Small Area Estimation of Non-Monetary Poverty with Geospatial Data

This paper evaluates the benefits of combining household surveys with satellite and other geospatial data to generate small area estimates of non-monetary poverty. Using data from Tanzania and Sri Lanka and applying a household-level empirical best (EB) predictor mixed model, we find that combining survey data with geospatial data significantly improves both the precision and accuracy of our non-monetary poverty estimates. While the EB predictor model moderately underestimates standard errors of those point estimates, coverage rates are similar to standard survey-based standard errors that assume independent outcomes across clusters.

[1]  Ryan N. Engstrom,et al.  Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions , 2021, Remote. Sens..

[2]  R. Engstrom,et al.  Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being , 2021, The World Bank Economic Review.

[3]  U. Pape,et al.  Small Area Estimation of Poverty Under Structural Change , 2018, Review of Income and Wealth.

[4]  Ryan Engstrom,et al.  Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data , 2020, PloS one.

[5]  Anne Driscoll,et al.  Using publicly available satellite imagery and deep learning to understand economic well-being in Africa , 2020, Nature Communications.

[6]  I. Molina,et al.  Pull your small area estimates up by the bootstraps , 2020, Journal of Statistical Computation and Simulation.

[7]  S. Ermon,et al.  Generating Interpretable Poverty Maps using Object Detection in Satellite Images , 2020, IJCAI.

[8]  Joseph E. Cavanaugh,et al.  Ordered quantile normalization: a semiparametric transformation built for the cross-validation era , 2019, Journal of applied statistics.

[9]  Timo Schmid,et al.  The fayherriot command for estimating small-area indicators , 2019, The Stata Journal: Promoting communications on statistics and Stata.

[10]  Tomomi Tanaka,et al.  Mapping Poverty and Slums Using Multiple Methodologies in Accra, Ghana , 2019, 2019 Joint Urban Remote Sensing Event (JURSE).

[11]  Ryan Engstrom,et al.  Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka , 2019, 2019 Joint Urban Remote Sensing Event (JURSE).

[12]  S. Haslett,et al.  A Comparison of Methods for Poverty Estimation in Developing Countries , 2019, International Statistical Review.

[13]  R. Engstrom,et al.  Estimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka , 2019 .

[14]  J. Rao,et al.  Small area estimation of complex parameters under unit‐level models with skew‐normal errors , 2018 .

[15]  Natalia Rojas-Perilla,et al.  From start to finish: a framework for the production of small area official statistics , 2018, Journal of the Royal Statistical Society: Series A (Statistics in Society).

[16]  Balgobin Nandram,et al.  Model‐based county level crop estimates incorporating auxiliary sources of information , 2018, Journal of the Royal Statistical Society: Series A (Statistics in Society).

[17]  João Pedro Azevedo,et al.  FHSAE: Stata module to fit an area level Fay-Herriot model , 2018 .

[18]  Andrew Head,et al.  Can Human Development be Measured with Satellite Imagery? , 2017, ICTD.

[19]  Neeti Pokhriyal,et al.  Combining disparate data sources for improved poverty prediction and mapping , 2017, Proceedings of the National Academy of Sciences.

[20]  Robust mean‐squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw , 2017 .

[21]  D. Morales,et al.  Poverty mapping in small areas under a twofold nested error regression model , 2017 .

[22]  Xin Lu,et al.  Mapping poverty using mobile phone and satellite data , 2017, Journal of The Royal Society Interface.

[23]  Small Area Estimation : An extended ELL approach , 2017 .

[24]  C. Morris,et al.  Rgbp: An R Package for Gaussian, Poisson, and Binomial Random Effects Models with Frequency Coverage Evaluations , 2016, 1612.01595.

[25]  CELL5M: A geospatial database of agricultural indicators for Africa South of the Sahara , 2016, F1000Research.

[26]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[27]  S. Haslett,et al.  Extended Structure Preserving Estimation (ESPREE) for updating small area estimates of poverty , 2016 .

[28]  Isabel Molina,et al.  A Comparison of Small Area Estimation Methods for Poverty Mapping , 2016, Statistics in Transition new series.

[29]  Ryan N. Engstrom,et al.  Determining the Relationship Between Census Data and Spatial Features Derived From High-Resolution Imagery in Accra, Ghana , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  S. Haslett.  Small Area Estimation Using Both Survey and Census Unit Record Data , 2016 .

[31]  Trevor Hastie,et al.  Statistical Learning with Sparsity: The Lasso and Generalizations , 2015 .

[32]  Yolanda Marhuenda,et al.  sae: An R Package for Small Area Estimation , 2015, R J..

[33]  Danny Pfeffermann,et al.  Single- and two-stage cross-sectional and time series benchmarking procedures for small area estimation , 2014 .

[34]  GLS estimation and empirical bayes prediction for linear mixed models with Heteroskedasticity and sampling weights: a background study for the POVMAP project , 2014 .

[35]  Mahmoud Torabi,et al.  On small area estimation under a sub-area level model , 2014, J. Multivar. Anal..

[36]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[37]  A. Belloni,et al.  Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011, 1201.0224.

[38]  Alessandro Tarozzi.  Can census data alone signal heterogeneity in the estimation of poverty maps , 2011 .

[39]  Victor Chernozhukov,et al.  High Dimensional Sparse Econometric Models: An Introduction , 2011, 1106.5242.

[40]  J. Rao,et al.  Small area estimation of poverty indicators , 2009 .

[41]  A. Belloni,et al.  Least Squares After Model Selection in High-Dimensional Sparse Models , 2009, 1001.0188.

[42]  Alessandro Tarozzi,et al.  Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas , 2007, The Review of Economics and Statistics.

[43]  Martino Pesaresi,et al.  A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  Wenceslao González-Manteiga,et al.  Bootstrap mean squared error of a small-area EBLUP , 2008 .

[45]  P. Lanjouw,et al.  Brazil within Brazil: Testing the Poverty Map Methodology in Minas Gerais , 2008 .

[46]  Monica Pratesi,et al.  Small area estimation: the EBLUP estimator based on spatially correlated random area effects , 2008, Stat. Methods Appl..

[47]  Liangpei Zhang,et al.  Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[48]  J. Lanjouw,et al.  How Good a Map? Putting Small Area Estimation to the Test , 2007 .

[49]  N. Lam,et al.  Urban Textural Analysis from Remote Sensor Data: Lacunarity Measurements Based on the Differential Box Counting Method , 2006 .

[50]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[51]  N. Tzavidis,et al.  M-quantile models for small area estimation , 2006 .

[52]  Jiming Jiang,et al.  Mixed model prediction and small area estimation , 2006 .

[53]  Tapabrata Maiti,et al.  On parametric bootstrap methods for small area prediction , 2006 .

[54]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[55]  Kim L. Boyer,et al.  Classifying land development in high-resolution panchromatic satellite images using straight-line statistics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[56]  J. Lanjouw,et al.  Micro-Level Estimation of Poverty and Inequality , 2003 .

[57]  Junyuan Wang,et al.  Small Area Estimation Under a Restriction , 2002 .

[58]  T. Maiti,et al.  MULTIVARIATE BAYESIAN SMALL AREA ESTIMATION : AN APPLICATION TO SURVEY ANDSATELLITE DATA , 1998 .

[59]  N. Ranganathan,et al.  Gabor filter-based edge detection , 1992, Pattern Recognit..

[60]  C. Stein,et al.  Estimation with Quadratic Loss , 1992 .

[61]  J. Rao,et al.  The estimation of the mean squared error of small-area estimators , 1990 .

[62]  Rachel M. Harter,et al.  An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data , 1988 .

[63]  C. Morris.  Parametric Empirical Bayes Inference: Theory and Applications , 1983 .

[64]  Noel J. Purcell,et al.  Postcensal Estimates for Local Areas (or Domains) , 1980 .

[65]  R. Fay,et al.  Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data , 1979 .

[66]  Rupert G. Miller.  The jackknife-a review , 1974 .

[67]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[68]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .

[69]  M. Bartlett,et al.  The use of transformations. , 1947, Biometrics.