Welfare competition in Norway*)

Jon Hernes Fiva and Jørn Rattsø, Department of Economics, Norwegian University of Science and Technology, N-7491 Trondheim, Norway (E-mail: [email protected] and [email protected])

Abstract

Local redistribution policy creates incentives for welfare migration and may result in 'underprovision' or even 'race to the bottom'. This paper contributes to the empirical evaluation of the welfare competition hypothesis and introduces two new measures of welfare benefit level. The first is based on individual data and calculates the expected welfare benefits of a standardized person, while the other is the welfare benefit norm decided by the local council. The estimated strategic interaction between local communities is statistically significant for both measures of welfare benefit level and confirms the hypothesis of welfare competition. We conclude that there is a geographic pattern in welfare benefits. This does not necessarily imply underprovision, since other aspects of the system, such as interest groups and grants financing, may work in the direction of excessive spending.

First version: December, 2003. This version: March 30, 2004 All results are preliminary, comments welcome.

*) We are grateful to Audun Langørgen and Dag Rønningen, Statistics Norway, for making available to us their computed welfare benefit levels, funding provided by the Norwegian Research Council under the FIFOS program, and comments at the Norwegian Research Forum 2004, in particular from Bjørg Langset and Erling Holmøy. 1

1. Introduction

Increased mobility of households and firms and consequent fiscal competition are often described as a threat to distribution policy. Some state this challenge in dramatic terms, with titles like 'Can the welfare state survive?'. In the EU context, Sinn (1994) has warned about the consequences for welfare of economic integration. Because of mobility, governments are encouraged to set fiscal variables to influence the location of households and firms. It follows that governments behave strategically in setting tax rates and expenditure levels. Cremer and Pestieau (2003) summarize the theoretical literature on mobility and redistribution. The many theoretical contributions on fiscal competition, however, are not matched by much empirical evidence.

Welfare competition, setting welfare levels down to avoid attracting welfare clients, is one aspect of fiscal competition. When taxpayers and welfare recipients are mobile it seems likely that the local governments will seek to attract wealthy households and avoid potential welfare recipients. In this situation municipalities’ welfare benefit levels are interdependent. A summary of the literature on welfare competition is provided by Brueckner (2000).

This paper contributes to the empirical evaluation of fiscal competition in welfare policy. We investigate the possibility that the decisions of Norwegian local governments about welfare benefit levels depend on the benefit level in ‘neighboring’ municipalities.1 The contribution is to introduce and analyze two different measures of welfare benefit level, the first based on individual data and the second measuring the politically determined norm. The first is a unique dataset of computed expected benefits in each municipality based on individual characteristics, worked out and documented by Langørgen and Rønningen (2003). They utilize data for most of the grown-up population in Norway (more than 2,5 million individuals) and estimate expected welfare benefits received for a set of individual characteristics. If welfare competition is important, we should detect strategic interaction in actual benefit levels of comparable individuals. Welfare benefits are means-tested and based

1 In most empirical analysis the neighborhood concept refers to geographic proximity, however neighbors may be selected on the basis of similarity in population size, demographic composition, income etc. In our empirical approach we apply a definition of neighbors based on contiguity.

2 on an evaluation of the demands of each individual on a case-by-case basis. This measure based on actual payments reflects local government policy, but also the implementation of the local welfare offices and their social workers. The existing empirical literature has concentrated on norms at the local government level and has not been able to check for implementation.

We also study the variation in norms for welfare benefit levels set by the local councils and expressing the direct political response to competition. The norms are set as guidelines for the administration and are specified as an amount paid to a 'standard user' (singe individual without children) per month. The norms are collected by Statistics Norway (‘Sosialstatistikk’). In the analysis we apply spatial econometrics methods to estimate the strategic interaction among local governments. The starting point is a fiscal demand function where the benefit level in each municipality is dependent on benefits in neighboring municipalities as well as economic and political characteristics. The endogeneity of other municipalities’ welfare benefits is handled with instrumental variables.

Section 2 outlines the welfare competition mechanism, section 3 presents the econometric design, and the data are described in section 4. Section 5 shows and discusses our estimated interaction models, while a short summary of results and challenges for future work are dealt with in the concluding section.

2. Welfare competition mechanism

Centralization or decentralization of redistribution policy is an old issue in the economics literature. Oates (1972) offers an early analysis of the role of the mobility of the poor, whereby local redistribution can chase the rich to other communities and attract the poor. Orr (1976) formalizes the altruistic argument for welfare benefits, and shows that poor living in communities where they are a small fraction of the population are expected to receive higher welfare benefits than in communities where they are a large fraction. This cost effect on redistribution of more poor implies that an inflow of poor to a community will reduce the benefit level. Brown and Oates (1987) extend the Orr framework to include a migration function explicitly, which shows the elasticity of the number of poor with respect to the

3 benefit level. They derive how the benefit level varies inversely with the elasticity of the migration function. The mobility of the poor is a source of inefficiency in decentralized systems.

The supply side argument formalized in the Brown-Oates-Orr model is to a large extent neglected by the empirical literature. The empirical literature has focused on the demand side of the market for welfare benefits, where decentralized governments choose welfare benefit levels in interaction with its neighbors. In these models preference for distribution is associated with increased benefit levels and increased share of recipients in the community.

A recent version of Wheaton (2000) motivates our empirical analysis. Welfare recipients have idiosyncratic location preferences and are represented by a finite elasticity logistic migration model due to McFadden (1978). The core mechanisms are discussed below.

When ri welfare recipients in community i of the fixed national welfare population (R) each receive welfare benefits bi, the welfare bill in the community is ribi. It is assumed that the welfare population is small relative the total population, and that the representative voter determining the welfare policy is an employed immobile taxpayer. The representative voter 2 cares about private consumption (ci) and redistribution to the poor. The altruistic utility function guiding the policy decision is: rb UUyg=+−(,)ii b (1) iii N i where yi is per capita income, gi is per capita central government grants to the community, and ribi/Ni is per capita welfare bill.

We assume that private consumption and welfare benefits are normal goods as well as strictly convex preferences. The formulation integrates the private budget constraint, whereby private

2 Following Orr (1976) we understand altruism as the rationale for support for redistribution to the poor. An alternative explanation is that the representative voter supports redistribution to the poor as a social insurance policy in case his income falls to low levels some time in the future. A third possible interpretation is that the representative voter wants to reduce poverty because of the negative externalities attached to it (crime etc). A forth possible interpretation is the vote-maximizing politician trying to get the votes of the poor.

4 income finances private consumption and local taxes, and the local government budget constraint, whereby local taxes and grants finance welfare spending. The local tax bill after grants is shared among the taxpayers. For simplicity, the central government financing of the grants are excluded. Communities in general differ in population size Ni, private income level yi, and grants gi.

The benefit level is set taking into account the endogeneity of the number of welfare recipients ri:

rrbiii ∂  UUbc/1=+  (2) Nbriii ∂ 

where Ub and Uc measure marginal utility of welfare benefits and private consumption respectively. If we assume that the welfare benefit decision is taken at the national level (and exclude grants), there is no welfare migration to take into account and the relevant tax price for benefits is the share of recipients in the population, ri/ Ni. This result is shown already by Orr (1976) in his analysis of redistribution as a public good, and a higher recipient share reduces the benefit level because redistribution is more costly. When welfare benefits are decentralized and welfare migration is taken into account, the response of the welfare recipients is internalized in the political decision. Brown and Oates (1987) show the inverse relationship between the benefit level and the elasticity of the recipients with respect to the benefit level. The elasticity raises the tax price of benefits and consequently contributes to underprovision compared to the national decision.

The migration part of the model assumes that welfare recipients have their own evaluation of the attractiveness of each community, and in addition to this their utility depends on the welfare benefits received in each community. The idiosyncratic attraction enters the individual utility function as a random component and is assumed to be independent and identically distributed. Recipient j living in community i compares his/her utility level in all other communities k (k≠i) and we can calculate a probability that this recipient chooses to stay in community i:

Prob(UUkiji> jk ,∀≠ ) (3)

5

The combined likelihood that recipients locate in community i follows a logistic function:    Neγ bi  rR= i (4) i  γ bj  ∑ Nej   j 

This is the supply side of the welfare market and shows the positive relationship between benefit level and welfare recipients in the migration equilibrium. In this case, when all communities have the same benefit level, the recipients will distribute themselves so that the communities end up with equal recipient shares of the total population. The parameter γ measures the migration response of the welfare recipients to the benefit level.

The logistic function implies a simple form of the elasticity of the number of recipients with r respect to the benefit level γ b (1− i ) . The elasticity is increasing in the number of i R communities, and the demand function for welfare benefits can be rewritten:

rrii  UUbc/1(1)=+− γ b i  (5) NRi  

The demand side shows how the political decision about the benefit level bi depends on the size of the welfare recipient group, and the benefit level will be reduced when the number of recipients goes up. It is important to notice that the decision is affected by the benefit level in all communities through the (endogenous) determination of welfare recipients.

A strength of the Wheaton (2000) model compared to the literature is that it allows for an asymmetric Nash equilibrium where the communities in general are different. When there are n communities, the model consists of n sets of equations (3) and (5) to be solved in benefit level bi and number of welfare recipients ri (i=1,..,n).

The geographic pattern of benefits and recipients will depend on the migration response of the welfare recipients, γ. When the migration response is strong, γ is large, all communities spend less on welfare. The supply of welfare recipients is responsive to the benefit level, the benefit levels will vary little, but the recipient shares will vary much. The overall pattern will

6 show small variation in benefits, but large variation in recipient shares. On the other hand, when the migration response (and γ) is small, we expect a pattern with large variation in benefits and small variation in recipient shares.

The model offers more specific hypotheses about community characteristics, in particular population size, private income level, and grants. Larger communities face lower migration elasticity (larger ri/R) and will choose higher benefit levels and have larger share of welfare recipients. Communities with higher private income level and higher grants have higher marginal benefit of altruism and will set a higher benefit level and have higher share of welfare recipients.

The mechanisms of the model are similar to the assumed moving costs in Smith (1991). When (psychic) moving costs vary by individual, the competitive mechanism mainly will be represented by the individual welfare recipients with low moving costs. The more popular understanding of the equilibrium mechanism in the US studies assumes wage adjustment. Brueckner (2000) offers an outline based on Wildasin (1991), also discussed by Saavedra (2000). In this setup, the welfare recipients earn wage income at the labor market, and the wage response to migration secures migration equilibrium. It seems unrealistic in our context to give such a prominent role to the (unskilled) wage adjustment.

There is a separate literature addressing the mobility of welfare recipients. Most observers will agree with the conclusion of Meyer (2000) based on the US evidence that there is welfare induced migration, but that it is modest in magnitude. There are serious methodological challenges to identify welfare migration. Actual migration flows may be small just because communities do respond to the competitive pressure. Welfare competition may also be observed even when (potential) welfare migration is negligible. The performance of neighboring communities may give voters information to evaluate their own community. Salmon (1987) discusses the argument for decentralization based on such yardstick competition, and Besley and Case (1995) offer empirical evidence. In this paper we will make no attempt to investigate the sources of welfare competition.

The understanding of welfare competition above implies a simultaneous determination of

7 welfare benefits and welfare recipients. We concentrate on the welfare benefits. The reduced form demand for benefits in community i can be written:

bbbbbii= (111 ,.., ii− ,+ ,.., bygN Iiii , , , ) (6)

Given data about the benefit level, private income level, grants and population size in all communities, we can in principle estimate this relationship. The response of the benefit level in community i to the benefit level in other communities indicates welfare competition, the decision about benefit level in each community is not taken in isolation.

3. Empirical modeling of welfare competition

In the econometric literature strategic interaction is known as spatial autocorrelation. The formal framework used for the statistical analysis of spatial autocorrelation is a so-called spatial stochastic process. Following Anselin (2001), spatial autocorrelation can be formally expressed as:

cov(bbij , )= Ebb ( ij ) − EbEb ( i ) ( j ) ≠ 0, for i ≠ j (7)

where bi is the welfare benefit level in community i. In this paper we apply the approach most frequently used to formally express spatial autocorrelation.3 We specify a functional form for the spatial stochastic process that relates the value of the random variable at a given location to its value at other locations:

bWbx= α ++β u (8) where b is a vector of benefit levels, W is the spatial weights matrix, x is a matrix of socio- economic characteristics of every municipality, β is a vector of parameters and u is a vector of error terms. For each municipality in the system W assigns municipalities of reference (referred to as ‘neighbors’ in the literature) and their relative weights to every municipality. These weights are determined apriori and can be considered as part of jurisdiction i’s

3 For other possible approaches see Anselin (2001).

8 characteristics. In this analysis we choose a definition of neighbors as municipalities with a common border.4 For ease of interpretation the elements of W are row-standardized, such that for each i, w =1.5 Then Wb yields a spatially weighted average of the welfare benefits ∑ j ij in the neighboring municipalities. While the choice of weights is based on prior evaluation concerning the pattern of interaction, the interaction effect, α, is estimated from the data. α can be interpreted as the slope coefficient of the reaction function and is the parameter of interest.

An econometric challenge is that the spatial lag term Wb is correlated with the disturbances, even when the latter are iid. This can be seen from the reduced form of (8). Assuming that ()IW−α is invertible, the reduced form is given by:

bI=−()()αα Wx−−11β +−I W u (9)

When municipalities’ benefit standards are potentially interdependent, we must model municipalities benefit-setting as simultaneous. Standard OLS estimation yields in this case biased and inconsistent estimators. There are two different approaches to handle the simultaneity. We can either estimate the reduced-form equation (9) by Maximum Likelihood methods or we can apply an instrument variable (IV) approach. In line with Figlio et al. (1998) and Besley and Case (1995) among others, we apply the latter approach. This means that we replace the spatially weighted average of benefit levels with fitted values from an auxiliary regression. The auxiliary regression is estimated with the solution proposed by Kelejian and Robinson (1993) using WX as instruments.

The spatial pattern in welfare benefits may result from various sources. In particular, common shocks and unobserved correlates will appear as spatial auto-correlation. The spatial process

4 At a later stage we plan to implement other weighting schemes based on geographic proximity to test the robustness of the results (one weighting scheme based on observed migration patterns as suggested by Shroder (1995: 185) and one weighting scheme where we include second-order-neighbors as Heyndeln and Vuchelen (1998: 95)). 5 This implies that Wij =1/mi when municipality i and j share a border and 0 otherwise (mi being the number of neighbours to municipality i).

9 can imply spatial lag dependence (equation (8)) or spatial error dependence. Correlated shocks are expected to show up as spatial error. If we face spatially correlated omitted variables, we have a pattern of spatial error dependence of the form:

uMu= λ + ε (10) whereε is a well behaved error vector. A model like this can lead to a false conclusion of welfare competition.6 If for example attitudes toward the welfare program vary across regions of the country, then benefits will tend to move together across contiguous municipalities although there is no welfare competition. Spatial interaction is not necessarily the result of welfare migration, but may be the result of yardstick competition. Bordignon et al. (2003) offer an attempt to separate yardstick competition from mobility competition, and they argue that yardstick competition is expected to generate spatial error dependence. Note that if the chosen instruments for the spatially weighted benefit levels are valid7, then an observed spatial autocorrelation (α) is not attributable to common unobservable shocks that may have hit neighboring municipalities (see Kelejian and Prucha (1998) for a formal proof of this property). An observed correlation will be caused by changes in the component of neighbors benefit levels that is attributable to neighbors’ (exogenous) observable variables.

To identify strategic interaction between local governments in Norway we estimate this (second stage) empirical counterpart of equations (6) and (8):

nK bwbCONTROLuiijjkkii=+βα0 ∑∑ + β + (11) jk==11

6 Consider for example the case where the error term is given by (10) and there is no strategic interaction (α = 0 ). Assuming that M=W and substituting (10) into (8) yields in this case: bWbx=+−+λ β λWxβ ε .Note that except for the extra term, −λWxβ , this model has the same “appearance” as the model in (8). The error autoregressive parameter λ appears now as the parameter of the lagged dependent variable Wb and we may falsely reject the null hypothesis of no strategic interaction. 7 Valid instruments are correlated with Wb, but uncorrelated with the error terms from (8).

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We apply a set of K control variables.8 From the theoretical model outlined in section 3 the key control variables are private income level (y), central government grants (g) and population size (N). In addition we include a set of control variables, based on findings from earlier studies of Norwegian local government behavior, notably Borge and Rattsø (1995, 2004).9 First, we take into account that the true budget constraint is intertemporal, and include interest payments (ip). Second, socio-demographic characteristics represent differences in preferences due to variation in the age composition of the population, and the population shares of children (ch), young (yo) and elderly (el) are included. Third, we control for variation in local cost conditions related to the local settlement pattern, and use the share of the population settled in rural areas (rural). Fourth we control for political characteristics potentially affecting the demand for local services and redistribution priorities. Both the share of socialists in the local council (soc) and the share of women in the local council (wom) measure preference orientation that has been shown to be important in local politics.

In addition to this, we have studied the effects of variables characterizing social aspects of the community of relevance for the welfare policy. Welfare benefits take into account costs of living, which vary in particular related to housing costs. We apply proxies that measure the degree of pressure in the housing market that is dummy variables representing the traveling distance to the nearest city with more than 25 000 inhabitants. No traveling distance is the point of reference.10 Our understanding is that the dummies reflect degree of urbanity important for the housing costs that welfare clients face. Socioeconomic characteristics of the municipalities influence the demand for welfare benefits. In the fiscal competition model, socioeconomic characteristics are endogenous. We have applied three variables that are assumed determined by factors outside welfare policy and also checked the results with and without these variables. The education level (lowedu) is measured as the share of the population with 9 years education or less. The local unemployment rate (unemp) follows

ˆ 8 The first stage regression is given by: nmnm , where ∑∑∑∑wbij j=+γν k wCONTROL ij kj + η k CONTROL ki jkjk====1111

γν, kk og ηare parameters to be estimated. 9 Description of variables and descriptive statistics is found in appendix table 1. 10 We’ve also applied another measure of cost of living – namely housing prices per square meter, but the traveling distance approach is found to be best suited.

11 standard definitions. The rate of divorce (divorce) often shows up with significant effects in studies of local policy.

4. Data: Chosen welfare benefit levels in Norway

Welfare benefits in Norway are decentralized to local governments. Assistance to the poor has been a local responsibility for more than 150 years, and the basic argument is that the local governments have the knowledge about the population and its living conditions needed to set the benefit level. The population of about 4,5 mill. is divided into 434 local governments with an average size of 9.000 inhabitants. The local governments are democratic institutions led by an elected local council. The financing of the local governments is quite centralized (grants and regulated income taxes) with some discretion related to user charges and property taxation.

In order to avoid too large differences between municipalities the central government regulates the level of social assistance through the Social Service Act. However, the local governments have substantial discretion in determining the welfare benefits. This discretion yields substantial variation in the benefit levels across municipalities as indicated in Appendix table 2. The central government also influences the incentives of the local governments by two elements of the grant system. The first is tax equalization where low per capita income tax revenues are compensated by about 90% below a national norm which is above the average income tax revenue. The second is expenditure equalization where characteristics of the population (in particular age composition) and local cost factors are taken into account in the calculation of the block grant. These elements imply that local governments do not face the full economic consequences of welfare migration.

Empirical research has addressed the differences in welfare benefit spending between local governments, in particular to give input to the expenditure equalization system. Langørgen (1995) concentrates on welfare benefit spending per capita and shows how they vary first and for all with social characteristics such as unemployment, share of refugees in the population, and family structure (share of divorced in the population). He also separates between welfare benefit spending per recipient on average and share of welfare recipients in the population.

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The social characteristics (unemployment, refugees, divorced) strongly increase the share of welfare recipients. The consequences for average benefits per recipient seem to reflect composition effects. Average benefits increase with the share of refugees and the share of divorced and decrease with social security participation. Midtsundstad et al. (1999) have written a reanalysis of Langørgen using more recent and more detailed data and with basically the same results. The political decision of setting the welfare benefit level and the welfare competition potentially involved are not explicitly addressed in these studies. In our context the social characteristics represent a potential endogeneity problem. The main lesson we draw is that studies of actual average benefits per recipient are likely to reflect important composition effects (refugees, unemployed etc.) that are hard to isolate.

Given the experiences with studies of welfare benefit spending at the local government level, we have looked for better empirical descriptions of the chosen benefit level. Langørgen and Rønningen (2003) have estimated the relationship between individual characteristics and welfare benefits based on a large dataset covering more than 2,5 million individuals in 433 municipalities in 1998. The analysis allows a calculation of the expected welfare benefits for an individual with specific characteristics in each community. The endogenous variable (b) is defined as expected welfare benefits for a standardized reference person11 measured in 1000 NOK per year. The variable is described in appendix Table 2 with an average of NOK 30.059 per year and varying from a minimum of NOK 24.060 to a maximum of NOK 35.596.

Welfare competition is primary a concern for local politicians. The actual benefit levels obtained by comparable individuals reflect the execution of the local policy by social workers. The policy decided at the local government level may not reach down to the social workers handling each individual. To investigate possible differences at the political and administrative level, and to check the robustness of the results, we also study the variation in norms for welfare benefit levels (bn) set by the local councils. The norms are set as guidelines for the administration and are specified as an amount paid to a 'standard user' per month. We utilize the reported norms for single persons without children receiving welfare benefit per month measured in 1000 NOK (bn). The norm set by the local council reflects the preferences

11 The reference person is a single, Norwegian man, 16-30 years old who is neither disabled, nor long-term unemployed. He has low education and pays no maintenance and receives no basic and supplementary benefits.

13 of the politicians and is consequently of interest independently of the actual individual benefit levels. The norm generally does not include housing expenses, and the two measures of welfare benefit level consequently show close to no correlation (the correlation coefficient is - 0,05). The expected welfare benefit of a standardized person and the welfare norm of the municipality anyway describe different aspects of the welfare benefits.

5. Estimated welfare competition

Empirical evidence for the welfare competition hypothesis implies a geographical pattern in welfare benefits. Looking for such a pattern we start out by a description of differences in welfare benefits. Differences between communities dependent on population size may reflect that factors other than welfare competition may be important for the determination of benefit levels. According to Appendix Table 3, there are no significant differences in expected welfare benefits (b) between communities when they are divided in four groups according to population size. The averages in all four groups are close to NOK 30.000 per year. The reported welfare benefit norms (bn) seem to be decreasing in population size.

In Appendix Table 4 a first look on the geographical pattern of welfare benefits is provided. The welfare benefit level varies between the 18 counties, and more important, the spread among communities within each county varies. In particular we notice that expected benefits are quite homogenous (low coefficient of variation) in counties with fairly small distances and low transportation costs such as Østfold, Akershus, Hedmark, Oppland, Sør-Trøndelag and Buskerud. On the other hand there are large differences in expected benefits in counties with large distances and high transportation costs such as Sogn og Fjordane, Nordland and Troms. These differences are consistent with welfare competition, but they may also reflect differences in social and demographic structure and urbanization.

The classical measure of spatial dependence is the Moran statistic (Moran, 1948). The statistic can be considered as a spatial analog to time series autocorrelation. Formally Moran’s I statistic is given by: I = uWu/uu′′ (12)

14 where u is a vector of OLS residuals and W is the spatial weight matrix.

A natural first investigation of the spatial structure is to regress the endogenous variables (b and bn) on a constant and evaluate the Moran statistic. This raw measure of spatial dependence indicates a strong spatial pattern based on neighborhood.12 Leaving out the strategic interaction term in equation (11) and estimating the model by OLS (reported as model A in Table 1 and 2) the Moran test still provides strong evidence for the existence of spatial dependence for both measures of welfare benefits. The Moran test yields a value of 5,08 and 5,02 for b and bn respectively, indicating that we confidently (at above the 99% confidence level) can reject the null hypothesis of absence of spatial auto-correlation. Note that the Moran test cannot say whether it is a spatial lag dependence or a spatial error dependence that is the driving force behind the spatial pattern.

Three different specifications of (11) are estimated for both b and bn. Model A (OLS without interaction effect) and Model B (OLS including interaction effects) are included as baselines for comparison. Model C is estimated with the neighboring controls as instruments as outlined in section 3.

In table 1 the focus is on the computed welfare benefits (b) based on individual data. We find an economically as well as statistically significant interaction effect. The reaction curves are found to be upward sloping indicating that higher benefits in neighboring communities raise the marginal utility of raising the benefits in the community and consequently leads to higher benefits. The quantitative effect of the instrument variable estimation implies that an increase in the welfare benefit level by NOK 1000 per year in neighboring communities will raise the benefit level in the community by NOK 260 per year.13 The OLS estimates indicate a somewhat larger interaction effect and predict an equivalent increase of benefits of NOK 420 per year. The reason for the bias in the OLS estimate is that the IV estimation controls for spatial dependence that are due to correlation in the residuals, while OLS does not.

12 The Moran statistic takes the value: 9,46 and 7,72 for b and bn respectively (both significant at well above the 99% confidence level). 13 The numbers of neighbors ranges from 1 to 11. The median number of neighbors is 5. This indicates that for a typical municipality, an increase in one of the neighbors’ benefit level with 1000 NOK will raise the benefit level in the municipality with 52 NOK.

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Table 1 about here

The welfare competition mechanism outlined in section 2 also gives a role for government grants, population size and private income level. We find no evidence suggesting that the governmental grants yields more generous welfare benefit, neither that interest payments yields lower welfare benefits. One possible explanation for this result is that governmental grants are highly correlated with some of the other control variables, notably population size and the share of low educated, divorced and unemployed respectively. Excluding the two latter variables yields a positive and statistically significant relationship between g and b in the OLS regression. Larger communities in population size are expected to have lower tax price for redistribution and therefore choose to have higher benefit level. In the data we do not find enough evidence to reject a hypothesis of no relationship between population size and welfare benefits, but the coefficients are in all specifications negative (with p-values ranging from 0,15 to 0,27). The distance dummies show the expected negative relationship between distance from city and benefit levels. The multicollinearity between these dummies and population size makes it hard to disentangle the effect of higher price level versus lower tax price.

In theory, the decision to redistribute using welfare benefits is motivated by altruism. Higher private income level consequently is associated with higher welfare benefit level. The estimated model implies the opposite result, higher private income level motivates lower welfare benefit level. This is a surprising result and questions our basic understanding of the political choice to redistribute. In future research we will investigate this more closely and check whether the private income level represents other characteristics important for the benefit setting.

Three other aspects of the political decision making are included in the model. First, the age distribution of the population represents the demand for local welfare services, which to a large extent are directed towards children, young and elderly. We expect that larger 'client' groups of the welfare services may crowd out welfare benefits, since they compete within the local government budget. Our estimates do not indicate such a competition between welfare

16 services and welfare benefits. Second, we have included variables measuring unemployment rate, education level and rate of divorced in the municipality to represent the demand for welfare benefits. We expect that a higher share of people with low education, a higher share of unemployed and a higher share of divorced all to be associated with lower welfare benefits, since a larger pool of welfare clients indicate stronger competition for funds. Only the share of the population with low education is consistent with this hypothesis. The two other are positively correlated with welfare benefits. Our understanding is that they incorporate composition effects that are not captured by the benefits calculated from individual data. Third, we test for political preferences by including socialist share and share of women in the local council as variables. Interestingly, these preference variables show little effect on welfare benefits.14

The estimates based on the alternative measure of reported welfare norms are shown in Table 2. Since the norms generally exclude housing expenses, we do not include the dummy variables for pressure in the housing market in these regressions. However, since 29 out of 433 municipalities do include housing benefits, these are represented by a dummy variable equal to 1. We find that the norm is approximately 460 NOK higher when housing expenses are included.

The results based on the welfare benefit norms also indicate strategic interaction and the size of the effect is larger than the interaction effect in expected welfare benefits shown in Table 1. The reaction coefficient is 0.90 for the welfare norm compared to 0.26 for computed welfare benefits, when estimated by instrument variables. The OLS estimate is here downward biased. Also with this dataset we find the ‘wrong’ sign on the private income variable. The effect of grants, interest payments and population size however are as expected, but the coefficients are not significant at conventional levels in the IV-specification. The effects of demographic and political variable variables seem modest. A large pool of people with lower education is again shown to yield lower welfare benefits. However, here a higher share of divorced is negatively correlated with the norm, possibly indicating that while the local council (who decides the norm) takes into account the total municipal budget when determining benefit level, the

14 Again multicollinearity might be confounding this conclusion. Social characteristics (lowedu, unemp, divorce) are all positively correlated with the share of socialists in the local councils.

17 bureaucrats (who have some discretion in deciding the actual outcome) is much less concerned with this. This can possibly explain the discrepancy between the two reaction coefficients based on the two measures of welfare benefits. The politicians in the local council are concerned about becoming a ‘welfare magnet’ and thereby have incentive to keep the benefit levels at moderate levels. However as the bureaucrats have substantial discretion to decide the actual outcome of welfare benefits and are not so concerned about a potential inflow of welfare clients (or getting reelected) the computed welfare benefits show a much less clear geographical pattern. If this is the case it moderates the underprovision argument suggested by the welfare competition hypothesis.

Table 2 about here

Utilizing neighbor’s control variables as instruments are problematic if we face endogenous sorting of households. As indicated above we have checked the results when some variables that might be endogenous are excluded from the model. In the IV-specifications excluding the rate of low educated, divorced and unemployed yields a small effect on the estimated reaction coefficient based on the welfare benefit norm ( αˆ =0,84 (0,17) ), but the estimated reaction coefficient based on expected welfare benefits are much higher than when these are included

( αˆ = 0,77 (0,17) ).

The empirical literature on welfare competition basically offers evidence about the US states and the AFDC-program (Aid to Families with Dependent Children). The two main studies of strategic interaction, Figlio et al. (1999) and Saavedra (2000), conclude that strategic interaction is important, Figlio et al. find reaction coefficients of the sign and size reported here, about 0.9. They show that the effect is asymmetric, and the competition effect is only significant downwards, that is when neighboring states reduce their benefit level. Saavedra’s result also suggests that American states behave strategically in setting their AFDC benefits. Brueckner (2000) has summarized the existing US evidence, including studies addressing welfare migration. We only know two studies outside the US states. Revelli (2003) analyses social service spending in UK local governments and identifies an interaction effect with elasticity of 0.2. He concludes that this is likely to follow from yardstick competition. His econometric design in particular attempts at separating between the consequences of common

18 shocks and interaction, and he finds no spatial error interdependence representing common shocks. Dahlberg and Edmark (2004) apply an approach similar to ours utilizing Swedish data for welfare benefits. They find statistically significant strategic interaction effects at a magnitude of 0,65.

6. Concluding remarks

When the allocation of welfare benefits is decentralized to local governments, incentives for welfare migration are created and may result in 'underprovision' or even 'race to the bottom'. It is an empirical issue whether this is important. We contribute to the empirical evaluation of the welfare competition hypothesis in an analysis of welfare benefits in Norway and introduce two new measures of the benefit level. The first is based on individual data and calculates the expected welfare benefits of a standardized person, while the other is the welfare benefit norm decided by the local council. We utilize spatial econometric methods in specifying a reaction function in which the welfare benefit level in one municipality is dependent on the benefit levels in neighboring municipalities and own socioeconomic characteristics. The estimated strategic interaction between local communities is statistically significant for both measures of welfare benefit level and confirms the hypothesis of welfare competition.

In theory, welfare competition implies underprovision of welfare benefits. In the Norwegian system of decentralized redistributive politics, interest groups, and grants financing there already may be a bias towards excessive spending. Also the central government intervenes with guidelines concerning the welfare benefit level. It follows that we cannot say that the welfare competition leads to ‘too low’ welfare benefits in Norway, but we can conclude that there is a geographic pattern in welfare benefits. The observed spatial autocorrelation is not attributable to common unobservable shocks that may have hit neighboring municipalities if we have chosen the instruments properly. With valid instruments the strategic interaction effect is not a spurious correlation that is really due to spatial error correlation. Correlation will be caused by changes in the component of neighbors benefit levels that is attributable to neighbors’ observable variables, which we use as instruments.

A factor that might be confounding our estimates is the potential endogeneity of the

19 municipalities’ characteristics. These variables are included both as controls as well as instruments for neighbor’s welfare benefits. If the municipalities’ characteristics are correlated with the error term, due to for example endogenous sorting of households across communities, then the IV estimates will be inconsistent. The challenge of unobserved correlates is best handled with panel data, whereby the statistical inference can be based on changes over time taking into account permanent characteristics of the communities. Future research should attempt at collecting data which vary in the time dimension as well as properly testing to discriminate between the two types of spatial interdependence.

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Table 1, Dependent variable: b (Expected welfare benefit of standardized recipient, based on individual data estimation) A OLS B OLS C IV W_b 0,42***(0,08) 0,26* (0,15) y -0,034***(0,006) -0,025*** (0,006) -0,028*** (0,007) g -0,001 (0,020) 0,001 (0,020) 0,00 (0,020) ip 1,85 (2,00) 0,83 (1,94) 1,21 (1,98) rural 0,42 (0,99) 0,44 (0,41) 0,43 (0,42) log N -0,50 (0,35) -0,31 (0,34) -0,38 (0,34) ch -6,78 (9,87) -3,03 (9,59) -4,43 (9,70) yo 3,72 (7,16) 3,00 (6,93) 3,27 (6,97) el 3,69 (4,25) 3,39 (4,12) 3,50 (4,14) wom 0,87 (0,88) 0,79 (0,85) 0,82 (0,86) soc 0,42 (0,62) -0,01 (0,60) 0,15 (0,62) lowedu -10,24*** (2,34) -8,27*** (2,29) -9,01*** (2,38) unemp 31,22* (16,19) 27,60* (15,69) 28,96* (15,80) divorce 46,10*** (7,65) 36,09***(7,64) 39,83*** (8,24) dist1 -0,52 (0,41) -0,42 (0,40) -0,46 (0,40) dist2 -0,36 (0,43) -0,19 (0,42) -0,25 (0,42) dist3 -0,88* (0,45) -0,64 (0,44) -0,73* (0,45) dist4 -0,96** (0,43) -0,64 (0,42) -0,76* (0,43) 2 0,254 0,301 Radj R2 0,300 # obs. 433 433 433 Moran 5,08 (p=0,0000) Note: Data on welfare benefits are from Langørgen and Rønningen (2003), Statistics Norway. Standard errors in parentheses. ***, ** and * denotes significance at 1%, 5% and 10% level respectively. A constant term is included in all regressions (not reported).

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Table 2, Dependent variable: bn (Welfare benefit norm per recipient) A OLS B OLS C IV W_bn 0,56*** (0,09) 0,90*** (0,18) y -0,0038* (0,0022) -0,002 (0,002) -0,001 (0,002) g 0,018** (0,008) 0,009 (0,007) 0,004 (0,008) ip -1,35* (0,78) -0,99 (0,74) -0,78 (0,76) rural -0,15 (0,17) 0,00 (0,16) 0,08 (0,17) log N -0,06 (0,12) -0,08 (0,12) -0,09 (0,12) ch 0,51 (3,88) -1,85 (3,72) -3,28 (3,84) yo 1,81 (2,75) 1,80 (2,63) 1,79 (2,77) el 1,46 (1,61) 1,22 (1,54) 1,07 (1,57) wom 0,55 (0,34) 0,41 (0,33) 0,32 (0,34) soc 0,38 (0,24) 0,21 (0,23) 0,11 (0,24) lowedu -2,07** (0,91) -1,83** (0,87) -1,69* (0,89) unemp 5,48 (6,32) 4,39 (6,02) 3,73 (6,14) divorce -9,46*** (2,91) -4,16 (2,88) -0,96 (3,27) housing 0,42*** (0,11) 0,45*** (0,10) 0,46*** (0,11) 2 0,148 0,226 Radj R2 0,376 # obs. 433 433 433 Moran 5,02 (p=0,0000) Note: Data on welfare benefits are from ’Sosialstatistikk’, Statistics Norway. Standard errors in parentheses. ***, ** and * denotes significance at 1%, 5% and 10% level respectively. A constant term is included in all regressions (not reported).

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Appendix Table 1: Data description and descriptive statistics – Mean and standard deviations

Variable Description Mean (st.dev) b Expected welfare benefits per standard recipient per year in 1000 NOK. The 30,058 standardized reference person is a single, Norwegian man, 16-30 years old, not (1,646) disabled, not long-term unemployed, low education, pays no maintenance, receives no basic and supplementary benefits, 1998. The variable is estimated and documented by Langørgen and Rønningen (2003). bn Reported welfare benefit norms for single persons, per month measured in 1000 NOK. 3,969 (0,613) y Average gross income for every person 17 years and older, measured in 1000 NOK. 184,476 (21,354) g The sum of lump-sum grants from the central government and regulated income and 22,968 wealth taxes, measured in 1000 NOK per capita. (6,031) ip Net interest payments as a fraction of exogenous local government revenue. 0,005 (0,037) rural The share of the population living in rural areas (1999). 0,52 (0,28) N Total population. 9048 (17094) ch The share of the population 0-5 years. 0,079 (0,011) yo The share of the population 6-15 years. 0,133 (0,015) el The share of the population 67 years and above. 0,158 (0,036) soc The share of socialist representatives in the local council. A socialist is defined as a 0,374 representative belonging to one of the following parties: NKP, RV, SV and AP. (0,142) wom The share of female representatives in the local council. 0,322 (0,082) lowedu The share of the population with 9 years education or less. 0,294 (0,057) unemp The share of the population that have no income from work (average for 1998) 0,012 (0,006) divorce The share of the population that are divorced or separated (january 98) 0,046 (0,015) dist1 Dummy equal 1 if traveling time to the closest city with more than 25 000 inhabitants 0,16 is between 0 and 30 minutes, 0 if not. (0,37) dist2 Dummy equal 1 if traveling time to the closest city with more than 25 000 inhabitants 0,15 is between 30 and 60 minutes, 0 if not. (0,36) dist3 Dummy equal 1 if traveling time to the closest city with more than 25 000 inhabitants 0,19 is between 1 and 2 hours, 0 if not. (0,39) dist4 Dummy equal 1 if traveling time to the closest city with more than 25 000 inhabitants 0,44 is larger than 2 hours, 0 if not. (0,49) housing Dummy equal 1 if support to housing is included in bn 0,067 (0,25)

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Appendix Table 2, Descriptives of the endogenous variables Minimum 1st Median 3rd Maximum Average quartile quartile (st.deviation) b 24,060 28,937 30,079 31,132 35,596 30,059 (1,65) bn 2,258 3,540 3,930 4,335 6,441 3,969 (0,613) Note: N=433.

Appendix Table 3, Welfare benefits according to population size Population size Average b Average bn <2300 (1st quartile) 30,120 (1,903) 4,154 (0,712) pop>2300, pop<4300 (2nd quartile) 29,967 (1,726) 4,028 (0,558) pop>4300, pop<9000 (3rd quartile) 29,831 (1,537) 3,917 (0,585) pop>9000 (4th quartile) 30,264 (1,268) 3,769 (0,491) Note: Standard deviation in parentheses.

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Appendix Table 4, Welfare benefit according to county, standard deviation and coefficient of variation County b St.dev Var-coeff # bn St.dev Var-coeff # Østfold 30.938 1.166 0.03818 3.638 0.323 0.089 18 Akershus 29.604 0.950 0.03222 3.800 0.366 0.096 22 Hedmark 31.075 1.413 0.04522 4.092 0.479 0.117 22 Oppland 30.778 1.334 0.04326 4.049 0.500 0.123 26 Buskerud 29.904 0.997 0.03321 3.1750.312 0.098 21 Vestfold 30.791 1.720 0.05615 3.858 0.341 0.088 15 Telemark 30.923 1.365 0.04418 3.382 0.396 0.117 18 Aust-Agder 31.626 1.724 0.055 15 3.875 0.525 0.136 15 Vest-Agder 29.616 1.365 0.046 15 3.754 0.529 0.141 15 Rogaland 28.543 1.539 0.05425 4.237 0.511 0.121 25 Hordaland 29.831 1.539 0.052 34 4.025 0.536 0.133 34 Sogn og Fjordane 29.513 1.924 0.065 26 4.330 0.801 0.185 26 Møre og Romsdal 28.959 1.432 0.049 38 4.077 0.684 0.168 38 Sør-Trøndelag 29.317 0.991 0.034 25 4.092 0.416 0.102 25 Nord-Trøndelag 30.249 1.373 0.045 24 4.164 0.591 0.142 24 Nordland 30.565 1.673 0.05545 3.956 0.549 0.139 45 Troms 29.555 1.880 0.06425 3.976 0.687 0.173 25 Finmark 31.027 1.134 0.03719 4.375 0.736 0.168 19 Overall 30.059 1.646 0.055433 3.9660.607 0.153 433