TY - JOUR
T1 - Evaluating the distributive effects of a micro-credit intervention
AU - Maitra, Pushkar
AU - Mitra, Sandip
AU - Mookherjee, Dilip
AU - Visaria, Sujata
N1 - Funding Information:
Funding was provided by the Australian Agency for International Development (CF09/650), the International Growth Centre, United Kingdom (1-VRA-VINC-VXXXX-89120), United States Agency for International Development (AID-OAA-F-13-00007) and the Hong Kong Research Grants Council (GRF16503014). We are grateful to Shree Sanchari for collaborating on the project. Jingyan Gao, Rina Lookman Jio, Arpita Khanna, Clarence Lee, Daijing Lv, Foez Mojumder, Moumita Poddar and Nina Yeung provided excellent research assistance at different stages of the project. Elizabeth Kwok provided exceptional administrative support. We thank three anonymous referees, the editor Andrew Foster, Gaurav Datt, Lakshmi Iyer, Christina Jenq, Xun Lu, Farshid Vahid, Diego Vera-Cossio and seminar participants at the Institute for Emerging Market Studies at HKUST, UNSW, the Workshop on The Role of the Private Sector in Development at the University of Sydney, the IEMS-CAG Workshop on Financial Inclusion in Asia and the Italian Summer School in Development Economics held in Prato, Italy for helpful feedback and comments. Internal review board clearance was received from Monash University, Boston University and The Hong Kong University of Science and Technology. The authors are responsible for all errors.
Funding Information:
Funding was provided by the Australian Agency for International Development ( CF09/650 ), the International Growth Centre, United Kingdom ( 1-VRA-VINC-VXXXX-89120 ), United States Agency for International Development ( AID-OAA-F-13-00007 ) and the Hong Kong Research Grants Council ( GRF16503014 ). We are grateful to Shree Sanchari for collaborating on the project. Jingyan Gao, Rina Lookman Jio, Arpita Khanna, Clarence Lee, Daijing Lv, Foez Mojumder, Moumita Poddar and Nina Yeung provided excellent research assistance at different stages of the project. Elizabeth Kwok provided exceptional administrative support. We thank three anonymous referees, the editor Andrew Foster, Gaurav Datt, Lakshmi Iyer, Christina Jenq, Xun Lu, Farshid Vahid, Diego Vera-Cossio and seminar participants at the Institute for Emerging Market Studies at HKUST, UNSW, the Workshop on The Role of the Private Sector in Development at the University of Sydney, the IEMS-CAG Workshop on Financial Inclusion in Asia and the Italian Summer School in Development Economics held in Prato, Italy for helpful feedback and comments. Internal review board clearance was received from Monash University, Boston University and The Hong Kong University of Science and Technology. The authors are responsible for all errors.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/9
Y1 - 2022/9
N2 - Most analyses of randomized controlled trials of development interventions estimate an average treatment effect on the outcome of interest. However, the aggregate impact on welfare also depends on distributional effects. We propose a simple method to evaluate efficiency–equity trade-offs in the utilitarian tradition of Atkinson (1970). This involves an estimation of the average treatment effect on a monotone concave function of the outcome variable, whose curvature captures the degree of inequality aversion in the welfare function. We argue this is preferable to the current practice of examining distributional impacts through sub-group analysis or quantile treatment effects. We illustrate the approach using data from a credit delivery experiment we implemented in West Bengal, India.
AB - Most analyses of randomized controlled trials of development interventions estimate an average treatment effect on the outcome of interest. However, the aggregate impact on welfare also depends on distributional effects. We propose a simple method to evaluate efficiency–equity trade-offs in the utilitarian tradition of Atkinson (1970). This involves an estimation of the average treatment effect on a monotone concave function of the outcome variable, whose curvature captures the degree of inequality aversion in the welfare function. We argue this is preferable to the current practice of examining distributional impacts through sub-group analysis or quantile treatment effects. We illustrate the approach using data from a credit delivery experiment we implemented in West Bengal, India.
KW - Agricultural finance
KW - Distributive impacts
KW - Program evaluation
UR - http://www.scopus.com/inward/record.url?scp=85132327083&partnerID=8YFLogxK
U2 - 10.1016/j.jdeveco.2022.102896
DO - 10.1016/j.jdeveco.2022.102896
M3 - Article
AN - SCOPUS:85132327083
VL - 158
JO - Journal of Development Economics
JF - Journal of Development Economics
SN - 0304-3878
M1 - 102896
ER -
