Convergence and divergence in regional agricultural productivity growth: evidence from Italian regions, 1951–2002

Abstract

This article analyzes long-term agricultural Total Factor Productivity (TFP) growth at regional level by testing its time-series properties and identifying factors associated with divergence as opposed to convergence. The empirical application concerns Italian regions over the 1951-2002 time period. TFP growth decomposition ultimately attributes the observed productivity growth performance to these contrasting (convergence vs. divergence) forces. We find that technological spillovers are the key convergence force regardless of how the spillover effects are computed. At the same time, forces favoring convergence are almost offset by divergence forces (mainly scale or learning effects). This decomposition may explain the persistence of TFP growth rate differences in Italian agriculture, and could be applicable elsewhere.JEL Classifications: O130, O180, Q110

Technological spillover in Japanese rice productivity under long-term climate change: evidence from the spatial econometric model

“…Esposti (2010) estimated the spatial autoregressive (SAR) model (spatial lag model) to show the causative factors on divergence in agricultural TFP for 20 Italian regions during 1951-2002. They concluded that (i) technological spillovers were the key convergence force and (ii) public agricultural R&D mostly behaved as a divergence force because it prevalently affected productivity through its region-specific part.…”

Convergencia regional en México considerando la participación del PIB agropecuario (1940-2010)

“…Los dos conceptos son idénticos solo si un grupo de economías tienden a converger hacia un mismo estado estacionario. Esposti (2015) formula y estima un modelo donde las fuerzas de convergencia y divergencia se combinan para generar un proceso de convergencia en regiones italianas. Las implicaciones teóricas y metodológicas apuntan a que la reducción de las disparidades regionales de la productividad agrícola sea un objetivo que hace que las políticas sectoriales (agrícolas) sean aceptables y deseables desde una perspectiva de desarrollo regional, y que pueden tener como objetivo principal promover la convergencia del crecimiento de la productividad, favoreciendo la naturaleza pública de las mejoras tecnológicas agrícolas.…”

“…The two concepts are identical only when one group of economies tends to converge toward the same stationary state. Esposti (2015) formulates and estimates a model where the forces of convergence and divergence are combined to generate a convergence process in Italian regions. The theoretical and methodological implications indicate that the reduction of regional disparities in agricultural productivity is an objective that makes sectorial (agricultural) policies acceptable and desirable from a perspective of regional development, and that they may have as main objective promoting the convergence of productivity growth, favoring the public nature of the best agricultural technologies.…”

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“…Since the 1990s, frontier analysis has gradually been introduced into the calculation of TFP, with technical efficiency taken into consideration. Based on whether a specific production function is assumed, the frontier analysis is divided into parametric methods (e.g., deterministic frontier analysis [DFA], stochastic frontier analysis [SFA]) (Aigner et al, 1976(Aigner et al, , 1977Battese and Coelli, 1992;Headey et al, 2010) and non-parametric methods (e.g., data envelopment analysis [DEA], MPI) (Alene, 2010;Charnes et al, 1978;Esposti, 2011;Headey et al, 2010). Compared with the parametric method, the non-parametric method has the advantages of simultaneously studying the multi-input and multi-output TFP issues, of having no need for a specific production function, and not being affected by subjective factors, so most scholars tend to use non-parametric frontier analysis.…”