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 H¨am¨al¨ainenand H¨am¨al¨ainen (2012) and H¨am¨al¨ainen 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 (H¨am¨al¨ainenet al., 2013) for Paltamo. As explained in e.g. H¨am¨al¨ainenand H¨am¨al¨ainen(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.