The Experts below are selected from a list of 9048 Experts worldwide ranked by ideXlab platform
Valuckaitė Laura - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of the factors influencing the shadow economy in Lithuania
'Vytautas Magnus University', 2017Co-Authors: Maksvytienė Inga, Valuckaitė LauraAbstract:The shadow economy is a phenomenon visible and often analyzed in scientific sources. Shadow economy is also known as a black, grey, hidden, invisible, irregular, unofficial or underground economy. The importance of this topic in the world as well as in Lithuania is revealed by statistics. It is shown that in the context of the developed countries (such as Norway or Switzerland) Lithuania has one of the largest shadow economy rate. The European countries that have the higher rate of shadow economy are Turkey, Romania and Bulgaria. Scientific research that shows the factors of shadow economy is required in order to reduce the extent of shadow economy. In order to evaluate the main causes of the shadow economy MIMIC approach is used. MIMIC (multiple causes and multiple indicators) approach is an econometric model with one latent Unobserved Variable. Also endogenous and exogenous Variables are observed. This method evaluates which factors are statistically significant and have the most influence on the shadow economy. Note that if exogenous Variables correlate with other Variables, it must be connected with two-headed arrows. According to the research results shadow economy in Lithuania is mostly influenced by unemployment, employment rate, taxes, consumer confidence, criminality, education and return on assets (ROA)
Zajankauskaitė Justina - One of the best experts on this subject based on the ideXlab platform.
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Šešėlinės ekonomikos modeliavimas
Institutional Repository of Vilnius University, 2021Co-Authors: Zajankauskaitė JustinaAbstract:Modeling Shadow Economy J. Zajankauskaitė. Modeling shadow economy: master thesis/ supervisor Prof., Habil. dr. Vydas Čekanavičius; Vilnius university, faculty of Mathematics and Informatics, department of Statistical Analysis. The scope of this master thesis includes the analysis of the shadow economy in Lithuania covering the period from 2000 Q1 to 2020 Q2. The study focuses on the problem related to an Unobserved Variable that cannot be measured directly. Shadow economy as immeasurable economic phenomenon might be an urgent economic problem causing serious consequences on the official economy of the country. Therefore, the aim of the master thesis is to identify the size of the shadow economy in Lithuania and to review determinants significantly influencing the shadow economy. As a result, three approaches of estimation of the extent of the shadow economy have been proposed. Currency demand model, multiple indicators, multiple causes model and newly developed approach of hybrid CDM-MIMIC exposed that the shadow economy in Lithuania tends to shrink over the period for 2000 to 2020 (except recession time and period for 2019-2020). Also, the output of the econometric modeling has presented that the main causal factors affecting the shadow economy are tax burden and indicators reflecting general economic situation in the country (GDP, unemployment rate, short-run interest rate, inflation, wages, etc.). The results of this paper serve to get a better understanding about the main tendencies of Lithuanian shadow market as well as help to identify principal instruments of controlling shadow economy in the country for policy makers
Maksvytienė Inga - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of the factors influencing the shadow economy in Lithuania
'Vytautas Magnus University', 2017Co-Authors: Maksvytienė Inga, Valuckaitė LauraAbstract:The shadow economy is a phenomenon visible and often analyzed in scientific sources. Shadow economy is also known as a black, grey, hidden, invisible, irregular, unofficial or underground economy. The importance of this topic in the world as well as in Lithuania is revealed by statistics. It is shown that in the context of the developed countries (such as Norway or Switzerland) Lithuania has one of the largest shadow economy rate. The European countries that have the higher rate of shadow economy are Turkey, Romania and Bulgaria. Scientific research that shows the factors of shadow economy is required in order to reduce the extent of shadow economy. In order to evaluate the main causes of the shadow economy MIMIC approach is used. MIMIC (multiple causes and multiple indicators) approach is an econometric model with one latent Unobserved Variable. Also endogenous and exogenous Variables are observed. This method evaluates which factors are statistically significant and have the most influence on the shadow economy. Note that if exogenous Variables correlate with other Variables, it must be connected with two-headed arrows. According to the research results shadow economy in Lithuania is mostly influenced by unemployment, employment rate, taxes, consumer confidence, criminality, education and return on assets (ROA)
Christina M Gibsondavis - One of the best experts on this subject based on the ideXlab platform.
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the effect of the wic program on the health of newborns
Health Services Research, 2010Co-Authors: Michael E Foster, Miao Jiang, Christina M GibsondavisAbstract:The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is the third largest food program in the United States, reaching nearly 8 million women and children at a cost of U.S.$5 billion (USDA 2007a). The program forms a key part of the safety net for poor families: nearly 60 percent of poor children under the age of 4 receive WIC, nearly twice as many as those who receive food stamps (Zedlewski and Rader 2005). Given the program's scope and prominence, the efficacy of WIC has been studied extensively. However, findings have been inconsistent, and the program remains an active focus of policy evaluation (Ludwig and Miller 2005). The methodological challenge facing researchers is that many characteristics are likely correlated with both WIC use and child health. This correlation introduces bias, leading one to either overstate or understate WIC's effects depending on the nature of the relationships between the measured and unmeasured confounding factors, children's outcomes and WIC participation. In this paper, we use propensity scores to examine the association between WIC participation and birth outcomes. Under key assumptions, propensity scores approximate a randomized experiment by creating matched groups comprising those from both the treatment and comparison groups who are comparable except for treatment status. We further estimate fixed-effects models that allow for mother-specific unobservables. In addition, we examine how strong confounding with an Unobserved Variable would have to be to explain the apparent effect (or noneffect) of WIC participation using approaches proposed by Rosenbaum (2002) and Imbens (2003). These analyses involve data from the Panel Study of Income Dynamics—Child Development Supplement (PSID-CDS), a study that has tracked more than 5,000 families for nearly four decades. The data included an over-sample of poor families, making it well suited for examining issues of poverty and child development in the United States. This study makes two contributions to the existing literature. First, by considering multiple outcomes—birth weight, born preterm, low birth weight, small for gestational age (SGA), neonatal hospitalizations, and maternal report of infant health—this study provides a more complete account of WIC's potential effects than most prior research. Second, because of the scope of the data, we account for the effect of heretofore unmeasured or omitted characteristics (such as maternal IQ and family income) that are likely to confound WIC estimates.
Melvyn Weeks - One of the best experts on this subject based on the ideXlab platform.
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home grown or imported initial conditions external anchors and the determinants of institutional reform in the transition economies
The Economic Journal, 2007Co-Authors: Maria Laura Di Tommaso, Martin Raiser, Melvyn WeeksAbstract:In this article we examine the determinants of institutional change using a panel dataset comprising 25 transition economies. A defining characteristic of our approach is that we treat institutional change as a multidimensional Unobserved Variable, accounting for the fact that each of our indicators represents a noisy signal. Our results suggest that institutional change is significantly path dependent. However, policy can to some extent break this dependence through economic and political liberalisation at the start of the transition and with the help of an external anchor such as EU accession.
