The Dynamics of Income Growth and Poverty: Evidence from Districts in India

Banerjee, Anurag; Banik, Nilanjan; Mukhopadhyay, Jyoti Prasad

doi:10.1111/dpr.12110

Cited by 14 publications

(11 citation statements)

References 13 publications

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“…For example, studies have examined poverty variation across states in India (Alkire & Seth, 2015;Baddeley et al, 2006;Dev & Ravi, 2007). Other studies have looked at the variation in deprivations across districts (Banerjee et al, 2015;Chaudhuri & Gupta, 2009). However, a more recent study employing multilevel modelling points to the importance of village level factors, in addition to state-level context, in shaping the distribution of household deprivations across India (Kim et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Jain

Rodgers²,

et al. 2021

Maternal & Child Nutrition

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Prior research has identified a number of risk factors ranging from inadequate household sanitation to maternal characteristics as important determinants of child malnutrition and health in India. What is less known is the extent to which these individual-level risk factors are geographically distributed. Assessing the geographic distribution, especially at multiple levels, matters as it can inform where, and at what level, interventions should be targeted. The three levels of significance in the Indian context are villages, districts, and states. Thus, the purpose of this paper was to (a) examine what proportion of the variation in 21 risk factors is attributable to villages, districts, and states in India and (b) elucidate the specific states where these risk factors are clustered within India. Using the fourth National Family Health Surveydataset, from 2015 to 2016, we found that the proportion of variation attributable to villages ranged from 14% to 63%, 10% to 29% for districts and 17% to 62% for states. Furthermore, we found that Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh were in the highest risk quintile for more than 10 of the risk factors included in our study. This is an indication of geographic clustering of risk factors. The risk factors that are clustered in states such as Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh underscore the need for policies and interventions that address a broader set of child malnutrition determinants beyond those that are nutrition specific.

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Section: Introductionmentioning

confidence: 99%

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Jain

Rodgers²,

et al. 2021

Maternal & Child Nutrition

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“…The economic capabilities of farm entrepreneurs, in this paper, includes farm income levels, access to market information, and chances to acquire and use new farm technologies, as well as children’s access to higher education. In studies by Koch [ 51 ] and Banerjee et al [ 52 ], they found that growth in agricultural income can reduce poverty. Therefore, the ability of farm entrepreneurs to increase farm and non-farm earnings is dependent on their economic capability levels.…”

Section: Introductionmentioning

confidence: 99%

Does farmer entrepreneurship alleviate rural poverty in China? Evidence from Guangxi Province

Naminse

Zhuang

2018

PLoS ONE

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In recent years, entrepreneurship has been gaining more prominence as a potential tool for solving poverty in developing countries. This paper mainly examines the relationship between farmer entrepreneurship and rural poverty alleviation in China by assessing the contribution of farm entrepreneurs towards overcoming poverty. Data were collected from 309 employees of farmer entrepreneurships in Guangxi Province through survey questionnaires. Structural equation modeling was used to conduct an analysis of the effects of three identified capabilities of farm entrepreneurs—economic, educational and knowledge, and socio-cultural capabilities—on attitude towards farmer entrepreneurship growth and the qualitative growth of farmer entrepreneurship and how these in turn affect rural poverty, using AMOS 21. The findings show that socio-cultural capability has the greatest influence on farmer entrepreneurship growth (β = 0.50, p<0.001). The qualitative growth of farmer entrepreneurship also more significantly impacts rural poverty (β = 0.69, p<0.001) than attitude towards farmer entrepreneurship growth. This study suggests that policy makers in China should involve more rural farmers in the targeted poverty alleviation strategies of the government by equipping rural farmers with entrepreneurial skills. This can serve as a sustainable, bottom-up approach to alleviating rural poverty in remote areas of the country. The study also extends the literature on the farmer entrepreneurship-rural poverty alleviation nexus in China, and this can serve as a lesson for other developing countries in the fight against rural poverty.

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“…Starting with the literature on the effect of economic growth on poverty, various studies have revealed that economic growth has been an important driver of poverty reduction (e.g., Banerjee et al, 2015;Bhagwati, 2001;Bourguignon, 2004;Datt and Ravallion, 2002;Dollar and Kraay, 2002;Fosu, 2015Fosu, , 2018Perera et al, 2013;Ravallion, 2004). However, in investigating the factors underpinning the slow pace of poverty reduction between the mid-1980s and the mid-2000s in Brazil, Ferreira et al (2010) have found that economic growth has played a negligible role in Brazil's poverty reduction.…”

Section: Discussion On the Effect Of Economic Complexity On Povertymentioning

confidence: 99%

“…Building on the aforementioned existing empirical literature on the effect of economic complexity, the present article examines the effect of economic complexity on poverty in developing countries through the channels of economic growth, income inequality and economic growth volatility. An extensive literature has shown that these three channels (i.e., economic growth, income inequality and economic growth volatility) can influence poverty rates (e.g., Banerjee et al, 2015;Bhagwati, 2001;Bourguignon, 2004;Datt and Ravallion, 2002;Dollar and Kraay, 2002;Fosu, 2015Fosu, , 2018Perera et al, 2013;Ravallion, 2004). For example, if economic complexity influences positively a country's economic growth (e.g., Hausmann and Hidalgo, 2009), and reduces the prevailing income inequality in this country (e.g., Hartmann et al, 2017), then it can be a potential driver of poverty reduction in that country, in particular if economic growth contributes to lowering poverty rates (e.g., Banerjee et al, 2015;Bhagwati, 2001;Dollar and Kraay, 2002;Fosu, 2015Fosu, , 2018Perera et al, 2013;Ravallion, 2004) and if income inequality is associated with poverty reduction (e.g., Fosu, 2010;Kulkarnia and Gaiha, 2020).…”

Section: Introductionmentioning

confidence: 99%

Economic Complexity and Poverty in Developing Countries

Gnangnon¹

2021

Preprint

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This paper has examined the effect of economic complexity on poverty in developing countries. The analysis has used a sample of 84 countries over the period 1980-2017. Results indicate that greater economic complexity results in lower poverty headcount rates. This is particularly the case for countries that enjoy higher economic growth rates, lower levels of income inequality and lower degrees of economic growth volatility, including due to lower sizes of export demand and financial flows shocks. These findings have important policy implications for developing countries that are exploring ways and means to recover from the current COVID-19 pandemic crisis, and prepare for future crises.

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The Dynamics of Income Growth and Poverty: Evidence from Districts in India

Cited by 14 publications

References 13 publications

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Multilevel analysis of geographic variation among correlates of child undernutrition in India

Does farmer entrepreneurship alleviate rural poverty in China? Evidence from Guangxi Province

Economic Complexity and Poverty in Developing Countries

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