Population Forecast with Focus on Emigration: Scenarios for the Case of Peripheral Region

Sustainable decision-making and policies for regional development need clear understanding of future trends in population change. Emigration as one of the core factors of population change causes the greatest uncertainty in the demographic forecasts. Population change due to emigration is bright especially in peripheral regions what strengthens their unfavourable socio-economic conditions. Within the present study, the authors forecast population change due to emigration in peripheral region with high emigration rates. The authors offer three scenarios – current, half of current, and zero emigration, which demonstrate how differentiated population change due to emigration may be in case of different suppositions. Such approach focuses on the issue at local level. For numerical expression of population change, the authors apply simulation model based on system dynamics methods. Forecast results of future population change due to emigration in each scenario may be as a basis for timely inclusion of issues in regional development policies.

TRENDS AND DETERMINANTS OF DEMOGRAPHIC PROCESSES IN RUSSIA

The article substantiates the need to identify trends in the development of processes demographic in individual countries and regions. The analysis of the determination of demographic processes is a necessary condition for the development of an effective demographic policy. The authors studied the dynamics of the population of the planet as whole, Europe, and Russia over the past 50 years. The assessment of the degree of influence of the measures taken in certain countries of the world on the demographic situation is given. The article deals with both methodological issues of studying the determination of demographic processes, and the results of research based on statistical information. Much attention is paid to the issues of determining the birth rate and mortality. An assessment of the impact of material payments on the birth rate is given. At the same time, the issues of determining migration processes and population aging are not left without attention. To analyze the causes of population migration in the region, the experimental planning method was used. The use of this method allowed us to identify the factors that affect migration processes and to assess the degree of influence of each of them on the level of the migration efficiency coefficient. The study showed the possibility of using the planning method in the study of socio-economic processes. The authors consider three variants of the population forecast of the Russian Federation until 2035, proposed by Rosstat, and assessed possible options for their implementation.

LINEAR ORDERING OF SELECTED GERONTECHNOLOGIES USING SELECTED MCGDM METHODS

For over last 20 years, significant changes have been observed in the age structure of the world’s population. The percentage of working-age population is steadily decreasing all over the world, and a relative number of retired people is increasing. It confirms that our society is ageing. Moreover, according to the United Nations population forecast the situation will get worse. The increasing number of seniors is also connected with the need to provide them with institutional support in the form of care. One of the key elements of helping older adults may be gerontechnology – an interdisciplinary field of research that uses technology to implement the aspirations and abilities of seniors. On the basis of a meticulous literature review, 9 groups of gerontechnology have been identified that have been rated with respect to 30 criteria. In the period December 2019 – January 2020 a representative sample of 1.152 Poles aged over 40 (acting as decision makers) took part in the research consisting of completing the prepared questionnaire. Based on selected Multiple Criteria Group Decision Making methods, linear ordering of gerontechnologies was prepared and the most preferred by respondents participating in the study was indicated.

Regional Population Forecast and Analysis Based on Machine Learning Strategy

Regional population forecast and analysis is of essence to urban and regional planning, and a well-designed plan can effectively construct a sound national infrastructure and stabilize positive population growth. Traditionally, either urban or regional planning relies on the opinions of demographers in terms of how the population of a city or a region will grow. Multi-regional population forecast is currently possible, carried out mainly on the basis of the Interregional Cohort-Component model. While this model has its unique advantages, several demographic rates are determined based on the decisions made by primary planners. Hence, the only drawback for cohort-component type population forecasting is allowing the analyst to specify the demographic rates of the future, and it goes without saying that this tends to introduce a biased result in forecasting accuracy. To effectively avoid this problem, this work proposes a machine learning-based method to forecast multi-regional population growth objectively. Thus, this work, drawing upon the newly developed machine learning technology, attempts to analyze and forecast the population growth of major cities in Taiwan. By effectively using the advantage of the XGBoost algorithm, the evaluation of feature importance and the forecast of multi-regional population growth between the present and the near future can be observed objectively, and it can further provide an objective reference to the urban planning of regional population.

Analisis Sektor Basis dan Sektor Non Basis dalam Upaya Peningkatan Sarana dan Prasarana Perkotaan di Kota Padang

[ID] Pembangunan berkelanjutan merupakan bagian dari fokus pembangunan daerah Kota Padang dengan tujuan menyediakan infrastruktur kota dan peningkatan perekonomian kota tahun 2019-2024. Pemerintah terus mengupayakan peningkatan Produk Domestik Regional Bruto (PDRB) Kota Padang dengan mengembangkan sektor kegiatan kota yang diimbangi dengan peningkatan sarana dan prasarana perkotaan. Penelitian ini bertujuan menganalisis sektor usaha yang belum mencapai kategori basis dan maju di Kota Padang. Metode yang digunakan dalam mengetahui sektor basis maupun non basis adalah metode location quotient (LQ). Sedangkan untuk mengetahui sektor maju atau mundur, digunakan analisis shift share. Dilakukan pula Analisis kependudukan melalui metode proyeksi trendline untuk memproyeksi peramalan penduduk pada tahun mendatang. Hasil analysis location quatien (LQ) menunjukkan terdapat dua sektor non basis di Kota Padang yaitu (1) sektor pertanian, kehutanan dan perikanan, (2) pertambangan dan penggalian. Selanjutnya, analisis shift-share menunjukkan terdapat empat sektor yang berada pada kategori mundur/ lamban yaitu sektor: (1) pertanian, (2) kehutanan dan perikanan, (3) industri pengolahan, pengadaan air, pengelolaan sampah, limbah dan daur ulang, (4) jasa keuangan dan asuransi. Berdasarkan trendlinepolynominal didapatkan model proyeksi perkiraan penduduk menurut persamaan y = -194.03x2 + 15535x + 815333 dengan R2 = 0.9964. Kebutuhan sarana prasarana untuk meningkatan kualitas hidup yang layak bagi warga Kota Padang diperkirakan meningkat secara dominan pada kelompok usia 20-24 tahun sebagai proyeksi angkatan kerja. Jumlahnya diprediksi sekitar 77.761 jiwa. Penelitian ini diharapkan bermanfaat dalam menentukan fokus penyediaan infrastruktur kota, agar sektor non basis dan sektor pada kategori mundur atau lambat dapat ditingkatkan menjadi kategori sektor basis dan maju. [EN] Sustainable development is part of the regional development focus of Padang City to provide urban infrastructure and improve the city's economy in 2019-2024. The government continues to strive to increase the Gross Regional Domestic Product (PDRB) of the City of Padang by developing a sector of city activities towards improving urban facilities and infrastructure.This research supports the government's efforts in directing the development of the city of Padang. This study aims to analyze the business sector that has not reached the basic and advanced category in Padang City. The method used to determine the basis and non-base sectors is the location quotient (LQ) method. Meanwhile, to determine the forward or backward sector, shift-share analysis is used. Population analysis is also carried out through the trendline projection method to project population forecasts in the coming year. The results of the location quotient (LQ) analysis show that there are two non-basic sectors in the city of Padang, namely (1) agriculture, forestry, and fisheries, (2) mining and quarrying. Furthermore, the shift-share analysis shows that four sectors are in a slow category, namely the sectors: (1) agriculture, (2) forestry and fisheries, (3) processing industry, water supply, waste management, waste, and recycling, (4) financial and insurance services. Based on the polynomial trendline, the population forecast projection model is obtained according to the equation y = -194.03x2 + 15535x + 815333 with R2 = 0.9964. The need for infrastructure and facilities to improve the quality of life that is feasible for the residents of Padang City is predicted to increase dominantly in the 20-24 year age group. The number is predicted to be around 77,761. It is hoped that this research will be useful in determining the focus of urban infrastructure provision, so that non-base sectors and sectors in the backward or later categories can be upgraded to the basic and advanced category.

Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method

Tayman and Swanson (J Popul Res 34(3):209–231, 2017) found in Washington State counties that a forecast based on the Hamilton–Perry method using a synthetic adjustment (SYN) of cohort change ratios and child-woman ratios had greater accuracy and less bias compared to forecasts holding these ratios constant (CONST). In this paper, we assess the robustness of SYN’s efficacy by evaluating forecast accuracy, bias, and distributional error across age groups in counties nationwide. We also investigate whether forecast errors and their patterns change for SYN and CONST if forecasts by age and gender are adjusted to an independent total population forecast for each county. Our main findings are as follows: (1) SYN lowers forecast error compared to CONST whether the forecasts are controlled or not; (2) controlling also leads to the improvements in forecast error, often exceeding those in SYN; and (3) using SYN and controlling together has the greatest effect in reducing forecast error. These findings remain after controlling for population size and growth rate, but the positive impacts on forecast error of SYN and controlling are most evident in counties with less than 30,000 population and that grow by 15% or more.

Evaluating probabilistic population forecasts

We demonstrate how a probabilistic population forecast can be evaluated, when observations for the predicted variables become available. Statisticians have developed various scoring rules for that purpose, but there are hardly any applications in population forecasting literature. A scoring rule measures the distance between the probability distribution of the predicted variable, and the actual outcome. We use scoring rules that reward accuracy (the outcome is close to the expected value of the prediction) and sharpness (the predictive distribution has low variance, which makes it difficult to hit the target).We evaluate probabilistic population forecasts for France, the Netherlands, and Norway. For all three countries, we use results from the UPE-project ("Uncertain Population of Europe"). We inspect prediction intervals for population size in the period 2004-2019 and 3000 sample paths for population pyramids for the year 2010. For the Netherlands and for Norway, we compare the UPE-results with findings from the official probabilistic population forecast by Statistics Netherlands (2001-2019) and from a probabilistic forecast for Norway (1997-2019). All forecasts were computed using the cohort-component method and stochastically varying parameters for fertility, mortality and migration. We show that the UPE-forecasts for the Netherlands and for Norway performed better than the other forecasts for these two countries. The error in the jump-off population caused a bad score for the French forecast.We evaluate the 3000 UPE-simulations of the age and sex composition predicted for the year 2010. When normalized for population numbers in each age-sex category, the predictions for the Netherlands received the best scores, except for the oldest old. The age pattern for the Norwegian score reflects the under-prediction of immigration after the enlargement of the European Union in 2005.

Uncertainty in Population Forecasts for the Twenty-First Century

The aim of this article is to review a number of issues related to uncertain population forecasts, with a focus on world population. Why are these forecasts uncertain? Population forecasters traditionally follow two approaches when dealing with this uncertainty, namely scenarios (forecast variants) and probabilistic forecasts. Early probabilistic population forecast models were based upon a frequentist approach, whereas current ones are of the Bayesian type. I evaluate the scenario approach versus the probabilistic approach and conclude that the latter is preferred. Finally, forecasts of resources need not only population input, but also input on future numbers of households. While methods for computing probabilistic country-specific household forecasts have been known for some time, how to compute such forecasts for the whole world is yet an unexplored issue.