Measuring the Inspectorate: Point and Interval Estimates for Performance Indicators

Article
  • 98 Downloads

Abstract

Border-based regulatory inspectorates, such as quarantine and customs organizations, intervene at national and state borders to ensure compliance of activities to relevant policies. Performance indicators can be used to help assess and compare the activities for the risk of non-compliance, and also to assess the inspectorate’s ability to detect and rectify non-compliance. We document a suite of three performance indicators that target inspectorate intervention at and before the border, and provide protocols for collecting the necessary data to compute point estimates of the indicators. For obtaining interval estimates, we then discuss mathematical models that account for the sources of uncertainty that are present during data collection. We cover three distinct setups, namely, the importation of certain classes of air cargo in Australia, both historically and under current policies, and passengers arriving at an international airport. The methodology developed here using the terminology of quarantine inspections can be applied in more general settings. Under the model which best describes the way data are collected, we provide confidence bounds for the performance indicators, with coverage close to the nominal level. The methodology is then illustrated on real and fabricated data.

Keywords

Raking Iterative proportional fitting Performance indicators Confidence intervals Inspectorates 

References

  1. Clopper, C. and Pearson, E.S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26, 404–413Google Scholar
  2. Crowder, M. and Sweeting, T. (1989). Bayesian inference for a bivariate binomial distribution. Biometrika, 76, 599–603Google Scholar
  3. Decrouez, G. and Hall, P. (2014). Split-sample methods for constructing confidence intervals for binomial and Poisson parameters. Journal of the Royal Statistical Society: Series B. 76(5), 949–975.Google Scholar
  4. Decrouez, G. and Robinson, A. (2012). Confidence intervals for the weighted sum of two independent binomial proportions. Australian and New Zealand Journal of Statistics 54(3), 281–299.Google Scholar
  5. Koopman, P.A.R. (1984). Confidence limits for the ratio of two binomial proportions. Biometrics 40, 513–517Google Scholar
  6. Korn, E.L. and Graubard, B.I. (1998). Confidence intervals for proportions with small expected number of positive counts estimated from survey data. Survey Methodology 24, 193–201Google Scholar
  7. Little, R.J.A. and Wu, M-M. (1991). Models for contingency tables with known margins when target and sampled populations differ. Journal of the American Statistical Association 86, 87–95Google Scholar
  8. Mumford, J. (2002). Economic issues related to quarantine in trade. European Review of Agricultural Economics 29 (3), 329–348Google Scholar
  9. Nam, J. (1995). Confidence limits for the ratio of two binomial proportions based on likelihood scores: non-iterative method. Biometrical Journal 37, 375–379Google Scholar
  10. Robinson, A.P., Cannon, R., and Mudford, R. (2011). DAFF Biosecurity Quarantine Operations Risk Return Study I: Performance Indicators, Report 1. ACERA Technical Report 1001i1, 58 p.Google Scholar
  11. Robinson, A.P., Mudford, R., Quan, K., Sorbello, P., and Chisholm, M. (2013). Adoption of meaningful performance indicators for quarantine inspection performance. ACERA Technical Report 1101d1 46 p.Google Scholar
  12. Sparrow, M. (2000). The Regulatory Craft: Controlling Risks, Solving Problems, and Managing Compliance. Brookings Institution Press, 370 p.Google Scholar

Copyright information

© International Biometric Society 2016

Authors and Affiliations

  1. 1. National Research UniversityHigher School of EconomicsMoscowRussia
  2. 2.CEBRA and Department of Mathematics and StatisticsThe University of MelbourneParkvilleAustralia

Personalised recommendations