Chapter 7. A dynamic spatial modelling of agricultural price transmission: evidence from the Niger millet market

Goundan, Anatole; Tankari, Mahamadou Roufahi

doi:10.3920/978-90-8686-873-5_7

Cited by 2 publications

(6 citation statements)

References 32 publications

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“…Employing a dynamic spatial panel econometric model, Mainardi (2011) found that spatial spillovers affect the millet and cowpea prices particularly strongly in Niger. Likewise, Goundan and Tankari (2016) constructed a dynamic spatial panel model to analyze the price transmission of the millet market in Niger. In a study of the spatial price transmission of maize, Ihle et al (2009) analyzed the transmission relationship between maize prices in Kenya and Tanzania using the Markov transformation vector autoregression model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…First, we follow Goundan and Tankari (2016) to construct a traditional panel model as a benchmark analysis tool without considering spatial elements:…”

Section: The Spatial Econometric Modelmentioning

confidence: 99%

“…The definitions of other variables and parameters are the same as those in model 9. 7 To capture the effects of dynamic factors on price transmission, we follow Goundan and Tankari (2016) by adding the first-order lag term of the explained variable to the model and finally constructing a dynamic spatial panel model:…”

Section: The Spatial Econometric Modelmentioning

confidence: 99%

“…To overcome this endogeneity issue, we follow Goundan and Tankari (2016) to use the third-order lagged moving average of explanatory variables in the above econometric models, that is, . 8 This works by blocking the reverse effect from maize price to the other three prices at the right-hand of the models, but the current maize price would still be affected by the lagged moving average prices of wheat bran, hog, and feed.…”

Section: Data Specificationmentioning

confidence: 99%

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Agricultural support and spatial price transmission: evidence from China’s maize sector

Dai

Feng

Wang

et al. 2023

IFAM

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A market-oriented reform of China’s 2016 Temporary Purchase and Storage Policy (TPSP) is important for improving the price-forming mechanism of maize. Using monthly, province-level panel data and a dynamic spatial econometric model, this study distinguishes the heterogeneous spatial price transmissions of maize under officially supportive and market-oriented regimes. Our empirical results show that the spatial transmission of maize prices was remarkably distorted under the TPSP. Simultaneously, its market-oriented reform has greatly reduced regional price differences and generated a more flexible and much smoother spatial price transmission. The reform provides important insights for improving agricultural support regimes for other products in China.

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Section: Literature Reviewmentioning

confidence: 99%

“…First, we follow Goundan and Tankari (2016) to construct a traditional panel model as a benchmark analysis tool without considering spatial elements:…”

Section: The Spatial Econometric Modelmentioning

confidence: 99%

Section: The Spatial Econometric Modelmentioning

confidence: 99%

Section: Data Specificationmentioning

confidence: 99%

See 2 more Smart Citations

Agricultural support and spatial price transmission: evidence from China’s maize sector

Dai

Feng

Wang

et al. 2023

IFAM

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“…For a comprehensive review of methods for analyzing spatial price transmission that account for information asymmetries, trade costs, intertemporal variations due to commodity stocks, and substitution effects, see von Cramon-Taubadel (2017); Goundan and Tankari (2016).…”

Section: Market Integrationmentioning

confidence: 99%

COVID‐19 policy responses, mobility, and food prices

Dietrich

Giuffrida

Martorano

et al. 2021

American J Agri Economics

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Governments around the world have taken drastic measures to contain the spread of COVID-19. Policy responses to the pandemic could affect local food prices in important ways. In this paper, we hypothesize that food prices in regionally integrated markets are more sensitive to mobility constraints than those in segmented markets. We use World Food Programme price data from 774 retail markets in 44 low and middle-income countries to test whether and how food prices have been affected by the stringency of COVID-19 containment measures. We assess market segmentation based on pre-COVID-19 price data and measure government responses using the Oxford Coronavirus Government Response Tracker. Our results show that more stringent policy responses increase food prices for integrated markets but not for segmented markets. The impact of the stringency of policy responses on food prices seems to be mediated by reductions in mobility and moderated by the dependence of markets on trade before COVID-19.

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Chapter 7. A dynamic spatial modelling of agricultural price transmission: evidence from the Niger millet market

Cited by 2 publications

References 32 publications

Agricultural support and spatial price transmission: evidence from China’s maize sector

Agricultural support and spatial price transmission: evidence from China’s maize sector

COVID‐19 policy responses, mobility, and food prices

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