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Estimating selected disaggregated socio-economic indicators using small area estimation techniques
Get PDFIn 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
Stichprobenselektion, unbeobachtete Heterogenität und Humankapitaleffekte bei der Schätzung von Einkommensfunktionen mit Paneldaten
No full textStichprobenselektion, unbeobachtete Heterogenität und Humankapitaleffekte bei der Schätzung von Einkommensfunktionen mit Paneldaten / Georg Licht u. Viktor Steiner. - In: Lebenslagen im Wandel: Zur Einkommensdynamik in Deutschland seit 1984 / Ulrich Rendtel ... (Hg.) - Frankfurt u.a. : Campus-Verl., 1991. - S. 100- 134. - (Sozioökonomische Daten und Analysen für die Bundesrepublik Deutschland ; 4
Stichprobenselektion, unbeobachtete Heterogenität und Humankapitaleffekte bei der Schätzung von Einkommensfunktionen mit Paneldaten
No full textStichprobenselektion, unbeobachtete Heterogenität und Humankapitaleffekte bei der Schätzung von Einkommensfunktionen mit Paneldaten / Georg Licht u. Viktor Steiner. - In: Lebenslagen im Wandel: Zur Einkommensdynamik in Deutschland seit 1984 / Ulrich Rendtel ... (Hg.) - Frankfurt u.a. : Campus-Verl., 1991. - S. 100- 134. - (Sozioökonomische Daten und Analysen für die Bundesrepublik Deutschland ; 4
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Short-Run and Long-Term Effects of Childbirth on Mothers' Employment and Working Hours across Institutional Regimes: An Empirical Analysis Based on the European Community Household Panel
Get PDFThe employment behavior of mothers is strongly influenced by labor market regulations and certain institutional arrangements, which both vary greatly across European countries. Using the European Community Household Panel (ECHP) 1994-2001 for Denmark, Germany, Italy and the United Kingdom, which represent four distinct 'institutional regimes', we estimate the short-run and long-term effects of childbirth on married women's employment and working hours. Estimation results show that these effects vary across the four countries in accordance with prevailing institutional regulations.employment and working hours, labor supply, childbirth, European Community Household Panel, panel data models
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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