1,720,966 research outputs found
Participation of Farmers in Market Value Chains: A Tailored Antràs and Chor Positioning Indicator
Full text linkThis 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 Measurement 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 various 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
Essays on positioning in value chains, proximity to markets and development
Full text linkThis thesis examines the relationship between value chain positioning, market proximity, and development. The research constituting this work focuses on the more recent debate in the development literature on market positioning and participation by farmers in rural developing areas. Although nowadays, crop commercialization is generally considered to have positive effects on development, the literature on the main determinants of this relationship still needs to reach definitive conclusions.
Market participation allows farmers to diversify their production, bringing significant advantages to small-scale producers, like access to new customers, lower costs, and business risk diversification. Nevertheless, this outcome could be very different in a risky environment like the one in developing countries. This scenario can be exacerbated (or eased) in agricultural product value chains. The literature has vastly debated how a market value chain is structured, but it has yet to discuss how it gets structured, especially at the farmer level.
From a “macro” point of view, the conceptual framework for (global) value chain participation and positioning stands on what was primarily outlined by Antràs & Chor in 2013 and 2018. At the “micro” level, particularly in development studies, the theoretical framework around positioning in VCs for smallholder farmers is still unsettled. At the same time, research on the links between market proximity and farmers’ vulnerability to food security is scarce.
Drawing from both the new trade literature on value chains and the development literature on household development and food security, this thesis addresses, at multiple scales of analysis, the research question of whether there is a connection between value chain positioning and welfare, as well as how proximity to markets affects the relationship between resilience and food security. More specifically, this work delivers a theoretical framework for farmers’ positioning in agricultural value chains; secondly, it explores the linkages across proximity to markets, resilience, and food security; finally, it tests whether better farmers' commercialization positioning both in terms of downstreamness in value chains and proximity to private companies, increases farmers' consumption as well as firms' productivity.
The contributions of this thesis to the current literature are conceptual and empirical. Essay 1 provides a first indicator for farmers’ positioning in value chains and tests its validity compared to its current alternatives. Based on a unique dataset, Essay 2 outlines the application of the Chaudhuri (2003) Vulnerability as Expected Poverty (VEP) framework to a subjective, non-monetary variable of food security and a new empirical approach using machine-learning techniques in the VEP calculation procedure. Lastly, Essay 3 goes beyond the boundaries of a sole-farmer value chain analysis with the integration in the analysis of a dataset focusing on firms, allowing to study the impacts of farming households' value chain positioning beyond the farm.
Although one cannot rely on ad hoc datasets for micro analyses on value chains, this work employs robust empirical methods and panel household data to test the validity of the proposed indicator. In addition, specifically in Essay 2, a mediation analysis estimates the indirect effect of resilience on food security volatility and vulnerability via proximity to markets (a standard proxy for market positioning in the development literature). Ultimately, the empirical assessment around the impact of farmers’ value chain positioning is improved by the inclusion of spatial panel models accounting for spatial spillovers in their calculations (see Essay 3).
Essay 1 proposes a micro-level measure for the positioning of farmers in value chains (in the absence of information on farmers’ inputs of production it turns out to measure positioning in “market chains”) inspired by the conceptual framework outlined by Antràs and Chor (2013, 2018), leveraging for farming households’ selling location and buyers. Using the World Bank's panel data for Ethiopia and Nigeria from "Living Standards Measurement Study: Integrated Surveys on Agriculture," this work also provides an empirical application of the proposed indicator showing how it performs better compared to the current alternatives at the micro-level. Secondly, by investigating the dynamics of farmers' food and total consumption over time and controlling for various household and production characteristics, as well as possible confounding factors, this Essay demonstrates that changes in farmers' welfare as proxied by food and total consumption, are positively affected by better positioning in the market chain.
Essay 2 advocates that proximity to final markets drives the link between resilience and food security. This work uses an exclusive dataset made available by the International Fund for Agricultural Development in 2017-2018 to contribute to the understanding of this impact. The paper applies a hybrid empirical approach combining machine learning algorithms with standard vulnerability approaches. Specifically, this work finds positive and statistically significant associations among proximity to markets, resilience, and food security. The work tests the plausibility of the exclusion restriction that market proximity does not affect food security fluctuations other than its impact on resilience capacity by implementing an instrumental variable approach. Moreover, using mediation analysis, this Essay reveals that market proximity accounts for a significant share of the positive correlation between household resilience and food security outcomes. The dampening role played by market proximity in decreasing welfare fluctuations is also confirmed when replacing food security outcomes with income ones. Overall, these findings suggest that policymakers should prioritize interventions to improve infrastructure and access to markets to boost household resilience and, in turn, decrease welfare fluctuations and vulnerability to food insecurity.
Essay 3 applies spatial panel regression models to a unique longitudinal dataset of firms and farmers’ surveys. This work stands on the availability of two datasets of surveys conducted by the World Bank Living Standards Measurement Study (LSMS-ISA) and by the Central Statistics Agency of Ethiopia in three data collection waves between 2010 and 2016 in Ethiopia. Based on the farmers’ positioning indicator developed in Essay 1, Essay 3 evaluates the welfare effects of positioning both in terms of market chains as well as geographical distance to firms with international exposure on farming households and its consequences on the productivity of local firms. Specifically, this paper tests the relationship between farmers' positioning in markets (estimated both in terms of geographical distance and positioning in value chains) and firms that import and export abroad, as well as the relationship between firms' closeness to farmers and their productivity levels. The key results are i) better farmers' positioning, both geographically to firms in global markets and in value chains, boosts households' welfare; ii) firms' proximity to farmers operating in value chains affects their total sales as well as productivity. These findings highlight how better farmer-to-firm and firm-to-farmer relationships represent a crucial means to foster local development
Economic and financial development as determinants of crypto adoption
No full textThis research investigates the macroeconomic determinants of crypto adoption, illuminating the potentials of cryptocurrencies to accelerate financial inclusion. By exploiting an extensive dataset from 165 countries between 2019 and 2021, this study employs various econometric methodologies, including Panel Feasible Generalized Least Squares (PFGLS), Robust Least Squares (RLS), and Quantile Regressions (QR). These classic econometric techniques are complemented by several machine learning techniques such as Bagging, Boosting, and Support Vector Machine (SVM) regressions, as well as Artificial Neural Networks (ANNs) and Naïve Bayes (NB) classification algorithms. The results show an interesting trend: cryptocurrency adoption is more prevalent in countries with robust financial markets and higher education levels. This suggests that crypto adoption is primarily a byproduct of sophisticated financial environments and an educated population, rather than a direct facilitator of financial inclusion
Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses
Full text linkThis study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises
The impact of socio-economic factors on the ecological footprint in Turkey: A comprehensive analysis using machine learning approaches
No full textThis study undertakes a comprehensive investigation into the impact of socioeconomic factors on the ecological footprint (EFP) in Turkey. It employs robust econometric methods and advanced Machine Learning (ML) models. The study employs Generalized Linear Models (GLM), Autoregressive Integrated Moving Average (ARIMA) models, Robust Least Squares (RLS) regression, and Granger causality tests to identify electric power consumption, real GDP, and life expectancy as significant positive drivers of the EFP. At the same time, trade openness and urbanization negatively impact the dependent variable. Advanced machine learning models, including Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) models, corroborate these findings, enhancing the study's comprehensiveness. The results underscore the importance of promoting energy efficiency, green growth, sustainable trade practices, and resource-efficient urban development to mitigate environmental impacts. The study provides robust empirical evidence and policy recommendations for reducing the EFP in Turkey, emphasizing the integration of sustainable practices in socioeconomic activities
Greenhouse gas emissions and road infrastructure in Europe: A machine learning analysis
No full textThis paper explores the determinants of greenhouse gas (GHG) emissions in Europe, focusing on transportation-related variables. By combining classical econometric models with Machine Learning (ML) techniques, we analyze data spanning from 2013 to 2021. The empirical findings highlight the complex relationship between newer passenger cars and GHG emissions, noting the significant impact of their production and increased usage. Conversely, the adoption of alternative fuel vehicles is found to significantly reduce emissions. This is further supported by ML models, which emphasize the critical role of car density and alternative fuel vehicles in determining emissions. Policy implications suggest the need for targeted interventions, including the promotion of electric and hybrid vehicles, enhancements in transportation infrastructure, and the implementation of economic incentives for clean technologies
Unleashing the power of innovation and sustainability: Transforming cereal production in the BRICS countries
Full text linkAmidst escalating food insecurity and climate change threats, which exacerbate food shortages and increase agricultural emissions, this paper explores transformative strategies in cereal production within the BRICS countries from 1990 to 2021. The uncontrolled growth of intensive agriculture, aimed at satisfying the growing global demand for food in a context already threatened by climate change, has led to a uniformity of crops with devastating impacts on biodiversity and ecosystem functioning, resulting in a transformation of soil and its
capacity to implement ecosystem services, such as food, fiber, and raw material production, nutrient recycling, carbon sequestration, clean water availability, and the regulation of water regimes and local temperatures. These changes have had negative consequences on agricultural production. Thus, sustainable agriculture faces three closely related challenges: reducing environmental impact, in-creasing productivity, and adapting to and mitigating
climate change. This analysis utilizes advanced econometric tools such as panel second-generation unit root tests, Westerlund’s cointegration test, second-generation long-run estimators, and the Dumitrescu-Hurlin causality test, together with several machine learning algorithms, to investigate the influence of technological innovations and improved land management on cereal yields. The findings demonstrate a positive correlation
between technological advancements, enhanced land management for cereal cultivation, and the food production index with increased cereal output. At the same time, emissions from agriculture significantly reduce yields over time. Furthermore, an interaction analysis reveals that the comprehensive integration of these factors significantly boosts cereal productivity. The study also identifies directional causal relationships between technological
and emission factors and cereal production, suggesting a complex interplay with land use. Sustainable land use is one of the key conditions for ensuring the ecological resilience of agricultural practices in terms of providing ecosystem services. Implementing these strategies calls for a collaborative approach among governments, policymakers, farmers, researchers, and other stakeholders, considering each BRICS nation’s unique environmental, socio-economic, and local contexts, and fostering regional cooperation to promote sustainable
agricultural practices
Balancing green power: Hydropower and biomass energy's impact on environment in OECD countries
No full textThe climate crisis, driven by greenhouse gas (GHG) emissions and environmental degradation, demands a transition to renewable energy for sustainable development. This paper analyzes the asymmetric effects of hydroelectric and biomass energy consumption on the ecological footprint (EFP) for 24 OECD countries from 1970 to 2022. By using a combination of advanced econometric approaches, including Method of Moments Quantile Regression (MMQR), Generalized Linear Models (GLM), and Robust Least Squares (RLS), with machine learning techniques such as Multivariate Adaptive Regression Splines (MARS) and Neural Networks (NN), this study will be able to identify complex nonlinearities that are not captured by traditional models. The results reveal that hydroelectric energy significantly reduces the EFP, particularly in high-pollution contexts, while biomass energy consumption worsens environmental degradation. These findings emphasize the urgent need for targeted policies to maximize the benefits of renewable energy sources and mitigate their risks. The study contributes to the literature by offering a comprehensive framework to analyze the environmental impacts of renewable energy, emphasizing the importance of methodological diversity and advanced modeling techniques as ways to achieve sustainability goals
Climate Change-Agrifood-Conflict Nexus Pathways: A Scoping Review of the Literature
No full textThis paper explores the pathways linking climate change and conflict, shedding light on the critical role the agrifood system can play as an intermediary. By conducting a scoping review of recent literature (2014-2024), this paper identifies two main pathways: increased competition over natural resources used in agriculture and decreased agricultural productivity. While some relationships, such as those examining the immediate causes of conflict – like threats to livelihoods, increased migration, and food insecurity – have been extensively studied, others, such as the impact of price changes and market forces, remain surprisingly underexplored. Various empirical approaches have been employed to identify these pathways, including ordinary least squares and logit/probit regressions as well as instrumental variables and structural equation modeling. Recently, the availability of high-resolution georeferenced datasets including socio-economic, environmental and conflict data, along with methodological advancements like spatial econometrics, have prompted more detailed and rigorous analyses. Current research gaps include the paucity of empirical studies at the micro level and the insufficient exploration of how market-based mechanisms influence the dynamics between climate change and conflict through the agrifood sector. The paper discusses future research directions, emphasizing the need for multidisciplinary approaches
Participation of farmers in market value chains: A tailored Antràs and Chor positioning indicator
No full textThis 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
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