Are China’s regional agricultural productivities converging: How and why?

Wang, Sun Ling; Huang, Jikun; Wang, Xiaobing; Tuan, Francis C.

doi:10.1016/j.foodpol.2019.05.010

Cited by 42 publications

(32 citation statements)

References 45 publications

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Section: Convergence Of Technology Expansionsupporting

confidence: 92%

“…Further study shows that there is no evidence of an overall σ convergence across all provinces. The results of the convergence test of this study are consistent with previous studies [36,37]. After calculating and discussing the TFP, the author then calculated the technical change on the grain yield using the following Equation 5:…”

Section: Convergence Of Technology Expansionsupporting

confidence: 87%

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The Impact of Agricultural Service on Grain Production in China

Qiao

2020

Sustainability

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The development of agricultural services was the most important change in China’s agricultural production in the past 20 years. Using nationally representative provincial-level panel data, this study shows that agricultural service has a positive impact on technical efficiency at a significance level of 0.06. Based on this study, if the share of agricultural service cost increases by 1%, technical efficiency will increase by 0.35%. In other words, this study provides an empirical explanation of the positive impact of agricultural service on productivity. Due to the heterogeneity of agricultural service, technical efficiency in Eastern China and major grain production regions is higher than that in other regions. Finally, this study confirms the convergence of technical change in China’s grain production.

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Section: Convergence Of Technology Expansionsupporting

confidence: 92%

Section: Convergence Of Technology Expansionsupporting

confidence: 87%

The Impact of Agricultural Service on Grain Production in China

Qiao

2020

Sustainability

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“…Lichtenberg 1994; Wang et al . 2019). We first test for absolute

σ

‐convergence and the results, shown in Table 4, indicate a clear rejection of the

σ

‐convergence hypothesis across all sub‐regions since the estimated parameter for the time trend is positive.…”

Section: Resultsmentioning

confidence: 99%

“…We first test for absolute

σ

‐convergence, based on cross‐sectional data, using the following model (Lichtenberg 1994; Wang et al . 2019):

V a r false[()(, {\log (C A T F P}_{t}) false] = θ_{1} + θ_{2} T + ε_{t}

where the dependent variable is the variance of CATFP in log across countries in period t ,

T

is a time trend,

ε

is a zero‐mean random error term,

θ_{1}

and

θ_{2}

are parameters to be estimated. According to this specification, absolute

σ

‐convergence occurs when the estimated parameter

\overset{true^}{θ_{2}} < 0

and is statistically significant.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Another different but important research question is whether agricultural TFP in the region is increasing and can be sustained while reducing its dispersion across countries over time, a notion known as absolute

σ

‐convergence (Lichtenberg 1994; Sala‐i‐Martin 1996; McCunn and Huffman 2000; Wang et al . 2019). An alternative convergence concept is whether countries with lower TFP levels grow relatively faster than those with higher ones, which is known as absolute

normalβ

‐convergence (Solow 1956; Baumol 1986; De Long 1988; Barro 1991, 1997; Mankiw et al .…”

Section: Introductionmentioning

confidence: 99%

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Agricultural productivity growth in Latin America and the Caribbean: an analysis of climatic effects, catch‐up and convergence*

Lachaud

Bravo‐Ureta

2020

Aus J Agri & Res Econ

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This paper investigates whether climatic adjusted total factor productivity (CATFP) in Latin America and the Caribbean (LAC) is converging, converging to different steady states or exhibit absolute convergence, that is the process does not require (government) interventions to reach its equilibrium path. We use data from the University of East Anglia’s Climatic Research Unit and from the Food and Agriculture Organization for 28 LAC countries over a 54‐year period (1961–2014) to estimate random parameters stochastic production frontier models to calculate CATFP and then use cross‐sectional regressions and an error correction model to analyse CATFP convergence across countries in the region. The results show that technological progress is the main driver of CATFP growth in the region and there is no absolute convergence, that is CATFP gaps across countries will not decrease over time and least performing countries will not grow faster than better performing ones without targeted policies. However, CATFP across LAC exhibits conditional convergence towards different steady states. Technological progress plays a critical role in raising the steady state level of CATFP with an overall average of 2.22 per cent per annum.

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Innovations in Agricultural Technologies in China

Huang

2021

From Food Scarcity to Surplus

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Are China’s regional agricultural productivities converging: How and why?

Cited by 42 publications

References 45 publications

The Impact of Agricultural Service on Grain Production in China

The Impact of Agricultural Service on Grain Production in China

Agricultural productivity growth in Latin America and the Caribbean: an analysis of climatic effects, catch‐up and convergence*

Innovations in Agricultural Technologies in China

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