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Operational Poverty Targeting by Proxy Means Tests: Models and Policy Simulations for Malawi New edition [Pehme köide]

  • Formaat: Paperback / softback, 136 pages, kõrgus x laius: 210x148 mm, kaal: 200 g
  • Sari: Development Economics & Policy 67
  • Ilmumisaeg: 26-Jul-2013
  • Kirjastus: Peter Lang AG
  • ISBN-10: 3631625707
  • ISBN-13: 9783631625705
Teised raamatud teemal:
Operational Poverty Targeting by Proxy Means Tests: Models and Policy Simulations for Malawi New editionSuurem pilt
  • Formaat: Paperback / softback, 136 pages, kõrgus x laius: 210x148 mm, kaal: 200 g
  • Sari: Development Economics & Policy 67
  • Ilmumisaeg: 26-Jul-2013
  • Kirjastus: Peter Lang AG
  • ISBN-10: 3631625707
  • ISBN-13: 9783631625705
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783631625705.html
Märksõnad:
This volume develops alternative methods for improving the targeting efficiency of development programs in terms of coverage of the poor and leakage to non-poor. The research sought to identify the best indicators for predicting how well off a household is. Using a set of innovative statistical methods and econometric tools, including out-of-sample validation tests, receiver operating characteristic curves, and bootstrapped simulations, this book demonstrates that using a proxy indicator-based system to target development programs such as fertilizer subsidy programs may be worth the extra effort and may improve program targeting and cost efficiency in Malawi. With increasing pressure to target better development programs at the poor and smallholder farmers in order to meet the Millennium Development Goals (MDGs), the methodology applied in this research can be extended to other developing countries. The research on which this volume is based received the 2012 Josef G. Knoll European Science Award of the Foundation Fiat Panis (Germany).
List of Tables
13(2)
Abbreviations and Acronyms 15(2)
Chapter I Introduction
17(22)
1.1 Background to the research
17(2)
1.2 Problem statement
19(2)
1.3 Research objectives and organization of the book
21(2)
1.4 Targeting in the literature
23(16)
1.4.1 The concept of poverty: Theoretical considerations
24(1)
1.4.2 Targeting the poor: Empirical methods
25(4)
1.4.3 Proxy means tests in the literature
29(6)
References
35(4)
Chapter II Targeting the Poor and Smallholder Farmers: Empirical evidence from Malawi
39(20)
1 Introduction
39(2)
2 Data and methodology
41(6)
2.1 Data and theoretical framework
41(1)
2.2 Model estimation methods
42(1)
2.2.1 Variable selection
42(1)
2.2.2 Estimating the proxy means tests
42(3)
2.3 Accuracy measures and robustness tests
45(1)
2.3.1 Accuracy measures
45(1)
2.3.2 Assessing the predictive power of the models
46(1)
3 Targeting accuracy of the proxy means tests: Empirical results
47(6)
3.1 Model predictive performances
47(2)
3.2 Targeting poverty using ROC curves: Examples from Malawi
49(1)
3.3 How sensitive are the models to the poverty line?
50(2)
3.4 Spatial distribution of targeting errors
52(1)
4 Conclusions
53(6)
References
53(3)
Annexes
56(1)
Annex
1. Malawi's poverty rates by region and poverty line
56(1)
Annex
2. Results of the maximum likelihood estimates (rural model)
57(1)
Annex
3. Malawi's rural poverty model calibrated to the national poverty line
58(1)
Chapter III Operational Models for Improving the Targeting Efficiency of Development Policies: A systematic comparison of different estimation methods using out-of-sample tests
59(34)
I Introduction
59(2)
2 Data and estimation methods
61(12)
2.1 Data
61(1)
2.2 Model estimation methods
62(1)
2.2.1 Poverty predictors and sample selections
62(2)
2.2.2 Estimation methods
64(4)
2.2.3 Predicting the household poverty status
68(2)
2.3 Targeting ratios and robustness tests
70(1)
2.3.1 Targeting ratios
70(1)
2.3.2 Model validations
71(2)
3 Results and discussions
73(11)
3.1 Modelling the household poverty status: Empirical results
73(4)
3.2 Aggregate performances of the estimation methods
77(3)
3.3 Assessing model sensitivity to the poverty line
80(2)
3.4 Comparing model targeting errors across welfare deciles
82(2)
4 Concluding remarks
84(9)
References
85(3)
Annexes
88(1)
Annex I. Sample size by model type
88(1)
Annex
2. Weighted Least Square estimates (rural model)
89(1)
Annex
3. Weighted Logit estimates (rural model)
90(1)
Annex
4. Weighted Least Square estimates (urban model)
91(1)
Annex
5. Weighted Logit estimates (urban model)
92(1)
Chapter IV To Target or Not to Target: The costs, benefits, and impacts of indicator-based targeting
93(32)
1 Introduction
93(2)
2 Targeted development programs: The Malawian context
95(2)
3 The principles of targeting: A theoretical perspective
97(3)
4 Data and methodology
100(10)
4.1 Data
100(1)
4.2 Estimating the models
4.2.1 Estimation method
101(1)
4.2.2 Out-of-sample tests
102(1)
4.2.3 Measuring model targeting performances
103(1)
4.3 Policy simulations
104(6)
5 Empirical results
110(7)
5.1 How well do the models identify the poor?
110(1)
5.2 Targeting performances of development policies
111(1)
5.3 Evaluating the cost-effectiveness and impacts of targeting the poor: Policy simulations
112(1)
5.3.2 Cost-efficiency, benefits, and impacts of targeting
112(3)
5.4 Cost efficiency of targeted agricultural input support programs versus the new system
115(2)
6 Conclusions and policy implications
117(8)
References
118(3)
Annexes
121(1)
Annex 1 Quantile regression results (rural model)
121(1)
Annex 2 Quantile regression results (urban model)
122(1)
Annex 3 Costs of targeting
123(2)
Chapter V General Conclusions
125(4)
General Appendices 129(1)
Appendix 1 Map of Malawi 129(1)
Appendix 2 Sample size and number of potential indicators by model type and estimation method 130(1)
Appendix 3 Descriptive statistics of variables used in the rural model (full sample) 131(1)
Appendix 4 Descriptive statistics of variables used in the rural model (calibration sample) 132(1)
Appendix 5 Descriptive statistics of variables used in the rural model (validation sample) 133(1)
Appendix 6 Descriptive statistics of variables used in the urban model (full sample) 134(1)
Appendix 7 Descriptive statistics of variables used in the urban model (calibration sample) 135(1)
Appendix 8 Descriptive statistics of variables used in the urban model (validation sample) 136
Nazaire Idriss Houssou holds a degree of Ingénieur Agronome from the University of Abomey-Calavi (Benin), an MSc and a PhD degree in Agricultural Economics from the University of Hohenheim (Germany). He is currently a Postdoctoral Fellow at the Development Strategy and Governance Division of the International Food Policy Research Institute (IFPRI) in Washington D.C. The author is outposted to Accra, Ghana where he works under IFPRIs Ghana Strategy Support Program (GSSP) which aims at triggering Agricultural Transformation in the country.