2. Review of the Empirical Evidence

Considering public spending as a whole, what does the empirical evidence tell us about the size of the multiplier? A first comprehensive answer to this question can be found in Ramey (Reference Ramey>2011a), which, based on a survey of the empirical literature available at that time, concludes that a reasonable range is between 0.8 and 1.5. Gechert and Rannenberg (Reference Gechert and Rannenberg>2018) extend the analysis to a broad set of empirical studies, finding a mean value of 0.9 and a sizable standard deviation of 0.8. Several reasons may explain this huge variability, ranging from the structural characteristics of the countries on which multipliers are estimated (Ilzetzki et al. (Reference Ilzetzki, Mendoza and Végh>2013))Footnote ² to the definition of multipliers adopted, which may dramatically affect the estimates of the multiplier (cf. Ramey (Reference Ramey>2019)). In this regard, Ramey (Reference Ramey>2019) finds that when multipliers are computed in a cumulative way, which is likely the more convincing approach (cf. Mountford and Uhlig (Reference Mountford and Uhlig>2009))Footnote ³, the bulk of the empirical estimates shrink to the narrower range of 0.6−1.0. Cumulative multipliers outside this range and above one, in any case, are found in several analyses, such as Ben Zeev and Pappa (Reference Ben Zeev and Pappa>2017) and Deleidi (Reference Deleidi>2022).

However, few studies have analyzed public consumption explicitly. Among them, Burriel et al. (Reference Burriel, De Castro, Garrote, Gordo, Paredes and Pérez>2010) estimate cumulative public consumption multipliers for the Euro area, as a whole, and for the USA, finding multipliers above 1.0 in the first case and approximately 0.5 in the second case; Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012) find a public consumption multiplier of 1.2 for the United States; Ilzetzki et al. (Reference Ilzetzki, Mendoza and Végh>2013) estimate cumulative public consumption multipliers for a large set of countries, concluding that the size of the multipliers is between 0.3 and 0.7 for high-income countries; Boehm (Reference Boehm>2020) finds a value of 0.8 for the cumulative multiplier, using a panel of OECD countries; Deleidi (Reference Deleidi>2022) estimates cumulative public consumption multipliers above 2.0 for Italy, and Deleidi et al. (Reference Deleidi, Romaniello and Tosi>2021b) finds cumulative public consumption multipliers in the range of 2.0, using regional Italian dataFootnote ⁴.

The results reported above refer to linear multipliers. What about state dependency? In particular, what does the literature tell us about the size of the multipliers in slack/recession and in good/expansion periods? In this regard, a very influential contribution is provided by Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012), who apply a smooth-transition SVAR model to the US data and find that government spending multipliers are much higher in recession periods (2.5) than in expansion periods (0.3). This conclusion is also confirmed for public consumption, with a multiplier of approximately 2.1 in recessions and 0.5 in expansions. By applying a panel smooth-transition local projections model to a set of OECD countries, Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2013, Reference Auerbach and Gorodnichenko>2017) confirm the evidence of state-dependent public spending multipliers for a broader set of countries and definitions of slack of an economy. Batini et al. (Reference Batini, Callegari and Melina>2012) estimate a threshold SVAR on a group of countries (United States, Japan, France, Italy, and Euro Area as a whole) and find that multipliers, associated with cuts in government expenditures, are larger during periods of a negative GDP growth rate (approximately 2.0) than in periods of a positive growth rate. Owyang et al. (Reference Owyang, Ramey and Zubairy>2013) apply a threshold local projections model and find evidence of higher multipliers in periods of high unemployment than in periods of low unemployment for Canada but not for the United States. Fazzari et al. (Reference Fazzari, Morley and Panovska>2015) apply a threshold SVAR to US data, using the capacity utilization rate as a measure of slack, and find that government spending multipliers are higher in slack (1.6) than in good periods (0.8). Caggiano et al. (Reference Caggiano, Castelnuovo, Colombo and Nodari>2015) estimate a smooth-transition SVAR on the US data and find evidence of higher government spending multipliers in “deep recessions” than in “strong expansionary periods,” but not the same evidence comparing “normal recessions” with “normal expansions.” Alloza (Reference Alloza>2017), estimating a nonlinear SVAR and a threshold local projections model on the US data, finds higher government spending multipliers in booms than in recessions and, therefore, procyclical multipliers. Ramey and Zubairy (Reference Ramey and Zubairy>2018) apply a threshold local projections model to a long sample of data for the USA and find no clear differences between government spending multipliers in periods of high and low unemployment rates, with multipliers below one in both states of the economy. This conclusion is also confirmed by comparing recessions vs. expansions. Gomes et al. (Reference Gomes, Sakurai and Soave>2022) investigate state dependency in a panel of emerging countries, finding government spending multipliers below one, regardless of whether the economy is in a slump or normal times, and find evidence of multipliers close to zero in slump conditions.

The empirical evidence on this relevant issue is therefore mixed. In addition, it is important to further highlight that some recent studies, such as Ramey and Zubairy (Reference Ramey and Zubairy>2018), have challenged the robustness of some of the most influential analyses supporting the conclusion of countercyclical government spending multipliers by showing that these results tend to be fragile to small changes in model specifications or improvements in the methods of calculating multipliersFootnote ⁵. Therefore, Ramey (Reference Ramey>2019, p.90) claims that “the evidence for higher spending multipliers during recessions or times of high unemployment is fragile, and the most robust results suggest multipliers of one or below during these periods.”

Finally, some studies have investigated the same question for the government fiscal stance and, therefore, have not specifically focused on public expenditure. In this regard, Jordà and Taylor (Reference Jordà and Taylor>2016), applying a panel threshold local-projections model to a set of OECD countries, find clear evidence of higher fiscal multipliers in recessions than in expansions for fiscal consolidation measures; Cohen-Setton et al. (Reference Cohen-Setton, Gornostay and Ladreit>2019), applying a panel smooth-transition local projections model to a set of OECD countries, find higher multipliers in slumps than in booms for large fiscal stimulus; and Banerjee and Zampolli (Reference Banerjee and Zampolli>2019) estimate a panel threshold local projections model to a set of advanced countries, finding that multipliers associated with consolidation policies are largely acyclical and less than one.

4. Linear and State-Dependent Estimates of the Multipliers

Turning to the results, Figure 2 shows the linear impulse response of GDP and public consumption, in GDP units, to a $1\%$ shock to public consumption (i.e. $\beta ^{gdp}_{h}$ and $\beta ^{g}_{h}$ ). Both impulse responses are shown together with their $90\%$ confidence intervalsFootnote ¹⁴. Two interesting results emerge from the analysis of these responses. First, public consumption shocks produce a statistically significant and long-lasting positive effect on GDP. The GPD reaction is hump-shaped, with a peak effect approximately 2−3 years after the shock. Second, public consumption shocks are highly persistent over time, with half of the shocks still there after 4 years.

By transforming these impulse responses to multipliers, I obtain the (cumulative) public consumption multipliers shown in the top row of Table 1 (baseline). The size of the multiplier is approximately 0.7 in the first year and above 1.0 in the medium term, stabilizing around a value of 1.25. Therefore, in the medium term, an increase (decrease) in public consumption of one euro raises (decreases) output by approximately $1.25{\unicode{x20AC}}$ .

Table 1. Linear cumulative multipliers

Figure 2. Linear impulse responses of GDP and public consumption to a $1\%$ shock to public consumption. Impulse responses in GDP units. Shaded areas represent $90\%$ confidence intervals based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors.

Table 1 also reports the results for subsample periods. In particular, it is interesting to control whether inclusion in the analysis of the period after the global financial crisis or periods of possible zero lower bound affects the results. The results show that this is not the case, as the estimates indicate strong subsample stability. Particularly, I do not find any clear difference between pre-crisis (Pre GFC) and post-crisis (Post GFC) multipliers, using 2008Q2 as the break period. I also find that the exclusion of zero lower bound periods (No ZLB), identified as periods of a zero or negative shadow monetary policy rate (cf. Wu and Xia (Reference Wu and Xia>2016), (Reference Wu and Xia>2017); Amendola et al. (Reference Amendola, Di Serio, Fragetta and Melina>2020)), does not affect the estimates.

In contrast, estimates from the nonlinear models reveal that public consumption multipliers clearly differ between slack and good economic conditions. Starting with the benchmark state variable that, using the metric described in the previous section, is represented by (negative) standardized deviations of the unemployment rate from the country mean, Figure 3 shows the state-dependent impulse responses of GDP and public consumption, in GDP units, to a $1\%$ shock to public consumption. As it emerges from the figure, the GDP reaction turns out to be highly different in the two regimes of the economy, resulting in a strong, long-lasting and statistically significant response in the slack regime and a rather weak and never significantly different from zero response in the good regime. Regarding the persistence of the shocks, as in Ramey and Zubairy (Reference Ramey and Zubairy>2018), I find greater persistence in the slack regime than in the good regime, but the difference is mild. Therefore, transforming these impulse responses to cumulative multipliers, the values of the public consumption multiplier turn out to be utterly different in the two regimes. In the medium term, multipliers are approximately 2.2 in the slack regime, whereas they are between 0.3 and 0.4 in the good regime (cf. Table 2, Baseline). The difference is sizeable even over shorter horizons. Two years after the shock, for example, multipliers are approximately 1.7 and 0.4 in the two regimes, respectively.

Figure 3. State-dependent impulse responses of GDP and public consumption to a $1\%$ shock to public consumption. Impulse responses in GDP units. Shaded areas represent $90\%$ confidence intervals based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors. State variable: negative standardized deviations of the unemployment rate from the country mean.

Table 2. State-dependent cumulative multipliers

Are these results robust to alternative state variables? In this regard, an interesting robustness check is to consider (negative) standardized deviations of the unemployment rate from the Hodrick−Prescott (HP) trend as the state variable. Indeed, a possible concern with the benchmark state variable is, being based on the country-mean unemployment rates, that it imposes a constant “threshold” (location parameter) over time, ruling out potential structural changes in the labor market. This alternative state variable does not introduce such an assumption, thus representing an important robustness check. Regarding the smoothing parameter ( $\lambda$ ) of the HP trend, I test two possibilitiesFootnote ¹⁵: (i) a very high $\lambda$ equal to $10^6$ ; and (ii) a smaller $\lambda$ equal to $4*10^4$ . Table 2 (Baseline) shows that the results are highly robust to the use of these alternative state variables.

Turning from the unemployment rate to other variables, I then consider both standardized deviations of GDP from the HP trend and, as in Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2013), standardized deviations of the (log) employment rate from the HP trend, as the state variableFootnote ¹⁶. As clearly emerges from Table 2 (Baseline), previous results are largely confirmed considering these alternative indicators of slack.

All in all, these results provide clear evidence in favor of higher public consumption multipliers in slack than in good economic conditions, with a sizable difference between the two regimesFootnote ¹⁷. This conclusion appears very robust, holding regardless of the specific state variable used and the horizon consideredFootnote ¹⁸. Remarkably, considering all the tested state variables, estimates suggest a medium-term multiplier between 1.9 and 2.2 in the slack regime and between 0.1 and 0.4 in the good regimeFootnote ¹⁹.

5. Addressing the Fiscal Foresight Problem: The Expectations-Augmented Model

A well-known potential concern with the result shown in the previous section is that, due to possible implementation lags in fiscal policy, retrieved fiscal shocks may be “nonfundamental,” meaning that they are unable to recover the true structural shocks of the underlying data-generating process. This, known as the fiscal foresight problem, is particularly problematic, as it can bias and undermine estimates if economic agents are responsive to fiscal news (e.g. Leeper et al. (Reference Leeper, Walker and Yang>2013)). If this is the case, in fact, due to lags between announcement and effective implementation of fiscal policy, economic agents may start reacting to shocks before they are observable by the econometricians. This, of course, leads to incorrect results.

Concerns about anticipation effects in the fiscal multiplier literature have recently gained particular attention, as several studies have shown that standard “SVAR-based” identification methodologies tend to produce fiscal shocks that are partly predictable using additional external information, such as professional forecasts or narrative shocks (Ramey (Reference Ramey>2011b); Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012), (Reference Auerbach and Gorodnichenko>2013)). This indicates that these methodologies are effectively exposed to the fiscal foresight problem, which, according to Ramey (Reference Ramey>2011b), produces strong distortions in the estimates if anticipation effects are not properly considered. However, several studies claim that the potential distortion due to fiscal foresight is relatively small in practice, somehow preserving the reliability of results based on standard approaches (Mertens and Ravn (Reference Mertens and Ravn>2010); Sims (Reference Sims>2012); Perotti (Reference Perotti>2014)). The actual relevance of the problem, thus, turns out to be an empirical question.

Nonfundamentalness can be seen as an omitted-variables problem that leads econometricians to have a smaller set of information than that of economic agents (Kilian and Lütkepohl (Reference Kilian and Lütkepohl>2017)). As such, whenever feasible, a solution is to add variables in the econometric model to align the two information sets. Accordingly, in line with Ramey (Reference Ramey>2011b) and Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012, Reference Auerbach and Gorodnichenko>2013), I tackle fiscal foresight by incorporating real-time professional forecasts into the model. Particularly, following Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012, Reference Auerbach and Gorodnichenko>2017), I use the forecasts provided by the OECD in the “Economic Outlook.” These forecasts are reliable and perfectly fit the present analysis, as they explicitly refer to public consumption expenditures and cover all the countries and the timespan considered in the workFootnote ²⁰. Accordingly, I augment the baseline models with these forecasts to directly control for expectations on public consumption:

(7) \begin{equation} y_{i,t+h} = \alpha _{i,h} + \beta _h g_{i,t} + \rho g^{fc}_{i,t-1} + \sum _{l=1}^L \phi _{l,h} w_{i,t-l} + \tau _{t,h} + \epsilon _{i,t+h}; \hspace{20pt} h=0,\ldots,H \end{equation}

(8) \begin{align} y_{i,t+h} & = \alpha _{i,h} + \beta _{1,h} g_{i,t} * F(z_{i,t-1}) + \rho _1 g^{fc}_{i,t-1}*F(z_{i,t-1}) + \sum _{l=1}^L \phi _{1,l,h} w_{i,t-l} * F(z_{i,t-1}) \nonumber\\ &\quad +\beta _{2,h} g_{i,t} * (1-F(z_{i,t-1})) + \rho _2 g^{fc}_{i,t-1}*(1-F(z_{i,t-1})) + \sum _{l=1}^L \phi _{2,l,h} w_{i,t-l} *(1-F(z_{i,t-1})) \nonumber\\ &\quad +\theta _h* F(z_{i,t-1}) + \tau _{t,h} + \epsilon _{i,t+h}; \hspace{20pt} h=0,\ldots,H \end{align}

where $g^{fc}_{t-1}$ is the forecast on public consumption made in period $t-1$ . This helps in purifying public consumption shocks from their potentially predictable component and in constructing impulse responses based on unanticipated structural shocks (Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2012); Perotti (Reference Perotti>2014); Boehm (Reference Boehm>2020); Deleidi et al. (Reference Deleidi, Romaniello and Tosi>2021b))Footnote ²¹. Indeed, directly controlling for public consumption expectations, the coefficients $\{\beta _h\}_{h=0}^{h=H}$ and $\{\beta _{1,h}\}_{h=0}^{h=H}$ , $\{\beta _{2,h}\}_{h=0}^{h=H}$ can now be interpreted as the linear and state-dependent impulse responses of $y$ to “unanticipated” public consumption shocks, as these shocks are orthogonal, by construction, to the set of information contained in the forecasts and lagged time series. I call this specification the E-model, which stands for the expectations-augmented model.

Multipliers estimated with the linear E-model are reported in the right part of Table 1. The results show that the inclusion of the forecasts in the model does not sizably affect the estimates, leaving the multipliers on values above one in the medium term. Furthermore, as emerges from the right part of Table 2, I continue to find strong evidence of higher public consumption multipliers in the slack regime than in the good regime, even controlling for expectations. As with the previous results, this conclusion holds regardless of the horizon and the state variable considered.

Summing up, it can be argued that the results are extremely robust to the inclusion of the forecasts into the model and, therefore, to potential problems connected to the predictability of the public consumption shocksFootnote ²².

5.1 An IV approach to test the significance of state-dependent results

Up to now, no statistical test has been performed on the difference between multipliers in slack and good periods. This is due to the difficulty of comparing results that are the ratio between two impulse responses. A possible solution to that, proposed by Ramey and Zubairy (Reference Ramey and Zubairy>2018), is to use an instrumental variable approach to estimate multipliers in a single step. This has the advantage of directly providing the standard errors associated with the multipliers, thus allowing, for example, to easily test state dependency.

To this aim, the method proposed by Ramey and Zubairy (Reference Ramey and Zubairy>2018) is adapted to the context of a panel smooth-transition model, and the following model is estimated:

(9) \begin{align} & \sum _{j=0}^{j=h} \frac{GDP_{i,t+h} - GDP_{i,t-1}}{GDP_{i,t-1}} = \alpha _{i,h} + \beta _{1,h} \sum _{j=0}^{j=h} \frac{G_{i,t+h} - G_{i,t-1}}{GDP_{i,t-1}} * F(z_{i,t-1}) + \rho _1 g^{fc}_{i,t-1}*F(z_{i,t-1}) \nonumber \\[5pt] &\quad +\sum _{l=1}^L \phi _{1,l,h} w_{i,t-l} * F(z_{i,t-1}) + \beta _{2,h} \sum _{j=0}^{j=h} \frac{G_{i,t+h} - G_{i,t-1}}{GDP_{i,t-1}} + \rho _2 g^{fc}_{i,t-1} + \sum _{l=1}^L \phi _{2,l,h} w_{i,t-l} \nonumber\\[5pt] &\quad +\theta _h* F(z_{i,t-1}) + \tau _{t,h} + \epsilon _{i,t+h}; \hspace{20pt} h=0,\ldots,H \end{align}

where $\sum _{j=0}^{j=h} \frac{G_{i,t+h} - G_{i,t-1}}{GDP_{i,t-1}} * F(z_{i,t-1})$ and $\sum _{j=0}^{j=h} \frac{G_{i,t+h} - G_{i,t-1}}{GDP_{i,t-1}}$ are instrumented by $ g_{i,t} * F(z_{i,t-1})$ and $g_{i,t}$ , respectively. The coefficient $\beta _{1,h}$ now reads as the difference between multipliers in slack and good regimes. The standard error attached to this coefficient reveals the significance of this difference. As seen from the results shown in Table 3, multipliers in slack and good periods are significantly different at high confidence levels. State-dependent results are, therefore, clearly supported from a statistical perspective.

Table 3. Size and significance level of the differences between multipliers in slack and good regime

The p-values reported are based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors.

6. Transmission Channels

This section examines some transmission channels underlying previous results by analyzing the linear and state-dependent impulse responses of variables other than the gross domestic product. Specifically, the reaction of private consumption, private investment, net taxes, and the real interest rate is analyzedFootnote ²³. The impulse responses are expressed in GDP units, except for the real interest rate, which is in basis points.

Table 4. Linear and state-dependent cumulative multipliers for private consumption and investment

State-dependent values are the averages across different state variables.

Figure 4 reports linear impulse responses. As it emerges from the figure, in line with the findings of several studies analyzing the effects of shocks to public spending (e.g. Blanchard and Perotti (Reference Blanchard and Perotti>2002); Galí et al. (Reference Galí, López-Salido and Vallés>2007); Caldara and Kamps (Reference Caldara and Kamps>2008); Mertens and Ravn (Reference Mertens and Ravn>2010); Auerbach and Gorodnichenko (Reference Auerbach and Gorodnichenko>2013)), I find a clear crowding-in effect on private consumption, which positively reacts to a positive public consumption shock. The reaction is statistically significant and long lasting. In terms of cumulative multipliers, the results suggest that an increase in public consumption of one euro stimulates approximately 0.45 ${\unicode{x20AC}}$ of additional private consumption (cf. Table 4). Controlling for expectations, such crowding-in dynamics strengthen slightly, with an increase in the medium-term private consumption multiplier of 0.1 units. The reaction of private investment is less clear. There is some evidence of crowding-out on impact and then some tendency of crowding-in in the short term, which explains the low but positive multipliers shown in Table 4 (0.1). In any case, the reaction of private investment is not significantly different from zero at any horizon, so no robust conclusions can be drawn.

Figure 4. Linear impulse responses of private consumption, private investment, net taxes, and real interest rate to a $1\%$ shock to public consumption. Impulse responses in GDP units, except the real interest rate response, which is in basis points. Shaded areas represent $90\%$ confidence intervals based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors.

Net taxes and the real interest rate turn out to be largely insensitive to public consumption shocks (cf. Figure 4). Concerning net taxes, this means that the results likely capture the effects of public consumption shocks in a scenario of no “offsetting forces” coming from this variable. The estimates can, therefore, be interpreted as the effects of “fully deficit-financed” public consumption shocks. The fact that the real interest rate does not show any increasing tendency in response to a positive public consumption shock is particularly interesting and in line with several empirical analyses (e.g. Ramey (Reference Ramey>2011b); Corsetti et al. (Reference Corsetti, Meier and Müller>2012); Batini et al. (Reference Batini, Callegari and Melina>2012); d’Alessandro et al. (Reference d’Alessandro, Fella and Melosi>2019)). Interestingly, this is also confirmed by excluding potential zero lower bound periods from the analysis.

Shifting the focus to the nonlinear analysis, the results show that the difference in the size of the multiplier between slack and good conditions is mainly driven by a completely different reaction of the private components of the GDP in the two regimesFootnote ²⁴. Indeed, while both variables are strongly crowded in the slack regime, they tend to be crowded out in the good regime, even if the absence of crowding out cannot be rejected, especially for consumption (cf. Figures 5, 6). In terms of medium-term multipliers, my findings suggest that (i) an increase in public consumption of one euro stimulates approximately 1.25 ${\unicode{x20AC}}$ of additional private consumption in the slack regime, while it crowds out approximately 0.30 ${\unicode{x20AC}}$ of private consumption in the good regime and (ii) one euro of increase in public consumption triggers approximately 1.3 ${\unicode{x20AC}}$ of additional private investment in slack conditions, while it translates into a reduction of private investment of about 1.1 ${\unicode{x20AC}}$ in the good regime (cf. Table 4).

Figure 5. Slack regime impulse responses of private consumption, private investment, net taxes, and real interest rate to a $1\%$ shock to public consumption. Impulse responses in GDP units, except the real interest rate response, which is in basis points. Shaded areas represent $90\%$ confidence intervals based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors. State variable: negative standardized deviations of the unemployment rate from the country mean.

Figure 6. Good-regime impulse responses of private consumption, private investment, net taxes, and real interest rate to a $1\%$ shock to public consumption. Impulse responses in GDP units, except the real interest rate response, which is in basis points. Shaded areas represent $90\%$ confidence intervals based on Driscoll and Kraay (Reference Driscoll and Kraay>1998) standard errors. State variable: negative standardized deviations of the unemployment rate from the country mean.

The analysis of the state-dependent impulse responses of net taxes and the real interest rate reveals that these variables do not seem to play a crucial role in explaining previous state-dependent findings (cf. Figures 5, 6). Regarding net taxes, this means that the cause behind state dependency does not seem to be sought in a less favorable way of financing public spending, that is, recurring less to the deficit and more to additional taxes, in the good than in the slack regime. Moreover, the response of the real interest rate hardly informs the results, as it remains not significantly different from zero in both regimes. Therefore, all in all, this suggests that the state-dependent effects of the public consumption shocks lie in more structural mechanisms at work in the economic systems analyzed here.

Further investigation of such potential mechanisms is beyond the scope of this paper, leaving room for future analysis. However, it is interesting to briefly mention some of the mechanisms highlighted by recent literature that seek to rationalize and model state dependency. Bachmann and Sims (Reference Bachmann and Sims>2012), for example, find that the confidence of private agents increases when public spending increases during periods of economic slack, thereby stimulating private activity and production. Hence, a possible channel behind state dependency can be sought in the effect of fiscal policy on the confidence of the private sector. Second, if financial frictions are more relevant during periods of slack, expansionary fiscal policies can stimulate private activity more in such circumstances by relaxing these frictions (Canzoneri et al. (Reference Canzoneri, Collard, Dellas and Diba>2016)). Empirical support for this channel is provided by Glocker et al. (Reference Glocker, Sestieri and Towbin>2019) for the UK. Similarly, the effects of fiscal shocks can be amplified if the fraction of hand-to-mouth consumers is higher in conditions of slack since this increases the economy’s (mean) marginal propensity to consume and, therefore, the responsiveness of private consumption to changes in current income (e.g. Galí et al. (Reference Galí, López-Salido and Vallés>2007)). Finally, Ghassibe and Zanetti (Reference Ghassibe and Zanetti>2020) build a model where search-and-matching frictions in the goods market generate fiscal multipliers that vary according to the level of congestion of this market, as this defines the level of the crowding-out of private consumption. Under conditions of demand-driven slack, congestion is low, crowding-out is low, and multipliers are higherFootnote ²⁵.