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RUSTDEC

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Rural Structural Transformation in Developing Countries

A key feature of economic development is structural transformation characterized by spatial and sectoral labor reallocation out of agriculture. Despite clear evidence of rural-urban migration flows in developing countries, recent... ver más

31/12/2028

BARCELONA GRADUATE...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

BARCELONA GRADUATE SCHOOL OF ECONOMICS FUNDAC... Educación secundaria técnica y profesional asociacion

TRL 4-5 | 50K€

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-27

ERC-2022-STG: ERC STARTING GRANTS Scope:Objectives

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1 Participantes

BARCELONA GRADUATE SCHOOL OF...

1.50M€ | Lider

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Duración del proyecto: 61 meses Fecha Inicio: 2023-11-27
Fecha Fin: 2028-12-31

Líder del proyecto

BARCELONA GRADUATE SCHOOL OF ECONOMICS FUNDAC... Educación secundaria técnica y profesional asociacion

TRL 4-5 | 50K€

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto A key feature of economic development is structural transformation characterized by spatial and sectoral labor reallocation out of agriculture. Despite clear evidence of rural-urban migration flows in developing countries, recent contributions show persistently lower productivity in agriculture compared to other sectors and large disparity in agricultural productivity across space. This points to the key question of this proposal: why do so many workers remain in agriculture? There is a pressing need for reliable answers to this question as rural areas host large shares of the population, facing high agricultural risk and poverty incidence. Any effective poverty eradication policy, such as those envisioned by the UN’s Sustainable Development Goals, must therefore necessarily focus on rural development. RUSTDEC will obtain novel answers to this key question by combining innovative micro panel data from Sub-Saharan Africa and Southeast Asia with an array of geospatial data on rural areas, exogenous variation in agricultural productivity, and quantitative theoretical models. The project employs remote-sensing and computer vision methods for generating community data as well as quasi-experimental and structural estimation techniques for rigorous causal estimation to pursue three major objectives: (i) provide first evidence on the role of dynamic adjustments in agricultural production for explaining the agricultural productivity gap and the magnitude of factor (mis-)allocation; (ii) dissect the microeconomic anatomy of spatial and sectoral labor reallocation and skill selectivity within and across rural communities; and (iii) identify the historic and future impact of climate change on agricultural production, adaptation, and rural development and its implications for the productivity gap. These objectives address three well-defined gaps in the literature, each going beyond the specific state-of-the-art and with the potential of generating ground-breaking impact.

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