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Matching Methods (matching + methods)
Selected AbstractsAccess to Telephone Services and Household Income in Poor Rural Areas Using a Quasi-natural Experiment for PeruECONOMICA, Issue 304 2009ALBERTO CHONG We take advantage of a quasi-natural experiment in Peru in which a privatized telecommunications company was required by the government to randomly install and operate public pay phones in small rural towns throughout the country. Using an especially designed household survey for a representative sample of rural towns, we are able to link access to telephone services with household income. We find that, regardless of income measurement, most characteristics of public telephone use are positively linked with income. Remarkably, the benefits are given at both non-farm and farm income levels. The findings hold when using propensity score matching methods. [source] Image matching based on relation between pixels located on the maximum and minimum gray-levels in local areaIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 2 2007Fumihiko Saitoh Member Abstract This paper proposes a template matching method maximum and minimum gray levels pixels sign matching (MMM) which is based on a comparison of the gray levels of a pair of pixels whose locations are on the pixels with the maximum and the minimum gray levels in a local area in a template image. In this method, the locations of the pixels with the maximum and the minimum gray levels are registered in a local area whose center is every pixel in a template image. The target image area is searched from the matching ratio which is obtained by the relation between the gray levels of the two pixels whose locations have been registered in the template image. The experimental results show that the proposed method had equal or better robustness to the inferior factors of objective images in comparison with the three typical conventional template matching methods. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] An automated method for peak detection and matching in large gas chromatography-mass spectrometry data setsJOURNAL OF CHEMOMETRICS, Issue 8-10 2006Sarah J. Dixon Abstract A new approach for peak detection and matching has been developed and applied to two data sets. The first consisted of the Gas Chromatography-Mass Spectrometry (GC-MS) samples of 965 human sweat samples obtained from a population of 197 individuals. The second data set contained 500 synthetic chromatograms, and was generated to validate the peak detection and matching methods. The size of both of the data sets (around 500,000 detectable peaks over all chromatograms in data set 1, and around 100,000 in data set 2) would make it unfeasible to check manually whether peaks are matched. In the method described, the first procedure involves pre-processing the data before carrying out the second procedure of peak detection. The final procedure of peak matching consists of three stages: (a) finding potential target peaks in the full data set over all chromatograms; (b) matching peaks in the chromatograms to these targets to form clusters of spectra associated with each target; (c) merging targets where appropriate. Peak detection and matching were applied to both data sets, and the importance of stage (c) of peak matching described. In addition to the analysis of the synthetic chromatograms, the method was also validated by shuffling the original order of the sweat chromatograms and performing the methods independently on the newly shuffled data. Copyright © 2007 John Wiley & Sons, Ltd. [source] How close is close enough?JOURNAL OF POLICY ANALYSIS AND MANAGEMENT, Issue 3 2007Evaluating propensity score matching using data from a class size reduction experiment In recent years, propensity score matching (PSM) has gained attention as a potential method for estimating the impact of public policy programs in the absence of experimental evaluations. In this study, we evaluate the usefulness of PSM for estimating the impact of a program change in an educational context (Tennessee's Student Teacher Achievement Ratio Project [Project STAR]). Because Tennessee's Project STAR experiment involved an effective random assignment procedure, the experimental results from this policy intervention can be used as a benchmark, to which we compare the impact estimates produced using propensity score matching methods. We use several different methods to assess these nonexperimental estimates of the impact of the program. We try to determine "how close is close enough," putting greatest emphasis on the question: Would the nonexperimental estimate have led to the wrong decision when compared to the experimental estimate of the program? We find that propensity score methods perform poorly with respect to measuring the impact of a reduction in class size on achievement test scores. We conclude that further research is needed before policymakers rely on PSM as an evaluation tool. © 2007 by the Association for Public Policy Analysis and Management [source] Channels through which Public Employment Services and Small Business Assistance Programmes Work,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 4 2010Núria Rodríguez-Planas Abstract Empirical evidence has found that public employment services (PES) and small business assistance (SBA) programmes are successful at getting the unemployed back to work. Policywise it is important to know which of these two programmes is more effective, for whom, and when. Using unusually rich survey data and matching methods, this study evaluates the relative effectiveness of PES and SBA for different subgroups in Romania in the late 1990s, where the outcome variables involve earnings, employment and unemployment in 2000,1 and early 2002. It finds that heterogeneity matters and that these programmes need to be tailored to the problem at hand. [source] Untangling the Causal Effects of Sex on JudgingAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 2 2010Christina L. Boyd We explore the role of sex in judging by addressing two questions of long-standing interest to political scientists: whether and in what ways male and female judges decide cases distinctly,"individual effects",and whether and in what ways serving with a female judge causes males to behave differently,"panel effects." While we attend to the dominant theoretical accounts of why we might expect to observe either or both effects, we do not use the predominant statistical tools to assess them. Instead, we deploy a more appropriate methodology: semiparametric matching, which follows from a formal framework for causal inference. Applying matching methods to 13 areas of law, we observe consistent gender effects in only one,sex discrimination. For these disputes, the probability of a judge deciding in favor of the party alleging discrimination decreases by about 10 percentage points when the judge is a male. Likewise, when a woman serves on a panel with men, the men are significantly more likely to rule in favor of the rights litigant. These results are consistent with an informational account of gendered judging and are inconsistent with several others. [source] Active labour market policy in East GermanyTHE ECONOMICS OF TRANSITION, Issue 4 2009Waiting for the economy to take off Matching estimation; causal effects; programme evaluation; panel data Abstract We investigate the effects of the most important East German active labour market programmes on the labour market outcomes of their participants. The analysis is based on a large and informative individual database derived from administrative data sources. Using matching methods, we find that over a horizon of 2.5 years after the start of the programmes, they fail to increase the employment chances of their participants in the regular labour market. However, the programmes may have other effects for their participants that may be considered important in the especially difficult situation experienced in the East German labour market. [source] Some Methods of Propensity-Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulationsBIOMETRICAL JOURNAL, Issue 1 2009Peter C. Austin Abstract Propensity-score matching is increasingly being used to reduce the impact of treatment-selection bias when estimating causal treatment effects using observational data. Several propensity-score matching methods are currently employed in the medical literature: matching on the logit of the propensity score using calipers of width either 0.2 or 0.6 of the standard deviation of the logit of the propensity score; matching on the propensity score using calipers of 0.005, 0.01, 0.02, 0.03, and 0.1; and 5 , 1 digit matching on the propensity score. We conducted empirical investigations and Monte Carlo simulations to investigate the relative performance of these competing methods. Using a large sample of patients hospitalized with a heart attack and with exposure being receipt of a statin prescription at hospital discharge, we found that the 8 different methods produced propensity-score matched samples in which qualitatively equivalent balance in measured baseline variables was achieved between treated and untreated subjects. Seven of the 8 propensity-score matched samples resulted in qualitatively similar estimates of the reduction in mortality due to statin exposure. 5 , 1 digit matching resulted in a qualitatively different estimate of relative risk reduction compared to the other 7 methods. Using Monte Carlo simulations, we found that matching using calipers of width of 0.2 of the standard deviation of the logit of the propensity score and the use of calipers of width 0.02 and 0.03 tended to have superior performance for estimating treatment effects (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] | ||||||||||