2012 Conference Presentation
Objective: In Norway, it is the responsibility of the county’s 430 municipalities to provide long term care services to their residents. The objective of this study is to understand how the demand for and supply of these LTC services has evolved over time. Such an understanding allows policy makers to plan more effectively for future demands for LTC. Not only does it help with forecasting future demand, but allows central government to better understand how it can influence the supply decisions made by the municipal governments. A related objective is to understand the interaction between municipalities’ provision of LTC services and other public services. For example, what sort of impact can we expect on the provision of pre-school education of a rising demand for LTC services? Understanding this process is essential in ensuring that attempts to achieve one policy objective do not adversely impact on attempts to achieve another.
Data: We utlise panel data on the 430 Norwegian municipalities from 1980 to 2010. Each municipality must report details of their annual accounts to the central government. This information is made available through Norway’s national statistics agency, Statistics Norway. The dataset includes a number of indicators on municipalities’ priorities, productivity and their coverage of needs. The data are consistent across municipalities, allowing comparisons to be made. Using data with such a long time span is important in allowing us to understand the dynamics of how resource use in the municipalities is evolving over time.
Methods: Given the nature of our data, we employ panel data and time series methods. The use of panel data methods allows us to understand the cross-sectional variation in the country. This is important, since there are clear spatial patterns in the demand for and supply of LTC. Modelling the evolution of these patterns over time is important in understanding where investments in the supply of LTC should be made in order to cater for expected future demand. The use of time series methods help to give an understanding of how the national situation developed over the study period, as well as giving an indication of what might be expected in the future.
Results: Preliminary results indicate that demand for LTC services depends strongly on each municipality’s demographic and geographic characteristics. In particular, municipalities with an older population tend to have higher resource use. We also see that more sparsely populated, rural municipalities have higher expenditure on LTC. Additionally, the municipalities’ revenue level strongly affects the supply of services and generate inequity. A finding from the last 10 years is that most of the growth in LTC is explained by the increase in younger users.