Agricultural and biotechnology patents as an adaptation strategy to climate change: A regional analysis of European farmer's efficiency

This chapter analyses the effect of innovation encouraged by climate change challenges on European farmers’ technical efficiency. Using the stochastic frontier approach, we estimate the impact of agri-cultural patents on farmers’ technical efficiency by taking into account both unobservable heterogeneity and heteroscedasticity in the inefficiency term. Our findings suggest that European farmers remain quite far from the maximum frontier and irrespective of the country in which they reside; farmers who innovate are more efficient than those who do not. Thus, the inefficiency of agricultural agents in the European context leaves space for policies that incentivise firms to adopt climate change adaptation strategies through technological innovation

A short survey on climate change and environmental innovations

Climate change is and will be in the coming years one of the major challenges facing the world. The best strategy to cope with climate anomalies seems to be fostering the ability to innovate and find tech-nological solutions. Therefore, understanding the relationship between the stimuli brought about by climate variability and the propensity to innovate is of paramount importance. To this end, this chapter provides some background on climate change and innovation economics and then focuses on climate-induced innovation in the context of mitigation technologies and adaptation strategies

Climate variability and agricultural production efficiency: evidence from Ethiopian farmers

It is known that climate and weather variability have negative impacts on agricultural production efficiency. The aim of this study is to analyse the impact of climatic variables on farms’ efficiency in Ethiopia making use of nationally representative datasets from Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) 2011/2012. By using the Stochastic Frontier Approach, we estimate simultaneously the farmers’ optimal production function and technical inefficiency equations, taking into account unobserved heterogeneity of farmers. Our main findings show that climate change variables have a positive effect on households’ efficiency but the impact depends on the different geo-climatic characteristics of the regions of the country

A Multidimensional poverty analysis: evidence from Italian data

Conventional poverty measures, showing that poverty and inequality have increased in Italy over the past fifteen years, are based on household income. The main drawback of this method is that it does not include other non-monetary variables relevant for defining households’ necessities. It is now widely agreed that poverty should be conceptualised as a multidimensional phenomenon, more related to the standard of living of the person or household than to the simple inability of satisfying basic subsistence needs. In this paper we propose to measure poverty in Italy by complementing income information with non-monetary indicators. To this end a multidimensional poverty analysis is performed by using a representative sample based on the first wave (2004) of the Italian component of the European Statistics on Income and Living Conditions (EU-SILC). Starting from the concept of deprivation, a non linear principal component analysis is applied to selected items in order to reveal underlying latent dimensions to be interpreted as deprivation indicators. We then examine how such measures can be combined with income measures in order to obtain a better identification of the poor. Finally we examine the overlapping between the income poor and the deprived and provide an analysis of deprivation profiles. Our results show that a more comprehensive poverty measure, combining deprivation criteria and income poverty, leads to a different identification of poor people, compared to analyses based only on income measures

Deriving multidimensional poverty indicators: methodological issues and an empirical analysis for Italy

Theoretical and empirical studies have recently adopted a multidimensional concept of poverty. There is considerable debate about the most appropriate degree of multidimensionality to retain in the analysis. In this work we add to the received literature in two ways. First, we derive indicators of multiple deprivation by applying a particular multivariate statistical technique, the non-linear principal component analysis (NLPCA), which overcomes traditional limits of many of the mostly used methodologies for poverty measurement. Second, on the basis of the aforementioned indicators, we provide an accurate identification of the poor in Italy by analyzing deprivation both as a distinct phenomenon in different life domains and as a single multidimensional concept. The main determinants of poverty in Italy are then investigated by estimating logit regressions and an ordered probit model. Our empirical analysis is based on data from the Italian component of European Statistics on Income and Living Conditions (EU-SILC-2004)

Recovering the counterfactual as part of ex-ante impact assessments: An application to the PASIDP-II project in Ethiopia

Real-world ex-ante impact assessments are far from the ideal experimental design, where the eligible population is supposed to be randomly assigned to treatment and control groups. Often, many surveys in developing contexts do not even collect data from a comparison group. We propose a methodology that recovers the counterfactual for ex-ante impact assessments of policy interventions under the conditions of distance decay in the exposure to continuous treatments and lack of control groups. We test this approach on data from a large-scale irrigation project in Ethiopia

Adoption of modern varieties, farmers' welfare and crop biodiversity: evidence from Uganda

This paper assesses the impact of modern varieties adoption on farmers' welfare and crop biodiversity conserved in-situ. Using nationally representative data collected in 2009/2010 in Uganda, an endogenous switching regression model estimates the net economic and environmental effects of switching from local landraces to modern species. Results showthat, after controlling for market and agro-ecological factors, the local varieties perform better thanmodern ones inmarginalized and climatic vulnerable areas. Crop biodiversity shows to play a fundamental role in farmers' risk minimizing strategies when the available modern varieties are not adaptable to the local context and not supported by the required level of agro-intensification. Rural development policies should consider the heterogeneity in the adoption returns and support diversity conservation as a national strategic asset for a suitable bioprospecting and a best-fitting agricultural system implementation