Fiscal shocks and asymmetric effects: a comparative analysis

We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4. In doing so, we use both a vector autoregressive and the novel support vector machines systems in order to extract the fiscal policy shocks series. The latter has never been used before in a similar macroeconomic setting. Within our research framework, in order to test the robustness of our results to alternative aggregate money supply definitions we use two alternative moentary aggregates. These are the commonly reported by central banks and policy makers simple sum monetary aggregates at the MZM level of aggregation and the alternative CFS Divisia MZM aggregate. From each of these four systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight different types of unanticipated fiscal policy shocks are next used to empirically examine their effects on the growth rate and the cyclical component of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock.

Introduction
In this paper we empirically test the existence of non-linearities that may be associated with the conduct of fiscal policy. In doing so, we try to detect two types of fiscal policy asymmetries: first, whether equal in magnitude contractionary or expansionary fiscal shocks have the same multiplier impact on real output, and second whether theoretically equal -in terms of their impact on the government budget fiscal policy tools, such as a tax cut or an increase in government spending, have the same impact on output.
Fiscal and monetary policies are the cornerstone of policymaking. However, until 2000 the main bulk of empirical research was dedicated solely to the effects of monetary policy. In the aftermath of the global crisis of 2008 there is a growing debate of whether governments should run fiscal stimulus packages in order to restore previous growth rates or run an austerity program to reduce deficits and in the long-run debt as a percent of GDP. Recently for example, highly indebted Eurozone countries (Greece, Ireland, Portugal and Spain) are required to implement fiscal austerity measures in order to balance their balance sheets. In this context it is interesting to see whether and how Keynesian principles may apply.
According to (Bertola and Drazen, 1993), governments should choose fiscal stimulus packages if they accept a positive and above unity fiscal multiplier regardless of the debt to GDP ratio. Keynesian economics assert that government spending and tax cuts, directly affect disposable private income and through the channel of active demand the economy tracks itself back to a growth path. The fiscal multiplier under Keynesian beliefs is well above unity as there is no crowding out effect and the wealth effect is not so strong. Due to various rigidities in the markets (labor, goods and services), this fiscal stimulus during recessions and fiscal contraction during boom times accordingly, is necessary and appropriate in order to restore equilibrium. Although, the exact value of the multiplier depends on various other factors, such as the simultaneous usage of monetary policy, the openness of the economy, the exchange rate regime e.t.c. its sign, however, is not under question: we expect a positive impact on GDP from an increase in government spending.
The neoclassical school on the other hand, asserts that government spending or tax cuts have no impact on GDP due to the Ricardian equivalence (Barro 1974). Agents fully anticipate the debt burden of the fiscal stimulus, expecting higher taxes in the future (wealth effect). Thus, in order to smooth out their level of consumption they save more as their disposable income increases leaving private consumption unchanged.. There is a crowding out effect of the private sector that fully offsets the increase of the demand from the public sector which renders the fiscal multiplier to zero. This is more apparent in periods of growth, since then the probability of a more efficient usage of resources from the government is lower than it is during a recession. On the other hand, there is room for a low positive multiplier during recessions, since resources are underused.
There is also a new class of research pointing to an exactly different direction than that of Keynesian economics: these find that the multiplier of fiscal contraction is positive and vice versa. This is known as contractionary fiscal expansion effect or expansionary fiscal contraction due mostly to a wealth effect that is, consumers put more weight to future consumption than to current one. At this notion, Alesina and Perotti (1997) and Giavazzi and Pagano (1990) among others, state that fiscal contraction based on expenditure cuts may be expansionary if it is accompanied by currency devaluation or by agreements with the unions. The greater this adjustment is the more is being anticipated by the agents leading to more powerful results. Furthermore, a tax increase in order to accommodate a deficit has the exact opposite results than a decrease of government spending because it reduces the competitiveness of the economy. This view is enhanced by Blanchard (1990), who states that fiscal consolidation may reduce uncertainty for the future leading to an increase in household's wealth today. This can be achieved through the decrease of interest rates as a result of the reduction of the risk premium of government bonds (Alesina and Ardagna, 2009).
In their seminal paper, Bertola and Drazen (1993), postulate that the sign of the fiscal multiplier depends on the GDP to debt ratio. In a hypothetical economy, where all agents are rational, and GDP to debt ratio is low, an increase of the government spending will be neutral to the real economy, featuring a Ricardian or even a negative effect. If the GDP to debt ratio is relatively large a fiscal consolidation signals a trial of the government to stabilize the economy and thus lifting future uncertainty leading to a positive multiplier or to an anti -Keynesian effect.
According to the above, the fiscal multiplier for an increase in government expenditures can range between negative and positive values and be large or small. According to the above, we can identify five potential sources of nonlinearities/asymmetries of fiscal policy: a) the phase of the business cycle, b) the GDP to debt ratio, c) the sign of the shock (positive versus negative shocks of the same instrument), d) the nature of the shock (spending versus revenues), e) the magnitude of the shock.
In this paper, we try to estimate the value of the fiscal multiplier taking into account the sign and the nature of the shock. Using VAR analysis with identified structural errors, Machine Learning (ML) techniques, a new dataset for the U.S. economy and running various tests, we come along some very interesting results. We cannot reject asymmetries in government spending between a positive unanticipated government spending shock and a negative such shock. The same asymmetries are detected for the unanticipated government revenue shocks. We also detect asymmetries in expansionary and contractionary unexpected fiscal policies.
The remainder of this paper is organized as follows: Section 2 presents the empirical literature review, Section 3 provides a detailed description of the data and the methodology used. The main results are presented in Section 4 and Section 5 concludes.

Literature Review
Despite this divergence of opinions, the empirical research is too narrow and is divided between linear and nonlinear policy analysis. Linear analysis covers most of the research, while nonlinear analysis is being implemented only in recent years. Empirical research focused into fiscal policy in the last decade following mostly the seminal work of Blanchard and Perotti (2002) in a VAR analysis which was built upon the innovative work of Sims (1980) in VAR analysis. Blanchard and Perotti (2002) introduced a new method of identification of structural errors using institutional information on tax and transfer system and under the main assumption, among others, that fiscal policy is a rather long process using quarterly data introduce their restrictions and identify structural fiscal shocks that are exogenous to the rest of the VAR variables. They conclude that, the U.S. economy experiences Keynesian effects regarding the sign of fiscal multipliers as well as there are asymmetries between tax and government purchases multipliers but not asymmetries of the effects on the output of a positive versus a negative change in taxes. Tagkalakis (2008) using an unbalanced yearly panel data set  of nineteen OECD countries, confirmed that in the presence of binding liquidity constraints during recessions both positive government spending and negative tax shocks have stronger stimulus effects on private consumption than in expansions. In a different analytical framework Leeper et al. (2010) show that government investment is contractionary in the short run, at worst, and has a muted impact, at best. This is mainly due to substantial time to build lags. The results over the long run are conditional upon the productivity of the public capital. Pereira and Lopes (2010) examining U.S. quarterly data over the 1965:2 to 2009:2 period in a Blanchard-Perron identification mode into a Bayesian simulation procedure, they find that policy effectiveness has come down substantially. More specifically, this trend is more evident for taxes net of transfers than for government expenditures, although, fiscal multipliers keep Keynesian signs. Cogan et al. (2009), focusing on an empirically estimated macroeconomic model for the U.S., find that the government spending multipliers are much less in new Keynesian that in old Keynesian models. The multipliers are less than one as consumption and investment are crowded out. On the other hand, Romer and Romer (2010), using new sources of data such as presidential speeches, executive-branch documents and Congressional reports, identify the size, timing and principal motivation for all major post-war tax policy actions. Their main findings indicate a very large effect of tax changes on output and on investments. This multiplier is well above unity, being in stark contrast with the findings of previous empirical researches. Barro and Redlick (2009), estimate a multiplier regarding responses of U.S. GDP to changes in defence spending between 0.6-0.7. As they point out in their paper, the exact volume of the multiplier is subject to economic slack, reaching unity as unemployment rate is quite high, around 12%. Positive tax rate shocks have significantly negative effects on real GDP growth. Mountford and Uhlig (2009), incorporating a VAR analysis and using new restrictions to identify revenue and spending shocks, as well as taking into account business cycle and monetary shocks, conclude that deficit financed tax cuts are the best fiscal policy to improve GDP, finding a very large multiplier. Gali et al. (2007), show that in an economy in which for some households (named rule of thumb consumers) consumption equals labor income and there exist sticky prices, it is possible that government spending shocks positively affect consumption. In this way, wealth effects are totally overshadowed by the sensitivity to current disposable income. Aggregate demand is partly insulated from the negative wealth effect generated by the higher levels of taxes needed to finance the fiscal expansion.
In a non-linear framework, Baum and Koester (2011), using a threshold VAR model, analyse the effects of fiscal policy on economic activity over the business cycle for Germany. They derive a fiscal multiplier around 0.7 for both revenues and spending in a linear model. When they take into account the phase of the business cycle, they find a spending multiplier around unity in boom times and 0.36 in recessions. There are also non linearities regarding the sign of government intervention through spending. With respect to revenue shocks they find less diverging results for both the phase of the business cycle and the type of fiscal policy implemented (expansionary or contractionary).
As it is clear from the above, empirical research spans a wide range of tests, including linear and non-linear models concerning the phase of the business cycle, the financial constraint of the agents, the nature and the sign of the fiscal intervention. Most of these studies, converge to fiscal multiplies below unity with the spending multiplier being of greater importance than the tax multiplier. In what follows we try to unfold the impact of fiscal policy using quarterly data for the U.S. economy for government spending, total government revenue, GNP (growth rate and cyclical component) and monetary variables such as the Treasury bill rate and the money supply. In this study we introduce four main innovations: first, to the best of our knowledge the Divisia monetary aggregates have not yet been used to previous research pertaining to fiscal policy. Second, following Cover's (1992) procedure of identifying monetary policy shocks we extract the unanticipated fiscal policy shocks on government spending and revenue. Moreover, we introduce Support Vector Regression models which have never been used before in this research area, but the empirical results of other implementations such as exchange rate , bank insolvency  and GDP forecasting  imply high generalization abilities with non-linear and non-stationary datasets. Finally, we explicitly test for the asymmetric effects on the growth rate and the cyclical component of real private GNP of a contractionary and expansionary fiscal policy. We come up with three key findings; first, all fiscal multipliers are below unity but with signs as predicted by Keynesian theory. Second, government expenditures have a larger impact as compared to the tax policy and finally, positive government spending shocks are more significant than negative spending shocks. All these results are in line with previous studies and are robust through many tests using structural identification proposed by Blanchard and Perotti (2002).

The Data
In this study we use quarterly data that span the period 1967Q1 to 2011Q4. The range of the data sample is limited by the availability of the monetary aggregates. The data are taken from the St. Louis Federal Reserve Economic Data (FRED) service. These include the real private Gross National Product, government consumption expenditures and gross investment, government current receipts and the 3-month Treasury bill rate 1 . All initial data are in current values and they are transformed -with the exception of the Treasury-bill rate-to real series by using the implicit price deflator of the GNP with 2005 as the base year. The two monetary aggregates used in this study are the official simple-sum aggregates in the MZM level of aggregation as they are reported by the Federal Reserve Bank of St. Louis and the Divisia MZM aggregates both in real terms. The Divisia monetary aggregate series are from the new Divisia monetary aggregates maintained within the Center of Financial Stability (CFS) program Advances in Monetary and Financial Measurement (AMFM), called CFS Divisia aggregates and documented in Barnett et al. (2013). We use both types of monetary aggregates in an effort to see whether our results are affected by the so-called "Barnett critique". In this regard, Barnett (1980) argues that official simple-sum monetary aggregates, constructed by the Federal Reserve, produce an internal inconsistency between the implicit aggregation theory and the theory relevant to the models and policy within which the resulting data are nested and used. That incoherence has been called the Barnett Critique [see, for example, Chrystal and MacDonald (1994) and Belongia and Ireland (2013)], with emphasis on the resulting inference and policy errors and the induced appearances of function instability. Finally, all data with the exception of the Treasury-bill rate are transformed to natural logarithms. To test the integration properties if our data we perform three different unit root tests: a) an augmented Dickey-Fuller test, b) a KPSS test where the null hypothesis is stationarity and finally c) an Elliott-Rothenberg-Stock test. In Table 1 we present the results of these unit root tests and we conclude that all variables used in this study are I(1). Thus for the rest of the empirical section we use the first differences of the variables unless otherwise stated.

Empirical Model and Identification
Since we determined in the previous section that all our variables are I(1) we proceed by testing our variables for a commons stochastic trend. Table 3, reports the results of the Johansen maximum likelihood cointegration tests on a VAR with lag length p = 3. We also report tail areas of residual misspecification tests. Two test statistics are used to test for the number of cointegrating vectors, the trace ( ) and maximum eigenvalue ( ) test statistics. In the trace test the null hypothesis that there are at most r cointegrating vectors is tested against a general alternative. In the case of the maximum eigenvalue test the alternative is explicitly stated. Using 99% critical values for the two tests we that the and test statistics provide evidence of one cointegrating relation in both VAR models we test: one with the simple sum MZM as the exogenous monetary aggregate and the other with the CFS Divisia instead. Since we detected one cointegrating vector, we proceed in our analysis by using a Vector Error Correction Model (VECM) including the lagged error correction term in the VARs regressors.
As it was previously mentioned, we use a structural VAR model with two alternative sets of exogenous variables. We perform a Blanchard-Perotti (2002) identification procedure to extract the structural errors. The basic reduced form VAR specification in order to identify the structural errors is: , where is a three dimensional vector of the endogenous variables, government revenue (r), government spending (g) and real private Gross National Product (y) and Z t is a vector of the two exogenous monetary variables, the 3-month Treasury bill rate (TB3) and the monetary aggregate, where we use alternatively a simple-sum and a CFS Divisia in the MZM level of aggregation. All variables are in first differences. The exogenous variables vector is two dimensional because we use each monetary aggregate separately along with the TB3 variable. represents the three dimensional vector of reduced form residuals with the corresponding ordering [r t ,g t ,y t ] and finally A 0 is the intercept coefficient vector, A(L q ) is a four lag polynomial and B is the exogenous variables coefficients vector. A four quarter lag length is chosen as there is a seasonality pattern in the response of taxes to outputsee Blanchard-Perotti (2002).

Blanchard-Perotti Identification
We employ the Blanchard-Perotti (2002) method of structural identification. As they well document in their seminal paper, the innovations in the fiscal variables, taxes and revenues are a linear combination of three types of shocks, a) the automatic response of these fiscal variables to output (automatic stabilizers), b) the discretionary effects of revenues to spending shocks and vice versa, c) the random fiscal shocks which are to be identified. Thus, the equation system is: In order to set the appropriate restrictions Blanchard and Perotti further assume that the first set of shocks (a 1 and b 1 for taxes and spending respectively) can be estimated as the elasticity of fiscal variables to output shocks as it takes more than a quarter for a fiscal policy measure to be decided and be implemented. As a measure for tax elasticity on output, a 1, we take into consideration Blanchard-Perotti's calculations who report an average value of 2. As for the spending multiplier, b 1, this is set to zero, as the main component of primary government spending, unemployment transfers is included in net revenues 2 . Then, contemporaneous effect of fiscal variables to output (c 1 and c 2 ) need to be estimated. Again in line with Blanchard et al (2002) and Baum et al. (2011), we use the cyclically adjusted reduced form fiscal policy shocks and we estimate the third equation of the equation system 2. Finally, under the assumption that revenue decisions come first, a 2 is set to zero. This is so, because a 2 represents the discretionary response of revenues to spending.

Support Vector Regression
The Support Vector Regression is a direct extension of the classic Support Vector Machine algorithm proposed by Vladimir Vapnik (1992). When it comes to SVR, the basic idea is to find a linear function that has at most a predetermined deviation from the actual values of the actual dataset. In other words we do not care about the error of each forecast as long as it doesn't violate the threshold, but we will not tolerate a higher deviation. The Support Vector (SV) set which bounds this "error-tolerance band" is located in the dataset through a minimization procedure.
One of the main advantages of the SVR in comparison to other machine learning techniques is the ability to identify global minima avoiding local ones, thus reaching an optimal solution. This aspect is crucial to the generalization ability of the SVR model in producing accurate and reliable forecasts. The model is built in two steps: the training step and the testing step. In the training step, the largest part of the dataset is used for the estimation of the function (i.e. the detection of the Support Vectors that define the band); in the testing step, the generalization ability of the model is evaluated by checking the model's performance in the small subset that was left aside in the first step performing out-of-sample forecasting.
Using mathematical notation and starting from a training dataset , where for each dataset pair, are observation samples and is the dependent variable (the target of the regression system that we need to approximate). As the scope is to minimize the loss function ( ‖ ‖ ) subject to| | , we enforce an upper deviation threshold and creating an "error-tolerance" band. Expanding this initial framework, Vapnik and Cortes (1995) proposed a soft margin model, i.e. accept the existence of vectors outside this error tolerance zone that are penalized according to their distance from the zone. So, they introduce slack variables to the loss function and controlled through a cost parameter C, resulting to a loss function ( ‖ ‖ ∑ ). In this way the primal problem that we wish to minimize is: where are the Lagrange multipliers from the Lagrangian primal function (2).
Instead of solving (1) we attack the dual form of the problem, which takes the form: Subject to ∑ and The solution of the primal problem (2) is ∑ and ∑ Real life phenomena, are rarely modelled by linear functions accurately. A simple way to bypass the problem is by projecting the observed dataset from the initial data space into a higher dimensional space were the linear model is appropriate. The "kernel trick" follows the projection idea while ensuring minimum computational cost: the dataset is mapped in an inner product space, where the projection is performed using only dot products (through special "kernel" functions) within the original space, instead of explicitly computing the mapping of each data point. The SV methodology coupled with the "kernel trick" is a very powerful tool for classification and regression. Non-linear kernel functions has evolved the SVR mechanism to a non-linear regression model, able of approximating non-linear phenomena.

Figure 1:
Upper and lower threshold on error tolerance indicated with letter ε. The boundaries of the error tolerance band are defined by Support Vectors (SVs). On the right we see the projection form 2 to 3 dimensions space and the projected error tolerance band. Forecasted values greater than ε get a penalty ζ according to their distance from the tolerance accepted band (source Scholckopf and Smola, 1998).
The described mapping was performed on four kernels: the linear, the radial basis function (RBF), the sigmoid and the polynomial. The mathematical representation of each kernel is: Polynomial with factors d, r, γ representing kernel parameters.
Again the procedure was repeated with SVR and we extract both fiscal policy structural errors: from government revenue and the government spending vector. Two sets of such errors are used: the ones from the simple sum MZM monetary aggregate as an exogenous variable VAR and the ones from the VAR with the Divisia MZM as an exogenous variable.

The Empirical results
Following Cover (1992), from each of the above two systems (MZM and CFS) we extract the residual series from the equations of government revenue (r) and government spending (g). These represent the unanticipated fiscal policy shocks. The series of the negative government spending shocks equals the government spending shock if the latter is negative otherwise it is equal to zero. The series of the positive government spending shocks equals the government spending shock if this is positive and otherwise it is equal to zero. In the same manner we construct the negative and positive government revenue shocks. Formally: where is the government spending shock extracted as described above. In a similar manner we construct the negative and positive government revenue shocks and .

Systems with contemporaneous shocks
In the previous section we extracted four series of unanticipated fiscal policy shocks, from each one of the two VARs considered in this study as well as for the support vector regression model. For each VAR these are the negative and positive government spending shocks and the negative and positive government revenue shocks series: , , and respectively. In order to investigate the possible existence of fiscal asymmetries, following Cover (1992), we run the following regression with each of the two sets of unanticipated fiscal policy shocks: where is the first difference of the real private GNP at period t, are two lags of the output first differences, , , and are the extracted unanticipated fiscal shocks to the economy as discussed above and , , , , and are parameters to be estimated. In these systems we assume that only current fiscal policy shocks affect the real output growth level and thus we include no lagged values of the fiscal shocks. We also estimate equation (11) with the cyclical component of the real private GNP as the dependent variable. The cyclical component of real private GNP is extracted using a standard Hodrick-Prescott filter with . The empirical results are presented in Table 4. Systems 1 and 3 present the results of the estimation of equation (11) with the fiscal policy shocks identified from the VAR including the simple sum MZM monetary aggregate and Divisia MZM monetary aggregate respectively. System 5 includes the estimates of equation (11) when the dependent variable is the cyclical component of real private GNP and the fiscal policy shocks are identified from a VAR with the Divisia MZM as the monetary aggregate variable 3 . In system ML we present the results of the estimation of equation (11) with the fiscal policy shocks identified from the support vector regression model.
The estimated coefficients and the reported p-values of the fiscal policy shocks provide evidence on the significance and magnitude of the multipliers of the various fiscal shocks on the growth rate of real private GNP and its cyclical component. Moreover, at the lower part of Table 4 we report the tail areas of the F-tests performed in testing for fiscal policy asymmetries. First, we test the null hypothesis that the multiplier of a positive government spending shock is equal to the multiplier of a negative government spending shock ( ) or in other words that a contractionary government spending shock has a symmetric effect on output as an equal expansionary government spending shock. Second, in a similar manner, we test for symmetric effects of the contractionary and expansionary government revenue shocks ( ). Next, we try to investigate whether equivalent in terms of their impact on government deficit fiscal policies have symmetric effects on the level and growth rate of real GNP. First, we test policies that increase the deficit, positive government spending and negative government revenue shocks ( ) and finally shocks that lead to fiscal consolidation, a decrease in government spending and an increase in government revenue shock ( ).
According to Table 4 we have some interesting results. First, all coefficients have the signs expected by theory in all systems except for the system ML. The coefficients of positive government spending shocks (SGP) have positive signs as they are expected to increase real private GNP. The same sign is expected on the coefficients of the negative government spending shocks (SGN): a negative shock multiplied by a positive coefficient produces a decrease in real private GNP. For analogous reasons the estimated coefficients on unanticipated revenue shocks have negative signs: an unexpected increase in government revenue (SRP) is expected to reduce real private GNP and an unexpected decrease in government revenue (SRN) will increase real private GNP.
We detect some asymmetries across all three systems with respect to fiscal policy shocks. It appears that in the System 1 and 2 where the dependent variable is the growth rate of real private GNP expansionary and contractionary unanticipated fiscal policies have asymmetric effects: the expansionary fiscal policy through either a positive unanticipated government spending shock (SGP) or a negative unanticipated government revenue shock (SRN) is statistically significant with p-values of 0.008 and 0.052 respectively. On the contrary, a contractionary fiscal policy through either a negative unanticipated government spending shock (SGN) or a positive unanticipated government revenue shock (SRP) is not significant even at the 0.10 significance level. In System 5 where the dependent variable is the cyclical component of real private GNP it appears that only a positive unanticipated government revenue shock has some impact on the cycle. Both government spending and revenue tests show that in general expansionary unanticipated fiscal policy shocks have asymmetric effects and appear to affect real private output more than contractionary fiscal shocks as the later appear insignificant. Expansionary fiscal policy is significant either through spending or revenue. Nonetheless, the coefficient of the positive unanticipated government spending shock is more than three times larger than the coefficient of the negative unanticipated government revenue shock in Systems 1 and 3 and more than two times larger when the dependent variable is the cyclical component of real private GNP. In the lower part of Table 4, the F-tests show that this asymmetry is statistically significant only for System 1. In system ML the unanticipated fiscal policy shocks does not seem to have any statistically significant impact on the level of the real private GNP.

Systems augmented with lagged shocks
In this section we augment the regressions run in equation (11) by assuming that not only the current values of the explanatory variables affect the level and growth rate of GDP but also four lags that correspond to a year's worth of historical information. The estimated equation now becomes: with similar specification as equation (11). The results from running equation (12) are presented in Table 5. Systems 2 and 4 have the growth rate of real private GNP as the dependent variable with fiscal policy shocks identified from a VAR with simple sum and Divisia MZM as the monetary aggregate exogenous variable respectively. System 6 is estimated with the cyclical component of real private GNP as the dependent variable and the identified residuals from the VAR with the Divisia MZM monetary aggregate 4 . System ML is estimated with the growth rate of real private GNP as the dependent variable with fiscal policy shocks identified from a support vector regression model.
The estimated coefficients and the reported p-values of the contemporaneous and lagged fiscal policy shocks provide evidence on the significance and magnitude of the multipliers of the various fiscal shocks on the level and growth rate of real GNP. In the lower part of Table  5 we report the tail areas of the F-tests performed in testing for fiscal policy asymmetries. In this specification with contemporaneous and four lagged fiscal shocks we are able to perform the following tests: First, we test the null hypothesis that the coefficient of a contemporaneous positive government spending shock is equal to the coefficient of a contemporaneous negative government spending shock ( ) or in other words that an expansionary government spending shock has a symmetric effect on output as an equal contractionary government spending shock. In a similar manner, we test for symmetric effects of the expansionary and contractionary government revenue shocks with the null hypothesis ( ). Next, we test whether equivalent in terms of their impact on government deficit fiscal policies have asymmetric effects on the growth rate of real private GNP and its cyclical component. First, policies that increase the deficit, i.e. positive government spending and negative government revenue shocks ( ) and second, shocks that lead to fiscal consolidation, a decrease in government spending and an increase in government revenue ( ).
Moreover, we perform F-tests that all lagged coefficients of the unanticipated fiscal policy shocks are jointly equal to zero: . Finally, in the last two rows of Table 5 we test for asymmetric cumulative effects of contractionary and expansionary unanticipated fiscal policy shocks: Table 5 summarizes the regressions results and the hypotheses testing evidence. According to these, the estimated coefficients of all unanticipated fiscal policy shocks that are statistically significant appear to have the correct sign. In System 2 where the dependent variable is the growth rate of real private GNP, no shock appears statistically significant. In System 4 with the same dependent variable but shocks identified from a VAR using the Divisia MZM as the exogenous monetary aggregate variable only the contemporaneous unexpected negative government spending shock appears statistically significant with a coefficient of 0.310 and a p-value of 0.075. In System 6 where the dependent variable is now the cyclical component of real private GNP, three types of unanticipated government shocks are significant: the contemporaneous positive and negative government spending with coefficients 0.237 and 0.423 respectively and the second lag of the positive government revenue shock with a coefficient of -0.161. the p-values of these estimates are 0.095, 0.012 and 0.031 respectively. In system ML, three types of unanticipated government shocks are significant: the third lag of the positive and the negative government spending shock and the third lag of the negative government revenue shock. Only the third lag of the positive government shock has the expected sign. The other two shocks have the opposite sign as expected from the theory. From these latter results it seems that both positive and negative government spending shocks have a positive impact, after three quarters, on the private real GNP. On the other hand, an unexpected reduction in government revenues will has a negative impact on the real private GNP in a three quarters period.
In the lower part of Table 5 we report the results of the F-tests discussed above. We find evidence of unanticipated fiscal shock asymmetries in three cases in System 6 and in system ML. In system 6, where the dependent variable is the cyclical component of real private GNP the three reported asymmetries are: a) asymmetry in the type of contractionary policy as negative government spending shocks appear to have a significant impact on the cycle of real private GNP while positive government revenue shocks appear statistically insignificant and the p-value of the test of equality between the two is 0.038; b) the joint test that all contemporaneous and lagged negative unanticipated government spending shocks appears statistically significant with p-value 0.094 while the positive joint government spending shock appears statistically insignificant; c) the joint test that all contemporaneous and lagged positive unanticipated government revenue shocks appears statistically significant with p-value 0.031 while the negative joint government revenue shock appears statistically insignificant. According to these results, only a contractionary unanticipated fiscal policy will have an impact on the cyclical component of GNP either through decreased government spending or increased government revenue. The joint impact of expansionary fiscal shocks appears statistically insignificant. In the last four rows of Table 5 we perform F-tests of equality between the cumulative effects of unanticipated fiscal policy shocks. We cannot reject any of these hypotheses.
In system ML, in which the dependent variable is the growth rate of the real private GNP there is an asymmetry in the expansionary fiscal policy, where the positive government spending shock has a greater impact on GNP than the negative government revenue shock. Moreover, the test that the impact of all contemporaneous and lagged negative unanticipated government revenue shocks is statistically different than the impact of all contemporaneous and lagged positive unanticipated government revenue shocks.

Conclusions
The aim of this paper was to empirically test the effects of fiscal policy shocks on the level and growth rate of real output and reveal possible asymmetries in fiscal policy implementation. The data are quarterly over the period 1967:1 to 2011:4. In doing so, we used two alternative vector autoregressive systems as well as a support vector regression model in order to construct the fiscal policy shocks. These systems differ in the monetary aggregate used as one of the exogenous variables: a simple sum MZM and a CFS Divisia MZM. From each one of these systems we extracted four types of shocks: a negative and a positive government spending shock and a negative and a positive government revenue shock. These eight sets of unanticipated fiscal shocks were used next to empirically examine their effects on the level and growth rate of real private GNP in two sets of regressions: one that assumes only contemporaneous effects of the shocks on output and one that is augmented with four lags of each fiscal shock. Our results are summarized as follows: In the regressions with no lagged shocks we detect some asymmetries across the three systems that extracted errors from the VARs used, with respect to fiscal policy shocks. When the dependent variable is the growth rate of real private GNP expansionary and contractionary unanticipated fiscal policies have asymmetric effects: the expansionary fiscal policy through either a positive unanticipated government spending shock (SGP) or a negative unanticipated government revenue shock (SRN) is statistically significant with p-values of 0.008 and 0.052 respectively while a contractionary fiscal policy through either a negative unanticipated government spending shock (SGN) or a positive unanticipated government revenue shock (SRP) is not significant. When the dependent variable is the cyclical component of real private GNP it appears that only a positive unanticipated government revenue shock has some impact on the cycle. We find that in general, expansionary unanticipated fiscal policy shocks have asymmetric effects and appear to affect real private output more than contractionary fiscal shocks as the later appear insignificant. Expansionary fiscal policy is significant either through spending or revenue even though the coefficient of the positive government spending shock is more than three times larger than the coefficient of the negative government revenue shock in the systems with growth rate of GNP as the dependent variable and more than two times larger in the when the dependent variable is the GNP cyclical component.
Finally, in the systems with lags, only the contemporaneous unexpected negative government spending shock appears statistically significant in System 4. In System 6, where the dependent variable is now the cyclical component of real private GNP, three types of unanticipated shocks are significant: the contemporaneous positive and negative government spending and the second lag of the positive government revenue shock. In system ML, the third lags of the unanticipated positive and negative government spending shocks and the third lag od the negative government spending shock are statistically significant, whereas only the positive government spending shock has the sign as it is expected by the theory.
In these systems, we find evidence of asymmetries in five cases and when the dependent variable is the cyclical component of real private GNP or the growth rate of the real private GNP: a) asymmetry in the type of contractionary policy (negative government spending shocks have a significant impact on the cycle of real private GNP while positive government revenue shocks appear statistically insignificant) b) the joint test that all contemporaneous and lagged negative unanticipated government spending shocks appears statistically significant while the positive joint government spending shock appears statistically insignificant c) the joint test that all contemporaneous and lagged positive unanticipated government revenue shocks appears statistically significant while the negative joint government revenue shocks appear statistically insignificant d) the positive government spending shock has a greater impact on GNP than the negative government revenue shock and e) the impact of all contemporaneous and lagged negative unanticipated government revenue shocks is statistically different than the impact of all contemporaneous and lagged positive unanticipated government revenue shocks.
Finally, the use of the support vector regression method did not gave to our results any extra explanatory power than the classical Blanchard and Perotti method.  0.004 *** 0.004 *** One, two and three asteriscs denote rejection of the null hypotheis at the 10%, 5% and 1% levels respectively. 0.460 0.578 0.109 -Note: *, ** and ***, denote a rejection of the null hypothesis at the 0.10, 0.05 and 0.01 levels respectively  1)  975 Note: *, ** and ***, denote a rejection of the null hypothesis at the 0.10, 0.05 and 0.01 levels respectively


extfieldsofstudy: Business Physics

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A comprehensive framework for tourism and recreation drought vulnerability reduction

The effects of drought are vast, but loss statistics often do not reflect the impacts on the tourism and recreation sector, which for many places is one of the most critical economic drivers. This is concerning because drought events are common across the globe, with varying frequency, duration, and intensity, and are therefore unavoidable. Over the years, drought conditions have been at record levels in many regions, causing deep societal and economic impacts. However, little research has been conducted on connections between tourism/recreation and drought, revealing a distinct disconnect between the tourism/recreation sector and drought management. To bridge this gap in the current understanding of, and approaches to, managing drought in the tourism/recreation sector, we present an interdisciplinary conceptual framework that integrates tourism/recreation into the drought management process to ensure sustainable economic development and community vitality. The model presented here promotes understanding of critical interactions through a bottom-up stakeholder engagement process balanced with formal top-down management approaches.

Introduction
Achieving sustainable water security requires appreciating the full range of interconnections between environment, economy, and society. Community vitality necessitates developing strong adaptive capacity to environmental stressors, including droughts and climate change. The widespread nature of droughts across the globe, coupled with the importance of Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. the tourism/recreation sector in many places, precipitates the need for investigation and action at this important intersection. Using a classic approach to defining drought (Wilhite and Glantz 1985), the tourism/recreation sector is most sensitive to hydrological and socio-economic droughts. Hydrological drought is linked to the effects of a shortfall in summer and winter precipitation on streamflow, reservoir and lake levels. Socio-economic drought occurs when the demand for water exceeds water supply, for example, due to reduced levels of streamflow or snowpack. Socio-economic drought can have significant impact on community vitality, as the ripple effects span across sectors.
Of all natural hazard types, drought is, in many ways, the most subtle. This may be because it has a slow onset and does not capture attention in the same way as other more distinctly defined and dramatic disasters. Yet, droughts have far-reaching impacts, both globally and in the US. From the 618 disaster events recorded between 1900 and 2012 internationally 5 , droughts caused over $100 billion 6 in unadjusted economic losses (EM-DAT 2012). In the US, an estimated average annual drought loss of $6-8 billion exceeds that of all other hazard types (NOAA 2002), consistently causing extensive local and regional impacts. Further, individual US historical drought events rank as some of the costliest when considering all types of hazard catastrophes. For example, the 1988 Drought alone cost the US economy $39 billion (Riebsame et al 1991) ($75 billion in 2012 dollars), making it the second costliest natural disaster after Hurricane Katrina (over $100 billion), and just exceeding estimates of the 2012 Midwest Drought at $35 billion and Hurricane Sandy at $65 billion (Aon Benfield 2013). In spite of the magnitude and widespread geographic extent of severe droughts, economic, environmental, and social impacts are not comprehensively documented for all sectors and regions (Wilhite et al 2007).
In many parts of the world, tourism/recreation is a critical economic sector, and anecdotal evidence is mounting linking economic losses to drought, even though the connection is minimally studied or systematically documented. In fact, very little research has been conducted on disasters and tourism generally (Thomas et al 2006), and even fewer studies document drought impacts in this sector (Faulkner 2001, Ritchie 2008, Wilhelmi et al 2008. An exception to this nearly absent tourism/recreation-drought connection is the monitoring and assessment of winter snowfall and snowpack, often in the context of climate change (Breiling and Charamza 1999, Scott and McBoyle 2001, Gossling and Hall 2006, Scott et al 2006, Dawson and Scott 2007, Scott and McBoyle 2007, Scott et al 2008, Shih et al 2009. In an attempt to bridge this notable gap in current knowledge at the intersection of drought, tourism, and recreation, we present an interdisciplinary conceptual framework that specifically integrates tourism/recreation into drought management to ensure economic stability and community vitality. The framework guides future investigations that are situated in International Assessment Efforts on Climate Change (IPCC) broadly, and the National Climate Assessment at a national level in the US, which taken together offer information to inform decision-making at the global, regional and local levels. Further, the framework promotes an increased understanding and appreciation of drought effects on tourism/recreation through a bottomup stakeholder engagement process balanced with formal top-down management approaches. This framework can be 5 Disasters in the EM-DAT database are recorded for: ten or more people reported killed, 100 people reported affected, a call for international assistance, or a declaration of a state of emergency. Thus, these do not represent moderate to smaller events, only those reaching this disaster threshold. 6 All losses are reported in US dollars. utilized to characterize the relationship between drought and tourism/recreation and also to identify vulnerabilities with a focus on increasing adaptive capacity. The knowledge gained from taking this comprehensive approach can be used to develop more proactive strategies for drought mitigation and climate adaptation that ensures increased communication across all affected groups in the short-and long-term. This approach may also be applied to many geographic locations, and modified for use in other economic sectors.

Interactions between drought and tourism/recreation
The economic and social effects of droughts are vast, but loss statistics often do not reflect the explicit impacts on the tourism/recreation sector, one of the most critical economic drivers for many regions around the world. This is particularly relevant because droughts are common in nearly all parts of the globe, with varying frequency, duration, and intensity. Their impacts are becoming increasingly complex (Wilhite et al 2007), emerging from a multifaceted interaction between demands for water by humans and the physical availability of water Wilhite 2002, Hayes et al 2004).
Drought has direct and indirect impacts on the tourism/recreation sector, and can span all seasons. The most obvious are reductions in water-or snow-dependent activities, such as boating, rafting, canoeing, fishing, snowmobiling, or skiing, resulting from lower water levels or snowpack, as well as from shortened or shifted seasons. Water restrictions can also pose distinct challenges to water-reliant recreation. Intangible relationships are more difficult to quantify and link to drought, such as decreased visitations, cancellations in hotel stays, or a reduction in booked holidays. These could stem from negative perceptions of dryness, fire bans, or wildfires burning nearby vacation destinations (even if these are in actually not in the vicinity). Wildlife viewing or hunting can also be affected through changes in animal and bird migratory patterns, causing reduced revenues for nearby towns and communities. Further, if drought results in negative experiences (for example, water restrictions or viewing a drought-stressed, brown landscape when visitors think it should be green), people may convey this to others, thereby reducing future visits from formerly interested visitors. The outcomes ultimately result in decreased tourist dollars for the local economy and a reduction of sales taxes, potentially even leading to unemployment or social and psychological impacts in the community. These may not be felt equally by all groups; seasonal workers, for example, might be disproportionately impacted.
Existing drought indices used to monitor and assess drought severity, such as the Surface Water Supply Index, Standardized Precipitation Index, or the US Drought Monitor do not incorporate impacts on tourism/recreation (Heim 2002). Certainly, the use of these indices could be utilized by the tourism/recreation sector to prepare for and adapt to drought, but the interplay between drought and this sector is not captured by any of them. In the US, The Drought Impact Reporter, launched by the National Drought Mitigation Center in July 2005, aims to catalog drought impacts, with 1102 reports out of 24 380 total entries categorized by tourism and recreation recorded since 2005 (National Drought Mitigation Center 2012). While this is not a systematic reporting system, nor does it provide a mechanism for comparison, it is at least the foundation of an inventory, is a mechanism for documenting themes in drought impacts on tourism/recreation, and begins to establish evidence of the drought and tourism/recreation connection.
Unfortunately, the tourism/recreation sector is easily shadowed by drought effects on agriculture. This is not to say that agriculture is unimportant, but rather that the tourism/recreation sector needs additional attention. Using Colorado as one example, tourism/recreation is a major driver of the state's economy, which is similar to economies throughout the western United States. The tourism/recreation sector is second only to agriculture, and generated $14.6 billion in direct spending and $750 million in local and state taxes in 2010 (Dean Runyan Associates 2011). Moreover, drought is relatively frequent, occurring in some region of the state nine out of every ten years (McKee et al 2000). Another such illustration is the 2011-2012 Drought in Texas, Oklahoma, and New Mexico, states that experience regular drought cycles. Several newspapers emphasized tourism and recreation impacts, even placing estimates at a $4.6 billion deficit (mywesttexas.com 2012, vindy.com 2011). Although state hearings addressed this sector (Shannon 2012), tourism is never mentioned in the report issued by the Texas Comptroller of Public Accounts, and recreation was given only one bullet point in the assessment (Combs 2012). This focus on agriculture extends to overall drought planning and mitigation, along with climate adaptation measures. Without a doubt, the tourism/recreation sector is equally important for investigation and policy-making, needing additional attention given overall drought losses (Ding et al 2011), particularly if and when drought is directly linked to other hazards like wildfires, extreme heat, or even shifts in vector-borne diseases.
Taken as a whole, the evidence illustrates the potential for drought to directly impact multiple sectors and further illuminates the need to incorporate tourism/recreation directly into drought management and planning. A healthy tourism/recreation sector is vital for overall social and economic strength in communities and regions, particularly in places where tourism/recreation makes up a significant portion of the overall economy. Consequently, systematic and consistent assessments and evaluation of drought impacts, along with direct inclusion in drought management strategies are fundamentally necessary, although thus far not commonly conducted, for tourism/recreation (Wilhelmi et al 2008).

Complexity of interactions
The severity of drought impacts vary significantly among communities, largely due to an imbalance between water supply and demand across sectors and regions, as well as a differential vulnerability to drought among water users. Vulnerability of the tourism/recreation sector to drought is complex, dynamic, and spatially variable. At its core, the conceptual framework presented in figure 1 guides consistent processes that increase dialog between the tourism/recreation sector and emergency and resource management organizations supported by a multifaceted, bottom-up stakeholder-driven process that integrates with, and informs, formal organizational structures. This preserves a community's values, while simultaneously creates a platform for common dialog and communication to identify measures, to conduct assessments, and to enhance the overall community vitality through improved adaptive strategies. The nuanced context of critical tourism/recreation activities with drought linkages are captured through the stakeholder engagement process. In turn, this information is combined with systematically collected drought monitoring data in order to evaluate potential impacts and to arrive at a comprehensive planning and climate adaptation solutions.
By integrating, rather than duplicating efforts, disaster/emergency management and the tourism/recreation sector create conditions and processes where measures from one may be used to inform the other. The framework incorporates the entire disaster management cycle, including preparedness, hazard mitigation, recovery, and response in order to explicitly acknowledge the relevance of these activities along with water management and climate change adaptation approaches. Ultimately, the goal is to reduce vulnerability of tourism/recreation to drought; the framework is structured to guide integrated research and practice through increased collaboration and communication.
Even though recreation and tourism are often treated as a single sector, the framework acknowledges that they have distinct planning needs with regard to water resources and drought, and also generate revenue in varying ways for consideration of economic impacts. The resource management orientation of recreation is perhaps more directly tied to water management than tourism. Further, the amount of money that each visitor spends is also fundamental to revenue generation, which will vary by season and purpose of visit. Active outdoor sporting activities have different water requirements than managed resort-type activities, and revenue generation streams vary. For example, if a campground on public lands does not fill due to fire bans, the direct economic implications are far less than a ski resort not filling rooms due to reduced snow pack. Or, mountain biking is potentially different from horseback riding both in terms of activity needs, water dependency, and revenue generation streams. Golf courses also have distinct water needs, and likely result in greater revenue generation than mountain biking, camping, and horseback riding.
To add to the complexity, drought impacts on tourism/ recreation may not be uniformly negative. For example, dirt roads are more accessible when dry than when wet. Access to trails may enable mountain biking or horseback riding to occur earlier or in a wider range in these conditions. In higher latitude areas with lakes, warm, dry weather is often essential to attract summer vacationers in large numbers. In places where cloudy summers are common, the lack of rainfall during drought could, in fact, have economic benefits for some businesses.
Recognizing that diversity is fundamentally critical for local economies, the framework explicitly incorporates various types of tourism and recreation, as well as their seasonality and dependencies on water use. Understanding individual components of tourism/recreation, and interactions among them, is essential for characterizing the relationships between drought impacts, vulnerabilities, and appropriate drought planning and climate adaptation strategies.
In addition to integrating sectors and directly relevant organizational management structures, this conceptual framework takes an interdisciplinary, comprehensive approach with regard to methods and hazard types. Capturing interactions among recreation and tourism activities at a given location through stakeholder engagement processes, as well as between different hazard types (e.g., drought, wildfire, etc), requires a multifaceted approach. For example, drought cannot be entirely disentangled from wildfires or extreme heat, and these interconnections should be explored, particularly identifying opportunities for cross-planning.
Frequently, drought management lacks quantitative data that provide evidence for informing policy creation and prioritization of drought mitigation measures for minimizing societal impacts, particularly for tourism and recreation. Many tools, indices, and data exist for monitoring and forecasting physical characteristics of droughts, but few objective, systematic, and comprehensive measures of tourism/recreation and societal impacts are included in vulnerability assessments. For example, visitations or lodging receipts are not linked to decreased water availability to evaluate the connections, thereby limiting drought mitigation opportunities. Additional quantitative and qualitative data, gathered through interviews, surveys, and focus groups can be used to verify, contextualize, and prioritize quantitative measures, as well as to capture local knowledge and perceptions. Fundamentally, these efforts can generate vulnerability reduction strategies specific to the strengths and needs of a particular community.

Vulnerability of tourism and recreation to drought
Vulnerability, broadly defined as susceptibility to harm, is a fundamental factor for drought disaster risk reduction. Although specific definitions of vulnerability vary, here we draw on constructs from risk and vulnerability research and conceptualize drought vulnerability for the tourism/recreation sector as a function of three interrelated components: exposure, sensitivity, and adaptive capacity (Turner et al 2003, Wisner et al 2004, Adger 2006, Wilhelmi and Hayden 2010, all depicted at the center of the framework (figure 1). Hydrometeorological conditions create an exposure to a drought hazard, while features of the socio-ecological system (e.g., land use, water demand, diversity of economic activities) influence the sensitivity of the system to negative drought impacts, and its capacity to respond to, recover from and cope with current and future droughts. Although several analytical frameworks defining these concepts and their interrelationships have been proposed (e.g., Morss et al 2011, Wilhelmi andHayden 2010), the specific indicators and the dynamics among them, especially in the context of drought and tourism/recreation are not well understood or characterized. Factors that contribute to differential exposure, sensitivity, and adaptive capacity increase or reduce drought impacts and directly affect the overall vitality of the communities. Therefore, having a comprehensive and interdisciplinary understanding of these elements of vulnerability and their underlying factors can lead to better characterization of indicators, impacts, and vulnerability relationships for targeting improved drought mitigation and climate adaptation strategies.
Exposure to drought is generally described as environmental and climatic conditions that position a system (e.g., community, industry, sector, population) to be negatively affected by a drought. Exposure is predominantly associated with the physical attributes of the environment, including local and regional climate, features of the landscape as well as hydrologic, land use, and ecosystem characteristics. Along with the variation in these characteristics, exposure to drought varies spatially and temporally. Exposure to droughts during certain seasons or during a particular time of a season can increase or decrease vulnerability to drought and the outcomes for the tourism/recreation sector. Geographic location, for example, can play a major role in how exposed the system is to drought and what the magnitude of the impacts may be. While meteorological drought can occur in virtually any climate (Wilhelmi and Wilhite 2002), certain areas may be particularly susceptible to hydrologic drought and the impacts on the water supply needed for operating tourism/recreation activities. The hydrologic cycle and its role in the water supply-demand relationships is an important factor that determines differential exposure to drought. Many water-based recreation and tourism activities largely depend on access to, and ability to use, surface water. Drought exposure is influenced by the relationships between the water users and their respective sources of water supply. If the area of the water source is exposed to drought, water users downstream can be affected as well. Therefore, basin hydrology, including stream and watershed characteristics, can affect the exposure of surface water users to drought. Previous droughts and other environmental stressors that affect health of natural ecosystems can further increase exposure to drought. Healthy ecosystems can withstand short-term drought without major long-term impacts. For example, the susceptibility of the forest ecosystem in Colorado to the rapid spread of the bark beetle was largely attributed to the stress to trees from previous prolonged droughts (Chapman et al 2012).
Traditionally, climate-sensitive industries developed resource management strategies that work with climate variability; however, they have typically assumed the regional climate as stationary (Fussel 2007). Climatic changes, such as warmer temperatures and decreased snowpack, can directly affect winter recreation like skiing, snowshoeing, and snowboarding; and indirectly affect a myriad of water-based recreation through decreased surface water supply, and water levels in streams, lakes and reservoirs (Ray et al 2008). A complex interaction between water supply and demand in any given area, droughts and the geographically varying impacts of climate change on temperature and precipitation creates differential exposure of the regions, communities, and businesses. For example, Alvord et al (2008) states: 'considering the impact that seasonal climate variability already has on tourism, projected impacts tied to rising temperatures threaten long-term profit margins of many tourism industries on the Colorado Plateau' (Alvord et al 2008, p 674).
Drought sensitivity is represented by non-climatic factors that determine the degree to which the tourism/recreation sector can be affected by a drought. These factors typically represent the attributes of the water demand in any given area and include characteristics of the land use and land management, local economy, and the water policies. Wilhite et al (2007) emphasized that the impacts of drought can be significantly exacerbated when the water demand exceeds the water supply either due overdevelopment and over-appropriation, or due to an overall degradation of the natural resource base. In many places, the sensitivity to droughts can be affected by the prior appropriation water rights system. During a drought event, water-right holders can place a 'call' on the river which limits or completely restricts the use of water by water users with lower priority water rights within a hydrologic basin. In Colorado, for instance, recreation water rights usually have more junior status than agricultural or mining water rights, which tend to be more senior (HydroBase 2013).
Economic development and dependency on the water supply plays a major role in the degree of drought sensitivity of regions and communities. Importantly, increased economic diversity in communities dependent on tourism and recreation results in greater ability to reduce drought sensitivity and, in turn, drought vulnerability. Wilhelmi et al (2008) illustrated that in Colorado, it is critical to diversify the economies in general, as well as diversifying the local businesses that are focused on tourism and recreation to reduce the impacts of drought. Water-dependent businesses, such as rafting, fishing, and reservoir-based recreation companies should have additional sources of income on which to rely during years when sufficient water supply is not as available. Therefore, dependency on water supply presents another factor of drought sensitivity, in particular for summer tourism/recreation activities. Since the 2002 statewide Colorado drought, many Colorado communities have strategically worked to expand the range of tourism/recreation activities and to increase the flexibility and diversity in local economies, in general. Examples include lighter boats for whitewater rafting or 'off season' activities, such as rock climbing, jeep touring, or road biking (Alvord et al 2008). In many cases, experiencing a severe drought positively affected the attitudes and the policies toward drought preparedness and mitigation, and decreases the sensitivity to future droughts. While the direct link to drought is not well documented or understood, there was evidence in some cases that businesses and communities that were operating in already marginal or depressed economic conditions, were unable to fully recover and may have become even more sensitive to future droughts.
Adaptive capacity refers to the potential of a system to adjust or modify its features or actions so as to better cope with current or future drought impacts (Turner et al 2003, Adger et al 2004, Adger 2006, Wilhelmi and Hayden 2010. Adaptive capacity influences adaptation, which refers to the actual adjustments or changes made to cope with droughts and reduce their impacts. In the context of tourism/recreation, adaptive capacity is influenced by factors such as social capital and social networks, in particular public relations, communication and marketing strategies, knowledge, attitudes, practices, and public drought risk perception, as well as access to information and alternative, adaptive technologies. Understanding adaptive capacity is a key gap in drought vulnerability research, particularly as it relates to the tourism/recreation sector. While exposure and sensitivity can be, for the most part, measured by quantitative data, adaptive capacity is often nuanced and best examined qualitatively or at the business or individual level (Morss et al 2011). Individual and institutional knowledge, attitudes, and practices regarding drought and tourism/recreation can have major influence on actual responses, economic and cultural adaptations, and water management. However, the combination of attitudes, access to resources, and adaptive technologies affects the willingness and ability of a community or business to change practices and operations in order to reduce drought vulnerability and increase community resilience.
Social and organizational networks among regions, communities, and business, along with improved communication mechanisms with the media and the public, can greatly increase adaptive capacity and lead to fewer economic losses and damages from a drought (Wilhelmi et al 2008). More recently, social media has added to the complexity of information dissemination, providing both opportunities and challenges for promoting tourism/recreation. The 2002 Colorado Drought illustrated that public perception of drought and climate conditions at popular tourism destinations are as important as the actual conditions themselves (Alvord et al 2008, Wilhelmi et al 2008. In fact, the Colorado Tourism Office developed the first crisis management plan for communication and messaging during times of disaster after the 2002 experiences (Colorado Tourism Office 2012, CWCB 2010. Effective communication and public relations are clearly keys to improved climate adaptation and drought vulnerability reduction.

Data collection and stakeholder engagement at all levels
Taken together, exposure, sensitivity, and adaptive capacity are direct drivers of drought vulnerability and overall community vitality. Thus, they can have positive or negative forces on outcomes that should be measured, recorded, and evaluated over time. Outcome measurement data might include: hotel stays, visitation, revenue, visitor spending, employment, mental health, species populations, or even species diversity. While some of these outcome measures are fundamental to recording, documenting, and tracking drought impacts, and by extension evaluating the effectiveness of adaptive measures (i.e. negative impacts would be reduced if adaptive capacities are effective), some measure will be locally defined. For example, information on snowpack and soil moisture coupled with data from resource managers about where fire mitigation has recently occurred could provide the tourism/recreation industry with information on the most appropriate places for issuing camping permits for the upcoming summer season. In doing so, there could be a corresponding reduction in possible wildfire exposure, reducing the burden on emergency managers. Outcome measures will also act as a feedback loop for informing overall drought preparedness and mitigation measures, such as water conservation. Importantly, these measures should be developed through direct engagement with local stakeholders (bottom-up) in the context of formal management structures at the local, state in the case of the US, and national (top-down).
Stakeholder engagement underpins the entire data collection process for arriving at improved adaptive strategies for drought vulnerability reduction in the tourism/recreation sector. Frequent interactions with, and among, the stakeholders can ensure that the data regarding exposure, sensitivity, and adaptive capacity inform local decisions to further build the adaptive capacity to drought. Although certainly challenging and requiring dedication and resources, community engagement has the potential to achieve the following: (1) interpret and evaluate current data sources, (2) identify additional data that could be relatively easily collected, (3) establish priorities for the development of new data sources, and (4) document best practices and adaptive strategies. Sustained stakeholder engagement hinges on effective and multipronged participation strategies, the generation of meaningful products from data, and a dissemination strategy.

Conclusions
This is the first conceptual framework that presents a research and operations roadmap for reducing tourism/recreation vulnerability to drought, having far-reaching potential for application to communities globally, and in the US. It suggests approaches and processes for producing empirical data that support the integration of tourism/recreation into drought management, and provides a model that should be evaluated for its applicability in different geographic settings. Although the key concepts included in the framework are transferable, the details of implementation would vary from location to location. For example, water rights and water access operate quite differently in various places, as would the particular stakeholders necessary in the engagement process. Still, the overarching framework guides a process for understanding the pathways for the reduction of tourism/recreation vulnerability. This is critical for increasing community vitality, particularly where this sector plays an important role in the community.
Additionally, the framework provides a mechanism for enhancing an understanding of the effects of regional and global climate change as it relates to tourism/recreation. For example, applying this framework would inform the development of a robust US National Climate Assessment, which aims to 'analyze the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change. . . ' (United States Code 1990). Drought impacts have the potential to influence nearly all of these areas, particularly as related to the health of the tourism/recreation sector.
While drought causes significant impacts on communities, currently no systematic approach comprehensively assesses tourism/recreation vulnerability to this devastating natural disaster. Without adequate information on tourism/recreation during non-drought periods, it is difficult, if not impossible, to properly evaluate opportunities and challenges for increasing preparedness and implementing adaptive capacity measures. Additionally, information generated from the application of the proposed framework can provide practitioners and decision makers alike with a more accurate and comprehensive assessment of drought vulnerabilities, along with a basis for comparison across communities and over time.