Macroeconomics of the Greek Depression: Chodorow-Reich, Karabarbounis & Kekre (2023)

What this is. The paper’s core results, the structural model it builds, and the Bayesian estimation approach with key equations: enough to know what Greece’s boom-bust cycle was driven by and how the model identifies those forces, without reading all 47 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper develops and estimates a dynamic general equilibrium model of a small open economy in a currency union to decompose the sources of Greece’s boom (1998-2007) and subsequent depression (2007-2017). It quantitatively confirms the central finding of Gourinchas, Philippon and Vayanos (2016) that fiscal consolidation drove about half of the bust in output, while substantially extending it with endogenous TFP via variable utilization, banking sector frictions, idiosyncratic income risk, and a more detailed tax structure. On the production side, external demand and government spending, mostly on nontraded consumption, account for essentially the entire boom; tax policy, amplified by a working capital constraint on firms and variable factor utilization, accounts for the largest fraction of the bust. On the consumption side, transfers to households and from EU structural funds, along with external demand, fuel the boom; the rise in uninsurable idiosyncratic income risk, tracked by the long-term unemployment rate, accounts for the largest fraction of the decline in consumption, prices, and wages.

Unlike the standard boom-bust narrative emphasizing downward nominal wage rigidity in a currency peg (Schmitt-Grohé and Uribe 2016), nominal rigidities play only a moderate role: wages and prices fell substantially during the Greek crisis, which this model attributes primarily to the rise of idiosyncratic risk as a negative demand and positive labor-supply shock. The model also extends the joint European boom-bust analysis of Martin and Philippon (2017) by adding endogenous TFP movements, capital accumulation, a banking sector, time-varying idiosyncratic risk, and multi-rate tax measurement.

Counterfactual experiments show that a more spending-based fiscal consolidation would have reduced the output bust by roughly 7 log points, and that avoiding the debt-financed boom in household transfers would have created fiscal space to lower distortionary capital taxes in the crisis. External and bank bailouts mitigated the depression; without the Economic Adjustment Programme, borrowing costs would have spiked roughly 30 percentage points in 2012.

Core results

Magnitudes and contributions are as reported in the source tables and figures. All log deviations are expressed as differences from the 1998 baseline after detrending at 1.6 percent per year (quantities) and 1 percent per year (prices and wages). Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	External demand and government spending account for essentially all of the production boom	Table 3, p. 2443	External demand $\bar{a}_T$ +0.04 log pts and total government spending +0.04 (including nontraded consumption $g_N^c$ +0.02) out of 0.09 total model log-output boom; data boom = 0.14
R2	Transfers and external demand contribute to the consumption boom	Table 3, p. 2443	Realized EU structural-fund transfers $T^g$ +0.02, anticipated transfers $T^l$ +0.01, rule-of-thumb household transfers $T^r$ +0.03, and external demand $\bar{a}_T$ +0.01 log pts; the external category total is +0.05 and includes $T^g$ and $T^l$ (model boom = 0.08; data = 0.15)
R3	Tax policy is the dominant driver of the bust in production	Table 4, p. 2445	Tax policy contributes -0.18 log pts out of -0.34 model (data -0.40) in log output 2007-2017; $\kappa_\tau$ -0.07, $\tau_N^k$ -0.05, $\tau^\ell$ -0.03
R4	Uninsurable idiosyncratic risk is the dominant driver of the bust in consumption and wages	Table 4, p. 2445; text p. 2444	Idiosyncratic risk $\pi^\theta$ contributes -0.14 out of -0.28 model log-consumption bust; accounts for 10 pp of price decline and 18 pp of wage decline
R5	Spending-based consolidation would have reduced the output bust by 7 log points	Figure 5, p. 2448	Shifting all fiscal adjustment from taxes to spending cuts raises log output by +7 log pts by 2017 vs baseline; roughly half via TFP gains from lower taxes
R6	Fiscal discipline in the boom and capital tax cuts in the bust raise output by 16 pp by 2017	Figure 6, p. 2452	Removing debt-financed transfers in boom and using freed resources to cut capital taxes: output +16 log pts, consumption +12 log pts by 2017; labor tax path adds only +2-4 log pts
R7	Fiscal multipliers: most spending multipliers below 1 (nontraded investment $g_N^x$ = 1.24 exceeds 1); capital-tax multipliers are large	Table 6, p. 2450; text p. 2451	$g_N^c$ output multiplier = 0.56; $g_N^x$ = 1.24 (nontraded investment); aggregate revenue-based tax multiplier = 1.34; capital tax cost-based multiplier $\tau_H^k$ = 4.46
R8	External bailout (EAP) prevented a 20 pp additional output shortfall; bank equity injections raised output 4 pp	Figures 7-8, pp. 2453-2454; text pp. 2414-2415	Without EAP, borrowing cost rises ~30 pp in 2012; government bailout raised output ~20 pp and consumption 20-40 log pts in 2013 by preventing further spending cuts or tax hikes; bank bailout raised output ~4 pp by 2017
R9	Greece’s output decline was unusually large and persistent relative to other modern high- and middle-income economies	Figure 1, p. 2412	Mean real output per capita was 18% below its 2007 peak over 2008-2017
R10	Variable utilization is needed to match the observed production and TFP bust	Table 5, p. 2447	Without variable utilization, model log output declines -0.24 rather than -0.34 and TFP declines -0.02 rather than -0.14; with ξH = ξN = 2.5, output and TFP declines are -0.44 and -0.23
R11	The working capital constraint amplifies production contractions	Table 5, p. 2447	Without working capital, output declines -0.18 rather than -0.34 log points in 2007-2017
R12	Idiosyncratic disaster risk is a mechanism for the consumption and price bust	Table 5, p. 2447	Setting φθ = 0 changes log consumption decline from -0.28 to -0.14; traded and nontraded prices change from -0.04 and 0.00 to +0.04 and +0.10
R13	Nominal price and wage rigidities have a moderate role over the full episode	Table 5, p. 2447; text p. 2414	With ψp = ψw = 0, output’s bust remains -0.34 log points; at ψp = ψw = 1,000 it is -0.42, while the authors report the main rigidity role is in the boom
R14	Bank equity injections mitigated the persistence of the depression	Figure 8, p. 2454; text p. 2415	Bank bailout raised output by roughly 4 log points by 2017 relative to use of the funds for tax cuts
R15	Bankers’ exit rate changes the amplification of real shocks through borrowing costs	Table 5, p. 2447	With δb = 0.3, output and consumption fall -0.29 and -0.25; with δb = 0.9, they fall -0.38 and -0.29, versus baseline changes of -0.34 and -0.28

Overall (paper’s conclusion). Greece’s depression differs profoundly from standard small-open-economy boom-bust narratives. The production bust was not driven by nominal rigidities preventing wage adjustment, since wages and prices fell substantially. Instead, tax increases, amplified through variable utilization and a working capital constraint, drove most of the output decline. The consumption decline, unusual in its persistence, reflected rising idiosyncratic income risk. The composition of fiscal adjustment and the timing of transfers in the boom period were as consequential as the aggregate size of the consolidation.

Theory / model

The model is a small open economy operating in a currency union, populated by heterogeneous households, traded and nontraded goods firms, a banking sector, and a government. Trend productivity grows at rate $(1-\alpha)\mu$, and all variables are expressed in detrended stationary form (p. 2417).

Households. Workers $\iota \in [0,1]$ belong to two types: a fraction $\zeta$ belongs to the rule-of-thumb household $r$ (more impatient, borrows at capacity, does not hold firm shares) and a fraction $1-\zeta$ belongs to the optimizing household $o$. Workers in the optimizing household face idiosyncratic income risk. Worker $\iota$ in household $h = \{r,o\}$ values consumption and labor via recursive preferences (equation (1), p. 2417):

$$V_{it}^h = \left\{(c_{it}^h)^{1-\frac{1}{\rho}} \left[1 + \left(\frac{1}{\rho}-1\right) \frac{\chi(\ell_{it}^h)^{1+\frac{1}{\varepsilon}}}{1+\frac{1}{\varepsilon}}\right] + \beta^h e^{(1-\frac{1}{\rho})\mu}\left[E_{it}(V_{it+1}^h)^{1-\sigma}\right]^{\frac{1-\frac{1}{\rho}}{1-\sigma}}\right\}^{\frac{1}{1-\frac{1}{\rho}}} \tag{1}$$

where $\sigma > 0$ governs risk aversion, $\rho > 0$ the intertemporal elasticity of substitution (estimated: $\hat{\rho} = 0.97$), and $\varepsilon > 0$ the Frisch elasticity (estimated: $\hat{\varepsilon} = 1.16$). Combining Epstein and Zin (1989) preferences with constant Frisch elasticity separates risk aversion from intertemporal substitution, which matters for the role of idiosyncratic risk in the bust.

Idiosyncratic income shocks for the optimizing household follow a random walk in logs (equation (4), p. 2418):

$$\log \theta_{it+1}^o = \log \theta_{it}^o + \nu_{it+1}^\theta \tag{4}$$

where innovations wash out at the household level, $\int \exp(\nu_{it}^\theta) d\iota = 1$. A permanent income loss $-\varphi^\theta$ occurs with probability $\pi_t^\theta$, measured by the long-term unemployment rate (rising from ~5% before the crisis to ~20% during it, Figure 3 panel I). This uninsurable risk is central to the model’s transmission of the bust into consumption, prices, and wages.

Firms. Intermediate goods firms produce traded goods $y_H$ and nontraded goods $y_N$ using Cobb-Douglas technology with variable utilization (equation (9), p. 2420):

$$y_{H,t} = z_{H,t} u_{H,t} (e^{-\mu} k_{H,t})^\alpha (\ell_{H,t})^{1-\alpha}, \quad y_{N,t} = z_{N,t} u_{N,t} (e^{-\mu} k_{N,t})^\alpha (\ell_{N,t})^{1-\alpha} \tag{9}$$

where $z_{H,t}$, $z_{N,t}$ are exogenous productivity in each sector, $u_{H,t}$, $u_{N,t}$ are endogenous utilization rates chosen by firms, and $k$ is capital (variable utilization raises depreciation, calibrated using firm surveys). The endogenous utilization mechanism is central: without it ($\xi_H = \xi_N = \infty$), the model would generate a bust in output and TFP more than 10 log points smaller (Table 5, p. 2447).

Firms face a working capital constraint that links production decisions to the endogenous borrowing cost $i_t$ (equation (12), p. 2421):

$$B_{t+1}^f + \kappa_y(P_{H,t} y_{H,t} + P_{N,t} y_{N,t}) = \kappa_x(1+\tau_t^x) P_{x,t} x_t + \kappa_\ell W_t \ell_t + \kappa_{\tau,t} T_t^f + (1+i_t) e^{-\mu} B_t^f \tag{12}$$

where $\kappa_x$, $\kappa_\ell$, $\kappa_{\tau,t}$ are the fractions of investment, labor, and tax payments requiring working capital financing. The fraction $\kappa_{\tau,t}$ rises from 50 to 100 percent during the crisis as firms are required to prepay income taxes before revenues realize. This constraint amplifies the production bust: without it, both the production boom and bust would have been smaller.

Banking sector. Banks follow Gertler and Kiyotaki (2011) and Bocola (2016). Incumbent banker net worth evolves as (equation (17), p. 2424):

$$N_{t+1}^c = (1+\bar{i}_{t+1}) e^{-\mu} N_t + (i_{t+1} - \bar{i}_{t+1})(B_{t+1}^f + \zeta B_{t+1}^r) \tag{17}$$

where $\bar{i}$ is the cost of funds from the rest of the world and $i$ is the domestic lending rate. An incentive compatibility constraint (equation (18), p. 2424) limits the lending spread via the threat of diversion:

$$\kappa_b(B_{t+1}^f + \zeta B_{t+1}^r) \leq J_t^b \tag{18}$$

where $J_t^b$ is bankers’ continuation value proportional to net worth $N_t$. Losses on sovereign debt (captured in $T_{Gd,t}^b$) erode bank net worth during the crisis, raise the lending spread $i_t - \bar{i}_t$, and reduce firms’ factor demand through the working capital constraint.

Driving forces. The model organizes exogenous shocks into six categories (p. 2425): (i) traded and nontraded productivity $z_H$, $z_N$; (ii) external demand $\bar{a}_T$ and import prices; (iii) financial conditions (sovereign borrowing limit, bank net worth shocks $T_W^b$, $T_{Gd}^b$, $T_{Ge}^b$); (iv) government spending ($g_T^c$, $g_N^c$, $g_T^x$, $g_N^x$, transfers $T^r$); (v) tax policy ($\tau^c$, $\tau^x$, $\tau^\ell$, $\tau_H^k$, $\tau_N^k$, prepayment fraction $\kappa_{\tau}$); and (vi) disaster risk (idiosyncratic $\pi^\theta$ and aggregate $\pi^a$). All processes follow a VAR(1) (equation (21), p. 2426):

$$\mathbf{z}_{t+1} = \bar{\mathbf{z}} + \mathbb{R}\, \mathbf{z}_t + \Sigma\, \nu_{t+1}, \quad \nu_{t+1} \sim \mathcal{N}(0, \mathbf{I}) \tag{21}$$

The remaining numbered model equations are transcribed below so the formal model is complete. Locators refer to printed AER pages.

Equation (2), p. 2418, gives the consumption aggregator and traded-goods nest:

$$c_t=\left[\omega_c^{1/\phi}c_{T,t}^{(\phi-1)/\phi}+(1-\omega_c)^{1/\phi}c_{N,t}^{(\phi-1)/\phi}\right]^{\phi/(\phi-1)},\quad c_{T,t}=\left[\gamma^{1/\eta}c_{H,t}^{(\eta-1)/\eta}+(1-\gamma)^{1/\eta}c_{F,t}^{(\eta-1)/\eta}\right]^{\eta/(\eta-1)} \tag{2}$$

Equation (3), p. 2418, aggregates sectoral varieties:

$$c_{H,t}=\left[\int_0^1c_{H,t}(j)^{(\epsilon_p-1)/\epsilon_p}\,dj\right]^{\epsilon_p/(\epsilon_p-1)},\quad c_{N,t}=\left[\int_0^1c_{N,t}(j)^{(\epsilon_p-1)/\epsilon_p}\,dj\right]^{\epsilon_p/(\epsilon_p-1)} \tag{3}$$

The labor agency objective and bundle are equation (5), p. 2419; its labor demand condition is equation (6), p. 2419:

$$\max_{\{\ell_t^{h\iota}\}}W_t(\ell_t^r+\ell_t^o)-\int W_t^{r\iota}\ell_t^{r\iota}d\iota-\int W_t^{o\iota}\ell_t^{o\iota}d\iota,\quad \ell_t^h=\left[\int(\ell_t^{h\iota})^{(\epsilon_w-1)/\epsilon_w}d\iota\right]^{\epsilon_w/(\epsilon_w-1)} \tag{5}$$ $$\ell_t^{h\iota}=\left(\frac{W_t^{h\iota}}{W_t}\right)^{-\epsilon_w}\ell_t^h \tag{6}$$

Household asset restrictions and the budget constraint are equations (7)-(8), pp. 2419-2420:

$$\varsigma_{t+1}^{h\iota}\geq0,\quad B_{t+1}^{h\iota}\leq\bar{B}_{t+1}^h \tag{7}$$ $$(1+\tau_t^c)P_{c,t}c_t^{h\iota}+[1+i(B_t^{h\iota})]e^{-\mu}B_t^{h\iota}-B_{t+1}^{h\iota}+Q_t^\varsigma\varsigma_{t+1}^{h\iota}=\theta_t^{h\iota}\left[(1-\tau_t^\ell)\int W_t^{h\iota}\ell_t^{h\iota}d\iota-\int AC_{w,\iota t}^h d\iota+T_t^h+\frac{\mathbb{I}(h=o)(\Pi_t^b+T_t^l)}{1-\zeta}\right]+(Q_t^\varsigma+\Pi_t^f)\varsigma_t^{h\iota} \tag{8}$$

Utilization-dependent depreciation and capital accumulation are equations (10)-(11), p. 2421:

$$\delta_{H,t}=\bar{\delta}_H+\frac{\bar{\xi}_H}{\xi_H}(u_{H,t}^{\xi_H}-1),\quad\delta_{N,t}=\bar{\delta}_N+\frac{\bar{\xi}_N}{\xi_N}(u_{N,t}^{\xi_N}-1) \tag{10}$$ $$k_{t+1}=\{1-[s_t\delta_{H,t}+(1-s_t)\delta_{N,t}]\}e^{-\mu}k_t+x_t+g_t^x \tag{11}$$

Equation (13), p. 2422, defines after-tax firm profits:

$$\begin{aligned} \Pi_t^f={}&(1-\tau_{H,t}^k)(P_{H,t}^fy_{H,t}-W_t\ell_{H,t}+\Pi_{H,t}^f)+(1-\tau_{N,t}^k)(P_{N,t}^fy_{N,t}-W_t\ell_{N,t}+\Pi_{N,t}^f)-AC_t^f+B_{t+1}^f-(1+\tau_t^x)P_{x,t}x_t\\ &-e^{-\mu}[(1+i_t)B_t^f-s_t\tau_{H,t}^k(\bar{\delta}_HQ_t^kk_t+i_tB_t^f)-(1-s_t)\tau_{N,t}^k(\bar{\delta}_NQ_t^kk_t+i_tB_t^f)] \end{aligned} \tag{13}$$

Retail demand equations (14)-(15), p. 2423, are:

$$y_{H,t}(j)=\left(\frac{P_{H,t}(j)}{P_{H,t}}\right)^{-\epsilon_p}\left[\gamma\left(\frac{P_{T,t}}{P_{H,t}}\right)^{-\eta}(c_{T,t}+x_{T,t}+g_{T,t}^c+g_{T,t}^x)+(1-\gamma)\left(\frac{P_{F,t}}{P_{H,t}}\right)^{-\eta}\bar{a}_{T,t}\right] \tag{14}$$ $$y_{N,t}(j)=\left(\frac{P_{N,t}(j)}{P_{N,t}}\right)^{-\epsilon_p}(c_{N,t}+x_{N,t}+g_{N,t}^c+g_{N,t}^x) \tag{15}$$

Bank net worth identities are equations (16) and (19), pp. 2424-2425:

$$N_t=e^{\mu}(B_{t+1}^f+\zeta B_{t+1}^r-B_{t+1}^b) \tag{16}$$ $$N_{t+1}=(1-\delta_b)N_{t+1}^c+N_{t+1}^e+T_{W,t}^b+e^{\mu}T_{G,t}^b \tag{19}$$

The government budget constraint is equation (20), p. 2425:

$$\begin{aligned} \bar{B}_{t+1}^g+T_t^g+\tau_t^cP_{c,t}[\zeta c_t^r+(1-\zeta)c_t^o]+\tau_t^xP_{x,t}x_t+\tau_t^\ell W_t[\zeta\ell_t^r+(1-\zeta)\ell_t^o]\\ +\sum_{i=H,N}\tau_{i,t}^k[P_{i,t}^fy_{i,t}-W_t\ell_{i,t}+\Pi_{i,t}^f-e^{-\mu}s_{i,t}(\bar{\delta}_iQ_t^kk_t+i_tB_t^f)]\\ =(1+\bar{r}_t)e^{-\mu}\bar{B}_t^g+T_{G,t}^b+P_{T,t}(g_{T,t}^c+g_{T,t}^x)+P_{N,t}(g_{N,t}^c+g_{N,t}^x)+\zeta T_t^r+(1-\zeta)T_t^o \end{aligned} \tag{20}$$

The external-demand measurement identity is equation (22), p. 2430:

$$P_{H,t}y_{H,t}=\gamma\left(\frac{P_{T,t}}{P_{H,t}}\right)^{\eta-1}P_{T,t}(c_{T,t}+x_{T,t}+g_{T,t}^c+g_{T,t}^x)+(1-\gamma)\left(\frac{P_{F,t}}{P_{H,t}}\right)^{\eta-1}P_{F,t}\bar{a}_{T,t} \tag{22}$$

Equation (24), p. 2444, defines the effective discount factor used to interpret idiosyncratic risk:

$$\widetilde{\beta}_t^o=\beta^o e^{(1-1/\rho)\mu}\left(1-\pi_t^a+\pi_t^a e^{(\sigma-1)\phi^a}\right)^{(1/\rho-1)/(\sigma-1)}\left[(1-\pi_t^\theta)e^{-\sigma\log\left(\frac{1-\pi_t^\theta e^{-\phi^\theta}}{1-\pi_t^\theta}\right)}+\pi_t^\theta e^{\sigma\phi^\theta}\right] \tag{24}$$

Method

The model is solved by a first-order perturbation around its steady state. It is estimated by Bayesian techniques following the bayesian-dsge-estimation approach (related to Smets and Wouters 2007). The key methodological discipline is that the time series of exogenous processes $\mathbf{z}$ are fed directly as observables without adding any measurement error; only the outcome variables receive measurement errors. This restricts the shocks to account for the data without slack from measurement noise, testing the model’s fit more stringently.

The estimation uses 16 observable outcome variables (equation (23), p. 2437):

$$\mathbf{y} = \left(\log \ell_H,\; \log \ell_N,\; \log \text{TFP}_H,\; \log \text{TFP}_N,\; \log u_H,\; \log u_N,\; s,\; \log c,\right.$$ $$\left.\log(P_N c_N),\; \log x_T,\; \log x_N,\; \log P_H,\; \log P_N,\; \log W,\; \Pi^f/(P_y y),\; \log N\right) \tag{23}$$

covering sectoral labor and TFP, utilization, capital share, aggregate and sectoral consumption, investment, prices, wages, firm profits, and bank net worth. The model achieves correlations with data above 0.9 for most variables (online Appendix Table C.10, p. 2437 footnote).

Parameters are divided into three groups: (a) parameters set without solving the model (Table 1, p. 2436: $\sigma=3$, trade elasticity $\eta=1.65$ estimated from a regression of relative expenditure on relative prices, $\varphi^a=0.24$ from Barro and Liao (2021)); (b) parameters calibrated from steady-state targets (Table 2 Panel A: discount factors, capital share $\alpha=0.44$, banking parameters); (c) parameters estimated by Bayesian MCMC from the time series (Table 2 Panel B: $\hat{\rho}=0.97$, $\hat{\phi}=3.17$, $\hat{\varepsilon}=1.16$, $\hat{\zeta}=0.34$, utilization elasticities $\hat{\xi}_H=3.12$, $\hat{\xi}_N=3.75$, price and wage adjustment costs $\hat{\psi}_{H,p}=79.3$, $\hat{\psi}_w=78.4$).

Source decompositions (Tables 3-4) are computed by shutting off the time evolution of each group of driving forces in turn, holding them constant at their steady-state values. Positive entries in a row indicate the group contributed to an increase in a variable; negative entries indicate it contributed to a decrease. By construction, contributions sum to the model total up to rounding.

Empirical specifications

The paper’s three main empirical exercises are source decompositions, structural element comparisons, and policy counterfactuals.

Bayesian structural estimation specification. The annual sample is Greece, 1998-2017. The authors estimate 16 structural parameters using the 16-element observable vector in equation (23), together with the measured exogenous process vector in equation (21). They use Bayesian MCMC with measurement error on the outcome observables only; measured shock paths enter without measurement error. The reported uncertainty is posterior uncertainty, including 90% credible bands in Figure 4 (p. 2441). This nonlinear structural system has no panel fixed effects or conventional regression standard errors. Written out from the Bayesian estimation description on pp. 2437-2438, the posterior target is proportional to the prior times the likelihood of the joint outcome and measured-shock series conditional on structural parameters; the measurement-error terms apply to the outcome vector only. The estimation sample has 20 annual observations. The model uses a first-order approximation around the steady state. The authors estimate the trade elasticity separately from the traded-goods CES first-order conditions. Written out from p. 2435, the annual first-difference regression is:

$$\Delta\log\left(\frac{P_{H,t}a_{H,t}}{P_{F,t}a_{F,t}}\right)=\beta\,\Delta\log\left(\frac{P_{H,t}}{P_{F,t}}\right)+\varepsilon_t$$

Here $a_{H,t}$ and $a_{F,t}$ are Greek expenditures on domestic and foreign traded goods. The reported elasticity is eta = 1.65 with standard error 0.25, estimated from annual Greek data over 1998-2017. It is a differenced time-series specification, with no fixed effects; the paper reports the standard error but does not describe a separate robust or clustered correction in the main text.

Source decompositions. The model is run once for the full sample with all shocks; then each group $g$ of driving forces is held at its steady-state mean while all others are fed in. Changes in endogenous variables across these runs identify the contribution of each group. Tables 3 and 4 (pp. 2443, 2445) report changes in log output, log labor, log capital, log TFP, log consumption, log traded and nontraded prices, log wage, and net-exports-to-GDP for the boom (1998-2007) and bust (2007-2017) periods respectively.

Written out from the shutdown procedure on pp. 2443-2445, for driving-force group $g$ the baseline and shutdown paths satisfy:

$$y_t^{\text{base}}=F(\theta,z_{1:T}),\quad y_t^{(-g)}=F(\theta,z_{1:T}^{(-g)}),\quad z_{g,t}^{(-g)}=\bar z_g,\quad \text{contribution}_{g,t}=y_t^{\text{base}}-y_t^{(-g)}$$

Structural-element checks. Table 5 (p. 2447) holds the estimated shock sequence fixed and changes one model feature at a time. Written out from the paper’s procedure, the comparison is $y_t^{(m)}=F(\theta^{(m)},z_{1:T})$, where $m$ identifies baseline or an alternative utilization elasticity, adjustment-cost parameter, idiosyncratic-risk parameter, banker exit rate, or the model without the working-capital constraint. The sample and shocked paths are the same as the baseline; there are no fixed effects or conventional standard errors because the outcomes are generated by the structural model.

Structural element analysis. Table 5 (p. 2447) re-runs the model with alternative parameter values (e.g. $\xi_H = \xi_N = \infty$, $\psi_p = \psi_w = 0$, $\varphi^\theta = 0$, no working capital) to identify which model features account for the boom-bust dynamics. Variable utilization and idiosyncratic risk are identified as the two structural elements that account for most of the boom-bust dynamics.

Fiscal multipliers. Fiscal multipliers are defined as the present-discounted ratio of the output response to the present-discounted change in the fiscal instrument, at a 7-year horizon (equation (25), p. 2448), discounted at the steady-state private interest rate $\bar{i} = 0.04$:

$$M_f^y(h) = \frac{\sum_{t=1}^h (1+\bar{i})^{1-t} \Delta y_t}{\sum_{t=1}^h (1+\bar{i})^{1-t} \Delta f_t} \tag{25}$$

The associated revenue-cost multiplier is equation (26), p. 2449:

$$M_f^r(h)=-\frac{\sum_{t=1}^{h}(1+\bar{i})^{1-t}\Delta[(1-\zeta)T_t^o]}{\sum_{t=1}^{h}(1+\bar{i})^{1-t}\Delta f_t} \tag{26}$$

where the impulse is a 1-percentage-point change in the fiscal instrument $f$ initiated from its autoregressive process. Revenue-cost multipliers divide $M_f^y(h)$ by the revenue counterpart $M_f^r(h)$ (defined symmetrically). Table 6 (p. 2450) reports output effects, revenue costs, and output-per-dollar-of-revenue for ten instruments: four spending categories, transfers, and five tax rates. The government nontraded investment multiplier $g_N^x = 1.24$ is the largest individual spending multiplier (investment > consumption, nontraded > traded, per the paper p. 2449); the nontraded consumption multiplier $g_N^c = 0.56$ equals the aggregate spending-weighted multiplier because $g_N^c$ is the largest spending category by expenditure share. Capital tax multipliers are the largest tax multipliers, consistent with the economy operating near the peak of the Laffer curve for capital income taxation (capital income tax cut approximately revenue-neutral at the margin, p. 2450).

Policy counterfactuals. Figures 5-8 (pp. 2448, 2452, 2453, 2454) evaluate three alternative scenarios: (i) shifting fiscal adjustment entirely from taxes to spending cuts, holding tax rates at 2009 values while expanding government spending innovations to balance the budget; (ii) eliminating debt-financed transfers $T^r$ in the boom and using the freed fiscal space to reduce distortionary taxes in the bust; (iii) removing the external bailout (EAP) resources and instead forcing Greece to balance the budget via additional spending cuts or tax hikes. All counterfactuals condition on the estimated sequence of shocks and compare the model-generated paths to the baseline path under observed fiscal policies.

Written out from Section V, each policy experiment compares $y_t^{(q)}=F(\hat\theta,z_{1:T},q)$ with the baseline $y_t^{(0)}=F(\hat\theta,z_{1:T},q_0)$, where fiscal instruments or bailout flows are changed under scenario $q$ and the stated alternative budget balancing instrument adjusts. These are annual structural counterfactuals over the Greece 1998-2017 model path, not panel regressions, so there are no fixed effects or regression standard errors.

Datasets used

Dataset	Role in paper	Wiki page
Eurostat European System of Accounts (ESA)	Output, prices, consumption, investment, labor, TFP for Greece 1998-2017 (baseline observables for estimation)	No page yet
EU Joint Harmonised Commission Surveys (JCS)	Firm-level capacity utilization (manufacturing sector) and services survey (services sector) for $u_H$ and $u_N$	No page yet
Bank of Greece Flow of Funds	Firm dividends $\Pi^f$ and bank net worth $N$; financial accounts	No page yet
Maastricht Treaty / OECD Economic Outlook	Government debt misreporting (anticipated transfers $T^l$ series from stated vs. revised deficits)	No page yet
Barro-Liao (2021) options-based disaster probability	Far-out-of-the-money put option prices on the Greek stock market for aggregate disaster probability $\pi^a$	No page yet

Sample: annual, 1998-2017 (20 years, Greece). Quantities detrended at 1.6% per year, TFP at 0.7%, prices and wages at 1% (euro inflation average).

When to read the full paper

Use the original if you are: studying the structural mechanisms that generate large depressions in currency unions; replicating the source decompositions in Tables 3-4 (the replication package is at the ICPSR repository linked in replicationCode); extending the model to other periphery euro-area economies; or evaluating the design of fiscal consolidation programs. The online appendix contains alternative specifications, robustness checks, and a full set of parameter estimates and model validation.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(9), September 2023. Published by the American Economic Association, paywalled. This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. Redistribution is extract-only; no verbatim PDF is hosted here.

Chodorow-Reich, Gabriel, Loukas Karabarbounis, and Rohan Kekre. “The Macroeconomics of the Greek Depression.” American Economic Review 113, no. 9 (September 2023): 2411-2457. DOI: 10.1257/aer.20210864.