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

Distilled by claude-sonnet-4-6 · extracted Jun 25, 2026, verified Jun 25, 2026

JEL (IAR-assigned): E32, E62, F41 · assigned from the abstract, not the journal

Full structured metadata (methods, scope, relatesTo, topics, datasets): raw Markdown (.md)

paper-summarymacrofiscal-policybusiness-cyclesopen-economy-macrostructural-estimationpeer-reviewedunreplicateddata:eurostat-esa

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.

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 non-traded 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, realized and anticipated EU transfers 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.

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.

#ResultLocatorMagnitude
R1External demand and government non-traded spending account for essentially all of the production boomTable 3, p. 2443External demand aˉT\bar{a}_T +0.04 log pts, government consumption gNcg_N^c +0.02 log pts out of 0.09 total model log-output boom; data boom = 0.14
R2Realized EU structural transfers and anticipated transfers drive the consumption boomTable 3, p. 2443TsT^s +0.02 log pts, TlT^l +0.01 log pts, external +0.05 log pts to log consumption out of 0.08 model boom; data consumption boom = 0.15
R3Tax policy is the dominant driver of the bust in productionTable 4, p. 2445Tax policy contributes -0.18 log pts out of -0.34 model (data -0.40) in log output 2007-2017; κτ\kappa_\tau -0.07, τNk\tau_N^k -0.05, τ\tau^\ell -0.03
R4Uninsurable idiosyncratic risk is the dominant driver of the bust in consumption and wagesTable 4, p. 2445; text p. 2444Idiosyncratic risk πθ\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
R5Spending-based consolidation would have reduced the output bust by 7 log pointsFigure 5, p. 2448Shifting 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
R6Fiscal discipline in the boom and capital tax cuts in the bust raise output by 16 pp by 2017Figure 6, p. 2452Removing 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
R7Fiscal multipliers: most spending multipliers below 1 (nontraded investment gNxg_N^x = 1.24 exceeds 1); capital-tax multipliers are largeTable 6, p. 2450; text p. 2451gNcg_N^c output multiplier = 0.56; gNxg_N^x = 1.24 (nontraded investment); aggregate revenue-based tax multiplier = 1.34; capital tax cost-based multiplier τHk\tau_H^k = 4.46
R8External bailout (EAP) prevented a 20 pp additional output shortfall; bank equity injections raised output 4 ppFigures 7-8, pp. 2453-2454; text pp. 2414-2415Without 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

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.

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 (1α)μ(1-\alpha)\mu, and all variables are expressed in detrended stationary form (p. 2417).

Households. Workers ι[0,1]\iota \in [0,1] belong to two types: a fraction ζ\zeta belongs to the rule-of-thumb household rr (more impatient, borrows at capacity, does not hold firm shares) and a fraction 1ζ1-\zeta belongs to the optimizing household oo. Workers in the optimizing household face idiosyncratic income risk. Worker ι\iota in household h={r,o}h = \{r,o\} values consumption and labor via recursive preferences (equation (1), p. 2417):

Vith={(cith)11ρ[1+(1ρ1)χ(ith)1+1ε1+1ε]+βhe(11ρ)μ[Eit(Vit+1h)1σ]11ρ1σ}111ρ(1)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 σ>0\sigma > 0 governs risk aversion, ρ>0\rho > 0 the intertemporal elasticity of substitution (estimated: ρ^=0.97\hat{\rho} = 0.97), and ε>0\varepsilon > 0 the Frisch elasticity (estimated: ε^=1.16\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):

logθit+1o=logθito+νit+1θ(4)\log \theta_{it+1}^o = \log \theta_{it}^o + \nu_{it+1}^\theta \tag{4}

where innovations wash out at the household level, exp(νitθ)dι=1\int \exp(\nu_{it}^\theta) d\iota = 1. A permanent income loss φθ-\varphi^\theta occurs with probability πtθ\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 yHy_H and nontraded goods yNy_N using Cobb-Douglas technology with variable utilization (equation (9), p. 2420):

yH,t=zH,tuH,t(eμkH,t)α(H,t)1α,yN,t=zN,tuN,t(eμkN,t)α(N,t)1α(9)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 zH,tz_{H,t}, zN,tz_{N,t} are exogenous productivity in each sector, uH,tu_{H,t}, uN,tu_{N,t} are endogenous utilization rates chosen by firms, and kk is capital (variable utilization raises depreciation, calibrated using firm surveys). The endogenous utilization mechanism is central: without it (ξH=ξN=\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 iti_t (equation (12), p. 2421):

Bt+1f+κy(PH,tyH,t+PN,tyN,t)=κx(1+τtx)Px,txt+κWtt+κτ,tTtf+(1+it)eμBtf(12)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 κx\kappa_x, κ\kappa_\ell, κτ,t\kappa_{\tau,t} are the fractions of investment, labor, and tax payments requiring working capital financing. The fraction κτ,t\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):

Nt+1c=(1+iˉt+1)eμNt+(it+1iˉt+1)(Bt+1f+ζBt+1r)(17)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 iˉ\bar{i} is the cost of funds from the rest of the world and ii is the domestic lending rate. An incentive compatibility constraint (equation (18), p. 2424) limits the lending spread via the threat of diversion:

κb(Bt+1f+ζBt+1r)Jtb(18)\kappa_b(B_{t+1}^f + \zeta B_{t+1}^r) \leq J_t^b \tag{18}

where JtbJ_t^b is bankers’ continuation value proportional to net worth NtN_t. Losses on sovereign debt (captured in TGd,tbT_{Gd,t}^b) erode bank net worth during the crisis, raise the lending spread itiˉti_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 zHz_H, zNz_N; (ii) external demand aˉT\bar{a}_T and import prices; (iii) financial conditions (sovereign borrowing limit, bank net worth shocks TWbT_W^b, TGdbT_{Gd}^b, TGebT_{Ge}^b); (iv) government spending (gTcg_T^c, gNcg_N^c, gTxg_T^x, gNxg_N^x, transfers TrT^r); (v) tax policy (τc\tau^c, τx\tau^x, τ\tau^\ell, τHk\tau_H^k, τNk\tau_N^k, prepayment fraction κτ\kappa_{\tau}); and (vi) disaster risk (idiosyncratic πθ\pi^\theta and aggregate πa\pi^a). All processes follow a VAR(1) (equation (21), p. 2426):

zt+1=zˉ+Rzt+Σνt+1,νt+1N(0,I)(21)\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 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 z\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):

y=(logH,  logN,  logTFPH,  logTFPN,  loguH,  loguN,  s,  logc,\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. log(PNcN),  logxT,  logxN,  logPH,  logPN,  logW,  Πf/(Pyy),  logN)(23)\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: σ=3\sigma=3, trade elasticity η=1.65\eta=1.65 estimated from a regression of relative expenditure on relative prices, φa=0.24\varphi^a=0.24 from Barro and Liao (2021)); (b) parameters calibrated from steady-state targets (Table 2 Panel A: discount factors, capital share α=0.44\alpha=0.44, banking parameters); (c) parameters estimated by Bayesian MCMC from the time series (Table 2 Panel B: ρ^=0.97\hat{\rho}=0.97, ϕ^=3.17\hat{\phi}=3.17, ε^=1.16\hat{\varepsilon}=1.16, ζ^=0.34\hat{\zeta}=0.34, utilization elasticities ξ^H=3.12\hat{\xi}_H=3.12, ξ^N=3.75\hat{\xi}_N=3.75, price and wage adjustment costs ψ^H,p=79.3\hat{\psi}_{H,p}=79.3, ψ^w=78.4\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.

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

Source decompositions. The model is run once for the full sample with all shocks; then each group gg 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.

Structural element analysis. Table 5 (p. 2447) re-runs the model with alternative parameter values (e.g. ξH=ξN=\xi_H = \xi_N = \infty, ψp=ψw=0\psi_p = \psi_w = 0, φθ=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 iˉ=0.04\bar{i} = 0.04:

Mfy(h)=t=1h(1+iˉ)1tΔytt=1h(1+iˉ)1tΔft(25)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}

where the impulse is a 1-percentage-point change in the fiscal instrument ff initiated from its autoregressive process. Revenue-cost multipliers divide Mfy(h)M_f^y(h) by the revenue counterpart Mfr(h)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 gNx=1.24g_N^x = 1.24 is the largest individual spending multiplier (investment > consumption, nontraded > traded, per the paper p. 2449); the nontraded consumption multiplier gNc=0.56g_N^c = 0.56 equals the aggregate spending-weighted multiplier because gNcg_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 TrT^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.

DatasetRole in paperWiki 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 uHu_H and uNu_NNo page yet
Bank of Greece Flow of FundsFirm dividends Πf\Pi^f and bank net worth NN; financial accountsNo page yet
Maastricht Treaty / OECD Economic OutlookGovernment debt misreporting (anticipated transfers TlT^l series from stated vs. revised deficits)No page yet
Barro-Liao (2021) options-based disaster probabilityFar-out-of-the-money put option prices on the Greek stock market for aggregate disaster probability πa\pi^aNo 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).

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.

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.

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