Gender, Nutrition Information Acquisition, and Dietary Diversity: Empirical Evidence from Rural Households in Zimbabwe

Gender, Nutrition Information Acquisition, and Dietary Diversity: Empirical Evidence from Rural Households in Zimbabwe | 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 Gender, Nutrition Information Acquisition, and Dietary Diversity: Empirical Evidence from Rural Households in Zimbabwe Terrence Kairiza, Wanglin Ma, Lloyd Chigusiwa, Hongyun Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6606637/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 Acquisition of nutrition information is crucial for enhancing dietary diversity among rural residents, yet the potential gender differences in its impact remain underexplored. This study investigates the gender-differentiated effects of nutrition information acquisition on the dietary diversity of rural households. Specifically, we consider the nutrition information acquired from mostly women village health workers and distinguish rural households headed or dominated by women and men. Households dominated by women refer to those in which the proportion of women over 18 is more than half the number of household members. An endogenous switching regression model with a count outcome variable addresses self-selection bias from observed and unobserved factors and estimates data from a nationally rural survey in Zimbabwe. The results indicate that households headed by women are more likely to obtain nutrition information, whereas households dominated by women are less likely to acquire such information. Nutrition information acquisition significantly improves dietary diversity. From a gender perspective, the impact of acquiring nutrition information on dietary diversity is more significant for rural households headed or dominated by women compared to those headed or dominated by men. Our findings underscore the crucial role of acquiring nutrition information from mostly women village health workers in enhancing dietary diversity. Particular policy attention and support should be directed towards disseminating nutrition information to rural households, especially those headed by women or where women play a dominant role. Dietary diversity Nutrition information acquisition Gender Zimbabwe 1 Introduction Poor-quality diets, predominately comprised of starchy staple foods and devoid of fruits, vegetables, and animal-source foods, are responsible for malnutrition (Iannotti et al. 2013 ; Ma et al. 2022a ; Ecker and Pauw 2024 ). These deficiencies damage rural residents’ physical and mental development and cause serious health issues in developing countries. For example, lacking iron, folate, and vitamins B12 and A can lead to anaemia. Gender norms at variance with women’s access to productive resources, services, and inputs have rendered women, girls, and households headed by women disproportionately vulnerable to malnutrition attendant to poor quality diets (World Bank et al. 2009; Meinzen-Dick et al. 2012 ; Kairiza and Kembo 2019 ; FAO et al. 2021; Kairiza et al. 2023b ). Specifically, FAO et al. (2021) highlighted that a third of women of reproductive age were globally affected by anaemia in 2019. By 2021, 31.9% of women were moderately or severely food insecure, compared to 27.6% of men. Malnutrition in poor countries is a function of behavioral factors and information access (Mulenga et al. 2021 ; Julius and Sebastian 2022). This makes the institution of social and behavioral change towards improved diets through the provision of nutrition information an imperative (Melesse 2021 ; Ma et al. 2022a ; Cui et al. 2023 ; Kairiza et al. 2023a ; Zheng et al. 2023 ). Previous studies have linked improvements in nutrition knowledge, delivered through various platforms, with enhanced diet quality across diverse settings (Caulfield et al. 1999 ; Bhutta et al. 2013 ; Aker and Ksoll 2016 ; Sekabira and Qaim 2017 ; Hirvonen et al. 2017 ; Hoddinott et al. 2017 ; Melesse 2021 ; Ma et al. 2022a ; Bidira et al. 2022 ; Cui et al. 2023 ; Kairiza et al. 2023b ; Zheng et al. 2023 ). For example, based on a randomized evaluation in Niger, Aker and Ksoll ( 2016 ) found that households treated with access to information technology planted a more diverse basket of crops, particularly marginal cash crops grown by women. Zheng et al. ( 2023 ) found that nutrition knowledge training not only enhances dietary diversity but also improves the intake of macronutrients (proteins and fats), micronutrients (zinc), and total calorie intake. Cui et al. ( 2023 ) found that internet access or usage significantly improves the dietary quality of rural households in China. Research consistently demonstrates that the impact of nutrition information on dietary outcomes exhibits significant gender differences (Sekabira and Qaim 2017 ; Kairiza et al. 2020 ; Ma et al. 2022a ; Zheng et al. 2023 ). For example, Kairiza et al. ( 2020 ) confirmed that food fortification benefits households headed by women more in reducing stunting in children under five than those headed by men in Zimbabwe. Ma et al. ( 2022a ) noted that females are significantly more sensitive to nutrition information in changing household nutrition intake than males. Zheng et al. ( 2023 ) found that the dietary diversity of females increases with nutrition knowledge training, whereas that of males is unaffected in China. These studies generally focus on the gender differences in the relationship between rural households’ nutrition information acquisition and their dietary but neglect the information provision sources. The motivation of this paper stems from the realization that despite gender heterogeneity in the efficacy of nutrition information in inducing behavioral change towards improved diets, in gender-stratified societies, the effectiveness of nutrition information provision is predicated on the form and caliber of the interaction between agents providing, and beneficiaries acquiring, nutrition information (Zeltzer 2020 ; Beugnot and Peterlé 2020 ; Raghunathan et al. 2023 ). Beugnot and Peterlé ( 2020 ) emphasized that gender-based homophily significantly influences the adoption of sound nutrition practices. The gender identities of nutrition information agents and beneficiaries bear upon the effectiveness of nutrition information and service delivery, potentially exacerbating or reducing existing gender inequalities in food and nutrition outcomes. This study explores the relationship between gender, nutrition information acquisition, and dietary diversity using nationally representative survey data from rural households in Zimbabwe. This study aims to answer two research questions: (1) Does nutrition information acquired improve the dietary diversity of rural households? (2) Are the impacts of nutrition information on dietary diversity different between households headed or dominated by women and those headed or dominated by men? In addressing these questions, this study endeavors to make the following three contributions to the existing literature. First, we consider nutrition information acquisition from village health workers (VHWs) who are almost exclusively women. This differs from previous studies discussing nutrition information acquisition from the internet (Moorman et al. 2020 ) or peers (i.e., relatives, neighbors, and friends) (Ma et al. 2022a ). VHWs in Zimbabwe are community-based health volunteers who play a critical role in the country’s primary healthcare system. They serve as the first point of contact for healthcare at the community level, especially in rural and underserved areas. Second, we consider two types of rural households: households headed by women and households dominated by women. Among them, households headed by women refer to those whose household heads are women. Households dominated by women refer to those in which the proportion of women over 18 is more than half the number of household members. Third, we employ the endogenous switching model with a count outcome variable to estimate the impacts of nutrition information acquisition on dietary diversity by acknowledging the presence of selection bias from both observed and unobserved heterogeneities. Importantly, this model is appropriate when the outcome variable (i.e., dietary diversity) is measured as a count variable, and the treatment variable (i.e., nutrition information acquisition) is measured as a dummy. Zimbabwe poses an interesting case. The Zimbabwe government has been trying to promote social and behavioral change towards improved diets, among other health and nutrition outcomes, through the provision of health and nutrition information using mostly women VHWs known in the vernacular as “mbuya hutano” loosely translated as “matriarchy of health”. The recruitment policy of VHWs deliberately targets trainable mature women within a specific community, and in the unlikely event that such women cannot be found, men can be recruited. However, in a few other circumstances, a few men can be recruited to move around with the women VHWs if it is believed that security may be a concern. This includes when the VHWs travel a long distance in remote bushy areas within the village. VHWs deliver health assistance to rural households by conducting health promotion services ranging from maternal, neonatal, and child health, nutrition, water, sanitation, and hygiene (WASH) related information to managing common childhood illnesses. Their work significantly improves health outcomes and reduces the burden on the formal healthcare system. The rest of this paper is organized as follows: Section 2 describes the data and key variable measurement, and the estimation strategy is presented in Section 3 . Section 4 discusses the empirical results, and Section 5 concludes with implications. 2 Data and key variable measurements 2.1 Data This study uses data from a nationally representative cross-sectional survey of rural Zimbabwe conducted by the Zimbabwe Vulnerability Assessment Committee in 2023. The survey covers all 60 rural districts in Zimbabwe’s eight rural provinces. We employ a two-stage sampling procedure. First, we randomly selected 25 enumeration areas within each of the 60 rural districts. Second, we randomly chose 10 households for interviews within the 1,500 enumeration areas. Finally, we obtained 1,5000 samples in total. After data cleaning, we obtained 14,725 samples for this study. We conducted face-to-face interviews with a knowledgeable household representative at their homestead, using a structured questionnaire translated into the respondents’ native language. 2.2 Key variable measurements 2.2.1 Nutrition information acquisition Nutrition information acquisition is measured as a binary variable. We included a question to capture this status: “Did your household acquire any nutrition information from mostly women village health workers (VHWs) in the past 12 months?”. Consequently, the nutrition information acquisition variable is assigned a value of one if the household has acquired nutrition information and zero otherwise. The measurement is consistent with existing research (Le et al. 2020 ; Yang et al. 2021 ; Ma et al. 2022a ). We did not capture quantitative details about the nutrition information provided by VHWs because the details are household-specific and cannot be directly compared. Focus group discussions and key informant interviews conducted alongside the 2023 survey revealed the significant roles VHWs play in primary health care in Zimbabwe. After formal training, VHWs undertake several responsibilities, such as (1) Preventative Care: They provide infant and young child feeding counseling and other health-related advice, including encouraging mothers to take iron folic acid tablets. (2) Promotional Activities: VHWs motivate mothers to attend antenatal sessions and disseminate nutrition-related messages through WhatsApp groups. (3) Surveillance: They screen children for malnutrition and refer those in need to the nearest health institutions for treatment initiation. 2.2.2 Dietary diversity Dietary diversity, the dependent variable, is a count variable measured using dietary diversity scores. Dietary diversity scores are a common tool to assess food security and dietary quality (Zhu et al. 2024 ; Ibrahim et al. 2024 ). In the survey, respondents were asked to select the food items they had consumed in the 24 hours preceding the survey from a list of 12 options. The food groups considered are (1) Cereals, (2) Roots and tubers, (3) Vegetables, (4) Fruits, (5) Meat, poultry, and offals, (6) Eggs, (7) Fish and seafood, (8) Pulses, legumes, and nuts, (9) Milk and milk products, (10) Oil/fats, (11) Sugar/honey, and (12) Condiments. Each item was counted once if it was consumed. Consequently, the aggregated scores generate a dietary diversity variable that ranges from 0 to 12. 2.2.3 Gender-related variables This study differentiates rural households headed or dominated by women from men using two variables: women head and women dominance. The women head variable was measured by the question, “ Does a woman head your household? ”. The women dominance variable was measured by the question, “ Do women dominate your household? ”. The two variables are measured as dummies. For example, the women head variable equals one if a rural household is headed by a woman and zero otherwise. Similarly, the women dominance variable is assigned a value of one if the number of females in the household over 18 years of age exceeds half of the total household members and zero otherwise. 3 Econometric strategy 3.1 Model selection Rural households that perceive themselves as more proficient in using nutritional information may actively seek it out, or those needing nutritional advice may pursue it. Consequently, these households self-select to acquire nutritional information (Smith and Todd 2005 ). Observing and unobserved factors affect this process and lead to a self-selection bias. When selection bias only stems from observed heterogeneity, researchers have employed the propensity score matching (PSM) approach to address the selection bias issues (Chagwiza et al. 2016 ; Tran and Goto 2019 ; Zheng et al. 2021 ; Macheka et al. 2021 ). However, the PSM approach becomes ineffective when self-selection exists from unobserved characteristics (e.g., people’s innate abilities and motivations to seek nutrition information) (Smith and Todd 2005 ). Our approach must address self-selection bias from observed and unobserved factors. In addition, the count nature of our dependent variable, dietary diversity, necessitates approaches that consider variations in count-dependent variables (Hasebe 2020 ; Ma et al. 2022b ). Therefore, we employ the endogenous switching model with the count outcome (ESMC) model introduced by Hasebe ( 2020 ). Notably, the ESMC model mitigates self-selection bias and considers the count nature of the dietary diversity. In addition, we provide the results estimated by the Propensity Score Matching (PSM) model for comparison purposes. 3.2 ESMC model The ESMC model simultaneously estimates one selection equation, which determines the probability of acquiring nutrition information and two outcome equations, which determine dietary diversity for those with nutrition information acquisition (Regime 1) and those without (Regime 2) as follows: Selection equation: \(\:{Pr}\left({NIA}_{i}\left|IV,\:X\right.\right)={X}_{i}^{{\prime\:}}\gamma\:+{IV}_{i}\alpha\:+{\epsilon\:}_{i}\) (1) Regime 1: \(\:\left({NIA}_{i}=1\right):{DDS}_{1i}={X}_{i}^{{\prime\:}}{\beta\:}_{1i}+{\eta\:}_{1i}\) (2a) Regime 2: \(\:\left({NIA}_{i}=0\right):{DDS}_{0i}={X}_{i}^{{\prime\:}}{\beta\:}_{0i}+{\eta\:}_{0i}\) (2b) where \(\:{Pr}\left({NIA}_{i}\left|IV,\:X\right.\right)\) represents the probability that a rural household \(\:i\:\) acquired nutrition information. \(\:{DDS}_{1i}\) and \(\:{DDS}_{0i}\:\) refer to dietary diversity scores (DDS) for nutrition information acquisitors and non-acquisitors, respectively. \(\:{X}_{i}^{{\prime\:}}\) is a vector of control variables that affect rural households’ decisions to acquire nutrition information and dietary diversity. \(\:{IV}_{i}\) represents an instrumental variable. \(\:\lambda\:,\:\alpha\:,\:{\beta\:}_{1i}\) and \(\:\:{\beta\:}_{0i}\:\) are parameters to be estimated. \(\:{\epsilon\:}_{i},\:\:{\eta\:}_{1i},\:\) and \(\:\:{\eta\:}_{0i}\) refers to error terms. Within the maximum likelihood estimation framework of the ESMC model, Eq. (1) is estimated by a probit model, and Equations (2a) and (2b) are estimated by the Poisson regression models. For model identification, the instrumental variable \(\:{IV}_{i}\) should be included in Eq. (1) but not Equations (2a) and (2b). In this study, \(\:{IV}_{i}\) is a variable representing the distance-weighted number of neighbors who acquired nutrition information within the 10-km radius of the households. We utilized the Global Positioning System coordinates of the households to calculate the distances between household i and all the households within its 10km radius that acquired nutrition information. Following Jackson ( 2014 ) and Kondo ( 2023 ), we calculate the inverse distance weighted number of households within household i ’s 10km radius that acquired nutrition information as follows: $$\:{IV}_{i}=\sum\:_{i=1}^{n}\frac{{NIA}_{j}}{{d}_{ij}}\:\:\forall\:{NIA}_{j}=1,\:{d}_{ij}\le\:10km$$ where d ij is the Euclidian distance between the homestead of household i and its neighbour j . We take the inverse distance weight as we speculate that the further away household j is from household i , the less likely that household j influences household i ’s information acquisition decision. Theoretically, the IV is expected to influence rural households’ nutrition information acquisition because they may imitate their peers’ acquisition behavior, but neighbors’ acquisition would not directly affect rural households’ dietary diversity. The existing literature has proven the role of peer effects in influencing rural households’ information acquisition (Bandiera and Rasul 2006 ; Conley and Udry 2010 ). This argument is confirmed by a simple falsification test following Di Falco et al. ( 2011 ) and Manda et al. ( 2019 ), who argue that an IV is valid if it affects a household’s decision to acquire nutrition information but does not affect nutritional outcomes among non-acquisitors. This is confirmed in Table A1 of the Appendix . After estimating Equations (1), (2a), and (2b), we calculate the treatment effects of nutrition information acquisition on dietary diversity. Specifically, following Hasebe ( 2020 ), we estimate the average treatment effects ( ATE ) in the total population, the average treatment effect on the treated ( ATT ), and the average treatment effects on the untreated ( \(\:ATU)\) , respectively, as follows: $$\:ATE=E[{DDS}_{1}-{DDS}_{0}|X]$$ 3a $$\:ATT=E[{DDS}_{1}-{DDS}_{0}|X,\:{NIA}_{i}=1]$$ 3b $$\:ATU=E[{DDS}_{1}-{DDS}_{0}|X,\:{NIA}_{i}=0]$$ 3c This study aims to compare the gender-differentiated effects of nutrition information acquisition. Therefore, we also present results by distinguishing between households headed by women and men and those dominated by women and men. 4 Results and discussion 4.1 Descriptive results 4.1.1 Characteristics between the status of nutrition information acquisition Table 1 shows variable definitions, descriptive statistics, and mean differences between nutrition information acquisitors and non-acquisitors. Significant differences between the two groups regarding the household heads’ age, marital status, education, household size, and provincial location can be observed. Specifically, households that acquire nutritional information tend to be headed by individuals who are older, married, and living with their spouses. Additionally, these households tend to be larger than those not acquiring nutritional information. The differences in personal and household-level characteristics between nutrition information acquisitors and non-acquisitors indicate the self-selection bias issue, necessitating the ESMC model’s need to unveil the impact of nutrition information acquisition on dietary diversity. Table 1 Personal and household-level characteristics by nutrition information acquisition status Nutrition information acquisition status Differences in means [Y – N] Acquisitors [Y] Non-acquisitors [N] Variables Definitions Mean S.D Mean S.D Observations # (%) 8,930 (60.6%) 5,795 (39.4%) Gender characteristics Woman head Household head (HH) is women (Yes = 1; No = 0) 0.328 0.469 0.331 0.471 -0.004 Women dominance Household is dominated by women (Yes = 1; No = 0) 0.058 0.234 0.089 0.285 -0.031*** Age Age of HH in years 51.904 16.360 50.693 17.255 1.212*** Marital status of household head Married living together (Yes = 1; No = 0) 0.613 0.487 0.577 0.494 0.037*** Married living apart (Yes = 1; No = 0) 0.079 0.269 0.086 0.280 -0.007 Divorced/separated (Yes = 1; No = 0) 0.055 0.227 0.080 0.272 -0.026*** Widow/widower (Yes = 1; No = 0) 0.230 0.421 0.224 0.417 0.007 Single (Yes = 1; No = 0) 0.001 0.030 0.003 0.059 -0.003*** Education of household head None 0.106 0.307 0.119 0.323 -0.013** Primary level (Yes = 1; No = 0) 0.369 0.483 0.345 0.475 0.024*** ZJC level (Yes = 1; No = 0) 0.150 0.357 0.149 0.356 0.001 O’ level (Yes = 1; No = 0) 0.339 0.473 0.342 0.474 -0.003 A’ level (Yes = 1; No = 0) 0.011 0.102 0.013 0.113 -0.002 Diploma/Certificate after primary (Yes = 1; No = 0) 0.006 0.078 0.007 0.083 -0.001 Diploma/Certificate after secondary (Yes = 1; No = 0) 0.012 0.109 0.015 0.123 -0.003* Graduate/Post-Graduate (Yes = 1; No = 0) 0.008 0.091 0.010 0.100 -0.002 Household size Number of people living and eating together 4.627 2.051 4.209 2.101 0.417*** Monthly income USD 311.48 1936.11 537.42 13,240.10 -225.95 Provincial location of household Manicaland (Yes = 1; No = 0) 0.140 0.347 0.084 0.277 0.056*** Mash Central (Yes = 1; No = 0) 0.121 0.327 0.155 0.362 -0.033*** Mash East (Yes = 1; No = 0) 0.128 0.334 0.185 0.389 -0.058*** Mash West (Yes = 1; No = 0) 0.108 0.311 0.131 0.337 -0.023*** Mat North (Yes = 1; No = 0) 0.132 0.338 0.089 0.285 0.043*** Mat South (Yes = 1; No = 0) 0.123 0.328 0.111 0.314 0.012** Midlands (Yes = 1; No = 0) 0.122 0.328 0.142 0.349 -0.020*** Masvingo (Yes = 1; No = 0) 0.126 0.332 0.103 0.304 0.023*** Notes: S.D. refers to standard deviation; total sample size is 14,725; the final column shows the results of a two-tailed t-test for the difference in the means: *** p < 0.01, ** p < 0.05, * p < 0.1. 4.1.2 Mean differences in dietary diversity Table 2 displays the difference in dietary diversity between nutrition information acquisition status and gender. The table shows that, on average, no matter whether households are headed (Panel (I)) or dominated (Panel (II)) by women or men, households that acquire nutrition information tend to have higher dietary diversity scores than those that do not. Table 2 also reflects on the gender dimensions. Panel (I) shows that irrespective of the nutrition information acquisition status, households headed by women tend to have poorer diets than those headed by men. This finding also applies when looking at households not acquiring nutrition information. Women-dominated households tend to have lower dietary diversity than those dominated by men, as reflected in Panel (II). This observation on gender is consistent with many studies reporting that girls, women, and households headed by women tend to have worse food and nutrition outcomes than those of boys, men, and headed by men (Kairiza and Kembo 2019 ; Kairiza et al. 2020 , 2023b ; FAO et al. 2021). Table 2 Mean differences in dietary diversity by nutrition information acquisition status and gender Nutrition information acquisition status Difference in means Acquisitors [Y] Non-acquisitors [N] [Y – N] Panel (I) Households headed by: Woman [W] 5.688 5.206 0.482*** Man [M] 5.886 5.479 0.407*** [W – M] -0.198*** -0.273*** Panel (II) Households dominated by: Woman [W] 5.805 4.996 0.809*** Man [M] 5.822 5.426 0.395*** [W – M] -0.016 -0.430*** Notes: The total sample size is 14,725; each panel’s final column and row show the results of a two-tailed t -test for the difference in the means: *** p < 0.01, ** p < 0.05, * p < 0.1. 4.2 ESMC estimation results Table 3 reports the ESMC estimates of the determinants of nutrition information acquisition and dietary diversity. The determinants of nutrition information acquisition are estimated based on Eq. (1), while the determinants of dietary diversity for acquisitors and non-acquisitors are estimated based on Equations (2a) and (2b), respectively. Table 3 Determinants of nutrition information acquisition and dietary diversity: ESMC model estimates Selection = nutrition information acquisition Outcome = dietary diversity Variables Non-acquisitors [N] Acquisitors [Y] Coefficient Standard Error Coefficient Standard Error Coefficient Standard Error IV 2.024*** (0.057) Gender characteristics Woman head 0.181*** (0.041) -0.004 (0.021) 0.005 (0.017) Women dominance -0.179*** (0.063) 0.022 (0.031) 0.026 (0.027) Age 0.002** (0.001) 0.001 (0.000) 0.002*** (0.000) Marital status of household head Married living together 0.138* (0.072) 0.005 (0.035) -0.030 (0.032) Married living apart -0.049 (0.079) 0.012 (0.038) -0.026 (0.034) Divorced/separated -0.178** (0.082) -0.026 (0.040) -0.038 (0.036) Widow/widower 0.036 (0.078) 0.005 (0.039) -0.038 (0.033) Education of household head Primary level 0.101*** (0.039) 0.067*** (0.021) 0.060*** (0.016) ZJC level 0.116** (0.046) 0.089*** (0.025) 0.059*** (0.019) O’ level 0.152*** (0.043) 0.091*** (0.023) 0.102*** (0.018) A’ level 0.019 (0.111) 0.097* (0.053) 0.108** (0.044) Diploma/Certificate after primary -0.006 (0.137) 0.165** (0.066) 0.089 (0.055) Diploma/Certificate after secondary -0.078 (0.102) 0.149*** (0.047) 0.110*** (0.041) Graduate/Post-Graduate 0.025 (0.121) 0.207*** (0.055) 0.137*** (0.047) Household size 0.046*** (0.006) 0.001 (0.003) -0.006*** (0.002) ln(Monthly income) 0.047*** (0.008) 0.085*** (0.004) 0.094*** (0.004) Provincial location of household Mash Central -0.113** (0.046) -0.084*** (0.025) -0.080*** (0.018) Mash East -0.176*** (0.045) 0.154*** (0.023) 0.081*** (0.016) Mash West -0.139*** (0.047) 0.021 (0.025) -0.097*** (0.018) Mat North -0.018 (0.047) -0.004 (0.028) 0.001 (0.017) Mat South -0.092** (0.047) -0.050* (0.027) -0.116*** (0.018) Midlands -0.177*** (0.046) 0.076*** (0.025) 0.069*** (0.017) Masvingo -0.090* (0.046) -0.014 (0.027) -0.021 (0.017) Constant -1.880*** (0.132) 0.563*** (0.066) 0.572*** (0.056) Observations 14,680 14,680 14,680 Notes: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; the reference marital status is single, the reference education level is none, and the reference provincial location is Manicaland. 4.2.1 Determinants of nutrition information acquisition The results show that the coefficient of the instrumental variable (IV) is positive and statistically significant, indicating that neighbors positively influence rural households’ nutrition information acquisition behavior. Being headed by a woman increases the probability of acquiring nutrition information at the 1% significance level. In comparison, women dominance decreases the possibility at the 1% significance level. These findings suggest that while households led by women are more proactive in obtaining nutrition information, households, where women dominate numerically may face barriers or challenges in accessing such information. These results are in resonance with several empirical studies which argue that women’s empowerment through access to ‘information’ resources in food system frameworks is hindered by gender norms (Kruijssen et al. 2018 ; Rao et al. 2019 ; Huyer and Partey 2020 ; Njuki et al. 2022 ). We speculate that initial conditions place women at a social disadvantage by limiting access to resources and asset ownership (Sekabira and Qaim 2017 ), causing dependence and lack of autonomy (Floyd and Sakellariou 2017 ), and cultural and gender norms that suppress women from actively searching for information outside ‘traditionally acceptable sources’ (Meinzen-Dick et al. 2012 ; Kruijssen et al. 2018 ; Njuki et al. 2022 ; Kairiza et al. 2023b ) makes obtaining information from mostly women VHWs an invaluable source among rural women. Lower levels of education up to O’ Level, compared to having no education at all, improve the probability of acquiring nutrition information. This points to the possibility that households headed by members with lower-level education need and can understand the nutrition information. Expectedly, higher levels of education, from A’ level to university or college education, have no statistically significant impact on the households’ probability of accessing information as the education probably already provides nutrition information. These results align with McKay et al. ( 2006 ), who argued that reliance on external sources for nutrition information is significantly higher among the less educated than among those with higher education in a population of older people. In addition, household size and income are positively associated with an increased probability of acquiring nutrition information. 4.2.2 Determinants of dietary diversity The results indicate that dietary diversity for both non-acquisitors and acquisitors is primarily influenced by women dominance, the education level of household heads, household size, and income. For example, being dominated by women increases the likelihood of a household having improved diet diversity if it has acquired nutritional information. This suggests that women are more effective users of nutritional information provided by mostly women VHWs. This finding aligns with Nayga ( 2000 ), who argued that women are more effective users of nutritional information as the primary meal planners. Higher education levels are associated with higher dietary diversity scores for households with and without nutritional information acquisition. This finding is consistent with previous studies, demonstrating that increased education influences people’s eating behaviors and health outcomes (Variyam 2008 ; Webbink et al. 2010 ; Binkley and Golub 2011 ). In addition, as expected, higher household income is correlated with better diet quality for both non-acquisitors and acquisitors. 4.3 Treatment effects of nutrition information acquisition on dietary diversity Table 4 presents the treatment effects of nutrition information acquisition on dietary diversity. The results indicate that acquiring nutritional information significantly increases dietary diversity. Specifically, the estimated ATE, ATT, and ATU are 0.420, 0.432, and 0.402, respectively, corresponding to improvements in dietary diversity of 7.79% for the total population, 8.01% for acquisitors, and 7.45% for non-acquisitors. We also employed the PSM method for comparison. The PSM estimation results, reported in Table A2 in the Appendix , indicate that ATE, ATT, and ATU are also positive and statistically significant, confirming the robustness of our estimates in Table 4 . Our findings are consistent with a host of studies emphasizing improvements in nutrition knowledge channeled through diverse platforms with improved diet quality in diverse settings (Cui et al. 2023 ; Chigusiwa et al. 2023 ; Kairiza et al. 2023b ). Table 4 Treatment effects of nutrition information acquisition on dietary diversity: ESMC model estimates Mean outcomes Change (%) Actual Counterfactual Treatment effects ATE 5.814 (0.878) 5.394 (0.807) 0.420 (0.041)*** 7.79 ATT 5.823 (0.856) 5.391 (0.778) 0.432 (0.042)*** 8.01 ATU 5.801 (0.910) 5.399 (0.851) 0.402 (0.041)*** 7.45 Notes: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. 4.4 Gender-differentiated treatment effects This study also examines the gender-differentiated effects across rural households headed or dominated by women and men. Table 5 presents the gendered differences in the treatment effects of nutrition information acquisition on dietary diversity. It shows that the treatment effects are higher for households headed and dominated by women than their male counterparts. Specifically, Panel (I) indicates that the ATE, ATT, and ATU for households headed by women are 0.493, 0.499, and 0.483, respectively. In contrast, the ATE, ATT, and ATU for households headed by men are 0.381, 0.389, and 0.369, respectively. Panel (II) indicates that the ATE, ATT, and ATU for women-dominated households are 0.711, 0.731, and 0.691, respectively. In contrast, the ATE, ATT, and ATU for households dominated by men are 0.398, 0.410, and 0.379, respectively. The PSM confirms the results in Table 5 estimates in Table A3 in the Appendix . Table 5 Gender-differentiated treatment effects of nutrition information acquisition on dietary diversity: ESMC model estimates Mean outcomes Change (%) Actual Counterfactual Treatment effects Panel (I) Between women- and men-headed households Women ATE 5.689 (0.932) 5.196 (0.831) 0.493 (0.071)*** 9.48 ATT 5.689 (0.892) 5.190 (0.785) 0.499 (0.073)*** 9.62 ATU 5.688 (0.990) 5.205 (0.897) 0.483 (0.072)*** 9.28 Men ATE 5.879 (0.851) 5.497 (0.795) 0.381 (0.050)*** 6.94 ATT 5.888 (0.838) 5.499 (0.773) 0.389 (0.051)*** 7.08 ATU 5.864 (0.872) 5.495 (0.829) 0.369 (0.051)*** 6.72 Panel (II) Between women- and men-dominated households Women ATE 5.790 (1.157) 5.079 (0.992) 0.711 (0.152)*** 14.00 ATT 5.818 (1.058) 5.087 (0.933) 0.731 (0.159)*** 14.37 ATU 5.762 (1.251) 5.071 (1.050) 0.691 (0.161)*** 13.62 Men ATE 5.818 (0.866) 5.420 (0.804) 0.398 (0.042)*** 7.35 ATT 5.823 (0.849) 5.413 (0.780) 0.410 (0.043)*** 7.58 ATU 5.809 (0.892) 5.430 (0.840) 0.379 (0.043)*** 6.99 Notes: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. Our results are consistent with various studies that confirm heterogeneity in the impact of interventions on nutritional outcomes (Sekabira and Qaim 2017 ; Kairiza et al. 2020 ). Given that nutrition information is provided by mostly women VHWs, our results, which show households that are headed or dominated by women benefit more from acquiring nutrition information supplied by mostly women VHWs, conforming to the notion of gender homophily (McPherson et al. 2001 ; Calvó-Armengol and Jackson 2004 ; Golub and Jackson 2012 ; Zeltzer 2020 ; Beugnot and Peterlé 2020 ; Raghunathan et al. 2023 ). The gender of the beneficiary matters in the benefits that are acquired from the nutrition information acquired from mostly women village health workers headed by women, or those that women dominate tend to benefit more. In resource-constrained environments, Mulungu et al. ( 2024 ) urged that resources to improve nutrition information should be concentrated on groups that benefit most from nutrition information. 5 Conclusion and implications Acquiring nutritional information plays a crucial role in improving the dietary diversity of rural residents. In Zimbabwe, village health workers (VHWs) are predominantly women, and rural residents obtain nutritional information from these VHWs to enhance their dietary diversity. However, there is a lack of empirical evidence to support this. This study uses rural household survey data from Zimbabwe to empirically analyze the impact of acquiring nutritional information from VHWs on dietary diversity. We specifically differentiate the gender-related effects across rural households headed or dominated by women and men. We employ the endogenous switching regression model with count dependent variable as it mitigates self-selection bias of nutrition information acquisition and accounting for the count nature of dietary diversity. The ESMC model estimates show that rural households’ decisions to acquire nutrition information are determined by social networks, gender, marital status, education of household heads, household size, and income. Dietary diversity is affected by female dominance, the education level of household heads, household size, and income. The treatment effects (ATE, ATT, ATU) are positive and statistically significant, suggesting that nutrition information acquisition significantly improves dietary diversity. From the gender perspective, households headed or dominated by women tend to benefit more in terms of improvements in dietary diversity when they access nutrition information than those headed or dominated by men. The finding indicates gender heterogeneity in favor of women in the impact of nutrition information on diet quality and gender homophily since nutrition information is provided via women’s VHWs. The findings of this study render an optimistic view of the efforts to close the gender gap in food and nutrition outcomes through village health workers who are mostly women. First, targeted efforts should be made to ensure that smaller and lower-income rural households can access nutrition information. Policies should focus on providing tailored support to these more vulnerable households, possibly through subsidies or community-based programs that enhance their ability to connect with VHWs. Second, it is crucial to leverage the existing networks and improve the reach of VHWs. Programs should focus on increasing the training and resources available to VHWs to better equip them with the necessary tools and knowledge to disseminate information effectively. Third, since the findings suggest that women-headed or dominated households benefit more from nutrition information, enhancing the gender balance among VHWs could improve the effectiveness of information dissemination across all households. Additionally, programs aiming to enhance household nutrition knowledge should continue targeting female household members. Declarations Funding Declaration: Hongyun Zheng acknowledges the financial support from the National Natural Science Foundation of China (72303076). Declaration of interest : The author declares no known interests related to the submitted manuscript. Data Availability Statement : Data will be made available on reasonable request. Author Contribution Terrence Kairiza and Lloyd Chigusiwa wrote the main manuscript text. Wanglin Ma supervised the work and reviewed and edited the manuscript writing. Hongyun Zheng reviewed and edited the manuscript text. 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Evidence from a selectivity-corrected analysis. Food Policy 102:102044 Zhu Y, An Q, Rao J (2024) The effects of dietary diversity on health status among the older adults: an empirical study from China. BMC Public Health 24:. https://doi.org/10.1186/s12889-024-18172-y Additional Declarations No competing interests reported. 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-6606637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458842394,"identity":"9d17363e-3740-41a1-b29e-f77dae712a31","order_by":0,"name":"Terrence Kairiza","email":"","orcid":"","institution":"Bindura University of Science Education","correspondingAuthor":false,"prefix":"","firstName":"Terrence","middleName":"","lastName":"Kairiza","suffix":""},{"id":458842395,"identity":"b75a70db-a43b-4db4-9f92-28d2be2eaba9","order_by":1,"name":"Wanglin Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie2QIYvDMBTH3whElc322OhneFAIq1m/SkbhVO84eXKqah9mcjIjYqZHbcREa8qJisiNm9hrx+BMRuVEfiThH5Jf8hIAj+cFQUVDjX1gByVpMh2lSFISxeWg8HEK9VQFQx6hHH/0r/w6gTiWVjX7VcTnm4k9wyl1KaL8fF9KbCl87NS6zGK+UOxtCy1zKioXKFFT6JWCWihhDqCdBYqquytYdfVDYX+kBC4lNXlcD4rJ4aHw/pbQpSSmE/TJOkDTIu2nt4TrItmiRpeCVR5be9URVlnTXIpVNAszbc7f2vljBO9L+F/5ZENHPREAmH267PF4PJ4b1GZgmJzrKzsAAAAASUVORK5CYII=","orcid":"","institution":"Lincoln University","correspondingAuthor":true,"prefix":"","firstName":"Wanglin","middleName":"","lastName":"Ma","suffix":""},{"id":458842396,"identity":"a8212444-1aaa-4398-a132-c8794145dfba","order_by":2,"name":"Lloyd Chigusiwa","email":"","orcid":"","institution":"Bindura University of Science Education","correspondingAuthor":false,"prefix":"","firstName":"Lloyd","middleName":"","lastName":"Chigusiwa","suffix":""},{"id":458842397,"identity":"c77221a3-c767-4eae-9a8c-9a70a88a9b88","order_by":3,"name":"Hongyun Zheng","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hongyun","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-05-06 23:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6606637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6606637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87139358,"identity":"306392c2-cccd-4425-bba8-40750d061dd7","added_by":"auto","created_at":"2025-07-20 15:31:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1389401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6606637/v1/45007795-a3f5-4865-bad6-ede44e8d9609.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender, Nutrition Information Acquisition, and Dietary Diversity: Empirical Evidence from Rural Households in Zimbabwe","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePoor-quality diets, predominately comprised of starchy staple foods and devoid of fruits, vegetables, and animal-source foods, are responsible for malnutrition (Iannotti et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Ecker and Pauw \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These deficiencies damage rural residents\u0026rsquo; physical and mental development and cause serious health issues in developing countries. For example, lacking iron, folate, and vitamins B12 and A can lead to anaemia. Gender norms at variance with women\u0026rsquo;s access to productive resources, services, and inputs have rendered women, girls, and households headed by women disproportionately vulnerable to malnutrition attendant to poor quality diets (World Bank et al. 2009; Meinzen-Dick et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kairiza and Kembo \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; FAO et al. 2021; Kairiza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Specifically, FAO et al. (2021) highlighted that a third of women of reproductive age were globally affected by anaemia in 2019. By 2021, 31.9% of women were moderately or severely food insecure, compared to 27.6% of men.\u003c/p\u003e \u003cp\u003eMalnutrition in poor countries is a function of behavioral factors and information access (Mulenga et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Julius and Sebastian 2022). This makes the institution of social and behavioral change towards improved diets through the provision of nutrition information an imperative (Melesse \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kairiza et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies have linked improvements in nutrition knowledge, delivered through various platforms, with enhanced diet quality across diverse settings (Caulfield et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bhutta et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Aker and Ksoll \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sekabira and Qaim \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hirvonen et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hoddinott et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Melesse \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Bidira et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kairiza et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, based on a randomized evaluation in Niger, Aker and Ksoll (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that households treated with access to information technology planted a more diverse basket of crops, particularly marginal cash crops grown by women. Zheng et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that nutrition knowledge training not only enhances dietary diversity but also improves the intake of macronutrients (proteins and fats), micronutrients (zinc), and total calorie intake. Cui et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that internet access or usage significantly improves the dietary quality of rural households in China.\u003c/p\u003e \u003cp\u003eResearch consistently demonstrates that the impact of nutrition information on dietary outcomes exhibits significant gender differences (Sekabira and Qaim \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kairiza et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, Kairiza et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) confirmed that food fortification benefits households headed by women more in reducing stunting in children under five than those headed by men in Zimbabwe. Ma et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) noted that females are significantly more sensitive to nutrition information in changing household nutrition intake than males. Zheng et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that the dietary diversity of females increases with nutrition knowledge training, whereas that of males is unaffected in China. These studies generally focus on the gender differences in the relationship between rural households\u0026rsquo; nutrition information acquisition and their dietary but neglect the information provision sources.\u003c/p\u003e \u003cp\u003eThe motivation of this paper stems from the realization that despite gender heterogeneity in the efficacy of nutrition information in inducing behavioral change towards improved diets, in gender-stratified societies, the effectiveness of nutrition information provision is predicated on the form and caliber of the interaction between agents providing, and beneficiaries acquiring, nutrition information (Zeltzer \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Beugnot and Peterl\u0026eacute; \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Raghunathan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beugnot and Peterl\u0026eacute; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasized that gender-based homophily significantly influences the adoption of sound nutrition practices. The gender identities of nutrition information agents and beneficiaries bear upon the effectiveness of nutrition information and service delivery, potentially exacerbating or reducing existing gender inequalities in food and nutrition outcomes.\u003c/p\u003e \u003cp\u003eThis study explores the relationship between gender, nutrition information acquisition, and dietary diversity using nationally representative survey data from rural households in Zimbabwe. This study aims to answer two research questions: (1) Does nutrition information acquired improve the dietary diversity of rural households? (2) Are the impacts of nutrition information on dietary diversity different between households headed or dominated by women and those headed or dominated by men? In addressing these questions, this study endeavors to make the following three contributions to the existing literature.\u003c/p\u003e \u003cp\u003eFirst, we consider nutrition information acquisition from village health workers (VHWs) who are almost exclusively women. This differs from previous studies discussing nutrition information acquisition from the internet (Moorman et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) or peers (i.e., relatives, neighbors, and friends) (Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). VHWs in Zimbabwe are community-based health volunteers who play a critical role in the country\u0026rsquo;s primary healthcare system. They serve as the first point of contact for healthcare at the community level, especially in rural and underserved areas. Second, we consider two types of rural households: households headed by women and households dominated by women. Among them, households headed by women refer to those whose household heads are women. Households dominated by women refer to those in which the proportion of women over 18 is more than half the number of household members. Third, we employ the endogenous switching model with a count outcome variable to estimate the impacts of nutrition information acquisition on dietary diversity by acknowledging the presence of selection bias from both observed and unobserved heterogeneities. Importantly, this model is appropriate when the outcome variable (i.e., dietary diversity) is measured as a count variable, and the treatment variable (i.e., nutrition information acquisition) is measured as a dummy.\u003c/p\u003e \u003cp\u003eZimbabwe poses an interesting case. The Zimbabwe government has been trying to promote social and behavioral change towards improved diets, among other health and nutrition outcomes, through the provision of health and nutrition information using mostly women VHWs known in the vernacular as \u003cem\u003e\u0026ldquo;mbuya hutano\u0026rdquo;\u003c/em\u003e loosely translated as \u003cem\u003e\u0026ldquo;matriarchy of health\u0026rdquo;.\u003c/em\u003e The recruitment policy of VHWs deliberately targets trainable mature women within a specific community, and in the unlikely event that such women cannot be found, men can be recruited. However, in a few other circumstances, a few men can be recruited to move around with the women VHWs if it is believed that security may be a concern. This includes when the VHWs travel a long distance in remote bushy areas within the village. VHWs deliver health assistance to rural households by conducting health promotion services ranging from maternal, neonatal, and child health, nutrition, water, sanitation, and hygiene (WASH) related information to managing common childhood illnesses. Their work significantly improves health outcomes and reduces the burden on the formal healthcare system.\u003c/p\u003e \u003cp\u003eThe rest of this paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the data and key variable measurement, and the estimation strategy is presented in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the empirical results, and Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with implications.\u003c/p\u003e"},{"header":"2 Data and key variable measurements","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis study uses data from a nationally representative cross-sectional survey of rural Zimbabwe conducted by the Zimbabwe Vulnerability Assessment Committee in 2023. The survey covers all 60 rural districts in Zimbabwe\u0026rsquo;s eight rural provinces. We employ a two-stage sampling procedure. First, we randomly selected 25 enumeration areas within each of the 60 rural districts. Second, we randomly chose 10 households for interviews within the 1,500 enumeration areas. Finally, we obtained 1,5000 samples in total. After data cleaning, we obtained 14,725 samples for this study. We conducted face-to-face interviews with a knowledgeable household representative at their homestead, using a structured questionnaire translated into the respondents\u0026rsquo; native language.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Key variable measurements\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Nutrition information acquisition\u003c/h2\u003e \u003cp\u003eNutrition information acquisition is measured as a binary variable. We included a question to capture this status: \u003cem\u003e\u0026ldquo;Did your household acquire any nutrition information from mostly women village health workers (VHWs) in the past 12 months?\u0026rdquo;.\u003c/em\u003e Consequently, the nutrition information acquisition variable is assigned a value of one if the household has acquired nutrition information and zero otherwise. The measurement is consistent with existing research (Le et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe did not capture quantitative details about the nutrition information provided by VHWs because the details are household-specific and cannot be directly compared. Focus group discussions and key informant interviews conducted alongside the 2023 survey revealed the significant roles VHWs play in primary health care in Zimbabwe. After formal training, VHWs undertake several responsibilities, such as (1) Preventative Care: They provide infant and young child feeding counseling and other health-related advice, including encouraging mothers to take iron folic acid tablets. (2) Promotional Activities: VHWs motivate mothers to attend antenatal sessions and disseminate nutrition-related messages through WhatsApp groups. (3) Surveillance: They screen children for malnutrition and refer those in need to the nearest health institutions for treatment initiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Dietary diversity\u003c/h2\u003e \u003cp\u003eDietary diversity, the dependent variable, is a count variable measured using dietary diversity scores. Dietary diversity scores are a common tool to assess food security and dietary quality (Zhu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ibrahim et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the survey, respondents were asked to select the food items they had consumed in the 24 hours preceding the survey from a list of 12 options. The food groups considered are (1) Cereals, (2) Roots and tubers, (3) Vegetables, (4) Fruits, (5) Meat, poultry, and offals, (6) Eggs, (7) Fish and seafood, (8) Pulses, legumes, and nuts, (9) Milk and milk products, (10) Oil/fats, (11) Sugar/honey, and (12) Condiments. Each item was counted once if it was consumed. Consequently, the aggregated scores generate a dietary diversity variable that ranges from 0 to 12.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Gender-related variables\u003c/h2\u003e \u003cp\u003eThis study differentiates rural households headed or dominated by women from men using two variables: women head and women dominance. The women head variable was measured by the question, \u0026ldquo;\u003cem\u003eDoes a woman head your household?\u003c/em\u003e\u0026rdquo;. The women dominance variable was measured by the question, \u0026ldquo;\u003cem\u003eDo women dominate your household?\u003c/em\u003e\u0026rdquo;. The two variables are measured as dummies. For example, the women head variable equals one if a rural household is headed by a woman and zero otherwise. Similarly, the women dominance variable is assigned a value of one if the number of females in the household over 18 years of age exceeds half of the total household members and zero otherwise.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Econometric strategy","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model selection\u003c/h2\u003e \u003cp\u003eRural households that perceive themselves as more proficient in using nutritional information may actively seek it out, or those needing nutritional advice may pursue it. Consequently, these households self-select to acquire nutritional information (Smith and Todd \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Observing and unobserved factors affect this process and lead to a self-selection bias. When selection bias only stems from observed heterogeneity, researchers have employed the propensity score matching (PSM) approach to address the selection bias issues (Chagwiza et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tran and Goto \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Macheka et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the PSM approach becomes ineffective when self-selection exists from unobserved characteristics (e.g., people\u0026rsquo;s innate abilities and motivations to seek nutrition information) (Smith and Todd \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our approach must address self-selection bias from observed and unobserved factors. In addition, the count nature of our dependent variable, dietary diversity, necessitates approaches that consider variations in count-dependent variables (Hasebe \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Therefore, we employ the endogenous switching model with the count outcome (ESMC) model introduced by Hasebe (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, the ESMC model mitigates self-selection bias and considers the count nature of the dietary diversity. In addition, we provide the results estimated by the Propensity Score Matching (PSM) model for comparison purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ESMC model\u003c/h2\u003e \u003cp\u003eThe ESMC model simultaneously estimates one selection equation, which determines the probability of acquiring nutrition information and two outcome equations, which determine dietary diversity for those with nutrition information acquisition (Regime 1) and those without (Regime 2) as follows:\u003c/p\u003e \u003cp\u003eSelection equation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Pr}\\left({NIA}_{i}\\left|IV,\\:X\\right.\\right)={X}_{i}^{{\\prime\\:}}\\gamma\\:+{IV}_{i}\\alpha\\:+{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eRegime 1: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({NIA}_{i}=1\\right):{DDS}_{1i}={X}_{i}^{{\\prime\\:}}{\\beta\\:}_{1i}+{\\eta\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003e (2a)\u003c/p\u003e \u003cp\u003eRegime 2: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({NIA}_{i}=0\\right):{DDS}_{0i}={X}_{i}^{{\\prime\\:}}{\\beta\\:}_{0i}+{\\eta\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003e (2b)\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Pr}\\left({NIA}_{i}\\left|IV,\\:X\\right.\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the probability that a rural household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003eacquired nutrition information. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DDS}_{1i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DDS}_{0i}\\:\\)\u003c/span\u003e\u003c/span\u003erefer to dietary diversity scores (DDS) for nutrition information acquisitors and non-acquisitors, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}^{{\\prime\\:}}\\)\u003c/span\u003e\u003c/span\u003e is a vector of control variables that affect rural households\u0026rsquo; decisions to acquire nutrition information and dietary diversity. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{IV}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents an instrumental variable. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:,\\:\\alpha\\:,\\:{\\beta\\:}_{1i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\beta\\:}_{0i}\\:\\)\u003c/span\u003e\u003c/span\u003eare parameters to be estimated. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i},\\:\\:{\\eta\\:}_{1i},\\:\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\eta\\:}_{0i}\\)\u003c/span\u003e\u003c/span\u003e refers to error terms. Within the maximum likelihood estimation framework of the ESMC model, Eq.\u0026nbsp;(1) is estimated by a probit model, and Equations (2a) and (2b) are estimated by the Poisson regression models.\u003c/p\u003e \u003cp\u003eFor model identification, the instrumental variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{IV}_{i}\\)\u003c/span\u003e\u003c/span\u003e should be included in Eq.\u0026nbsp;(1) but not Equations (2a) and (2b). In this study, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{IV}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a variable representing the distance-weighted number of neighbors who acquired nutrition information within the 10-km radius of the households. We utilized the Global Positioning System coordinates of the households to calculate the distances between household \u003cem\u003ei\u003c/em\u003e and all the households within its 10km radius that acquired nutrition information. Following Jackson (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Kondo (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we calculate the inverse distance weighted number of households within household \u003cem\u003ei\u003c/em\u003e\u0026rsquo;s 10km radius that acquired nutrition information as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{IV}_{i}=\\sum\\:_{i=1}^{n}\\frac{{NIA}_{j}}{{d}_{ij}}\\:\\:\\forall\\:{NIA}_{j}=1,\\:{d}_{ij}\\le\\:10km$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the Euclidian distance between the homestead of household \u003cem\u003ei\u003c/em\u003e and its neighbour \u003cem\u003ej\u003c/em\u003e. We take the inverse distance weight as we speculate that the further away household \u003cem\u003ej\u003c/em\u003e is from household \u003cem\u003ei\u003c/em\u003e, the less likely that household \u003cem\u003ej\u003c/em\u003e influences household \u003cem\u003ei\u003c/em\u003e\u0026rsquo;s information acquisition decision.\u003c/p\u003e \u003cp\u003eTheoretically, the IV is expected to influence rural households\u0026rsquo; nutrition information acquisition because they may imitate their peers\u0026rsquo; acquisition behavior, but neighbors\u0026rsquo; acquisition would not directly affect rural households\u0026rsquo; dietary diversity. The existing literature has proven the role of peer effects in influencing rural households\u0026rsquo; information acquisition (Bandiera and Rasul \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Conley and Udry \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This argument is confirmed by a simple falsification test following Di Falco et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Manda et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who argue that an IV is valid if it affects a household\u0026rsquo;s decision to acquire nutrition information but does not affect nutritional outcomes among non-acquisitors. This is confirmed in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA1\u003c/span\u003e of the \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter estimating Equations (1), (2a), and (2b), we calculate the treatment effects of nutrition information acquisition on dietary diversity. Specifically, following Hasebe (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we estimate the average treatment effects (\u003cem\u003eATE\u003c/em\u003e) in the total population, the average treatment effect on the treated (\u003cem\u003eATT\u003c/em\u003e), and the average treatment effects on the untreated (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ATU)\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e,\u003c/sub\u003e respectively, as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:ATE=E[{DDS}_{1}-{DDS}_{0}|X]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3a\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:ATT=E[{DDS}_{1}-{DDS}_{0}|X,\\:{NIA}_{i}=1]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3b\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:ATU=E[{DDS}_{1}-{DDS}_{0}|X,\\:{NIA}_{i}=0]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3c\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis study aims to compare the gender-differentiated effects of nutrition information acquisition. Therefore, we also present results by distinguishing between households headed by women and men and those dominated by women and men.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Descriptive results\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.1 Characteristics between the status of nutrition information acquisition\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows variable definitions, descriptive statistics, and mean differences between nutrition information acquisitors and non-acquisitors. Significant differences between the two groups regarding the household heads\u0026rsquo; age, marital status, education, household size, and provincial location can be observed. Specifically, households that acquire nutritional information tend to be headed by individuals who are older, married, and living with their spouses. Additionally, these households tend to be larger than those not acquiring nutritional information. The differences in personal and household-level characteristics between nutrition information acquisitors and non-acquisitors indicate the self-selection bias issue, necessitating the ESMC model\u0026rsquo;s need to unveil the impact of nutrition information acquisition on dietary diversity.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePersonal and household-level characteristics by nutrition information acquisition status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eNutrition information acquisition status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eDifferences in means\u003c/p\u003e\n \u003cp\u003e[Y \u0026ndash; N]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAcquisitors [Y]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-acquisitors [N]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinitions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.D\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.D\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eObservations # (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e8,930 (60.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e5,795 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoman head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold head (HH) is women (Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen dominance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold is dominated by women (Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.031***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of HH in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.212***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMarital status of household head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried living together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried living apart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidow/widower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEducation of household head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZJC level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO\u0026rsquo; level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u0026rsquo; level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma/Certificate after primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma/Certificate after secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate/Post-Graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of people living and eating together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1936.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e537.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13,240.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-225.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eProvincial location of household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManicaland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.023***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMat North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMat South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMasvingo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Yes\u0026thinsp;=\u0026thinsp;1; No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNotes: S.D. refers to standard deviation; total sample size is 14,725; the final column shows the results of a two-tailed t-test for the difference in the means: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.2 Mean differences in dietary diversity\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the difference in dietary diversity between nutrition information acquisition status and gender. The table shows that, on average, no matter whether households are headed (Panel (I)) or dominated (Panel (II)) by women or men, households that acquire nutrition information tend to have higher dietary diversity scores than those that do not. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e also reflects on the gender dimensions. Panel (I) shows that irrespective of the nutrition information acquisition status, households headed by women tend to have poorer diets than those headed by men. This finding also applies when looking at households not acquiring nutrition information. Women-dominated households tend to have lower dietary diversity than those dominated by men, as reflected in Panel (II). This observation on gender is consistent with many studies reporting that girls, women, and households headed by women tend to have worse food and nutrition outcomes than those of boys, men, and headed by men (Kairiza and Kembo \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kairiza et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2023b\u003c/span\u003e; FAO et al. 2021).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean differences in dietary diversity by nutrition information acquisition status and gender\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNutrition information acquisition status\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference in means\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcquisitors [Y]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-acquisitors [N]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[Y \u0026ndash; N]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePanel (I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eHouseholds headed by:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoman [W]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMan [M]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.407***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[W \u0026ndash; M]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.198***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.273***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePanel (II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eHouseholds dominated by:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoman [W]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMan [M]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[W \u0026ndash; M]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.430***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eNotes: The total sample size is 14,725; each panel\u0026rsquo;s final column and row show the results of a two-tailed \u003cem\u003et\u003c/em\u003e-test for the difference in the means: *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 ESMC estimation results\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reports the ESMC estimates of the determinants of nutrition information acquisition and dietary diversity. The determinants of nutrition information acquisition are estimated based on Eq.\u0026nbsp;(1), while the determinants of dietary diversity for acquisitors and non-acquisitors are estimated based on Equations (2a) and (2b), respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDeterminants of nutrition information acquisition and dietary diversity: ESMC model estimates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSelection\u0026thinsp;=\u0026thinsp;nutrition information acquisition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eOutcome\u0026thinsp;=\u0026thinsp;dietary diversity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNon-acquisitors [N]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAcquisitors [Y]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWoman head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen dominance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.179***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status of household head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried living together\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried living apart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidow/widower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation of household head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZJC level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO\u0026rsquo; level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u0026rsquo; level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma/Certificate after primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma/Certificate after secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate/Post-Graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eln(Monthly income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvincial location of household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.113**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.084***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.080***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.176***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMash West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.139***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMat North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMat South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.116***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.177***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMasvingo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.880***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.563***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.572***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eNotes: Standard errors in parentheses; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1; the reference marital status is single, the reference education level is none, and the reference provincial location is Manicaland.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Determinants of nutrition information acquisition\u003c/h2\u003e\n \u003cp\u003eThe results show that the coefficient of the instrumental variable (IV) is positive and statistically significant, indicating that neighbors positively influence rural households\u0026rsquo; nutrition information acquisition behavior. Being headed by a woman increases the probability of acquiring nutrition information at the 1% significance level. In comparison, women dominance decreases the possibility at the 1% significance level. These findings suggest that while households led by women are more proactive in obtaining nutrition information, households, where women dominate numerically may face barriers or challenges in accessing such information. These results are in resonance with several empirical studies which argue that women\u0026rsquo;s empowerment through access to \u0026lsquo;information\u0026rsquo; resources in food system frameworks is hindered by gender norms (Kruijssen et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rao et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huyer and Partey \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Njuki et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). We speculate that initial conditions place women at a social disadvantage by limiting access to resources and asset ownership (Sekabira and Qaim \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), causing dependence and lack of autonomy (Floyd and Sakellariou \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and cultural and gender norms that suppress women from actively searching for information outside \u0026lsquo;traditionally acceptable sources\u0026rsquo; (Meinzen-Dick et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kruijssen et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Njuki et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kairiza et al. \u003cspan class=\"CitationRef\"\u003e2023b\u003c/span\u003e) makes obtaining information from mostly women VHWs an invaluable source among rural women.\u003c/p\u003e\n \u003cp\u003eLower levels of education up to O\u0026rsquo; Level, compared to having no education at all, improve the probability of acquiring nutrition information. This points to the possibility that households headed by members with lower-level education need and can understand the nutrition information. Expectedly, higher levels of education, from A\u0026rsquo; level to university or college education, have no statistically significant impact on the households\u0026rsquo; probability of accessing information as the education probably already provides nutrition information. These results align with McKay et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), who argued that reliance on external sources for nutrition information is significantly higher among the less educated than among those with higher education in a population of older people. In addition, household size and income are positively associated with an increased probability of acquiring nutrition information.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.2 Determinants of dietary diversity\u003c/h2\u003e\n \u003cp\u003eThe results indicate that dietary diversity for both non-acquisitors and acquisitors is primarily influenced by women dominance, the education level of household heads, household size, and income. For example, being dominated by women increases the likelihood of a household having improved diet diversity if it has acquired nutritional information. This suggests that women are more effective users of nutritional information provided by mostly women VHWs. This finding aligns with Nayga (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), who argued that women are more effective users of nutritional information as the primary meal planners. Higher education levels are associated with higher dietary diversity scores for households with and without nutritional information acquisition. This finding is consistent with previous studies, demonstrating that increased education influences people\u0026rsquo;s eating behaviors and health outcomes (Variyam \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Webbink et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Binkley and Golub \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, as expected, higher household income is correlated with better diet quality for both non-acquisitors and acquisitors.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Treatment effects of nutrition information acquisition on dietary diversity\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the treatment effects of nutrition information acquisition on dietary diversity. The results indicate that acquiring nutritional information significantly increases dietary diversity. Specifically, the estimated ATE, ATT, and ATU are 0.420, 0.432, and 0.402, respectively, corresponding to improvements in dietary diversity of 7.79% for the total population, 8.01% for acquisitors, and 7.45% for non-acquisitors. We also employed the PSM method for comparison. The PSM estimation results, reported in Table \u003cspan class=\"InternalRef\"\u003eA2\u003c/span\u003e in the \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e, indicate that ATE, ATT, and ATU are also positive and statistically significant, confirming the robustness of our estimates in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Our findings are consistent with a host of studies emphasizing improvements in nutrition knowledge channeled through diverse platforms with improved diet quality in diverse settings (Cui et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chigusiwa et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kairiza et al. \u003cspan class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTreatment effects of nutrition information acquisition on dietary diversity: ESMC model estimates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean outcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eChange (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCounterfactual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.814 (0.878)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.394 (0.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.420 (0.041)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.823 (0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.391 (0.778)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.432 (0.042)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.801 (0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.399 (0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402 (0.041)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eNotes: Standard errors in parentheses; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Gender-differentiated treatment effects\u003c/h2\u003e\n \u003cp\u003eThis study also examines the gender-differentiated effects across rural households headed or dominated by women and men. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the gendered differences in the treatment effects of nutrition information acquisition on dietary diversity. It shows that the treatment effects are higher for households headed and dominated by women than their male counterparts. Specifically, Panel (I) indicates that the ATE, ATT, and ATU for households headed by women are 0.493, 0.499, and 0.483, respectively. In contrast, the ATE, ATT, and ATU for households headed by men are 0.381, 0.389, and 0.369, respectively. Panel (II) indicates that the ATE, ATT, and ATU for women-dominated households are 0.711, 0.731, and 0.691, respectively. In contrast, the ATE, ATT, and ATU for households dominated by men are 0.398, 0.410, and 0.379, respectively. The PSM confirms the results in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e estimates in Table \u003cspan class=\"InternalRef\"\u003eA3\u003c/span\u003e in the \u003cspan class=\"InternalRef\"\u003eAppendix\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGender-differentiated treatment effects of nutrition information acquisition on dietary diversity: ESMC model estimates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean outcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eChange (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCounterfactual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment effects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel (I) Between women- and men-headed households\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.689 (0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.196 (0.831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.493 (0.071)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.689 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.190 (0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.499 (0.073)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.688 (0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.205 (0.897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483 (0.072)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.879 (0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.497 (0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381 (0.050)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.888 (0.838)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.499 (0.773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389 (0.051)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.864 (0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.495 (0.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.369 (0.051)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel (II) Between women- and men-dominated households\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.790 (1.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.079 (0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.711 (0.152)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.818 (1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.087 (0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731 (0.159)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.762 (1.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.071 (1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691 (0.161)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.818 (0.866)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.420 (0.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398 (0.042)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.823 (0.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.413 (0.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.410 (0.043)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.809 (0.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.430 (0.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379 (0.043)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eNotes: Standard errors in parentheses; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOur results are consistent with various studies that confirm heterogeneity in the impact of interventions on nutritional outcomes (Sekabira and Qaim \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kairiza et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given that nutrition information is provided by mostly women VHWs, our results, which show households that are headed or dominated by women benefit more from acquiring nutrition information supplied by mostly women VHWs, conforming to the notion of gender homophily (McPherson et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Calv\u0026oacute;-Armengol and Jackson \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Golub and Jackson \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zeltzer \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Beugnot and Peterl\u0026eacute; \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Raghunathan et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The gender of the beneficiary matters in the benefits that are acquired from the nutrition information acquired from mostly women village health workers headed by women, or those that women dominate tend to benefit more. In resource-constrained environments, Mulungu et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) urged that resources to improve nutrition information should be concentrated on groups that benefit most from nutrition information.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Conclusion and implications","content":"\u003cp\u003eAcquiring nutritional information plays a crucial role in improving the dietary diversity of rural residents. In Zimbabwe, village health workers (VHWs) are predominantly women, and rural residents obtain nutritional information from these VHWs to enhance their dietary diversity. However, there is a lack of empirical evidence to support this. This study uses rural household survey data from Zimbabwe to empirically analyze the impact of acquiring nutritional information from VHWs on dietary diversity. We specifically differentiate the gender-related effects across rural households headed or dominated by women and men. We employ the endogenous switching regression model with count dependent variable as it mitigates self-selection bias of nutrition information acquisition and accounting for the count nature of dietary diversity.\u003c/p\u003e \u003cp\u003eThe ESMC model estimates show that rural households\u0026rsquo; decisions to acquire nutrition information are determined by social networks, gender, marital status, education of household heads, household size, and income. Dietary diversity is affected by female dominance, the education level of household heads, household size, and income. The treatment effects (ATE, ATT, ATU) are positive and statistically significant, suggesting that nutrition information acquisition significantly improves dietary diversity. From the gender perspective, households headed or dominated by women tend to benefit more in terms of improvements in dietary diversity when they access nutrition information than those headed or dominated by men. The finding indicates gender heterogeneity in favor of women in the impact of nutrition information on diet quality and gender homophily since nutrition information is provided via women\u0026rsquo;s VHWs.\u003c/p\u003e \u003cp\u003eThe findings of this study render an optimistic view of the efforts to close the gender gap in food and nutrition outcomes through village health workers who are mostly women. First, targeted efforts should be made to ensure that smaller and lower-income rural households can access nutrition information. Policies should focus on providing tailored support to these more vulnerable households, possibly through subsidies or community-based programs that enhance their ability to connect with VHWs. Second, it is crucial to leverage the existing networks and improve the reach of VHWs. Programs should focus on increasing the training and resources available to VHWs to better equip them with the necessary tools and knowledge to disseminate information effectively. Third, since the findings suggest that women-headed or dominated households benefit more from nutrition information, enhancing the gender balance among VHWs could improve the effectiveness of information dissemination across all households. Additionally, programs aiming to enhance household nutrition knowledge should continue targeting female household members.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eHongyun Zheng acknowledges the financial support from the National Natural Science Foundation of China (72303076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u003c/strong\u003e: The author declares no known interests related to the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: Data will be made available on reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTerrence Kairiza and Lloyd Chigusiwa wrote the main manuscript text. Wanglin Ma supervised the work and reviewed and edited the manuscript writing. 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BMC Public Health 24:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-18172-y\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-18172-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Dietary diversity, Nutrition information acquisition, Gender, Zimbabwe","lastPublishedDoi":"10.21203/rs.3.rs-6606637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6606637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcquisition of nutrition information is crucial for enhancing dietary diversity among rural residents, yet the potential gender differences in its impact remain underexplored. This study investigates the gender-differentiated effects of nutrition information acquisition on the dietary diversity of rural households. Specifically, we consider the nutrition information acquired from mostly women village health workers and distinguish rural households headed or dominated by women and men. Households dominated by women refer to those in which the proportion of women over 18 is more than half the number of household members. An endogenous switching regression model with a count outcome variable addresses self-selection bias from observed and unobserved factors and estimates data from a nationally rural survey in Zimbabwe. The results indicate that households headed by women are more likely to obtain nutrition information, whereas households dominated by women are less likely to acquire such information. Nutrition information acquisition significantly improves dietary diversity. From a gender perspective, the impact of acquiring nutrition information on dietary diversity is more significant for rural households headed or dominated by women compared to those headed or dominated by men. Our findings underscore the crucial role of acquiring nutrition information from mostly women village health workers in enhancing dietary diversity. Particular policy attention and support should be directed towards disseminating nutrition information to rural households, especially those headed by women or where women play a dominant role.\u003c/p\u003e","manuscriptTitle":"Gender, Nutrition Information Acquisition, and Dietary Diversity: Empirical Evidence from Rural Households in Zimbabwe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 11:44:02","doi":"10.21203/rs.3.rs-6606637/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":"54565b00-460d-4caf-a8af-bec0f0d93cd5","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-20T15:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 11:44:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6606637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6606637","identity":"rs-6606637","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}