Impact of integrated aquaculture-agriculture value chain participation on welfare of marginalized indigenous households in Bangladesh: A panel data analysis

AbstractThis study examines the impact of integrated aquaculture-agriculture (IAA) value chain participation dynamics on the welfare of indigenous households using a three-wave household panel dataset from Bangladesh. An innovation of this study is that distributional effects of IAA value chain participation dynamics is investigated by examining economic impacts on all actors across the IAA value chains. We applied random-effects, standard fixed-effects, Heckit panel, and control function approaches to control for endogeneity of IAA value chain participation and unobserved heterogeneity. We found that IAA value chain participation is positively associated with household income and diet quality depicted by the consumption frequency of certain foods, especially fish consumption, and the benefits do not continue to accrue after discontinuing participation. The results also reveal that IAA value chain participation has higher impacts on the welfare of households who were involved in production related IAA value chain activities than upstream and downstream IAA value chain activities.Keywords: Integrated aquaculture-agriculture (IAA)welfare impactfixed effects modelcontrol functionindigenous householdsBangladesh AcknowledgementsThis article is part of the author doctoral dissertation, for which he was awarded the Joseph G. Knoll European Science Award from the Foundation fiat panis. The author very much grateful particularly to Prof. Dr. Joachim von Braun for his invaluable comments and guidance. He wish to acknowledge helpful methodological suggestions from Prof. Dr. Matin Qaim and this work benefited from the critical comments and constructive suggestion by the participants at the International Rice Congress (IRC), Bangkok, Thailand, International Institute of Fisheries Economics & Trade (IIFET) conference, Seattle and International Conference of Agricultural Economists in Vancouver. The author received Young Rice Scientist (YRS) award at IRC, Bangkok and Best Aquaculture Economics Paper Prize at IIFET in Seattle for this paper. The author gratefully acknowledges financial support from the German Academic Exchange Service (DAAD) and from the Dr. Hermann Eiselen Doctoral Program of the Foundation fiat panis. Both the DAAD and the Dr. Hermann Eiselen Doctoral Program of the Foundation fiat panis provided funding during the course of the doctoral studies of the author and had no further role in the analysis or completion of this article. The author further acknowledge the WorldFish Bangladesh office for sharing the first two wave panel data sets thereby enabling the construction of a three- wave panel dataset, and to the individual IAA value chain participators who participated in the field survey interviews. I am also grateful to the editor of the journal, Prof. Dr. Frank Asche, and three anonymous reviewers for their valuable suggestions. All errors remain my own.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data and the STATA codes employed in the analysis will be made available upon request to the author.Notes1 IAA is based on the concept of integrated resource management, utilizing synergies among subsystems that result in greater farm productivity. For detailed discussions of IAA related technologies see Edwards (Citation1998), Prein (Citation2002), and Pant et al. (Citation2005).2 The terms adivasi, indigenous, ethnic minority and tribal are used interchangeably in this study. In Bangladesh adivasi communities are typically the most marginalized and extremely poor segments of society; live in densely populated border areas; face dispossession and eviction from their ancestral lands; are often excluded from social safety net programs; are frequently trapped in poverty; and a significant proportion of them live below the absolute national poverty line (Pant et al., Citation2014).3 Within-group variability with respect to the treatment variable (in this case IAAp and IAAd) is necessary in order to estimate an efficient FE model (Kikulwe et al., Citation2014). Thus, there needs to be a sufficient number of households that participate in IAA value chains or that discontinued participation in the first year of the survey, but not in another year. Such variability is present in the data, especially between the survey wave one (2007) and three (2012), and between waves two (2009) and three (2012), because in the third wave a large number of IAA value chain ‘participators’ became ‘dis-participators.’ Variability in IAA participation in this case only comes from dis-participation in IAA value chain. Thus, the FE model was estimated by using only an IAA participation dummy and other covariates. Otherwise, different estimates for IAA participation and dis- participation will give similar results because the change in participation status is the mirror image of change in dis-participation. Thus the dis-participation effect on welfare was based on the RE models because they do not need within variability.4 Here we only used IAA participation in the FE and subsequent models because of the variability of IAA participation resulting from dis-partiticaption. Thus changes in the IAA participation are the mirror image of changes in the dis-participation.5 Although theoretically (e.g. according to agricultural household models) consumption is a function of income and other covariates, but empirically income is endogenous to consumption and in this case current income is highly correlated with IAA participation. Here we used lagged income instead of current income to avoid the endogeneity problem (e.g. Asfaw et al., Citation2012 also used lagged income instead of current income for estimating welfare impacts of technology adoption).Additional informationFundingThis work was supported by Deutscher Akademischer Austauschdienst; Stiftung fiat panis.