Does Urbanization Affect Rural Poverty? Evidence from Indian Districts

Calì, Massimiliano; Menon, Carlo

doi:10.1093/wber/lhs019

Cited by 89 publications

(54 citation statements)

References 48 publications

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“…In our estimations of the input measure, the capital-city effect is negative: subdistricts with high levels of market gravity tend to have lower-than-average poverty rates. Farmers concentrated in subdistricts around Jakarta have access to a larger pool of consumers and suppliers than those farther away-proximity to the city increases crop sales and production inputs (Cali and Menon 2013). Despite this advantage, the capital-city effect can also have drawbacks for farming practices, as shown in table 3.…”

Section: Agro-clusters and Urban Proximitymentioning

confidence: 99%

“…The literature on the Downloaded by [Wageningen University & Research -Library] at 04:34 10 November 2017 relation between spatial concentration and the incidence of poverty often neglects the importance of spatial effects (see, for example, Cali and Menon 2013;Giang, Nguyen, and Tran 2016). These spatial effects show the spatial interactions in which endogenous variables of different regions may be dependent (Anselin and Bera 1998).…”

Section: Introductionmentioning

confidence: 99%

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Agro-clusters and Rural Poverty: A Spatial Perspective for West Java

Wardhana

Ihle

Heijman

2017

Bulletin of Indonesian Economic Studies

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Neighbouring economies are likely to influence one another. The concentration of farming activities referred to as an 'agro-cluster' generates opportunities for income and employment in a given region and its surrounding area. We analyse the link between poverty rates and agro-clusters by accounting for spatial spillovers. To quantify agroclusters, we employ one input-oriented and one output-oriented measure. Our analysis applies six spatial econometric specifications and focuses on 545 subdistricts of West Java, where about 10% of the population live in poverty. We find that the concentration of agricultural employment substantially reduces poverty in a subdistrict as well as in neighbouring subdistricts. We also find that specialisation in crop outputs has positive impacts on poverty reduction and that localisation externalities are fundamental to agriculture's success. These findings imply that policy interventions may be applied in a spatially selective manner because they will generate spatial-spillover effects on poverty reduction in surrounding areas.

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Section: Agro-clusters and Urban Proximitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agro-clusters and Rural Poverty: A Spatial Perspective for West Java

Wardhana

Ihle

Heijman

2017

Bulletin of Indonesian Economic Studies

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“…For example, increased urban consumption and changing preferences for high-value and higher quality agricultural products increase the urban demand and willingness-to-pay for agricultural products , Reardon and Timmer 2014, Djurfeldt 2015. As urban residents most often do not produce their own food, this will have important implications on rural agricultural production systems (von Thünen 1826, Wiggens 2000, Cali and Menon 2012. 1 There is however relatively limited evidence on how increased urbanization and urban demand affects agriculture in the surrounding areas supplying these cities.…”

Section: Introductionmentioning

confidence: 99%

Cities and agricultural transformation in Africa: Evidence from Ethiopia

Vandercasteelen

Beyene

Minten³

et al. 2018

World Development

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Version: February 2016 AbstractDue to the rapid growth of cities in Africa, a larger number of farmers is living in the rural hinterland providing food to urban residents. However, empirical evidence on how urbanization affects these farmers is scarce. To fill this gap, this paper explores the relationship between proximity to a city and the production behavior of rural staple crop producers. More in particular, we analyze unique data from teff producing farmers in major producing areas around Addis Ababa, the Ethiopian capital. We find that farmers more closely located to Addis Ababa face higher wages and land rental prices, but because they receive higher teff prices they have better incentives to intensify production. Moreover, we observe that modern input use, land and labor productivity, and profitability in teff production improve with urban proximity. There is a strong and significant direct effect of urban proximity, which is suggested to be related with more use of formal factor markets, less transaction costs, and better access to information. In contrast, we do not find strong and positive relationships of rural population density increases -as an alternative source of agricultural transformation -as they seem to lead to immiserizing effects in these settings. Our results show that urban proximity should be considered as an important determinant of the process of agricultural intensification and transformation in developing countries.

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“…Nunn and Qian (2011) argue that the introduction of potatoes to the Old World improved nutrition and agricultural productivity which lead to higher urbanization rates and higher economic development. Finally, Cali and Menon (2013) find that urbanization causes rural poverty to decline through increased demand for agricultural products in the case of Indian districts. Furthermore, disaggregated urbanization data for developing countries is generally more easy to find whereas GDP per capita estimates were often inexistent or lacked precision as the analysis moved back in time.…”

Section: Methodology and Datamentioning

confidence: 98%