Abstract
This paper empirically explores how fiscal policy (represented by increases in government spending) has asymmetric effects on economic activity across different levels of real interest rates. It suggests that the effect of fiscal policy depends on the level of real rates because the Ricardian effect is smaller at lower financing costs of fiscal policy. Using threshold vector autoregression models on U.S. data, the paper provides new evidence that expansionary government spending is more conducive to short-term growth when real rates are low. It also finds asymmetric effects on interest rates and inflation and threshold effects associated with substitution between financing methods.
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Notes
Davig, Leeper, and Chung (2004) suggest that tax policy reactions can shift between periods when taxes are adjusted in response to government indebtedness and periods when other priorities drive tax decisions. We focus on government spending and its implications for future tax liabilities but not on the tax policy behavior itself.
In an open economy, higher interest rates induce capital inflows and real exchange rate appreciations, which result in a deteriorating current account and offset the increase in domestic demand arising from a fiscal expansion.
Since public services can be considered as an input to private production, government spending on public goods and infrastructure can lead to faster economic growth (Aschauer, 1989; Barro and Sala-ì-Martin, 1992; and Tanzi and Zee, 1997). Such supply-side effects of fiscal policy are regarded as more important over the longer term.
Findings from micro data help explain why consumption is strongly associated with current income: consumption is affected by anticipated tax refunds (Shapiro and Slemrod, 1995) or predictable income changes resulting from Social Security taxes (Parker, 1999); and a substantial fraction of households have near-zero net worth (Wolff, 1998), implying that many consumers do not engage in the intertemporal consumption-smoothing (Mankiw, 2000).
We are grateful to the referee for suggesting that we look at implications of borrowing constraints and rule-of-thumb consumers for the consumption response to a government spending shock.
In stage 1, we estimate a single threshold (τ1). In stage 2, the first-stage threshold is taken as the upper (lower) threshold if it is above the 65th (below the 35th) percentile of the switching index. The grid set for the other threshold (τ2) is composed of 50 grids on the side with the longer leg of the τ1 estimate. If the first-stage threshold is between the 35th and the 65th percentile, the grid set for τ2 is composed of 25 grids on each side of the τ1 estimate. In stage 3, we take the τ2 estimate as its refinement estimator \(\tau _2^r\) and repeat stage 2 to obtain the refinement estimator of τ1 \({\tau _1}(\tau _1^r)\).
We generate J (= 1,000) realizations of the Wald statistics, \(\chi _T^{2j}(\tau)\,(j = 1,2, \ldots,\,J)\), under the null of symmetry for each grid and construct empirical distributions for three functionals of the collection of the statistics over grid space \(\Gamma:SupW = \tau \in \Gamma \sup \chi _T^2(\tau)\), \(AveW = {1 \over {\# \Gamma }}\sum\nolimits_{\tau \in \Gamma } {\chi _T^2(\tau)} \), \(ExpW = \ln \left\{ {\left. {{1 \over {\# \Gamma }}\sum\nolimits_{\tau \in \Gamma } {\exp \left({\chi _T^2(\tau)/2} \right)} } \right\}} \right.\), where # Γ is the number of grid points in Γ.
Hansen’s (1999) procedure, by minimizing the sum of squared errors in the threshold autoregressive model, enables one to compute the confidence intervals of thresholds for a single equation. Hansen’s procedure for computing confidence intervals, however, is not readily applicable to the thresholds that are obtained by the maximization of the conditional log-likelihood for multiple equations.
Inflation was rather persistent in the late 1960s and high and persistent after the 1973 oil shock. The Federal Reserve’s anti-inflation policy kept inflation in check in 1982, and thereafter policy has consistently aimed at keeping inflation low.
The law of motion of government debt can be written in a simple form as \({D_{t + 1}} = AG_t^\alpha D_t^\beta \). This can be rewritten in a log-differenced form, ΔlnDt+1 = αΔlnG t + βΔlnD t , which can be extended to a more general form in a VAR.
For the whole period, all variables in levels in model 1 are stationary, and Johansen’s maximum-eigenvalue test and trace test reject the null hypothesis of cointegration in model 1 when private spending is measured by consumption but not when it is measured by investment. For model 2, the growth of monetary base and inflation have a unit root, while the ex post real rate is stationary. We find mild evidence of cointegration for model 2 when private spending is measured by investment. We also estimate TVARs in levels to account for possible cointegrations among level variables but the conclusion we obtain is qualitatively the same.
Alesina and others (2002) note that, in the United States, the yearly budget is discussed and approved during the second half of the preceding year and that additional small fiscal measures are sometimes decided during the year but most of the time become effective by the end of the year. Fatás and Mihov (2003) suggest that spending is less prone to simultaneity problems in determining fiscal policy effects than the budget deficit is because spending is not related to the current state of the economy whereas the budget deficit is largely affected by the cycle.
As a result, the use of an alternative switching index—the real interest minus the regime-mean output growth—will not affect the result, while it requires an iterative estimation to obtain the regime-mean output growth.
Blanchard and Perotti (2002) estimate structural vector autoregression models, which contain tax, government spending, output, and an individual GDP component (such as consumption or investment) in a level form controlling for trends, for the post-1960 U.S. data. Alesina and others (2002), using a simple structural model for a panel of industrial countries, find that government spending shocks lead to a decrease in the investment-GDP ratio.
Commodity price inflation can be included in model 2 to cope with the “price puzzle”—the finding that a monetary tightening leads to a rising rather than falling price level (Leeper, Sims, and Zha, 1996; and Christiano, Eichenbaum, and Evans, 1996). We find that the inclusion of commodity price inflation does not alter our main results.
Kormendi (1983) finds from U.S. data that defense spending is between government investment and government consumption in terms of the size of the crowding-out effect on private consumption. Evans and Karras (1998), using cross-country data analysis, suggest that private consumption and nonmilitary government spending are substitutes, whereas private consumption and military spending are complements.
To allow for shifts to other regimes at the margin, one may consider the estimation averages over the actual histories of real rates conditional on each regime, given a fixed size of shocks. This approach will somewhat smooth out differences across regimes but will not affect our results qualitatively, given that the moderate-rate regime, as a middle ground in the characteristics of responses, buffers a transition from one extreme regime to the other extreme unless the shock is extremely large.
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The authors thank Tamim Bayoumi, Andrew Feltenstein, Robert Flood, Iryna Ivaschenko, Sung In Jun, Sunil Sharma, and an anonymous referee for valuable comments and suggestions. The authors are also grateful to seminar participants at the 2004 Far Eastern Meeting of the Econometric Society in Seoul for useful comments and discussions.
Appendix
Appendix
Data Sources and Description of the Variables
We use the U.S. quarterly series, obtained from Federal Reserve Economic Data (FRED) at the website of the Federal Reserve Bank of St. Louis, in our analysis. Variable definitions and FRED code names are as follows: X = real GDP, chained 1996 dollars (GDPC1); nominal GDP (GDP); P = GDP deflator (= GDP/GDPC1); G = real government consumption expenditures and gross investment, chained 1996 dollars (GCEC1); real national defense spending = nominal national defense consumption expenditures and gross investment (FDEFX) divided by the GDP deflator; C = real personal consumption expenditure, chained 1996 dollars (PCECC96); I = real fixed private domestic investment, chained 1996 dollars (FPIC1); D = nominal federal government debt (defined below) divided by GDP deflator; M = the Federal Reserve Board of Governors’ adjusted monetary base (BOGAMBSL); money stock M1 (M1SL); and R = the three-month treasury bill rate, percent per annum (TB3MS). The data available at monthly frequency from the source are averaged to obtain quarterly observations. The nominal federal government debt is taken from the IMF’s International Financial Statistics and seasonally adjusted (by X12).
The growth rate of a variable x in annual percentage is defined as Δ ln x t = 400 · ln(x t / xt−1). The lagged ex post real interest rate is defined as rrt−1 = Rt−1 − 400 · (P t / Pt−1 − 1). The lagged ex ante real interest rate is measured by R t−1 minus the expected inflation rate for period t, for which the six-months-ahead forecast of the monthly base value of CPI taken from the Livingston Survey is interpolated at quarterly frequency. —
