Rural demographic change, rising wages and the restructuring of Chinese agriculture

Li, Tianxiang; Yu, Wusheng; Baležentis, Tomas; Zhu, Jieming; Ji, Yueqing

doi:10.1108/caer-02-2016-0025

Cited by 24 publications

(12 citation statements)

References 23 publications

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“…The results are shown in Table 8.It is shown from the results that the coefficients of GDP of import countries to China's agricultural, vegetable and aquatic products exports are significantly positive at the level of 1% and the elasticities (abbreviated as εgdpj,) are 0.787, 0.860 and 1.155, respectively. The coefficients are basically consistent with that measured by Wang and Wen (2016) at 0.696, using the countries along the Silk Road and with Hu and Huo (2008) and Zhang et al (2013), at 1.17 and 0.7629–0.7829, respectively.The coefficient of domestic GDP is positive for exports in agricultural products in general, but with significantly negative sign for the export of vegetable products, which may be the result of rapid climbing of labor costs along with the income growth, for vegetable products in particular and the resultant decline in export competitiveness (Li et al , 2017). The coefficient of relative price index is negative for exports of agricultural products in general and for vegetable products, but statistically insignificant and positive for that of aquatic products.…”

Section: Model Specificationsmentioning

confidence: 99%

Impact of COVID-19 on China's agricultural trade

Cao

Wang

et al. 2020

CAER

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PurposeThe outbreak of the novel COVID-19 virus has spread throughout the world, causing unprecedented disruption to not only China's agricultural trade but also the world's agricultural trade at large. This paper attempts to provide a preliminary analysis of the impact of the COVID-19 pandemic on China's agricultural importing and exporting from both short- and long-term perspectives.Design/methodology/approachThis study seeks to analyze how the outbreak of COVID-19 could potentially impact China's agricultural trade. With respect to exports, the authors have pinpointed major disruptive factors arising from the pandemic which have affected China's agricultural exports in both the short and long term; in doing so, we employ scenario analysis which simulates potential long-term effects. With regard to imports, possible impacts of the pandemic regarding the prospects of food availability in the world market are investigated. Using scenario analysis, the authors estimate the potential change in China's food market—especially meat import growth—in light of the implementation of the newly signed Sino-US Economic and Trade Agreement (SUETA).FindingsThe results show that China's agricultural exports have been negatively impacted in the short-term, mostly due to the disruption of the supply chain. In the long term, dampened external demand and potential imposition of non-tariff trade barriers (NTBs) will exert more profound and lasting negative effects on China's agricultural export trade. On the other hand, despite panic buying and embargoing policies from some exporting and importing countries, the world food availability and China's food import demand are still optimistic. The simulation results indicate that China's import of pork products, in light of COVID-19 and the implementation of SUETA, would most likely see a sizable climb in quantity, but a lesser climb in terms of value.Originality/valueAgricultural trade in China has been a focal-point of attention in recent years, with new challenges slowing exports and increasing dependence on imports for food security. The outbreak of the COVID-19 pandemic adds significant uncertainty to agricultural trade, giving rise to serious concerns regarding its potential impact. By exploring the impact of the unprecedented pandemic on China's agricultural trade, this study should contribute to a better understanding of the still-evolving pandemic and shed light on pertinent policy implications.

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Section: Model Specificationsmentioning

confidence: 99%

Impact of COVID-19 on China's agricultural trade

Cao

Wang

et al. 2020

CAER

Self Cite

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“…Feder (2018) provided a measurement method of TFP considering the change in bias technology: Specifically when the productivity of lower (or higher) cost factors increases, the bias factor's productivity component increases (or decreases). Li et al (2016) and Li, Yu, Baležentis, Zhu, and Ji (2017) use LMDI to measure China's productivity, and Miao et al (2018Miao et al ( , 2019 combine DEA with Luenberger indicators to measure environmental production technology and environmental performance.…”

Section: Introductionmentioning

confidence: 99%

Total factor productivity and the factors of green industry in Shanxi Province, China

Song

2019

Growth and Change

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Based on industrial panel data from 11 prefecture‐level cities in Shanxi Province from 2008 to 2016, two kinds of total factor productivity (TFP) are measured: With and without the consideration of environmental factors. The specific decomposition of TFP under these two conditions is then discussed. Finally, the factors of TFP are studied in a green industry context. The results indicate the following: (a) The TFP's annual growth rate considering environmental conditions is higher than that without such consideration; (b) if environmental factors are neglected, TFP improvements primarily depend on technological progress; (c) when considering environmental factors, both technological progress and improved technological efficiency promote the growth of green TFP (GTFP), but this still primarily depends on substantial technological progress, with an annual average growth rate that reaches 10.2%; and (d) among factors, the economic development level, degree of openness, and urbanization positively correlate with the GTFP, while the industrial structure and degree of informatization significantly and negatively correlate with GTFP.

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“…Urban-rural relative income gap decomposition based on logarithmic mean Divisia index method (LMDI) [17,18]…”

Section: Logarithmic Mean Divisia Index (Lmdi) Decompositionmentioning

confidence: 99%

Rural - urban income gap and labour market in Romania

Băncescu

2021

SHS Web Conf.

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Rural – urban income gap is an important social-economic development indicator for a society. A large rural - urban income gap within a society reveals a split of it into two distinct societies, one extremely poor compared to the other. In Romania, almost half of the resident population lives in rural areas (46.02% in 2019), while the urban-rural migration flow is higher than the traditional reverse flow since 1997. However, the country is characterized by regional economic disparities, Bucharest-Ilfov region being the most economically developed. Labour market in rural areas is underdeveloped, rural population being highly depended on subsistence agriculture. Furthermore, rural areas have a low level of income and living standards. In this paper, we analysis the rural-urban labour market dynamics and rural-urban income gap using a LMDI (logarithmic mean Divisia index) decomposition for 2005-2019 period. Factors such as income gap effect and structural rural income effect are considered. Results show that the total urban–rural income gap has decreased with 8.91%, while structural rural income effect contributed with an increase of only 0.63%, the income gap effect (of different employed population groups) being of -9.49%.

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Rural demographic change, rising wages and the restructuring of Chinese agriculture

Cited by 24 publications

References 23 publications

Impact of COVID-19 on China's agricultural trade

Impact of COVID-19 on China's agricultural trade

Total factor productivity and the factors of green industry in Shanxi Province, China

Rural - urban income gap and labour market in Romania

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