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AbstractThe study was carried out in Murehwa, Mutoko, and Goromonzi district in Mashonaland East Province, Zimbabwe guided by the objective of analyzing factors that determine commercialization of African indigenous vegetables (AIVs) by smallholder farmers with different characterization. Quantitative techniques were used to collect data from three purposively sampled districts and 250 randomly sampled smallholder farmers using a questionnaire. The fractional regression model (FRM) was used to regress the explanatory variables. The paper revealed that the bulk of the households who participated were male-headed, married households and unemployed. Having a bulk of participants as male-headed households has a possible negative effect on commercialization. About 54.8% of households of the sampled population participated in the selling of African indigenous vegetables. Commercialization was significantly affected by the age of the household head, the quantity of AIVs sold, estimated income, employment status and distance to the main market which in this case is Mbare Musika. Mutoko has many households participating in the African indigenous vegetables commercialization. However, their index is low compared to Goromonzi which had few farmers but with a higher commercialization index, reflecting the volume per farmer that reaches the market. Commercialization of AIVs was found to be viable at 60 kg and above of quantities sold to the market. Household income had an inverse relationship with the commercialization index. Capacitating the younger generation in line with benefits of AIVs can enhance their participation in production and marketing and propel commercialization.

Micro and small wood-processing enterprises represent the heart of European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the provision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is used in the current research, which is a nonparametric, linear-programming-based approach that is commonly used to analyse the efficiency of organisational units. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterprises in EU countries and reveal the hidden inputs that facilitate efficiency generation. The economic efficiency evaluation was carried out on the basis of the official statistical data for the micro and small wood-processing companies in EU member states for the period 2015–2020 by performing a two-stage DEA analysis. The data used were standardised by value per employee. In addition to the first stage of DEA, a fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically efficient entities. The only contextual variable affecting the economic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase in the investments. [ABSTRACT FROM AUTHOR]

This paper uses the theoretical underpinnings of the real options theory framework to investigate whether uncertainty affects a firms' decision to directly export versus indirectly export and to determine whether indirect exports may offset the negative effects of uncertainty on export intensity. Using firm-level survey data of 40 emerging countries, we find that uncertainty and firms' export intensity are negatively correlated. We also find that an increase in uncertainty increases a firms' willingness to export through trade intermediaries. These results when considered holistically imply that trade intermediaries are able to countervail the negative effect of uncertainty on firms' export intensity. [ABSTRACT FROM AUTHOR]

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ABSTRACT FROM AUTHOR]

Abstract Background The agricultural economy has little room for emerging farmers and there is no strong support system available for the small-scale farmers venturing in agro-processing. In this study, “access to agro-processing training” refers to any processing training rendered to small-scale crop farmers to equip them when venturing in to agro-processing. Small-scale crop farmers trained with high knowledge in processing and skills are pre-disposed to adopting processing as a strategy of making their processed products penetrate the agro-processing market. Methodology Data were collected from 307 small-scale crop farmers and STATA version 15 was used to perform fractional regression analyses to determine factors influencing access to training from the five types of agro-processing training (marketing training, processing training, record-keeping training, financial management training and business-plan training). Results The results revealed that 26% of the small-scale crop farmers had no access to agro-processing training and 74% of the small-scale crop farmers had access to training. Farming experience had significant influence on the access to agro-processing training for small-scale farmers to function efficiently in the agro-processing industry at 5% level of significance and their coefficient was positive. Conclusion Small-scale crop farmers with less farming experience should be encouraged to participate in the agro-processing sector as their participation can result in improved income and food security at the household levels. Furthermore, new agro-processing training programmes should be encouraged as trained farmers are more likely to participate in the value addition activities of agro-processing.

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production. [ABSTRACT FROM AUTHOR]

We extend the notion of a two-part fractional regression model with conditional free disposal hull efficiency responses to accommodate two-stage regression analysis. The two-part regression model includes the binomial model with a nonlinear specification for the expected response in (0,1] and is a more general formulation in the context of fractional regressions. We use nonlinear least squares to assess the effect of covariates in the conditional efficiency response. The approach is applied to Brazilian agricultural county data, as reported in the Brazilian agricultural census of 2006. The efficiency measure is output oriented and assumes variable returns to scale. Output is rural gross income and inputs are land expenses, labor expenses and expenses on other technological inputs. The covariates affecting production are credit, technical assistance, a rural development index, income concentration, measured by the Gini index, and regional dummies. Overall Brazilian rural production performance responds positively to all covariates. [ABSTRACT FROM AUTHOR]

The moderating role of corporate life cycle stages with the impact of relative economic value added (EVA) indicator on corporate social responsibility participation index (CSRPI). Chinese A-share listed companies are investigated. The CSRPI weights are calculated by Analytic Network Process. Fractional regression with interaction is used. The corporate life cycle stages moderate the relationship between relative EVA measure and CSRPI. Surprisingly, this impact is confirmed for companies at non-mature stages, but not for mature companies. Model type, weights and corporate life cycle robustness were confirmed. The findings have implications for stakeholders in understanding companies' social behaviour in the Chinese market. • The moderating role of corporate life cycle on corporate social responsibility in China • Corporate social responsibility participation index construction by analytic network process • Fractional regression with moderating effect application, model robustness verification • Confirmation of moderating effect of the corporate life cycle only for non-mature companies [ABSTRACT FROM AUTHOR]

This article uses 2017 Brazilian agricultural census data (aggregated at county level) to evaluate the impact of external factors on agricultural efficiency in Brazil. The external factors are defined as access to credit, participation in cooperatives, proportion of literacy, technical assistance, and environmental production practices. All variables are transformed as the log of the municipal (county) rank. The response variable is defined as the free disposal hull (FDH) conditional ratio and a one-inflated beta regression is estimated. The results show that in counties where the FDH ratio is equal to 1, cooperatives have the biggest impact on efficiency, and where the FDH ratio is not equal to 1, environmental practices, which are most often associated with technology, have the largest effect on efficiency. Finally, the regions that are expected to see the largest gains in efficiency with increases in external factors are the Northeastern and the Northern regions of Brazil. • We assessed the effect of covariates on the production process of the Brazilian agriculture in 2017. • The response variable is an FDH conditional ratio measure of efficiency computed for county data. • We modeled the ratio as a beta-inflated distribution, using maximum likelihood. • The highest expected gains from public policies will occur in the North and Northeast. [ABSTRACT FROM AUTHOR]