

Drought w/in 3 yrs Floods w/in 3 yrs Cyclones w/in 3 yrs Hailstorms w/in 3 yrs Respondent-Level Age Female Household-Level Wave 1 Lagged DV # Persons in HH Highest Education < Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste

(A10)

(A11)

(A12)

(A13)

Social Cohesion

Collective Efficacy

Formal Networks

Assoc Member

0.653** (0.221) 0.402 (0.261) 0.016 (0.345) 0.187 (0.293)

0.291 (0.215) 0.173 (0.217) 0.106 (0.268) 0.044 (0.230)

0.314 (0.170) 0.112 (0.168) 0.396 (0.247) 0.638* (0.254)

0.281 (0.254) 0.041 (0.207) 0.192 (0.196) 0.333 (0.235)

0.004* (0.002) 0.075 (0.088)

0.002 (0.003) 0.031 (0.082)

0.003 (0.002) 0.382*** (0.063)

0.012** (0.004) 0.251** (0.093)

0.006 (0.095) 0.002 (0.019) 0.055 (0.040) 0.229 (0.127) 0.235 (0.141) 0.158 (0.133) 0.190 (0.117) 0.285 (0.167)

0.076 (0.080) 0.027 (0.016) 0.037 (0.036) 0.233 (0.141) 0.074 (0.137) 0.123 (0.106) 0.106 (0.123) 0.102 (0.195)

0.483*** (0.087) 0.059*** (0.014) 0.606*** (0.046) 0.704*** (0.069) 0.404*** (0.115) 0.254*** (0.074) 0.486*** (0.088) 0.504*** (0.134)

0.674*** (0.119) 0.044 (0.023) 0.337*** (0.052) 0.421** (0.131) 0.400** (0.128) 0.172 (0.126) 0.270 (0.159) 0.333 (0.173)

0.001 (0.003) 0.006 (0.004) 0.003

0.007** (0.003) 0.003 (0.003) 0.003

0.002 (0.003) 0.003 (0.003) 0.001

0.004* (0.002) 0.001 (0.003) 0.000 (continued on next page)

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Appendix Table 5 (continued )

% Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(A10)

(A11)

(A12)

(A13)

Social Cohesion

Collective Efficacy

Formal Networks

Assoc Member

(0.005) 0.007 (0.005) 0.005 (0.005) 0.166 (0.271) 10,269 5494

(0.003) 0.003 (0.003) 0.000 (0.006) 0.023 (0.163) 10,269 5028

(0.003) 0.004 (0.005) 0.000 (0.006) 0.009 (0.170) 10,269 5036

(0.003) 0.005 (0.003) 0.006 (0.005) 0.050 (0.241) 10,269 5328

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001. Appendix Table 6 Multilevel Logistic Regression of Social Capital on Recent Disaster Experience, for Households with Different Respondents

Drought w/in 3 yrs Floods w/in 3 yrs Cyclones w/in 3 yrs Hailstorms w/in 3 yrs Respondent-Level Age Female Household-Level Wave 1 Lagged DV # Persons in HH Highest Education < Poverty Line Muslim Other Backward Caste Scheduled Caste Scheduled Tribe Village-Level % Employed (NREGA) % Hindu % Othr Backward Caste % Scheduled Caste % Scheduled Tribe Jatis live together Observations LL

(A14)

(A15)

(A16)

(A17)

Social Cohesion

Collective Efficacy

Formal Networks

Assoc Member

0.533* (0.228) 0.300 (0.230) 0.063 (0.319) 0.246 (0.284)

0.201 (0.214) 0.202 (0.235) 0.105 (0.268) 0.107 (0.227)

0.460** (0.151) 0.047 (0.149) 0.276 (0.212) 0.697** (0.239)

0.349 (0.267) 0.131 (0.214) 0.032 (0.229) 0.278 (0.285)

0.002 (0.002) 0.047 (0.051)

0.002 (0.002) 0.063 (0.059)

0.002 (0.002) 0.186*** (0.055)

0.002 (0.003) 0.116 (0.061)

0.060 (0.073) 0.001 (0.015) 0.032 (0.030) 0.075 (0.113) 0.107 (0.117) 0.012 (0.100) 0.141 (0.118) 0.233* (0.091)

0.045 (0.052) 0.012 (0.011) 0.061 (0.037) 0.166 (0.174) 0.068 (0.172) 0.060 (0.116) 0.040 (0.132) 0.191 (0.142)

0.424*** (0.074) 0.098*** (0.013) 0.544*** (0.032) 0.799*** (0.102) 0.416*** (0.121) 0.100 (0.110) 0.552*** (0.126) 0.630*** (0.134)

0.575*** (0.095) 0.096*** (0.014) 0.312*** (0.042) 0.644*** (0.097) 0.311 (0.178) 0.165* (0.073) 0.300** (0.102) 0.529*** (0.114)

0.002 (0.003) 0.007 (0.004) 0.002 (0.005) 0.012* (0.005) 0.001 (0.004) 0.217 (0.257) 13,694 6967

0.008** (0.003) 0.004 (0.003) 0.002 (0.004) 0.002 (0.004) 0.004 (0.004) 0.119 (0.148) 13,694 6551

0.002 (0.002) 0.001 (0.002) 0.001 (0.003) 0.000 (0.004) 0.002 (0.005) 0.193 (0.196) 13,694 6628

0.003 (0.002) 0.003 (0.004) 0.001 (0.003) 0.003 (0.004) 0.011* (0.005) 0.152 (0.250) 13,694 6557

Standard errors in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001.

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