No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis*

Eberhardt, Markus; Teal, Francis

doi:10.1111/j.1468-0084.2012.00720.x

Cited by 154 publications

(193 citation statements)

References 67 publications

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“…Common factor specifications for TFP, similar to equation (3), can also be found in Costantini and Destefanis (2009) and Eberhardt and Teal (2013). The main difference is that we allow for time-varying factor loadings λ it to capture shifts in a country's access to worldwide technology.…”

Section: Aggregate Production Function and Modeling Tfpmentioning

confidence: 99%

Fiscal policy and TFP in the OECD: measuring direct and indirect effects

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Statement of purpose:The purpose of these working papers is to promote the circulation of research results (Research Series) and analytical studies (Documents Series) made within the National Bank of Belgium or presented by external economists in seminars, conferences and conventions organised by the Bank. The aim is therefore to provide a platform for discussion. The opinions expressed are strictly those of the authors and do not necessarily reflect the views of the National Bank of Belgium. JEL classification: C31, C33, E62, O38 Orders

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Section: Aggregate Production Function and Modeling Tfpmentioning

confidence: 99%

Fiscal policy and TFP in the OECD: measuring direct and indirect effects

2014

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“…As pointed out by Eberhardt and Teal (2013), the CEE estimator is robust when the cross-section dimension N is small; when variables are nonstationary (cointegrated or not), subject to structural breaks;…”

Section: B2 Ccemg Estimatormentioning

confidence: 99%

Does Economic Growth Matter? Technology-Push, Demand-Pull and Endogenous Drivers of Innovation in the Renewable Energy Industry

2014

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This paper aims to contribute to the longstanding technology-push vs. demand-pull debate and to the literature on renewable energy policy assessment. We argue that in addition to the traditional push-pull dichotomy, the drivers of technological change must be differentiated by whether they are exogenous or endogenous to the economic system and must be assessed with respect to their contribution to both the creation and the diffusion of innovation. We apply this perspective to study innovation in the renewable energy (RE) industry in 15 European Union countries from 1990 to 2012. Using different panel data estimators, we find that public R&D investments, policies supporting RE and per capita income all have a positive effect on either innovation creation or diffusion, whereas the variability of policy support has a negative impact on diffusion. However, impacts are heterogeneous and differ depending on the innovation dimension considered. Most importantly, we find that economic growth is a stronger driver of RE diffusion than technology-push or exogenous demand-pull mechanisms, whereas it is relatively ineffective at stimulating innovation creation. The effect of economic growth on RE diffusion exhibits a nonlinear, U-shaped pattern that resonates with the Environmental Kuznets Curve hypothesis. RE penetration remains negligible at low levels of growth whereas it increases sharply only after income per capita has reached a given threshold. This effect has both a direct cause (with increased affluence demand for environmental quality rises) and an indirect cause (with increased affluence expensive RE policies become more affordable and get implemented more extensively). Our findings have implications for policy making. They suggest that for RE diffusion to increase, innovation policies should be carefully balanced. Government action should be directed not only at shielding renewables from competition with fossil fuel technologies, but also at stimulating aggregated demand and economic growth.

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“…These data encapsulate human activities (human population 156 density, landcover, planted crops, and roads) as well as the geographical setting of I. purpureaand climate; Eberhardt & Teal, 2013). We first processed all these data into landscape layers at a 163 common spatial resolution of 10km 2 and a common spatial extent around the US states with 164 available samples (Fig.…”

Section: Data Compilation 153mentioning

confidence: 99%

The relative contribution of natural landscapes and human-mediated factors on the connectivity of a noxious invasive weed

Alvarado-Serrano

Etten

Chang³

et al. 2016

Preprint

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Landscape connectivity of a noxious invasive weed: promises and challenges of landscape 23genomics for knowledge-based weed management? 24 ABSTRACT 25Examining how the landscape may influence gene flow is at the forefront of 26 understanding population differentiation and adaptation. Such understanding is crucial in light of 27 ongoing environmental changes and the elevated risk of ecosystems alteration. In particular, 28 knowledge of how humans may influence the structure of populations is imperative to allow for 29 informed decisions in management and conservation. Here we characterize the population genetic 30 structure of Ipomoea purpurea, a noxious invasive weed, and assess the interaction between 31 natural and human-driven landscapes on genetic differentiation. By combining rigorous statistical 32 analyses and different molecular markers (nuclear microsatellites and a genome-wide panel of 33 SNPs), we detect both common and marker-specific patterns of genetic connectivity and identify 34 human population density as an important predictor of pairwise population differentiation, 35suggesting that the agricultural and/or horticultural trade may be involved in maintaining some 36 level of connectivity across distant agricultural fields. Climatic variation appears as an additional 37 predictor. We discuss the implications of these results and the approach we followed in the 38 context of understanding agricultural weed and invasive species' connectivity, as well as the 39 challenges and promises of current landscape genomics research for knowledge-based weed 40 management. 41

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No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis*

Cited by 154 publications

References 67 publications

Fiscal policy and TFP in the OECD: measuring direct and indirect effects

Fiscal policy and TFP in the OECD: measuring direct and indirect effects

Does Economic Growth Matter? Technology-Push, Demand-Pull and Endogenous Drivers of Innovation in the Renewable Energy Industry

The relative contribution of natural landscapes and human-mediated factors on the connectivity of a noxious invasive weed

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