Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 Bio-based and Applied Economics BAE © 2024 Author(s). Open access article published, except where otherwise noted, by Firenze University Press under CC-BY-4.0 License for content and CC0 1.0 Universal for metadata. Firenze University Press | www.fupress.com/bae Citation: Gattone, T. (2024). Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator. Bio-based and Applied Eco- nomics 13(3): 245-264. doi: 10.36253/bae- 15464 Received: November 29, 2023 Accepted: April 19, 2024 Published: October 16, 2024 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Francesco Pagliacci, Valentina Raimondi, Luca Salvatici ORCID TG: 0000-0002-7727-8049 Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Tulia Gattone Department of Social Sciences and Economics, Sapienza University of Rome, Italy E-mail: tulia.gattone@uniroma1.it Abstract. This study presents a micro-level indicator of farmers’ positioning in the market chain, based on the conceptual framework outlined by Antràs and Chor (2013, 2018). The indicator considers the selling location of a farming household and its crop buyers. Using panel data from the World Bank’s ‘Living Standards Measure- ment Study: Integrated Surveys on Agriculture’ for Ethiopia and Nigeria, this paper applies the proposed indicator empirically and showcases its superior performance in comparison to existing alternatives at the micro-level. Furthermore, by analyzing the dynamics of farmers’ food and total consumption over time and controlling for vari- ous household and production characteristics, as well as potential confounding factors, this study shows that moving towards a downstream position in the market chain has a positive impact on farmers’ food and total consumption levels. The results are validated through sensitivity analysis and robustness checks. Keywords: value chains, economic development, market chain, farming households. JEL-Codes: Q12, O12, O13, C23. INTRODUCTION The discourse on the effects of farmers’ participation in global markets remains nuanced. One segment of the literature highlights that smallholder farmers’ engagement in traditional markets catalyzes pro-poor outcomes through a cycle of enhanced household income, increased consumption, greater food security, and improved nutrition (Bellemare, 2012; Montalbano et al., 2018). Conversely, another segment postulates that market participa- tion might not significantly benefit those unable to leverage increased market orientation’s advantages (von Braun, 1995; Carletto et al., 2017). Market chain participation encompasses essential activities for food production delivery to consumers, including trading (Kaplinsky & Morris, 2001). In development scenarios, farmers often find themselves limited to lower-value activities, positioning them at the backward stages of the mar- ket value chain, which contrasts with increased employment, better jobs, resources, governance, and food security associated with downstream posi- https://doi.org/10.36253/bae-15464 https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode http://www.fupress.com/bae https://doi.org/10.36253/bae-15464 https://doi.org/10.36253/bae-15464 https://orcid.org/0000-0002-7727-8049 mailto:tulia.gattone@uniroma1.it 246 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 tioning (Minten et al., 2009; Cattaneo & Miroudot, 2013; African Development Bank et al., 2014; Swinnen, 2014; Swinnen & Vandeplas, 2014). Antràs and Chor (2013) offer a foundational model on positioning, illustrat- ing a dependency of downstream stages on upstream activities, yet discussions on the structuring of the most upstream sectors within value chains remain limited. This research merges insights from trade and devel- opment literature on value chain positioning, focusing on supplier positioning in global chains as per Antràs and Chor (2013; 2018), and the commercialization deci- sions of rural farmers as detailed by Migose et al. (2018), Minten et al. (2018), and Montalbano et al. (2018). It introduces a novel downstreamness measure for rural farmers in market value chains, inspired by Antràs and Chor’s framework. This study tests the new position- ing indicator using the LSMS-ISA dataset for Ethio- pian households, selected for its detailed commodity exchange market data, and conducts parallel testing with Nigerian LSMS-ISA data and analyses related to food quantity and market positioning. The indicator outper- forms traditional measures in empirical tests. The study examines how farmers’ market position- ing affects their consumption levels. Findings show that improved positioning significantly boosts farmers’ food and total consumption, supporting existing literature on agricultural commercialization’s impact, validated through extensive sensitivity and robustness checks. The paper is organized as follows: Section 2 reviews the literature and theoretical framework. Section 3 intro- duces the market positioning indicator. Section 4 details crop value chain structure and methodology. Section 5 describes the data and statistics. Section 6 discusses the empirical strategy and results. Section 7 concludes the study, summarizing key findings and implications. 2. LITERATURE REVIEW Agricultural commercialization is widely regarded as a key mechanism for poverty alleviation in rural set- tings, underpinned by literature suggesting its positive impact on rural households’ development (von Braun & Kennedy, 1994; de Janvry & Sadoulet, 2006). This transi- tion allows smallholder farmers to shift from subsistence farming practices to the cultivation of market-specific crops, facilitating specialization, the adoption of modern agricultural technologies, and ultimately, higher produc- tivity (van Asselt & Useche, 2022). Studies like those of Key et al. (2000), Yanagizawa (2009). and Svensson & Jensen (2010) have documented that market participation and positioning are affected by access costs and risk pref- erences, affirming the benefits of effective market posi- tioning. However, agricultural trade may yield several effects on production constraints, land use, and environ- mental sustainability (Minten et al., 2007), with small- holder farmers facing barriers such as low productivity, stringent standards compliance, and elevated transaction costs that limit market entry (Montalbano et al., 2015). Vertical market integration turns out to be critically relevant in these contexts characterized by fragmented markets, weak contract enforcement, and political insta- bility (Fackler & Goodwin, 2001). The nature of the crop buyer significantly influences market positioning, with farmers navigating interactions with intermediar- ies, large processing firms, and state-managed markets. Despite the perception of intermediaries as monopo- listic rent-seekers (Montalbano et al., 2018), empirical evidence suggests that farmers’ involvement in contract schemes and export chains generally yields positive out- comes for smallholders (Minten et al., 2009; Barrett et al., 2012; Bellemare, 2012; Subervie & Vagneron, 2013; Bellemare & Novak, 2017). The interaction between global and local value chains raises questions about the impact of global market participation on local agricultural systems and food con- sumption. While some argue that global value chains can undermine traditional local markets (Ríos Guayasamín et al., 2016), others point to the competition for resources that such integration entails (Feyaerts et al., 2020). The debate extends to the efficacy of local versus global value chains, with some evidence suggesting local markets may offer better performance or serve as gateways to global chains (D’Souza & Jolliffe, 2014; Wegerif & Martucci, 2019). The importance of market positioning within these distribution networks cannot be overstated, yet the lack of comprehensive data and theoretical frameworks for micro-level analysis underscores the complexity of draw- ing definitive conclusions (Feyaerts et al., 2020). Selling to immediate social circles is often seen as a strategy of last resort for farmers constrained by high transaction costs or market access issues, highlighting the challenges faced by rural farmers in developing economies (Timmer, 1997; Key et al., 2000; Fackler & Goodwin, 2001; Faf- champs & Hill, 2005). Given the disparate nature of existing studies, often limited to specific case studies, this paper aims to bridge the gap by proposing a micro-level measure of market positioning. This contribution seeks to enrich the ongo- ing discussion on the nuanced relationship between market participation and food consumption, providing a new analytical lens to examine the intricate dynamics at play in agricultural commercialization and its broader socioeconomic impacts. 247Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 3. THE PROPOSED POSITIONING MEASURE Value chain downstream positioning, which denotes the proximity of production to final demand, inte- grates development and trade concepts, highlighting the importance of geographical distance and market access on agricultural decision-making (von Thünen, 1966; Chamberlin & Jayne, 2013; Oosting et al., 2014; Mon- talbano & Nenci, 2022). This approach reveals the pro- found effect of location on farming strategies, extending beyond mere physical distance to include factors like travel costs (Nanyeenya et al., 2007; Duncan et al., 2013). Kaplinsky and Morris (2001) outline three value chain elements: key buyers, transaction dynamics, and critical factors. Montalbano et al. (2018) further refine this by introducing a “Positioning Dummy”, based on the identity of market outlets, for distinguishing between upstream and downstream positions, highlight- ing the significance of broader market access. However, the challenge remains in developing a theoretical model that accurately captures value chain participation, espe- cially the volume of sales, a crucial aspect in Global Val- ue Chains (GVCs) discussions (Nenci, 2020). Traditional Input-Output (I-O) tables, despite their utility, fall short in detailing the entire value chain network (Montalbano & Nenci, 2022). Antràs and Chor (2018; 2022) expand on this by incorporating the sequence of production stages into the analysis, defining upstreamness (U) as the weight- ed average distance of a stage from final demand, and downstreamness from the proximity to primary produc- tion factors. The formula for upstreamness is given by: (1) where represents the dollar amount of each country’s sector needed to produce one dollar’s worth of industry output in another country (i.e., ). Downstream- ness is similarly defined, focusing on the distance from primary factors, emphasizing the role of value addition in determining chain positioning. Applying theoretical models to agricultural value chains reveals challenges, notably with data limitations and the non-linear structure of these chains, which often resemble “flatter” or “spider” configurations, complicat- ing the application of Antràs and Chor’s (2018) frame- work. Antràs & Chor (2019) distill market positioning into the share of output sold directly to consumers, cre- ating a micro-level downstreamness indicator. How- ever, this indicator faces limitations in capturing the intricacies of market chains due to data scarcity. Build- ing on the insights from Veugelers et al. (2013), Giunta et al. (2022), and Nenci et al. (2022), who examine val- ue chain participation through the share of imported intermediates, this study proposes a refined indicator for agricultural value chain positioning that accounts for the sequence of intermediaries from farmers to final retailers, emphasizing the critical role of selling posi- tions within the chain. It accounts for the intermediary sequence from farmers to end retailers, highlighting the critical role of selling positions within the chain. This is quantitatively represented as: = Selling Position n.1 × + Selling Position n.2 × + ⋯; (2) where the first integer term indicates the Selling Position number (i.e., the chain positioning of acquiring inter- mediaries), equals the quantity of crop sold by each household, and Y is the total quantity of that crop sold along the crop-selling chain. The current literature reveals numerous shortcom- ings: the absence of a comprehensive, standalone indica- tor; incomplete data that lead to partial interpretations; and a neglect of the impact of vertical integration on positioning. These deficiencies underscore the neces- sity for a refined micro-level downstreamness indicator. This improved indicator should account for the selling position, incorporate the geographical selling location, and consider the multiplicity of buyers. Furthermore, it should integrate the welfare effects of positioning, net of geographical distance, and the impact of trade costs on positioning (Fafchamps & Hill, 2005; Mancini et al., 2023). To address these concerns, an enhanced formula is proposed: (3) where equals , equals , equals the quantity of crop sold, and Y equals the total quantity of that crop sold along the crop market selling chain. It is important to note that farming households are commonly involved in multiple crop value chains. Hence, the resulting posi- tioning value attached to them will be the average of their positioning score in each single crop selling chain. The proposed indicator, following the theory of Antràs and Chor (2013), incorporates crop demand elasticities as a tuning parameter, suggesting that lower elasticity values (ρ) increase the likelihood of vertical integration 248 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 in the value chain. This tuning parameter, formulated as 1/(1 - ρ), reflects the observation that own-price elastici- ties are negative for most commodities, as indicated by Deaton & Muellbauer (1980), and particularly low for crops like maize and sorghum, which exhibit among the lowest values (Tafere et al., 2010). Finally, the proposed indicator facilitates comparability across different types of value chains and fields by being structured as an index ranging from 0 to 1. The adaptation of Antràs and Chor’s framework assumes farmers as a type of firm, with the analy- sis specifically targeted at a singular stage of the chain. The investigation is confined to the dynamics of selling chains, under the premise that scrutinizing solely the farmers’ roles does not encompass the evaluation of add- ed value. Additionally, it is assumed that farmers have the capability to engage in multiple crop value chains simultaneously, illustrating a diversified strategy to mar- ket participation. The proposed approach integrates ele- ments from development and trade literature, such as “selling position,” “selling location,” and “crop ratio,” while updating the model to reflect non-sequential pro- duction stages and the diverse nature of agricultural sales, as suggested by recent insights (Davis et al., 2018; Antràs & Chor, 2022). 4. EMPIRICAL FRAMEWORK In the establishment of the empirical framework for this analysis, Figure 1 systematically delineates the array of market outlets available to smallholder farm- ers. By illustrating the comprehensive network through which agricultural products transition from production to the end consumer, Figure 1 methodically outlines the agricultural value chain, beginning with input suppli- ers – such as seeds, fertilizers, pesticides, and herbicides – primarily provided by either agricultural development agencies or private entities (Audet-Bélanger et al., 2013; Ugonna et al., 2015; Ayele et al., 2021). Notably, village collectors often constitute the ini- tial market entry point in countries such as Nigeria and Ethiopia (Ayele et al., 2021; Babama’aji et al., 2022), lead- ing to further engagement with agricultural coopera- tives and processors. These entities are instrumental in vertical integration, offering essential services like free storage and facilitating transactions with exporters, or local food agencies (Gabre-Madhin & Goggin, 2006; USAID, 2017 Additionally, the figure highlights the role of wholesale markets situated in main districts, which acquire crops either directly from farmers or via inter- mediaries, thereby augmenting access to storage and communication channels (Ayele et al., 2021). The significance of private companies in providing downstream positioning benefits is also emphasized, noting their contribution to higher income levels and the facilitation of technology spillovers, which in turn enhance income stability and food security (Case, 1992; Bandiera & Rasul, 2006; Matuschke & Qaim, 2009; Bar- rett et al., 2017). The analysis further acknowledges the importance of mobile markets and commodity exchange markets as additional, critical conduits connecting smallholders with formal market segments (FAO, 2020). The variability in the length of value chains necessitates that farmers engage at various stages, with their posi- tioning influenced by external contingencies such as nat- ural disasters (Biggeri et al., 2018). Leveraging insights from Montalbano et al. (2018), this research assumes that direct sales to primary mar- kets or private entities potentially yield higher profitabil- ity, indicative of sophisticated management expertise. Consequently, market outlets are classified into seven distinct groups, spanning from upstream positions, characterized by lesser reward, to downstream positions, associated with greater economic benefit. Specifically, · Outlet n.1: Roadside Selling Position n.1 · Outlet n.2: Agricultural Cooperatives Selling Position n.2 · Outlet n.3: Farm-Based Association Figure 1. A Standard Crop Value Chain in Ethiopia and Nigeria. Source: Author’s adaptation from Gabre-Madhin & Goggin (2006); Rashid & Negassa (2013), Gashaw & Kibret (2018); FAO (2020); Ayele et al. (2021); Babama’aji et al. (2022). 249Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 · Outlet n.4: Government Agencies Selling Position n.3 · Outlet n.5: Political Leader · Outlet n.6: Private Trader in Local Market Selling Position n.4 · Outlet n.7: Local Merchant/Grocery · Outlet n.8: Local Market Selling Position n.5 · Outlet n.9: Mobile Market · Outlet n.10: Private Trader in Main Market Selling Position n.6 · Outlet n.11: Main Market · Outlet n.12: Private Company Selling Position n.7 · Outlet n.13: Auction Market Also, a final note must be made for selling locations, whose score scale of 3 is defined, due to limited observa- tions, as follows1: – Selling Location n.1: Selling within the village or near the village – Selling Location n.2: Selling near the town or near the district – Selling Location n.3: Selling outside the district or outside the region 5. DATA AND DESCRIPTIVE STATISTICS This study utilizes the LSMS-ISA dataset from Ethi- opia and Nigeria, gathered by the Ethiopian Central Sta- tistics Agency, the National Bureau of Statistics of Nige- ria, and the World Bank across three survey waves from 2010 to 2016. The final dataset, nationally representative, comprises approximately 1460 and 1178 observations for Ethiopian and Nigerian farmers, respectively, commer- cializing their crops. The analysis draws from household and agricultural data within the LSMS-ISA dataset, focusing on farm- ers’ responses about their main crop buyers, encapsu- lated in a network roster of over 30 actors, allowing identification of primary and secondary commercial partners. Variable definitions and descriptive statistics for household variables are detailed in Tables A.1 (vari- able descriptions), A.2 (Ethiopia - summary statistics), and A.3 (Nigeria - summary statistics) in the Appendix, noting omissions in the Nigerian dataset due to miss- ing data. Geographical analysis reveals that households are generally located far from main markets, with Figure 2 depicting the regional distribution of households in Ethiopia and Nigeria. Selling patterns, as shown in Figure A.1 in the Appendix, indicate a preference for selling large crop 1 If households resides in the main market, this measure can be bypassed. amounts outside formal markets, particularly with rela- tives, friends, and neighbors. Notably, events like the 2011 floods in Ethiopia significantly influenced these trends, with a marked shift in the selling outlets used by farmers. Figure 3 and 4 categorize crop sales quantities from Figure A.1 by selling position and location, respectively. Specifically, as shown in Figure 3, Ethiopian farmers tend to sell upstream, mainly to agricultural coopera- tives and farm-based associations, while Nigerian farm- ers predominantly sell downstream but also through local markets. The distribution of sales by location (see Figure 4) shows a majority within or near villages, with a notable por- tion of Nigerian crops sold outside the region before 2012. Table 1 presents summary statistics for food and total consumption2,3, alongside food quantity for sensi- tivity analysis. Consumption data in nominal values, adjusted for inflation using the 2010 CPI,4 shows that food consump- tion constitutes over 70% of total expenditure for house- holds in both Ethiopia and Nigeria. Table 2 shows the downstreamness indicator results, indicating that Ethiopian rural households have an aver- age downstreamness score of 0.02, suggesting a pre- dominant upstream positioning within market chains, a trend also observed in Nigeria but with more variability. These figures indicate that in the Ethiopian sample, the positioning indicator for crop-specific value chains ranges from 0 to 0.7, with rural households having an average downstreamness value of approximately 0.02. In Nigeria, there is greater heterogeneity in downstream- ness values, with a maximum of 1 in 2011 and a decrease to 0.45 in 2013. These findings support the transition of food supply chains from local and fragmented to longer and geographically connected ones (IFAD, 2016). Farm- ers in the market chain predominantly position them- selves upstream (Montalbano et al., 2018), and the crops they sell exhibit low price elasticity of demand, as dem- onstrated by studies from which crop elasticities are tak- en: World Bank Group (1982), Akinleye & Rahji (2007), Pan et al. (2009), Tafere et al. (2010), Ashagidigbi (2019), Adeniji (2019), and Obayelu et al. (2019) Moreover, ana- lyzing the data while excluding outliers reveals micro- 2 Following the LSMS-ISA documentation on the Ethiopia Socioeco- nomic Survey, consumption total expenditures include three sources: food, non-food and education expenses for each household. 3 As specified in the “Basic Information Document” for the LSMS-ISA Nigeria General Household Survey, total consumption is calculated as the sum of all food, education, non-food, and imputed rent expendi- tures. Expenditures were calculated and aggregated to household level and converted to per capita terms. 4 Available at http://data.worldbank.org/indicator/FP.CPI.TOTL. http://data.worldbank.org/indicator/FP.CPI.TOTL 250 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 trends in market positioning dynamics over the years (Figure A.2, A.3 and A.4 in the Appendix). 6. IDENTIFICATION STRATEGY, RESULTS, AND SENSITIVITY This section details the identification strategy and results of this study, including analyses of alternative positioning indicators, primary findings for the amend- ed indicator, and subsequent sensitivity and robustness assessments (Subsections 6.1 to 6.3). 6.1. Identification Strategy The empirical strategy tests the correlation between the amended value-chain positioning indicator and the natural log of food and total household consumption, utilizing a semi-logarithmic econometric model. This approach incorporates household and production char- Ethiopia Nigeria Figure 2. Household Density per Region/State. Source: Author’s own elaboration from LSMS-ISA data. Ethiopia Nigeria Figure 3. Quantity of Crop Sold (in Kilos) per Position. 251Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 acteristics to control for heterogeneity, following Dercon (2004), Chaudhuri (2003), and Montalbano et al. (2018). The specification employed is: Ch,t = αh + βt + ϕ1Downh,t + δXh,t + εh,t; (4) where Ch,t is alternatively the natural log of house- hold per capita5 of food consumption and total con- 5 LSMS-ISA household surveys for Nigeria do not provide per adult equivalencies in consumption aggregates. Considering the current debate around the likelihood of incurring in mistakes when self-calculating equivalencies (see, Deaton & Margaret, 1998) and to make estimates across the two samples comparable, the consumption levels for Ethiopia are reported in terms of per capita in line with those for Nigeria. Ethiopia Nigeria Figure 4. Ethiopia - Quantity of Crop Sold (in Kilos) per Selling Location. Table 1. Dependent Variables Summary Statistics. N. of Observations Mean Standard Deviation Minimum Value Maximum Value Et hi op ia Food Consumption (decimals, ETB) 1,394 1,666.08 1891.68 156.24 41,616.74 Total Consumption (decimals, ETB) 1,394 2,021.67 1986.22 188.59 42,073.02 Sens. Test Food Quantity (decimals, Kg) 1,459 7.15 37.77 0.07 1,004.40 N ig er ia Food Consumption (decimals, NGN) 1,178 56,075.51 74,259.26 4,751.17 1,672,537 Total Consumption (decimals, NGN) 1,178 78,349.05 88,541.40 9,334.46 1,699,927 Sens. Test Food Quantity (decimals, Kg) 1,175 32.9454 156.10 0.04 3268.39 Table 2. Downstreamness Indicator Results. N. of Obserbations Mean Standard Deviation Minimum Value Maximum Value Et hi op ia Downstreamness in 2011 (decimals) 521 0.02 0.07 0.00 0.70 Downstreamness in 2013 (decimals) 1,026 0.02 0.07 0.00 0.70 Downstreamness in 2015 (decimals) 883 0.02 0.07 0.00 0.70 N ig er ia Downstreamness in 2011 (decimals) 346 0.05 0.12 0.00 1.00 Downstreamness in 2013 (decimals) 757 0.00 0.03 0.00 0.45 Downstreamness in 2015 (decimals) 515 0.03 0.09 0.00 0.86 252 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 sumption, Downh,t represents the value of the proposed downstreamness indicator, and Xh,t is the vector of con- trol variables for household heterogeneity and includes observable household and production characteristic. A non-zero ϕ1 coefficient suggests a significant relation- ship between market positioning and consumption. The model also accounts for unobserved heterogeneity, time, and location effects, with fixed effects to mitigate time- variant unobserved biases. The study exclusively considers households engaged in value chains to focus on the impact of market chain positioning.6 Possible reverse causality between food/ total consumption and market positioning is not expect- ed to impact the estimates because proxies for food con- sumption and commercialization are measured in dif- ferent time periods. Robustness checks in the Appendix include the Heckman correction for selection bias and the control function method to address self-selection bias, as suggested by Wooldridge (2015). 6.2. Main Empirics Table 3 contrasts the proposed adjusted «à la Antràs and Chor» (AC) indicator from Equation [3] with com- mon downstreamness indicators like the crop share ratio, the geographical distance to the main market and 6 Households selling their crop in non-market outlets account for around 7-8% of the final sample for Ethiopia. Montalbano et al. (2018)’s positioning in terms of crop market outlets. Model comparison using adjusted R-squared, AIC, and BIC coefficients reveals the superior performance of the proposed indicator with respect to traditional mar- ket positioning proxies. This finding challenges the com- monly used proxies for marketing factors, orientation, and positioning that have been traditionally employed in empirical studies (e.g., inter alia, Montalbano et al., 2018; Migose et al., 2018; Mkuna & Wale, 2022). Table 4 reports the positive impact of downstream positioning on consumption levels in Ethiopia. All esti- mates were adjusted for household production character- istics to account for additional latent variables that could explain variations in market positioning, effectively reducing potential endogeneity resulting from selectivity bias (Fafchamps & Hill, 2005). By accounting for time- and geography-related factors, it is observed that Ethiopian farmers posi- tioned downstream in the market experience signifi- cantly higher per-capita consumption levels compared to farming households with similar characteristics but lower positioning scores. Specifically, a 0.01 increase in their market positioning boosts per-capita food consumption by over 50% and total consumption by more than 40%, challenging the view that consump- tion patterns solely depend on food price shifts. Ignor- ing household and geographic specifics leads to under- estimating the “market positioning effect.” The impact Table 3. Downstreamness Indicators Comparison – Main Results for Ethiopia. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) (7) (8) Proposed Indicator (ln) Crop Share (ln) Distance to Market Market Outlets Adjusted Down. (ln) Crop Share (ln) Distance to Market Market Outlets Downstreamness 42.01*** 0.12* -0.20 0.07 35.96*** 0.08 -0.06 0.04 (12.91) (0.07) (1.64) (0.10) (11.01) (0.05) (1.44) (0.09) Controls Yes Yes Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Yes Yes Yes Yes Trends Yes Yes Yes Yes Yes Yes Yes Yes Constant 6.04*** 6.73*** 9.25 6.28*** 6.66*** 7.20*** 8.84+ 6.86*** (0.99) (1.09) (6.58) (1.03) (0.85) (0.94) (5.82) (0.89) N. of Observations 1,387 1,387 1,381 1,387 1,387 1,387 1,381 1,387 N. of HH_id 1,097 1,097 1,093 1,097 1,097 1,097 1,093 1,097 R-squared Adj. 0.72 0.71 0.64 0.71 0.73 0.72 0.69 0.75 AIC -1316.97 -1266.77 -1013.52 -1251.08 -1697.19 -1644.86 -1371.93 -1633.08 BIC -615.49 -565.29 -375.39 -549.61 -995.71 -943.38 -733.80 -931.60 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 253Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 is consistent across food and total consumption, with accuracy improving when location controls are includ- ed. Despite the size of the hypothesized change in posi- tioning score is observed in less than 2% of cases, its significant effect highlights the importance in driving consumption changes among households with varying initial downstream positions. Similarly, Table 5 presents the Nigerian results, mir- roring the Ethiopian findings. A 0.01 enhancement in positioning indicator value corresponds to approximate- ly 40% and 37% increases in per-capita food and total consumption, respectively. In Nigeria, like Ethiopia, farmers sell through vari- ous channels including local markets, cooperatives, and directly to processors, with a crop range extending to non-food items like cotton. The empirical strategy to Nigerian data7 yields results mirroring Ethiopia’s: a 0.01 improvement in market positioning leads to roughly a 40% increase in per-capita food consumption and a 37% increase in total consumption. This confirms that better market positioning, after accounting for variables like district characteristics and time trends, significantly enhances consumption levels for farmers in both coun- tries. 7 The variable “crop code” is not controlled for in the case of Nigeria, given the few changes in labeling across the years that may have altered the panel dataset combined “crop code” variable. Also, interview month is omitted due to several missing observations. Consumption data rely on the postharvest surveying visit. Data on fertilizer use are from the post-planting questionnaire. 6.3. Sensitivity and Robustness Checks Table 6 shows the result of the sensitivity analysis for food quantity in both samples. Food quantity is also measured in logarithmic form, just like consumption. Results in both countries are very similar. Food quan- tity is positively affected by higher positioning scores for all the specifications provided for both samples. If rural households are able to increase their positioning indica- tor value by 0.01, on average, and ceteris paribus, they are able to more than double their food quantity level both in Ethiopia and Nigeria. Therefore, impact of increased posi- tioning in value chains on food quantity per household is greater, in terms of magnitude, than the impact on food and total consumption levels per capita. Robustness checks are reported in Table 7 above for Ethiopia and Table 9 for Nigeria. Table 7 shows the results of Table 4 replicated with population sampling weights.8 Results are robust and consistent with what was previously obtained. As in Table 4, results for both food and total consumption show the same dynamics: lower significance for the baseline specification and a downward bias if district dummies are not in the control group but only the wave dummies are considered. Similarly, in Table 8 above, the results for Nige- ria (shown in Table 5) are replicated with the provided 8 Conversely to Nigeria, combined population weights are not reported in the LSMS-ISA Ethiopia Rural Socioeconomic Surveys. To avoid mis- takenly corrections, population weights were adjusted across the years by attaching the latest weight to the household’s highest surveying wave. Table 4. Main Results for Ethiopia – Panel Fixed Effects Clustered by Household ID. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 31.04** 43.11*** 42.01*** 27.17* 36.13*** 35.96*** (15.03) (12.74) (12.91) (14.31) (11.03) (11.01) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes Constant 7.65*** 5.95*** 6.04*** 7.99*** 6.55*** 6.66*** (0.63) (0.99) (0.99) (0.58) (0.85) (0.85) N. of Observations 1,387 1,387 1,387 1,387 1,387 1,387 N. of HH_id 1,097 1,097 1,097 1,097 1,097 1,097 R-squared Adjusted 0.31 0.72 0.72 0.31 0.73 0.73 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, + p<0.15. 254 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 population sampling weights. Coefficient estimates for the proposed amended positioning indicator in Table 7 and 8 are significant for almost all the specifications provided in both samples. Controlling for factors such as time and district fixed effects, including region, district, and village dummies, as well as trends, Ethiopian and Nigerian households who participate and have a better position in the market chain register, on average and cet- eris paribus, have a per-capita equivalent food and total consumption level around 20% times higher than those farming households with the same characteristics and who have a position-indicator score lower than 0.01 unit. To address potential selection bias from excluding about 100 households not commercializing their crops within value chains, this study utilizes the xtheckmanfe Stata module by Rios-Avila (2020) to account for endo- geneity and sample selection. The results, adjusted for time effects and Heckman correction, are in Appen- Table 5. Main Results for Nigeria – Panel Fixed Effects Clustered by Household ID. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 31.50*** 33.39*** 33.85*** 26.79** 31.56** 31.46** (11.94) (12.16) (12.50) (10.75) (13.97) (14.19) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes Constant 10.75*** 11.45*** 10.93*** 11.08*** 11.49*** 11.03*** (0.24) (0.28) (0.51) (0.28) (0.24) (0.58) N. of Observations 1,178 1,178 1,178 1,178 1,178 1,178 N. of HH_id 979 979 979 979 979 979 R-squared Adjusted 0.41 0.82 0.82 0.32 0.74 0.74 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. Table 6. Sensitivity Testing with Food Quantity. Food Quantity (Ethiopia) Food Quantity (Nigeria) (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 61.86** 70.51* 81.38** 61.07** 82.03*** 78.18*** (26.55) (36.81) (36.54) (25.31) (26.60) (28.09) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes Constant -1.09 7.68*** 7.76*** 2.07*** 2.08*** 2.89*** (1.05) (1.97) (1.92) (0.43) (0.38) (0.77) N. of Observations 1,452 1,452 1,452 1,175 1,175 1,175 N. of HH_id 1,121 1,121 1,121 977 977 977 R-squared Adjusted 0.13 0.53 0.54 0.59 0.88 0.88 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 255Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 dix Table A.4. Moreover, the issue whether households’ participation and positioning in markets could be influ- enced by characteristics affecting both consumption and market position, is addressed using a control function approach. This approach involves adding the residual of a first-stage regression, which predicts the “Down- streamness Positioning” binary variable, to the main regression as an exclusion restriction. This residual, denoted as ρ, is designed to be uncorrelated with the endogenous variable, thereby providing unbiased esti- mators in the main equation and mitigating self-selec- tion bias (Wooldridge, 2015). Table A.5 in the Appendix reports the results, showing very consistent outcomes with the previous regressions. 7. DISCUSSION AND CONCLUSIONS To summarize, the empirical outcomes indicated that changes in market positioning significantly and Table 7. Main Results with Population Sampling Weights for Ethiopia. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 22.68+ 21.85+ 21.47+ 20.08+ 21.81* 22.39* (15.07) (14.50) (14.61) (13.58) (13.00) (12.97) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes Constant 7.24*** 6.91*** 7.05*** 7.61*** 7.56*** 7.68*** (0.62) (1.23) (1.22) (0.58) (1.07) (1.05) N. of Observations 1,387 1,387 1,387 1,387 1,387 1,387 N. of HH_id 1,097 1,097 1,097 1,097 1,097 1,097 R-squared Adjusted 0.33 0.72 0.73 0.34 0.70 0.71 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. Table 8. Main Results with Population Sampling Weights for Nigeria. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 15.96 20.52* 21.58** 11.98 18.26 18.56+ (12.26) (10.46) (10.79) (10.89) (13.01) (13.02) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes Constant 10.90*** 11.27*** 10.38*** 11.29*** 11.38*** 10.52*** (0.27) (0.23) (0.51) (0.38) (0.23) (0.65) N. of Observations 1,172 1,172 1,172 1,172 1,172 1,172 N. of HH_id 973 973 973 973 973 973 R-squared Adjusted 0.33 0.83 0.83 0.23 0.76 0.77 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 256 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 consistently matters to increasing the consumption levels of Ethiopian farmers selling crops in the market chain. From this perspective, the findings of Montalbano et al. (2018) extend to Ethiopia and Nigeria regarding the positive role of farmers’ market participation in Uganda. However, the results contradict the conclusion of Mon- talbano et al. (2018), arguing instead for the significance of market intermediaries. Finally, a concern should be sounded concerning the external validity of these findings. Since the focus is on investigating market positioning, the overwhelming majority of farmers who produce crops only for home consumption are excluded from the analysis. This gap hampers the ability of the analysis to derive consistent estimates for the entire population of a crop producer. Nevertheless, results of the parallel test conducted for Nigeria are highly reassuring regarding the proposed amended indicator’s external validity. Historically, the examination of farmers’ market decisions traces back to the early 1990s, with seminal works by Fafchamps (1992), von Braun (1995), and Key et al. (2000). Yet, a comprehensive analysis of market structure from the farmers’ perspective remains elu- sive. The motivation behind this work lies on the idea that farmers selling to wholesalers/producers are better off than farmers that sell to the most proximate mar- kets. This work adjusts Antràs and Chor’s downstream- ness indicator to farming households’ selling locations and buyer-market chains. It contributes to the literature by creating a conceptual framework for farmers’ mar- ket positioning and a replicable setting for assessing the effects of market positioning on both food security and welfare levels. Utilizing national, representative household sur- veys from Ethiopia and Nigeria, this paper investigates the relationship between market positioning scores and consumption levels, revealing that farmers posi- tioned further downstream in the value chain experi- ence enhanced food and overall consumption. This study evidences the significant impact of micro-variations in market positioning on rural development and establishes the superiority of the Antràs and Chor-informed indica- tor over other alternatives for assessing market position- ing’s welfare effects. The findings, robust across various empirical models and further supported by sensitivity analyses focusing on food quantity, underscore the reli- ability of the research question addressed. This work fills a critical void in existing literature by offering a nuanced, well-validated indicator that assesses farmers’ value chain positioning with a novel empha- sis on market outlet identities and selling locations. By incorporating demand elasticity as a pivotal param- eter for vertical integration, as suggested by Antràs and Chor (2013), the indicator not only adheres to but also expands upon the theoretical underpinnings of value chain analysis. Empirical validation from Ethiopia and Nigeria illustrates that slight enhancements in market positioning lead substantial increases in consumption, with 0.01 rise in positioning yielding over a 40% uplift in per-capita consumption levels. The study also acknowledges the challenges in com- paring across countries due to incomplete data in exist- ing datasets, especially regarding the network roster for inputs acquisition. It advocates for a broader data collec- tion strategy encompassing trade flows for all actors in the agricultural chain, aiming to elucidate the value add- ed along a farmer’s selling line. This approach promises a more holistic understanding of the agricultural value chain’s dynamics and its implications for farmer welfare. ACKNOWLEDGMENTS For this paper, I am grateful to my supervisors, Prof. Pierluigi Montalbano and Prof. Marco Letta, for their con- stant invaluable feedback on this work. I would also like to express my gratitude to the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team for providing the data. Furthermore, I extend my thanks to Prof. Carlo Pietrobelli. Prof. Enrico Marvasi, Prof. Marco Sanfilippo, Prof. Donato Romano, Prof. Fabio Santeremo, Prof. Luca Salvatici, Dr. Sara Savas- tano and the participants at the 1st DevEconMeet, the Sapienza PhD Meeting 2022, the 12th AIEAA Confer- ence, IFAD & FAO “Working & Networking” Workshop on Climate Change and Food Systems, the 3rd Annual Southern PhD Economics Conference (ASPEC), as well as the 7h Nordic Development Conference (NorDev) for their comments on previous versions of this paper. I am also thankful for the doctoral research support received from Sapienza University of Rome. 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Variable name Definition Time period Source Gender of the Household Head Gender of the household head (binary, 1=female) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Age of the Household Head (decimals) Age years of the household head (decimals) 2011-2015 World Bank LSMS-ISA Ethiopia only Number of Household Members in the Labor Force (decimals) Number of household members (binary, 1=female) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Household Size (decimals) Number of people in the household (decimals) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Average Years of Education for Household Adults (decimals, years of schooling) Average education level attained by the household adult members (values from 0 to 8) 2011-2015 World Bank LSMS-ISA Ethiopia only Average Years of Education for Household Head (decimals, years of schooling) Average education level attained by the household head (values from 0 to 8) 2011-2015 World Bank LSMS-ISA Ethiopia only Number of Household Infants (decimals) Number of household members in the infant age range (decimals) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Number of Household Children (decimals) Number of household members in the children age range (decimals) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Household Years of Education (decimals, years of schooling) Average education level attained by all household members (values from 0 to 8) 2011-2015 World Bank LSMS-ISA Ethiopia only Harvest Crop (decimals, Kg) Quantity of crop harvest in the surveying period (decimals, Kg) 2011-2015 World Bank LSMS-ISA Ethiopia only Field Size (decimals, Ha) Average field size in the surveying period (decimals, Ha) 2011-2015 World Bank LSMS-ISA Ethiopia only Free Seed Event of receiving free seed (binary, 1=no and 2=Yes) 2011-2015 World Bank LSMS-ISA Ethiopia only Seed Purchase Necessity of purchasing seed (binary, 1=no and 2=Yes) 2011-2015 World Bank LSMS-ISA Ethiopia only Fertilizer Use Use of fertilizers (binary, 1=no and 2=Yes) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Fertilizer Purchase Purchase of fertilizers (binary, 0=no and 1=Yes) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Leftover Fertilizer Presence of leftover fertilizers (binary, 0=no and 1=Yes) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria Free Fertilizer Event of receiving free fertilizers (binary, 0=no and 1=Yes) 2010-2016 World Bank LSMS-ISA Ethiopia and Nigeria https://doi.org/10.1080/21683565.2018.1530716 https://doi.org/10.1080/21683565.2018.1530716 https://doi.org/10.3368/jhr.50.2.420 261Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 Table A.2. Households Summary Statistics for Ethiopia. N. of observations Mean Standard Deviation Minimum Value Maximum Value Gender of the Household Head (binary, 1=female) 1,460 0.18 0.39 0 1 Age of the Household Head (decimals) 1,460 45.72 14.21 18 97 Number of Household Members in the Labor Force (decimals) 1,460 2.69 1.38 0 10 Household Size (decimals) 1,460 5.77 2.19 1 14 Average Years of Education for Household Adults (decimals, years of schooling) 1,460 1.70 1.83 0 8 Number of Household Infants (decimals) 1,460 0.58 0.80 0 5 Number of Household Children (decimals) 1,460 2.39 1.68 0 10 Household Years of Education (decimals, years of schooling) 1,460 1.70 1.83 0 8 Harvest Crop (decimals, Kg) 1,460 914.13 752.98 0 3,249.61 Field Size (decimals, m2) 1,460 9030.31 9370.73 0 38,917.46 Free Seed (binary, 2=Yes) 1,459 1.99 0.12 1 2 Seed Purchase (binary, 2=Yes) 1,462 1.94 0.24 1 2 Fertilizer Use (binary, 2=Yes) 1,462 1.81 0.40 1 2 Table A.3. Households Summary Statistics for Nigeria. N. of Observations Mean Standard Deviation Minimum Value Maximum Value Gender of the Household Head (binary, 1=female) 1,178 0.20 0.40 0 1 Number of Household Members in the Labor Force (decimals) 1,178 2.48 2.13 0 13 Household Size (decimals) 1,178 6.41 3.27 1 28 Number of Household Infants (decimals) 1,178 0.55 0.92 0 6 Number of Household Children (decimals) 1,178 1.90 2.22 0 14 Fertilizer Purchase (binary, 1=Yes)) 1,178 0.33 0.47 0 1 Letfover Fertilizer (binary, 1=Yes) 1,178 0.03 0.17 0 1 Free Fertilizer (binary, 1=Yes) 1,178 0.01 0.10 0 1 Fertilizer Use (binary, 1=organic) 1,178 1.69 0.46 1 2 Ethiopia Nigeria Figure A.1. Quantity of Crop Sold (in Kilos) Mean Values per Selling Outlet. 262 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 Figure A.2. Kernel Density Downstreamness Positioning Indicator Ethiopia 2011. Figure A.3. Kernel Density Downstreamness Positioning Indicator Ethiopia 2013. 263Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 Figure A.4. Kernel Density Downstreamness Positioning Indicator Ethiopia 2015. Table A.4. Sample Bias – Panel FE with the Heckman Correction. Food Consumption Total Consumption (1) (2) (3) (4) Heckman FE Heckman FE Wave Fixed Effects Wave Fixed Effects Wave Fixed Effects Wave Fixed Effects Downstreamness 50.88* 26.41** 48.15+ 22.92** (30.07) (12.26) (30.42) (11.63) Controls Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Constant 7.57*** 7.79*** 7.64*** 8.15*** (0.42) (0.61) (0.31) (0.57) N. of Observations 1,457 1,389 1,457 1,389 N. of HH_id 1,098 1,098 R-squared Adjusted 0.26 0.25 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. Note: Control variables “household average education level” and “crop code” are excluded as their inclusion in the regression models does not allow convergence in the Heckman Fixed Effect computa- tional tools. Bootstrap replications are set to 50. 264 Tulia Gattone Bio-based and Applied Economics 13(3): 245-264, 2024 | e-ISSN 2280-6172 | DOI: 10.36253/bae-15464 Table A.5. Self-Selection Bias – Control Function Method. Food Consumption Total Consumption (1) (2) (3) (4) (5) (6) Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Wave Fixed Effects District-Wave Fixed Effects District-Wave FE HH Trends Downstreamness 27.23* 43.03*** 41.03*** 24.28+ 36.23*** 35.86*** (15.71) (13.04) (13.07) (14.97) (11.23) (11.20) Controls Yes Yes Yes Yes Yes Yes Wave FE Yes Yes Yes Yes Yes Yes District FE Yes Yes Yes Yes Trends Yes Yes r 0.14 0.01 -0.01 0.10 -0.01 0.01 (0.09) (0.10 (0.10) (0.08) (0.09) (0.09) Constant 7.97*** 6.24*** 6.25*** 8.31*** 6.84*** 6.95*** (0.63) (0.99) (0.98) (0.59) (0.85) (0.86) N. of Observations 1,387 1,387 1,387 1,387 1,387 1,387 N. of HH_id 1,097 1,097 1,097 1,097 1,097 1,097 R-squared Adjusted 0.22 0.71 0.69 0.24 0.72 0.72 Standard errors, clustered by households id, in parentheses: *** p<0.01, ** p<0.05, * p<0.1, +p<0.15. 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