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
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.
Core results
Section titled “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 non-traded spending account for essentially all of the production boom | Table 3, p. 2443 | External demand +0.04 log pts, government consumption +0.02 log pts out of 0.09 total model log-output boom; data boom = 0.14 |
| R2 | Realized EU structural transfers and anticipated transfers drive the consumption boom | Table 3, p. 2443 | +0.02 log pts, +0.01 log pts, external +0.05 log pts to log consumption out of 0.08 model boom; data consumption boom = 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; -0.07, -0.05, -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 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 = 1.24 exceeds 1); capital-tax multipliers are large | Table 6, p. 2450; text p. 2451 | output multiplier = 0.56; = 1.24 (nontraded investment); aggregate revenue-based tax multiplier = 1.34; capital tax cost-based multiplier = 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 |
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
Section titled “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 , and all variables are expressed in detrended stationary form (p. 2417).
Households. Workers belong to two types: a fraction belongs to the rule-of-thumb household (more impatient, borrows at capacity, does not hold firm shares) and a fraction belongs to the optimizing household . Workers in the optimizing household face idiosyncratic income risk. Worker in household values consumption and labor via recursive preferences (equation (1), p. 2417):
where governs risk aversion, the intertemporal elasticity of substitution (estimated: ), and the Frisch elasticity (estimated: ). 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):
where innovations wash out at the household level, . A permanent income loss occurs with probability , 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 and nontraded goods using Cobb-Douglas technology with variable utilization (equation (9), p. 2420):
where , are exogenous productivity in each sector, , are endogenous utilization rates chosen by firms, and is capital (variable utilization raises depreciation, calibrated using firm surveys). The endogenous utilization mechanism is central: without it (), 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 (equation (12), p. 2421):
where , , are the fractions of investment, labor, and tax payments requiring working capital financing. The fraction 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):
where is the cost of funds from the rest of the world and is the domestic lending rate. An incentive compatibility constraint (equation (18), p. 2424) limits the lending spread via the threat of diversion:
where is bankers’ continuation value proportional to net worth . Losses on sovereign debt (captured in ) erode bank net worth during the crisis, raise the lending spread , 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 , ; (ii) external demand and import prices; (iii) financial conditions (sovereign borrowing limit, bank net worth shocks , , ); (iv) government spending (, , , , transfers ); (v) tax policy (, , , , , prepayment fraction ); and (vi) disaster risk (idiosyncratic and aggregate ). All processes follow a VAR(1) (equation (21), p. 2426):
Method
Section titled “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 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):
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: , trade elasticity estimated from a regression of relative expenditure on relative prices, from Barro and Liao (2021)); (b) parameters calibrated from steady-state targets (Table 2 Panel A: discount factors, capital share , banking parameters); (c) parameters estimated by Bayesian MCMC from the time series (Table 2 Panel B: , , , , utilization elasticities , , price and wage adjustment costs , ).
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
Section titled “Empirical specifications”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 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. , , , 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 :
where the impulse is a 1-percentage-point change in the fiscal instrument initiated from its autoregressive process. Revenue-cost multipliers divide by the revenue counterpart (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 is the largest individual spending multiplier (investment > consumption, nontraded > traded, per the paper p. 2449); the nontraded consumption multiplier equals the aggregate spending-weighted multiplier because 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 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.
Datasets used
Section titled “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 and | No page yet |
| Bank of Greece Flow of Funds | Firm dividends and bank net worth ; financial accounts | No page yet |
| Maastricht Treaty / OECD Economic Outlook | Government debt misreporting (anticipated transfers 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 | 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
Section titled “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
Section titled “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.