Study on a resource allocation formula for social services in Finland

This article considers how a specific need-based resource allocation formula for children's day care, income support, child welfare and other social services was developed. The formula is needed for the municipal allocation of state subsidies for social services, and the work was performed for the Ministry of Social Welfare and Health. Cross-sectional data were collected from the 436 Finnish municipalities and 37 small areas within the six largest cities. Because of the simultaneous relationship between supply and demand of services, two-stage least-square estimation and structural equations models were used in the analysis. After the effect of supply was removed from the service utilisation data, group-need factors (municipal variables) were found for these services. The new formula is financially and administratively feasible, transparent and reasonably simple.     

Social and health‐care priorities of local politicians in Finland: do the attitudes of politicians reflect the actual processes in municipalities?

The aim of this study was to find out whether elected politicians' attitudes influence policy-making processes in terms of social and health-care resource allocation. A preliminary hypothesis was developed on the basis of previous empirical findings on the attitudes of politicians. If health-care costs decreased during the period 1993–1999, then secondary care costs should have increased and vice versa; if the total amount of resources increased, then secondary care must have been allocated fewer resources. However, the results of this study showed that the actual resource-allocation processes did not reflect the attitudes of local politicians. Expenditure on secondary health care increased during this period, while primary health-care services were allocated fewer resources. Analysing the actual decision-making processes rather than investigating attitudes to priority setting may prove more useful in gaining a better understanding of priority-setting processes and the mechanisms used in different contexts.     

Correlates of Sexual Abuse in a Sample of Adolescent Girls Admitted to Psychiatric Inpatient Care

We examined correlations of child sexual abuse among 300 adolescent girls in psychiatric inpatient treatment. Diagnostic and Statistical Manual of Mental Disorders (4th ed.)-based psychiatric diagnoses were obtained from the Schedule for Affective Disorder and Schizophrenia for School-Age Children—Present and Lifetime and from data on family and behavioral characteristics from the European Addiction Severity Index (EuropASI). A total of 79 girls (26.3%) had experienced child sexual abuse during their lifetime. Child sexual abuse was associated with an adolescent’s home environment, sibling status, smoking, posttraumatic stress disorder diagnosis, self-mutilating behavior, and suicidal behavior. At least 62% of the perpetrators were acquaintances of the victims. Correlates of child sexual abuse can be used to identify child sexual abuse victims and persons at heightened risk for child sexual abuse.     

Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys

We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit. Using the systematic approach, the sampling probabilities and the participation probabilities can be considered separately. This is beneficial when the performance of missing-data methods are to be compared. Using data from Health 2000 and 2011 Surveys and from national registries, it was found that multiple imputation removed almost all differences between full sample and estimated prevalences. The inverse probability weighting removed more than half and the doubly robust method 60% of the differences. These findings are encouraging since decreasing participation rates are a major problem in population surveys worldwide.     

On future household structure

Summary.  We develop a method for computing probabilistic household forecasts which quantifies uncertainty in the future number of households of various types in a country. A probabilistic household forecast helps policy makers, planners and other forecast users in the fields of housing, energy, social security etc. in taking appropriate decisions, because some household variables are more uncertain than others. Deterministic forecasts traditionally do not quantify uncertainty. We apply the method to data from Norway. We find that predictions of future numbers of married couples, cohabiting couples and one-person households are more certain than those of lone parents and other private households. Our method builds on an existing method for computing probabilistic population forecasts, combining such a forecast with a random breakdown of the population according to household position (single, cohabiting, living with a spouse, living alone etc.). In this application, uncertainty in the total numbers of households of different types derives primarily from random shares, rather than uncertain future population size. A similar method could be applied to obtain probabilistic forecasts for other divisions of the population, such as household size, health or disability status, region of residence and labour market status.     

Estimating heterogeneity in the probabilities of enumeration for dual-system estimation

"We show how conditional logistic regression can be used to estimate the probability of being enumerated in a census and apply the model to the 1990 Post-Enumeration Survey (PES) in the United States.... We discuss some special problems caused by the fact that the PES sample area is open to migration between the captures. We also consider the effect of data errors in estimation. We characterize hard-to-enumerate populations and give some tentative estimates of correlation bias."     

Uncertain population forecasting

"Errors in population forecasts arise from errors in the jump-off population and errors in the predictions of future vital rates. The propagation of these errors through the linear (Leslie) growth model is studied, and prediction intervals for future population are developed. For U.S. national forecasts, the prediction intervals are compared with the U.S. Census Bureau's high-low intervals." In order to assess the accuracy of the predictions of vital rates, the authors "derive the predictions from a parametric statistical model and estimate the extent of model misspecification and errors in parameter estimates. Subjective, expert opinion, so important in real forecasting, is incorporated with the technique of mixed estimation. A robust regression model is used to assess the effects of model misspecification."     

Accountability as a Warrant for Trust: An Experiment on Sanctions and Justifications in a Trust Game

Accountability is present in many types of social relations; for example, the accountability of elected representatives to voters is the key characteristic of representative democracy. We distinguish between two institutional mechanisms of accountability, i.e., opportunity to punish and requirement of a justification, and examine the separate and combined effects of these mechanisms on individual behavior. For this purpose, we designed a decision-making experiment where subjects engage in a three-player trust game with two senders and one responder. We ask whether holding the responder accountable increases senders’ and responders’ contributions in a trust game. When restricting the analysis to the first round, the requirement of justification seems to have a positive impact on senders’ contributions. When the game is played repeatedly, the experience of previous rounds dominates the results and significant treatment effects are no longer seen. We also find that responders tend to justify their choices in terms of reciprocity, which is in line with observed behavior. Moreover, the treatment combining punishment and justification hinders justifications that appeal to pure self-interest.

     

Efficient initial designs for binary response data

In this paper we introduce a binary search algorithm that efficiently finds initial maximum likelihood estimates for sequential experiments where a binary response is modeled by a continuous factor. The problem is motivated by switching measurements on superconducting Josephson junctions. In this quantum mechanical experiment, the current is the factor controlled by the experimenter and a binary response indicating the presence or the absence of a voltage response is measured. The prior knowledge on the model parameters is typically poor, which may cause the common approaches of initial estimation to fail. The binary search algorithm is designed to work reliably even when the prior information is very poor. The properties of the algorithm are studied in simulations and an advantage over the initial estimation with equally spaced factor levels is demonstrated. We also study the cost-efficiency of the binary search algorithm and find the approximately optimal number of measurements per stage when there is a cost related to the number of stages in the experiment.