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Enregistrement W7061521368

Remittances, household food security, and entrepreneurship development: A case study of Mzuzu, Malawi

2023· dissertation· en· W7061521368 sur OpenAlexfundno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueThermal Analysis in Power Transmission
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Waterloo
Mots-clésLivelihoodEntrepreneurshipContext (archaeology)Investment (military)Developing countryQualitative researchFood processing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

With the substantial increase in migrant remittances to developing countries since the 1990s, there is a growing interest in migration and development among academics and development practitioners. Remittances, if channelled as investments into income-generating activities (IGAs), can be a crucial source of development finance to improve people's livelihoods from the bottom up. In the context of migration from low-income households, however, channelling remittances into IGAs can be more challenging as they first need to use remittances to meet their basic needs. It is also evident that remittance-receiving households use a major portion of the remitted income for food, indicating a strong link between remittances and household food (in)security. As such, the impact of remittances on livelihoods, food security, and IGA investment among migrant-sending families seems to be positive but context-dependent. However, the linkages between migrant remittances, household food security, and entrepreneurship development are not extensively explored empirically. To address this knowledge gap, this dissertation focuses on the case study of Mzuzu, Malawi and investigates whether or not remittances benefit receiving households by (i) improving livelihoods; (ii) increasing food security; and (iii) bolstering IGAs. The in-depth interview of 42 migrant-sending households and 10 returnee migrants from Mzuzu was conducted as part of the field study. In addition to that, some perspectives of 37 key informants were collected through interviews. The collected information was analyzed using qualitative techniques.
\nThe analysis demonstrates several key findings. Firstly, migration and remittances can have a positive impact on the livelihood of the families in sending areas through the improvements in the education of children as well as in the status of women in the family, in addition to an increase in family capital. Secondly, remittances help improve the households’ food security status as they can buy food directly and grow more food by investing in agricultural inputs. However, these positive impacts are mostly dependent on the volume and frequency of remittances, indicating that the improvements in household food security are likely to be short-term unless remittances are used to expand household income sources. As a result, households maintain improved food security status over the longer term. However, for such investments to occur, the households should be receiving remittances that exceed the family’s cash requirement to buy the food and fulfill immediate needs. As such, there needs to be an attractive incentive structure in place that enables families to invest remittances in IGAs. Thirdly, although remittances are not enough for leveraging investments, the returnee migrants were able to invest in micro-enterprises, create jobs, and even transfer skills, suggesting that informal migrants from low-income households can also be catalysts to development at the local level through brain gain. The study shows that remittances are mostly a support mechanism for the families left behind. The channelling of the remittances in promoting entrepreneurship through migration and remittances requires investment-friendly policy measures and an attractive incentive structure. The findings suggest that New Economics of Labour Migration (NELM) theory needs to extend to incorporate the broader context that influence the developmental impact of remittances on migrant-sending communities.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,155
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,196
Écart entre enseignants0,179 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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