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The Federal Planning Bureau (FPB) is an independent public agency. It draws up studies and projections on economic, social and environmental policy issues and on the integration of these policies within a context of sustainable development.
In the context of the significant increase in the number of beneficiaries of the health and disability insurance observed over the last 20 years in Belgium, we seek to develop an explanatory model for work incapacity and disability. On the basis of SILC data, we attempt to identify and prioritise the effects of different factors that may influence the probability of employees' transitions between socio-economic statutes, and in particular to and from primary incapacity and disability.
Dans le cadre du chiffrage des programmes électoraux, le modèle PROMES du Bureau fédéral du Plan sera utilisé pour calculer l’impact budgétaire de mesures dans le domaine des dépenses publiques de soins de santé. PROMES est un modèle de microsimulation alimenté par des données administratives individuelles sur les dépenses de soins de santé. Il permet de calculer l'impact de mesures qui s’appliquent à l’ensemble des dépenses de soins de santé relevant de l'assurance maladie et invalidité ou de mesures ciblées sur certains groupes de dépenses spécifiques. Ce document décrit les caractéristiques, la structure et les possibilités de simulation du modèle. Enfin, il présente les résultats de simulation du scénario de référence et d’un exemple de mesure pour illustrer son fonctionnement.
In het kader van de doorrekening van de verkiezingsprogramma’s zal het projectiemodel PROMES van het Federaal Planbureau worden ingezet om de budgettaire impact te berekenen van beleidsmaatregelen binnen het domein van de publieke gezondheidszorguitgaven. PROMES is een microsimulatiemodel dat gebruik maakt van administratie gegevens over uitgaven voor geneeskundige verzorging op individueel niveau. Het model laat toe om de impact van beleidsmaatregelen gericht op de totale uitgaven voor geneeskundige verzorging van de ziekte- en invaliditeitsverzekering of op specifieke uitgavengroepen te berekenen. Dit document schetst de kenmerken, structuur en simulatiemogelijkheden van het model. Ter illustratie van de werking van het model worden, naast de resultaten van het basisscenario, simulatieresultaten voor een voorbeeldmaatregel gepresenteerd.
PROMES (PROjecting Medical Spending) est un modèle de microsimulation élaboré au Bureau fédéral du Plan pour le compte de l’Institut national d’assurance maladie-invalidité et en collaboration avec ce dernier. Ce modèle procure une vision analytique détaillée des déterminants de l’évolution des dépenses de soins de santé couvertes par l'assurance maladie-invalidité (AMI) et permet de projeter celles-ci à court et moyen terme.
PROMES (PROjecting Medical Spending) is een microsimulatiemodel ontwikkeld op het Federaal Planbureau in opdracht van en samenwerking met het Rijksinstituut voor Ziekte- en Invaliditeitsverzekering. Het model heeft als doel de evolutie van de ZIV-uitgaven voor gezondheidszorg beter te begrijpen en te projecteren op korte en middellange termijn.
This paper proposes a new model to account for unobserved heterogeneity in empirical modelling. The model extends the well-known Finite Mixture (or Latent Class) Model by using the Johnson family of distributions for the component densities. Due to the great variety of distributional shapes that can be assumed by the Johnson family, the method does not impose the usual a priori assumptions regarding the type of densities that are mixed.
Les « Perspectives économiques 2015-2020 » annoncent une reprise de la croissance de l’économie belge. Bien que relativement modeste (1,5 % en moyenne annuelle), cette croissance irait de pair avec une progression assez soutenue de l’emploi (près de 34 000 par an en moyenne). Le poids économique de l’ensemble des administrations publiques, notamment en termes d’emploi, serait en recul, ce qui contribue, avec la baisse des charges d’intérêt, à la réduction très significative du déficit public, qui s’établirait à 1,1 % du PIB à l’horizon 2020.
De ‘Economische vooruitzichten 2015-2020’ kondigen een groeiherstel van de Belgische economie aan. Die groei is nog relatief bescheiden (gemiddeld 1,5 % per jaar), maar zou gepaard gaan met een vrij sterke werkgelegenheidsgroei (gemiddeld bijna 34 000 jobs per jaar). Het economisch gewicht van de gezamenlijke overheid zou afnemen, o.m. in termen van werkgelegenheid, en samen met de daling van de rentelasten bijdragen tot de aanzienlijke vermindering van het overheidstekort, dat 1,1 % van het bbp zou bedragen in 2020.
Dit rapport vormt een bijdrage tot de voorbereiding van het nieuwe Stabiliteitsprogramma en van het nieuwe Nationaal Hervormingsprogramma (NHP). Het vermeldt de voornaamste resultaten van de voorlopige versie van de “Economische vooruitzichten 2015-2020” die in mei 2015 gepubliceerd zullen worden.
Ce rapport constitue une contribution à la préparation du nouveau Programme de Stabilité et du nouveau Programme National de Réforme (PNR). Il reprend les principaux résultats de la version préliminaire des « Perspectives économiques 2015-2020 » qui seront publiées en mai 2015.
This paper presents the models developed at the FPB to project public spending on curative care and long-term care in the medium and long term. The variables explaining curative care spending are income, the age composition of the population, the unemployment rate and technological and medical progress. This variable is approximated using two indicators, the number of new drug approvals (Farmanet data) and the approvals for non-pharmaceutical products (Food and Drug Administration data). With the exception of the latter, all drivers mentioned above increase the cost of curative care. As for long-term care spending, it is explained by income, the proportion of older people in the population and their life expectancy. Long-term care spending is positively impacted by income and ageing. Yet, due to the increase in life expectancy, the impact of ageing shifts gradually towards the oldest age group.
At the occasion of its 50th anniversary, the National Institute for Health and Invalidity Insurance (NIHDI) asked the Federal Planning Bureau to draft a report on the social impact of public health care and health care insurance. We focused on three specific questions. First, what was the contribution of health care to population health during the past half-century? Lacking sufficient data on other dimensions of health, we look at mortality and life expectancy. Two approaches to this question lead to the same conclusion: the expansion of health care has contributed substantially to the increase in life years. The second question concerns the role of health care in the economy. The value added and employment in the branches Health care and Social services has increased vastly between 1970 and making health care an ever more important part of the Belgian economy . The third question is about the impact of health care and public health care insurance on inequalities in health and income. Among other findings, we report that in Belgium there is no social inequality in the use of general practitioner, though there is in specialist consultations. Also, a fairly large number of older persons have to cope with own contributions to health care that exceed 10% of their income.
This Working Paper is aimed at describing the current version of Federal Planning Bureau’s medium-term macrosectoral model, named HERMES. This model is used to produce on a regular basis medium-term outlooks for the Belgian economy. In addition to the main macroeconomic aggregates (GDP, private consumption, external trade, investments,…), those outlooks also concern labour market aggregates, detailed public finances, energy consumption and greenhouse gas emissions. The HERMES model is also used to compute the impact of policy measures and external shocks on the Belgian economy.
While rising health care expenditures as a percentage of national income is a well-known and widely documented feature across the industrialized world, it has proved difficult to quantify the effects of the underlying cost drivers. The main difficulty is to find suitable proxies to measure medical technological innovation, which is believed to be a major determinant of steadily increasing health spending. This paper’s main contribution is the use of data on approved medical devices and drugs to proxy for medical technological progress. The effects of these variables on total real per capita health spending are estimated using a panel model for 18 OECD countries covering the period 1981-2009. The results confirm the substantial cost-increasing effect of medical technology, which may account for at least 50% of the explained historical growth of spending. Excluding the approval variables causes a significant upward bias of the estimated income elasticity of health spending and negatively affects some model specification tests. Despite the overall net positive effect of technology, the effect of two subgroups of approvals on expenditure is significantly negative. These subgroups can be thought of as representing ‘incremental medical innovation’, while the positive effects are related to radically innovative pharmaceutical products and devices. The results are consistent with those reported in other studies which suggest that some new products, despite their high price when they are introduced, can ultimately save money by reducing spending on other medical interventions.
This paper presents an allocation model of Belgian household consumption over 23 categories of goods and services. We have formulated and estimated an extension of the classic Almost Ideal Demand System. The original model has been modified by introducing a dynamic adjustment mechanism and by the inclusion of demographic variables. These capture shifts in consumption patterns related to the changing age composition of the population. The paper is an extension of earlier work (Willemé, 2008), in particular with respect to the number and composition of the consumption categories considered.
Projections of use and supply of formal and informal carried out in Work Package 6 of the ANCIEN project show that if current patterns of care use and supply prevail, supply of care is likely to fall behind demand. This paper discusses the key policy implications of these findings. Meeting the required care capacity poses multifarious challenges for European welfare states, namely: how to limit the growing burden of LTC expenditure on social security or government budgets, especially in countries that rely heavily on formal care, and how to avoid an increased informal caregiver burden, while at the same time ensuring adequate care for disabled older persons. Technological advances could help close the care gap, by reducing the need for care and boosting the productivity of formal and informal care workers, or by lessening the need for care. As it is impossible to assess whether these efficiency gains will suffice to bridge the care gap, policies should anticipate an increasing care burden and plan accordingly for how to deal with its consequences.
This report presents results of projections of use and supply of long-term care for older persons in four countries representative of different long-term care systems: Germany, the Netherlands, Spain and Poland. Using a standardised methodology, the projections show that between 2010 and 2060, the numbers of users of residential care, formal home care and informal care are projected to increase in all countries, but at different rates. The results also indicate that if current patterns of care use and supply prevail, supply of informal and formal care is likely to fall behind demand. Measures to increase LTC capacity will be needed in all countries; the key policy implications of these findings are discussed in Policy Brief No. 12 in this series.
This report describes the organization of the Belgian long-term care system. It can be characterized as a mixed system with extensive public care provision and substantial support from informal care mainly within the family. While the current volume and quality of services appears to be adequate, the future increase in the number of dependent elderly persons over the next two decades as a result of demographic ageing can be expected to become a serious challenge, both in terms of required formal and informal care capacity and financially.
In the health economics literature, it is customary to model total (private and public) aggregate health expenditures as a function of income, demographic ageing and a host of other variables. Private health expenditures are rarely modelled separately and if they are, the models are often based on individual data and limited to specific medical services such as physician visits. In this paper we specify a model of aggregate private health expenditures, embedded in a generalisation of Deaton & Muellbauer’s Almost Ideal Demand System (AIDS). The main advantage of specifying a complete demand system is that all household consumption decisions, including the ones about the use of health care services, are subject to the same budget restriction.
This paper presents a model of Belgian household consumption, with a focus on private health expenditures. To do so, we have formulated and estimated an extension of the classic Almost Ideal Demand System. The original model has been modified by introducing a dynamic adjustment mechanism and by the inclusion of demographic variables. These were expected to capture shifts in consumption patterns related to the changing age composition of the population. The results confirm the expected effects: the ageing of the population is likely to increase the share of private health expenditures (and consumer durables) in the household budget over the coming decades.
The Belgian Study Group on Ageing of the High Council of Finance, in its Annual Report, publishes the results of research on the budgetary and social effects of ageing. In this context, the Federal Planning Bureau, in its capacity as secretariat and main research body of the Committee, has in recent years been stepping up its efforts to deve lop models based on socioeconomic micro data. The results of one of these models, de signed to make short-term projections of poverty indicators, are presented in this paper.
Federal Planning Bureau
