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Do Red-Flagged Clients Exit Social Assistance Earlier than Others? The Case of the Finnish Social Assistance System

Published online by Cambridge University Press:  11 July 2022

Merita Mesiäislehto*
Affiliation:
Finnish Institute for Health and Welfare, Helsinki, Finland
Pasi Moisio
Affiliation:
Finnish Institute for Health and Welfare, Helsinki, Finland
Ilari Ilmakunnas
Affiliation:
Finnish Centre for Pensions, Helsinki, Finland
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Abstract

We examine an early ‘red flag system’ in the Finnish social assistance system and its efficiency in reducing the length of social assistance spells. We utilise the age-threshold in the policy that requires notifying social services on beneficiaries under twenty-five years of age after four months of social assistance receipt. Using monthly administrative data covering all twenty-three to twenty-six-year-old social assistance beneficiaries in Finland in 2018-2019, we compare the social assistance receipt of those below and above the age-threshold. Our findings show that those who are ‘red flagged’ do not exit social assistance earlier than others. On the contrary, four months after the notification is sent to social services, they are more likely to still have social assistance as their primary source of income compared to the twenty-five to twenty-six-year-old individuals. Our results shed light on the little discussed question on how to combine social work with digitalised and automatised welfare.

Type
Article
Creative Commons
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Introduction

In many countries, digitalisation of welfare services has become increasingly common in the last two decades. ICT software is used to manage client information and processes in health and social care, but it may also contain other purposes, such as profiling unemployed individuals or identifying clients with special needs (Caswell and Marston, Reference Caswell and Marston2010; Desiere et al., Reference Desiere, Langenbucher and Struyven2019).

Social assistance is a benefit that is typically closely connected with social work. The recent trend to digitalise social services, which typically involves standardisation of application processes and benefit receipt, aims at increasing the efficiency of these services. At the same time, it is also viewed to equalise the benefit applications processes (see Buffat, Reference Buffat2015). However, the shift towards ‘e-social work’ (Devlieghere and Roose, Reference Devlieghere and Roose2018) is argued to affect particularly individuals who not only seek financial support but also need face-to-face service and individual counselling (see Hansen et al., Reference Hansen, Lundberg and Syltevik2018; Schou and Pors, Reference Schou and Pors2019).

In our study, we examine the role of a digitalised and automatised program in identifying social assistance beneficiaries’ need for social work counselling and reducing the beneficiaries’ dependency on social assistance. In Finland, digitalisation of the welfare state has moved fast in health and social services. Around 70 per cent of clients who apply for social benefits at the Social Insurance Institution (later referred to as Kela) use the institution’s online services. When the administration of basic social assistance was centralised to Kela in 2017, the application of social assistance became digitalised. Before the reform, digital applications were only possible in some of the largest municipalities in the 2010s. Kela’s digital services and equal treatment of social assistance applicants were among the motivations for the reform. Thus, centralisation made it easier for individuals receiving other benefits to use the same system when applying for social assistance. Moreover, there was hope that stigma related to social assistance would decrease due to centralisation. Finally, it was expected that the reform would reduce bureaucracy in municipalities’ social services and leave more time for social work (Blomberg and Kroll, Reference Blomberg and Kroll2020; Korpela et al., Reference Korpela, Heinonen, Laatu, Raittila, Ylikännön, Korpela, Heinonen, Laatu, Raittila and Ylikännön2020). Municipalities were still left responsible for the provision of social services.

Kela has a statutory obligation to inform municipalities about its residents in a need of social services. Alongside the social assistance reform, a ‘red flag’ program was introduced to identify beneficiaries in need of other than financial support and to send a notification to social services of the municipality. Notifications are sent using a shared database that was created to share information between Kela and municipalities. Several client categories were introduced in the red flag system, including social assistance recipients under twenty-five years of age who have lived on social assistance for four consecutive months. In this study, we use this age-threshold in policy to estimate how ‘red-flagging’ long-term social assistance recipients to guide them to social work counselling is associated with subsequent recipiency. Meeting a social worker is voluntary, and not conditional for receiving social assistance.

Current ideologies of welfare programs across Europe support activation policies with strict sanctioning and this issue is frequently studied. However, less attention has been paid on policies or practices that include non-sanctioning elements, such as counselling, and their possible effect on welfare receipt (see, however Dall and Danneris, Reference Dall and Danneris2019). This article studies the possibilities of tackling disadvantage among young adults when social work is not (anymore) incorporated or conditional for social assistance.

Maintaining the link between last resort benefits and social work is a timely topic in Finland. The recent Finnish Basic Income Experiment has brought interest on how to identify benefit recipients who also need social work counselling and other social services; during the basic income experiment it has been understood that simply money is not enough, and social work is crucial for some citizens (Hiilamo, Reference Hiilamo2016; Kangas et al., Reference Kangas, Jauhiainen, Simanainen and Ylikännö2019; Kangas et al., Reference Kangas, Jauhiainen, Simanainen and Ylikännö2021). Thus, studying Kela’s digitalised red flagging and notification system on long-term social assistance recipients to social work counselling will give valuable information on how to reconcile cash benefits and social work. A vital question with regards to the effectiveness of the policy is whether the system - guiding individuals to a municipal social worker without using sanctioning elements - actually helps in providing social work for vulnerable young adults.

The Finnish social assistance scheme and the automatised ‘red flag’ system

In 2019, 7.2 per cent of the population in Finland received social assistance. Among individuals between ages eighteen and twenty-four, the proportion was 16 per cent (Kelasto, 2022). The prevalence of social assistance receipt among young adults is higher in Finland than its neighbouring countries Norway and Sweden (Lorentzen et al., Reference Lorentzen, Angelin, Dahl, Kauppinen, Moisio and Salonen2014), and it is also more prevalent between ages eighteen and twenty-four than later in young adulthood (Ilmakunnas et al., Reference Ilmakunnas, Kauppinen and Kestilä2015). In Finland, social assistance benefit is often received simultaneously with other benefits. Around 90 per cent of unemployed households who receive social assistance also receive housing benefit and unemployment benefit (Tervola et al., Reference Tervola, Mesiäislehto and Ollonqvistforthcoming).

Cross-country comparisons on social assistance (e.g. Lødemel and Schulte, Reference Lødemel and Schulte1992; Gough et al., Reference Gough, Bradshaw, Ditch, Eardley and Whiteford1997; Gough, Reference Gough2001) have typically categorised the Finnish and other Nordic social assistance schemes as strongly residual systems where due to an encompassing welfare state and nearly full employment, means-tested benefits have played a minor role. Still, compared to other countries benefit levels are relatively generous and Nordic social assistance systems are effective in alleviating poverty (Marx et al., Reference Marx, Lina and Gerlinde2016). In later studies, a Nordic model of social assistance has been questioned (Kuivalainen and Nelson, Reference Kuivalainen, Nelson, Kvist, Fritzell, Hvinden and Kangas2012; Nelson, Reference Nelson2013). The recession of the early 1990’s increased the number of social assistance recipients and resulted in a number of social policy reforms and cutbacks in public benefits, which also reduced the effectiveness of the minimum income benefits to reduce poverty (Alm et al., Reference Alm, Nelson and Nieuwenhuis2020). In addition, Nordic countries have introduced elements of activation and thus emphasise individual responsibility instead of viewing poverty as a structural problem (Kuivalainen and Nelson, Reference Kuivalainen, Nelson, Kvist, Fritzell, Hvinden and Kangas2012; Saikkonen and Ylikännö, Reference Saikkonen and Ylikännö2020). In Finland, the traditionally close connection between cash and care has slowly started to deteriorate.

The Finnish social assistance scheme is categorised as a simple and comprehensive scheme open to all with insufficient means to support themselves (Frazer and Marlier, Reference Frazer and Marlier2016). The social assistance scheme consists of three components. The basic social assistance is strictly rights-based (conditions for the eligibility are set out in the legislation) while both supplementary and preventive social assistance include discretionary elements. Basic social assistance is administrated by Kela and it covers almost 90 per cent of social assistance expenditure (Sotkanet, 2022). Together with general social assistance legislation, Kela uses its own much more detailed instructions on what expenditures are compensated, to what amount and in which situations. The two other components of social assistance, supplementary and preventive, are administrated by the local municipalities’ social work and are highly discretionary. In this study we focus on basic social assistance since it is by far the most important form of social assistance. Also, a decision on basic social assistance is needed when applying supplementary or preventive social assistance.

The development of social assistance into a subjective right, separated from social work, started in the 1980s. The current strictly rightbased social assistance law was enacted in 1984, aiming at ‘getting rid of the last traces of poor law’. In the Constitution reform of 1995, the minimum income protection was set not just as a basic but subjective right. Strictly rights-based social assistance eventually led to the separation of social assistance and social work. More and more municipalities created separate benefits units to handle social assistance applications with the written procedure, separately from social work. Finally, in 2017, basic social assistance was centralised and transferred from municipalities to the Social Insurance Institution (Kela), which enhanced its nature as a fully right-based benefit even further. Simultaneously, the application of social assistance was digitalised, although it is still possible to apply using a paper form. In Kela, social assistance is handled much like any social security benefit by benefit handlers without a degree in social work. Thus, there is no more a direct link between basic social assistance and (municipality’s) social work. Recently, the Finnish basic income experiment 2017-2018 (see Kangas et al., Reference Kangas, Jauhiainen, Simanainen and Ylikännö2021) may be seen as a link, or as an outcome, of a long political development towards minimum income protection as subjective right.

Similar to other Nordic countries, the Finnish social protection is a universal basic right and residence based. But unlike in other Nordic countries, minimum income protection in Finland is a subjective constitutional right and social assistance guarantees this right in practice. This makes the Finnish case very exceptional and de facto to an effective guaranteed minimum resources system: social assistance can never be rejected via sanctioning, though it can be reduced temporarily.

The automatised ‘red flag’ notification system

Kela is obliged to notify municipalities’ social services of social assistance beneficiaries who (i) have received social assistance as their primary source of income for four consecutive months and are under twenty-five years old, or (ii) are over twenty-five years old and have had social assistance as their primary source of income for twelve months, or (iii) are immigrants and have received social assistance as their primary source of income for two consecutive months, or (iv) have their social assistance reduced due to sanctioning, or (v) are considered to be in need of social work counselling for some other reason, such as issues with life management, housing, health or child welfare. All five notification categories derive from legislation, including the Social Welfare Act, Youth Act and Child Welfare Act. A digital system identifies individuals who belong to one of the three first groups and sends an automatic notice to the individuals’ home municipalities about the need for social work intervention (Figure 1). For the two other groups that concern sanctioning and need of social work counselling, notice is sent manually by the benefit handler at Kela.

Figure 1. The process of red flagging social assistance beneficiaries in need of social work in the Finnish social assistance system

Source. Mesiäislehto et al. (Reference Mesiäislehto, Kivipelto and Ylikännö2022)

The electronic system not only sends a notice but also links the data on social assistance recipients between Kela and municipalities and, thus, serves as a platform for the administration of social assistance more generally. However, the system has been criticised by social workers as in its current form it does not allow them to communicate back to Kela on issues regarding the clients (Jokela et al., Reference Jokela, Kivipelto and Ylikännö2019). Another challenge related to the notification system, but also the current social assistance system more generally, is the last part of the process: taking part in social work counselling offered by the municipality is not conditional to the social assistance payment. In other words, Kela continues to pay social assistance without sanctions even if the individual does not meet with the municipal social worker. Some municipalities have reported difficulties in contacting particularly younger clients who do not show up in the meetings (Mesiäislehto et al., Reference Mesiäislehto, Kivipelto and Ylikännö2022).

Table 1 presents the criteria and number of notifications sent through the digital notification system in 2018-2019. In 2019, a total of 57 700 notifications were sent from Kela to municipalities’ social services. In 13 per cent of these cases the notification concerned an individual under twenty-five years with four months consecutive SA receipt while 9 per cent of the notifications concerned individuals who were at least twenty-five years old with social assistance as a primary source of income for twelve months. The figures do not include notifications that are sent automatically to municipalities’ social services when the applicant of basic social assistance also applies preventive or supplementary social assistance, which concerns around 70 000 applications yearly (Kelasto, 2022).

Table 1 Needs assessment notifications sent through the digital notification system in 2018-2019 by type of notification, %

Source. The Social Insurance Institution of Finland (kelasto.fi)

Previous research on digitalisation and the incentive effects of welfare policies

The social and economic disadvantages experienced by social assistance recipients explain why it has been seen important that social work is closely linked with social assistance. Previous studies show that recipients of social assistance are disadvantaged in multiple ways: they are much more likely to experience over-indebtedness, problems in paying the bills and have other financial problems compared to the rest of the population (Hannikainen-Ingman et al., Reference Hannikainen-Ingman, Kuivalainen, Sallila and Kuivalainen2013). They are also more likely to have problems related to mental and physical health (Vaalavuo and Bakkum, Reference Vaalavuo and Bakkum2021). Particularly among young adults, mental health problems have been identified as an important obstacle for exiting social assistance (Vaalavuo and Bakkum, Reference Vaalavuo and Bakkum2021). Furthermore, around one third of social assistance recipients are also clients of social work (Jokela and Kivipelto, Reference Jokela and Kivipelto2021).

Previous research on utilising automatised client categories in welfare services mostly deals with the administration processes and clients’ employment outcomes. Automatised systems to profile clients are typically used in employment services, where algorithms place jobseekers in different categories based on the jobseeker’s employability (e.g. Caswell and Marston, Reference Caswell and Marston2010; Brébion and Leschke, Reference Brébion and Leschke2020; van Landeghem et al., Reference van Landeghem, Desiere and Struyven2021). Findings of these studies show that on one hand, profiling unemployed helps in ‘red flagging’ – that is, identifying those who are more at risk of becoming long-term unemployed. On the other hand, automatised categories may misclassify individuals and reinforce existing discriminative patterns (van Landeghem et al., Reference van Landeghem, Desiere and Struyven2021).

While social assistance beneficiaries are usually included in the profiling systems if they are jobseekers, automatised categories in the social assistance system itself are less used. This may be explained by the fact that social assistance benefit is in many countries managed on the local level, and centralised systems are rarer (for European countries, see Frazer and Marlier, Reference Frazer and Marlier2016). The strong connection with activation policies and employment services may also explain why profiling and the need for social work inside the social assistance system rarely exists.

Studies on the effect of welfare policies on labour market outcomes or welfare participation focus on the threat effects of welfare programs on employment rates (Lemieux and Milligan, Reference Lemieux and Milligan2008; Jonassen, Reference Jonassen2013; Persson and Vikman, Reference Persson, Vikman, Carcillo, Immervoll, Jenkins, Königs and Tatsiramos2014) or the length of welfare spells (Fortin et al., Reference Fortin, Lacroix and Drolet2004). Generally, the underlying assumption of studies on incentive effects of welfare programs is that they reduce employment and incentives to work. The assumption is based on the theory of welfare trap stating that high benefit levels may decrease incentives to work particularly among those with lower education and skill level (Lemieux and Milligan, Reference Lemieux and Milligan2008; Jonassen, Reference Jonassen2013). However, it has been also argued that providing support (economic, social) to economically disadvantaged individuals increases their capabilities and thus decreases their dependency on welfare benefits (Bargain and Dooley, Reference Bargain and Dooley2016).

Another strand of research has examined the impact of counselling and monitoring on welfare recipients’ labour market behaviour. These studies, mostly dealing with unemployment benefit, have found a modest increase in the transition rate from unemployment to employment (Gorter and Kalb, Reference Gorter and Kalb1996; Crépon et al., Reference Crépon, Dejemeppe and Gurgand2005; van den Berg and van der Klaauw, Reference van den Berg and van der Klaauw2006; Persson and Vikman, Reference Persson, Vikman, Carcillo, Immervoll, Jenkins, Königs and Tatsiramos2014) but a significant increase among those with a higher risk of unemployment and with poor labour market prospects (Crépon et al., Reference Crépon, Dejemeppe and Gurgand2005; van den Berg and van der Klaauw, Reference van den Berg and van der Klaauw2006). The review of the literature shows that counselling and monitoring tend to reduce unemployment especially in the long-term and when the counselling is more intensive (Eichhorst et al., Reference Eichhorst, Kaufmann and Konle-Seidl2008).

Research hypotheses

Although the policy we focus on does not include sanctioning, we assume that a meeting with the social worker would have a positive effect on social assistance recipients to look for more suitable options, such as entering job search instead of staying in the last-resort benefit. In the Nordic model, social assistance is considered to be a residual temporal aid, supporting people back to the universal residence-based social security or employment. Thus, we hypothesise that 1) social assistance spells of social assistance recipients under twenty-five years of age are shorter compared to those who are twenty-five years old or older due to the early red flagging notification to municipality’s social services. This naturally only applies to individuals whose physical or mental condition allows them to work or study.

It is also possible that counselling helps individuals to seek primary benefits that are more suitable for their situation (e.g. disability or rehabilitation benefit). Since in Finland social assistance often works as a top-up for other benefits (Kuivalainen and Nelson, Reference Kuivalainen, Nelson, Kvist, Fritzell, Hvinden and Kangas2012; Tervola et al., Reference Tervola, Mesiäislehto and Ollonqvistforthcoming), receiving other benefits would not necessarily mean an exit from social assistance. We also know that among long-term social assistance beneficiaries, it is less likely to suddenly (completely) exit social assistance. We could assume that having other sources of income apart from social assistance benefit would be an improvement to their situation and that social work counselling could increase the probability of the individual to seek primary benefits, such as disability or rehabilitation benefit, or unemployment benefit that requires active job search. Thus, we also hypothesise that 2) the probability of long-term social assistance beneficiaries to receive social assistance as their main source of income after four months of continuous benefit receipt is lower for individuals who are under twenty-five years old compared to those who are twenty-five years old and over due to the early red flagging notification.

Data and methods

In this study we analyse the differences in social assistance receipt patterns among two groups of (long-term) social assistance beneficiaries: twenty-three-to-twenty-four-year-old unemployed social assistance beneficiaries who after four months of consecutive social assistance receipt are ‘red flagged’ by the digital notification system as long-term social assistance beneficiaries in need of social work counselling, and twenty-five- to twenty-six-year-old unemployed social assistance beneficiaries who do not receive a red flag in the digital notification system. We expect that the individuals in these two age groups are similar in terms of socioeconomic characteristics and their life situations – particularly the twenty-four- and twenty-five-years old individuals who are just below and just above the age threshold. The only difference is that a notification is sent to social services on those under twenty-five years of age but not on those who are twenty-five years of age or older.

For the analysis we use administrative register-based monthly data from January 2018 to July 2019 covering all twenty-three-to-twenty-six-year-old recipients of social assistance in Finland who receive social assistance during the reference period (N=55 233).

Variables

Age

In our analysis, we will concentrate on individuals whose age is within one or two years below (twenty-three and twenty-four years) and one or two years above (twenty-five and twenty-six years) the cut-off that defines which individuals are contacted by social work. The cut-off is set at age twenty-five. It is important to note that we do not actually measure age per se but the age when the individual has been on social assistance for four months. Additionally, we focus only on those individuals who fulfil the criteria set by Kela (see ‘Guidance to social work counselling’ below).

Social assistance receipt

Our main outcome of interest is the probability of receiving social assistance as the primary source of income – that is, the individual does not have other income sources than social assistance and housing benefit.

Guidance to social work

In order to analyse whether the social assistance spells of the younger age groups differ from the older ones we need to apply the same criteria that is used by Kela to identify the individuals who are long-term social assistance beneficiaries in need of social work counselling: 1) Long-term social assistance receipt refers here to four months of consecutive benefit receipt where the individual’s main source of income has been social assistance benefit – that is, apart from social assistance the individual may only receive housing benefit, 2) Age refers to the month when the individual has been on social assistance for four months, and 3) The individual is unemployed (not employed nor on parental/ care leave or other benefits). Individuals who receive a red flag in the system for another reason (sanctioning) are excluded from the analysis.

Control variables

In the regression analysis, we control for gender, nationality (Finnish/ Foreign), and household type (Childless couples/ One person living alone/ Other one-person households).

Table 2 presents the background characteristics of social assistance beneficiaries by age. On the left-hand side of the table are shown characteristics of all young adults who receive social assistance during the reference period while the right-hand side of the table includes information on the long-term social assistance recipients – that is, those whose primary source of income during four consecutive months is social assistance. Over half of twenty-three to twenty-six-year old social assistance beneficiaries are male and most of them are Finnish. Having social assistance as a primary source of income is more common among the twenty-three-year-old social assistance beneficiaries than among the older age groups. Compared to all social assistance beneficiaries among young adults, long-term social assistance beneficiaries are more frequently men and most of them live alone, or they live in shared housing (or with their parents) but are considered as one-person households by Kela.

Table 2 Sociodemographic characteristics of 23–26-year-old social assistance beneficiaries in January 2018 to July 2019 by age, %

Note. Long-term social assistance receipt refers here to 4 consecutive months of social assistance as a primary source of income. * Usually this refers to persons who live in shared apartment but do not belong to the same household.

Methods

We use descriptive analyses to examine the length and number of social assistance spells among young adults. To analyse the differences in social assistance spells between the long-term social assistance beneficiaries who are red flagged in the system (twenty-three- or twenty-four-year-old) and those who are not (twenty-five- or twenty-six-year-old), we conduct a Kaplan-Meier survival analysis that estimates the probabilities for exiting social assistance for the two groups of social assistance beneficiaries. In research on social protection systems, survival analysis is commonly used to estimate the occurrence of an event such as leaving or entering a benefit scheme or (un)employment (Jenkins, Reference Jenkins2004; see also Rønsen and Skarðhamar, Reference Rønsen and Skarðhamar2009; Bäckman and Bergmark, Reference Bäckman and Bergmark2011). In order to capture individuals whose benefit spells did not start before the follow-up period we use left-censoring by excluding individuals whose benefit spells started before January 2018. In addition, we use right-censoring to account for individuals whose benefit spells continued after the last month of the follow-up period (July 2019). For estimating the probability of receiving social assistance as the primary source of income, we use logistic regression analysis.

Results

Before examining the probabilities for exiting social assistance, we first take a look at the length and number of social assistance spells among all twenty-three-to-twenty-six-year-old young adults who have received social assistance during the reference period (Figures 2a and 2b).

Figure 2a. Length of social assistance benefit spells in months between January 2018 and July 2019 by age (%)

Figure 2b. Number of social assistance benefit spells between January 2018 and July 2019 by age (%)

We find that around 40 per cent of social assistance benefit spells only last for one month and one fourth of them last for two or three months. There is practically no difference between the four age groups in the prevalence of benefit spells that are longer than six months except that they are most prevalent among the twenty-three-year-old social assistance beneficiaries. It is also worth noting that around one fifth of twenty-three-year-old social assistance beneficiaries and one third of social assistance beneficiaries in the other age groups had two or more benefit spells during the reference period. The majority of social assistance beneficiaries had one benefit spell during the reference period, and this was most frequently the case among the twenty-three-year old social assistance beneficiaries.

Figure 3 depicts the survival function for the probability of receiving social assistance after four months of continuous social assistance receipt. The follow-up period (value 0 in the x-axis) starts at April 2018: that is the earliest point in time in the data when an individual can have a four-month benefit spell. Since the data are total population data, no confidence intervals or standard errors are presented. The results show that the oldest age group (twenty-six-year-old social assistance beneficiaries) exit social assistance earlier than other age groups. The survival functions of the three other age groups do not differ significantly – that is, the likelihood of receiving social assistance after four months is not lower for the younger age groups who have been notified for the need of social work counselling to their municipality compared to the older ones who have not.

Figure 3. Probability of receiving social assistance after four months of continuous social assistance receipt by time and age between April 2018 and July 2019

Since no difference could be observed in the probability of exiting social assistance, we next examine whether the notification to social work (and possible counselling) could be reflected in reduced dependence on social assistance; or in other words, increased prevalence of other sources of income. Figure 4 shows the probability of having social assistance as a primary source of income for long-term social assistance beneficiaries four months after the younger age groups (twenty-three-to-twenty-four years old) are red flagged in the social assistance system.

Figure 4. Predicted probabilities for social assistance as main source of income four months after under twenty-five-year-old beneficiaries are red flagged in the social assistance system

Note. Regression models controlled for gender, nationality and household type.

Our findings show that four months after social services receive a notification on the younger social assistance beneficiaries the likelihood of living on social assistance is in fact higher among the younger age groups than the older ones who are not red flagged by the system.

Conclusions and discussion

In this study we assessed the effect of red flagging individuals in need of social work counselling on the individual’s dependency on social assistance. For the analysis we utilised a policy that requires Kela, the institution responsible for the administration of social assistance, to notify social services when an under twenty-five year old individual’s primary source of income has been social assistance for four consecutive months. For an individual who has turned twenty-five, the notification is sent after twelve months of social assistance receipt (as a primary source of income).

The results show that social assistance spells among the under twenty-five year old long-term social assistance beneficiaries do not differ significantly from those who are twenty-five years old or older. Furthermore, we found that four months after social services receive a notification on the younger social assistance beneficiaries, the likelihood of living on social assistance is in fact higher among the younger age groups than the older ones who are not red flagged by the system.

Our findings suggest that red flagging clients in the social assistance system is not likely to have an effect on the dependency on social assistance, nor does it decrease the role of social assistance among the long-term social assistance beneficiaries.

It needs to be borne in mind when interpreting the results that being assigned to the treatment is not the same as receiving or complying with the treatment (Cattaneo et al., Reference Cattaneo, Idrobo and Titiunik2018). In our case, once a person has received social assistance for four consecutive months, Kela sends a notice to municipalities’ social services that then invites the person for counselling. Social work counselling is voluntary, and no information is registered in the digital system about the social assistance recipient’s meeting with social worker. Earlier research suggests that while red flag notifications are handled systematically in municipalities, social workers often have difficulties to reach particularly young adults after the notification (Jokela et al., Reference Jokela, Kivipelto and Ylikännö2019). Our results indicate that the digitalised red flag notification system does not seem to shorten the benefit spells of young adults – regardless of what or where is the reason for this failure. Though our research design does not make it possible to interpret the estimates as causal relationships. Our aim was to compare recipiency of social assistance among groups that share similar background characteristics. The two age groups, twenty-four- and twenty-five-year-olds, resemble each other in terms of gender, household type, activity status and share of those who have social assistance as primary source of income. However, when comparing the two other groups, twenty-three- and twenty-six-year-old individuals, it should be noted that having social assistance as a primary source of income is much more prevalent among the youngest age group which may also affect the dependency on the benefit.

It is also important to note that the situations of social assistance beneficiaries are often complex and for long-term social assistance beneficiaries, the (complete) transition from last-resort to primary benefits or employment may take longer. This is particularly the case among young adults with mental health problems (Vaalavuo and Bakkum, Reference Vaalavuo and Bakkum2021). At the same time, we recognise that there are other dimensions related to young adults’ situations (e.g. life management, social contacts, health and wellbeing more generally) that may have improved due to social work counselling, despite their continuing dependency on social assistance, and that measures such as employment or income are only one part of the question on ‘what works’ (Dall and Danneris, Reference Dall and Danneris2019). However, to study the other outcomes would require different type of data that were not available in this study. On the other hand, since red flag notifications are sent based on the length of recipiency, the length of recipiency may be seen as an important measure. In other words, while the red flag notification system and social work may have stronger impact on some other dimensions of the beneficiary’s life, we consider it as a reasonable hypothesis that the effects should also be seen in recipiency of social assistance. In addition, in the increasingly digitalised welfare states, it is crucial to analyse and discuss the opportunities (and limitations) of digitalised services, including red flag systems that are supposed to identify clients in need of support. The crucial question is how to develop these services.

We know that for some, digitalised cash benefit is not enough, at least if the society wants to actively prevent social exclusion. How do we identify those in need of services, and ways to get the services to them – or them to seek for help voluntarily? More generally, how could we find a way to link digitalised, subjective right-based cash benefits with voluntary services and activation? This means that we need to find out what kind of social work and activation works when participation to services is voluntary. This is a timely question since the technological progress pushes towards digitalised welfare, whether we like it or not.

References

Alm, S., Nelson, N. and Nieuwenhuis, R. (2020) ‘The diminishing power of one? Welfare state retrenchment and rising poverty of single-adult households in Sweden 1988–2011’, European Sociological Review, 36, 2, 198217.CrossRefGoogle Scholar
Bäckman, O. and Bergmark, Å. (2011) ‘Escaping welfare? Social assistance dynamics in Sweden’, Journal of European Social Policy, 21, 5, 486500.CrossRefGoogle Scholar
Bargain, O. and Dooley, K. (2016) ‘The effect of social benefits on youth employment: combining RD and a behavioral model’, Journal of Human Resources, 11157510R.Google Scholar
Blomberg, H. and Kroll, C. (2020) ‘For better and for worse? On the transformation of the Finnish social assistance scheme’, Social Work and Society, 18, 1, 110.Google Scholar
Brébion, C. and Leschke, J. (2020) ‘Selecting the suitable job quality items in profiling and job matching algorithms for public employment services’, HECAT - Disruptive Technologies Supporting Labour Market Decision Making No. D2.1, http://hecat.eu/wp-content/uploads/2021/11/Deliverable_2_1_final-2.pdf [accessed 30.05.2022].Google Scholar
Buffat, A. (2015) ‘Street-level bureaucracy and E-government’, Public Management Review, 17, 1, 149–61.CrossRefGoogle Scholar
Caswell, D. and Marston, G. (2010) ‘Unemployed citizen or ‘at risk’ client? Classification systems and employment services in Denmark and Australia’, Critical Social Policy, 30, 3, 384404.CrossRefGoogle Scholar
Cattaneo, M., Idrobo, N. and Titiunik, R. (2018) A Practical Introduction to Regression Discontinuity Designs: Volume I, Cambridge: Cambridge University Press.Google Scholar
Crépon, B., Dejemeppe, M. and Gurgand, M. (2005) ‘Counseling the unemployed: does it lower unemployment duration and recurrence?’, IZA Discussion paper No 1796.CrossRefGoogle Scholar
Dall, T. and Danneris, S. (2019) ‘Reconsidering ‘what works’ in welfare-to-work with the vulnerable unemployed: the potential of relational causality as an alternative approach’, Social Policy and Society, 18, 4, 583–96.CrossRefGoogle Scholar
Desiere, S., Langenbucher, K. and Struyven, L. (2019) ‘Statistical profiling in public employment services: an international comparison’, OECD Social, Employment and Migration Working Papers, No. 224, Paris: OECD Publishing.Google Scholar
Devlieghere, J. and Roose, R. (2018) ‘Electronic information systems: In search of responsive social work’, Journal of Social Work, 18, 6, 650–65.CrossRefGoogle Scholar
Eichhorst, W., Kaufmann, O. and Konle-Seidl, R. (eds.) (2008) Bringing the Jobless into Work? Experiences with Activation Schemes in Europe and the US, Berlin and Heidelberg: Springer.Google Scholar
Fortin, B., Lacroix, G. and Drolet, S. (2004) 'Welfare benefits and the duration of welfare spells: evidence from a natural experiment in Canada', Journal of Public Economics, 88, 7–8, 14951520.CrossRefGoogle Scholar
Frazer, H. and Marlier, E. (2016) Minimum Income Schemes in Europe. A Study of National Policies 2015, Brussels, European Commission.Google Scholar
Gorter, C. and Kalb, G. R. (1996) ‘Estimating the effect of counselling and monitoring the unemployed using a job search model’, Journal of Human Resources, 31, 3, 590610.CrossRefGoogle Scholar
Gough, I. (2001) ‘Social assistance regimes: a cluster analysis’, Journal of European Social Policy, 11, 2, 165–70.CrossRefGoogle Scholar
Gough, I., Bradshaw, J., Ditch, J., Eardley, T. and Whiteford, P. (1997) ‘Social assistance in OECD countries’, Journal of European Social Policy, 7, 1, 1743.CrossRefGoogle Scholar
Hannikainen-Ingman, K., Kuivalainen, S. and Sallila, S. (2013) ’Toimeentulotuen asiakkaiden elinolot ja hyvinvointi’, in Kuivalainen, Susan (ed.), Toimeentulotuki 2010-luvulla. Tutkimus toimeentulotuen asiakkuudesta ja myöntämiskäytännöistä [Social Assistance in the 2010s. A Study on Social Assistance Clients and Granting Practices], Helsinki: National Institute for Health and Welfare (THL).Google Scholar
Hansen, H., Lundberg, K. and Syltevik, L. (2018) ’Digitalization, street-level bureaucracy and welfare users’ experiences’, Social Policy and Administration, 52, 1, 6790.CrossRefGoogle Scholar
Hiilamo, H. (2016) ’Miten perustulokokeilu onnistuisi?’ [How BI experiment could work?], Yhteiskuntapolitiikka 81, 3, 343–51.Google Scholar
Ilmakunnas, I., Kauppinen, T. and Kestilä, L. (2015) ’Sosioekonomisten syrjäytymisriskien kasautuminen vuonna 1977 syntyneillä nuorilla aikuisilla’ [Accumulation of risks of social exclusion among young adults born in 1977], Yhteiskuntapolitiikka, 80, 3, 247–62.Google Scholar
Jenkins, S. P. (2004) Survival Analysis, Institute for Social and Economic Research, University of Essex.Google Scholar
Jokela, M., Kivipelto, M. and Ylikännö, M. (2019) ’Toimeentulotuelta sosiaalityön asiakkaaksi. Sosiaalityöhön ohjaaminen Kelan ja kunnan rajapinnassa’ [From social assistance to social work. Guiding to social work at Kela and municipalities], Working paper 26/2019, Helsinki: National Institute for Health and Welfare.Google Scholar
Jokela, M. and Kivipelto, M. (2021) Sosiaalityön ja toimeentulotuen riittämättömyys asiakkaiden näkökulmasta, Yhteiskuntapolitiikka, 86, 2, 190200.Google Scholar
Jonassen, A. B. (2013) ‘Regression discontinuity analysis of the disincentive effects of increasing social assistance’, PhD thesis, Department of Economics and Business, Aarhus University.Google Scholar
Kangas, O., Jauhiainen, S., Simanainen, M. and Ylikännö, M. (2019) The Basic Income Experiment 2017–2018 in Finland: Preliminary Results, Reports and Memorandums of the Ministry of Social Affairs and Health 2019:9.Google Scholar
Kangas, O., Jauhiainen, S., Simanainen, M. and Ylikännö, M. (2021) Experimenting with Unconditional Basic Income - Lessons from the Finnish BI Experiment 2017-2018, Cheltenham: Edward Elgar Publishing.CrossRefGoogle Scholar
Kelasto (2022) ‘Social assistance’, Statistical database Kelasto, https://www.kela.fi/kelasto [accessed 02.06.2022].Google Scholar
Korpela, T., Heinonen, H. -M., Laatu, M., Raittila, S. and Ylikännön, M. (2020) ‘Johdanto’, in (eds.) Korpela, T., Heinonen, H. -M., Laatu, M., Raittila, S. and Ylikännön, M. (eds.), Ojista allikkoon? Toimeentulotukiuudistuksen ensi metrit, Helsinki: Kela, 827.Google Scholar
Kuivalainen, S. and Nelson, K. (2012) ‘Eroding minimum income protection in the Nordic countries? Reassessing the Nordic model of social assistance’, in Kvist, J., Fritzell, J., Hvinden, B. and Kangas, O. (eds.), Changing Social Equality: The Nordic Welfare Model in the 21st Century, Bristol: Policy Press, 6988.Google Scholar
Lemieux, T. and Milligan, K. (2008) ‘Incentive effects of social assistance: a regression discontinuity approach’, Journal of Econometrics, 142, 2, 807–28.CrossRefGoogle Scholar
Lødemel, I. and Schulte, B. (1992) ‘Social Assistance: a part of social security of the poor law in new disguise?’, in European Institute of Social Security (ed.), Reforms in Eastern and Central Europe: Beveridge 50 Years After, Yearbook. Leuven: Acco, 515–38.Google Scholar
Lorentzen, T., Angelin, A., Dahl, E., Kauppinen, T., Moisio, P. and Salonen, T. (2014) ‘Unemployment and economic security for young adults in Finland, Norway and Sweden: from unemployment protection to poverty relief’, International Journal of Social Welfare, 23, 1, 4151.CrossRefGoogle Scholar
Marx, I., Lina, S. and Gerlinde, V. (2016) ‘For the poor, but not only the poor: on optimal pro-poorness in redistributive policies’, Social Forces, 95, 1, 124.CrossRefGoogle Scholar
Mesiäislehto, M., Kivipelto, M. and Ylikännö, M. (2022) ‘Social assistance beneficiaries’ access to social services in a digitalized social assistance system’, Sozialer Fortschritt, 71, 6-7, 385404.CrossRefGoogle Scholar
Nelson, K. (2013) ‘Social assistance and EU poverty thresholds 1990–2008. Are European welfare systems providing just and fair protection against low income?’, European Sociological Review, 29, 2, 386401.CrossRefGoogle Scholar
Persson, A. and Vikman, U. (2014) ‘The effects of mandatory activation on welfare entry and exit rates’, in Carcillo, S., Immervoll, H., Jenkins, S. P., Königs, S. and Tatsiramos, K. (eds.), Safety Nets and Benefit Dependence, Bingley: Emerald Group Publishing Limited, 189217.CrossRefGoogle Scholar
Rønsen, M. and Skarðhamar, T. (2009) ‘Do welfare-to-work initiatives work? Evidence from an activation programme targeted at social assistance recipients in Norway’, Journal of European Social Policy, 19, 1, 6177.CrossRefGoogle Scholar
Saikkonen, P. and Ylikännö, M. (2020) ‘Is there room for targeting within universalism? Finnish social assistance recipients as social citizens’, Social Inclusion, 8, 1, 145–54.CrossRefGoogle Scholar
Schou, J. and Pors, A. S. (2019) ‘Digital by default? A qualitative study of exclusion in digitalized welfare’, Social Policy and Administration, 53, 3, 464–77.CrossRefGoogle Scholar
Sotkanet (2022) ‘Basic social assistance during year, euros 1000’, Sotkanet.fi Statistics and Indicator Bank, The Finnish Institute for Health and Welfare, https://sotkanet.fi/sotkanet/en/index [accessed 02.06.2022].Google Scholar
Tervola, J., Mesiäislehto, M. and Ollonqvist, J. (forthcoming) ‘Smaller net or just fewer to catch? Disentangling the causes for the varying sizes of minimum income schemes’, International Journal of Social Welfare.Google Scholar
Vaalavuo, M. and Bakkum, B. (2021) ‘Mental health problems at a critical juncture: exit from social assistance among young Finns’, Journal of Social Policy, 50, 4, 764–87.CrossRefGoogle Scholar
van den Berg, G. J. and van der Klaauw, B. (2006) ’Counselling and monitoring of unemployed workers: theory and evidence from a controlled social experiment’, International Economic Review, 47, 3, 895936.CrossRefGoogle Scholar
van Landeghem, B., Desiere, S. and Struyven, L. (2021) ‘Statistical profiling of unemployed jobseekers’, IZA World of Labor, 2021, 483.Google Scholar
Figure 0

Figure 1. The process of red flagging social assistance beneficiaries in need of social work in the Finnish social assistance systemSource. Mesiäislehto et al. (2022)

Figure 1

Table 1 Needs assessment notifications sent through the digital notification system in 2018-2019 by type of notification, %

Figure 2

Table 2 Sociodemographic characteristics of 23–26-year-old social assistance beneficiaries in January 2018 to July 2019 by age, %

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Figure 2a. Length of social assistance benefit spells in months between January 2018 and July 2019 by age (%)

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Figure 2b. Number of social assistance benefit spells between January 2018 and July 2019 by age (%)

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Figure 3. Probability of receiving social assistance after four months of continuous social assistance receipt by time and age between April 2018 and July 2019

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Figure 4. Predicted probabilities for social assistance as main source of income four months after under twenty-five-year-old beneficiaries are red flagged in the social assistance systemNote. Regression models controlled for gender, nationality and household type.