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Demographic situation of Lithuania has deteriorated for more than two decades and continues to be very difficult. Data of the vital statistics and of various sample surveys are commonly used for an assessment of the demographic situation. Meanwhile, a very rich data of population census of the entire population are employed only rarely. Therefore, this study has several aims. The main aim of the study is to obtain the new knowledge on demographic development of Lithuania using census data and to reveal the impact of long-term historical changes of socio-demographic structures of population and demographic processes on current demographic situation as well as their possible continuity in the future. Other important aim of this study is to draw attention of researchers and public policy-makers to the rich population census data which allow to understand the demographic situation not only on the total population level but also on various socio-demographic segments and in the long-term context of the demographic development. The information basis of this study includes: first, the micro-data of the Lithuanian 2011 population and housing census which have been a major source of the research; second, for the comparative analysis, the micro-data of Lithuanian 2001 population and housing census have been used; third, available and comparable aggregate data of previous censuses (1989, 1979, 1970, 1959, 1923, and 1897), which permit to disclose demographic trends in the long historical perspective, have been analyzed. The study consists of three parts: the first part – “Socio-demographic composition of Lithuanian population” (by the age, education, ethnicity, foreign born population); the second part – “The changes in the demographic processes” (marital behaviour and fertility); and the third part – “The empirical sources and methodology of the study.” [...]

Population research --- Population change --- Demografija --- Demographic processes --- Population census --- Gyventojų surašymas --- Populiacijos kaita --- Lithuania --- Population --- Population change --- Demografija --- Demographic processes --- Population census --- Gyventojų surašymas --- Populiacijos kaita

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Demographic situation of Lithuania has deteriorated for more than two decades and continues to be very difficult. Data of the vital statistics and of various sample surveys are commonly used for an assessment of the demographic situation. Meanwhile, a very rich data of population census of the entire population are employed only rarely. Therefore, this study has several aims. The main aim of the study is to obtain the new knowledge on demographic development of Lithuania using census data and to reveal the impact of long-term historical changes of socio-demographic structures of population and demographic processes on current demographic situation as well as their possible continuity in the future. Other important aim of this study is to draw attention of researchers and public policy-makers to the rich population census data which allow to understand the demographic situation not only on the total population level but also on various socio-demographic segments and in the long-term context of the demographic development. The information basis of this study includes: first, the micro-data of the Lithuanian 2011 population and housing census which have been a major source of the research; second, for the comparative analysis, the micro-data of Lithuanian 2001 population and housing census have been used; third, available and comparable aggregate data of previous censuses (1989, 1979, 1970, 1959, 1923, and 1897), which permit to disclose demographic trends in the long historical perspective, have been analyzed. The study consists of three parts: the first part – “Socio-demographic composition of Lithuanian population” (by the age, education, ethnicity, foreign born population); the second part – “The changes in the demographic processes” (marital behaviour and fertility); and the third part – “The empirical sources and methodology of the study.” [...]

Population research --- Lithuania --- Population --- Population change --- Demografija --- Demographic processes --- Population census --- Gyventojų surašymas --- Populiacijos kaita

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This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample. The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with "other towns" and "census towns" being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues.

Crowd Sourcing --- Earth Sciences and GIS --- Google Images --- Health, Nutrition and Population --- Human Judgment --- Lasso --- Logit Model --- Machine Learning --- Population Census --- Population Sciences --- Random Forests Method --- Satellite Imagery --- Science and Technology Development --- Statistical and Mathematical Sciences --- Urban Area --- Urban Development --- Urban Economic Development --- Urban Economics --- Urbanization

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The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

Capita Expenditure --- Degrees of Freedom --- Delta Method --- Econometrics --- Education --- Estimates of Poverty --- Explanatory Variables --- Finance and Financial Sector Development --- Financial Literacy --- Health, Nutrition and Population --- Household Survey --- Household Survey Data --- Households --- Macroeconomics and Economic Growth --- Parameter Estimates --- Population Census --- Population Policies --- Poverty Mapping --- Poverty Mapping Methodology --- Poverty Maps --- Poverty Measures --- Poverty Reduction --- Pro-Poor Growth --- Rural Development --- Rural Poverty Reduction --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Simulation Procedures --- Simulations --- Small Area Estimation --- Small Area Estimation Poverty Mapping --- Standard Deviation --- Standard Errors --- Statistical and Mathematical Sciences --- Variance-Covariance Matrix

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The hub-periphery development pattern of the Guangdong economy, to some extent, is a miniature of that of the Chinese economy. The Pearl River Delta, drawing from its first-nature comparative advantages in factor endowments and proximity to Hong Kong SAR, China, and Macau SAR, China, and the second-nature advantages as first-movers in the reforms in attracting and retaining domestic and foreign resources, has developed into a regional economic center. This paper examines the pattern of inter- and intra-provincial migration and that of the concentration of production, to explore the challenges and opportunities for the success of "double transfer." The paper suggests a four-prong approach, to improve the business environment, support the realization of latent comparative advantages, increase the skill level of the labor force to support the upgrade of the production structure, and protect the vulnerable, to support the inclusive growth of the economy in Guangdong in a sustainable manner.

Capital --- Certain extent --- Cheap labour --- Citizens --- Comparative advantages --- Comparative analysis --- Competitiveness --- Credit --- Development --- Development plans --- Development policy --- Development strategy --- Discrimination --- Disposable income --- Economic development --- Economic growth --- Economic integration --- Economic opportunity --- Economic structure --- Economic theories --- Economic theory & research --- Economic welfare --- Economies of scale --- Economy --- Effective policies --- Empirical analysis --- Environment --- Environmental economics & policies --- Equal access --- Equity --- Exploitation --- Exports --- Financial crisis --- Foreign direct investment --- Forestry --- GDP --- GDP per capita --- Gini coefficient --- Government initiatives --- Growth rate --- Health care --- Health services --- Health, nutrition and population --- Immigration --- Incentives --- Income --- Income inequality --- Industrialization --- Intensive industries --- Internal migration --- International migration --- International migration review --- Investment --- Job creation --- Job training --- Labor force --- Labor market --- Labor policies --- Labor productivity --- Labor supply --- Land --- Large cities --- Life expectancy --- Living standards --- Local governments --- Macroeconomics and economic growth --- Market economy --- Migrant --- Migrant workers --- Migration --- Migration data --- Migration flows --- National development --- National level --- National policies --- National population --- Old-age --- Policy --- Policy discussions --- Policy research --- Policy research working paper --- Pollution --- Population --- Population and development --- Population association --- Population census --- Population migration --- Population policies --- Production --- Progress --- Property --- Public goods --- Public services --- Real GDP --- Real wages --- Regional policy --- Relocation of industries --- Resource allocation --- Resources --- Return migration --- Revenue --- Rural areas --- Rural residents --- Sanitation --- Savings deposits --- Skill level --- Skilled workers --- Social protections and labor --- Social science --- Social security --- Social services --- Spatial distribution --- Sustainable access --- Sustainable development --- Taxes --- Temporary migration --- Trade --- Training --- Training opportunities --- Transportation --- Urban areas --- Urban centers --- Urban migration --- Urban population --- Urbanization --- User fees --- Value --- Value added --- Vocational training --- Vulnerable populations --- Wage differentials --- Wages --- Welfare