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Research has provided robust evidence for the use of GPS technology to be the scalable gold standard in land area measurement in household surveys. Nonetheless, facing budget constraints, survey agencies often seek to measure with GPS only plots within a given radius of dwelling locations. Subsequently, it is common for significant shares of plots not to be measured, and research has highlighted the selection biases resulting from using incomplete data. This study relies on nationally-representative, multi-topic household survey data from Malawi and Ethiopia that exhibit near-negligible missingness in GPS-based plot areas, and validates the accuracy of a multiple imputation model for predicting missing GPS-based plot areas in household surveys. The analysis (i) randomly creates missingness among plots beyond two operationally relevant distance measures from the dwelling locations; (ii) conducts multiple imputation under each distance scenario for each artificially created data set; and (iii) compares the distributions of the imputed plot-level outcomes, namely, area and agricultural productivity, with the known distributions. In Malawi, multiple imputation can produce imputed yields that are statistically undistinguishable from the true distributions with up to 82 percent missingness in plot areas that are further than 1 kilometer from the dwelling location. The comparable figure in Ethiopia is 56 percent. These rates correspond to overall rates of missingness of 23 percent in Malawi and 13 percent in Ethiopia. The study highlights the promise of multiple imputation for reliably predicting missing GPS-based plot areas, and provides recommendations for optimizing fieldwork activities to capture the minimum required data.

Global Positioning System --- GPS --- Land Area Measurement --- Missing Data --- Multiple Imputation --- Survey Methodology

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Land area is a fundamental component of agricultural statistics, and of analyses undertaken by agricultural economists. While household surveys in developing countries have traditionally relied on farmers' own, potentially error-prone, land area assessments, the availability of affordable and reliable Global Positioning System (GPS) units has made GPS-based area measurement a practical alternative. Nonetheless, in an attempt to reduce costs, keep interview durations within reasonable limits, and avoid the difficulty of asking respondents to accompany interviewers to distant plots, survey implementing agencies typically require interviewers to record GPS-based area measurements only for plots within a given radius of dwelling locations. It is, therefore, common for as much as a third of the sample plots not to be measured, and research has not shed light on the possible selection bias in analyses relying on partial data due to gaps in GPS-based area measures. This paper explores the patterns of missingness in GPS-based plot areas, and investigates their implications for land productivity estimates and the inverse scale-land productivity relationship. Using Multiple Imputation (MI) to predict missing GPS-based plot areas in nationally-representative survey data from Uganda and Tanzania, the paper highlights the potential of MI in reliably simulating the missing data, and confirms the existence of an inverse scale-land productivity relationship, which is strengthened by using the complete, multiply-imputed dataset. The study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer-reported areas, and with regards to systematic bias in plot selection for GPS-based area measurement.

Global Positioning System --- Land Area Measurement --- Land Productivity --- Macroeconomics and Economic Growth --- Missing Data --- Multiple Imputation --- Poverty Reduction --- Sub-Saharan Africa

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Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to understand the underlying issues and difficulties that come about from missing data and their impact on subsequent analysis. There has been a great deal written on the theory developed for analyzing missing data for finite-dimensional parametric models. This includes an extensive literature on likelihood-based methods and multiple imputation. More recently, there has been increasing interest in semiparametric models which, roughly speaking, are models that include both a parametric and nonparametric component. Such models are popular because estimators in such models are more robust than in traditional parametric models. The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject. This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is at a level that is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible. Anastasios A. Tsiatis is the Drexel Professor of Statistics at North Carolina State University. His research has focused on developing statistical methods for the design and analysis of clinical trials, censored survival analysis, group sequential methods, surrogate markers, semiparametric methods with missing and censored data and causal inference and has been the major Ph.D. advisor for more than 30 students working in these areas. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the recipient of the Spiegelman Award and the Snedecor Award. He has been an Associate Editor of the Annals of Statistics and Statistics and Probability Letters and is currently an Associate Editor for Biometrika.

Parameter estimation. --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Stochastic systems --- Mathematical statistics. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Multivariate analysis --- Multiple imputation (Statistics) --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Imputation, Multiple (Statistics) --- Monte Carlo method --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices

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This 2001 book presents a general theory as well as a constructive methodology to solve 'observation problems', that is, reconstructing the full information about a dynamical process on the basis of partial observed data. A general methodology to control processes on the basis of the observations is also developed. Illustrative but also practical applications in the chemical and petroleum industries are shown. This book is intended for use by scientists in the areas of automatic control, mathematics, chemical engineering and physics.

Observers (Control theory) --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Observability (Control theory) --- State estimator (Control theory) --- State observer (Control theory) --- Control theory