One cow per poor family: Effects on the growth of consumption and crop production

Nilsson, Pia; Bäckman, Mikaela; Bjerke, Lina; Maniriho, Aristide

doi:10.1016/j.worlddev.2018.09.024

Cited by 17 publications

(13 citation statements)

References 57 publications

(76 reference statements)

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“…The main results from econometric estimations in Tables 2 and A1 show that the socioeconomic characteristics are very important drivers of household expenditures (here the household size, the age of the household head, and education level of the household head). This finding is in line with the findings of Donkoh and Amikuzuno (2011), Umeh and Asogwa (2012) and Nilsson et al (2019) who reported the age and education of the household head as well as the household size 2 among the determinants of household expenditures but is in contrast with Davis et al's (1983) finding that the education has no significant impact on household education expenditure. These results are reflective of the vital importance of household control factors in their demand decisions.…”

Section: Discussion Of the Findingssupporting

confidence: 90%

“…The findings also revealed that having productive assets (livestock units, land size) and financial resources (access to credit, non-farm income share) have a substantial impact on household demand. This result supports the findings that the land size (Umeh & Asogwa, 2012), possession of durable assets (Donkoh & Amikuzuno, 2011), the house size (Hussain and Asad, 2012), the household income and wealth (Davis et al, 1983;Khan & Abdullah, 2010;Umeh & Asogwa, 2012;Wang et al, 2016) and the number of livestock units (Nilsson et al, 2019) are among the factors that significantly affect household expenditures. This is reflective of the importance of possession of productive assets, financial capital, productive and high wage employment, and income and wealth status to household demand behaviour.…”

Section: Discussion Of the Findingssupporting

confidence: 77%

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An Analysis of the Determinants of Household Expenditures in Rwanda

Maniriho

Musabanganji

Nkikabahizi

et al. 2021

UKHJSS

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Economists use two different approaches, unitary and collective, to analyze household decisions. The unitary approach ignores the differences between single-person and multi-person households, whereas the collective approach states that each person in the household must be characterized by specific preferences. The household’s decisions concern mainly the allocation of their income to current consumption or for savings and future consumer expenditures. This study uses the Comprehensive Food Security and Vulnerability Analysis (CFSVA) data collected from a random sample in 2015 in Rwanda. The ordinary least squares (OLS) method was applied to a linear regression model to estimate the household demand functions (total household consumption expenditures, household food consumption expenditures and household nonfood consumption expenditures). The results show that the socioeconomic characteristics of the household, the possession of productive assets and wealth conditions as well as the household locational controls are among the primary drivers of its consumption expenditures. The findings highlight the policy efforts that improve household human capital (education, health), access to and capitalization of productive assets and financial capital, continuous urbanization of rural areas, and sustained provision of quality infrastructure, to achieve high standards of household welfare.

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Section: Discussion Of the Findingssupporting

confidence: 90%

Section: Discussion Of the Findingssupporting

confidence: 77%

An Analysis of the Determinants of Household Expenditures in Rwanda

Maniriho

Musabanganji

Nkikabahizi

et al. 2021

UKHJSS

Self Cite

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“…Our motivation for using the CEM is that the CEM allows a balance between the policy-on and the policy-off group to be chosen ex-ante rather than being revealed through an iterative process of ex-post balance checking ( Nilsson et al., 2019 ). The balance between the treated and the control groups is chosen ex-ante, reducing model dependence.…”

Section: Methodsmentioning

confidence: 99%

“…Within each stratum, the treated farmers are matched with those in the control groups. Unmatched farmers in the control group are discarded ( Nilsson et al., 2019 ). The success of the matching is measured by the multivariate imbalance measures L 1 ; that is, the distances in covariate values between the treated and control before and after the matching are compared with a reduction indicating success.…”

Section: Methodsmentioning

confidence: 99%

“…Even though CEM is preferred to propensity score matching (PSM) due to the latter's tendency to increase the imbalance between the two different groups, control and treated ( Nilsson et al., 2019 ), a combination of the two approaches, i.e., CEM preceding PSM was used ( King and Nielsen, 2019 ), our primary analysis, therefore, involves two lines of modelling; CEM and CEM combined with PSM. The CEM, combined with PSM, means that the PSM analysis is restricted to the CEM matched data ( Blackwell et al., 2009 ).…”

Section: Methodsmentioning

confidence: 99%

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The impact of output price support on smallholder farmers' income: evidence from maize farmers in Ghana

Abokyi

Strijker

Asiedu³

et al. 2020

Heliyon

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Instability in smallholder farmers' income in developing countries due to unstable farm prices has been a challenge for farmers and agricultural policymakers over the years. Sustained price stabilization mechanisms are mostly lacking. In some countries, output price support has been initiated to stabilize incomes and as an incentive to enhance farmer investment and boost production. This paper investigates the impacts of output price support on smallholder farmers' income in Ghana, using a household and farm-level data from 252 beneficiaries and 268 non-beneficiaries of buffer stock operations in Ghana. We applied the Coarsened Exact Matching and Propensity Score Matching methods to balance the data among the two groups. We estimate the smallholder farmer income effect from participating in buffer stock operations by combining the matching methods in a regression framework. The results affirm that buffer stock operations increase the incomes of participating smallholder farming households by at least 12%, providing evidence that output price support via buffer stocks is a critical tool for improving incomes and alleviating poverty among farmers in Ghana. The results further indicate that age, gender, access to market, and use of extension services, as well as transport and packaging costs, drive the participation of smallholder farmers in the buffer stock operations in Ghana. The findings are relevant to local policymakers and development partners who develop tailored interventions to stabilize and increase income for smallholder maize farmers in Ghana.

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Introduction and Summary

Nilsson¹,

Heshmati²

2019

Efficiency, Equity and Well-Being in Selected African Countries

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One cow per poor family: Effects on the growth of consumption and crop production

Cited by 17 publications

References 57 publications

An Analysis of the Determinants of Household Expenditures in Rwanda

An Analysis of the Determinants of Household Expenditures in Rwanda

The impact of output price support on smallholder farmers' income: evidence from maize farmers in Ghana

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