Why population forecasts should be probabilistic - illustrated by the case of Norway

Deterministic population forecasts do not give an appropriate indication of forecast uncertainty. Forecasts should be probabilistic, rather than deterministic, so that their expected accuracy can be assessed. We review three main methods to compute probabilistic forecasts, namely time series extrapolation, analysis of historical forecast errors, and expert judgement. We illustrate, by the case of Norway up to 2050, how elements of these three methods can be combined when computing prediction intervals for a population’s future size and age-sex composition. We show the relative importance for prediction intervals of various sources of variance, and compare our results with those of the official population forecast computed by Statistics Norway.cohort component method, forecast errors, forecasting, simulation, stochastic population forecast, time series, uncertainty

Cohort Analysis Documentation for the cohort analysis component of the OECD data table delivery

This document specifies the data sources, assumptions, and constraint that are relevant to the tables that have been produced and delivered in response to the Cohort Analysis-part of a larger data delivery of tables to OECD (Statistics Norway’s reference W15/0716). The specific data sources are not discussed in detail here, but references to further documentation are provided. Also provided here are some simple descriptive statistics of the target population of the analysis. The delivery for the Cohort Analysis consists of three main tables (plus a presentational variation of each table): NEET spell count Total NEET periods Timing of NEET periods These tables are not included with this document. The delivered tables are based on a longitudinal perspective, in that they follow a specific birth cohort, the 1990 cohort, month by month in the period January 2006 to December 2013, and compares various registered statuses for each person in each month

Benefit Analysis. Documentation for the benefit analysis component of the OECD data table delivery

This document specifies the data sources, assumptions, and constraint that are relevant to the tables that have been produced and delivered in response to the Benefit Analysis-part of a larger data delivery of tables to OECD (Statistics Norway’s reference W15/0716). The specific data sources are not discussed in detail here, but references to further documentation are provided. Also provided here are some descriptive statistics of the data used in the tables delivered. The benefit analysis consists of four tables, one for each of four areas of benefit/allowance: 1. Unemployment benefit 2. Disability benefit 3. Social assistance 4. Family allowance

Benefit Analysis. Documentation for the benefit analysis component of the OECD data table delivery

This document specifies the data sources, assumptions, and constraint that are relevant to the tables that have been produced and delivered in response to the Benefit Analysis-part of a larger data delivery of tables to OECD (Statistics Norway’s reference W15/0716). The specific data sources are not discussed in detail here, but references to further documentation are provided. Also provided here are some descriptive statistics of the data used in the tables delivered. The benefit analysis consists of four tables, one for each of four areas of benefit/allowance: 1. Unemployment benefit 2. Disability benefit 3. Social assistance 4. Family allowancespublishedVersio

Yrker og næringer blant nye mottakere av uføretrygd og arbeidsavklaringspenger. Nye mottakere av uføretrygd og arbeidsavklaringspenger (AAP) i 2020

Arbeidsavklaringspenger (AAP) og uføretrygd er to sentrale ytelser i det norske velferdssystemet, som begge gir inntektssikring for personer som av helsemessige årsaker har reduserte muligheter til å være i arbeid. Yrkesbakgrunnen til nye mottakere av disse ytelsene kan bidra til å belyse sammenheng mellom yrkesaktivitet og tap av arbeids- og inntektsevne, og denne rapporten beskriver hvilke yrker og næringer nye mottakere av AAP og uføretrygd har hatt før mottak av ytelsene. I alt 16,1 prosent av de nye uføretrygdede i 2020 har ikke hatt noe registrert yrke tilbake til 2009. De største andelene finner vi naturlig nok i de yngste aldersgruppene som i mindre grad har hatt muligheten til å etablere seg i arbeidslivet. Andelen uten registrert yrke er noe mindre blant mottakere av AAP, 14,9 prosent. Både blant uføretrygdede og mottakere av AAP er det bakgrunn fra salgs- og serviceyrker som er det klart mest dominerende yrkesfeltet, henholdsvis 28,0 og 29,8 prosent av de nye mottakerne i 2020. Her finner vi pleie- og omsorgsarbeid som det største yrkesområdet, men det er også en stor andel av salgsyrker. Det nest største yrkesfeltet er akademiske yrker med 12,5 og 12,9 prosent av de nye mottakerne av henholdsvis uføretrygd og AAP. Det er en tydelig kjønnsforskjell der nesten 4 av 10 nye kvinnelige mottakere av ytelsene kommer fra salgs- og serviceyrker. Blant nye mannlige mottakere i 2020 var også salgs- og serviceyrker det mest vanlige yrkesfeltet, men her er de nye mottakerne noe jevnere fordelt på de resterende yrkesfeltene med blant annet en stor andel også blant prosess- og maskinoperatører og håndverkere. Når vi sammenligner yrkesfordelingen blant nye mottakere av uføretrygd og AAP med alle lønnstagere i alderen 18-67 år, finner vi at salgs- og serviceyrker er det klart mest overrepresentert yrkesfeltet. Andelen nye mottakere av uføretrygd og AAP fra salgs- og serviceyrker er omtrent 13 prosentpoeng høyere enn andelen lønnstagere i alderen 18-67 år i 2020. Selv om akademiske yrker er det nest største yrkesfeltet blant nye uføretrygdede og nye mottakere av AAP, er det også det mest underrepresenterte yrkesfeltet. Disse funnene er basert på en kartlegging av det sist registrerte yrket i november over en tiårsperiode før mottaket av ytelsen, men det finnes flere alternativer for å kartlegge yrkesbakgrunnen. En mulighet som diskuteres i rapporten, er en mer detaljert kartlegging av sist registrerte yrke på månedsnivå. Denne datakilden er imidlertid noe begrenset fordi opplysningene om yrke ikke strekker seg lenger tilbake enn fem år. Siden mange mottakere reduserer yrkesaktiviteten lenge før mottak av AAP eller uføretrygd, gir denne kilden en stor andel med uoppgitt yrke. I rapporten diskuteres det også om det bør være ulike betingelser knyttet til det siste yrket. Nye uføretrygdede og mottakere av AAP får fastsatt et tidspunkt når inntekts- eller arbeidsevnen ble vurdert til å være nedsatt med minst halvparten, men en kartlegging av siste yrke betinget av uføretidspunktet gir oss en stor andel uten registrert yrke. Vi har også kartlagt yrker tettest mulig opp mot det året mottakerne sist hadde en yrkesinntekt over 2G. Også da ender vi opp med en langt større andel med uoppgitt yrke. I tillegg viser rapporten at uføretidspunktet ikke nødvendigvis er sammenfallende med reduksjon i yrkesinntekt, og at en del reduserte sin yrkesinntekt lenge før mottaket av ytelsene. Siden de alternative metodene for kartlegging av yrker øker andelen uten et registrert yrke, samtidig som den relative fordelingen mellom yrkesfelt ikke endrer seg vesentlig sammenlignet kartleggingen basert på sist registrerte yrke, konkluderer vi med at sist registrerte yrke over en tiårsperiode før mottak av ytelsen antagelig er den beste metoden for å kartlegge siste yrke og næring for nye mottakere av uføretrygd og AAP

Norway's Uncertain Demographic Future

When using material from this publication, please give Statistics Norway as your source.The demographic future of any population is uncertain, but some of the many possible trajectories are more probable than others. Therefore, an exploration of the demographic future should include two elements: a range of possible outcomes, and a probability attached to that range. Together, these two constitute a prediction interval for the population variable concerned. This report presents the findings of a research project, the aim of which was to compute prediction intervals for the future population of Norway broken down by age and sex to the horizon 2050. We estimate that the odds are four against one (80 per cent chance) that Norway’s population, now 4.5 million, will number between 4.3 and 5.4 million in the year 2025, and 3.7-6.4 million in 2050. This illustrates that uncertainty increases with time. There is a clear trade-off between greater accuracy (higher odds) and higher precision (narrower intervals). Odds of 19 against one (95 per cent chance) result in a wider interval: 4.1-5.7 million in 2025, and 3.2-7.3 million in 2050. The probabilistic population forecasts of the youngest and the oldest age groups show largest uncertainty, because fertility and mortality are hard to predict. As a result, prediction intervals in 2030 for the population younger than 20 years are so wide, that the forecast is not very informative. International migration shows large prediction intervals around expected levels, but its impact on the age structure is modest. In 2050, uncertainty has cumulated so strongly, that intervals are very large for virtually all age groups, in particular when the intervals are judged in a relative sense (compared to the median forecast). According to our statistical model, the expected accuracy of the total population size forecast published by Statistics Norway is somewhat below two-thirds on the long run, and a little above that level on the short run. The results have been obtained on the basis of stochastic simulation of each of the three components of population change; fertility, mortality, and international migration. Simulation of the components relied heavily on three complementary methods: time series analysis for the historical development of key demographic indicators, such as the TFR, the life expectancy, and numbers of immigrants and emigrants;an analysis of historical forecast errors, assembled on the basis of forecasts produced by Statistics Norway since 1969;and finally expert judgement, which was used, for instance, to restrict the prediction interval for the TFR or that for the numbers of immigrants and emigrants to a reasonable range. The predictions for each component were calibrated in such a way that the median coincided with the Medium Variant value of the 1999-based official population forecast of Statistics Norway. The time series predictions indicated that assumptions on future TFR as employed by Statistics Norway in its official population forecasts have estimated coverage probabilities of only 46, 31, and 24 per cent in the years 2010, 2030, and 2050. The official mortality (i.e. life expectancy) assumptions have higher expected accuracy in 2050 (just over 60 per cent), but lower accuracy in the beginning of this century (just over a third in the period 2000-2010)

A Cross-Sectional Study of Educational Aspects and Self-Reported Learning Difficulties among Female Prisoners in Norway

The aim of this cross-sectional study was to analyse the educational background, educational desires and participation in education among three samples of female prisoners with Norwegian citizenship in Norwegian prisons over the period from 2009 to 2015. The female participants were n = 106 in 2009, n = 74 in 2012 and n = 79 in 2015, respectively, with a mean age of 38 years. Moreover, the study examined whether self-reported learning difficulties could predict participation in education activity while incarcerated. The results show that the female prisoners included in this study increased their educational level over the studied years. Similar education patterns were observed in the 2009 and 2012 samples regarding all educational levels for the female prisoners. A different pattern was observed in the 2015 data, with 44.3 % having mandatory education as their highest level compared to 57.6 in 2009 and 53.4 in 2012, respectively. However, these differences in percent between the samples at any education level were not significant. Significant differences were, however, found regarding the desire for upper secondary education between the samples in 2009 and 2012, and 2009 and 2015, respectively. In 2009, 20.2% reported upper secondary education as an educational desire, whereas 35.2% reported this as a desire in 2012, and 36.7% in 2015. Participation in educational activity during incarceration also changed during the time period of these studies. Many of the female prisoners participate in educational activity, but a significant difference was found between the samples in 2012 and 2015 as there was a decrease in activity. In 2012, 41.9% did not participate, whereas in 2015, almost 60% (58.2) of the female prisoners did not participate in any educational activity. Both the highest completed education level and self-reported learning difficulties predicted participation in education activity among the female prisoners in the 2015 sample.publishedVersio

Prisoners’ academic motivation, viewed from the perspective of self‑determination theory: Evidence from a population of Norwegian prisoners

The study presented in this article explores prisoners’ academic motivation structure from the theoretical perspective of self-determination theory, using the Academic Motivation Scale (AMS). Analysing survey responses from 529 (29 female, 500 male) prisoners with Norwegian citizenship who participated in education while being incarcerated, the authors investigate how prison students’ motivation might be “reduced” or summarised using a smaller set of factors or components than extant studies. A confirmatory factor analysis suggested that a five-factor model, including intrinsic motivation, three types of extrinsic motivation (namely identified regulation, introjected regulation, and external regulation) and amotivation, yielded the best fit with the data provided by the prisoners. An alternative three-factor model created by collapsing the three extrinsic dimensions into a single dimension was found to fit the data poorly. The structural model revealed that younger prisoners displayed more controlled academic motivations than older ones, who displayed more autonomous motivations. Contrary to the authors’ expectations, prisoners with a higher level of education did not display more autonomous academic motivations than those with a lower level

A Cross-Sectional Study of Educational Aspects and Self-Reported Learning Difficulties among Female Prisoners in Norway

The aim of this cross-sectional study was to analyse the educational background, educational desires and participation in education among three samples of female prisoners with Norwegian citizenship in Norwegian prisons over the period from 2009 to 2015. The female participants were n = 106 in 2009, n = 74 in 2012 and n = 79 in 2015, respectively, with a mean age of 38 years. Moreover, the study examined whether self-reported learning difficulties could predict participation in education activity while incarcerated. The results show that the female prisoners included in this study increased their educational level over the studied years. Similar education patterns were observed in the 2009 and 2012 samples regarding all educational levels for the female prisoners. A different pattern was observed in the 2015 data, with 44.3 % having mandatory education as their highest level compared to 57.6 in 2009 and 53.4 in 2012, respectively. However, these differences in percent between the samples at any education level were not significant. Significant differences were, however, found regarding the desire for upper secondary education between the samples in 2009 and 2012, and 2009 and 2015, respectively. In 2009, 20.2% reported upper secondary education as an educational desire, whereas 35.2% reported this as a desire in 2012, and 36.7% in 2015. Participation in educational activity during incarceration also changed during the time period of these studies. Many of the female prisoners participate in educational activity, but a significant difference was found between the samples in 2012 and 2015 as there was a decrease in activity. In 2012, 41.9% did not participate, whereas in 2015, almost 60% (58.2) of the female prisoners did not participate in any educational activity. Both the highest completed education level and self-reported learning difficulties predicted participation in education activity among the female prisoners in the 2015 sample