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Introduction: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. Methodology: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. Results: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p

Research background: Despite the growing social recognition of the positive role played by organic farming in the conservation of natural resources and the reduction or elimination of the negative externalities of modern agriculture, the economic competitiveness of organic versus conventional agriculture is a contentious issue. Studies on scale efficiency in the agricultural economics literature, in general, did not address the differences in production practices such as organic and conventional production. Purpose of the article: We estimated scale efficiency of organic and conventional production, tested for differences between organic and conventional agriculture scale efficiency, and explored the sources of inefficiencies. Methods: This was accomplished using cross-sectional data on 658 organic and conventional cocoa farmers, for the 2012/13 production season in the Eastern Region of Ghana. The analysis accounted for selection bias and recognised the fractional property of the scale efficiency measure. Findings & Value added: Organic agriculture is less scale efficient than conventional agriculture. Whilst we recommend that both producer groups improve scale efficiency, organic producers require greater work to do to make up for the almost 50% scale inefficiency. We also found farmer-based organisations to significantly influence scale efficiency. This calls for the need to strengthen farmer-based organisations to increase participation, among other reasons. We departed from the existing scale efficiency literature in a three of ways. We accounted for selection-bias using propensity score matching in the organic and conventional samples in analysing scale efficiency, modelled scale inefficiency using fractional regression and empirically selected the appropriate link function using a battery of tests. Finally, we accounted for an important policy variable; farmer-based organisation. We employed propensity score matching that accounted from observable biases. Further research may consider other methods that account for both observed and unobserved variations.

International business researchers commonly estimate proportions, percentages, rates, or fractions – so-called “proportional dependent variables”. In this paper, we posit that two regression strategies are particularly pertinent to the international business field: Tobit and fractional regression. Reviewing recent international business research, we find that, while fractional regression is rarely used, analyses from Tobit regression are often incomplete or erroneously interpreted with consequences for the validity of the reported results. Accordingly, we clarify how researchers should choose between Tobit and fractional regression and interpret their results. We present insights based on simple simulations and data examples with associated Stata code and a decision tree for choosing between types of models for use with proportional dependent variables.

The study sets out to measure the technical, allocative and cost efficiencies of maize farming in the Northern Region of Ghana for the 2014-2015 farming season. The region has 73% of its population as farmers, with maize being the most cultivated and consumed cereal. Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) are employed in the estimation. Interviews were conducted on 121 farmers selected through a mixed sampling technique. The study also segregated the quantities and prices of nitrogen and phosphorus from compound fertilizers. Under DEA, the study found an average efficiency of 79%, 67% and 53% for technical, allocative and cost efficiencies respectively. For SFA the results respectively are 77%, 27% and 21%. Cost and allocative efficiency estimates were very low especially for SFA. Using fractional regression analysis, it was found that household structure of farmers determined their technical efficiencies. Maize land size, marital status, education, and maize farming experience were found to affect allocative efficiency whiles marital status, household structure and maize farming experience affected cost efficiency. The study also found that labor was excessively used in the production process. Fertilizer application increased maize productivity more than other inputs. Policy recommendations were made following these findings.

Research background: Despite the growing social recognition of the positive role played by organic farming in the conservation of natural resources and the reduction or elimination of the negative externalities of modern agriculture, the economic competitiveness of organic versus conventional agriculture is a contentious issue. Studies on scale efficiency in the agricultural economics literature, in general, did not address the differences in production practices such as organic and conventional production. Purpose of the article: We estimated scale efficiency of organic and conventional production, tested for differences between organic and conventional agriculture scale efficiency, and explored the sources of inefficiencies. Methods: This was accomplished using cross-sectional data on 658 organic and conventional cocoa farmers, for the 2012/13 production season in the Eastern Region of Ghana. The analysis accounted for selection bias and recognised the fractional property of the scale efficiency measure. Findings & Value added: Organic agriculture is less scale efficient than conventional agriculture. Whilst we recommend that both producer groups improve scale efficiency, organic producers require greater work to do to make up for the almost 50% scale inefficiency. We also found farmer-based organisations to significantly influence scale efficiency. This calls for the need to strengthen farmer-based organisations to increase participation, among other reasons. We departed from the existing scale efficiency literature in a three of ways. We accounted for selection-bias using propensity score matching in the organic and conventional samples in analysing scale efficiency, modelled scale inefficiency using fractional regression and empirically selected the appropriate link function using a battery of tests. Finally, we accounted for an important policy variable; farmer-based organisation. We employed propensity score matching that accounted from observable biases. Further research may consider other methods that account for both observed and unobserved variations., doi: 10.15414/raae.2020.23.02.112-123; https://roaae.org/issue/review-of-agricultural-and-applied-economics-raae-vol-23-no-2-2020-2/?article=is-organic-agriculture-more-scale-efficient-than-conventional-agriculture-the-case-of-cocoa-cultivation-in-ghana

This study aims to assess 58 Iberian banks´ performance and its relationship with Intellectual Capital efficiency. Therefore, a two-stage analysis was conducted in order to address several proposed research questions related to Iberian (Portuguese and Spanish) banks in general, and each country, individually, during the period from 2013 to 2016. In a first-stage, sampled banks´ performance and respective rankings were assessed, through the measurement of their efficiency scores, i.e. using DEA´s (Data Envelopment Analysis) Constant and Variable Returns to Scale (CRS and VRS) and Super efficiency models, and in second-stage, both quantile and fractional regression models were applied as way of inferring about the impact of selected independent variables, i.e. Value Added Intellectual Coefficient (VAIC™) components, Leverage and Size, on the DEA scores of Iberian banks´. Findings suggest that Portuguese banks have constantly better average Technical, Pure Technical, and Scale Efficiency (i.e. TE, PTE, and SE) scores throughout the studied period, in comparison to Spanish banks. Also, findings show an increase on average efficiency scores (for both CRS and VRS), over the studied four-year period, for all sampled Iberian banks. Finally, second-stage analysis findings suggest a positive and significant relationship between Human Capital Efficiency (HCE) and sampled banks´ performance. Conversely, results suggest a negative and significant impact of both Structural and Employed Capital Efficiency (i.e. SCE and CEE) on sampled banks´ performance. This may be an indication of the pivotal importance of Human Resources Management (HRM) practices and the impact that application of the “best practices” may have on Iberian banking industry´s performance in general, and also on Portuguese and Spanish banks´ performance in particular. Este estudo tem por objetivo a avaliação do desempenho de 58 bancos Ibéricos e a sua relação com a eficiência do Capital Intelectual. Por conseguinte, uma análise de dois estágios foi aplicada de forma a responder às questões de investigação propostas relacionadas com a banca Ibérica (Portuguesa e Espanhola) no geral, e com cada país em particular, durante o período compreendido entre 2013 e 2016. Num primeiro estágio, foi feita uma avaliação e respetiva classificação do desempenho dos bancos selecionados, através da estimação dos seus resultados de eficiência, i.e. aplicando os modelos Constant e Variable Returns to Scale (CRS e VRS) e de Super-Eficiência do Data Envelopment Analysis (DEA). Num segundo estágio, e de modo a aferir a relação entre o desempenho global dos bancos e a eficiência do seu Capital Intelectual, foram aplicados modelos de regressão por quantis e fracionários. Recorreu-se ainda, ao método Value Added Intellectual Coefficient (VAIC™), considerando-se as suas componentes como variáveis independentes, para além das variáveis Endividamento, e Dimensão. Os resultados obtidos sugerem que os bancos Portugueses apresentam melhores resultados médios de technical, pure technical, e scale efficiency (i.e. TE, PTE e SE), durante o período de estudo, comparativamente aos bancos espanhóis. Para além disso, os resultados demonstram um aumento das médias dos resultados obtidos (para ambos os modelos, CRS e VRS), durante o período de quatro anos estudado. Finalmente, os resultados obtidos durante a análise de segundo-estágio sugerem uma relação positiva e significativa entre a eficiência do capital humano (HCE) e o desempenho dos bancos selecionados. Contrariamente, os resultados sugerem um impacto negativo e significativo de ambos os componentes, eficiência do capital estrutural e do capital aplicado (SCE e CEE), no desempenho dos bancos que constituem a amostra. Esta poderá ser uma indicação do papel preponderante das práticas aplicadas pela Gestão de Recursos Humanos (HRM), e no impacto que a aplicação das “melhores práticas” poderá ter no desempenho do sector bancário Ibérico no geral, e também no desempenho dos bancos Portugueses e Espanhóis em particular. Mestrado em Gestão

This study examines the adoption of high-yielding varieties (HYVs) of groundnut by smallholders in eastern Uganda. The primary focus of this work is the analysis of the sustainability of impacts from a regional farmer-led HYV dissemination and multiplication program. Data collected in 2014 is used to determine the lasting impact of the program conducted a decade prior, from 2001 to 2004. The structure of the data, which includes a treatment and 2-part control group, is critical to the identification of project impacts, measured as groundnut land allocation to groundnut HYVs (%). Fractional regression, propensity score matching and instrumental variable techniques are utilized to address potential bias from model specification, selection, and endogeneity. We find that, on average, participating households allocated 21% more of their land in groundnuts to HYVs relative to controls. Diffusion of program benefits through spillover is revealed by statistically significant differences in mean adoption between neighbor and non-neighbor controls, such that benefits are transferred from participants to their neighbors but not to the non-neighbor control group. We also find that, because of seed saving practices, the average yield for HYVs decreased over time to levels below those obtained from landrace varieties. Thus, the program effectively aided in information dissemination and technology transfer within rural communities. However, additional knowledge transfer is critical to the sustainability of food security outcomes among participant farmers.

O desempenho da agricultura brasileira é calculado por medidas de eficiência técnica por meio de um modelo de envoltória com disponibilidade plena ? Free Disposal Hull (FDH). A modelagem do processo de produção é condicional a variáveis contextuais potencialmente associadas a imperfeições de mercado. A fronteira de produção é gerada por uma medida de probabilidade produto. As observações de produção são agregadas por município, tomando-se os microdados do Censo Agropecuário 2006. A medida de eficiência é orientada a produto e assume retornos variáveis à escala. A produção é medida pela renda total rural, e os insumos considerados são gastos com terra, trabalho e insumos tecnológicos. As covariáveis do processo de produção são crédito, assistência técnica, indicador social (infraestrutura), indicador ambiental, indicador demográfico e concentração de renda, medida pelo índice de Gini. O desempenho da produção responde favoravelmente no nível de 95% aos indicadores social e demográfico. As demais variáveis não são estatisticamente significantes no nível de 95%. As intensidades dos efeitos marginais diferem por região. Desse modo, políticas públicas direcionadas à inclusão de pequenos produtores no fluxo produtivo demandam orientação regional. Made available in DSpace on 2019-01-03T23:35:02Z (GMT). No. of bitstreams: 1 Imperfeicoesdemercadoeconcentracaoderenda.pdf: 343050 bytes, checksum: 269a4388188aee2103750f86c8927c77 (MD5) Previous issue date: 2019-01-03 Título em inglês: Market imperfections and income concentration in agricultural production.

Economists often treat pesticide resistance as a common-pool resource problem. While pecuniary economic incentives are the standard prescription for open-access market failures arising with such resources, non-pecuniary behavioral approaches are also effective in some cases. Yet non-pecuniary instruments have not previously been evaluated for managing pesticide resistance. I empirically evaluate the performance of such an intervention to manage pest resistance to genetically engineered Bacillus thuringiensis (Bt) corn. Regulations by the US Environmental Protection Agency mandate refuges to delay the evolution of Bt resistance. To satisfy Bt product permitting requirements with the EPA, the agricultural company Monsanto piloted a social marketing program to promote refuge in 17 North Carolina counties in 2013-2014. Using seed sales data from 2013 to 2016 for the whole of North Carolina, I estimate multiple econometric models, including linear difference-in-differences (DID), fractional DID, discrete changes-in-changes, and matching to identify the average treatment effect (ATE) of the program on growers’ refuge planting decisions. Results suggest that if it had covered all corn growers in North Carolina, the intervention would have led the average grower to plant between 2.6% and 5.8% (depending on model) more refuge in the 2014 season immediately following the program, than would have been the case without the program. The ATE roughly halves in 2015 and vanishes by 2016. Econometric analysis suggests the program increased by 12% the average probability of planting any refuge in 2014. Evidence is mixed for effects of the intervention on grower compliance with mandated refuge thresholds. Informed by behavioral economics research on other environmental and resource policies, I discuss the implications of these findings for pesticide resistance management.

In this article we assess the effect of market imperfections and income inequality on rural production efficiency. The analysis is carried out using the notion of stochastic conditional efficiency computed in terms of free disposal hull (FDH) efficiency measurements. Free disposal hull and conditional FDH are output oriented with variable returns to scale. They are evaluated for rural production at the county level, considering the rank of rural gross income as the output and the ranks of land expenses, labor expenses, and expenses on other technological factors as inputs. The conditional frontier is dependent on income inequality and other indicators related to market imperfections. The econometric approach is based on fractional regression models and the generalized method of moments (GMM). Overall, the market imperfection variables act to reduce performance, and income dispersion is positively associated with technical efficiency.

This study updates previous meta-analysis of farm-level frontier function studies in order to provide a detailed systematic and comprehensive analysis of the effects that different studyspecific attributes have on mean technical efficiency (MTE) scores. Before presenting the technical efficiency (TE) analysis, we provide an overview of the evolution of key methodological approaches that have been developed and applied to measure and examine TE. A detailed descriptive analysis is then performed for a meta-dataset that includes 408 farm level TE studies, published between 1981 and mid-2014. Some studies report several MTEs, resulting in 900 observations or cases. A key result from the descriptive analysis is that the Average of the Mean Technical Efficiencies (AMTE) reported for all studies is 74.2%. The AMTE across methodological attributes tend to be quite similar but several significant differences are observed when comparisons are made across geographical regions, income levels, and types of product. The paper goes on to report the results of meta-regressions estimated using the fractional regression procedure, which is well suited for dependent variables that are defined on the unit interval or as a fraction (between 0 and 1), as is the case with TE. In the concluding section, we provide some thoughts concerning recent work that uses stochastic production frontier methodologies to evaluate the impact of developments projects while addressing biases from observable and unobservable variables.

We consider the use of the non-Archimedean infinitesimal epsilon in DEA-CCR models. The application of interest is defined by the performance measure of the Brazilian Agricultural Research Corporation research centers. We characterize an assurance region for the non-Archimedean element and suggest a value for it. Types of DMUs are compared using fractional regression models and quasi maximum likelihood inference. We conclude that the research centers aimed at studying specific agricultural products are dominant. The classic DEA-CCR performance measures and the solution provided by non-Archimedean model have Spearman correlation over 90%.

We consider the use of the non-Archimedean infinitesimal epsilon in DEA-CCR models. The application of interest is defined by the performance measure of the Brazilian Agricultural Research Corporation research centers. We characterize an assurance region for the non-Archimedean element and suggest a value for it. Types of DMUs are compared using fractional regression models and quasi maximum likelihood inference. We conclude that the research centers aimed at studying specific agricultural products are dominant. The classic DEA-CCR performance measures and the solution provided by non-Archimedeanmodel have Spearman correlation over 90%.

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are often derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Validation exercises show that our approach is effective at predicting cropland allocation at multiple levels of resolution. In addition, the model provides marginal effects of changes in climate and biophysical factors on cropland allocation at the pixel level that can be used in a variety of research and policy contexts.

In this paper, we measure the performance for each of the Brazilian Agricultural Research Corporation research centers by means of a Data Envelopment Analysis model. Performance data are available for a panel covering the period 2002–2009. The approach is instrumentalist, in the sense of Ramalho, Ramalho, and Henriques (2010) . We investigate the effects on performance of contextual variable indicators related to the intensity of partnerships and revenue generation. For this purpose, we propose a fractional nonlinear regression model and dynamic GMM (Generalized Method of Moments) estimation. We do not rule out the endogeneity of the contextual variables, cross-sectional correlation or autocorrelation within the panel. We conclude that revenue generation and previous performance scores are statistically significant and positively associated with actual performance.

In this article, we use Brazilian census data (1995/1996 and 2006) to model agricultural production at the state level in Brazil. Cost-efficiency measurements are computed using data envelopment analysis techniques, and the response is assessed via fractional regression. We examine the effects of time, geographic region, education, and investment in agricultural research on economic efficiency. We found that investment in agricultural research and regional dummies have a significant effect on efficiency measurements. On average, South and Southeast states are more efficinet than other states. An increase in cost efficiency can be accomplishe through investment in agricultural research.

This article examines the variations in mean technical efficiency estimates in organic agriculture and the factors that explain the observed variations. A three-stage process was employed in data collection. Firstly, journals on organic agriculture and related disciplines were identified and searched. Secondly, several publishers’ websites and databases, namely Cambridge Journals, Elsevier, Emerald, Oxford University Press, Sage, Taylor and Francis, and Wiley, among others, were covered. Databases included AgEcon Search, CAB Abstracts, DOAJ, EBSCOhost, Google Scholar and ScienceDirect. Thirdly, the reference lists of studies found in the first and second stages were searched to identify additional literature. In all, 42 studies constituting 109 observations covering the period 2002-2014 were found. Unlike existing literature on technical efficiency quantitative reviews in agriculture, this article employs a battery of tests to select the appropriate solution for multiple observations from the same primary study, as well as the appropriate functional form for the selected fractional regression model. The mean technical efficiency of organic agriculture for the period of study and the effects of other study characteristics are thoroughly discussed., http://dx.doi.org/10.7896/j.1512

The study investigates whether African agricultural efficiency levels has been improving or not and what drives its over the years based on 442 frontier studies using meta-regression analysis. The results show that mean efficiency estimates from studies decrease significantly as year of survey in the primary study increases. Also studies published in Journals, with parametric specification and with panel data produced significantly higher efficiency estimates, while those with a focus on grain crops reported significantly lower efficiency estimates. Other results show that education, followed by experience; extension and credit are the major drivers of agricultural efficiency levels in Africa.

This study examines how the U.S. crop mix pattern has responded to climate and in turn the potential effects of projected climate change. We find that there are significant effects of temperature and precipitation on the crop choice decisions.

As Switzerland experiences a severe shortage of nurses, this paper investigates the impact of students’ ex ante wage expectations on their choice to pursue a nursing college education. This analysis contributes to a small yet rapidly developing body of literature that uses subjective expectation data to predict educational choices. We surveyed a full cohort of healthcare trainees in their third year of training. The main result is that those trainees (in upper-secondary education) who expected a greater return from nursing college (tertiary education) were more likely to enroll in nursing college later on. This suggests that policies that increase returns from studying nursing can attract students to nursing. In addition, the results confirm that subjective wage expectation data are useful in modeling individual choice.

The present study investigates the development (i.e., rise or decline) in African agricultural efficiency level and what drives the efficiency over the years. A total of 379 frontier studies resulting in 534 farm level efficiency estimates were considered using meta-regression analysis (MRA) for the empirical analysis. The results show that mean efficiency estimates from the selected case studies decrease significantly as year of survey in the primary study increases. Apparently, this implies that over the years, negative efficiency change characterized the growth of African agriculture and food production. The effect of other study attributes considered in the MRA show that studies published in Journals, with parametric and primal technology specification produced significantly higher efficiency estimates, while those published in top ranking journals and with Cobb-Douglass and Translog functional forms produced significantly lower efficiency estimates. Other results show that education, followed by experience; extension and credit are the major drivers of agricultural efficiency levels in Africa over the years. Given these findings; we suggest policies that encourage investment in human capital development associated with education and extension should be prioritized to enhance the growth of agriculture and food production in the region.

This paper measures and explains technical efficiency of 371 dairy farms located in seventeen districts in East African Countries. Four output and nine input types were used to calculate the efficiency scores for each farm. A two-stage analysis was conducted to measure and explain the efficiency scores. First, the efficiency scores were measured by using a data envelopment analysis (DEA) approach which was implemented with a linear programming method. About 18% of the farms were fully productive, each with efficiency scores of unity, which meant this group is currently operating on the production possibility frontier. On the other hand, about 32% of the farms have efficiency scores below 0.25, which means about a third of the dairy farms would need to expand dairy production by at least 75% from the current level without any increase in the level of inputs. Second, a fractional regression method was used to explain the efficiency scores by relating then to a range of explanatory variables. The findings indicate that technology adoption factors such as the existence of improved breeds; feed and fodder innovations (e.g. growing legumes) have positive and statistically significant effects on the level of efficiency. Similarly, zero-grazing seem have positive and highly significant effects. As far as marketing variables are concerned, interestingly selling milk to individual consumers or organizations seems to contribute to dairy efficiency positively and significantly than other marketing outlets such as traders of chilling plants. Membership of dairy cooperative has a positive effect but this is not statistically significant., Replaced with revised version of paper 11/18/10.