Journal of Economic Literature | Macro Paper Warehouse

Can Government Purchases Stimulate the Economy?

This essay surveys the state of knowledge on the government-spending multiplier as of 2011, drawing together theory, U.S. aggregate time-series evidence, and the newer cross-state/cross-locality “natural experiment” literature, to answer the question most relevant to the post-2008 stimulus debate: how much output does a temporary, deficit-financed increase in government purchases buy? Ramey first shows that “the multiplier” is not one number: in neoclassical models the multiplier operates through a negative wealth effect on labor supply and can range from as low as -2.5, when spending is temporary and financed by concurrent distortionary taxes, to about 1.2 for a permanent increase financed by lump-sum taxes, because the size and even sign of the multiplier depends critically on the persistence of the spending and how it is financed; in New Keynesian models the multiplier is typically smaller than in traditional Keynesian models (often at or below one) unless the model assumes widespread rule-of-thumb consumers and demand-determined employment, or unless the nominal interest rate is stuck at the zero lower bound, in which case rising expected inflation lowers the real interest rate and multipliers well above one become possible (Christiano, Eichenbaum, and Rebelo’s estimate peaks at 2.3). Turning to the data, she reviews a range of aggregate U.S. time-series studies – from the large-scale econometric models of the 1960s through modern structural VARs using military-spending instruments, narrative “news” shocks, and Blanchard-Perotti-style identification – and finds that, despite substantial differences in sample, experiment, and identification strategy, most estimates cluster between roughly 0.6 and 1.8, with standard errors that are always large; she argues the lower end of this range mostly reflects episodes (such as the Korean War) in which spending was partly tax-financed, and that after truncating for that and for concerns about persistence and measurement in the highest estimates, the range most relevant to a temporary, deficit-financed increase is 0.8 to 1.5, with multipliers likely toward the top of that range during severe recessions. She also surveys the newer literature using cross-state and cross-region panel data (military contracts, ARRA allocations, New Deal spending), which mostly finds income multipliers of about 0.5 to 2.0 and implied costs of roughly $25,000-$35,000 per job created, while cautioning that these local multipliers answer a conceptually different question from the aggregate multiplier and require an explicit model (of the kind developed by Nakamura and Steinsson 2011 and Shoag 2010) to translate into an aggregate estimate. The essay closes by noting that despite the proliferation of estimates, there remains no consensus on the underlying transmission mechanism – some studies find consumption falls in response to spending increases, consistent with the neoclassical wealth effect, while others find it rises, consistent with rule-of-thumb consumers – and that none of the multiplier estimates reviewed speaks to the welfare consequences of stimulus spending, which would require additional assumptions about mechanisms and about whether government purchases enter the utility function.

Capital Account Liberalization: Theory, Evidence, and Speculation

Two decades of cross-country regressions found no relationship between capital account openness and economic growth, which earlier surveys read as a verdict against the textbook case for liberalization. This survey argues those regressions never tested the theory. In the neoclassical growth model, liberalizing a capital-poor country’s capital account permanently lowers its cost of capital and permanently raises the level of GDP per capita, but raises the growth rate only temporarily, because capital accumulation – subject to diminishing returns – is the only channel available and long-run growth differences come solely from total factor productivity, which the capital account regime does not touch. So a regression of average growth on the fraction of years a country was judged open “do[es] not provide a test of any causal theory”; Henry adds a worked example in which a country that liberalizes halfway through a twenty-year window genuinely gets a temporary growth boost yet, because its openness share is 0.5 against an always-open comparator’s 1.0, the regression returns a negative coefficient. Reoriented to what theory predicts – do the cost of capital, investment and growth move in the years right after a country opens? – the evidence lines up: across 18 developing countries that opened their stock markets between 1986 and 1993, stock markets revalue by roughly 26 to 30 percent in real dollar terms, dividend yields fall by 5 to 75 basis points, the growth rate of the capital stock rises from 5.4 to 6.5 percent a year between the five years before and after, real private investment growth rises by 22 percentage points in an eleven-country sample, and GDP-per-capita growth rises about a percentage point a year. Henry then turns the same scepticism on these results. The revaluation is small relative to what large capital-labour gaps should imply, and he canvasses three explanations – incremental rather than one-shot liberalization (which he rejects, since later openings move prices little and continuous investability measures give a cumulative dividend-yield fall of only about 140 basis points), genuinely lower returns in developing countries because weak institutions depress total factor productivity, and persistent return differentials from capital market imperfections and weak investor protection. He shows the aggregate investment response cannot be financed by the initial country fund (Chile’s 37.7 million dollar Toronto Trust fund accounts for under 5 percent of the extra capital implied by its subsequent 2.2 percentage points of abnormal annual capital-stock growth), and argues the country-fund date is a proxy for a broader opening rather than the whole inflow. He is blunter still about growth: with capital growth up about one percentage point and an output elasticity of capital near one-third, liberalization “cannot raise the growth rate of GDP per capita by much more than one-third of a percentage point,” so the one-point estimate “is implausibly large,” and the measured jump in TFP growth from 0.19 to 1.82 percent a year cannot be attributed to liberalization within the model, because contemporaneous inflation stabilizations, trade liberalizations, privatizations and Brady debt relief supply an accounting for it that the theory does not. Firm-level work resolves some of this and complicates the rest: firm revaluations do track firm-specific changes in systematic risk, and the average firm’s capital stock growth exceeds its pre-liberalization mean by 3.8 percentage points a year, but investment does not respond to firm-specific changes in the equity premium at all – which Henry calls “a powerful blow to the Allocative Efficiency view.” On crises, he insists the answer depends on which liberalization is meant: crises also occur under capital controls and are positively correlated with them, the median stock market liberalization predates the Mexican crisis by five years and the Asian crisis by nearly ten, and the proximate cause was short-term dollar-denominated bank debt, whose reversal in the five Asian crisis countries amounted to nearly 80 billion dollars in a single year while portfolio flows fell by about half and stayed positive. His bottom line is that debt-flow liberalization – especially short-term and dollar-denominated – “can cause problems,” while “all the evidence we have indicates that countries derive substantial benefits from opening their equity markets to foreign investors,” and that the profession’s attachment to cross-sectional growth regressions reflects tradition and “a professional obsession” with policies that raise steady-state growth rather than anything the theory supports.

Exchange Rate Predictability

This article asks whether anything forecasts exchange rates and, if so, what – and answers that it depends, systematically, on five choices the forecaster makes. Since Meese and Rogoff (1983a,b, 1988) it has been known that a simple random walk usually beats economic models out of sample, and the survey opens by insisting on what that does not mean: the finding “should not be interpreted as a validation of the efficient market hypothesis,” which “does not mean that exchange rates are unrelated to economic fundamentals, nor that exchange rates should fluctuate randomly around their past values. Hence the puzzle.” The first half reviews the predictors, models, data choices and forecast evaluation methods proposed over the preceding decade, with deliberately limited scope: a reduced-form, out-of-sample focus at monthly and quarterly frequencies, on nominal rather than real exchange rates, because structural models “are too stylized to be literally taken to the data and successfully used for forecasting.” The second half re-runs the horse race on up-to-date data for a set of 19 countries against the US dollar, using a single common data transformation (seasonal adjustment), a rolling estimation window equal to half the sample, short (one month or quarter) and long (four-year) horizons, and a battery of tests: a Granger-causality test robust to instabilities, the Diebold-Mariano-West and Clark-West out-of-sample tests against a random walk without drift, and two tests that check robustness to the forecast sample and to the estimation window size. The literature’s consensus negatives are confirmed – purchasing power parity and monetary fundamentals do not forecast at horizons under about two to three years, and non-linear models are the least successful – and the one positive consensus is that Taylor-rule and net foreign asset fundamentals have more out-of-sample content than traditional interest rate, price, output and money differentials, with the disagreement being over how much of the puzzle they resolve. In the author’s own exercise most traditional predictors show in-sample predictive ability while almost none survives out of sample; Taylor-rule fundamentals are strongly significant under Clark-West for four countries and marginally for five more at the one-month horizon, and for none at four years, but are not significant at all under Diebold-Mariano-West – a discrepancy the author reads as the difference between judging a model “in population” and judging it “at the actual estimated parameter values.” A model loading up on real interest rates, the trade balance and the current account has the strongest in-sample fit of any considered and “extremely poor” out-of-sample performance, “possibly due to the large number of parameters to estimate.” A panel monetary model beats the random walk at long horizons for four countries and a Bayesian model averaging specification beats it nowhere. The sharpest result is about instability: the Fluctuation test finds predictive ability for monetary fundamentals only occasionally and briefly – never for Canada, for France in the late 2000s, Japan in the mid-2000s, the UK in 2009 – and window-size analysis shows that only small estimation windows detect German predictability (concentrated in the late 1980s) while only large ones detect Japan’s (emerging in the late 2000s). The author’s conclusion is therefore that although some predictors work somewhere sometimes, “none of the predictors, models, or tests systematically find empirical support for superior exchange rate forecasting ability of a predictor for all models, countries and time periods,” so “Meese and Rogoff’s (1983a,b) finding does not seem to be entirely and convincingly overturned” – and that the time-varying, occasional nature of predictability is itself a new puzzle, since existing time-varying-parameter models fail to capture it.

Home Bias in Open Economy Financial Macroeconomics

This is a survey of the “home bias” puzzle: the well-documented fact that investors everywhere hold a disproportionate share of their wealth in domestic equities, bonds and bank assets, well beyond what standard portfolio theory recommends. Coeurdacier and Rey organize the literature into three broad classes of explanation – hedging motives in frictionless financial markets, asset trade costs, and informational frictions and behavioral biases – and give particular attention to a “new” macroeconomics literature they label Open Economy Financial Macroeconomics, which embeds non-trivial international portfolio choice into standard two-country DSGE models. Using such a model with both bonds and equities, they show that once real-exchange-rate risk is hedged through bond positions, the remaining equity position optimally hedges human-capital risk conditional on bond payoffs – a channel that, unlike the unconditional hedge used in earlier equity-only models, robustly generates home bias in both the model and in the authors’ own new cross-country evidence. The survey also reviews the transaction-cost literature (concluding that plausible costs are generally too small to explain observed home bias, except when diversification gains are themselves small), the literature on informational asymmetries and endogenous information acquisition, and behavioral explanations built on differences in investor beliefs. It closes by presenting new descriptive evidence on cross-border bond holdings, bank lending and institutional (mutual fund) holdings, and by arguing that “the home bias puzzle is now less of a puzzle” than it once was, while flagging major open areas – modeling the official sector, financial intermediaries, delegated investment and heterogeneous investors – for future work.

Local Projections

This 2025 Journal of Economic Literature survey by Òscar Jordà (who introduced the method in a 2005 AER paper) and Alan M. Taylor takes stock of local projections (LP) as a general framework for estimating impulse responses, arguing that LPs “help bridge the divide between current best practices in applied microeconomics and standard time series methods in macroeconomics.” The baseline LP estimates the impulse response at horizon h directly as the coefficient beta_h in a single OLS regression of y_{t+h} on the intervention s_t and lagged controls x_t, requiring one separate regression per horizon rather than iterating forward a fitted VAR; the authors identify four practical advantages over VARs – no cross-equation system constraints, straightforward accommodation of nonlinearities and heterogeneity, direct estimation of cumulative multipliers, and a natural extension to panel and difference-in-differences settings – while showing LP remains asymptotically equivalent to VAR-implied impulse responses (and to VAR-based Cholesky and long-run identification) whenever the underlying process truly is a VAR of the assumed order (citing Plagborg-Møller and Wolf 2021), and retains identification via LP-IV even in “non-invertible” settings where VAR identification from the reduced-form covariance matrix breaks down. The survey then works through the full LP toolkit: levels versus long-difference specifications (long-differencing “considerably reduces” the O(T^-1) small-sample bias that is severe for persistent, near-unit-root outcomes in short samples); horizon-by-horizon smoothing via B-splines or a Gaussian basis function; inference via Newey-West HAC standard errors, lag-augmented LP (which restores ordinary White standard errors with, per Montiel Olea and Plagborg-Møller 2021, correct uniform coverage across stationary, near-unit-root, and unit-root processes), system GMM for joint hypothesis tests across horizons, sup-t simultaneous confidence bands, and Lagrange-multiplier significance bands constructed under the null; identification via selection on observables, inverse-propensity weighting, long-run restrictions, and LP-IV; projection minimum distance (PMD) for recovering structural parameters from LP-estimated auxiliary parameters; counterfactual policy-path analysis subject to a Mahalanobis-distance “modesty” test; state-dependent and nonlinear LPs; and panel LP / LP-DiD for staggered-adoption settings. Worked illustrations throughout convey magnitudes without claiming new empirical results of their own: a state-dependent local-projection estimate of the OECD fiscal-consolidation multiplier (1978-2019) finds the average four-year output response is more than twice as large in slumps (-1.78, chi-squared(5)=58.3, p=0) as in booms (-0.80, chi-squared(5)=17.2, p=0.004); a projection-minimum-distance estimate of a UK Phillips curve (1975m1-2007m12, using the Cloyne-Hürtgen 2016 monetary shock) yields coefficients of 0.838 (SE 0.593) on inflation and -1.990 (SE 0.180) on the output term; and an LP-IV estimate of the unemployment response to a Romer-Romer monetary shock (1985:1-1999:12) peaks at roughly 1.25 percentage points around 24 months before returning to zero by about 48 months.

The Science of Monetary Policy: A New Keynesian Perspective

This widely cited survey derives monetary policy design from a simple forward-looking New Keynesian model built from first principles – a forward-looking IS-type output-gap equation and a forward-looking Phillips curve – and states its conclusions as a numbered sequence of general “Results” meant to be robust across a wide variety of macroeconomic frameworks. Under discretion (no commitment), optimal policy embeds implicit inflation targeting: the central bank should adjust the nominal rate more than one-for-one with expected future inflation (Result 3, later known as the Taylor principle), should perfectly offset demand shocks but let the nominal rate stay put in the face of shocks to potential output (Result 4), and, more generally, should raise real rates whenever inflation is forecast above target and let it return only gradually. Under commitment, the paper derives a genuinely new result: even when the central bank has no temptation to push output above its natural level (ruling out the traditional Kydland-Prescott/Barro-Gordon inflationary-bias motive for commitment), a credible commitment to a rule still improves the current output-inflation trade-off, because current inflation depends on expectations of future policy, and a rational private sector will discount an un-committed promise of future toughness (Result 7). The paper also formalizes Alan Blinder’s “opportunistic” approach to disinflation as optimal when policy-makers weight small output deviations more heavily than small inflation deviations, showing it is equivalent to targeting inflation within a zone rather than at a point (Result 12), and works through practical complications including imperfect information, interest-rate smoothing, and model uncertainty. Turning from theory to practice, the paper applies its “Taylor principle” criterion to U.S. monetary history, arguing pre-Volcker policy “tended to accommodate rather than fight increases in expected inflation” while Volcker-Greenspan policy adopted the kind of implicit inflation targeting the theory recommends, and closes with simple rules (including Taylor’s and the authors’ own forward-looking variant) and open questions for future research, including endogenous inflation persistence, open-economy extensions, and the zero lower bound.