Economic resilience and childhood growth: the construction of a household economic resilience index in Indonesia

Economic resilience and childhood growth: the construction of a household economic resilience index in Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic resilience and childhood growth: the construction of a household economic resilience index in Indonesia Rayinda Putri Meliasari, Gumilang Aryo Sahadewo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909202/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the face of rising economic uncertainty, household economic resilience is a pivotal concern, particularly in developing countries. Concurrently, child stunting and cognitive impairment stand as critical developmental challenges, significantly impacting the prospects of low socioeconomic status households. This study seeks to establish a comprehensive and valid measure of household economic resilience, employing multidimensional household characteristics for index construction. Leveraging data from the 2014 Indonesian Family Life Survey (IFLS), the study forms a latent variable for household economic resilience through factor analysis. This variable encompasses indicators of economic welfare, living conditions, social protection, and financial literacy, each of which is itself a latent variable assembled from multiple constituent variables. Furthermore, we investigated the influence of household resilience on child growth, utilizing rainfall as an instrumental variable (IV). The results indicate a noteworthy decrease in stunting likelihood with an increase in the resilience index. Nevertheless, although positive, the effects on HAZ, WAZ, and WHZ did not yield statistical significance. Notably, an elevation in children’s total cognitive z-score and math cognitive z-score was observed, while encounters with economic shocks in the past five years did not yield significant results. The resilience index stands poised to aid policymakers in targeting vulnerable groups, and channeling resources, and social protection programs to those most in need. Household economic resilience welfare well-being poverty child growth Figures Figure 1 Figure 2 1. Introduction Evolving temporal dynamics fueled by climate change, natural disasters and concurrent economic disruptions have led to increased investigation into household resilience in the context of potential vulnerability. The need to address this challenge has given rise to various initiatives to establish robust metrics of economic resilience as a basic foundation for policy formulation. The application of this analytical framework has proven important in looking at the capacity of households to withstand and adapt to certain shocks, which include economic disruptions (Feng et al., 2023 ; Holling, 1973 ; Proag, 2014 ; Skondras et al., 2020 ; Spaans and Waterhout, 2017 ). Accurate measurement of resilience stands to assist policymakers in gauging the efficacy of governmental programs (Boorman et al., 2013 ; Coaffee et al., 2018 ; Kontokosta and Malik, 2018 ), as well as in identifying the precision of their targeting strategies (Garbero and Letta, 2022 ). Moreover, it facilitates the design of government initiatives that prioritize the alleviation of economic shocks for socioeconomically marginalized segments, particularly within developing nations (Jones et al., 2021 ). A number of studies have analyzed the concept of resilience from various disciplinary perspectives, such as economic, natural disaster, social, health, and institutional (Davydov et al., 2010 ; De Stefano et al., 2012 ; Ribeiro and Pena Jardim Gonçalves, 2019 ). Previous literature examining economic resilience has focused on the macro level (Feng et al., 2023 ; Spaans and Waterhout, 2017 ; Wang et al., 2022 ). Some studies then attempt to measure resilience at the household level, but these measures emphasize natural disaster shocks. A study that investigated economic resilience at the household level is Feeny ( 2016 ) with measurements based on household responses in the face of rising food and fuel prices. However, this indicates that the resilience measured is a post-shock measurement. This research contributes to addressing these gaps by developing a household-level measure of economic resilience that includes factors that can predict household preparedness in the face of shocks. Drawing on the Indonesian context, we construct an economic resilience index. As a developing nation, Indonesia accommodates a substantial populace of individuals living in poverty, numbering at 26.2 million in 2022 (BPS, 2023 ). Given their disadvantaged socioeconomic status, such households are particularly vulnerable to health-related vulnerabilities that impact various aspects of their livelihoods. Moreover, their vulnerability to adverse shocks, such as income declines, price increases of essential commodities, and natural disasters, is further compounded by limited resources. These risks often go uncompensated due to insufficient resources (Alam and Mahal, 2014 ; Frankenberg et al., 2003 ). Even for extremely poor households, resource constraints can push them into a prolonged cycle of poverty (Fields, 2003 ). In Indonesia, the agricultural sector still dominates (World Bank, 2022 ). What is unique about farming households is their dual function as producers and consumers. However, the dominance of net consumer farmers in Indonesia makes them vulnerable to shocks (McCulloch, 2008 ). This is supported by Warr and Yusuf ( 2014 ) who found that rural households are more vulnerable to falling into poverty as a result of food price shocks. The sector’s income is also strongly influenced by weather (Christian et al., 2019 ). Despite the significant presence of the informal sector and micro, small, and medium enterprises (MSMEs) in Indonesia’s labor force, a majority of MSMEs remain informal, contributing to households’ vulnerability to shocks (BPKM, 2021 ; Feeny, 2016 ). Several attempts have been made to measure the resilience index in Indonesia (Kusumastuti et al., 2014 ; Purwandari et al., 2022 ). However, the district-level analysis in these studies cannot be applied directly to households for several reasons. The large population size indicates diverse characteristics. In addition, inequality is still an issue in Indonesia, both in terms of income and infrastructure (Kusumastuti et al., 2014 ), and its consequences are only felt in specific groups. This is not accounted for in macro-level measurements, motivating more specialized resilience measurements at the household level. This study measures household resilience in Indonesia using four dimensions, namely household economic welfare, living conditions, social protection net, and financial inclusion. Each dimension is a latent variable that is measured based on indicator variables. The welfare dimension is measured by household characteristics, such as years of education, proportion of breadwinners, expenditure, number of assets, and food security. Household living conditions include the availability of basic needs such as water source, toilet, television, and floor type. The social protection net includes social assistance received by the household. Meanwhile, financial behavior consists of household knowledge of financial facilities and ownership of savings and/or stocks. The use of this dimension is driven by a series of studies in developing countries that have found a significant effect of financial literacy on welfare (Kass-Hanna et al., 2022 ) and poverty reduction (Matewos et al., 2016 ; Wang et al., 2021 ; Xu et al., 2021 ). While the resulting resilience index can be directly utilized as a policy basis, we further undertook an extended analysis to investigate the influence of resilience on child growth. This specific variable was selected due to its potential to anticipate enduring effects on household socio-economic status, a pivotal consideration when exploring developmental contexts within emerging economies. This research contributes to policy in several ways. By defining economic resilience as households’ ability to overcome shocks, the generated resilience index in Indonesia acts as a government tool to pinpoint areas for enhancing household economic resilience pre, during, and post-shocks, minimizing potential risks. Additionally, the economic resilience framework, coupled with social protection indicators, aids in precisely evaluating program targeting. In instances where low-resilience households lack program assistance, it necessitates a government review and realignment of interventions to address these vulnerable areas. Secondly, this study uniquely gauges economic resilience at the household level, distinguishing it from previous macro-level assessments (Mızrak and Çam, 2022 ; Siebeneck et al., 2015 ; Zhang et al., 2023 ). Notably, macro-level indicators hinge on household reactions to shocks. While some studies have explored household-level measurements, they often focus on natural disaster-related shocks. Third, to further analyze and test the obtained resilience index, we proceeded to examine its impact on child growth, gauged through cognitive capacity and stunting measurements. Although external risk factors experienced by children have a substantial and prolonged impact on the future (Seifan et al., 2015 ), very few studies have investigated the relationship of household resilience to child growth. This aspect of development economics was chosen because it has a sustained and intergenerational impact on the socioeconomic status of households, which is a concern in policy-making in developing countries. Existing policies that focus on this aspect tend to emphasize nutrition programs themselves (Christian et al., 2020 ; Giles and Satriawan, 2015 ; Olney et al., 2018 ) and access to health facilities as demand-side policies (Kofinti et al., 2022 ). Examining the impact of economic resilience on child growth stands to make a substantive contribution to the formulation of policies addressing poverty and developmental concerns. 2. Construction of Economic Resilience Index Previous studies have measured economic resilience to inform the adoption of the estimation model (Cumming et al., 2005 ; Mancini et al., 2012 ; Pasteur, 2011 ). As resilience is an abstract concept, some of these studies have been criticized and updated in subsequent studies. Cissé & Barrett ( 2018 ) constructed a resilience score using the interaction between the probability of nonlinear poverty dynamics and poverty traps. They base their analysis on well-being proxied by livestock ownership. Resilience is measured through the probability of households exceeding normative well-being under shocks. While their analysis is quite complex and can be used for forecasting, it only uses one characteristic of resilience expressed as a probability. An alternative method for predicting economic resilience is to utilize the latent variable framework. Economic resilience qualifies as a latent variable due to its conceptual nature, as latent variables are typically regarded as theoretical constructs that cannot be directly measured (Borsboom, 2008 ; Hermann et al., 1980 ). Economic resilience, characterized as the capacity of households to withstand and adapt to shocks, defies direct quantification. This parallels the concept of quality of life, which, though intangible, is inferred through a suite of indicators encompassing socioeconomic dimensions, health, and more. To generate latent variables, several statistical techniques can be utilized, namely factor analysis and principal component analysis (PCA). Table 1 Comparison of methodologies for building economic resilience indices C&B (Cisse & Barrett) Resilience Measurement Principal Component Analysis (PCA) Factor Analysis (FA) Objective C&B treats resilience as a dependent variable. The C&B method compares each household’s resilience score, with the minimum acceptable likelihood of achieving some normative welfare standard, such as the poverty line. PCA, a dimension reduction technique, minimizes dimensionality while preserving information in the original data. It transforms variables into orthogonal components that capture the most variance. Like PCA, FA is a dimensionality reduction technique. It uncovers latent variables (factors) underlying some indicator variables, aiding in identifying data patterns. FA serves both explanatory purposes by revealing data structure and confirmatory purposes by validating theoretical relationships between variables. Mechanism C&B utilizes OLS regressions to estimate household welfare variables’ mean and variance based on characteristics, shocks, or risk exposure. This informs the probability of a household meeting or surpassing a predefined normative standard, e.g., poverty line. PCA generates weights (eigenvectors) for indicator variables, which are then utilized to derive latent variables explaining the overall data variation. In essence, PCA yields a summary latent variable based on its indicators. FA does not predict a latent variable from observed variables; instead, it assumes the existence of latent variables among correlated indicators. The latent variable value is derived after running factor loadings. Advantages and disadvantages [+] Consider nonlinear dynamics within a first-order Markov process, aligning with the empirical literature on poverty dynamics estimation. (Barrett et al., 2006 ). [-] Analysis requires panel data ( lags ) and time series analysis. [+] PCA assigns weights to indicators without assuming an underlying latent variable structure. [-] Sensitive to outliers and missing data. [+] Unlike PCA, it doesn't force all components to account for the correlation structure. [-] Too strong correlations between indicator variables can make it difficult to identify latent factors. Data type The regression outcome is a probability so the data type is binary. However, other characteristics can be categorical or continuous. 1. Continuous data only 2. Categorical data only (MCA/multiple correspondence analysis ) 3. Combination of continuous and categorical data (PCAmix) 1. Continuous data only 2. Binary data only (tetrachoric correlation) 3. Ordinal data only (polychoric correlation) 4. Combination of continuous and categorical data (FAMD/factorial analysis of mixed data) Sources (Cissé and Barrett, 2018 ) (Upton et al., 2022 ) (Bro and Smilde, 2014 ) (Jolliffe and Cadima, 2016 ) (Kim and Mueller, 1978 ) (Bekele et al., 2022 ) Factor analysis and PCA are two statistical techniques that aim to reduce dimensions. Both techniques use several indicator variables to generate a latent variable, which in this case is economic resilience. Therefore, unlike Cissé & Barrett ( 2018 ) who define resilience as a change in normative well-being or poverty, factor analysis and PCA allow to predict resilience based on other factors that are important to include. While the goals and steps of factor analysis and PCA are similar, they have fundamental differences. PCA creates new variables that are not correlated with the indicator variables. In factor analysis, latent variables, or factors, already exist in each of the correlated indicator variables and are then extracted as new variables. Table 1 shows a more detailed comparison between the three methods. Given that PCA generates latent variables by emphasizing the most prominent variation in the dataset, it follows that the selection of indicator variables need not conform to a predetermined pattern or rationale. PCA “forces” all components to explain the correlation structure of the indicators (Bekele et al., 2022 ). Meanwhile, factor analysis produces latent variables that are more “realistic” because they are “drawn” from the relationships between indicator variables. This study utilizes a two-stage factor analysis to generate an economic resilience index. In the first stage, we calculated the latent variable value of each sub-indicator. Then, factor analysis in the second stage will produce an economic resilience index. 3. Conceptual Framework: Indicators of Household Economic Resilience Being a developing country, Indonesia often experiences a lower socio-economic status among its population, rendering them more susceptible to unforeseen disruptions. The prevalence of rural areas compounds challenges for households in the face of shocks, stemming from uneven development in small villages. With a predominantly agricultural and informal sector (Alatas and Newhouse, 2010 ), vulnerability persists due to income fluctuations and external influences like weather. Assessing household resilience necessitates considering indicators beyond socioeconomic status, encapsulated in the concept of “resilience.” The term resilience was first analyzed by Holling ( 1973 ) who explored the resilience of natural dynamics. He defined resilience as the persistence of a perturbed entity to maintain its position within its environment. Various studies then measured resilience, from the level of individuals, households, villages, and districts, to certain institutions. The resilience framework is also not limited to the field of economic policy but can extend to various aspects as long as it significantly disrupts the subject’s activities (Melketo et al., 2021 ). Although many resilience measurements have been made, defining resilience and putting it into a quantitative measurement is still ambiguous. This is because of the difficulty in accurately assessing and describing the true essence of resilience (Scherzer et al., 2019 ). Despite resilience’s abstract nature, the inevitability of various shocks, such as natural disasters, economic downturns, civil conflicts, and health crises, necessitates a tool for assessing survival capability in these situations. Resilience measurement can be the foundation of government policy to identify program targets to increase household resilience capacity. The results of this analysis can then be used to minimize the risk of shocks that may arise, especially for households with low socioeconomic status, and prevent them from falling into poverty. This research constructs an Indonesian resilience index based on four dimensions. The first, household economic well-being, gauges satisfaction with economic aspects affecting resilience. Economic well-being correlates directly with the ability to withstand economic shock (Mahmud and Riley, 2021 ). To comprehensively measure economic resilience, household farm assets, alongside traditional indicators like expenditure and assets, are vital, particularly in an agrarian country like Indonesia (Ansah et al., 2022 ; Peng et al., 2022). Additionally, the index incorporates food security (Vaitla et al., 2020 ) and the education level of the household head (Ramilan et al., 2022 ). In assessing economic resilience, living conditions, closely tied to socioeconomic status, serve as an additional dimension. Inadequate access to basic amenities elevates the risk of economic shocks significantly (Wu et al., 2021 ). The presence of sufficient facilities also influences household preparedness for economic shocks (ADB, 2022 ). Social protection, alongside basic amenities, is crucial for determining households’ coping capacity. In Indonesia, social safety net policies focus on poverty reduction (Hadna and Kartika, 2017 ; Laurens and Putra, 2020 ; McCulloch, 2008 ). Including governmental initiatives in resilience measurement facilitates assessing program efficacy, as confirmed by studies such as Abay et al. ( 2022 ) in Ethiopia and Mujuru et al. ( 2022 ) in South Africa, demonstrating increased resilience with higher household transfers. Measuring economic resilience also needs to include financial literacy. Forms of loans such as microcredit that can be utilized by vulnerable households are an effort to provide equal access to financial facilities that then contribute to improving household micro-enterprises and alleviating poverty (Gatto and Sadik-Zada, 2022 ). A study in Kenya by Yao et al. ( 2023 ) was conducted to investigate the role of financial services on household economic resilience. Their findings showed a positive and significant relationship between financial access and resilience. These results were supported by Suri et al. ( 2021 ) who found that households with access to digital loans were significantly better able to survive under shocks. Resilience is closely related to socioeconomic status. Households with low socioeconomic status are more dependent on agriculture and government transfers. In addition, they are also more prone to falling back into poverty due to economic shocks. This indicates vulnerability for those with low socioeconomic status. A strand of studies has proven the significant effect of household vulnerability on several economic development variables such as child growth (Hadley et al., 2011 ; Khongrangjem and Marwein, 2020 ; Yuan et al., 2022 ). The question remains as to the significance of resilience in compensating for these vulnerabilities. Specifically, further exploration is needed to determine under what conditions households are considered resilient enough to compensate for the risks of low socioeconomic status. we employ child growth as an indicative dimension of vulnerability that households confront in the face of shocks. 4. Data We leveraged the Indonesian Family Life Survey (IFLS) data collected by the RAND Corporation. To date, the survey has been conducted 5 times, namely in 1993/1994, 1997/1998, 2000/2001, 2007/2008, and the last wave in 2014/2015. The data in this study is cross-section data and we used the last wave of IFLS in the analysis. we utilized information on household characteristics to form an economic resilience index, and individual characteristics to analyze child growth and cognition. Due to some missing data, we obtained 8,009 household data in the construction of the economic resilience index. This household data was then combined with individual child data. As a result, the study utilized 3,598 samples of under-fives for stunting analysis and 8,027 samples of children aged 7–14 years for cognitive ability analysis. In analyzing the relationship between economic resilience and several outcomes, we utilized rainfall as an instrument variable. To obtain rainfall data, we used precipitation data collected by Climatic Data Online (CDO) and available through the National Oceanic and Atmospheric Administration (NOAA) portal. The precipitation data is a monthly time series data with a high-resolution grid with a degree of 0.5 × 0.5 from 1980 to 2014. We used the 2014 precipitation data for analysis and integrated it with IFLS5 data by matching subdistricts based on longitude and latitude. 5. Methodology 5.1 Measurement of Household Economic Resilience Index Figure 1 displays the dimensions and indicators utilized in constructing the household economic resilience index. Thus, the resilience index for household \(h\) , \(R{E}_{h}\) , is expressed as: $$R{E}_{h}=f\left(Wellbein{g}_{h}, Livin{g}_{h}, SocialProtectio{n}_{h}, Financia{l}_{h}\right) \left(1\right)$$ Resilience is a latent variable whose value is determined by the four indicators above. Meanwhile, the value of these indicators is also determined by their sub-indicators. This research utilizes two-stage factor analysis in determining the values of these latent variables. This method assumes that the observed variables (indicators) are linear combinations of several underlying factor variables. The emphasis on factor analysis is to explain the correlation between variables. If there are a number of \(n\) variables, each of the correlated variables is the weighted sum of a factor (or more, as long as \(n<r\) , where \(r\) is the number of factors) and the remainder is error. The resulting latent factors infer the intercorrelation between variables (Chiwaula et al., 2022 ). Well-being was predicted by six continuous variables. In contrast, the three remaining sub-indicators comprised binary variables, prompting the utilization of factor analysis grounded in tetrachoric correlation to analyze them. Measuring well-being using traditional factor analysis begins with standardizing the data, as different units of measurement within the dataset can exert an influence on the outcomes. we ran the Kaiser-Meyer-Olkin (KMO) test to test the suitability of the data for factor analysis and the Bartlett test to test the uniformity of the data (Hakan and Seval, 2011 ). The KMO measurement is in the range of 0 to 1. The higher the KMO number, the stronger the variable correlation, with the threshold (cutoff point) being at 0.5 (Chiwaula et al., 2022 ). After the value of each dimensional latent variable was obtained, we ran the factor analysis again to produce the economic resilience variable so that it could be analyzed again. $$R{E}_{h}={\gamma }_{h1}Wellbein{g}_{h}+{\gamma }_{h2}Livin{g}_{h}+{\gamma }_{h3}SocialProtectio{n}_{h}+{\gamma }_{h4}Financia{l}_{h} \left(2\right)$$ where \(h\) are scoring coefficients generated from factor loadings. The resilience index is then standardized using minimum-maximum formation (Haile et al., 2022 ; Smith and Frankenberger, 2018 ). This process results in values spanning from 0 to 1, wherein higher index values correspond to heightened levels of resilience. We found 3,498 missing values of agricultural asset data due to subsampling in the interview process. However, information from RAND stated that the questionnaire was only administered to farmers. With this assumption, we replaced the missing values with zero. On the other hand, missing values were also found in the variables of years of education of the household head and household assets (see Table 2 ). To deal with this problem, we used Expectation-Maximization (EM) estimation in the welfare factor analysis and resilience factor analysis. The EM method generates complete data expectations from the available data in the form of a log likelihood and finds parameters that maximize the log likelihood expectation (Do and Batzoglou, 2008 ). 5.2 Estimation Model The resilience index is not associated with any measurement (D’errico and Smith, 2020 ). In addition to predicting the resilience index, this study aims to examine the relationship between resilience and child growth. The econometric model used in the analysis is as follows: $${y}_{ihv}={\beta }_{0}+{\beta }_{1}R{E}_{hv}+{\beta }_{2}{{\rm X}}_{1ihv}^{{\prime }}+{\beta }_{3}{{\rm X}}_{2hv}^{{\prime }}+{\beta }_{4}{{\rm X}}_{3v}^{{\prime }}+{\epsilon }_{ihv} \left(3\right)$$ where \({y}_{ihv}\) is the outcome variable to be tracked for individual we in household h in village v , which in this context includes a number of dependent variables measuring child nutritional status, namely HAZ, WAZ, WHZ; as well as several outcome variables related to child cognitive ability, namely total raw cognitive score, math cognitive score, nonverbal cognitive score, total cognitive z-score, math cognitive z-score, and nonverbal cognitive z-score. \({\beta }_{1}\) is the estimated parameter. The vector \({{\rm X}}_{1ihv}^{{\prime }}\) includes child characteristics, \({{\rm X}}_{2ihv}^{{\prime }}\) includes household characteristics, and \({{\rm X}}_{3ihv}^{{\prime }}\) represents village characteristics. There are differences in the characteristics that control between the dependents of nutritional status and child cognitive ability. Meanwhile, the notation \({\epsilon }_{ihv}\) represents the error. On the other hand, to investigate the effect on the probability of a child being stunted \((HAZ<-2\) ) and severely stunted \((HAZ<-3),\) we leveraged the following probit model, $$P\left(Stunte{d}_{ihv}=1\right)=\varphi ({\beta }_{0}+{\beta }_{1}R{E}_{hv}+{\beta }_{2}{{\rm X}}_{1ihv}^{{\prime }}+{\beta }_{3}{{\rm X}}_{2hv}^{{\prime }}+{\beta }_{4}{{\rm X}}_{3v}^{{\prime }}+{\epsilon }_{ihv}) \left(4\right)$$ $$P\left(SevStunte{d}_{ihv}=1\right)=\varphi ({\beta }_{0}+{\beta }_{1}R{E}_{hv}+{\beta }_{2}{{\rm X}}_{1ihv}^{{\prime }}+{\beta }_{3}{{\rm X}}_{2hv}^{{\prime }}+{\beta }_{4}{{\rm X}}_{3v}^{{\prime }}+{\epsilon }_{ihv}) \left(5\right)$$ Given that the resilience index is a latent variable that is predicted from several indicators, resilience is an endogenous variable (Smith and Frankenberger, 2018 ). There is a potential reverse causality, where child malnutrition can also affect household resilience. Poor nutrition in children makes them vulnerable to illness. As a result, the household’s health expenditure increases and this will affect its resilience. In addition, some factors can affect household resilience and children’s nutritional status simultaneously. Examples include parents’ knowledge and attitudes, and the connections they may have (d’Errico and Pietrelli, 2017 ). Endogeneity issues arise because the resilience index must have a relationship with the household characteristics included in the regression model. Endogeneity will result in biased parameter estimates on the variables to be measured. To accommodate this problem, we utilized instrument variables (IV). To select a good instrument, the variable should predict the endogenous variables in the model well and not have a direct impact on the dependent variables. we used rainfall as an instrument variable. Based on Le & Nguyen ( 2021 ) rainfall is precipitation standardized through $${R}_{s}=\frac{{TR}_{s}-LRA{R}_{s}}{LRS{D}_{s}} \left(6\right)$$ where \({R}_{s}\) is the rainfall anomaly in subdistrict \(s\) , \(T{R}_{s}\) is the rainfall level in subdistrict \(s\) . The long-term rainfall average ( \(LRA{R}_{s}\) ) and long-term rainfall standard deviation \(\left(LRS{D}_{s}\right)\) are the average and standard deviation of rainfall in subdistrict s over the period from 1980 to 2014. The instrument variable must meet two key assumptions. In terms of the relevance assumption, rainfall was chosen due to its significant impact on the Indonesian agricultural sector, directly affecting farmers’ income, as well as the vulnerability of the informal sector and MSMEs to income fluctuations tied to rainfall. Thus, changes in rainfall are relevant in explaining household economic resilience. The instrument variable must also fulfill the exclusion restriction. Rainfall does not directly affect children’s health and cognition. While it impacts agricultural production and income, its cascading effects on food availability, diseases like diarrhea and malaria, and children’s nutritional status form conditions illustrating household economic resilience (Randell et al., 2020 ; Kinyoki et al., 2016). 6. Results 6.1 Descriptive Statistics Table 2 Descriptive Statistics: Household resilience indicators (household-level data) Variables Mean SD Observation Year of education of the head of household 11.437 5.500 6,673 Share of workers in the household 0.701 0.265 8,009 Expenditure log 14.012 0.883 7,972 Food consumption score 60.400 17.392 7,843 Asset log 15.010 1.319 7,852 Agricultural asset log 6.804 8.420 8,009 Adequate drinking water sources 0.889 0.314 8,009 Adequate bathing water sources 0.387 0.487 8,009 Has adequate toilets 0.743 0.437 8,009 Have a television 0.935 0.246 8,009 Tile floor 0.512 0.500 8,009 Receiving PKH 0.032 0.175 8,009 Receiving BLSM 0.134 0.341 8,009 Receiving BLT 0.154 0.361 8,009 Receiving/buying Raskin 0.483 0.500 8,009 Knowing where to borrow 0.858 0.349 8,009 Knowing financial institutions 0.795 0.403 8,009 Own savings/shares 0.310 0.463 8,009 Table 2 displays the descriptive statistics of the variables to be used in constructing the household economic resilience index. Most households already have adequate drinking water sources and toilets. However, only 38.7 percent of households have an adequate bathing water source. The proportion of social assistance recipients is also quite small, especially the Family Hope Program (PKH) because there are still few recipients of this program. 6.2 Household economic resilience We utilized factor analysis as the initial stage to estimate the four resilience indicators. In Table 3 , factor loading coefficients demonstrate variable contributions to predicting each indicator, with values between − 1 and 1; with 0 indicating no contribution. We also tested the reliability of latent variables using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test. KMO assesses factor analysis feasibility, with a threshold of 0.5 for validity (Chiwaula et al., 2022 ). Thus, the results confirmed the acceptability of conducting factor analysis for each dimension. Bartlett’s test revealed significant intercorrelations in each dimension, affirming their presence. Table 3 First factor analysis Variables Factor loadings Household economic well-being Year of education of household head 0.542 Share of workers in the household 0.048 Log of household expenditure per week 0.709 Food consumption score (FCS) 0.573 Log of household assets 0.584 Log of farm asset -0.124 KMO 0.700 Bartlett’s test (Chi2) 4,595.675 Living conditions Adequate drinking water sources 0.524 Adequate restrooms 0.702 Household has a television 0.696 Types of tile flooring 0.657 KMO 0,755 Bartlett’s test (Chi2) 1,696.956 Social protection Households receiving PKH 0.653 Households receiving BLSM 0.863 Households receiving BLT 0.848 Households receiving/buying Raskin 0.722 KMO 0.808 Bartlett’s test (Chi2) 3,731.785 Financial behavior Households know where to borrow money 0.819 Households are aware of financial institutions 0.975 Households have savings/shares 0.328 KMO 0.558 Bartlett’s test (Chi2) 2,896.043 Household expenditure and assets significantly contribute to the welfare indicator, while the proportion of workers in the household shows minimal correlation with welfare, possibly due to the indication of child labor. The agricultural assets have negative factor loadings, indicating lower welfare for farming households. In addition, other indicators such as food consumption score and years of education of the household head also have considerable contributions. The three remaining sub-indicators exhibit relatively equal contributions, except for savings ownership in the financial behavior dimension, highlighting that households’ financial knowledge doesn't always determine their saving decisions. Table 4 Second factor analysis to derive economic resilience latent Resilience capacity Factor loadings Household economic well-being 0.828 Living conditions 0.555 Social protection -0.485 Financial behavior 0.271 KMO 0.648 Bartlett’s test (Chi2) 3,085.572 After the values of the four latent variables were obtained, we ran the factor analysis once again to produce the economic resilience latent variable. Table 4 shows the factor loadings of each latent dimension on economic resilience. The negative sign of the social protection dimension can be interpreted that households receiving government assistance are households that have a lower ability to survive when faced with economic shocks. The economic resilience index is then obtained by standardizing the minimum-maximum so that the value is between 0 and 1. A higher index indicates that households are more resilient to economic shocks. Table 5 and Fig. 2 show the distribution of the household economic resilience indices obtained. About 71 percent of households have resilience in the range of 0.33 to 0.67. Households with an economic resilience index of more than 0.67 accounts for 15.1 percent of the total sample. Meanwhile, 13 percent of households have a resilience index below 0.33 percent. This reveals that the majority of households in Indonesia have medium household resilience. Table 5 Distribution of household economic resilience index Distribution of economic resilience index Mean % 0–33 25.807 13.372 33–67 50.389 71.520 67–100 74.343 15.108 6.3 The Relationship between Household Economic Resilience and Child Growth The descriptive statistics of the variables included in the regression are shown in Table 6 a and 6 b. To overcome the problem of missing data, we imputed the data by creating a binary variable that identified the missing data in each control variable. The relationship between economic resilience and child growth was tested by regression using the instrument variable of rainfall. We utilized several dependent variables, namely height-for-age-zscore (HAZ), weight-for-age-zscore (WAZ), weight -for-height-zscore (WHZ), probability of being stunted , probability of being severely stunted , and cognitive score for a sample of children aged 7–14 years. Stunted is a variable that takes a value of 1 if a toddler has a stunted score ( \(HAZ<-2)\) , while severely stunted occurs when \(HAZ<-3\) . Table 6 a. Descriptive statistics for stunting analysis (0–5 years old) (1) (2) (3) Variables Mean SD Mean SD Mean SD Resilience index 51.19 15.28 48.75 14.38 51.86 15.45 Rainfall 0.55 1.03 0.48 1.01 0.57 1.03 Dependent variables HAZ -1.42 1.53 -1.54 1.44 1.55 -5.96 WAZ -0.98 1.31 -1.07 1.24 1.33 -5.52 WHZ -0.25 1.54 -0.31 1.44 1.56 -5.9 Stunted 0.35 0.48 0.38 0.49 0.48 0 Severely stunted 0.12 0.33 0.14 0.35 0.32 0 Child characteristics Gender 0.51 0.50 0.53 0.50 0.51 0.50 Child’s age (months) 30.06 17.44 30.34 17.35 29.98 17.47 Birth order 1.95 1.06 2.06 1.15 1.93 1.03 Household characteristics HH Head’s gender 0.96 0.19 0.97 0.17 0.96 0.19 Mother’s age 34.07 10.17 34.55 10.68 33.94 10.02 Father’s age 38.07 10.69 38.98 11.72 37.82 10.37 Mother’s height 151.31 5.52 150.88 5.74 151.43 5.45 Father’s height 162.92 6.21 162.46 6.42 163.06 6.14 Mother is working 0.50 0.50 0.51 0.50 0.50 0.50 Father is working 0.94 0.23 0.94 0.24 0.95 0.23 Number of toddlers 1.01 0.62 1.03 0.71 1.01 0.60 Log per capita food expenditure 2.55 1.04 2.38 1.02 2.60 1.04 Log cigarette expenditure 10.99 0.89 11.00 0.82 10.99 0.91 Mother’s education Primary school 0.26 0.44 0.28 0.45 0.26 0.44 Junior high school 0.19 0.39 0.19 0.39 0.19 0.39 Senior high school 0.29 0.45 0.29 0.45 0.29 0.45 Higher education 0.15 0.35 0.12 0.32 0.15 0.36 Father’s education Primary school 0.29 0.45 0.31 0.46 0.28 0.45 Junior high school 0.19 0.39 0.23 0.42 0.18 0.39 Senior high school 0.33 0.47 0.33 0.47 0.33 0.47 Higher education 0.15 0.36 0.10 0.30 0.17 0.38 Household size 6.21 3.31 6.72 3.69 6.07 3.18 Health insurance 0.50 0.50 0.46 0.50 0.51 0.50 Village characteristics Urban 0.60 0.49 0.53 0.50 0.61 0.49 Observations 3,693 3,693 797 797 2,896 2,896 Tabel 6b. Descriptive statistics for cognitive analysis (7–14 years old) (1) (2) (3) Variables Mean SD Mean SD Mean SD Resilience index 50.20 15.56 47.29 14.33 50.95 15.77 Rainfall 0.62 1.00 0.63 0.97 0.62 1.01 Dependent variables Cognitive score – all 67.36 19.73 66.97 19.81 67.46 19.71 Cognitive score – math 56.88 25.99 56.82 25.77 56.90 26.05 Cognitive score – nonverbal 71.72 21.83 71.20 22.13 71.86 21.75 Cognitive z-score – all 0.01 0.99 0.00 0.99 0.01 0.99 Cognitive z-score – math 0.02 0.99 0.02 0.98 0.02 0.99 Cognitive z-score – nonverbal 0.00 0.99 -0.02 0.99 0.01 0.99 Child characteristics Age (years) 10.37 2.26 10.36 2.26 10.37 2.26 Gender 0.52 0.50 0.53 0.50 0.52 0.50 Household characteristics HH head’s age 44.01 10.53 44.39 10.74 43.91 10.47 HH head’s gender 0.96 0.18 0.96 0.20 0.97 0.18 HH head is working 0.95 0.21 0.95 0.23 0.96 0.20 HH head’s education Primary school 0.39 0.49 0.41 0.49 0.38 0.49 Junior high school 0.18 0.38 0.22 0.41 0.17 0.38 Senior high school 0.31 0.46 0.29 0.45 0.31 0.46 Higher education 0.13 0.33 0.08 0.28 0.14 0.35 Number of children 1.89 1.09 1.93 1.10 1.88 1.09 Household size 6.78 3.27 7.22 3.62 6.66 3.17 Log per capita food expenditure 12.86 0.62 12.81 0.62 12.87 0.62 Log education expenditure 14.35 0.93 14.25 0.90 14.38 0.93 Log per capita income 12.68 1.10 12.35 1.07 12.77 1.09 Domiciled in Java 0.51 0.50 0.50 0.50 0.51 0.50 Village characteristics Urban 0.61 0.49 0.56 0.50 0.63 0.48 Observations 8,027 8,027 1,640 1,640 6,387 6,387 The utilization of instrument variables can overcome endogeneity problems that arise in economic resilience variables. We chose rainfall as an instrument variable because it is an exogenous variable and affects household resilience. Table 7 shows the results of the first-stage regression (complete first-stage regression results can be seen in Table A.1 and Table A.2 in the Appendix ). The strong F-statistic value signifies that rainfall serves as a robust instrumental variable, thereby substantiating the viability of proceeding with the regression analysis. Table 7 First-stage regressions Dependent: 0–5 y.o. 7–14 y.o. Economic resilience index (1) (2) Rainfall 0.297 * -0.475 *** (0.154) (0.146) Constant -123.0 *** -133.9 *** (9.375) (3.435) Control variables Yes Yes F Statistics 203.7 656.3 RMSE 9.113 9.442 Observations 3,693 8,027 Note: The regression controls for individual characteristics, household characteristics, and village characteristics. Column 1 shows the regression results for the under-five sample. Column 2 shows the regression results for the 7–14 years old sample. we include the value of the F statistic to show the power of the instrument in explaining the endogenous variables. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 In our instrumental variable analysis, the reduced-form strategy involves regressing outcome variables on instrument variables and control variables. Results in Table 8 and Table 9 indicate a positive correlation between rainfall levels and HAZ, WAZ, and WHZ indices, and a negative correlation with the probability of stunting and severe stunting. Additionally, we obtain a negative correlation between rainfall and child cognitive scores. Most models show statistically significant rainfall coefficients, affirming its validity as an instrument, implying its significant influence on outcomes through the resilience index. This strengthens the credibility of estimating the impact of resilience on outcomes without bias. Table 8 Reduced-form regression for stunting analysis (0–5 years old) OLS Probit (1) (2) (3) (4) (5) HAZ WAZ WHZ Stunted Severely stunted Rainfall 0.0443 0.0755 *** 0.0513 ** -0.016 * -0.005 (0.025) (0.021) (0.026) (0.008) (0.005) Control variables Yes Yes Yes Yes Yes Note: The regression controls for individual characteristics, household characteristics, and village characteristics. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Tabel 9. Reduced-form regression for cognitive analysis (7–14 years old) (1) (2) (3) (4) (5) (6) Cognitive score – total Cognitive score – math Cognitive score – nonverbal Cognitive z score – total Cognitive z score – math Cognitive z score - nonverbal Rainfall -0.966 *** -1.946 *** -0.558 * -0.051 *** -0.0760 *** -0.0262 * (0.278) (0.385) (0.310) (0.0149) (0.0151) (0.0149) Control variables Yes Yes Yes Yes Yes Yes Note: The regression controls for individual characteristics, household characteristics, and village characteristics. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 The ordinary least squares (OLS) results are presented in Table A.5 , Appendix , revealing a significant positive impact of household resilience on HAZ and WAZ. Probit results in Table A.7 show a significant negative effect on stunting likelihood, but endogeneity concerns may bias these findings. Table 10 presents instrumental variable estimations, indicating a positive but statistically insignificant relationship between economic resilience and HAZ and WHZ. Only WAZ shows significance, with a slight increase (0.255 standard deviations) per resilience index unit, holding other factors constant. In heterogeneity analysis, no significant resilience index effects on outcomes are found for both samples with or without shocks in the last five years. However, a negative effect on WHZ emerges for shock-exposed children, suggesting increased resilience among affected households may not effectively ensure children’s nutritional intake, leading to a decline in their nutritional status. This effect, however, is not statistically significant. Table 10 Results of estimation using instrument variables (HAZ, WAZ, WHZ) Model Independent: Economic Resilience Index (1) (2) (3) HAZ 0.165 0.505 0.029 (0.129) (0.437) (0.135) WAZ 0.255 * 0.505 0.203 (0.147) (0.468) (0.166) WHZ 0.204 -0.041 0.322 (0.163) (0.190) (0.306) Observation 3,678 795 2,892 Note: Models (1), (2), and (3) are regression analyses for the whole sample, the sample exposed to shocks in the past five years, and the sample not exposed to shocks in the past five years, respectively. For missing data, we fill in the control variables with zero and create a dummy variable to indicate missing values for each variable. All regressions include control variables. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 The IV-Probit regression results regarding the probability of a child being stunted are documented in Table 11 . We found that there is a significant decrease in the probability of stunting as a result of an increase in household economic resilience. More specifically, a one-index increase in household economic resilience significantly decreases the probability of stunting by 9.5 percentage points. Similarly, household resilience is negatively associated with severely stunted. However, this relationship is not statistically significant. There was a significant negative association between health insurance and the likelihood of stunting, indicating the ability of health insurance to compensate for the decline in children’s nutritional status. We continued the regression for heterogeneity analysis. While no significant effect was found for children who did not experience shocks, we found interesting results related to children who experienced shocks in the last five years. It was evident that a one-unit increase in the resilience index reduced the probability of being stunted by 11.3 percentage points and severely stunted by 11.6 percentage points, with high statistical significance. It should be noted that this result may be due to the sample restriction related only to those who experienced shocks. Households that experience shocks and have higher levels of resilience tend to have a lower probability of stunting their children compared to households with lower levels of resilience. It is also important to note that these results could be affected by the relatively small sample size, which in turn results in lower standard errors. Table 11 Probit results of estimation using instrument variables (stunted and severely stunted) Model Dependent: Resilience Index (1) (2) (3) Stunted \((HAZ<-2)\) -0.095 *** -0.113 *** -0.056 (0.021) (0.007) (0.091) Severely stunted \((HAZ<-3)\) -0.077 -0.116 *** 0.079 (0.051) (0.004) (0.055) Observation 3,598 775 2,823 Note: All regressions include control variables. Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Table 12 presents the regression results of economic resilience on children’s cognitive abilities. The results show that there is a strong positive effect of household economic resilience on children’s cognitive ability. Using raw scores, it was found that an increase of one index significantly increased 203.5 percentage points for total score and 409.7 percentage points for children’s math score. we also regressed the standardized cognitive scores by age and found similar results. There was a significant increase of 0.107 standard deviations in the total cognitive z-score and 0.16 standard deviations in the math z-score. Meanwhile, no significant results were found in the nonverbal scores, either from the raw or standardized scores. Different results were observed when the sample was narrowed down to those who experienced economic shocks in the last five years, as shown in column (2). In this case, we found no significant effect of resilience on children’s cognitive scores. In fact, a negative coefficient was found on the nonverbal score. In contrast, we found significant results for all outcomes in the estimation results for children who did not experience shocks, and all of them have a positive direction. More specifically, every one increase in the resilience index significantly increased the total cognitive score by 312.1 percentage points, the math cognitive score by 542.8 percentage points, and the nonverbal cognitive score by 216 percentage points. When using scores standardized by age, an increase of 0.166 standard deviations in total cognitive z-score, 0.212 standard deviations in mathematics z-score, and 0.103 standard deviations in nonverbal cognitive z-score was observed. However, significance was only reached at the 10 percent level for the nonverbal score. Table 12 Results of IV estimation of household resilience on children’s cognitive scores (7–14 years old) Model Dependent: Resilience Index (1) (2) (3) Raw score Cognitive – all 2.035 ** 0.128 3.121 ** (0.822) (0.736) (1.507) Cognitive – math 4.097 *** 1.661 5.428 ** (1.476) (1.169) (2.550) Cognitive – nonverbal 1.176 -0.511 2.160 * (0.720) (0.870) (1.239) Observation 8,027 1,640 6,387 Standardized (z-score) Cognitive – all 0.107 ** 0.005 0.166 ** (0.044) (0.039) (0.080) Cognitive – math 0.160 *** 0.064 0.212 ** (0.057) (0.045) (0.100) Cognitive – nonverbal 0.055 -0.026 0.103 * (0.034) (0.042) (0.059) Observation 8,025 1,638 6,387 Note: All regressions include control variables. Standard errors in parentheses . * p < 0.10, ** p < 0.05, *** p < 0.01 7. Conclusion Constructing the economic resilience index involves various multidimensional factors crucial for developing countries like Indonesia. Utilizing multivariable factor analysis, the results of this study indicate that 71.52 percent of Indonesian households exhibit medium economic resilience with an average index of 50.4. Key contributors are economic welfare and household living conditions, reflecting socio-economic status. Social protection negatively influences resilience, suggesting lower resilience in households receiving assistance. Meanwhile, the financial behavior indicator has a small contribution. This indicates the need to strengthen this aspect to improve the economic resilience of Indonesian households. The study also identifies a negative contribution of the agricultural assets, underscoring the potential benefits of empowering farmers in the country. The estimation results using instrumental variables did not show a significant effect of household resilience on child growth outcomes such as HAZ and WHZ. However, we found a significant impact on increasing WAZ and decreasing the likelihood of stunting. This means that while there is no significant difference in HAZ between households with low and high economic resilience, there are more cases of stunting in households with lower resilience. On the other hand, it should be noted that WAZ and WHZ may not be good indicators of child growth as they are sensitive to short-term changes. For example, a child who is sick at the time of the interview may lose weight at that time and gain it back after recovery. Furthermore, we found a significant negative effect on the probability of stunting and severely stunted children exposed to economic shocks in the last five years. This study revealed evidence that increased economic resilience in the household significantly improved children’s cognitive scores. The study findings showed a significant increase in children’s overall cognitive scores as well as cognitive scores in math. In addition, we also found that this increase applies to different age groups of children when cognitive scores are normalized by age. On the other hand, we found no significant effect on the group of children who were exposed to economic shocks in the last five years. This is logical because economic shocks can affect children in terms of physical and mental health. This result is also in line with the framework that more resilient households have higher levels of expenditure, assets and food consumption scores. In addition, more resilient households also tend to have more adequate facilities and household heads with higher levels of education. Household financial behavior also plays a role in increasing household resilience. As such, there is evidence to suggest that children raised in more resilient households have a higher chance of receiving a better education from their parents, either through direct teaching or through investments made by parents in the child’s education. 8. Limitation This research seeks to define economic resilience as a condition influenced by a multidimensional set of household factors, and uses this framework to create a resilience index. In addition to including factors that tend to be consistent such as assets and living conditions, we also included variables that are dynamic in nature, which include expenditure, food consumption score, and savings holdings. While this provides a comprehensive and holistic assessment, it has the consequence that the usefulness of the index in this study is limited to a specific time. There is a difficult interpretation of the index when it is estimated in a regression model. For example, in IV regression model to explore the effect of the index on cognitive scores, we can interpret that a one index increase is associated with a 203.5 percentage point increase in total cognitive scores. But what measure explains this 1 index increase? In other words, to what extent does an improvement in household living standards increase 1 index of resilience and its association with child cognition? What is clear is that more resilient households are associated with improved child cognitive ability. The interpretation of resilience remains ambiguous. This is a consequence of the multidimensional factors involved in measuring resilience. Nonetheless, measuring resilience will be helpful in identifying policy directions to increase households’ resilience under shocks so as to prevent them from falling into poverty. On the financial behavior indicator, we only managed to include three relevant variables due to data limitations. It is important to include other variables that are more representative of household financial behavior, such as household budgeting habits, understanding of retirement plans, and knowledge of bank accounts such as loan deposits and credit cards, as well as understanding of investment risks. We highlighted the importance of inclusion of additional variables to gain a more holistic understanding of financial behavior. In future research, efforts to broaden the scope of variables will provide richer and deeper insights in understanding the factors that influence financial behavior which then contribute to resilience. Declarations Data Availability The dataset underpinning the findings of this study is accessible via The Indonesian Family Life Survey (IFLS), conducted by RAND. It can be accessed at the following link: https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html Conflict of Interest The authors declare no competing interest. Ethical Approval Not applicable. References Abay, K.A., Abay, M.H., Berhane, G., Chamberlin, J., 2022. Social protection and resilience: The case of the productive safety net program in Ethiopia. Food Policy 112, 102367. https://doi.org/10.1016/j.foodpol.2022.102367 ADB, 2022. 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International Journal of Disaster Risk Reduction 81, 103311. https://doi.org/10.1016/j.ijdrr.2022.103311 Mujuru, N.M., Obi, A., Mdoda, L., Mishi, S., Lesala, M.E., 2022. Investigating the contribution and effect of cash transfers to household food security of rural smallholder farmers in the Eastern Cape Province of South Africa. Cogent Soc Sci 8. https://doi.org/10.1080/23311886.2022.2147138 Olney, D.K., Leroy, J., Bliznashka, L., Ruel, M.T., 2018. PROCOMIDA, a Food-Assisted Maternal and Child Health and Nutrition Program, Reduces Child Stunting in Guatemala: A Cluster-Randomized Controlled Intervention Trial. J Nutr 148, 1493–1505. https://doi.org/10.1093/jn/nxy138 Pasteur, K., 2011. From Vulnerability to Resilience. Practical Action Publishing. Proag, V., 2014. The Concept of Vulnerability and Resilience. Procedia Economics and Finance 18, 369–376. https://doi.org/10.1016/S2212-5671(14)00952-6 Purwandari, T., Sukono, S., Hidayat, Y., Ahmad, W.M.A.W., 2022. Determining the urban economic resilience planning through ratio of original local government revenue. Decision Science Letters 11, 509–520. https://doi.org/10.5267/j.dsl.2022.5.005 Ramilan, T., Kumar, S., Haileslassie, A., Craufurd, P., Scrimgeour, F., Kattarkandi, B., Whitbread, A., 2022. Quantifying Farm Household Resilience and the Implications of Livelihood Heterogeneity in the Semi-Arid Tropics of India. Agriculture 12, 466. https://doi.org/10.3390/agriculture12040466 Randell, H., Gray, C., Grace, K., 2020. Stunted from the start: Early life weather conditions and child undernutrition in Ethiopia. Soc Sci Med 261, 113234. https://doi.org/10.1016/j.socscimed.2020.113234 Ribeiro, P.J.G., Pena Jardim Gonçalves, L.A., 2019. Urban resilience: A conceptual framework. Sustain Cities Soc 50, 101625. https://doi.org/10.1016/j.scs.2019.101625 Scherzer, S., Lujala, P., Rød, J.K., 2019. A community resilience index for Norway: An adaptation of the Baseline Resilience Indicators for Communities (BRIC). International Journal of Disaster Risk Reduction 36, 101107. https://doi.org/10.1016/j.ijdrr.2019.101107 Seifan, A., Schelke, M., Obeng-Aduasare, Y., Isaacson, R., 2015. Early Life Epidemiology of Alzheimer’s Disease - A Critical Review. Neuroepidemiology 45, 237–254. https://doi.org/10.1159/000439568 Siebeneck, L., Arlikatti, S., Andrew, S.A., 2015. Using provincial baseline indicators to model geographic variations of disaster resilience in Thailand. Natural Hazards 79, 955–975. https://doi.org/10.1007/s11069-015-1886-4 Skondras, N.A., Tsesmelis, D.E., Vasilakou, C.G., Karavitis, C.A., 2020. Resilience–Vulnerability Analysis: A Decision-Making Framework for Systems Assessment. Sustainability 12, 9306. https://doi.org/10.3390/su12229306 Smith, L.C., Frankenberger, T.R., 2018. Does Resilience Capacity Reduce the Negative Impact of Shocks on Household Food Security? Evidence from the 2014 Floods in Northern Bangladesh. World Dev 102, 358–376. https://doi.org/10.1016/j.worlddev.2017.07.003 Spaans, M., Waterhout, B., 2017. Building up resilience in cities worldwide – Rotterdam as participant in the 100 Resilient Cities Programme. Cities 61, 109–116. https://doi.org/10.1016/j.cities.2016.05.011 Suri, T., Bharadwaj, P., Jack, W., 2021. Fintech and household resilience to shocks: Evidence from digital loans in Kenya. J Dev Econ 153, 102697. https://doi.org/10.1016/j.jdeveco.2021.102697 Upton, J., Constenla-Villoslada, S., Barrett, C.B., 2022. Caveat utilitor: A comparative assessment of resilience measurement approaches. J Dev Econ 157. https://doi.org/10.1016/j.jdeveco.2022.102873 Vaitla, B., Cissé, J.D., Upton, J., Tesfay, G., Abadi, N., Maxwell, D., 2020. How the choice of food security indicators affects the assessment of resilience—an example from northern Ethiopia. Food Secur 12, 137–150. https://doi.org/10.1007/s12571-019-00989-w Wang, X., Wang, L., Zhang, X., Fan, F., 2022. The spatiotemporal evolution of COVID-19 in China and its impact on urban economic resilience. China Economic Review 74, 101806. https://doi.org/10.1016/j.chieco.2022.101806 Wang, Y., Zhang, Q., Li, Q., Wang, J., Sannigrahi, S., Bilsborrow, R., Bellingrath-Kimura, S.D., Li, J., Song, C., 2021. Role of social networks in building household livelihood resilience under payments for ecosystem services programs in a poor rural community in China. J Rural Stud 86, 208–225. https://doi.org/10.1016/j.jrurstud.2021.05.017 Warr, P., Yusuf, A.A., 2014. World food prices and poverty in Indonesia. Australian Journal of Agricultural and Resource Economics 58, 1–21. https://doi.org/10.1111/1467-8489.12015 World Bank, 2022. Financial Deepening for Stronger Growth and Sustainable Recovery. Wu, X., Li, X., Lu, Y., Hout, M., 2021. Two tales of one city: Unequal vulnerability and resilience to COVID-19 by socioeconomic status in Wuhan, China. Res Soc Stratif Mobil 72, 100584. https://doi.org/10.1016/J.RSSM.2021.100584 Xu, S., Yang, Z., Tong, Z., Li, Y., 2021. KNOWLEDGE CHANGES FATE: CAN FINANCIAL LITERACY ADVANCE POVERTY REDUCTION IN RURAL HOUSEHOLDS? The Singapore Economic Review 1–36. https://doi.org/10.1142/S0217590821440057 Yao, B., Shanoyan, A., Schwab, B., Amanor-Boadu, V., 2023. The role of mobile money in household resilience: Evidence from Kenya. World Dev 165, 106198. https://doi.org/10.1016/j.worlddev.2023.106198 Yuan, B., Huang, X., Li, J., He, L., 2022. Socioeconomic disadvantages and vulnerability to the pandemic among children and youth: A macro-level investigation of American counties. Child Youth Serv Rev 136, 106429. https://doi.org/10.1016/j.childyouth.2022.106429 Zhang, X., Mao, F., Gong, Z., Hannah, D.M., Cai, Y., Wu, J., 2023. A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding. Urban Clim 48, 101402. https://doi.org/10.1016/j.uclim.2022.101402 Additional Declarations No competing interests reported. Supplementary Files IREENGsupplementarydoc.docx Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3909202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272185649,"identity":"f6fb3a13-388f-42ef-82da-6a753a8bb77c","order_by":0,"name":"Rayinda Putri Meliasari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYJCCAwxsMAYDgxyY8YAoLWwQLcZgkQSC9kC1gEBiA4jEp8W8/Yzh4YIyuzyD+70PDzDuuZM+P+zwQ6AtdnK6Ddi1yJzJMTg841xyscExdoMDDM+e5W68nWYA1JJsbHYAuxYJBqAW3jbmxA3H2IB+OXA4d+PsBJCWA4nbcGnhfwPSUg/Xkm44O/0Dfi0SYFsOw7UkyEvnELBF4lnBYZ5zx4slj6UBw/bAM8MN0jkFBxIM8PiFP3nzZ56y6jy+w8eYP3w4cEdefnb65g8fKuzkcGlhYOAwAJEJDBDyAIMBWKUBLuUgwP4AoQWUAOQb8KkeBaNgFIyCkQgAmyxpd90roH8AAAAASUVORK5CYII=","orcid":"","institution":"Gadjah Mada University","correspondingAuthor":true,"prefix":"","firstName":"Rayinda","middleName":"Putri","lastName":"Meliasari","suffix":""},{"id":272185650,"identity":"70734030-ef01-4559-b66c-3608e84c9fae","order_by":1,"name":"Gumilang Aryo Sahadewo","email":"","orcid":"","institution":"Gadjah Mada University","correspondingAuthor":false,"prefix":"","firstName":"Gumilang","middleName":"Aryo","lastName":"Sahadewo","suffix":""}],"badges":[],"createdAt":"2024-01-29 15:59:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51081365,"identity":"e024998f-49c3-4fa1-a2f4-30ae38c045a6","added_by":"auto","created_at":"2024-02-13 19:16:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruct of Economic Resilience Indicators\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909202/v1/fc07d73dcd409e7e4bdff74e.png"},{"id":51081368,"identity":"f3b1292d-d678-40f1-adc2-22ae6ad9acf8","added_by":"auto","created_at":"2024-02-13 19:16:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of household economic resilience indices\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909202/v1/ac98cb8085843d1b4ccede0c.png"},{"id":67559805,"identity":"08ca66a7-cdd4-4127-8bba-3fd995bb8cbe","added_by":"auto","created_at":"2024-10-26 23:01:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1569948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909202/v1/b5dcdfca-ada0-46c4-9148-33e60d3c16d2.pdf"},{"id":51081364,"identity":"d28094e9-964e-4899-82f7-9aaa91ac9403","added_by":"auto","created_at":"2024-02-13 19:16:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22221,"visible":true,"origin":"","legend":"","description":"","filename":"IREENGsupplementarydoc.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909202/v1/5144e2059b37c758f4ed6ede.docx"},{"id":51081366,"identity":"8fa50d72-bb27-4a9a-8217-bf69fe2e859f","added_by":"auto","created_at":"2024-02-13 19:16:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":162627,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909202/v1/bf6a1b0743739dff15010db4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Economic resilience and childhood growth: the construction of a household economic resilience index in Indonesia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEvolving temporal dynamics fueled by climate change, natural disasters and concurrent economic disruptions have led to increased investigation into household resilience in the context of potential vulnerability. The need to address this challenge has given rise to various initiatives to establish robust metrics of economic resilience as a basic foundation for policy formulation. The application of this analytical framework has proven important in looking at the capacity of households to withstand and adapt to certain shocks, which include economic disruptions (Feng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holling, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Proag, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Skondras et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spaans and Waterhout, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Accurate measurement of resilience stands to assist policymakers in gauging the efficacy of governmental programs (Boorman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Coaffee et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kontokosta and Malik, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as in identifying the precision of their targeting strategies (Garbero and Letta, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, it facilitates the design of government initiatives that prioritize the alleviation of economic shocks for socioeconomically marginalized segments, particularly within developing nations (Jones et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA number of studies have analyzed the concept of resilience from various disciplinary perspectives, such as economic, natural disaster, social, health, and institutional (Davydov et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; De Stefano et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ribeiro and Pena Jardim Gonçalves, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous literature examining economic resilience has focused on the macro level (Feng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Spaans and Waterhout, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some studies then attempt to measure resilience at the household level, but these measures emphasize natural disaster shocks. A study that investigated economic resilience at the household level is Feeny (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with measurements based on household responses in the face of rising food and fuel prices. However, this indicates that the resilience measured is a post-shock measurement. This research contributes to addressing these gaps by developing a household-level measure of economic resilience that includes factors that can predict household preparedness in the face of shocks.\u003c/p\u003e \u003cp\u003eDrawing on the Indonesian context, we construct an economic resilience index. As a developing nation, Indonesia accommodates a substantial populace of individuals living in poverty, numbering at 26.2\u0026nbsp;million in 2022 (BPS, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given their disadvantaged socioeconomic status, such households are particularly vulnerable to health-related vulnerabilities that impact various aspects of their livelihoods. Moreover, their vulnerability to adverse shocks, such as income declines, price increases of essential commodities, and natural disasters, is further compounded by limited resources. These risks often go uncompensated due to insufficient resources (Alam and Mahal, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Frankenberg et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Even for extremely poor households, resource constraints can push them into a prolonged cycle of poverty (Fields, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Indonesia, the agricultural sector still dominates (World Bank, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). What is unique about farming households is their dual function as producers and consumers. However, the dominance of net consumer farmers in Indonesia makes them vulnerable to shocks (McCulloch, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This is supported by Warr and Yusuf (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who found that rural households are more vulnerable to falling into poverty as a result of food price shocks. The sector’s income is also strongly influenced by weather (Christian et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite the significant presence of the informal sector and micro, small, and medium enterprises (MSMEs) in Indonesia’s labor force, a majority of MSMEs remain informal, contributing to households’ vulnerability to shocks (BPKM, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Feeny, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral attempts have been made to measure the resilience index in Indonesia (Kusumastuti et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Purwandari et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the district-level analysis in these studies cannot be applied directly to households for several reasons. The large population size indicates diverse characteristics. In addition, inequality is still an issue in Indonesia, both in terms of income and infrastructure (Kusumastuti et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and its consequences are only felt in specific groups. This is not accounted for in macro-level measurements, motivating more specialized resilience measurements at the household level.\u003c/p\u003e \u003cp\u003eThis study measures household resilience in Indonesia using four dimensions, namely household economic welfare, living conditions, social protection net, and financial inclusion. Each dimension is a latent variable that is measured based on indicator variables. The welfare dimension is measured by household characteristics, such as years of education, proportion of breadwinners, expenditure, number of assets, and food security. Household living conditions include the availability of basic needs such as water source, toilet, television, and floor type. The social protection net includes social assistance received by the household. Meanwhile, financial behavior consists of household knowledge of financial facilities and ownership of savings and/or stocks. The use of this dimension is driven by a series of studies in developing countries that have found a significant effect of financial literacy on welfare (Kass-Hanna et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and poverty reduction (Matewos et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While the resulting resilience index can be directly utilized as a policy basis, we further undertook an extended analysis to investigate the influence of resilience on child growth. This specific variable was selected due to its potential to anticipate enduring effects on household socio-economic status, a pivotal consideration when exploring developmental contexts within emerging economies.\u003c/p\u003e \u003cp\u003eThis research contributes to policy in several ways. By defining economic resilience as households’ ability to overcome shocks, the generated resilience index in Indonesia acts as a government tool to pinpoint areas for enhancing household economic resilience pre, during, and post-shocks, minimizing potential risks. Additionally, the economic resilience framework, coupled with social protection indicators, aids in precisely evaluating program targeting. In instances where low-resilience households lack program assistance, it necessitates a government review and realignment of interventions to address these vulnerable areas. Secondly, this study uniquely gauges economic resilience at the household level, distinguishing it from previous macro-level assessments (Mızrak and Çam, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Siebeneck et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, macro-level indicators hinge on household reactions to shocks. While some studies have explored household-level measurements, they often focus on natural disaster-related shocks.\u003c/p\u003e \u003cp\u003eThird, to further analyze and test the obtained resilience index, we proceeded to examine its impact on child growth, gauged through cognitive capacity and stunting measurements. Although external risk factors experienced by children have a substantial and prolonged impact on the future (Seifan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), very few studies have investigated the relationship of household resilience to child growth. This aspect of development economics was chosen because it has a sustained and intergenerational impact on the socioeconomic status of households, which is a concern in policy-making in developing countries. Existing policies that focus on this aspect tend to emphasize nutrition programs themselves (Christian et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giles and Satriawan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Olney et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and access to health facilities as demand-side policies (Kofinti et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Examining the impact of economic resilience on child growth stands to make a substantive contribution to the formulation of policies addressing poverty and developmental concerns.\u003c/p\u003e "},{"header":"2. Construction of Economic Resilience Index","content":"\u003cp\u003ePrevious studies have measured economic resilience to inform the adoption of the estimation model (Cumming et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mancini et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pasteur, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As resilience is an abstract concept, some of these studies have been criticized and updated in subsequent studies. Cissé \u0026amp; Barrett (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) constructed a resilience score using the interaction between the probability of nonlinear poverty dynamics and poverty traps. They base their analysis on well-being proxied by livestock ownership. Resilience is measured through the probability of households exceeding normative well-being under shocks. While their analysis is quite complex and can be used for forecasting, it only uses one characteristic of resilience expressed as a probability.\u003c/p\u003e\u003cp\u003eAn alternative method for predicting economic resilience is to utilize the latent variable framework. Economic resilience qualifies as a latent variable due to its conceptual nature, as latent variables are typically regarded as theoretical constructs that cannot be directly measured (Borsboom, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hermann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Economic resilience, characterized as the capacity of households to withstand and adapt to shocks, defies direct quantification. This parallels the concept of quality of life, which, though intangible, is inferred through a suite of indicators encompassing socioeconomic dimensions, health, and more. To generate latent variables, several statistical techniques can be utilized, namely factor analysis and principal component analysis (PCA).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of methodologies for building economic resilience indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026amp;B (Cisse \u0026amp; Barrett) Resilience Measurement\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor Analysis (FA)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026amp;B treats resilience as a dependent variable. The C\u0026amp;B method compares each household’s resilience score, with the minimum acceptable likelihood of achieving some normative welfare standard, such as the poverty line.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA, a dimension reduction technique, minimizes dimensionality while preserving information in the original data. It transforms variables into orthogonal components that capture the most variance.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLike PCA, FA is a dimensionality reduction technique. It uncovers latent variables (factors) underlying some indicator variables, aiding in identifying data patterns. FA serves both explanatory purposes by revealing data structure and confirmatory purposes by validating theoretical relationships between variables.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026amp;B utilizes OLS regressions to estimate household welfare variables’ mean and variance based on characteristics, shocks, or risk exposure. This informs the probability of a household meeting or surpassing a predefined normative standard, e.g., poverty line.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCA generates weights (eigenvectors) for indicator variables, which are then utilized to derive latent variables explaining the overall data variation. In essence, PCA yields a summary latent variable based on its indicators.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFA does not predict a latent variable from observed variables; instead, it assumes the existence of latent variables among correlated indicators. The latent variable value is derived after running factor loadings.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvantages and disadvantages\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[+] Consider nonlinear dynamics within a first-order Markov process, aligning with the empirical literature on poverty dynamics estimation. (Barrett et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[-] Analysis requires panel data (\u003cem\u003elags\u003c/em\u003e) and \u003cem\u003etime series analysis.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[+] PCA assigns weights to indicators without assuming an underlying latent variable structure. [-] Sensitive to \u003cem\u003eoutliers\u003c/em\u003e and \u003cem\u003emissing data.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[+] Unlike PCA, it doesn't force all components to account for the correlation structure.\u003c/p\u003e \u003cp\u003e[-] Too strong correlations between indicator variables can make it difficult to identify latent factors.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe regression \u003cem\u003eoutcome\u003c/em\u003e is a probability so the data type is binary. However, other characteristics can be categorical or continuous.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Continuous data only \u003c/p\u003e \u003cp\u003e2. Categorical data only (MCA/multiple \u003cem\u003ecorrespondence analysis\u003c/em\u003e) \u003c/p\u003e \u003cp\u003e3. Combination of continuous and categorical data (PCAmix)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. Continuous data only \u003c/p\u003e \u003cp\u003e2. Binary data only (tetrachoric correlation) \u003c/p\u003e \u003cp\u003e3. Ordinal data only (polychoric correlation) \u003c/p\u003e \u003cp\u003e4. Combination of continuous and categorical data (FAMD/factorial analysis of mixed data)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Cissé and Barrett, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Upton et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Bro and Smilde, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Jolliffe and Cadima, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Kim and Mueller, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1978\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(Bekele et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eFactor analysis and PCA are two statistical techniques that aim to reduce dimensions. Both techniques use several indicator variables to generate a latent variable, which in this case is economic resilience. Therefore, unlike Cissé \u0026amp; Barrett (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) who define resilience as a change in normative well-being or poverty, factor analysis and PCA allow to predict resilience based on other factors that are important to include. While the goals and steps of factor analysis and PCA are similar, they have fundamental differences. PCA creates new variables that are not correlated with the indicator variables. In factor analysis, latent variables, or factors, already exist in each of the correlated indicator variables and are then extracted as new variables.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a more detailed comparison between the three methods. Given that PCA generates latent variables by emphasizing the most prominent variation in the dataset, it follows that the selection of indicator variables need not conform to a predetermined pattern or rationale. PCA “forces” all components to explain the correlation structure of the indicators (Bekele et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, factor analysis produces latent variables that are more “realistic” because they are “drawn” from the relationships between indicator variables. This study utilizes a two-stage factor analysis to generate an economic resilience index. In the first stage, we calculated the latent variable value of each sub-indicator. Then, factor analysis in the second stage will produce an economic resilience index.\u003c/p\u003e"},{"header":"3. Conceptual Framework: Indicators of Household Economic Resilience","content":"\u003cp\u003eBeing a developing country, Indonesia often experiences a lower socio-economic status among its population, rendering them more susceptible to unforeseen disruptions. The prevalence of rural areas compounds challenges for households in the face of shocks, stemming from uneven development in small villages. With a predominantly agricultural and informal sector (Alatas and Newhouse, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), vulnerability persists due to income fluctuations and external influences like weather. Assessing household resilience necessitates considering indicators beyond socioeconomic status, encapsulated in the concept of “resilience.”\u003c/p\u003e\u003cp\u003eThe term resilience was first analyzed by Holling (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) who explored the resilience of natural dynamics. He defined resilience as the persistence of a perturbed entity to maintain its position within its environment. Various studies then measured resilience, from the level of individuals, households, villages, and districts, to certain institutions. The resilience framework is also not limited to the field of economic policy but can extend to various aspects as long as it significantly disrupts the subject’s activities (Melketo et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although many resilience measurements have been made, defining resilience and putting it into a quantitative measurement is still ambiguous. This is because of the difficulty in accurately assessing and describing the true essence of resilience (Scherzer et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite resilience’s abstract nature, the inevitability of various shocks, such as natural disasters, economic downturns, civil conflicts, and health crises, necessitates a tool for assessing survival capability in these situations. Resilience measurement can be the foundation of government policy to identify program targets to increase household resilience capacity. The results of this analysis can then be used to minimize the risk of shocks that may arise, especially for households with low socioeconomic status, and prevent them from falling into poverty.\u003c/p\u003e\u003cp\u003eThis research constructs an Indonesian resilience index based on four dimensions. The first, household economic well-being, gauges satisfaction with economic aspects affecting resilience. Economic well-being correlates directly with the ability to withstand economic shock (Mahmud and Riley, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To comprehensively measure economic resilience, household farm assets, alongside traditional indicators like expenditure and assets, are vital, particularly in an agrarian country like Indonesia (Ansah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al., 2022). Additionally, the index incorporates food security (Vaitla et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the education level of the household head (Ramilan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn assessing economic resilience, living conditions, closely tied to socioeconomic status, serve as an additional dimension. Inadequate access to basic amenities elevates the risk of economic shocks significantly (Wu et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The presence of sufficient facilities also influences household preparedness for economic shocks (ADB, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Social protection, alongside basic amenities, is crucial for determining households’ coping capacity. In Indonesia, social safety net policies focus on poverty reduction (Hadna and Kartika, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Laurens and Putra, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McCulloch, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Including governmental initiatives in resilience measurement facilitates assessing program efficacy, as confirmed by studies such as Abay et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Ethiopia and Mujuru et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in South Africa, demonstrating increased resilience with higher household transfers.\u003c/p\u003e\u003cp\u003eMeasuring economic resilience also needs to include financial literacy. Forms of loans such as microcredit that can be utilized by vulnerable households are an effort to provide equal access to financial facilities that then contribute to improving household micro-enterprises and alleviating poverty (Gatto and Sadik-Zada, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A study in Kenya by Yao et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was conducted to investigate the role of financial services on household economic resilience. Their findings showed a positive and significant relationship between financial access and resilience. These results were supported by Suri et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who found that households with access to digital loans were significantly better able to survive under shocks.\u003c/p\u003e\u003cp\u003eResilience is closely related to socioeconomic status. Households with low socioeconomic status are more dependent on agriculture and government transfers. In addition, they are also more prone to falling back into poverty due to economic shocks. This indicates vulnerability for those with low socioeconomic status. A strand of studies has proven the significant effect of household vulnerability on several economic development variables such as child growth (Hadley et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Khongrangjem and Marwein, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The question remains as to the significance of resilience in compensating for these vulnerabilities. Specifically, further exploration is needed to determine under what conditions households are considered resilient enough to compensate for the risks of low socioeconomic status. we employ child growth as an indicative dimension of vulnerability that households confront in the face of shocks.\u003c/p\u003e"},{"header":"4. Data","content":"\u003cp\u003eWe leveraged the Indonesian Family Life Survey (IFLS) data collected by the RAND Corporation. To date, the survey has been conducted 5 times, namely in 1993/1994, 1997/1998, 2000/2001, 2007/2008, and the last wave in 2014/2015. The data in this study is cross-section data and we used the last wave of IFLS in the analysis. we utilized information on household characteristics to form an economic resilience index, and individual characteristics to analyze child growth and cognition. Due to some missing data, we obtained 8,009 household data in the construction of the economic resilience index. This household data was then combined with individual child data. As a result, the study utilized 3,598 samples of under-fives for stunting analysis and 8,027 samples of children aged 7–14 years for cognitive ability analysis.\u003c/p\u003e\u003cp\u003eIn analyzing the relationship between economic resilience and several outcomes, we utilized rainfall as an instrument variable. To obtain rainfall data, we used precipitation data collected by Climatic Data Online (CDO) and available through the National Oceanic and Atmospheric Administration (NOAA) portal. The precipitation data is a monthly time series data with a high-resolution grid with a degree of 0.5 × 0.5 from 1980 to 2014. We used the 2014 precipitation data for analysis and integrated it with IFLS5 data by matching subdistricts based on longitude and latitude.\u003c/p\u003e"},{"header":"5. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Measurement of Household Economic Resilience Index\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the dimensions and indicators utilized in constructing the household economic resilience index. Thus, the resilience index for household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(R{E}_{h}\\)\u003c/span\u003e\u003c/span\u003e, is expressed as:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$R{E}_{h}=f\\left(Wellbein{g}_{h}, Livin{g}_{h}, SocialProtectio{n}_{h}, Financia{l}_{h}\\right) \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eResilience is a latent variable whose value is determined by the four indicators above. Meanwhile, the value of these indicators is also determined by their sub-indicators. This research utilizes two-stage factor analysis in determining the values of these latent variables. This method assumes that the observed variables (indicators) are linear combinations of several underlying factor variables. The emphasis on factor analysis is to explain the correlation between variables. If there are a number of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e variables, each of the correlated variables is the weighted sum of a factor (or more, as long as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\u0026lt;r\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r\\)\u003c/span\u003e\u003c/span\u003e is the number of factors) and the remainder is error. The resulting latent factors infer the intercorrelation between variables (Chiwaula et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWell-being was predicted by six continuous variables. In contrast, the three remaining sub-indicators comprised binary variables, prompting the utilization of factor analysis grounded in tetrachoric correlation to analyze them. Measuring well-being using traditional factor analysis begins with standardizing the data, as different units of measurement within the dataset can exert an influence on the outcomes. we ran the Kaiser-Meyer-Olkin (KMO) test to test the suitability of the data for factor analysis and the Bartlett test to test the uniformity of the data (Hakan and Seval, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The KMO measurement is in the range of 0 to 1. The higher the KMO number, the stronger the variable correlation, with the threshold (cutoff point) being at 0.5 (Chiwaula et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After the value of each dimensional latent variable was obtained, we ran the factor analysis again to produce the economic resilience variable so that it could be analyzed again.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$R{E}_{h}={\\gamma }_{h1}Wellbein{g}_{h}+{\\gamma }_{h2}Livin{g}_{h}+{\\gamma }_{h3}SocialProtectio{n}_{h}+{\\gamma }_{h4}Financia{l}_{h} \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h\\)\u003c/span\u003e\u003c/span\u003e are scoring coefficients generated from factor loadings. The resilience index is then standardized using minimum-maximum formation (Haile et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith and Frankenberger, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This process results in values spanning from 0 to 1, wherein higher index values correspond to heightened levels of resilience.\u003c/p\u003e \u003cp\u003eWe found 3,498 missing values of agricultural asset data due to subsampling in the interview process. However, information from RAND stated that the questionnaire was only administered to farmers. With this assumption, we replaced the missing values with zero. On the other hand, missing values were also found in the variables of years of education of the household head and household assets (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To deal with this problem, we used Expectation-Maximization (EM) estimation in the welfare factor analysis and resilience factor analysis. The EM method generates complete data expectations from the available data in the form of a log likelihood and finds parameters that maximize the log likelihood expectation (Do and Batzoglou, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Estimation Model\u003c/h2\u003e \u003cp\u003eThe resilience index is not associated with any measurement (D’errico and Smith, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to predicting the resilience index, this study aims to examine the relationship between resilience and child growth. The econometric model used in the analysis is as follows:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${y}_{ihv}={\\beta }_{0}+{\\beta }_{1}R{E}_{hv}+{\\beta }_{2}{{\\rm X}}_{1ihv}^{{\\prime }}+{\\beta }_{3}{{\\rm X}}_{2hv}^{{\\prime }}+{\\beta }_{4}{{\\rm X}}_{3v}^{{\\prime }}+{\\epsilon }_{ihv} \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{ihv}\\)\u003c/span\u003e\u003c/span\u003e is the outcome variable to be tracked for individual we in household \u003cem\u003eh\u003c/em\u003e in village \u003cem\u003ev\u003c/em\u003e, which in this context includes a number of dependent variables measuring child nutritional status, namely HAZ, WAZ, WHZ; as well as several outcome variables related to child cognitive ability, namely total raw cognitive score, math cognitive score, nonverbal cognitive score, total cognitive z-score, math cognitive z-score, and nonverbal cognitive z-score. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e is the estimated parameter. The vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rm X}}_{1ihv}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e includes child characteristics, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rm X}}_{2ihv}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e includes household characteristics, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rm X}}_{3ihv}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e represents village characteristics. There are differences in the characteristics that control between the dependents of nutritional status and child cognitive ability. Meanwhile, the notation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ihv}\\)\u003c/span\u003e\u003c/span\u003e represents the error.\u003c/p\u003e \u003cp\u003eOn the other hand, to investigate the effect on the probability of a child being stunted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((HAZ\u0026lt;-2\\)\u003c/span\u003e\u003c/span\u003e) and severely stunted \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((HAZ\u0026lt;-3),\\)\u003c/span\u003e\u003c/span\u003e we leveraged the following probit model,\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$P\\left(Stunte{d}_{ihv}=1\\right)=\\varphi ({\\beta }_{0}+{\\beta }_{1}R{E}_{hv}+{\\beta }_{2}{{\\rm X}}_{1ihv}^{{\\prime }}+{\\beta }_{3}{{\\rm X}}_{2hv}^{{\\prime }}+{\\beta }_{4}{{\\rm X}}_{3v}^{{\\prime }}+{\\epsilon }_{ihv}) \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$P\\left(SevStunte{d}_{ihv}=1\\right)=\\varphi ({\\beta }_{0}+{\\beta }_{1}R{E}_{hv}+{\\beta }_{2}{{\\rm X}}_{1ihv}^{{\\prime }}+{\\beta }_{3}{{\\rm X}}_{2hv}^{{\\prime }}+{\\beta }_{4}{{\\rm X}}_{3v}^{{\\prime }}+{\\epsilon }_{ihv}) \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eGiven that the resilience index is a latent variable that is predicted from several indicators, resilience is an endogenous variable (Smith and Frankenberger, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There is a potential reverse causality, where child malnutrition can also affect household resilience. Poor nutrition in children makes them vulnerable to illness. As a result, the household’s health expenditure increases and this will affect its resilience. In addition, some factors can affect household resilience and children’s nutritional status simultaneously. Examples include parents’ knowledge and attitudes, and the connections they may have (d’Errico and Pietrelli, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Endogeneity issues arise because the resilience index must have a relationship with the household characteristics included in the regression model.\u003c/p\u003e \u003cp\u003eEndogeneity will result in biased parameter estimates on the variables to be measured. To accommodate this problem, we utilized instrument variables (IV). To select a good instrument, the variable should predict the endogenous variables in the model well and not have a direct impact on the dependent variables. we used rainfall as an instrument variable. Based on Le \u0026amp; Nguyen (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) rainfall is precipitation standardized through\u003c/p\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${R}_{s}=\\frac{{TR}_{s}-LRA{R}_{s}}{LRS{D}_{s}} \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{s}\\)\u003c/span\u003e\u003c/span\u003e is the rainfall anomaly in subdistrict \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(s\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T{R}_{s}\\)\u003c/span\u003e\u003c/span\u003e is the rainfall level in subdistrict \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(s\\)\u003c/span\u003e\u003c/span\u003e. The long-term rainfall average (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(LRA{R}_{s}\\)\u003c/span\u003e\u003c/span\u003e) and long-term rainfall standard deviation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(LRS{D}_{s}\\right)\\)\u003c/span\u003e\u003c/span\u003e are the average and standard deviation of rainfall in subdistrict s over the period from 1980 to 2014.\u003c/p\u003e \u003cp\u003eThe instrument variable must meet two key assumptions. In terms of the relevance assumption, rainfall was chosen due to its significant impact on the Indonesian agricultural sector, directly affecting farmers’ income, as well as the vulnerability of the informal sector and MSMEs to income fluctuations tied to rainfall. Thus, changes in rainfall are relevant in explaining household economic resilience. The instrument variable must also fulfill the exclusion restriction. Rainfall does not directly affect children’s health and cognition. While it impacts agricultural production and income, its cascading effects on food availability, diseases like diarrhea and malaria, and children’s nutritional status form conditions illustrating household economic resilience (Randell et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kinyoki et al., 2016).\u003c/p\u003e "},{"header":"6. Results","content":"\u003cp\u003e\u003c/p\u003e\u003ch2\u003e6.1 Descriptive Statistics\u003c/h2\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics: Household resilience indicators (household-level data)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of education of the head of household\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.437\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,673\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers in the household\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpenditure log\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,972\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood consumption score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.392\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,843\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsset log\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,852\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural asset log\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.804\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.420\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate drinking water sources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate bathing water sources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas adequate toilets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave a television\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTile floor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiving PKH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiving BLSM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiving BLT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiving/buying Raskin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowing where to borrow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowing financial institutions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn savings/shares\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the descriptive statistics of the variables to be used in constructing the household economic resilience index. Most households already have adequate drinking water sources and toilets. However, only 38.7 percent of households have an adequate bathing water source. The proportion of social assistance recipients is also quite small, especially the Family Hope Program (PKH) because there are still few recipients of this program.\u003c/p\u003e\u003ch2\u003e6.2 Household economic resilience\u003c/h2\u003e\u003cp\u003eWe utilized factor analysis as the initial stage to estimate the four resilience indicators. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, factor loading coefficients demonstrate variable contributions to predicting each indicator, with values between − 1 and 1; with 0 indicating no contribution. We also tested the reliability of latent variables using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test. KMO assesses factor analysis feasibility, with a threshold of 0.5 for validity (Chiwaula et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the results confirmed the acceptability of conducting factor analysis for each dimension. Bartlett’s test revealed significant intercorrelations in each dimension, affirming their presence.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFirst factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFactor loadings\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHousehold economic well-being\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of education of household head\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers in the household\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog of household expenditure per week\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood consumption score (FCS)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog of household assets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog of farm asset\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett’s test (Chi2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,595.675\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLiving conditions\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate drinking water sources\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate restrooms\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold has a television\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of tile flooring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,755\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett’s test (Chi2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,696.956\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocial protection\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds receiving PKH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds receiving BLSM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds receiving BLT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds receiving/buying Raskin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett’s test (Chi2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,731.785\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFinancial behavior\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds know where to borrow money\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds are aware of financial institutions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds have savings/shares\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett’s test (Chi2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,896.043\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eHousehold expenditure and assets significantly contribute to the welfare indicator, while the proportion of workers in the household shows minimal correlation with welfare, possibly due to the indication of child labor. The agricultural assets have negative factor loadings, indicating lower welfare for farming households. In addition, other indicators such as food consumption score and years of education of the household head also have considerable contributions. The three remaining sub-indicators exhibit relatively equal contributions, except for savings ownership in the financial behavior dimension, highlighting that households’ financial knowledge doesn't always determine their saving decisions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecond factor analysis to derive economic resilience latent\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience capacity\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFactor loadings\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold economic well-being\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving conditions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial protection\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.485\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial behavior\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartlett’s test (Chi2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,085.572\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAfter the values of the four latent variables were obtained, we ran the factor analysis once again to produce the economic resilience latent variable. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the factor loadings of each latent dimension on economic resilience. The negative sign of the social protection dimension can be interpreted that households receiving government assistance are households that have a lower ability to survive when faced with economic shocks. The economic resilience index is then obtained by standardizing the minimum-maximum so that the value is between 0 and 1. A higher index indicates that households are more resilient to economic shocks. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the distribution of the household economic resilience indices obtained. About 71 percent of households have resilience in the range of 0.33 to 0.67. Households with an economic resilience index of more than 0.67 accounts for 15.1 percent of the total sample. Meanwhile, 13 percent of households have a resilience index below 0.33 percent. This reveals that the majority of households in Indonesia have medium household resilience.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of household economic resilience index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of economic resilience index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0–33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.807\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.372\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33–67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.389\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.520\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e67–100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.343\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.108\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003e6.3 The Relationship between Household Economic Resilience and Child Growth\u003c/h2\u003e\u003cp\u003eThe descriptive statistics of the variables included in the regression are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb. To overcome the problem of missing data, we imputed the data by creating a binary variable that identified the missing data in each control variable. The relationship between economic resilience and child growth was tested by regression using the instrument variable of rainfall. We utilized several dependent variables, namely \u003cem\u003eheight-for-age-zscore\u003c/em\u003e (HAZ), \u003cem\u003eweight-for-age-zscore\u003c/em\u003e (WAZ), weight\u003cem\u003e-for-height-zscore\u003c/em\u003e (WHZ), probability of being \u003cem\u003estunted\u003c/em\u003e, probability of being \u003cem\u003eseverely stunted\u003c/em\u003e, and cognitive score for a sample of children aged 7–14 years. \u003cem\u003eStunted\u003c/em\u003e is a variable that takes a value of 1 if a toddler has a stunted score (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HAZ\u0026lt;-2)\\)\u003c/span\u003e\u003c/span\u003e, while \u003cem\u003eseverely stunted\u003c/em\u003e occurs when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(HAZ\u0026lt;-3\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Descriptive statistics for stunting analysis (0–5 years old)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.52\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunted\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverely stunted\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild’s age (months)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth order\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH Head’s gender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother’s age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather’s age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother’s height\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather’s height\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother is working\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather is working\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of toddlers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog per capita food expenditure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog cigarette expenditure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother’s education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather’s education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVillage characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e797\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e797\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,896\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,896\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003eTabel 6b. Descriptive statistics for cognitive analysis (7–14 years old)\u003c/b\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDependent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive score – all\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive score – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive score – nonverbal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive z-score – all\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive z-score – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive z-score – nonverbal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head’s age\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head’s gender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head is working\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH head’s education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog per capita food expenditure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog education expenditure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog per capita income\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomiciled in Java\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVillage characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,640\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,640\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,387\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe utilization of instrument variables can overcome endogeneity problems that arise in economic resilience variables. We chose rainfall as an instrument variable because it is an exogenous variable and affects household resilience. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results of the first-stage regression (complete first-stage regression results can be seen in Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003eA.1\u003c/span\u003e and Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003eA.2\u003c/span\u003e in the \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e). The strong F-statistic value signifies that rainfall serves as a robust instrumental variable, thereby substantiating the viability of proceeding with the regression analysis.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFirst-stage regressions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent:\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–5 y.o.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7–14 y.o.\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic resilience index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.475\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.146)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-123.0\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-133.9\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9.375)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.435)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Statistics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e656.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.113\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.442\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,027\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: The regression controls for individual characteristics, household characteristics, and village characteristics. Column 1 shows the regression results for the under-five sample. Column 2 shows the regression results for the 7–14 years old sample. we include the value of the F statistic to show the power of the instrument in explaining the endogenous variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cem\u003eStandard errors in parentheses\u003c/em\u003e \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\u003cp\u003eIn our instrumental variable analysis, the reduced-form strategy involves regressing outcome variables on instrument variables and control variables. Results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cb\u003eTable\u0026nbsp;9\u003c/b\u003e indicate a positive correlation between rainfall levels and HAZ, WAZ, and WHZ indices, and a negative correlation with the probability of stunting and severe stunting. Additionally, we obtain a negative correlation between rainfall and child cognitive scores. Most models show statistically significant rainfall coefficients, affirming its validity as an instrument, implying its significant influence on outcomes through the resilience index. This strengthens the credibility of estimating the impact of resilience on outcomes without bias.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReduced-form regression for stunting analysis (0–5 years old)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eProbit\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStunted\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSeverely stunted\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0443\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0755\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0513\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The regression controls for individual characteristics, household characteristics, and village characteristics. \u003cem\u003eStandard errors in parentheses\u003c/em\u003e \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003eTabel 9. Reduced-form regression for cognitive analysis (7–14 years old)\u003c/b\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive score – total\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive score – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive score – nonverbal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCognitive z score – total\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCognitive z score – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCognitive z score - nonverbal\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.966\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.946\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.558\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.051\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0760\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0262\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.278)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.385)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.310)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0149)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0151)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0149)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The regression controls for individual characteristics, household characteristics, and village characteristics. \u003cem\u003eStandard errors in parentheses\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe ordinary least squares (OLS) results are presented in Table \u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003eA.5\u003c/span\u003e, \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e, revealing a significant positive impact of household resilience on HAZ and WAZ. Probit results in Table \u003cspan refid=\"Tab18\" class=\"InternalRef\"\u003eA.7\u003c/span\u003e show a significant negative effect on stunting likelihood, but endogeneity concerns may bias these findings. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents instrumental variable estimations, indicating a positive but statistically insignificant relationship between economic resilience and HAZ and WHZ. Only WAZ shows significance, with a slight increase (0.255 standard deviations) per resilience index unit, holding other factors constant.\u003c/p\u003e\u003cp\u003eIn heterogeneity analysis, no significant resilience index effects on outcomes are found for both samples with or without shocks in the last five years. However, a negative effect on WHZ emerges for shock-exposed children, suggesting increased resilience among affected households may not effectively ensure children’s nutritional intake, leading to a decline in their nutritional status. This effect, however, is not statistically significant.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of estimation using instrument variables (HAZ, WAZ, WHZ)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003eIndependent:\u003c/p\u003e \u003cp\u003eEconomic Resilience Index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.129)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.437)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.135)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.255\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.468)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.166)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.190)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.306)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,678\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e795\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,892\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Models (1), (2), and (3) are regression analyses for the whole sample, the sample exposed to shocks in the past five years, and the sample not exposed to shocks in the past five years, respectively. For missing data, we fill in the control variables with zero and create a dummy variable to indicate missing values for each variable. All regressions include control variables. Standard errors in parentheses. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe IV-Probit regression results regarding the probability of a child being stunted are documented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e11\u003c/span\u003e. We found that there is a significant decrease in the probability of stunting as a result of an increase in household economic resilience. More specifically, a one-index increase in household economic resilience significantly decreases the probability of stunting by 9.5 percentage points. Similarly, household resilience is negatively associated with severely stunted. However, this relationship is not statistically significant. There was a significant negative association between health insurance and the likelihood of stunting, indicating the ability of health insurance to compensate for the decline in children’s nutritional status.\u003c/p\u003e\u003cp\u003eWe continued the regression for heterogeneity analysis. While no significant effect was found for children who did not experience shocks, we found interesting results related to children who experienced shocks in the last five years. It was evident that a one-unit increase in the resilience index reduced the probability of being stunted by 11.3 percentage points and severely stunted by 11.6 percentage points, with high statistical significance. It should be noted that this result may be due to the sample restriction related only to those who experienced shocks. Households that experience shocks and have higher levels of resilience tend to have a lower probability of stunting their children compared to households with lower levels of resilience. It is also important to note that these results could be affected by the relatively small sample size, which in turn results in lower standard errors.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProbit results of estimation using instrument variables (stunted and severely stunted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003eDependent: Resilience Index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunted\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((HAZ\u0026lt;-2)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.113\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.091)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverely stunted\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((HAZ\u0026lt;-3)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,598\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e775\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,823\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: All regressions include control variables. Standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e12\u003c/span\u003e presents the regression results of economic resilience on children’s cognitive abilities. The results show that there is a strong positive effect of household economic resilience on children’s cognitive ability. Using raw scores, it was found that an increase of one index significantly increased 203.5 percentage points for total score and 409.7 percentage points for children’s math score. we also regressed the standardized cognitive scores by age and found similar results. There was a significant increase of 0.107 standard deviations in the total cognitive z-score and 0.16 standard deviations in the math z-score. Meanwhile, no significant results were found in the nonverbal scores, either from the raw or standardized scores.\u003c/p\u003e\u003cp\u003eDifferent results were observed when the sample was narrowed down to those who experienced economic shocks in the last five years, as shown in column (2). In this case, we found no significant effect of resilience on children’s cognitive scores. In fact, a negative coefficient was found on the nonverbal score. In contrast, we found significant results for all outcomes in the estimation results for children who did not experience shocks, and all of them have a positive direction. More specifically, every one increase in the resilience index significantly increased the total cognitive score by 312.1 percentage points, the math cognitive score by 542.8 percentage points, and the nonverbal cognitive score by 216 percentage points. When using scores standardized by age, an increase of 0.166 standard deviations in total cognitive z-score, 0.212 standard deviations in mathematics z-score, and 0.103 standard deviations in nonverbal cognitive z-score was observed. However, significance was only reached at the 10 percent level for the nonverbal score.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of IV estimation of household resilience on children’s cognitive scores (7–14 years old)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003eDependent: Resilience Index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – all\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.035\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.121\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.822)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.736)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.507)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.661\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.428\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.476)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.169)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.550)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – nonverbal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.511\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.160\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.720)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.870)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.239)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,640\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,387\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized (z-score)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – all\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.107\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.080)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – math\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive – nonverbal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,638\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,387\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: All regressions include control variables. \u003cem\u003eStandard errors in parentheses\u003c/em\u003e. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"7.\tConclusion","content":"\u003cp\u003eConstructing the economic resilience index involves various multidimensional factors crucial for developing countries like Indonesia. Utilizing multivariable factor analysis, the results of this study indicate that 71.52 percent of Indonesian households exhibit medium economic resilience with an average index of 50.4. Key contributors are economic welfare and household living conditions, reflecting socio-economic status. Social protection negatively influences resilience, suggesting lower resilience in households receiving assistance. Meanwhile, the financial behavior indicator has a small contribution. This indicates the need to strengthen this aspect to improve the economic resilience of Indonesian households. The study also identifies a negative contribution of the agricultural assets, underscoring the potential benefits of empowering farmers in the country.\u003c/p\u003e\n\u003cp\u003eThe estimation results using instrumental variables did not show a significant effect of household resilience on child growth outcomes such as HAZ and WHZ. However, we found a significant impact on increasing WAZ and decreasing the likelihood of stunting. This means that while there is no significant difference in HAZ between households with low and high economic resilience, there are more cases of stunting in households with lower resilience. On the other hand, it should be noted that WAZ and WHZ may not be good indicators of child growth as they are sensitive to short-term changes. For example, a child who is sick at the time of the interview may lose weight at that time and gain it back after recovery. Furthermore, we found a significant negative effect on the probability of stunting and severely stunted children exposed to economic shocks in the last five years.\u003c/p\u003e\n\u003cp\u003eThis study revealed evidence that increased economic resilience in the household significantly improved children\u0026rsquo;s cognitive scores. The study findings showed a significant increase in children\u0026rsquo;s overall cognitive scores as well as cognitive scores in math. In addition, we also found that this increase applies to different age groups of children when cognitive scores are normalized by age. On the other hand, we found no significant effect on the group of children who were exposed to economic shocks in the last five years. This is logical because economic shocks can affect children in terms of physical and mental health. This result is also in line with the framework that more resilient households have higher levels of expenditure, assets and food consumption scores. In addition, more resilient households also tend to have more adequate facilities and household heads with higher levels of education. Household financial behavior also plays a role in increasing household resilience. As such, there is evidence to suggest that children raised in more resilient households have a higher chance of receiving a better education from their parents, either through direct teaching or through investments made by parents in the child\u0026rsquo;s education.\u003c/p\u003e"},{"header":"8. Limitation","content":"\u003cp\u003eThis research seeks to define economic resilience as a condition influenced by a multidimensional set of household factors, and uses this framework to create a resilience index. In addition to including factors that tend to be consistent such as assets and living conditions, we also included variables that are dynamic in nature, which include expenditure, food consumption score, and savings holdings. While this provides a comprehensive and holistic assessment, it has the consequence that the usefulness of the index in this study is limited to a specific time.\u003c/p\u003e \u003cp\u003eThere is a difficult interpretation of the index when it is estimated in a regression model. For example, in IV regression model to explore the effect of the index on cognitive scores, we can interpret that a one index increase is associated with a 203.5 percentage point increase in total cognitive scores. But what measure explains this 1 index increase? In other words, to what extent does an improvement in household living standards increase 1 index of resilience and its association with child cognition? What is clear is that more resilient households are associated with improved child cognitive ability. The interpretation of resilience remains ambiguous. This is a consequence of the multidimensional factors involved in measuring resilience. Nonetheless, measuring resilience will be helpful in identifying policy directions to increase households\u0026rsquo; resilience under shocks so as to prevent them from falling into poverty.\u003c/p\u003e \u003cp\u003eOn the financial behavior indicator, we only managed to include three relevant variables due to data limitations. It is important to include other variables that are more representative of household financial behavior, such as household budgeting habits, understanding of retirement plans, and knowledge of bank accounts such as loan deposits and credit cards, as well as understanding of investment risks. We highlighted the importance of inclusion of additional variables to gain a more holistic understanding of financial behavior. In future research, efforts to broaden the scope of variables will provide richer and deeper insights in understanding the factors that influence financial behavior which then contribute to resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset underpinning the findings of this study is accessible via The Indonesian Family Life Survey (IFLS), conducted by RAND. It can be accessed at the following link: https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbay, K.A., Abay, M.H., Berhane, G., Chamberlin, J., 2022. Social protection and resilience: The case of the productive safety net program in Ethiopia. Food Policy 112, 102367. https://doi.org/10.1016/j.foodpol.2022.102367\u003c/li\u003e\n\u003cli\u003eADB, 2022. BUILDING RESILIENCE OF THE URBAN POOR IN INDONESIA.\u003c/li\u003e\n\u003cli\u003eAlam, K., Mahal, A., 2014. Economic impacts of health shocks on households in low and middle income countries: a review of the literature. 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A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding. Urban Clim 48, 101402. https://doi.org/10.1016/j.uclim.2022.101402\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Household economic resilience, welfare, well-being, poverty, child growth","lastPublishedDoi":"10.21203/rs.3.rs-3909202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the face of rising economic uncertainty, household economic resilience is a pivotal concern, particularly in developing countries. Concurrently, child stunting and cognitive impairment stand as critical developmental challenges, significantly impacting the prospects of low socioeconomic status households. This study seeks to establish a comprehensive and valid measure of household economic resilience, employing multidimensional household characteristics for index construction. Leveraging data from the 2014 Indonesian Family Life Survey (IFLS), the study forms a latent variable for household economic resilience through factor analysis. This variable encompasses indicators of economic welfare, living conditions, social protection, and financial literacy, each of which is itself a latent variable assembled from multiple constituent variables. Furthermore, we investigated the influence of household resilience on child growth, utilizing rainfall as an instrumental variable (IV). The results indicate a noteworthy decrease in stunting likelihood with an increase in the resilience index. Nevertheless, although positive, the effects on HAZ, WAZ, and WHZ did not yield statistical significance. Notably, an elevation in children\u0026rsquo;s total cognitive z-score and math cognitive z-score was observed, while encounters with economic shocks in the past five years did not yield significant results. The resilience index stands poised to aid policymakers in targeting vulnerable groups, and channeling resources, and social protection programs to those most in need.\u003c/p\u003e","manuscriptTitle":"Economic resilience and childhood growth: the construction of a household economic resilience index in Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 19:16:07","doi":"10.21203/rs.3.rs-3909202/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3fe4c227-b080-40fd-abe8-d45210dd3df8","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-26T22:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 19:16:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909202","identity":"rs-3909202","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}