Publication: Propensity Score Matching and Policy Impact Analysis : A Demonstration in EViews
dc.contributor.author | Essama-Nssah, B. | |
dc.date.accessioned | 2012-06-21T21:18:34Z | |
dc.date.available | 2012-06-21T21:18:34Z | |
dc.date.issued | 2006-04 | |
dc.description.abstract | Effective development policymaking creates a need for reliable methods of assessing effectiveness. There should be, therefore, an intimate relationship between effective policymaking and impact analysis. The goal of a development intervention defines the metric by which to assess its impact, while impact evaluation can produce reliable information on which policymakers may base decisions to modify or cancel ineffective programs and thus make the most of limited resources. This paper reviews the logic of propensity score matching (PSM) and, using data on the National Support Work Demonstration, compares that approach with other evaluation methods such as double difference, instrumental variable, and Heckman's method of selection bias correction. In addition, it demonstrates how to implement nearest-neighbor and kernel-based methods, and plot program incidence curves in E-Views. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation, and on the quality and quantity of available data. In any case, PSM can act as an effective adjuvant to other methods. | en |
dc.identifier | http://documents.worldbank.org/curated/en/2006/04/6708043/propensity-score-matching-policy-impact-analysis-demonstration-eviews | |
dc.identifier.doi | 10.1596/1813-9450-3877 | |
dc.identifier.uri | https://hdl.handle.net/10986/8730 | |
dc.language | English | |
dc.publisher | World Bank, Washington, DC | |
dc.relation.ispartofseries | Policy Research Working Paper; No. 3877 | |
dc.rights | CC BY 3.0 IGO | |
dc.rights.holder | World Bank | |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/igo/ | |
dc.subject | ALGORITHMS | |
dc.subject | COMPARISON GROUPS | |
dc.subject | COMPUTATION | |
dc.subject | COUNTERFACTUAL | |
dc.subject | DESCRIPTIVE STATISTICS | |
dc.subject | DUMMY VARIABLES | |
dc.subject | ECONOMIC GROWTH | |
dc.subject | ESTIMATORS | |
dc.subject | EVALUATION METHODS | |
dc.subject | IMPACT ANALYSIS | |
dc.subject | IMPACT EVALUATION | |
dc.subject | IMPACT INDICATORS | |
dc.subject | INCOME | |
dc.subject | INCOME GAINS | |
dc.subject | INEQUALITY | |
dc.subject | INSTRUMENTAL VARIABLES | |
dc.subject | INTERVENTION | |
dc.subject | INTERVENTIONS | |
dc.subject | MATCHING METHODS | |
dc.subject | MAXIMUM LIKELIHOOD ESTIMATION | |
dc.subject | MISSING DATA | |
dc.subject | NONLINEARITY | |
dc.subject | ORTHOGONALITY | |
dc.subject | POVERTY REDUCTION | |
dc.subject | PROBABILITY | |
dc.subject | PROGRAM EFFECTIVENESS | |
dc.subject | PROGRAMS | |
dc.subject | PROPENSITY SCORE MATCHING | |
dc.subject | RANDOMIZATION | |
dc.subject | RATE OF CHANGE | |
dc.subject | REGRESSION ANALYSIS | |
dc.subject | RESEARCH WORKING PAPERS | |
dc.subject | SAMPLE SELECTION | |
dc.subject | SAMPLE SIZE | |
dc.subject | SELECTION BIAS | |
dc.subject | SOCIAL EXPERIMENTS | |
dc.subject | STANDARD DEVIATION | |
dc.subject | TREATMENT EFFECTS | |
dc.subject | EVIEWS | |
dc.subject | DOUBLE DIFFERENCE | |
dc.subject | IMPACT ANALYSIS | |
dc.subject | INSTRUMENTAL VARIABLES | |
dc.subject | KERNEL FUNCTION | |
dc.subject | MATCHING | |
dc.subject | PROPENSITY SCORE | |
dc.subject | SAMPLE SELECTION | |
dc.title | Propensity Score Matching and Policy Impact Analysis : A Demonstration in EViews | en |
dspace.entity.type | Publication | |
okr.crossref.title | Propensity Score Matching And Policy Impact Analysis - A Demonstration In Eviews | |
okr.date.doiregistration | 2025-04-10T10:05:16.267906Z | |
okr.doctype | Publications & Research::Policy Research Working Paper | |
okr.doctype | Publications & Research | |
okr.docurl | http://documents.worldbank.org/curated/en/2006/04/6708043/propensity-score-matching-policy-impact-analysis-demonstration-eviews | |
okr.globalpractice | Education | |
okr.globalpractice | Transport and ICT | |
okr.globalpractice | Poverty | |
okr.guid | 802731468135581892 | |
okr.identifier.doi | 10.1596/1813-9450-3877 | |
okr.identifier.externaldocumentum | 000012009_20060405111629 | |
okr.identifier.internaldocumentum | 6708043 | |
okr.identifier.report | WPS3877 | |
okr.language.supported | en | |
okr.pdfurl | http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2006/04/05/000012009_20060405111629/Rendered/PDF/wps38770rev0pdf.pdf | en |
okr.topic | Statistical and Mathematical Sciences | |
okr.topic | Poverty Reduction::Poverty Impact Evaluation | |
okr.topic | Poverty Monitoring and Analysis | |
okr.topic | Scientific Research and Science Parks | |
okr.topic | Science Education | |
okr.topic | Education | |
okr.topic | Science and Technology Development | |
okr.unit | Development Research Group (DECRG) | |
okr.volume | 1 of 1 | |
relation.isAuthorOfPublication | 1a351482-543d-53c1-9937-6b1df30573b3 | |
relation.isAuthorOfPublication.latestForDiscovery | 1a351482-543d-53c1-9937-6b1df30573b3 | |
relation.isSeriesOfPublication | 26e071dc-b0bf-409c-b982-df2970295c87 | |
relation.isSeriesOfPublication.latestForDiscovery | 26e071dc-b0bf-409c-b982-df2970295c87 |
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