Policy evaluation of income support schemes

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Department of Economics and Management

Master of Science in Economics

Policy evaluation of income support schemes

Supervisor:

Prof. Lorenzo Corsini

Candidate:

Maria Cristina Maurizio

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Introduction

Public support programs have, in history, become more and more relevant all around the world. Their objective is to influence socio-economic aspects which do not reach a satisfying level when left without control. Examples of support programs available in almost all coutries are the ones linked to the control of poverty and of unemployment. The economic evolution and in particular the technologial aspects of it, have naturally make our life easier but on the other hand it has endangered delicate situation. One of which is the equilibrium of in labour markets. The functioning of labour markets is a very sensible matter: a issue in labour market can easily become a serious economic problem. If people are not able to find a job and firms to employ individuals, this could surely attempt to paralize the economy. The procedure of finding a job must be as smooth as possible, with a major percentage of people seeking for a position matched to an adequate job. People finding themselves without a job, who start looking for it, automatically increase the number of unvoluntary unemployed. It goes without saying, that unemployment is one of the most relevant problems most of developed economics. For this, governaments must try to take actions in order to control the unemployment rate and to prevent it from rising. In reality, though, the critical issues are not only linked to being unemployed but regards also how long you have been in this situation and what are the causes for this unemployment spell. The governament has the difficult task to decide how to help and sustain them in the job research in the best way possible. However, as in every framework linked to people behaviour, have a universally approved and effective procedure is impossible. Accordingly, the unemployment support schemes are different among countries even though the objective is the same.

In this framework appears clear the necessity to evaluate a policy (in particular, we are referring to unemployment support schemes) and to assess its effects on the economy. It

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is well known that public policies are a cost for the state and when the government finds itselfs in deciding where to allocate resources, having at least an estimation of the impacts would be very useful in choosing the right alternative. For example, one might want to know if the effect of increasing the duration of unemployment benefits would be a better option with respect to establish an unemployment assistance program.

Nevertheless, assessing such effects is not a small thing because it is linked to the concept of causality. Causal effects stems directly from causal relations, but the problem is that the reality is complex and we can not be sure whether a results stems only from such link or there are also other factors which are relevant for it. However, this is what a researcher studying program evaluation want to assess.

Studying the effects of a policy has a huge impact in:

• trying to improve the positive effects of it and to reduce the downsided ones; • analysing whether the use of the resources have been optimal or maybe there are

policies more effective than others;

• increasing the transparency by divulgation of results;

• trying to simplify the process and maybe understanding which particular features are more effective than others.

This analysis is usually run after the implementation of the program, but in some situa-tions it is possible to use prior and intermediate data to try to predict the possible effects. This kind of analysis would be clearly more approximative because it can not rely on the exact data but nevertheless it can be useful in assessing a sort of qualitative relation. A prior analysis would be based only on data from other programs and, as much as they can be similar, the result can not be generalized to a different situation. This happens because the results of a policy are linked to the economic and institutional framework in which they are applied. However, this can be useful in many situations, to begin analysing in a critical way a possible programs. An intermediate policy evaluation, instead, would be led after the implementation of the program but too early to have official data. It can be helpful in finding aspects on which the governament can intervene to make the policy more effective.

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great space to debate. This thematic is still at the center of the economic reasearches, because there is not a universal method able to solve this assessment in every situations, but there are various options which need to be tried and discussed. It is a relatively new field, born with the modern definition of state when the welfare of citizens became relevant, and I think that there is still the need to explore, try and also fail to make the researches to progress.

We decided to concentrate on unemployment support programs with particular attention to the differences between unemployment insurance schemes and unemployment assis-tance. Specifically in the first chapter we analysed the fundamental features of unem-ployment benefits and assistance; in the second, instead, we reported the causal analysis framework necessary to understand the problems in impact evaluation; finally, the third and fourth chapters are dedicated to two specific cases. In particular, in the third we applied propensity score matching to try to evaluate the effect of unemployment benefit and in the fourth we applied instrumental variable to attempt to solve a possible omitted variable problem in assessing the effect of unemployment assurance. Both the analysis have been carried out on the data collecting in the EU-SILC survey have been presented in further details at the end of chapter two.

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Chapter 1 Unemployment support schemes

1.1 Unemployment insurance schemes

A fundamental part in the study of economics is the one of labour markets: they have an essential role in people’s life and they affect almost all economic activities. After the 2008 financial crisis, the situation in labour markets all around the world have worsened drastically. Millions of people lost their job finding themselves in a situation of poverty. In that situation of uncertainty, came up with strenght the debate about the economic support that the State can provide. In the context of labour markets, the central kind of assistance is the money income that the State gives to people when they become unem-ployed. This kind of support is referred as income insurance (UI) schemes.

The word insurance is known also in common speaking: it is a contract by which an individual pay a premium to an institution (a bank or an insurance company) in order to be covered in case of some events (usually illness, accidents, etc...). When referring to unemployment insurance the concept is the same but the premium is constituted by the contributions workers pay while working.

Income insurance schemes are nowadays widely spread and "all developed economies have (them) to protect workers against major income losses during spells of unemployment". [25]

However, the general idea underlying UI programs is obviously the same everywhere: help unemployed workers meeting their basic needs without selling their assets and/or accept-ing jobs below their qualifications. As a consequence, unemployment insurance programs

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act also as automatic stabilizers in the business cycle, since, by supporting the incomes of unemployed people while they are searching for another job, they enable them not to reduce dramatically their consumption level. Furthermore, UI schemes also make the "reallocation of labour across the economy a smoother process",[25] as job seekers can devote more time to finding an occupation that matches their skills and expectations. But of course, no discussion would be needed if there were not negative effects in the implementation of UI schemes. In fact in reality, unemployment insurance schemes do have also downsides.

The most important one is that, if the amount given to unemployed is too generous and too prolonged, this can cause people to reduce the intensity they put in searching for a new job. Specifically, if the job seekers realize that receiving the subsidies enable them to live a decent life also without working, they would not feel the need to search for it. Accordingly, this would cause them to be out of the work for a longer time, increasing the so called unemployment duration1_{. Obviously as a consequence, this could prevent}

unemployment rate to stay at an acceptable level and it can also act as a detriment of economic growth.

From these principal impacts, both the positive and negative ones, stem also other mech-anisms that can impact on the final effect of UI schemes, for example:

• Since people receive subsidies, unemployment becomes less worrying and for this workers quit more easily from their job.

• People now are more selective in choosing to accept a job and as a consequence firms have to rise wages: jobs becomes less profitable from the firm’s point of view. • On the side of positive mechanisms, there is also the so called re-entaitelment effect : having a job entitles people on receiving benefits in the future and people search harder.

Of course, also other consequential effects can be stated, but the one listed above are the principal found by the literature in past years.

1_{Unemployment duration "refers to the duration of the period during which the person recorded as}

unemployed was seeking or available for work."(OECD Glossary of statistical terms). It can be computed at individual level or also at aggregated level consisting in this case in the mean unemployed duration in the group.

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The obvious consequence is that, when deciding to implement an unemployment insur-ance policy, the governament must consider to add some other policies, called active labour policy, to contrast the negative effects. So, active labour policies should help unemployed individuals in finding a new job as quickly as possible. They can consist on the obligation for unemployed people to attend courses and internships or on a limit in the number of jobs they can turn down. Generally, the suspension of the benefits is the consequence in cases of non-compliance with the active policies requirements.

As said before, nowadays UI schemes can be found in almost all countries around the world. Nevertheless, unemployment insurance programs wary consistently among coun-tries because they are clearly affected by the economics and institutional context.

However, "unemployment benefits programs in advanced industrialized economies share many features" among which "three of the most important (...) are eligibility require-ments, benefit level and maximum duration of benefits". [25]

Eligibility requirements represent a fundamental feature of UI, since they determine both the coverage of UI schemes (meaning the share of unemployed allowed to participate) and the groups that are more likely to receive the benefits (for example, young people compared to prime-age ones) [1]. As the abovementioned ILO’s working paper highlights "in order to provide an adequate level of protection, UI schemes need to consider workers who confront a different risk of unemployment" and for that it is important to construct a program which is able to provide adequate coverage being also financially sustainable. The paper takes into account also two different categories of workers, the ones from pub-lic sector and the sef-employed ones. Concerning pubpub-lic sector workers, they are almost always included in the unemployment benefits receptions. In particular, their analysis shows that the UI programs in 10 out of the 15 advanced economies considered and 12 out of 17 of the emerging one do include them.

The picture is almost the contrary when considering self-employed workers: in fact, they find out that there is very limited coverage for this category of workers among the ex-amined countries. Furhermore, in the majority of the cases in which they are considered, their UI schemes are based on voluntary affiliations. Specifically, among the advanced economies the only state that provides unconditional coverage to self-employers is

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Fin-land, while, among emerging countries, 4 have this possibility.

Another key point is whether the eligibility depends on the previous lenght of employ-ment records or rather on paid social contributions. The fact is that, considering just the latter, all the informal workers are not considered while, if the governament decides to include also them, the risk is to have a scheme which is not sustainable. The trend among countries is to demand the payment of social security contribution and, for example, just Finland that allows participation considering just previous employment records.

Regarding the ending of the previous job, most of the countries consider for benefits only people who have been fired and not the ones who voluntary quit. This happens because the participation in the UI schemes of job-quitters "might create perverse effects, as peo-ple might leave their job simply to take advantage of the benefits".[1]

Furthermore, a great number of countries with a UI scheme, require unemployed workers to register at a governament unemployed office in order to check who is unemployed and to help them in finding a new job. Nevertheless, seeking for a job is always required to avoid moral hazard and there are a lot of different way to enforce this obligation: the abovementioned active labour policies.

Concerning the required period of payed contributions, it varies substantially across states. Frequently, to be considered, an unemployed needs to have worked / payed contribution for a minimum percentage of the previous one or two years and in other cases also a mini-mum level of earnings is needed. An extreme case is the one of Australia and New Zealand where there is no length requirement for the previous period of unemployment.[25] Look-ing at European case, in Figure 1.1 [6], we can see that "the number of weeks required to access unemployment benefits varies between 25 or less in Greece, France, Italy and Malta, to more than 80 in Lithuania and Slovakia, with one year (52 weeks) being the most common." When considering the ratio between the contribution period and the pe-riod taken into account (so the pepe-riod in which the contributions should have been paid), "the most frequent value across Member States is 0.5 (that is, beneficiaries should have contributed for at least half of the reference period)" [6]

About the benefits amount, it is generally computed as a percentage of the previus wage income which is called replacement rate. Usually the wage of the last three or six months is considered. Another common possibility is to make the benefit level decreasing with time: the replacement rate is higher at the beginning of unemployment spell. In

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Length of the required qualifying period, situation in 2015

Figure 1.1: Picture taken from European Semester Thematic Factsheet: Unemployment benefits, European Commission, 2017

the Figure 1.2, also taken from [6], are reported the replacement rate among european countries. It is important to consider that often the level of unemployment benefits is related also to certain characteristics of the individual: for example, the number of children. As a consequence the measurements reported are the average across different characteristics. Another possibility for governaments is to put upper and/or lower bounds to the level of benefits which overwrite the replacement rate. Upper bounds are needed to avoid the assignment of too generous benefits to people who have already an advantaged position. Lower bound, instead, are put in place in order provide income support sufficient to meet basic needs.

The third feature considered is the maximum duration of the benefits. It is straight-forward that there is not a fixed optimal duration but it depends on many factors that can affect the unemployment spell at individual level as well as at macroeconomic level. Regarding the first, we can consider the specific characteristics of the person, like for example his/her experience, and in the latter we have to take into account everything that can affect the state of the labour market. In any case, as the ILO working paper states, "the duration should be set in a way to provide adequate protection durind peri-ods of involuntary unemployment and it shuold be benchmarked against the generosity

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Net replacement rate of unemployment benefits at 67 % of Average Wage, January 2016

of the benefits". Accordingly benefits duration is frequently computed at an equivalent rate which is the number of monthly wages that the unemployed is entitled to receive as unemployed benefits2. In Figure 1.3 are reported some data about benefit duration in european countries.

1.2 Unemployment assistance programme

However, there exists another kind of unemployment benefits called unemployment as-sistance. "Unemployment assistance primarily aims to prevent unemployment-related poverty".[6] Generally, it is means-tested which means that only people under a certain level of income (frequently at household level) can apply for it. Moreover, it is frequently made available to unemployed people when they are not eligible or no more entitled to receive unemployment insurance. So for example, unemployed people which did not pay the necessary amount of contributions to be eligible for unemployment insurance schemes can apply for unemployment assistance. In addition, also people whose maximum dura-tion of unemployment benefits is expired, can be eligible for unemployment assistance.

2_{So it is the amount of benefits multiplied by the number of months in which the unemployed is}

entitled to receive them, divided by the wage income. It states how many months of wage income I’m entitled to receive.

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Maximum duration of benefits for a one-year work record, January 2017

Unemployment assistance is linked to the concept of unconditional basic income applied in the context of labour market. Unconditional basic income is a monetary transfer given to all the individual whose resources are below a certain treshold. It is considered uncondi-tional in contrast to other policies which can be adressed as selective. A selective policy is one regarding a specific group of individuals, while a conditional one would be potentially open to every individual possessing a certain condition. In this context, unemployment insurance targets only unemployed people, while, unconditional income support regard virtually all individuals as long as they meet specific requirements.

The discussion about establishing an unconditional basic income, or even an universal ba-sic income, came back just after the financial crisis, when the number of people considered poor grew substantially all around the world. The term unconditional is clearly different from universal, which implies that all people are entitled to have the basic income. In past years, also the possibility to establish a universal basic income has been discussed by many researchers trying to evaluate if the cost of such a policy would be outstanded by the positive effects.

In the labour market framework, of course people receiving it must be unemployed and having a household income below a determined level. It’s fundamental to consider income at household level otherwise also the people without personal resources but living in a

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family with sustainable economic conditions would be entitled to receive the unemploy-ment assistance.

Unemployment assistance schemes consist in an amount of money paid as income to cer-tain unemployed with specific requirements. The obvious difference with unemployment insurance programs is that in that case the provision of benefits is strictly linked to the payment of a certain amount of contributions. Unemployment assistance, instead, is not given according to paid contributions but considering household income. Other relevant things among the requirement are the number of children and/or disable people depen-dent on the claimant.

Also considering this type of unemployment support, it is important to fix the require-ments trying to balance the positive with the negative effects. The problems are exactly the same arising in the UI framework: receiving an income without working can act as an incentive to reduce the effort put in searching for a job and this would lead to an increase in unemployment duration and in general in unemployment rate. So, also for un-employment assistance schemes, it is important to discourage certain behaviours in order to reduce these downsided effects. As a consequence, in fact, also in these programs, it is always required to be actively searching for a job and to be available to accept a position in the occurrency of having offered a suitable job.

The organization of unemployment assistance differs highly across countries and one rea-son can be that unemployment assistance may or may not be organized separately from unemployment insurance. [7] This makes difficult the comparison among different coun-tries in terms of the specific features, even though it seems generally accepted that the amount must be sufficient to mantain a decent lifestyle but should also be less than the benefit from UI schemes. However, in 2017 "most Member States do not have a separate unemployment assistance scheme in place, but mostly rely on general means-tested social assistance made available to low-income households". [6]

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Chapter 2 Causal analysis

In everyday speaking it is easy to meet statements regarding whether something is effective or not; the problem is that, in addition, we use these judgements to assess a causal relation. Very often the situation is also not well stated: an example from Imbens and Rubin 2015 is "she has long hair because she is a girl". Not only this conclusion is incorrect in many cases, but also it is not well posed considering that the objective is to state a causal effect. The thing of which we want to see the effect must be an action able to manipulate and so to change a variable. "Being a girl" is not such thing. Maybe the previous sentence can be use to state a correlation between having long hair and the sex of a person, but surely we cannot consider "having long hair" as a consequence of being a female.

For every economist, in particular the ones approaching the field of causal inference, should be clear that correlation is a different thing with respect to causation: two things can be correlated but this does not mean that one is the cause of the other. This can happens because there are some counfonding factors and/or there can be a problem of reversal causality.

So, to be able to understand how to compute a causal effet, we need to consider a precise context in order to comprehend what can be considered a causal relation. For this we consider the framework called Rubin Causal Model (RCM). The model was named by Holland in his 1986 paper after Donal Rubin, which is the first one to study causal effect in observational studies in a paper in 1974. The model is divided in 2 principal parts:

1) The definition of causal effect throught the assessment of potential outcome. 2) The discussion about the assignment mechanism.

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2.1 Potential outcomes and causal effect

The RCM is also often called the Neyman-Rubin model because, even though the role of Rubin has been fundamental in the develompment of causal inference’s literature, it was Neyman that, in 1923, introduced the concept of potential outcome.

Getting deeper in this first part of the model, we need to identify the key features. In particular we have an individual i for which we consider:

• D which is called the treatment variable and it is the variable indicating the treat-ment applied to the individual;

• Y which is called the outcome variable and it is the variable on which we want to assess the effect of the treatment.

The concept of treatment need to be specified in order to consider situation in which we can properly talk about causal effect. A treatment is something we can apply to the individual (if we speak in medical terms, can be a drug, or in economics a specific policy like unemployment benefits) and that can affect the outcome variable. Usually the treatment is considered dichotomous and it takes D=1 if the individual receives the treatment and D=0 otherwise. However there exist extentions to include also a continuous treatment variable.

The key concept in assessing the causal effect is the so called potential outcome. Taking the ex-ante situation with respect to the assignment of the treatment, we can consider two possible, and for this potential, level of the outcome variable:

• Yi (0) = the level of the outcome variable if the individual does not take the

treat-ment. It is also called the counterfactual outcome.

• Yi (1) = the level of the outcome variable if the individual takes the treatment.

For example, following Imbens and Rubin 2015, we can consider the case in which we have an individual with headache who is thinking about taking an aspirin. Obviously, in this formulation the treatment is "taking or not an aspirin" and the outcome variable is the level of headache after the decision. So, before deciding to take it or not we have two potential outcome: the level of headache if he takes the drug and the one corresponding to the opposite situation.

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The individual causal effect (ITE) in this framework is defined as the difference in the two potential outcomes. Following the example

IT E = Yi(Aspirin) − Yi(N oAspirin)

which in more general terms can be written as:

IT E = Yi(D = 1) − Yi(D = 0)

Going fourther with the example, the authors build the following table assuming that the level of headache is a dichotomous variable which is equal to 1 if the headache remains and 0 otherwise. In particular, they consider the situation in which, after taking the drug, the headache disappers while in the other case it does not.

Table 2.1 from Imbens and Rubin 2015 pag. 6

Thanks to this example, it is straightforward to assess what Holland in 1986 called the fundamental problem of causal inference. It is clear that in reality we can observe just one realization of the potential outcome because the individual can either take the aspirin or not, but never both. Therefore we are in a situation of missing values and consequently to estimate the causal effect we cannot proceed following exactly the definition because "with only a point observation after treatment, it is impossible to reach a conclusion about the impact". Hence, to try to assess the causal effect we need to find an appropriate counterfactual, which "constitutes the main challenge of impact evaluation".[20]

The usual way of proceeding is to add more individuals in our framework, specifically some exposed to the active treatment and some exposed to the alternative one (that can be called control treatment). The first option to go through this, according to the authors, is to consider the same unit in different times as multiple units. This can be useful to make an idea about the impact of the treatment but the problem is that time do matter in when considering human beings. Coming back to the aspirin example, the individual

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can become more or less sensitive to the drug or maybe the headache can be more or less intense and this may impact the effectiveness of the aspirin.

According to this, usually it is not a good idea to consider different moments in time to assess the causal effect when considering effects on human beings. At this point the most obvious way to proceed is to consider physically different units.

But to be able to avoid imprecise situation we need to impose some hypothesis.

Firstly, we need to consider the stable unit treatment value assumption (also known as SUTVA), formally introduced by Rubin in 1980, which states that:

"The potential outcomes for any unit do not vary with the treatments assigned to other units, and, for each unit, there are no different forms or versions of each treatment level,

which lead to different potential outcome" (Rubin and Imbens, 2015, pag.10) Analizing it, we are able to split the SUTVA in 2 sub assumptions, which specifically are:

1. The treatment status of an unit does not affect the potential outcome of other units. This part is known as non-interference assumption and following the aspirin example can be described it as the exclusion of the possibility that one individual taking or not the aspirin can affect the headache of someone else.

2. There is no variation in the treatment applied to different units. This is known as the no hidden variations of treatments assumption and it rules out the eventuality that my aspirin is different from the one given to another individual.

It is important to note that, these assumptions are not directly informed by observations but they rely on previously acquired knowledge. So, to verify if they are reliable or not, we need to consider each specific situation and see whether they are acceptable assumption. In particular, in economics the general equilibrium effect can make the statement of the non interference assumption controversial: for example, in a job training program, the outcome of an individual can affect the one of another by increasing competition for certain job position. Despite these possible problems, the SUTVA are fundamental because causal inference is generally impossible without them.[18]

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2.2 The assignment mechanism

At this point, having accepted the SUTVA, let’s assume that we can consider a number of individuals to which we assign or not the treatment. The way in which we decide to assign it is called the assignment mechanism and it is the second important part of the RCM. For the moment, before dealing with the assignment mechanism, it is important to concentrate on how to use the multiple units that we have. The generally considered measure are the average treatment effect (ATE) and the average treatment effect on the treated (ATT.).

For the ATE, the formula is the following:

AT E = E(Yi|D = 1) − E(Yi|D = 0)

But, again either the individual receives the treatment or not. For this, even considering more units, we have the problem of the missing counterfactual and we are not able to compute it.

Let’s consider the ATT, we have:

AT T = E[E(Yi|D = 1) − E(Yi|D = 0)|D = 1]

As can easily be seen, also in this case we have missing values.

Considering what we have, we can build an estimator of the ATE making the difference between the outcome levels for the individual receiving the treatment and the levels for the controls. But making just a simple adding and subtracting we can see that this estimator would not unbiased:

ˆ

AT E = E[Yobs_{|D = 1] − E[Y}obs_{|D = 0]}

which is equal to write ˆ

AT E = E[Y (D = 1)|D = 1] − E[Y (D = 0)|D = 0] Adding and subtracting E[Y(D=0)|D=1] we obtain ˆ

AT E = E[Y (D = 1)|D = 1] − E[Y (D = 0)|D = 1] + E[Y (D = 0)|D = 1] − E[Y (D = 0)|D = 0]

ˆ

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Accordingly, when we compute the AT E we are not computing only the average causalˆ effect but there is also a bias which is called selection bias. If this bias is not equal to zero, the estimate can be misleading. The selection bias emerge because "the treated and non treated group may not be the same prior to the intervention, so the expected difference between those groups may not be due entirely on the program evaluation".[20] But why there are these differences? Among the possible causes there are:

• Self-selection: when the individuals are allowed to select themselves into a group. Their voluntary participation can make them different from the other participants. • Targeted policy: when a program is targeted to a specific kind of people, and for

this the selected individuals may be different from the control.

• Observable and unobservable characteristics which can affect the selection. Hence, to have an unbiased estimate we need to make the selection bias equal to zero and for this we can consider another hypothesis: the strong ignorability assumption. It states that the potential outcomes of an individual are independent of the treatment he/she receives.

Strong ignorability assumption Di ⊥ (Yi(0), Yi(1))

This assumption is very a strong one and it is also quite difficult to understand, expecially if one has just entered the world of causal inference. The struggle in grasping it stems from the complexity in separationg the observed outcome from the potential ones. Obviously, the observed outcome has to be related to the treatment, otherwise it would be senseless trying to assess the causal relation between the two. With this is mind, it is easier to get that the ignorability assumption regards just the potential outcome. The two potential outcomes exist prior to the actual treatment assignment, in fact, it just reveals which of the two becomes the observed outcome.

This hypothesis would lead the AT E to be equal to the AT T because the selection biasˆ would be equal to 0 and, since one would be replicating a randomization, the AT T would be equal to the AT E.

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ˆ

ATE = E[Y (D = 1)|D = 1] − E[Y (D = 0)|D = 1] + E[Y (D = 0)|D = 1] − E[Y (D = 0)|D = 0]

BIAS = E[Y (D = 0)|D = 1] − E[Y (D = 0)|D = 0] but since (Yi(0), Yi(1)) ⊥ Di we have

BIAS = E[Y (D = 0)] − E[Y (D = 0)] = 0

ATT = E[Y (D = 1)|D = 1] − E[Y (D = 0)|D = 1] ATT = E[Y (D = 1) − Y (D = 0)|D = 1] ATT = E[Y (D = 1) − Y (D = 0)|D = 1]

and because of (Yi(0), Yi(1)) ⊥ Di

ATT = E[Y (D = 1) − Y (D = 0)] = ATE

But, as mentioned before, this is a very strong assumption and whether it is acceptable in a context depends on which assignment mechanism is used. A way to make this assumption more easy to consider, is to relax it a bit, considering the independence of the potential outcome from the treatment conditioned on some pre-treatment variables X.

Conditional independence assumption Di ⊥ (Yi(0), Yi(1))|Xi

This allows the selection of a specific group of the population which has particular char-acteristics, for example we can consider just unemployed people in a income support program.

Speaking more in details of the assignment mechanisms, according to Imbens and Rubin (2015), we can consider three basic restrictions on them:

1. Individualistic assignment: which limits the dependence of a particular unit’s as-signment probability on the values of covariates and potential outcomes for other units.

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2. Probabilistic assignment: which instead requires the assignment mechanism to imply a non-zero probability for each treatment value, for every unit.

3. Unconfounded assignment: which disallows dependence of the assignment mecha-nism on the potential outcomes.

The authors, following Cochran (1965), highlight that usually a distinction between exper-iments, where the assignment mechanism is both known and controlled by the researcher, and observational studies, where the assignment mechanism is not under the researcher’s control, is also made.

The class of assignment mechanisms with all these three restrictions and the researcher’s knowledge and control of the functional form of it, is the one of classical randomized experiments. These are the first category of experiments for which the causal effect has been studied. The leading case of this group of mechanisms is the complitely randomized experiment where the units are randomly selected to be in the treatment or in the con-trol group. Thus, the assignment probability for every unit is the same and in this case the unconfoundedness assumption holds. Consequently in this context we can unbiasedly estimate the ATE.

The problem is that random experiments are very often unfeasible. This can be link, for example, to ethical problem or to the design of the treatment: it is not always ethically correct to give to some individuals a treatment and not to others or, maybe because of the formulation of the policy, no control observation are comparable to the treated ones. Futhermore, in reality the researchers do not always have control over the assignment mechanism and, consequently, they do not know the assignment probability for each unit. This is the case of previosly cited observational studies.

In particular, Imbens and Rubin (2015) refer to the class of observational studies that possesses all the three abovementioned restrictions as regular assignment mechanisms. In these situations we cannot rely on the perfect randomization of the experiments and for this on the assumption of independence, both strong and weak, between potential out-comes and treatment. Accordingly, estimating the AT E we do not necessarily obtain theˆ ATE.

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As said in the introduction, the birth of the causal inference, as here intended, is usu-ally referred to the Neyman’s 1923 paper and until 1974 it mostly relied on randomized studies. In that year, Rubin realized that researchers can not always wait to performe an "ideal experiment" but they have to exploit also the data coming from observational studies. From that moment, the discussion on how to use this data for causal inference has exponentially increased.

Over the years, there have been a great number of papers concerning how to be able to estimate the causal effect in observational study and still today there is not a perfect solution. Researchers formulated different methods that can be applied to different kinds of situations with a level of reliability that differes according to each specific case.

In particular, the first distinction to be consider is whether the treatmend is given accord-ing to specific observable characteristics or on unobservable one. The most classical tecniques in these two cases are:

• Selection on observable characteristics:

- Matching or weighting using propensity score or other measures (Rosenbaum & Rubin, 1983 )

- Regression discontinuity approach (Cook, 2007). • Selection on unobservable characteristics

- Difference in Difference (Ashenfelter and Card, 1985; Athey and Imbens, 2006); - Instrumental variable approach (Imbens & Angrist, 1994; Angrist, Imbens &

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2.3 Database description

Before ending this chapter and starting the analysis, it is necessary to consider a brief description of the data we used. The data came from the European Union Statistics on Income and Living Conditions (EU-SILC) and we considered, in particular the one for Spain, since it is the country interested by our analysis1. As the Eurostat webpage2 states "The EU-SILC project was launched in 2003" but "The EU-SILC legal basis entered into force in 2004 and covers now all EU countries, Iceland, Norway, Switzerland; some other countries participated on the voluntary basis." The EU-SILC database constitutes one of the leading sources for the computation of indicators on the social situation and on the diffusion of poverty. In particular, the "information on social exclusion and housing conditions is collected mainly at household level, while labour, education and health information is obtained from individual persons aged 16 and over". Moreover, income variables are collected at personal level.

Looking at how the data can be used, the sample design is built in order to obtain reliable estimates, when considering the statistical weights attached to it, for NUTS2 regions. Moreover, it is divided into two types of dataset:

• Cross-sectional data concerning a certain time period;

• Longitudinal data reporting "individual-level changes over time, observed periodi-cally over a four-year period";

Since our analysis regards the ability of finding a job the year after having participated to an unemployment support programs, we considered the longitudinal data which enable us to follow the same individual over the two years. In particular, the year considered are 2017 and 2018: specifically so, we want to see whether being unemployed and receiving an income support in 2017 influenced the employment condition in 2018.

Starting with the data they were divided into 4 subdatasets:

• UDB_lES18P : the dataset containing all the socio-economic information (labour, income, etc...) at personal level only for people over 16 years;

1_{all data handling have been done under the direct supervision of Prof. Corsini}

2

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• UDB_lES18R: the dataset contining all personal (age, etc...) information for all the individuals in the household (so children are included);

• UDB_lES18H : the dataset contining all household socio-economic information for all the the households with statistical weight higher than 0.

• UDB_lES18D :the dataset contining all household socio-economic information for all the the households

So, first of all, for each year one should merge these four datasets into one containing all the information, the personal and the household ones, for every individual. Then we to selected only the individuals present in the dataset of 2017 and 2018. At this point we constructed the variables we need for our analysis from the 2017 and 2018 data:

• UB_2017: this variable is built upon PL092G which reports the amount of con-ditional and non-means tested unemployment subsidies. The resulting variable is a dummy taking 1 if PL092G is greater than 0 and o otherwise. This variable would be used in the first section of the analysis. We took the variable from the 2018 dataset because it refers to the year before;

• RDC_2017: this variable is built upon PL093G which reports the amount of non-conditional and means tested unemployment subsidies. The resulting variable is a dummy taking 1 if PL093G is greater than 0 and 0 otherwise. This variable would be used in the second section of the analysis. We took the variable from the 2018 dataset because it refers to the year before;

• month_unemp_2016: the variable indicating the number of month spent being unemployed in 2016. We built it using the twelve EU-SILC variables from PL211A to PL211L which report the self-defined condition in each 2016 month. So, before we had to make them dummies taking 1 if the individual in that month was unemployed and 0 otherwise. Then we summed the dummies and we obtained the number of months each individual spent being unemployed in 2016.

• children_hh: the variable reporting the number of people in th efamily with less than 16 years. To built this variable we used the register variable RX020 reporting

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the age of all the members and, for each family, we counted how many individuals have less that 16 years.

• IV1, IV2, IV3 and IV4: these are the variables used in the fourth chapter for the instrumental variable analysis. In particular, they were built upon the income variable PY010G, PY050G, PY080G, PY100G. Specifically, PY010G reports the Employee cash or near cash income (Gross), PY050G is cash benefits or losses from self-employment (gross), PY080G Pension from individual private plans (gross) and PY100G Old-age benefits (gross). So, since we wanted to compute the number of members in the individual’s family not receiving an income from work or pension (a part from the individual himself), we needed to sum all these 4 variables and that we make an individual dummy out of them taking 1 if the sum was equal to zero and 1 otherwise. for each individual, we summed the family members with the dummy equal to 1 and than we subtract his dummy. This was our IV1. For IV2, which was equal to IV1 divided by the number of family member (minus one, the individual under analysis) we divided IV1 by HX040-1, where HX040 is the number of people in the household. For IV3 and IV4 we did exactly the same procedure but counting how many people (beside the specific individual) had an income from work or pension.

• emp_2018: we considered the variable PL031, which is the self-defined employ-ment position, in 2018 and we make out of that a dummy taking 0 if the individual was still unemployed, and 1 otherwise.

Finally, the final dataset was restricted in consider to consider only unemployed people in 2017.

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Chapter 3 Unemployment benefits and propensity

score

3.1 Propensity score matching

As already said in the first chapter, a way to overcome the fundamental problem of causal inference is to randomly assign the treatment: doing this we can unbiasedly estimate the causal effect. Nevetheless, the situations in social sciences in which it is possible to run a randomized experiment are few. The other possibility is to try to mimic randomization, which consists in trying to construct a randomized experiment out of an observational one. One way to obtain this, is constructing a control group with characteristics as similar as possible to the treated one: making this we should be able to recreate the unobserved counterfactual outcome.

One may think that under the previously stated unconfoundedness assumption, the prob-lem should be solved. In reality, this assumption is almost never feasible.

Under unconfoundedness, we can remove all biases in comparison between treated and control units by adjusting for differences in observed covariates. Although feasible in principle, in practice this will be difficult to implement with a large number of covariate

(Imbens & Rubin, 2015)

The problem is that, when considering a great number of covariates, it is very difficult to find two individuals with exactly the same value for every covariate. Which means that it is impossible to perform an exact match because we have too many dimensions to take

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into account. The possible way to overcome this "curse of multidimensionality" [20] is to consider a balancing score b(Xi).

According to the definition in the Imbens & Rubin [18] book:

A balancing score score b(X) is a function of the covariate such that Di ⊥ Xi|b(Xi)

So, it is a way of combining all the considered covariates into a number with the objective to summarize them. According to this definition, it is easy to state that there exists un infinite number of balancing scores.

A fundamental property of balancing score is that "if assignment to treatment is uncorre-lated given the full set of covariates, then the assignment is also unconfounded conditioning only on a balancing score".[18] So, if unconfoundedness is assumed with respect to certain covariates then it is respected also considering a balancing score1_.

Di ⊥ (Yi(0), Yi(1))|Xi =⇒ Di ⊥ (Yi(0), Yi(1))|b(Xi)

This means that, even though certain covariates’ values may differ between a treated and a control units having the same balancing score, there should be the same distribution of covariates in the two groups.

As already states, there are an infinite number of balancing scores but maybe the most famous is the propensity score. The propensity score is defined as the probability to receive the treatment given the covariates. It can be used to evaluate how different the treated and the control group are and can be a way of subselecting the two in order to have more similar characteristics.

Before coming to that part, it is important to notice that in practice we rarely have the true population propensity score and so to use it we need to estimate it ˆp(Xi). If

unconfoundedness is considered a good assumption, a researcher can then estimate the propensity score as a function of certain characteristics. In particular, we can start by selecting the covariates which we expect to be a priori important in the treatment assign-ment regardless of their statistical association.

In case of evaluation of labour market policies, almost always the assignment mechanism happens by self-selection. Considering this, when we want to estimate the propensity

1_{The demonstration is at page 267 of the Imbens & Rubin book. Here it is omitted for the sake of}

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score, we need to consider all the covariates that might be relevant determining the par-ticipation. It is important that also another hypothesis is verified, namely the overlap assumption, which is:

0 < P r(D = 1|X) < 1

So, the probability (as it is embodied in the definition of probability) to receive the treatment, given the covariates, must be between 0 and 1. Accordingly, the propensity score must be bounded and to be able to apply properly the matching procedure we must have that the distributions of propensity score in the 2 groups are similar. We will come back to this point further in this section.

It is difficult, though, to decide which model is the "correct" one because we cannot ob-serve the real values. For this, the specification of the model is something which depends completely on the researcher: econometric tools can help deciding whether to include a covariate or not, but often the intuition is the master. For example, it is frequently a good idea to include age squared, but it is not so easy to decide what other covariates squared and which interactions one should include. Nevertheless, it is worth to note that also in-cluding too many covariates is not a recommended decision because it can lead to higher standard errors and also "in perfectly predicting participation for many households".[20] For all these reasons, as I stated before, a good intuition can be determinant or, as an alternative, one can made several regressions to find the best model in terms of ability to explain.

The increasing ability to use computers to analise data, considering in particular ma-chine learning tecniques, is very helpful for this specific task. There are, for instance, algorithms able to try a great number of model and automatically decide, according to certain criteria, which is the preferable one. I will use one of these algorithms from an R package in the analysis of this part.

However, in practice the most known and used procedure to estimate the propensity score is a logit or a probit regression, which are able to estimate a dependent variable bounded between 0 and 1. With this methods, we can estimate the coefficients of the covariates and with them we can finally estimate the propensity score. At this point all individuals, participant and non-participant, have an estimated ˆp(Xi).

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from the regression this will lead to a biased propensity score. In this situation we would find ourself in presence of an hidden bias.[29] Since all the propensity score mechanism lies on the assumption that all the counfounding variables are included in the estimation of such probability measure, the presence of an omitted variable would make this assumption to fail.

Furthermore, the formulation of the regression is not all that matter though. Heckman, Ichimura and Todd (1995) demonstrated that, for example, another relevant feature in determining the bias is the used survey. They found out that when "the same question-naire is administered to both groups, so outcomes and characteristics are measured in the same way for both groups" and "participants and controls are placed in a common economic environment" [10] the matching procedure would considerably reduce the bias. This stems, of course, from the fact that each questionnarie can be formulated differently and that there can be variations in measurament, like for example when considering an-swers for categorical variables.

After having estimated a propensity score which is considered satisfing, we can look at its density in the two group to see if there is overlapping between the propensity score in control and in treatment group. Ideally, we want to have the same distribution, but, since in reality it is quite impossible, in practice it is sufficient to have correspondence between the possible propensity scures in the control and in the treatment group. It is easy to state this feature graphically with the support of a computer.

Good level of overlap

Figure 2.1: Picture taken from the the handbook on impact evaluation [20]

In Figure 2.1 a good level of overlapping can be assessed: the two density curves, the one of propensity score in the treated group and the one in the control, are for a great part overlaid.

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Poor level of overlap

Figure 2.2: Picture taken from the the handbook on impact evaluation [20]

In Figure 2.2, the region of common support is narrower and so, in this case, we are in a situation of weak commun support.

A frequent advise is to drop all the observations with weak common support; making this, the two groups become more balanced.

In addition to this graphical assessment, there are of course measures able to indicate whethe the covariates are more or less balanced between the two groups. A frequently use measure is the standardize mean difference2_:

SM D =

√

µt−µc (σ_t2+σ_c2)/2

The correspondent empirical estimator is SM D =ˆ

√

X¯t− ¯Xc

(s2 t+s2c)/2

where ¯

X

is the sample mean of the covariate, while s2 _{is the sample variance estimator for the 2 groups. It gives}

a scale-free measure of the difference in that specific covariate between the treated and the control. Frequently, it is used also in its standardized version, omitting, though, the direction of the difference.

So, as I was saying before, two balanced groups can be obtained also by dropping the observations considered outliers, for example, regarding their propensity score. There exist several methods to obtain this, but the most famous are3_:

• Matching • Weighting

2_{In the Imbens and Rubin’s book it is called normalized difference (p.311)}

3_{Of course there are other methods and ever more often researchers tend to use also combination of}

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The matching procedure can use the propensity score to compute the distance between units belonging to different treatment group (which in our case are just treatment an control). There are several possible algorithms to perform matching based on propensity score. Among which:

• Exact matching

It is the simplest way of matching two units. It consists in finding two units, one in the control and the other in the treated, with exactly the same value of propensity score. It is often used even without propensity score and it consists in finding two units with exactly the same values for every variables. Nevertheless, when having a great number of variables is very difficult to find two exactly equal ones. The problem, however, persists even when matching on propensity score and for this it is hardly used.

• Nearest neighbour matching

Given the high difficulty in the first kind of matching procedure I cited, the near-est neighbour method loosens the matching condition and allows to use a greater number of units. In particular, taken a treated unit, it finds in the control group the one with the smaller difference in propensity score. There are several possible variants to the standard algorithm. One difference can be found in matching with or without replacement: in the former case, an untreated individual can be used more than once as a match, whereas in the latter case it is considered only once. A problem related to matching without replacement is that estimates depend on the order in which observations get matched and for this it is recommendable to have them randomly ordered. Another thing that can be changed is that on can allow for each treated unit to be matched to more than one control. In this context to the choosen controls would be given weights so that they would count as one. • Nearest neighbour with caliper

This matching technique uses the same algorithm of the simple nearest neighbour but add a limit to the difference in propensity score between two units. This reduce the possible distance in the two group but often implies also to have to drop some other individuals which can not find a match respecting the treshold.

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• Radius matching

It is a variant of caliper matching suggested by Dehejia and Wahba (2002). "The basic idea of this variant is to use not only the nearest neighbour within each caliper but all of the comparison members within the caliper. A benefit of this approach is that it uses only as many comparison units as are available within the caliper and therefore allows for usage of extra (fewer) units when good matches are (not) available. Hence, it shares the attractive feature of oversampling mentioned above, but avoids the risk of bad matches" [4] In this case, the difficulty is to choose an adequate radius to consider all the control units in it, because if it too narrow there can be the possibility of not finding any match and if it is too loose the matches would be unreliable.

• Stratification matching

This method is known also as interval matching, blocking and subclassification (Rosenbaum and Rubin, 1983). It consists in dividing the common support of the propensity score into a number of intervals (called strata) and to compute the impact within each interval by taking the mean difference in outcomes between treated and control. Here the decision of how many strata to consider appears very relevent. To decide how many strata should be included one can run a few tests and check if within a stratum the propensity score is balanced. Otherwise strata are maybe too large and need to be increased in number.[4]

• Kernel matching

Kernel matching is a non parametric matching procedure "that uses weighted aver-ages of all individuals in the control group to construct the counterfactual outcome".[4] This leads to a reduction in variance but can also increase bias because it uses all the observations, although weighted, to estimate the treatment effect. The weights are computed according to the difference in propensity score between each individual from the control group and the participant observation. In particular, a function must been chosen such that it gives higher weight to individuals which have similar values of propensity score and lower otherwise.

• Optimal matching

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complex way of matching in which already matched couples can be reconsidered to achieve a greater level of balance among all the couple, according to a predefinite unit of measure.

Obviously, these are not the only possible ways of matching units, but are the more used in the context of propensity score.

However, there is not a universally recognised best way to match. It depends on the researcher’s knowledge and aim. A recommendation often given is to try to perform more than one method to see whether the results change dramatically according to it.

After having reached a satisfing level of balance between the two group, one can proceed in estimating the causal effect. In particular, if the balance is very good this means that the researcher have likely replicated the context of a randomized experiment. This enable the computation of the average treatment effect via a linear regression. One should firsty computes it by regressing the outcome variable onto the treatment, obviously only considering the matched data. As a confirmation, also the covariates can be included, in order to control for the remaining difference among the two groups. It is important to note that the result obtained would not be generalizeable to other population because of course our result are linked to the sample used.

The weighting procedure, instead, is based on the use of propensity score via weights. To use a weithing procedure, instead of a matching one, was proposed, among the others, by Imbens in 2000. When estimating the average treatment effect, this implies weighting the treated units by the inverse of the propensity score and the controls by one over one minus the propensity score. [12]

ω

T

=

1_e_ˆ and

ω

C

=

_1−ˆ1_e

The weights change if we consider another quantity, like for example the average treatment on the treated (ATT).

Weighting allows to keep all the control and treated units without having to drop nothing. Accordingly, it increases the number of observations and makes the ones with an higher weight to count more. This way of weighting is called inverse probability weighting (IPW) and it has been the more famous method in the literature.

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conditioned on the propensity score and if there is something omitted in that estimation all the results would be biased.

The two methods, matching and weighting, have been largely discussed in literature, and they have found both supporters and people against them. However, nowadays, a clear solution to which procedure should be used has not come yet. The impossibility of knowing the real causal effect makes impossible for the researcher to compare the results. At the moment they are both used, alone or in combination with other tecniques, to try to obtain the more reliable estimates possible.

3.2 Unemployment benefits in Spain

It is common knowledge that "the great recession of 2008-09 led to record unemployment rates across the OECD"[19] and that the situation became very alarming for certain eu-ropean states. One of this is Spain.

In 2017 (which is the year we take into consideration for the analysis), even if unemploy-ment rates were below (or close to) pre-crisis levels in many countries, they were still considerably high in the Southern European countries hit hardest by the crisis, such as Greece (22%), Spain (17%) and Italy (11%).

In Spain, in order to overcome the striking increase of unemployment, which had been unstoppable from 2008 to 2013, the governament started adopting, in the first part of the after crisis period, reforms to strenghten the labour market. For example, on August 2010 the unemployment insurance was extended (under some conditions) to self-employed workers[23].

"However, in the aftermath of the Great Recession, the fears of the European sovereign-debt crisis led the European Commission to recommend a decrease in the generosity of the UI benefits as one of a series of austerity measures aiming at slashing spending and raising taxes (European Commission 2012)."[31]. Accordingly, on July 13 2012, the Span-ish government announced that all workers whose unemployment spell began on July 15 2012, would have their repalcement rate after 180 days of unemployment spell reduced from 70% to 50% [23] – before this reform, after 180 days of unemployment spell the

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percentage went from 70% to 60%.

After that, since the level of unemployment rate keep being high, there have been more reforms regarding delicate categories: for example long term unemployed and young un-employed.

Since the aim of this part is to apply propensity score matching to estimate the effect of unemployment benefits in Spain, in the following paragraph I’m going to describe the feature of UI scheme.

The unemployment benefits, called in spanish Prestación por desempleo, are managed by the Servicio Público de Empleo Estatal, SEPE (State Public Employment Service) and so the first step to obtain the benefits is to register as a job seeker in one of their offices and, once there, apply for the benefits within two weeks after you become legally unemployed. Following the webpage of the European Commission4 _{and the OECD document reporting}

the benefits in Spain in 2017, the elegibility requirements are:

• Be legally unemployed and register as a job seeker in the public employment service. Moreover the individual must actively be seeking for a job and willing to accept a suitable position;

• Have not left the previous job voluntary; • Have more than 16 years;

• Not having reached the ordinary age to retire;

• Have paid contributions for at least 360 days in the 6 years before becoming unem-ployed or before the end of your obligation to pay contributions.

Regarding the amount of the unemployment benefits, it depends on the contributions paid to Social Security during the last 180 days of contributions, without taking into account overtime. The regulatory base is computed as the average of the contribution base of the indicated period. The amount received is 70% of the regulatory base during the first 180 days of benefits and 50% from day 181 until the end of it.

Moreover, the spanish governament established a minimum and a maximum amount for

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to benefits, hence it may in no cases be higher or lower than the set limits. These limits are computed on the Indicador Público de Renta de Efectos Múltiples or IPREM (public indicator of multiple effects income) and depend on the number of family members de-pendent on the unemployed person. In 2017 IPREM is EUR 537.84 per month, that is EUR 7 529.76 yearly (including 13th and 14th bonus payments)[19].

Table 3.1: Minimum and maximum amount of unemployment benefits in Spain 2017

Minimum amount

Without children With children

80% of the IPREM increased by a sixth 107% of the IPREM increased by a sixth

Maximum amount

Without children With one child With two children or more

175% of the IPREM, increased by a sixth 200% of the IPREM, increased by a sixth 225% of the IPREM, increased by a sixth

In the event of unemployment due to losing a part-time job, benefits are calculated in proportion to the reduction in working hours. Minimum and maximum limits are reduced proportionally, after applying the same percentage that results from hours worked divided by the usual company hours.

The last fundamental feature according to the IZA paper of Moffit [25] is the unemploy-ment benefits duration. In Spain there is no waiting period and the maximum duration varies between 120 and 720 days, depending on the length of time for which the person paid social security contributions during the previous 6 years. It follows a step function as it is shown in the following Figure 3.3.

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Table 3.2: Duration of unemployment benefts according to the contribution paid

Days of contribution Benefits Duration (days) from 360 to 539 120 from 540 to 719 180 from 720 to 899 240 from 900 to 1079 300 from 1080 to 1259 360 from 1260 to 1439 420 from 1440 to 1619 480 from 1620 to 1799 540 from 1800 to 1979 600 from 1980 to 2159 660 from 2160 720

Figure 3.3: Picture taken from the slides of Yolanda F. Rebollo-Sanz (Universidad Pablo de Olavide) of her Seminar on Coverage of Unemployment Benefits 9 December 2015

3.3 The analysis

3.3.1 Preliminary checks

As stated before, the propensity score can be used to build a comparison group which is more similar to the treated one.

However, before starting with the estimation of it, we ran a couple of preliminary checks, which have no role in assessing the causal effect but they are a starting point on seeing how the propensity score matching can change the results.

First of all, we checked for the proportion of individuals employed in 2018 in the two groups. The outcome variable, emp_2018 was built on the variable PL031 which is described as self-defined current economic status. Reading the description in the EU-SILC Guideline 2018 we can see that "The target variable captures the person’s own perception of their main activity at present. It differs from the ILO concept to the extent that people’s own perception of their main status differs from the strict definitions used

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in the ILO definitions". So, it is clear that the people considered unemployed according to this variable (PL031=5 correspond to people who declare to be unemployed) can be differently considered by the ILO definition. Our first idea was to use the ILO definition of unemployment which is the following:

The “unemployed” comprise all persons above the age specified for measuring the economically active population, who during the reference period were: (a) “without

work”, i.e. were not in paid employment or self-employment as specified by the international definition of employment; (b) “currently available for work”, i.e. were available for paid employment or self-employment during the reference period; and (c)

“seeking work”, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. [26]

where generally, "persons are to be considered as without work if they did not work at all during the reference period (not even for one hour)" [26]. The problem was that, in the cross section EU-SILC data, there is the variable PL035 which is a dummy reporting whether an individual worked at least one hour during the previous week. But since for our analysis we needed to observe the evolution of individuals over time, we used the longitudinal data where PL035 is not present. For this reason from now on, when we consider unemployed people, we refer to the one who self-define so.

Coming back to the employed percentage, the results are in the following Table 3.3: Table 3.2: Distribution of employed people among the two groups

Group 2017 Total Employed 2018 Percentage employed 2018

Control 1412 585 0.414

Treated 376 194 0.516

For those who received the unemployment benefits in 2017, we found out that the 51% found a job in 2018, while in the control group the percentage drops to 41%. A z-test for proportions was performed and it confirmed that the 2 proportions were statistically different, which seemed to indicate a positive effect of receiving unemployment benefits.

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model of the employment condition on all the covariates considered relevant. In particular, we chose to consider:

• UB_2017: The treatment variable. It takes 1 if the person received the benefits in 2017 and 0 otherwise and it indicates the treatment effect;

• PB150: Sex of the individual;

• PE040: Highest ISCED level attained;

• PH010: General health level (it goes from 1=verygood to 5=very bad);

• PL020: It takes 1 if the individual is actively looking for a job and 0 otherwise; • PL025: It takes 1 if the individual is available for work and 0 otherwise;

• PX020: Age of the individual;

• HS120: It express how easily the household, to which the individual belong, can make ends meets (it goes from 1=with great difficulty to 6=very easily);

• HX040: The number of household members;

• DB040: The region where the household lives (this is a factor variables taking all the 17 existing regions in Spain);

• children_hh: the number of children in the household5_;

• agesquared: the age of the individual squared. It is included because frequently it increase the R2.

The results are showed in the first colomun ofTable 3.4. It can be seen that the coefficient associated to the treatment variable (UB_2017) is positive and highly significant. This gets along with the findings in the first check. Accordingly, this first look would point at a positive effect of receiving unemployement benefits to the employment condition of the following years.

Nevertheless, getting deeper in the analysis, just adding a new variable this first result is questioned. In particular, we added the variable labeled as months_unemp_2016. This

Policy evaluation of income support schemes

Department of Economics and Management

Master of Science in Economics