Small area estimation of poverty indicators under partitioned area-level time models

This paper deals with small area estimation of poverty indicators. Small area estimators of these quantities are derived from partitioned time-dependent area-level linear mixed models. The introduced models are useful for modelling the different behaviour of the target variable by sex or any other dichotomic characteristic. The mean squared errors are estimated by explicit formulas. An application to data from the Spanish Living Conditions Survey is given

Small area estimation of poverty indicators under partitioned area-level time models

This paper deals with small area estimation of poverty indicators. Small area estimators of these quantities are derived from partitioned time-dependent area-level linear mixed models. The introduced models are useful for modelling the different behaviour of the target variable by sex or any other dichotomic characteristic. The mean squared errors are estimated by explicit formulas. An application to data from the Spanish Living Conditions Survey is given

Small area estimation of poverty indicators under partitioned area-level time models

This paper deals with small area estimation of poverty indicators. Small area estimators of these quantities are derived from partitioned time-dependent area-level linear mixed models. The introduced models are useful for modelling the different behaviour of the target variable by sex or any other dichotomic characteristic. The mean squared errors are estimated by explicit formulas. An application to data from the Spanish Living Conditions Survey is given.Peer Reviewe

Estimating selected disaggregated socio-economic indicators using small area estimation techniques

In 2015, the United Nations (UN) set up 17 Sustainable Development Goals (SDGs) to be achieved by 2030 (General Assembly, 2015). The goals encompass indicators of various socioeconomic characteristics (General Assembly, 2015). To reach them, there is a need to reliably measure the indicators, especially at disaggregated levels. National Statistical Institutes (NSI) collect data on various socio-economic indicators by conducting censuses or sample surveys. Although a census provides data on the entire population, it is only carried out every 10 years in most countries and it requires enormous financial resources. Sample surveys on the other hand are commonly used because they are cheaper and require a shorter time to collect (Sarndal et al., 2003; Cochran, 2007). They are, therefore, essential sources of data on the country’s key socio-economic indicators, which are necessary for policy-making, allocating resources, and determining interventions necessary. Surveys are mostly designed for the national level and specific planned areas or domains. Therefore, the drawback is sample surveys are not adequate for data dis-aggregation due to small sample sizes (Rao and Molina, 2015). In this thesis, geographical divisions will be called areas, while other sub-divisions such as age-sex-ethnicity will be called domains in line with (Pfeffermann, 2013; Rao and Molina, 2015). One solution to obtain reliable estimates at disaggregated levels is to use small area estimation (SAE) techniques. SAE increases the precision of survey estimates by combining the survey data and another source of data, for example, a previous census, administrative data or other passively recorded data such as mobile phone data as used in Schmid et al. (2017). The results obtained using the survey data only are called direct estimates, while those obtained using SAE models will be called model-based estimates. The auxiliary data are covariates related to the response variable of interest (Rao and Molina, 2015). According to Rao and Molina (2015), an area or domain is regarded as small if the area or domain sample size is inadequate to estimate the desired accuracy. The field of SAE has grown substantially over the years mainly due to the demand from governments and private sectors. Currently, it is possible to estimate several linear and non-linear target statistics such as the mean and the Gini coefficient (Gini, 1912), respectively. This thesis contributes to the wide literature on SAE by presenting three important applications using Kenyan data sources. Chapter 1 is an application to estimate poverty and inequality in Kenya. The Empirical Best Predictor (EBP) of Molina and Rao (2010) and the M-quantile model of Chambers and Tzavidis (2006) are used to estimate poverty and inequality in Kenya. Four indicators are estimated, i.e. the mean, the Head Count Ratio, the Poverty Gap and the Gini coefficient. Three transformations are explored: the logarithmic, log-shift and the Box-Cox to mitigate the requirement for normality of model errors. The M-quantile model is used as a robust alternative to the EBP. The mean squared errors are estimated using bootstrap procedures. Chapter 2 is an application to estimate health insurance coverage in Kenyan counties using a binary M-quantile SAE model (Chambers et al., 2016) for women and men aged 15 to 49 years old. This has the advantage that we avoid specifying the distribution of the random effects and distributional robustness is automatically achieved. The MSE is estimated using an analytical approach based on Taylor series linearization. Chapter 3 presents the estimation of overweight prevalence at the county level in Kenya. In this application, the Fay-Herriot model (Fay and Herriot, 1979) is explored with arcsine square-root transformation. This is to stabilize the variance and meet the assumption of normality. To transform back to the original scale, we use a bias-corrected back transformation. For this model, the design variance is smoothed using Generalized Variance Functions as in (Pratesi, 2016, Chapter 11). The mean squared error is estimated using a bootstrap procedure. In summary, this thesis contributes to the vast literature on small area estimation from an applied perspective by; (a) Presenting for the first time regional disaggregated SAE results for selected indicators for Kenya. (b) Combining data sources to improve the estimation of the selected disaggregated socioeconomic indicators. (c) Exploring data-driven transformations to mitigate the assumption of normality in linear and linear mixed-effects models. (d) Presenting a robust approach to small area estimation based on the M-quantile model. (e) Estimating the mean squared error to access uncertainty using bootstrap procedures

Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach

Poverty maps are used to aid important political decisions such as allocation of development funds by governments and international organizations. Those decisions should be based on the most accurate poverty figures. However, often reliable poverty figures are not available at fine geographical levels or for particular risk population subgroups due to the sample size limitation of current national surveys. These surveys cannot cover adequately all the desired areas or population subgroups and, therefore, models relating the different areas are needed to 'borrow strength" from area to area. In particular, the Spanish Survey on Income and Living Conditions (SILC) produces national poverty estimates but cannot provide poverty estimates by Spanish provinces due to the poor precision of direct estimates, which use only the province specific data. It also raises the ethical question of whether poverty is more severe for women than for men in a given province. We develop a hierarchical Bayes (HB) approach for poverty mapping in Spanish provinces by gender that overcomes the small province sample size problem of the SILC. The proposed approach has a wide scope of application because it can be used to estimate general nonlinear parameters. We use a Bayesian version of the nested error regression model in which Markov chain Monte Carlo procedures and the convergence monitoring therein are avoided. A simulation study reveals good frequentist properties of the HB approach. The resulting poverty maps indicate that poverty, both in frequency and intensity, is localized mostly in the southern and western provinces and it is more acute for women than for men in most of the provinces.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS702 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

DETERMINING POVERTY MAP USING SMALL AREA ESTIMATION METHOD

Poverty is a phenomenon that always occurs in every country especially in the developing country such as Indonesia. Poverty is defined as a condition where someone has not capability to fulfill their basic needs (food and non food). The difference of geographic condition and the unequal of demography always become some problems in the geographic targeting of the poor in the poverty reduction program. One of method that is accurately effective and sensitive with poverty in the small area is Small Area Estimation method by Elbers et al. It is known as Elbers, Lanjouw, Lanjouw method (ELL method). The objective of this method is to map the incidence of poverty in every county or city using the steps in ELL method. In this study, we use Central Java as our case study. The results of this study are the model consumption of Central Java, poverty indicators for each city in Central Java and the poverty maps so that can give information and facilitate the government for making priority in poverty reduction programs. Keywords: Poverty map, ELL Metho

Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

Mapping poverty in rural China: how much does the environment matter?

A recently developed small area estimation technique is used to geographically derive detailed estimates of consumption-based poverty and inequality in rural Shaanxi, China. These estimates may be helpful for targeting since there is wide variability in poverty rates within Shaanxi but low levels of inequality within most counties and townships. We also investigate whether including environmental variables in the equation used to predict consumption and poverty improves upon typical approaches that only use household survey and census data. Ignoring environmental variables appears likely to produce targeting errors

Mapping poverty in rural China: How much does the environment matter?

In this paper, we apply a recently developed small-area estimation technique to derive geographically detailed estimates of consumption-based poverty and inequality in rural Shaanxi, China. We also investigate whether using environmental variables derived mainly from satellite remote sensing improves upon traditional approaches that only use household survey and census data. According to our results, ignoring environmental variables in statistical analyses that predict small-area poverty rates leads to targeting errors. In other words, using environmental variables both helps more accurately identify poor areas (so they should be able to receive more transfers of poor area funds) and identify non-poor areas (which would allow policy makers to reduce poverty funds in these better off areas and redirect them to poor areas). Using area-based targeting may be an efficient way to reach the poor since many counties and townships in rural Shaanxi have low levels of inequality, even though, on average, there is more within-group than between-group inequality. Using information on locations that are, in fact, receiving poverty assistance, our analysis also produces evidence that official poverty policy in Shaanxi targets particular areas which in reality are no poorer than other areas that do not get targeted