Does state spending on mental health lower suicide rates?

The Journal of Socio-Economics 41 (2012) 408–417

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Does state spending on mental health lower suicide rates? Justin M. Ross a,∗ , Pavel A. Yakovlev b , Fatima Carson c a

School of Public & Environmental Affairs, 1315 East Tenth Street, Indiana University, Bloomington, IN 47405, United States Department of Economics and Quantitative Sciences, A.J. Polumbo School of Business, 600 Forbes Avenue, Duquesne University, Pittsburgh, PA 15282, United States c Strategic Development Group, Inc., 2901 North Walnut Street, Bloomington, IN 47404, United States b

a r t i c l e

i n f o

Article history: Received 30 December 2009 Received in revised form 2 September 2010 Accepted 5 October 2010 JEL classification: I12 I18 I31

a b s t r a c t Using recently released data on public mental health expenditures by U.S. states from 1997 to 2005, this study is the first to examine the effect of state mental health spending on suicide rates. We find the effect of per capita public mental health expenditures on the suicide rate to be qualitatively small and lacking statistical significance. This finding holds across different estimation techniques, gender, and age groups. The estimates suggest that policies aimed at income growth, divorce prevention or support, and assistance to low income individuals could be more effective at suicide prevention than state mental health expenditures. © 2010 Elsevier Inc. All rights reserved.

Keywords: Suicide mortality Mental health Public spending

1. Introduction Suicide is the eleventh leading cause of death in the United States.1 In an attempt to combat this problem, spending by state mental health agencies (SMHA) in the U.S. totalled $29.4 billion dollars in 2005. However, there have been limited attempts to estimate the effectiveness of state-level spending on a systematic basis, as most research has focused on program-specific and socio-economic determinants of suicide. Since each public spending category or government program carries an opportunity cost in terms of foregone spending on other potentially life-saving programs, it is important that the effectiveness of these expenditures is thoroughly analyzed. This study is the first to use the recently released data on public mental health spending in order to examine its effectiveness in reducing suicide rates by gender and age groups. Previous research on the effect of state public expenditures of any form on suicide rates is limited, but has generally been supportive. Zimmerman (1995, 2002) and Flavin and Radcliff (2009) both found that state welfare expenditures have a significant negative effect on the incidence of suicide. The study closest to ours is Minoiu and Rodríguéz Andres (2008), who find that public health and wel-

∗ Corresponding author. Tel.: +1 812 856 7559. E-mail addresses: [email protected] (J.M. Ross), [email protected] (P.A. Yakovlev), [email protected] (F. Carson). 1 Centers for Disease Control, Suicide Facts at a Glance (2008). 1053-5357/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.socec.2010.10.005

fare expenditures have a significant negative effect on state suicide rate. However, Minoiu and Rodríguéz Andres measure public health and welfare spending as a share of total state expenditures. Like Hanson (2008), they argue that these shares signal intensity or effort level of support by the state to those considering suicide. Unfortunately, the Minoiu and Rodríguéz Andres (2008) measure of spending on health and welfare literally implies that suicides can be reduced more by spending $1 million out of a $10 million budget rather than $10 million out of a $1 billion budget. Alternatively stated, the implications are that a state could reduce suicide rate by holding constant its level of health spending and lowering its spending on other programs. Thus, it is difficult to know what the coefficient on public health’s budget share is really capturing.2 It could be argued that the Minoiu and Rodríguéz Andres (2008) measure ends up capturing the effect from public spending in levels if states with higher spending shares also have higher public spending per capita. Our study differs from Minoiu and Rodríguéz Andres in three important ways: (1) it replaces overall state health expenditures with state mental health expenditures; (2) it uses a per capita measure of expenditures as in Zimmerman (1995, 2002) and Flavin and Radcliff (2009) instead of expenditure shares; and (3) it focuses on a different time period (1997–2005 in ours vs. 1982–1997 in Minoiu

2 A review of the use of suicidal behavior in signaling games can be found in Yang and Lester (1996).

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Table 1 Variable descriptions and sources. Label

Description

Source

Male, all

Male age-adjusted suicide rate for entire male population

Female, all

Female age-adjusted suicide rate for entire female population

Male, 25–64

Male age-adjusted suicide rate for population aged 25–64

Female, 25–64

Female age-adjusted suicide rate for population aged 25–64

PCPI PCMHEXP

Real Per Capita Personal Income (thousands) Per Capita State Mental Health Agency Expenditures (thousands)

PCWE PCHE CUR PopDen SHRMIG DR

Per Capita Public Welfare Expenditures (thousands) Per Capita Public Health Expenditures (thousands) Civilian unemployment rate Population density Net migration as a proportion of the population Divorce rate

ShrNW

Share non-white

MSD

Mountain state dummy

Sunny

Number of sunny days in the year, in tens.

Centers for Disease Control WISQARS Injury Mortality Reports. Rate per 100,000 of relevant population Centers for Disease Control WISQARS Injury Mortality Reports. Rate per 100.000 of relevant population Centers for Disease Control WISQARS Injury Mortality Reports. Rate per 100,000 of relevant population Centers for Disease Control WISQARS Injury Mortality Reports. Rate per 100,000 of relevant population Bureau of Economic Analysis, Adjusted to 2000 dollars National Association of State Mental Health Directors Research Institute. Adjusted to 2000 dollars U.S. Census Bureau. Adjusted to 2000 dollars U.S. Census Bureau. Adjusted to 2000 dollars Bureau of Labor Statistics U.S. Census Bureau. Population (in thousands) divided by land area U.S. Census Bureau For 2005, U.S. Census: for 1997–2004. National Center for Health Statistics, Center for Disease Control. When a year was missing in a state, the average of the nearest surrounding years was used in its place, or if an end-year was missing the nearest available year was used. This occurred in six states and accounted for less than 5 percent of the sample U.S. Census. Proportion of the population that does not identify as Caucasian U.S. Census. Indicates the state is member of the Rocky Mountain Census region Dunn (2008)

and Rodríguéz Andres). Recently released figures on state mental health expenditures enable this study to be the first to examine a more accurate measure of the public resources devoted to mental health aid than the general public health or welfare expenditures. Public welfare expenditures, public health expenditures, and various socioeconomic variables such as income, unemployment, and divorce rates are used as additional control variables. The results indicate that per capita state mental health expenditures have neither a statistical nor a quantitatively significant effect on suicide rates. The same conclusion holds across different empirical specifications, estimation techniques, gender, and age groups. As in previous studies, we find several socio-economic factors to be among significant determinants of suicide rates. Similar to Minoiu and Rodríguéz Andres (2008), our empirical models have greater explanatory power for male than female suicides. 2. Socioeconomic determinants of suicide The common theoretical frameworks for modeling social determinants of suicide are the Durkheim (1897/1966) theory of social integration and the Hammermesh and Soss (1974) model of lifetime utility.3 These works motivate many of the control variables commonly employed in a variety of econometric studies of macrolevel determinants of suicide. The intuition of these variables in Durkheim’s theory is that they represent or proxy for the extent to which a person becomes detached, withdrawn, or in some way isolated from society at large. Hammermesh and Soss (1974) model suicide as a choice made when the present value of lifetime utility turns negative, and find empirical support for the suicide decision to be a function of income, age, and unemployment. A vast literature has tried to determine the significance and magnitude of the effect of various social and economic indicators on suicide (Chuang and Huang, 1997; Kunce and Anderson, 2002; Neumayer, 2003; Chen et al., 2009). The socioeconomic control variables used in this paper

3 This theory of social integration is cited in all of the previously discussed literature (Zimmerman, 1995, 2002; Minoiu and Rodríguéz Andres, 2008; Flavin and Radcliff, 2009).

were based on those used in the previous literature that examined state level public spending variables (Zimmerman, 1995, 2002; Minoiu and Rodríguéz Andres, 2008; Flavin and Radcliff, 2009). The descriptions and sources of all the variables can be found in Table 1, while their descriptive statistics are reported in Table 2. Divorce and unemployment rates are perhaps the most intuitive control variables, as both events can be traumatic for the individuals who experience them. These individual traumas can serve as a trigger for suicidal tendencies, or in the context of the Durkheim’s theory they may be proxies for the level of detachment between the individual and society as families and friends become separated. In a cross-section of the states, Stack (1980) found that a one percent increase in the divorce rate increased the suicide rate by 0.54 persons per 100,000. Neumayer (2003) found divorce rates across countries to be among the most robust determinants of both

Table 2 Summary statistics. Variable

Mean

S.D.

Min

Max

Male, all Female, all Male, 25–64 Female, 25–64 PCMHEXP PCWE PCHE PCPI ShrMig CUR PopDen MSD DR ShrNW Sunny

20.52 4.61 25.13 6.70 0.08 0.91 0.15 28.33 0.00 4.84 0.28 0.16 4.17 0.67 147.19

5.74 1.51 6.72 2.24 0.05 0.35 0.08 4.59 0.01 2.26 1.14 0.36 1.12 0.31 36.83

6.20 1.22 6.59 1.22 0.02 0.26 0.00 19.85 −0.02 2.30 0.00 0.00 1.90 0.02 56.60

38.85 11.12 45.15 16.22 0.40 2.67 0.47 48.29 0.04 46.00 9.48 1.00 10.40 0.98 248.40

Abbreviations: suicide rates reported by gender (male, female) and age group (all, 25–64); PCMHEXP is real per capita mental health expenditures; PCWE is real per capita public welfare expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; CUR is current unemployment rate; PopDen is the population density; MSD is a mountain state dummy; DR is the divorce rate; ShrNW is the share of the population that is non-white; Sunny is the number of sunny days in a year.

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male and female suicide rates. Stevenson and Wolfers (2006) find a strong and robust relationship between divorce and female suicide in a panel of U.S. states, but not a statistically significant relationship for men. Platt (1984) provides a discussion and review of the correlation between unemployment and suicide with the conclusion that the association between the two measures at aggregate levels of data represents an important antecedent variable even if it is not a causal relationship. A cohort study of New Zelanders by Blakely et al. (2003) found unemployment to be correlated with a two to three fold increase in the log-odds ratio of death by suicide. Kposowa (2001) found qualitatively similar results in a cohort study of Americans. Hammermesh and Soss (1974) use permanent lifetime income as an explicit argument in the lifetime utility function of individuals. Marcotte (2003) extended the utility model to explain suicide attempts as a utility maximizing approach when there is a probability of survival and when the attempt will affect future utility. Marcotte (2003) goes on to show that individuals who made a suicide attempt had higher future earnings than those who did not attempt suicide but gave it serious consideration. Empirically, the significance of income effects on suicide rates remains an issue that is unresolved in the suicide literature (Chuang and Huang, 1997; Minoiu and Rodríguéz Andres, 2008). Regardless, to capture the possibility of such effects a control for real per capita personal income is incorporated. The proportion of the population that is non-white also has several possible motivations for including it as a control variable. Whites are known to have higher suicide rates than non-whites, with the exception of Native Americans (NAHIC, 2006). Lester (1992) argued that the extent to which subgroups are minorities might affect the rate of suicide. It could be argued that minority groups work harder on the social connections that they have, which lowers suicidal tendencies. For instance, Chatters et al. (1994) find that African Americans have more extensive social networks with their kin, which may account for their lower rates of suicide. The population density of the state, measured as the population over square land miles, is also included among the control variables. Some of the older literature refers to the social problems of urban life (Gulick, 1973; Breault and Kposowa, 1983), while the more recent literature has focused on the social isolation of rural life (Fernquist and Cutright., 1998; Singh and Mohammad Siahpush, 2002). For instance, Singh and Mohammad Siahpush (2002) found that the estimated rural–urban suicide gradients increased in the United States from 1970 to 1997 after controlling for other relevant socioeconomic indicators. Two indicators for regional effects are also included: a dummy for whether or not the state was part of the Rocky Mountain Census region and the number of sunny days in the year. Minoiu and Rodríguéz Andres (2008) and Hammermesh and Soss (1974) each found regional indicators to be important. While initial sunshine exposure has been found to be a trigger for suicide, long stretches of daily sunlight can ultimately be mood enhancing (Papadopoulosa et al., 2005). This paper attempts to estimate the influence of this effect by including the number of sunny days in a given year.

rates in the country over the period of the data, with a low of 3.6 suicides per 100,000 persons in 2000 to a high of 6.4 in 2001. These rates are far below the average suicide rate of 11.7 for the data period. Alaska and Wyoming are consistently among the highest, averaging 20.4 and 19.0 for the period, respectively. Ultimately, the regression analysis in this paper will be based upon the age-adjusted suicide rates for each gender.4 Separating the suicide rates by gender will allow us to observe if women and men respond differently to these measures. Males commit suicide at a considerably higher rate than females, as can be seen by the respective means of 20.5 and 4.6 in Table 2. Furthermore, since socioeconomic factors like divorce and unemployment would intuitively seem less important to the relatively young and elderly, the age-adjusted suicide rates for the population that is age 25–64 will be estimated separately. As can be seen in Table 2, the mean suicide rate for this group is higher than the rate for all ages in both men and women. The primary public spending variable of interest is State Mental Health Agency (SMHA) controlled mental health expenditures. SMHA controlled mental health expenditures is an aggregate measure that includes a variety of state spending on subcategories such as state psychiatric hospitals, in-patient hospital care, community based programs, grants, prevention research and training, as well as administration costs.5 In 2004, the most recent year of data available on these subgroups, the average state spent about 2.5 percent of these total costs on administration, 33 percent on state psychiatric hospitals, and 64 percent on community support based programs.6 Table 3 provides a breakdown of suicide rates and SMHA expenditures by state in 2005. Both per capita expenditures and total expenditures are listed in Table 3. The District of Columbia spends the second most on a per capita basis and has the lowest suicide rate. Wisconsin spent the most on a per capita basis but had only the 21st lowest suicide rate, while West Virginia spent the least amount and had just 1.9 more suicides per 100,000 people than Wisconsin. To get a better view of a direct correlation, Fig. 1 provides a scatter plot of state suicide rates against real per capita mental health expenditures between 1997 and 2005. On each y-axis in Fig. 1 is the age-adjusted suicide rate according to different population groups. For each group, the best-fit line exhibits an inverse relationship between mental health expenditures and the suicide rate. It should be noted that the District of Columbia was excluded from Fig. 1 because their extremely high spending levels and low suicide rates would incorrectly give the impression that the District of Columbia was driving the negative slope. State mental health expenditures are reported annually from 2005 to 2001, with a reoccurring observation in 1997 and in 1990. However, going state by state, mental health expenditures appear to be very predictable on an annual basis from the recently observed data. In short, if one were to exclude a year of the most recent data, they could quite accurately predict it by just averaging the two surrounding periods. Assuming that this would also similarly be true in the missing 1998–2000 periods, each state’s expenditures were interpolated using a weighted average of the state’s spending in 2001 and 1997.7 In estimating the econometric model,

3. Suicide and mental health treatment Suicide itself is a phenomenon with many important variations that complicate the understanding of causal mechanisms. Suicide has been extensively examined over various age, racial, ethnic, and gender groups. The main intention of this paper is to determine the effectiveness of statewide policy via mental health expenditures on some of the broadest definitions of suicide, particularly those that would be the most indicative measures of effectiveness to policy makers. Washington, DC has consistently held the lowest suicide

4 For making comparisons across states and over time, the accepted practice is to use age-adjusted suicide rates that standardize the rate across the age distribution of the population of interest. 5 State mental health agencies are specifically instructed to exclude capital expenditures in the surveys used to generate the data. 6 Source: http://www.nri-inc.org/projects/profiles/RevExp2004/2004Table7.pdf. 7 Specifically, in 1998, a state’s mental health expenditures was set equal to (3/4) of 1997s expenditures and (1/4) of 2001s expenditures. In 1999, a state’s mental health spending was treated as an average of its 1997 and 2001 levels. In 2000, it was computed as (1/4) 1997 expenditures plus (3/4) 2001 expenditures.

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Table 3 2005 State Mental Health Agency Expenditures. State

Total (millions)

Rank

Per capita

Rank

Age-adjusted suicide rate

Rank

Alabama Alaska Arizona Arkansas California Colorado Connecticut District of Columbia Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington Wisconsin West Virginia Wyoming Average

$273.70 $173.54 $867.30 $98.64 $4270.20 $343.75 $549.20 $233.80 $74.77 $647.17 $443.97 $192.63 $53.88 $1021.70 $518.66 $235.55 $253.70 $208.44 $258.50 $180.30 $776.50 $685.60 $973.50 $669.28 $305.90 $414.01 $124.82 $106.05 $150.50 $154.91 $1215.83 $46.40 $3977.50 $1027.80 $46.76 $757.73 $157.30 $434.56 $2541.28 $102.35 $285.20 $55.08 $522.00 $832.20 $159.88 $109.00 $531.50 $585.49 $579.73 $118.90 $49.95 $576.41

27 35 8 45 1 24 17 31 46 14 21 33 48 6 20 30 29 32 28 34 10 12 7 13 25 23 40 43 39 38 4 51 2 5 50 11 37 22 3 44 26 47 19 9 36 42 18 15 16 41 49

$60.29 $259.24 $145.71 $35.58 $118.65 $73.55 $157.52 $278.15 $128.46 $36.49 $48.75 $151.96 $37.79 $80.33 $82.89 $79.70 $92.54 $49.97 $57.50 $137.40 $139.33 $106.64 $96.31 $130.88 $105.47 $71.53 $133.38 $60.46 $62.48 $118.88 $140.44 $24.21 $206.49 $118.42 $73.53 $66.12 $44.49 $119.71 $205.48 $95.95 $67.03 $70.61 $87.16 $36.43 $63.83 $175.88 $70.33 $93.37 $321.07 $21.46 $98.62 $99.35

41 3 9 49 18 31 7 2 15 47 44 8 46 29 28 30 26 43 42 12 11 20 23 14 21 33 13 40 39 17 10 50 4 19 32 37 45 16 5 24 36 34 27 48 38 6 35 25 1 51 22

11.5 20.0 16.0 14.3 9.0 17.1 8.1 5.2 9.7 12.5 10.3 8.3 16.2 8.5 11.8 11.1 13.2 13.4 11.2 12.3 8.4 7.2 10.9 10.4 12.8 12.5 21.9 11.0 20.0 11.8 6.0 17.9 6.0 11.5 13.9 11.5 14.9 14.8 11.2 6.3 11.9 15.3 14.0 10.9 14.9 12.2 11.2 12.7 11.4 13.3 17.4 12.3

24 49 44 39 10 46 6 1 11 31 12 7 45 9 25 17 34 36 20 29 8 5 14 13 33 30 51 16 50 25 3 48 2 23 37 22 41 40 18 4 27 43 38 15 42 28 19 32 21 35 47

one of the robustness checks will be to omit the interpolated data, which demonstrate that the conclusions regarding mental health expenditures are not sensitive to this approach. 4. The model The empirical model used to explain the suicide rate in state i at time t (SRi,t ) is a log-linear function of per capita mental health expenditures (PCMHEXPi,t ), per capita public health expenditures (PCHEi,t ), per capita public welfare spending (PCWEi,t ), and other significant determinants (Zi,t ) as specified by previous research:

other socioeconomic variables to vary proportionally at different levels.8 The full set of state and year fixed effects is represented by ˛i and i , respectively.9 All nominal variables are converted into constant 2000 dollars. In addition to the model of suicide rates for the entire population, we estimate the same model for various population subgroups such as males, females, males aged 25–64, and females aged 25–64. Since the explanatory variables consist largely of socioeconomic indicators like divorce and unemployment, senior citizens and teens could experience different responses to these events, motivating their exclusion as in previous research.

ln(SRi,t ) =  ln(PCMHEXPi,t ) +  ln(PCHEi,t ) + ı ln(PCWEi,t ) + Zi,t ˇ + ˛i + i + εi,t .

(1)

The traditional Gaussian error term is represented by εi,t . The logarithmic value of the suicide rate and the public spending variables helps mitigate a heteroskedastic relationship, while allowing the

8 This implicitly assumes that the suicide rate has an underlying mental health (mh) production function that is multiplicative in the spending variables (s), and multiplicative of the exponential values of the socioeconomic variables (x): mh = s␤1exp(xˇ2 + ε). 9 The Hausman test indicates the presence of fixed effects in our model, but in the GMM estimation state fixed effects are replaced with a lagged dependent variable.

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Fig. 1. State suicide rates and per capita public mental health expenditures (excluding the District of Columbia), 1997–2005. Note: spending is in constant 2000 dollars.

The estimation of Eq. (1) using panel data has its advantages as well as challenges. The main advantage of panel data is the ability to control for unobserved effects (heterogeneity) among cross-sectional units. The challenge is that panel data may exhibit contemporaneous correlation, autocorrelation, heteroskedasticity, and endogeneity. The aforementioned challenges are typically present in many panel datasets, including the data employed in this paper.10 Nevertheless, as a first pass, Eq. (1) will be estimated using a two-way fixed-effects estimator with state-clustered robust standard errors. However, our main results will be based on the estimation of Eq. (1) with the lagged dependent variable via generalized method of moments (GMM) in order to control for potential endogeneity in state mental health and general health expenditures. The Arellano and Bond (1991) and Arellano and Bover (1995)/Blundell and Bond (1998) dynamic GMM panel estimators are designed to deal with this potential endogeneity problem (i.e. when regressors are correlated with past or future disturbance). For example, the endogeneity problem would arise if an increase in public mental health spending in a given year was implemented in response to a rise in suicides in the previous year, which is the argument employed by Minoiu and Rodríguéz Andres (2008) as a justification for using a dynamic GMM rather than a fixed-effect estimator. As in Minoiu and Rodríguéz Andres (2008), we use the system GMM estimator with the endogenous variable’s lagged values and lagged first-differences as the instruments. According to Roodman (2006), the dynamic GMM estimator generally works well for (1) linear hypothesis testing; (2) when there are few time periods and many cross sections; (3) there is an autoregressive lag of the

10 We test for and find evidence of autocorrelation, group-wise heteroskedasticity, and contemporaneous correlation in our data.

dependent variable; (4) the independent variables are not strictly exogenous; (5) there are time-invariant fixed effects; and (6) within group heteroskedasticity and autocorrelation is likely present. The aforementioned issues are rather descriptive of the dataset in this paper and motivate our choice of dynamic system GMM as the preferred estimator. The dynamic system GMM estimator used in this study also utilizes the robust two-step estimation procedure with Windmeijer’s (2005) finite-sample correction. Windmeijer’s (2005) finite-sample correction in two-step estimation prevents the standard errors from being severely biased downward. Following Roodman’s (2006) recommendation, we include time dummies in our GMM regressions rather than a second order time polynomial as in Minoiu and Rodríguéz Andres (2008). The use of time dummies is more likely to yield reliable autocorrelation tests and robust standard errors if disturbances are correlated across panels. 5. Results 5.1. Model estimation results Table 5 presents the panel fixed effect estimates with robust standard errors clustered by state for the model in equation (1).11 The regressions in Table 5 are estimated using the natural log of the state suicide rate for each gender and age group as the dependent variable. In contrast to the pair-wise correlations shown in Table 4, public mental health expenditures in the multivariate fixed effect estimates of female suicides take on a counterintuitive positive sign

11 The fixed effects estimates in Table 5 omit the lagged dependent variable for it being simultaneously determined with the dependent variable by the fixed effects as explained in “Chapter 14: Models for Panel Data” in Greene (2002). Time invariant variables and slowly changing variables like the mountain state dummy and number of sunny days are also excluded due to collinearity with state fixed effects.

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Table 4 Pair-wise correlations.

Male, all Female, all Male, 25–64 Female, 25–64 PCMHEXP PCWE PCHE PCPI ShrMig CUR PopDen MSD DR ShrNW Sunny

PCPI ShrMig CUR PopDen MSD DR ShrNW Sunny

Male, all

Female, all

Male, 25–64

Female, 25–64

PCMHEXP

PCWE

PCHE

1 0.7960* 0.9564* 0.7673* −0.3888* −0.3583* −0.0931* −0.5514* 0.3429* 0.0135 −0.3488* 0.6422* 0.6157* 0.0204 0.1082*

1 0.7588* 0.9573* −0.2855* −0.2654* 0.0394 −0.3401* 0.4407* 0.0361 −0.3034* 0.5738* 0.5509* 0.0553 0.0993*

1 0.7384* −0.3758* −0.3306* −0.0672 −0.5484* 0.3375* 0.0238 −0.3400* 0.6057* 0.5843* 0.0646 0.1071*

1 −0.3063* −0.2688* 0.0165 −0.3738* 0.4363* 0.039 −0.2978* 0.5565* 0.5377* 0.0873 0.1258*

1 0.7404* 0.1342* 0.6235* −0.2891* 0.1514* 0.7034* −0.1342* −0.3783* −0.0268 −0.1769*

1 0.1208* 0.4474* −0.3627* 0.2102* 0.5752* −0.2901* −0.3546* 0.1063* −0.2334*

1 0.1616* −0.0658 0.0067 −0.1950* −0.0467 −0.1677* 0.0654 −0.2875*

PCPI

ShrMig

CUR

PopDen

MSD

DR

ShrNW

1 −0.0837 0.0045 0.5064* −0.1872* −0.4430* 0.1150* −0.0637

1 −0.0817 −0.1758* 0.3558* 0.4522* 0.0309 0.3235*

1 0.0896 −0.0266 −0.0102 −0.0577 −0.1102*

1 −0.0997* −0.2507* −0.0739 0.0274

1 0.3836* 0.0657 0.3721*

1 −0.0915 0.1111*

1 0.035

Abbreviations: PCMHEXP is real per capita mental health expenditures; PCWE is real per capita public welfare expenditures; PCHE is real per capita public health expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; CUR is current unemployment rate; PopDen is the population density; MSD is a mountain state dummy; DR is the divorce rate; ShrNW is the share of the population that is non-white; Sunny is the number of sunny days in a year. * p-Value of 0.05 or less. Table 5 Two-way fixed effect estimates of suicide rates by age and gender.

ln(PCMHEXP) ln(PCWE) ln(PCHE) ln(PCPI) ShrMig CUR PopDen DR ShrNW Intercept Within R2 Between R2 Overall R2

Male, all

Female, all

Male, 25–64

Female, 25–64

0.020 (0.026) 0.024 (0.038) −0.034*** (0.008) −0.215 (0.261) 0.040 (1.619) 0.001 (0.001) −0.039 (0.042) −0.005 (0.016) −0.001 (0.027) 3.709*** (0.866) 0.112 0.246 0.216

0.109** (0.045) 0.059 (0.078) 0.009 (0.019) −0.327 (0.474) 0.768 (2.562) −0.001 (0.001) 0.050 (0.086) −0.009 (0.015) 0.092 (0.066) 2.880* (1.579) 0.077 0.090 0.010

0.033 (0.042) 0.015 (0.051) −0.041*** (0.011) −0.189 (0.359) −0.600 (2.588) 0.001 (0.001) −0.004 (0.044) 0.001 (0.023) 0.031 (0.035) 3.822*** (1.158) 0.107 0.155 0.390

0.077* (0.045) 0.119 (0.117) 0.010 (0.026) −0.555 (0.669) 2.239 (3.089) −0.001 (0.001) 0.147 (0.145) −0.004 (0.021) 0.145* (0.073) 3.874* (2.227) 0.098 0.006 0.006

Notes: dep. var.: natural log of suicide rate. Sample size is 443. State and year fixed effects (dummies) are not reported. Abbreviations: PCMHEXP is real per capita mental health expenditures; PCWE is real per capita public welfare expenditures; PCHE is real per capita public health expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; CUR is current unemployment rate; PopDen is the population density; DR is the divorce rate; ShrNW is the share of the population that is non-white. * Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 10 percent level. ** Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 5 percent level. *** Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 1 percent level.

and are statistically significant. Public welfare expenditures are correlated with increases in suicide but are not statistically significant in any of the models. Public health expenditures are negative and statistically significant in male suicide models, but positive and not statistically significant in the female suicide models. These results will be informative of the direction and magnitude of endogeneity bias when being compared to the main (GMM) estimates in Table 6. Regarding the other control variables, they generally fit the sign expectations, but few carry any statistical significance. Their statistical significance is actually dependent on state fixed effects, as almost all fit the expected signs and become statistically significant when estimated via OLS without fixed effects.12

12

Those results can be provided upon request by the authors.

Table 6 features the GMM estimates with the endogenous treatment of the lagged dependent variable, per capita mental health, and per capita health expenditures. Like in Minoiu and Rodríguéz Andres (2008), measures of model fit are higher for male than for female suicide rates. The most striking difference in going from the fixed effect estimates in Table 5 to the GMM estimates in Table 6 is the reversal of the signs on per capita public mental health and public welfare expenditures. Similar to the fixed effect estimates, the GMM estimates for per capita welfare expenditures lack statistical significance. Notably, the counterintuitive positive coefficient for public welfare expenditures in Table 5 turns negative in Table 6 when the other public spending variables are treated as

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Table 6 System GMM estimates of suicide rates by age and gender.

AR(1) ln(PCMHEXP) ln(PCWE) ln(PCHE) ln(PCPI) ShrMig CUR PopDen MSD DR ShrNW Sunny Intercept R2 A–B test AR(1) A–B test AR(2) Hansen test

Male, all

Female, all

Male, 25–64

Female, 25–64

0.420** (0.171) −0.039 (0.114) −0.026 (0.069) −0.038** (0.016) −0.177 (0.282) 2.333 (4.098) 0.001 (0.002) −0.229*** (0.081) 0.166** (0.073) 0.029 (0.020) 0.028 (0.065) −0.001 (0.001) 2.117 (1.280) 0.854 0.006 0.111 1.0

0.302* (0.164) −0.006 (0.236) −0.031 (0.145) 0.054 (0.067) −0.656 (0.710) 8.258 (6.238) 0.000 (0.004) −0.183 (0.175) 0.202*** (0.068) 0.022 (0.036) −0.018 (0.078) −0.001 (0.001) 3.433 (2.832) 0.596 0.006 0.276 0.997

0.369** (0.183) 0.070 (0.138) −0.042 (0.095) −0.030 (0.025) −0.469 (0.487) 7.558 (4.669) 0.000 (0.002) −0.185 (0.142) 0.146** (0.062) 0.024 (0.034) 0.039 (0.103) −0.001 (0.001) 3.696** (1.793) 0.77 0.01 0.55 1.0

0.229* (0.130) −0.146 (0.177) −0.059 (0.125) 0.079 (0.064) −0.757 (1.082) 5.097 (7.279) −0.002 (0.004) −0.116 (0.286) 0.186** (0.077) 0.024 (0.053) 0.156 (0.152) −0.001 (0.001) 3.706 (3.932) 0.507 0.007 0.284 0.999

Notes: dep. var.: natural log of suicide rate. Sample size is 443. The lagged dependent variable, PCMHEXP, and PCHE are treated as endogenous (instrumented with their own lags and lagged first-differences, t − 1 and deeper). Year fixed effects (dummies) are not reported. A pair-wise correlation of the predicted values with the observed values of the dependent variable is reported as the R2 statistic in the GMM regressions since the GMM regression output does not provide the R2 statistic. Abbreviations: AR(1) is one-year lag of dependent variable; PCMHEXP is real per capita mental health expenditures; PCHE is real per capita public health expenditures; PCWE is real per capita public welfare expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; CUR is current unemployment rate; PopDen is the population density; MSD is a mountain state dummy; DR is the divorce rate; ShrNW is the share of the population that is non-white; Sunny is the number of sunny days in a year. * Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 10 percent level. ** Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 5 percent level. *** Robust standard errors clustered by state are reported in parentheses, with statistical significance indicated at 1 percent level.

endogenous in our GMM estimator.13 The point estimates for public mental health expenditures across demographic groups ranges from −0.146 among females age 25–64, to 0.070 in males of the same age. All of the point estimates for public mental health spending lack statistical significance. Public health spending per capita is negative and statistically significant among men, but not statistically significant and positive for women. This suggests that, at least for men, there are complementary effects since the health care system plays a role in determining whether or not a suicide attempt actually results in a fatality. The literature on female suicide suggests that their attempts are less likely to have the intention of actually ending their own life, and as such the health care system may play a less important role.14

even when the effect is negative. The most favorable estimate is among 25–64 females, whose estimate implies that a one percent increase in public mental health expenditures per capita would reduce the incidence of suicide among that group by 0.91 per 100,000 women in this age group. Arguably, per capita welfare expenditures may be a more reliable mechanism for deterring suicide, as it is negative in every demographic group shown in Table 7, and has magnitudes similar to public mental health expenditures. The largest effect on suicide that can be found in Table 7 comes from per capita personal income increases, though this effect is not statistically significant. A one percent increase in per capita personal income, an admittedly difficult policy achievement, reduces the suicide rate by more than a standard deviation in the sample’s

5.2. Qualitative significance

Table 7 Marginal effect of a one percent increase of independent variable on the number of suicides (evaluated at the mean).

While Table 6 contains the main estimation results in terms of statistical significance, Table 7 provides context to their analytical significance. Specifically, Table 7 uses the coefficients from Table 6 to construct the point estimate change from the mean suicide rate that would result from a one percent increase in each of the continuous independent variables. For instance, from Table 7 it can be seen that a one percent increase in per capita welfare expenditures is correlated with −0.52 fewer male suicides per 100,000. This represents about 9.1 percent of the standard deviation (5.74) for this group, and is about five fewer suicides per 10 million males. In addition to lacking statistical significance, the marginal effect of per capita state mental health expenditures is relatively small

13 The Arellano–Bond autocorrelation and Hansen over-identification tests are the two commonly used methods of evaluating the appropriateness of the dynamic GMM estimator for a particular model. For the GMM regressions shown in Table 6, the Arellano–Bond tests reveal the presence of first-order but no second-order autocorrelation in residuals, while the Hansen test fails to reject the null hypothesis that the instruments used in the regression are exogenous. As in Minoiu and Rodríguéz Andres (2008), these tests confirm the appropriateness of the system GMM estimator for our model. 14 See Moscicki (1994) for a literature review on gender differences in completed and attempted suicides.

ln(PCMHEXP) ln(PCWE) ln(PCHE) ln(PCPI) ShrMig CUR PopDen MSD DR ShrNW Sunny

Male, all ( = 5.74)

Female, all ( = 1.51)

Male, 25–64 ( = 6.72)

Female, 25–64 ( = 2.24)

−0.78 −0.52 −0.76** −3.32 0.48 0.01 −0.01*** 3.70** 0.60 0.01 0.00

−0.03 −0.14 0.26 −2.21 0.40 0.00 0.00 1.03*** 0.10 0.00 0.00

1.83 −1.04 −0.74 −9.41 1.97 0.00 −0.01 3.95** 0.60 0.01 0.00

−0.91 −0.38 0.55 −3.56 0.35 −0.01 0.00 1.37** 0.16 0.01 0.00

Notes: calculated using coefficients from Table 6, and the means/standard deviations of the corresponding independent variables reported on Table 2. Standard deviations of the corresponding population group’s suicide rate are reported in parentheses. Abbreviations: PCMHEXP is real per capita mental health expenditures; PCWE is real per capita public welfare expenditures; PCHE is real per capita public health expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; Unemployment is current unemployment rate; PopDen is the population density; MSD is a mountain state dummy; Divorce is the Divorce Rate; ShrNW is the share of the population that is non-white; Sunny is the number of sunny days in a year. ** Statistical significance indicated at 5 percent level. *** Statistical significance indicated at 1 percent level.

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Table 8 Instrument Variable/Two-Stage-Least Squares Regression on cross-section of states when variables are 1997–2005 average.

ln(PCMHEXP) ln(PCWE) ln(PCHE) ln(PCPI) ShrMig CUR PopDen MSD DR ShrNW Sunny Intercept R2

Male, all

Female, all

Male, 25–64

Female, 25–64

0.014 (0.212) −0.580 (0.478) 0.370 (0.307) −0.573 (0.507) −3.536 (6.666) 0.046 (0.030) −0.486** (0.216) 0.034 (0.187) 0.090** (0.044) 1.356* (0.807) 0.000 (0.001) 4.148** (1.801) 0.601

−0.407* (0.247) −0.050 (0.571) 0.326 (0.371) 0.375 (0.568) 8.660 (6.151) 0.041 (0.035) −0.608** (0.239) 0.251 (0.237) 0.049 (0.047) 0.149 (1.001) −0.001 (0.001) −0.520 (2.024) 0.582

0.014 (0.220) −0.624 (0.493) 0.427 (0.309) −0.743 (0.522) −3.671 (7.738) 0.044 (0.033) −0.315 (0.218) −0.008 (0.187) 0.091* (0.047) 1.462* (0.803) 0.000 (0.001) 4.946*** (1.866) 0.485

−0.511* (0.285) −0.102 (0.687) 0.399 (0.438) 0.298 (0.662) 9.293 (7.311) 0.050 (0.043) −0.581** (0.255) 0.212 (0.281) 0.043 (0.055) 0.414 (1.197) −0.001 (0.001) −0.217 (2.369) 0.513

Notes: dep. var.: natural log of suicide rate. Sample size is 51 (50 U.S. states and D.C.). PCMHEXP and PCHE are instrumented by their corresponding value in 1990. Abbreviations: PCMHEXP is real per capita mental health expenditures; PCHE is real per capita public health expenditures; PCWE is real per capita public welfare expenditures; PCPI is real per capita personal income; SHRMIG is net migration as a proportion of the population; CUR is current unemployment rate; PopDen is the population density, MSD is a mountain state dummy, DR is the divorce rate; ShrNW is the share of the population that is non-white; Sunny is the number of sunny days in a year. * Robust standard errors are reported in parentheses, with statistical significance indicated at 10 percent level. ** Robust standard errors are reported in parentheses, with statistical significance indicated at 5 percent level. *** Robust standard errors are reported in parentheses, with statistical significance indicated at 1 percent level.

suicide rate in three of the four cases. The effect of per capita income is larger for men than for women, just as it is with per capita welfare and health expenditures. According to the estimates, it would seem to suggest that public policies directed at income supports and growth would be a better use of public resources for combating suicide than mental health expenditures.15 The effect of unemployment on suicide is analytically small across all models in Table 7. Admittedly, the period of this data is marked by some historically low levels of unemployment, and other periods with greater variation might turn up different findings. Other studies that have more closely examined the link between unemployment and welfare using micro-data have verified that some link exists. A one percentage point increase in the divorce rate (e.g. a change from five to six percent) is correlated with 6 more suicides per million for men, compared to just 1–1.6 more suicides per million for women. This is very similar to Stack’s (1980) finding that a one percent increase in the divorce rate increased the suicide rate by 0.54 persons. 5.3. Robustness checks The model specifications in this study were based primarily on the work of Minoiu and Rodríguéz Andres (2008) and Zimmerman (1995, 2002). However, alternative empirical specifications and control variables were used and are discussed here briefly, but are not reported to conserve space (these estimates are available upon request). First, estimating the same models with variables in levels instead of their logarithmic counterparts had no qualitative impact on the results. Second, as previously described, the mental health expenditure data is based on extrapolations from the observed data for the missing years (1998, 1999, and 2000). Excluding these years of interpolated spending data has no qualitatively significant effect on the reported estimates. Adding a control variable to indicate the use of extrapolated data in that year had a p-value of 0.98 when time fixed effects were included. Thus, the main difference from the inclusion of the extrapolated observations is increased statistical significance for the control variables due to the larger sample size. Third, Eq. (1) was also estimated without state fixed effects

15 If mental health expenditures are excluded and the dataset is expanded down to 1980, the effect of per capita public welfare expenditures increases in magnitude to a correlation coefficient of −0.05 for men and −0.08 for women, though p-values are around 0.2 so they are not statistically significant either.

using a pooled cross-section OLS approach, which made most of the socioeconomic control variables statistically significant. However, the use of a pooled cross-section OLS estimator is ill-advised in the presence of unobserved heterogeneity as affirmed by the Hausman and Breusch-Pagan LM test results (available upon request). Using one-year lags of the public spending measures produced similar insignificant results for these variables. Finally, the negative sign on the per capita mental health expenditures is sensitive to the inclusion of per capita health expenditures, as omitting this variable causes state mental health expenditures to become positive yet not statistically significant.16 Some research in the “happiness” literature has suggested a connection between self-reported happiness and income inequality. If individuals have interdependent utility functions, then their own personal happiness may decline if their incomes are lower than their social reference group. This is a controversial string of research (e.g. see Epstein, 2006; Wilkinson, 2007) questioned by other studies (Andres, 2005). Nevertheless, we attempted to include different measures of state-level income inequality derived from the IRS Statistics of Income (SOI) state-level income data, but found them to be highly collinear ( = 0.89) with per capita personal income, similar to Yakovlev (2010).17 Using an income inequality measure in place of, or in addition to, per capita personal income showed that increases in income inequality reduced suicide by a margin that is not statistically significant. An alternative transformation of the data that was used by Flavin and Radcliff (2009) at the recommendation of the American Association of Suicidology was also tested. This alternative approach uses the averages of the variables from 1997 to 2005 for the 50 states plus the District of Columbia and the 1990 per capita public health and public mental health expenditures as instruments for their time-averaged counterparts. The results from this cross-sectional regression that are shown in Table 8 are somewhat mixed, as mental health expenditures is negative and statistically significant among female suicides at the 10 percent level, but not statistically significant and positive for men. Using the same approach as in Table 7 to calculate the marginal qualitative effect, this represents a 1.5–2.7 suicide reduction per 100,000 females, but a

16 We thank an anonymous referee for suggesting the inclusion of the per capita health expenditures variable in the regression. 17 The measure we used calculated the percentage of households in the state reporting more than $200,000 to the percentage reporting less than $50,000.

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0.30–0.35 increase among their male counterparts. Welfare expenditures continue to be negative but not statistically significant for all groups, with marginal qualitative effects around 10 male suicide reductions per 100,000 and 0.30 fewer female suicides per 100,000 women. Many of the socioeconomic control variables become statistically significant, and in general retain their expected signs. A notable exception is per capita income, which changes signs from those in Table 6 for female suicide rates. 6. Conclusions and areas for future research Unlike previous research on various forms of public spending, this study finds little evidence that public mental health spending per capita have reduced suicide rates among the U.S. states from 1997 to 2005. While a statistically significant pair-wise correlation exists between suicide rates and per capita public mental health spending, this relationship is small and not statistically significant when socioeconomic variables and other forms of public spending are considered. Public welfare expenditures are about as effective in suicide prevention as public mental health expenditures, though they are also not statistically different from zero. Furthermore, public health expenditures appear to have complementary effects in reducing male suicide rates. After comparing estimates for other socio-economic variables, we conclude that policies aimed at income growth and financial support for low income individuals are more likely to be effective in suicide prevention than public mental health spending. There are several areas for future research on the relationship between state policies and suicide or mental health in general. Suicide attempts could similarly be a worthy alternative dependent variable, as females attempt suicide at a much higher rate than men, but are much less likely to die from these attempts (CDC, 2008). Public mental health expenditures may also have other effects on mental health outcomes aside from suicide. For instance, the incidence of eating disorders or substance abuse rates may be targets of mental health programs that receive public funding. Even though the intended function of the spending may be to influence an aspect of mental health not directly related to suicide, estimates of its impact on suicide are still relevant because it may have the unintended consequence of redistributing resources away from alternative programs that aim at suicide prevention. Nevertheless, future work might examine individual components of spending as the data becomes available. Three further important points of qualification should be noted. First, these results do not imply that mental health treatment is ineffective at preventing suicide. Suicide prevention centers, which are often supported by private funding, have repeatedly been found to be effective (e.g. see Lester, 1997), and these results for public mental health expenditures should not be interpreted as saying otherwise. Secondly, regression analysis does not imply that eliminating all mental health expenditures would have no effect on suicide rates. Rather it implies that, at current per capita levels, state spending could be reduced or increased by some margin without observing a statistically significant difference in the incidence of suicide. Finally, since this is a test of total public spending on mental health, it is implicitly testing how the money is being spent. Reallocating spending or resources could result in different outcomes. Acknowledgements The authors wish to express their appreciation for helpful comments from Nichole Quon and Haeil Jung, and two anonymous referees. Furthermore, we appreciate undergraduate research assistant Joseph Min Kim for his assistance in data collection. The authors are solely responsible for any errors.

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