Paltamo Full Employment Experiment: an Application of The

Home , Hyrynsalmi, Kuhmo, Paltamo, Puolanka, Sotkamo, Vaala

Paltamo Full Employment Experiment: An Application of the

Synthetic Control Method

Kosti Takala∗

February 26, 2015

Abstract

In this paper I study the eﬀects of the Paltamo Full Employment Experiment, an activation measure for

the unemployed which combines their prior unemployment beneﬁts and a bonus into a salary. Thus, the

unemployed have to work for their beneﬁts unless they are willing to go on social assistance. To estimate

the eﬀects on unemployment and on the related beneﬁts and allowances, I employ the synthetic control

method to construct counterfactuals for Paltamo, a Finnish municipality in which the experiment was

run from 2009 to 2013. I ﬁnd that there was a real and statistically signiﬁcant drop in unemployment

and related beneﬁts that lasted throughout the experiment. However, the experiment ended up costing

more to the government than what could be retrieved through savings in beneﬁt payments.

Keywords: Labor market experiment, Unemployment, Synthetic control method

∗Department of Economics, Massachusetts Institute of Technology, [email protected]

1 1 INTRODUCTION 2

1 Introduction

The ever aging Finnish population means more dependents (children and pensioners, but mostly pensioners) and, therefore, more allowances, beneﬁts and pensions to cover by the eﬀorts of a stagnating labor force.

Add to the equation a high unemployment, especially in the long term, and its related (generous) benefits and health problems, and you get a government deficit tantamount to a house of cards waiting to collapse, unless something can be done to the different costs stemming from unemployment.

To tackle the above problem, many countries have implemented measures such as active labor market policies, the scale and evaluation of which has been inadequate. This makes the Paltamo Full Employment

Experiment a rather unique attempt at curbing the costs of unemployment in Paltamo, a small Finnish municipality. The basic idea of the experiment is to take all of Paltamo’s unemployment beneﬁts, add a bonus to the mix, and use these funds to employ the unemployed in the Paltamo Employment House, where they would do all kinds of handiwork such as crafts and bakery. The unemployed were to work for their beneﬁts but could make some extra money in the process compared to what they used to receive. This was the carrot. The stick, on the other hand, was ending up on social assistance in case one refused employment.

What made the experiment unique were its scale, costs (eight ﬁgures in euros), and length (four years from

2009 until 2012, and a run-down year 2013).

Because the experiment was implemented in only one municipality, involving everyone in it, statistical analysis would seem to be quite difficult with the apparent lack of a control group. There are municipalities very similar to Paltamo but none of them can convincingly replicate its characteristics. This is why I turn to the synthetic control method by Abadie et al. (2010), combining multiple towns into a Paltamo-like control municipality such that it resembles Paltamo in some relevant dimensions prior to the treatment. However, adding additional covariates rarely has a big effect on the results, making them more robust. The obtained effects are then tested using placebo tests, tests that let us pretend the experiment took place in each of the towns, one at a time.

The Paltamo experiment has been evaluated before by Hämäläinenand Hämäläinen (2012) and Hämäläinen et al. (2013), but these papers only have data until the end of 2011 and 2012, respectively. I worked on the same project in 2011 using even older data. Now enough time has passed to estimate the short run effects, although they only give a lower bound for the savings the experiment has produced because the improved characteristics (such as employability and health) of the unemployed may reduce costs in the future.

I find that unemployment did indeed go down due to the experiment, even during an economic downturn, and that this made the related benefits go down by a considerable amount. The savings produced by the experiment were not nearly sufficient to cover the costs, at least not during the first four years. However, 2 ACTIVE LABOR MARKET POLICIES AND THEIR EFFECTS 3 as mentioned in the previous paragraph, I only analyze a few short-term effects and leave the long-term completely in the dark.

The paper is organized as follows. Section 2 will give a ridiculously short overview of active labor market policies, whereas sections 3 and 4 introduce the experiment in question and the synthetic control method, respectively. Section 5 is reserved for description of the data, analysis and results. Section 6 concludes the journey.

2 Active labor market policies and their eﬀects

The US has typically not only implemented more extensive public sector employment programs, but also evaluated them more rigorously than her European cousins. However, the scale of these measures has still been low enough not to have a big impact on the overall US labor market (Kluve and Schmidt, 2002). The policies employed by the US and many European countries are often referred to as active labor market policies (ALMPs) because they aim to activate the unemployed and thereby improve their employment and earnings potential. According to Van Ours (2007), the effects of these programs can be divided into two: treatment and compulsion effects. By treatment effects he means the increase in search effectiveness that stems from these policies, whereas compulsion effects can be understood as coming from decreased utility of unemployment making it more difficult for a person to stay unemployed and just keep claiming their monthly benefits. A meta-study by Kluve (2006) finds that ALMPs differ widely in their effectiveness, but, generally, training programs seem to have the largest effects when transitioning from unemployment to work. Direct public sector employment programs can even be detrimental for the participants’ employment prospects. The short-term effects may be worsened by a lock-in effect where the unemployed participating in the program reduce their job-seeking activities for its duration, thereby making the program look worse.

This is why it is of paramount importance to evaluate ALMPs not only in the short but also in the long run.

In this paper I seek to evaluate the Paltamo Full Employment Experiment, which was in eﬀect from the beginning of 2009 until the end of 2012, after which it was gradually run down during 2013. It clearly falls under the ALMP category, although it is much more extensive (employing almost all of the unemployed in

Paltamo) and, therefore, very costly. Like many previous evaluations of ALMPs, I will focus on the effects and benefits of the experiment but not so much on the costs. This is because information on the actual costs may have been hidden behind a curtain by those who seek to prove that the experiment was a success. Even if this is not the case, it is often difficult to separate the relevant costs from those that would have occurred anyway. However, I will present some cost estimates in the next section. 3 PALTAMO EXPERIMENT IN SHORT 4

Figure 1: Unemployment rates in Paltamo, Puolanka, and Finland

3 Paltamo experiment in short

Paltamo is a small municipality located in the Kainuu region in Eastern Finland. In December 2013, it had

a population of 3620, a signiﬁcant drop from the 4994 at the end of 1991. Paltamo has had a notoriously

high unemployment ever since the Finnish depression in the early to mid-90s. During this grim period, the

rate of unemployment1 hovered around 25 percent, from where it had dropped to 16 percent by the start of the experiment in 2009. In Figure 1 we can see the unemployment rates in Paltamo, Finland, and Puolanka, a very Paltamo-like town. I will analyze unemployment more closely in section 5, but one can see that there was a signiﬁcant drop in 2009. Paltamo was chosen to be treated because of its small size (the experiment was not free) and the problems it had been facing, such as the notoriously high unemployment and a high dependency ratio2. Paltamo can also be thought of as a window to the Finland of tomorrow with an aging population and stagnating labor force3.

Sonkaj¨arviwas chosen as the oﬃcial control municipality, possibly because it is located in the proximity of Paltamo (about 70 miles south) and is about the same size (its population has been in sharp decline, too).

I will not pay much attention to Sonkaj¨arvi,other than noting that it seems to be a pretty good control

1I deﬁne the annual rate of unemployment as an average of the monthly rates. You are unemployed in a given month if you have registered as ’looking for work’. 2Dependency ratio is the ratio of people outside the labor force to those in the labor force. Usually you are not in the labor force if you are under 16 or over 64 years old. 3Arguably, this might also be the fate of many other European countries in the not-too-distant future. 3 PALTAMO EXPERIMENT IN SHORT 5

Direct operating costs of the experiment (in euros) 2009 2010 2011 2012 Total 1,396,291 2,923,451 3,372,612 3,989,695 11,682,049

Table 1: Direct operating costs of the experiment, in euros (Hämäläinenet al., 2013)

for Paltamo. As explained in e.g. Hämäläinenand Hämäläinen(2012), an average Finnish municipality is of very small size (about 6000) and rather autonomous, providing services to its citizens. The Ministry of

Employment and the Economy is responsible for public employment services and ALMPs.

In a nutshell, the idea of the experiment was to re-allocate passive unemployment benefits, such as earnings-related and basic unemployment allowances to activating measures such as wages and employment services. The goal was to rehabilitate the unemployed – especially those long-term unemployed that had been without work for 500 days – and to help them find a job in the open market, improving their health and earnings potential in the process, and to cut the immense costs unemployment puts on the Finnish government. To achieve these goals, the experiment was to employ as many of the unemployed as possible in the publicly funded Paltamo Employment House (PEH). Implemented by Paltamo Employment Association, there was an orientation phase (the Job Club) of one to three weeks of job search assistance and the like, before the unemployed could start working at the PEH. While working there, they were paid based on their work histories (and the level of their unemployment benefits prior to the experiment) such that they would receive a little more than without working at all. The gross wage range was from 918 to 2260 euros per month, with an average of 1078 euros – clearly exceeding the minimum benefits of 551 euros in 2011

(Hämäläinenand Hämäläinen,2012). The actual work was mostly not too productive and included arts and crafts, recycling, renovation, cafe, and bakery. The unemployed had the option to refuse work, in which case they would end up on social assistance unable to enjoy the unemployment benefits.

The costs of the experiment mainly come from the wages paid to the unemployed and to the people working at the PEH and the Job Club. There are also indirect costs such as the possible loss of private

ﬁrms’ business and tax payments because of the cheap labor used by the PEH. These types of costs are not easily evaluated using the publicly available data I have, but due to the small scale of the PEH operation

I believe them to be of minor importance. Hämäläinenet al. (2013) have estimated the total operating costs from 2009 to 2012 at about 11.7 million euros (see Table 1). To find out how much the net cost of the experiment was, one needs to estimate the savings in categories like unemployment-related benefits, social assistance and general housing allowance. There are also other allowances, but here I will focus on the previous three. 4 SYNTHETIC CONTROL METHOD 6

4 Synthetic control method

As explained in Abadie et al. (2010), comparative case studies often suﬀer from at least two concerns if not

problems. First, it is often diﬃcult to ﬁnd a control unit that can believably replicate what the treated unit

would have experienced in absence of the treatment. Second, data are rarely available on an aggregate level

which implies that the researcher has to aggregate smaller units into larger ones using his or her discretion.

Both of these problems may obviously lead to inaccuracies in the analysis that follows. In the case of

Paltamo, I have data on an aggregate level, so the second concern is not as big of an issue as the ﬁrst.

To solve, or at least try to tackle, the second problem, one can use synthetic control methods. Abadie et

al. (2010) is a seminal contribution, but the idea has been around at least since Abadie and Gardeazabal’s

(2003) case study into the eﬀects of terrorism on the economy of the Basque Country, where they combine

Catalonia and Madrid to construct a synthetic control region for the Basque Country. The method extends

the usual linear panel data model by letting the unobserved eﬀects vary over time. It uses the diﬀerences

in diﬀerences framework, comparing the treated unit to its synthetic control. It allows us to see the weight

each non-treated unit has in the synthetic control, thereby increasing transparency, and avoids extrapolation

by restricting these weights to be nonnegative and sum to one. The method also promotes research honesty,

since it is possible to ﬁx study design without knowing how this will aﬀect the results.

To be more precise, and following Abadie et al. (2010), suppose there are J + 1 regions (municipalities in

this paper) and denote the one being treated by j = 1. Let the time periods be t = 1, ..., T0, ..., T , where T0 I is the last period before the experiment/treatment. Let also Yjt be the variable of interest, where Yjt and N Yjt are the outcomes with and without the treatment, respectively. If there are no anticipation eﬀects, then I N 4 I N Yjt = Yjt ∀j ∀t = 1, ..., T0. What we are interested in is α1t = Y1t − Y1t for t = T0 + 1, ..., T . Assume further N that the outcome, Yjt , can be written using the following factor model:

N Yjt = δt + Zjθt + µjλt + jt, (1)

where λt is an unknown common factor, Zj is a (1×r) vector of observed covariates, θt is an (r ×1) vector of

unknown parameters, µj is a (1×F ) vector of unknown factor loadings, λt is an (F ×1) vector of unobserved

common factors, and jt is an unobserved transitory shock (with mean zero).

′ K1 KM ′ Ki Next, we deﬁne a (k×1) vector X1 = (Z1, Y 1 , ..., Y 1 ) , where for each i, Y 1 is some linear combination

of the pre-treatment outcomes of the variable of interest for our treatment region. Similarly, we deﬁne X0

5 as a (k × J) matrix where each row corresponds to a unit in the donor pool . Thus, k = r + M. Now, the 4This should be true for the Paltamo experiment unless the threat of being forcefully employed has driven people to move out or ”to ﬂy under the radar” by not registering as unemployed. 5Donor pool is used to refer to the group of units that are not treated but are used in constructing the counterfactual. 5 DATA, ANALYSIS AND RESULTS 7

′ J+1 synthetic control method ﬁnds a (J ×1) vector of nonnegative weights W = (w2, ..., wJ+1) , with ∑j=2 wj = 1, » ′ such that the distance YX1 − X0WYV = (X1 − X0W ) V (X1 − X0W ) is minimized for some symmetric and positive deﬁnite weighting matrix V . As in both Abadie and Gardeazabal (2003) and Abadie et al. (2010),

I will choose V such that the root mean squared prediction error (RMSPE) is minimized for the variable of interest and the periods before the treatment.

∗ N This will yield a minimizer W which we can use to construct an estimate of Y1t for t = T0 + 1, ..., T : ˆ N N ∗ N Y1t = Y0t W , where Y0t is a (1 × J) vector of the outcomes in the non-treated regions. Finally, we get an I N ∗ estimate for the treatment eﬀect:α ˆ1t = Y1t − Y0t W . As discussed and proved in Abadie et al. (2010), the estimator will be unbiased under fairly standard conditions.

When it comes to statistical inference and the significance of results, Abadie et al. (2010) propose running a placebo test, something that was already used in Abadie and Gardeazabal (2003), although the idea had been around even before that (see e.g. refutability tests in Angrist and Krueger (1999)). To do a placebo test, one applies the synthetic control method to all or a subset of the regions one by one, as if they had been treated or affected by the experiment. Doing this, one can evaluate whether the observed effect in the true treatment region is out of the ordinary or if it is just an artifact of the method (a method that clearly is not working). We can be fairly certain that there was a real effect if the observed differences-in-differences estimate is among the top-5% of all the placebo treatments.

5 Data, analysis and results

5.1 Data

The data used in this study come from three Finnish, publicly available databases: Kela (Social Insurance

Institution of Finland), SOTKAnet (National Institute for Health and Welfare), and StatFin (Statistics

Finland). These municipality-level panel data span the years 1991 through 2013. Thus, the experiment period 2009–2012/2013 is covered, but we are unable to use these data to study whether the observed treatment eﬀects last post experiment or new eﬀects emerge. This is actually an important question that should be addressed in future work.

I have restricted the donor pool to 189 Finnish municipalities (out of 320) by removing those that have been merged with another municipality between 1991 and 2013 and those that are part of a region deemed too diﬀerent from Kainuu (where Paltamo is located). There are a total of 19 regions in Finland, out of which four have been removed (these four include the Helsinki region which is far more urban than Kainuu, and the Swedish-speaking regions located near the coast). 5 DATA, ANALYSIS AND RESULTS 8

Figure 2: Unemployment rates in Paltamo and synthetic Paltamo

5.2 Analysis

I will make and maintain the assumption that the experiment only affects Paltamo and that indirect spillover effects on other towns are minor. If this was not the case, the treatment effects would be attenuated toward zero since the comparison group would also have experienced, say, a decline in unemployment. This assumption is not entirely valid because it is well possible that some people have left the municipality in an attempt to escape employment and having to work for their benefits. It may also be that there were job seekers that moved to Paltamo to join the experiment. Moreover, one has to remember that, simultaneously with the experiment, a global financial crisis made the rate of unemployment and the number of those receiving benefits go up in many municipalities. For actual computations I have used the Stata package synth provided by Abadie, Diamond and Hainmueller.6

First I will analyze whether the experiment had an eﬀect on the unemployment in Paltamo, as it would be worrisome if it did not, considering that people are being forced into employment somewhat artiﬁcially

– without real demand for labor. Looking at Figure 2, we see that synthetic Paltamo replicates the actual one quite accurately in the pre-treatment period, and there is a considerable drop in the unemployment rate due to the experiment. It also looks like rate of unemployment starts to return to its normal level after the experiment, which is why it would be interesting to see the data for 2014.7 If we believe what the synthetic

6See Jens Hainmueller’s website: http://web.stanford.edu/ jhain/synthpage.html 7We have to remember that the experiment was gradually run down during 2013, so it still had some eﬀect that year 5 DATA, ANALYSIS AND RESULTS 9

Figure 3: Placebo test for unemployment, one graph for each town in the donor pool. Paltamo in red.

control method gives us, unemployment was reduced by 2.4, 6.8, 9.9, 10.8, and 6.2 percent in 2009 through

2013, respectively.

The covariates I have used in constructing the synthetic control can be found in Appendix B. Adding more covariates does not seem to have any effect on the unemployment rate in synthetic Paltamo and, thus, on the pre-intervention fit; the results are essentially unchanged and the synthetic control is mostly comprised of the same three municipalities (Enontekiö,Nurmes and Vaala, see Appendix A). The placebo test in Figure 3 suggests that it is not the synthetic control method that is driving the result since the difference between the two Paltamos is by far the biggest of all. I also computed a time placebo by starting the experiment in

2008 instead of 2009, but there was no eﬀect in the ﬁrst year, as expected.

Let us then investigate what kind of an eﬀect the experiment had on the costs of unemployment and poverty for the government. Social assistance in Finland is the assistance of last resort which can be applied for if a household’s total income (including unemployment beneﬁts) does not exceed some threshold.

Therefore, it is not directly related to unemployment but the unemployed usually receive part of their income as social assistance. One would expect the costs of social assistance to go down because of the experiment as people earn more. However, as can be seen in Figure 4, per-capita social assistance seems to have increased in 2009 due to the experiment. Afterwards there has been a clear downward trend, and the yearly diﬀerences between Paltamo and synthetic Paltamo between 2009–2013 have been 11.8, −5.2, −12.1, −19.2, and −27.3 –especially since the unemployment rate used here is an average of the monthly numbers. 5 DATA, ANALYSIS AND RESULTS 10

Figure 4: Total social assistance paid out in Paltamo and its synthetic counterpart, euro/capita.

euro/capita, respectively. However, based on a placebo test (the ﬁgure of which I have omitted), these

diﬀerences are not signiﬁcant; according to this test, 21 municipalities out of the 185 in this donor pool8 have managed to reduce their per-capita social assistance by more than Paltamo since 2009 (meaning that

21 there is a 185 ≈ 11.4% chance of observing an eﬀect at least as big as Paltamo’s, but without the experiment). It might be possible to attribute the increase in 2009 to the increased awareness of social assistance among

the unemployed, but it is also conceivable that some have refused to participate in the experiment, in which

case they have ended up on social assistance, unable to claim their unemployment beneﬁts.

General housing allowance is a lot like social assistance, and its size depends mainly on one’s income and

rent payments. Figures 5 and 6 show how these per-capita allowances have evolved in the two Paltamos9.

The takeaway is that there has been a signiﬁcant drop in paid housing allowances, although the eﬀect in

2011 is within normal variation and might not have been caused by the experiment. The pool of towns used for the placebo test in Figure 6 has been shrunk to 81 because over a hundred municipalities had synthetic counterparts that did not track them too well as compared to Paltamo (I have dropped a municipality if its root mean squared prediction error is more than twice as big as Paltamo’s). Only one of the 81 showed an eﬀect as large as Paltamo which indicates that the drop in Paltamo was indeed statistically signiﬁcant.

Last, but deﬁnitely not least, I will consider the actual, total unemployment beneﬁts and allowances

8The size of the donor pool varies slightly from case to case because of missing data. 9The diﬀerences since 2009 for Paltamo are −23.9, −33.7, −6.4, −36.3, and −28.3 euro/capita, respectively. 5 DATA, ANALYSIS AND RESULTS 11

Figure 5: General housing allowance in Paltamo and synthetic Paltamo, euro/capita.

Figure 6: Placebo test for per-capita general housing allowance, with donor pool restricted to only those 81 municipalities that had a pre-treatment RMSPE of less than twice that of Paltamo’s. Paltamo in red. 5 DATA, ANALYSIS AND RESULTS 12

Figure 7: Total unemployment beneﬁts in Paltamo and synthetic Paltamo, euro/capita.

Figure 8: Placebo test for per-capita total unemployment beneﬁts with 187 municipalities (two dropped because of missing data). Paltamo in red. 6 CONCLUDING REMARKS 13

(per capita, as usual). Figures 7 and 8 paint a clear picture: the amount of these beneﬁts paid in Paltamo has been greatly (and signiﬁcantly) reduced thanks to the experiment. The gaps for the treatment years estimated by the synthetic control method are −147, −326, −415, −530 and −496 euro/capita, respectively.

5.3 Costs versus savings

Estimated savings (in thousands of euros) year population unemp. beneﬁts hous. allow. soc. assist. TOTAL 2009 3917 576 94 -46 623 2010 3884 1266 131 20 1417 2011 3807 1580 24 46 1650 2012 3743 1984 136 72 2192 2013 3620 1796 102 99 1997

Table 2: Rough, back-of-the-envelope-type calculation of estimated savings generated by the experiment (multiplying the per-capita numbers by the population).

I have made some very rough calculations in Table 2 to estimate the total savings the experiment has generated through the decreased unemployment-related allowances and beneﬁts I analyzed here. It has been made assuming that the fall in each variable was as big as the synthetic control method suggests. I estimate the total savings somewhere in the neighborhood of 7.8 million euros. Because I have cost data only for years from 2009 through 2012, I can compare the costs and savings over this period. The cost estimate I presented earlier in Table 1 was about 11.7 million, whereas savings for the same period are estimated at around 5.9 million, suggesting that the true costs for the ﬁrst four years were about 5.8 million euros. This number can be understood as an estimate for how much it costs to activate people through employment without any real demand for labor.

6 Concluding remarks

In this paper I have used the synthetic control method to analyze the Paltamo Full Employment Experiment, a very special active labor market experiment. I have compared its costs to the savings that have been created due to decreased unemployment benefits, housing allowances and social assistance. I acknowledge that these are only rough estimates, but they still give some sort of an idea about whether experiments like this could be run elsewhere, possibly in a region with a much bigger population. One does not have to believe in the synthetic control method to see that the experiment has had a real effect. I consider the method a nice way of estimating things that might be very hard to estimate otherwise. One must bear in mind that I have only analyzed a few short-term effects, and left everything else for future studies. It is an interesting question REFERENCES 14 whether the experiment has improved the general level of physical and mental health in Paltamo and might, thereby, produce savings in the much-longer-term.

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202:67–78, 2007. Appendix A: Variables and weights in synthetic Paltamo

Variable Explanation Source Period agriculture % of total active labor force in agriculture or forestry SOTKAnet 1991–2007 industry % of total active labor force in industry SOTKAnet 1991–2007 services % of total active labor force in services SOTKAnet 1991–2007 % 16-64-year-old Population share of 16-64-year-olds SOTKAnet 1991–2013 unemployment Registered as unemployed, % of labor force, avg over months SOTKAnet 1991–2013 social assist. Social assistance, euro/capita SOTKAnet 1991–2013 morbidity index Kela’s morbidity index, country avg=100 (higher is worse) SOTKAnet 1991–2013 Swedish Swedish-speaking popuation as, % of total population at year end SOTKAnet 1991–2013 social sector net exp. Net expenditure of the municipal social sector, euro/capita SOTKAnet 1993–2013 education higher People with higher education, aged 15+, % of population SOTKAnet 1995–2013 housing allow. General housing allowance, euro/month/household Kela 1994–2013 unemp. benef. Total unemployment allowanced and beneﬁts, euro/capita Kela 2000–2013 taxable income Mean taxable income (euro) StatFin 2005–2013 wage income Wage income, dwelling unit average StatFin 1995–2013

Table 3: Variables used in the study; explanation, source and time period.

Weights in synthetic Paltamo Municipality Unemployment Social assistance Housing allowance Unemployment benefits Enontekiö 0.122 0.099 Nurmes 0.459 Vaala 0.299 Hyrynsalmi 0.338 0.136 Jalasjärvi 0.119 Kittilä 0.088 Lestijärvi 0.072 Lieksa 0.141 Liperi 0.152 Puolanka 0.091 Forssa 0.071 0.057 Kaavi 0.150 Kuhmo 0.104 0.157 Pyhtää 0.056 Rautavaara 0.107 0.279 Sotkamo 0.132 Taivalkoski 0.165 Kolari 0.156 Rautjärvi 0.188

Table 4: Weights in synthetic Paltamo for the four diﬀerent variables of interest. Only showing those municipalities with a weight of 5 percent or more, since in some cases there are more than ﬁve towns with a minuscule weight. Appendix B: Values of key variables in synthetic Paltamo

Synthetic Paltamo Covariate Paltamo Unemployment Social assist. Housing allow. Unemp. beneﬁts 16-64-year-old (%) 61.8 61.8 61.9 62.5 63.2 Social assistance, eur/cap 67.8 78.8 68.8 68.2 Morbidity index 134 123 124 129 127 Swedish (%) 0.067 0.131 0.083 0.714 0.211 higher education (%) 11.7 12.6 11.6 12.1 11.3 industry (%) 14.2 12.1 11.3 15.5 15.4 agriculture (%) 14.9 17.6 28.9 17.5 16.9 services (%) 31.1 31.8 32.0 Taxable income, eur, mean 16,884 17,113 17,348 Wage income, eur/hh 19,057 19,954 19,277 Social sector net exp., e/cap 690 760 832 % unemployed (91–08, mean) 20.7 19.1 20.2 20.7 % unemployed in 1995 25.4 25.4 % unemployed in 2001 22.7 22.5 % unemployed in 2003 18.6 18.8 % unemployed in 2007 16.2 16.1 Social assist. (1994), e/cap 61 61 Social assist. (1998), e/cap 82 82 Social assist. (2000), e/cap 70 70 Social assist. (2002), e/cap 69 69 Social assist. (2004), e/cap 55 55 Social assist. (2006), e/cap 55 56 Social assist. (2008), e/cap 87 87 Housing allow. (1995), e/m/hh 130 131 Housing allow. (2002), e/m/hh 174 174 Housing allow. (2004), e/m/hh 174 174 Housing allow. (2006), e/m/hh 187 187 Housing allow. (2008), e/m/hh 196 196 Unemp. benef. (2001), e/cap 800 800 Unemp. benef. (2003), e/cap 864 864 Unemp. benef. (2004), e/cap 870 868 Unemp. benef. (2005), e/cap 810 811 Unemp. benef. (2008), e/cap 699 699

Table 5: Values of the covariates I have used in constructing the diﬀerent synthetic Paltamos for each of the four variables of interest. These correspond to the rows of the matrices X1 (for Paltamo) and X0 (for municipalities in Table 4).