Is Microfinance Truly Useless for Poverty Reduction and Women Empowerment? A Bayesian Spatial-Propensity Score Matching Evaluation in Bolivia

Gonzales, Rolando; Aranda, Patricia; Mendizabal, Joel

doi:10.2139/ssrn.3167321

Cited by 6 publications

(9 citation statements)

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“…Because the typical NN matching method is believed to have the potential risk of bad matching when the closest neighbour is too far away (Gonzales et al, 2018), we used a distance calliper to test for the robustness of the typical NN matching when the spatial effects are controlled in the sample selection equation (Equation 3) (Papadogeorgou et al, 2019). By applying a distance calliper, the tolerance level on the maximum propensity score distance was captured through the distance relationship:…”

Section: Spatial Propensity Score Matchingmentioning

confidence: 99%

“…Different spatial models (SAR, SEM and SDM) show a similar result. 8 The inaccuracy of ignoring spatial effects can lead to either an over-or underestimation of the treatment effect (Chatzopoulos & Lippert, 2016;Gonzales et al, 2018).…”

Section: Factors Influencing the Uptake Of Soil Management Practicesmentioning

confidence: 99%

“…The literature on farmer decision-making acknowledges the presence of spatial dependence arising from peers' involvement in the programmes: peers motivate one another to pursue environmental learning (Defrancesco et al, 2008;Oreszczyn et al, 2010;Sligo & Massey, 2007;Tamini, 2011). Furthermore, literature in agricultural economics highlights the importance of developing existing evaluation models that include spatial effects (Gonzales et al, 2018;Holloway et al, 2002). Empirical studies outside the field of PEP evaluations address the issue of spatial dependence effects on farmers' choice behaviours, mainly focusing on farmers' uptake of technology and adoption of good management practices.…”

Section: Introductionmentioning

confidence: 99%

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Spatial evaluation of the impact of a climate change participatory extension programme on the uptake of soil management practices*

Yang¹,

Knook²

2021

Aus J Agri & Res Econ

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Participatory extension programmes (PEPs) are a popular policy tool to stimulate the uptake of climate change mitigation practices on a farm level. Given the public investment in PEPs, reliable evaluation is important. However, few studies evaluate climate change PEPs. Moreover, the evaluations conducted so far do not correctly account for potential spatial effects, such as the influence of neighbouring farms on PEP participation. Therefore, this paper estimates the impact of PEP participation on the uptake of a climate change mitigation practice and soil management, and identifies the importance of spatial effects on PEP participation. A spatial propensity score matching method is applied to a dataset from Scotland, consisting of 134 PEP and 184 control farmers. The results show that PEP participation facilitates the uptake of soil management practices and that spatial dependence exists in farmers' decision-making, indicating the need for the inclusion of spatial factors. This study contributes to the current literature by combining spatial econometric analysis and propensity score matching to conduct a quantitative evaluation of a climate change PEP. The evaluation methodology provides decision-makers with reliable insights into the potential contribution of PEPs towards climate change mitigation targets.

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Section: Spatial Propensity Score Matchingmentioning

confidence: 99%

Section: Factors Influencing the Uptake Of Soil Management Practicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial evaluation of the impact of a climate change participatory extension programme on the uptake of soil management practices*

Yang¹,

Knook²

2021

Aus J Agri & Res Econ

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“…While there is some previous work on propensity score matching in the context of multilevel data [ 22 ] and aggregate (region-level) data [ 23 – 25 ], we are interested in an integrative approach that allows spatial information to augment patient-level information through a hierarchical data structure when patients are clustered at the spatial unit. Recent work in spatial propensity score analysis for point-referenced data has focused on incorporating distance between units into the propensity score model [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies

et al. 2021

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Background Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. Methods We employ a spatial propensity score matching method to account for “geographic confounding”, which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. Results In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. Conclusions These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

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“…11 The central aim of this paper is to develop a spatial DR estimator that minimizes bias in the presence of observed and potentially unobserved geographic confounding. While there has been some recent work incorporating spatial information into PSA, [15][16][17][18] these methods have been limited to non-clustered data in which the response variable is a region-level proportion. Arpino and Mealli 19 and Li et al 11 have recently introduced PSA approaches for multilevel data.…”

Section: Introductionmentioning

confidence: 99%

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes

Davis

Neelon

Nietert

et al. 2017

Stat Methods Med Res

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Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need.

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Is Microfinance Truly Useless for Poverty Reduction and Women Empowerment? A Bayesian Spatial-Propensity Score Matching Evaluation in Bolivia

Cited by 6 publications

References 0 publications

Spatial evaluation of the impact of a climate change participatory extension programme on the uptake of soil management practices*

Spatial evaluation of the impact of a climate change participatory extension programme on the uptake of soil management practices*

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes

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