Statistical matching for conservation science

The awareness of the need for robust impact evaluations in conservation is growing and statistical matching techniques are increasingly being used to assess the impacts of conservation interventions. Used appropriately matching approaches are powerful tools, but they also pose potential pitfalls. We outlined important considerations and best practice when using matching in conservation science. We identified 3 steps in a matching analysis. First, develop a clear theory of change to inform selection of treatment and controls and that accounts for real‐world complexities and potential spillover effects. Second, select the appropriate covariates and matching approach. Third, assess the quality of the matching by carrying out a series of checks. The second and third steps can be repeated and should be finalized before outcomes are explored. Future conservation impact evaluations could be improved by increased planning of evaluations alongside the intervention, better integration of qualitative methods, considering spillover effects at larger spatial scales, and more publication of preanalysis plans. Implementing these improvements will require more serious engagement of conservation scientists, practitioners, and funders to mainstream robust impact evaluations into conservation. We hope this article will improve the quality of evaluations and help direct future research to continue to improve the approaches on offer.Peer reviewe

Asymptotic Loss in Privacy due to Dependency in Gaussian Traces

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.Comment: IEEE Wireless Communications and Networking Conferenc

ADAPTATION OF STATISTICAL MATCHING IN MICRO-REGIONAL ANALYSIS OF AGRICULTURAL PRODUCTION

Agricultural production and agricultural policy has many special dimensions. The production structure, income positions, and labour input have large regional disparities, the production intensity is not homogenous in space, and farms have different risk factors and market possibilities in the different regions of Hungary. Land use and production technology also varies largely, in many regions farming is competitive, highly specialized with big corporate farms, while other regions have small individual farms with mixed production structure and less concentration in land use. There are no direct data for spatial analysis less aggregated than NUTS 3 level. Only the data of agricultural census and administrative database for direct payments are available at settlement and microregional level, but these databases do not provide information of farm income. The income statistics either cannot be disaggregated to micro-regional level (agricultural accounts) or are not representative at this level (FADN). The administrative database of the Paying Agency contains the land use data and limited livestock numbers for all farms receiving direct payments. The FADN database contains a large accountancy dataset for a low number of farms. Statistical matching combines these two databases and provides a possibility for detailed regional analysis using estimated data.micro-regional, statistical matching, administrative data, FADN, Agricultural and Food Policy, Community/Rural/Urban Development,

Disordered Heteropolymers with Crosslinks - Phase Diagram and Conformational Transitions

We study the phase behavior of random heteropolymers (RHPs) with quenched cross-links, a novel polymer class of technological and biological relevance, and show the possible occurrence of freezing with few chain conformations sampled. The sensitivity of the frozen phase microstructure to the disorder components is elucidated at positive solubility parameter values; at low T's segregated microphases form, while at a finite T, a first order conformational transition occurs, and is attributed to statistical matching of large microphases bounded by cross-links. The end of the symmetry broken regime stabilization by cross-links occurs at a higher T by a second order conformational transition. \\icrophases form, while at a finite T, a first order conformational transition occurs, and is attributed to statistical matching of large microphases bounded by cross-links. The end of the symmetry broken regime stabilization by cross-links occurs at a higher T by a second order conformational transition.Comment: 5 pages, 2 ps. figures. submitted to Chem. Phys. Let

Statistical Matching of Administrative and Survey Data : An Application to Wealth Inequality Analysis

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Using population representative survey data from the German Socio-Economic Panel (SOEP) and administrative pension records from the Statutory Pension Insurance, the authors compare four statistical matching techniques to complement survey information on net worth with social security wealth (SSW) information from the administrative records. The unique properties of the linked data allow for a straight control of the quality of matches under each technique. Based on various evaluation criteria, Mahalanobis distance matching performs best. Exploiting the advantages of the newly assembled data, the authors include SSW in a wealth inequality analysis. Despite its quantitative relevance, SSW is thus far omitted from such analyses because adequate micro data are lacking. The inclusion of SSW doubles the level of net worth and decreases inequality by almost 25 percent. Moreover, the results reveal striking differences along occupational lines.Hans Böckler-Foundation, 2006-835-4, Erstellung und Analyse einer konsistenten Geld- und Realvermögensverteilungsrechnung für Personen und Haushalte 2002 und 2007 unter Berücksichtigung der personellen Einkommensverteilun

Unemployed and their Caseworkers: Should they be Friends or Foes?

In many countries, caseworkers in a public employment office have the dual roles of counselling and monitoring unemployed persons. These roles often conflict with each other leading to important caseworker heterogeneity: Some consider providing services to their clients and satisfying their demands as their primary task. Others may however pursue their strategies even against the will of the unemployed person. They may assign job assignments and labour market programmes without consent of the unemployed person. Based on a very detailed linked jobseeker-caseworker dataset, we investigate the effects of caseworkers' cooperativeness on the employment probabilities of their clients. Modified statistical matching methods reveal that caseworkers who place less emphasis on a cooperative and harmonic relationship with their clients increase their employment chances in the short and medium term.Public employment services, unemployment, statistical matching methods

Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost

While static microsimulation models of the tax-transfer system are now available throughout the developed world, health microsimulation models are much rarer. This is, at least in part, due to the difficulties in creating adequate base micro-datasets upon which the microsimulation models can be constructed. In sharp contrast to tax-transfer modelling, no readily available microdata set typically contains all the health status, health service usage and socio-demographic information required for a sophisticated health microsimulation model. This paper describes three new techniques developed to overcome survey data limitations when constructing \'MediSim\', a microsimulation model of the Australian Pharmaceutical Benefits Scheme. Comparable statistical matching and data imputation techniques may be of relevance to other modellers, as they attempt to overcome similar data deficiencies. The 2001 national health survey (NHS) was the main data source for MediSim. However, the NHS has a number of limitations for use in a microsimulation model. To compensate for this, we statistically matched the NHS with another national survey to create synthetic families and get a complete record for every individual within each family. Further, we used complementary datasets to impute short term health conditions and prescribed drug usage for both short- and long-term health conditions. The application of statistical matching methods and use of complementary data sets significantly improved the usefulness of the NHS as a base dataset for MediSim.Base Data, Drug Usage, Microsimulation, Pharmaceutical Benefits, Scripts, Statistical Matching

An Empirical Evaluation of Statistical Matching Methodologies

Using known data, the methodologies available for microdata file merging are compared. Results indicate that various techniques work or do not work in specific circumstances. An optimal-constrined merge model with an absolute difference distance function provide the best results