Ad-hoc analysis / Germany / September 8, 2026

Germany’s recovery,
interrupted.

Orders are one thing. Output is another. A data investigation into the distance between demand, industrial production and the capacity to execute.

Thirty exhibits test a single proposition: has Germany become less able to turn demand into domestic output? The evidence includes the arguments against it.

By Dr. Michael Schymura · Industrial data through July 2026 · Election result provisional

The conditional production gap

13.3

index points between July output and the pre-2020 order benchmark.

The gap is observed relative to a fitted relationship. Its causes and permanence remain open.

01 Demand 02 Conversion 04 Energy 07 Politics 09 BASF The verdict

Reopen the conversion case.

The written findings describe manufacturing. Select a matched sector or change the historical benchmark; dates affect industrial time-series charts.

SectorBenchmarkCountry comparator

History 2010–2026

StartEnd

Manufacturing · July 2026 · expected 103.6 · actual 90.3 · gap 13.3 index points.

Germany’s industrial recovery has a conversion problem. The data do not yet establish that conversion is its primary problem.

That distinction matters. A factory order is a promise of future activity, shaped by product mix, delivery schedules and the location of production. It is neither current output nor a guarantee of a profitable sale. In July 2026, German orders rose while production fell. The tempting explanation is that demand has returned and the machinery that turns it into output has broken. This ad-hoc analysis puts that proposition on trial.

The evidence contains a substantial conditional production gap, persistent energy exposure and increasingly restrictive coalition arithmetic. It also contains weak broad demand, considerable spare capacity and statistical results that resist a single structural explanation. These findings belong in the same story. They do not establish a single causal chain.

13.3 pointsJuly manufacturing gap against the pre-2020 order benchmark

37.5%Manufacturers reporting insufficient demand in Q3

54.52%German storage fill on gas-day September 6

01 / THE DEMAND TEST

First establish what has recovered.

July’s 2.5% increase in new orders was accompanied by a 1.4% decline when major contracts were removed. Over three months, the corresponding movements were +2.9% and −2.2%. Other transport equipment, a category that includes aircraft and ships, supplied a large part of the monthly impulse. Its orders rose 126.4%; automotive orders fell 12.5%. Large contracts can sustain activity over several years. They do not imply a broad expansion across next month’s factory floor.

The order headline overstates the breadth of recovery. July 2026. Headline orders rose 2.5%; orders excluding major contracts fell 1.4%. Exports are nominal; other measures are real volume. — July 2026. Headline orders rose 2.5%; orders excluding major contracts fell 1.4%. Exports are nominal; other measures are real volume.

indicator	change pct	time
Orders	2.5	2026-07
Orders excluding major contracts	-1.4	2026-07
Industrial output	-1.1	2026-07
Manufacturing turnover	-1.5	2026-07
Automotive output	-9.2	2026-07
Exports (nominal)	-0.8	2026-07

The production release also contains a composition warning. Total industrial output fell 1.1%, but automotive output fell 9.2%; the statistical office pointed to several weeks of plant shutdowns as a likely contributor. Manufacturing production was 90.3 on a 2021=100 index. The movement is real, but a summer shutdown should not be relabeled a new structural regime.

Orders and output occupy different recoveries. 2010–July 2026; broad orders from 2015. Broad pre-2025 history retains the August vintage. Matched sector orders and output are available in the selector. — 2010–July 2026; broad orders from 2015. Broad pre-2025 history retains the August vintage. Matched sector orders and output are available in the selector.

time	series	value
2010-01	Orders	81.1881188119
2010-02	Orders	81.298129813
2010-03	Orders	85.2585258526
2010-04	Orders	87.6787678768
2010-05	Orders	88.1188118812
2010-06	Orders	90.099009901
2010-07	Orders	89.3289328933
2010-08	Orders	91.7491749175
2010-09	Orders	90.4290429043
2010-10	Orders	90.8690869087
2010-11	Orders	95.7095709571
2010-12	Orders	92.6292629263
2011-01	Orders	96.8096809681
2011-02	Orders	98.0198019802
2011-03	Orders	94.7194719472
2011-04	Orders	96.1496149615
2011-05	Orders	99.2299229923
2011-06	Orders	98.6798679868
2011-07	Orders	96.4796479648
2011-08	Orders	96.1496149615
2011-09	Orders	92.6292629263
2011-10	Orders	94.1694169417
2011-11	Orders	90.9790979098
2011-12	Orders	92.7392739274
2012-01	Orders	91.5291529153
2012-02	Orders	92.1892189219
2012-03	Orders	94.2794279428
2012-04	Orders	92.299229923
2012-05	Orders	93.7293729373
2012-06	Orders	91.199119912
2012-07	Orders	92.0792079208
2012-08	Orders	91.9691969197
2012-09	Orders	89.7689768977
2012-10	Orders	92.8492849285
2012-11	Orders	90.099009901
2012-12	Orders	90.7590759076
2013-01	Orders	90.6490649065
2013-02	Orders	92.6292629263
2013-03	Orders	94.2794279428
2013-04	Orders	91.7491749175
2013-05	Orders	92.1892189219
2013-06	Orders	96.2596259626
2013-07	Orders	94.0594059406
2013-08	Orders	94.8294829483
2013-09	Orders	97.3597359736
2013-10	Orders	96.1496149615
2013-11	Orders	97.1397139714
2013-12	Orders	96.1496149615
2014-01	Orders	97.9097909791
2014-02	Orders	97.9097909791
2014-03	Orders	95.7095709571
2014-04	Orders	97.9097909791
2014-05	Orders	94.499449945
2014-06	Orders	94.3894389439
2014-07	Orders	100.6600660066
2014-08	Orders	95.4895489549
2014-09	Orders	96.8096809681
2014-10	Orders	99.0099009901
2014-11	Orders	96.5896589659
2014-12	Orders	100.0

The long view is harder to dismiss. Production has failed to regain the path that a pre-pandemic relationship with orders would suggest. The phase portrait below treats orders and output as separate coordinates. The economy can move toward stronger demand without moving proportionately toward greater production. It can also move through shutdowns and inventories before the two series reconnect.

The path back is not the path out. Follow time along the path. Movement to the right means stronger orders; upward means stronger domestic production. There is no causal direction in the connecting line. — Follow time along the path. Movement to the right means stronger orders; upward means stronger domestic production. There is no causal direction in the connecting line.

time	orders 2019	production 2019
2015-01	98.3498349835	97.5911251981
2015-02	98.4965163183	97.5277337559
2015-03	97.7631096443	96.8938193344
2015-04	98.0931426476	97.4326465927
2015-05	98.533186652	97.8446909667
2015-06	99.633296663	98.1933438986
2015-07	99.8166483315	98.7004754358
2015-08	99.633296663	98.2567353407
2015-09	98.0931426476	97.93977813
2015-10	97.066373304	97.4960380349
2015-11	97.1763843051	97.4643423138
2015-12	97.1030436377	97.5911251981
2016-01	97.6897689769	97.9080824089
2016-02	97.6164283095	98.6053882726
2016-03	98.7898789879	98.795562599
2016-04	98.8632196553	98.7004754358
2016-05	99.0832416575	98.0348652932
2016-06	98.6798679868	98.351822504
2016-07	98.7898789879	97.93977813
2016-08	99.1199119912	98.6370839937
2016-09	99.1565823249	98.5736925515
2016-10	99.9266593326	99.2709984152
2016-11	100.0	99.3343898574
2016-12	101.7968463513	98.763866878
2017-01	100.9534286762	98.4469096672
2017-02	102.566923359	98.6053882726
2017-03	102.3469013568	99.3977812995
2017-04	104.2537587092	100.1267828843
2017-05	103.7037037037	100.5705229794
2017-06	104.033736707	101.1093502377
2017-07	103.8870553722	101.4263074485
2017-08	105.4272093876	102.4088748019
2017-09	106.6740007334	103.1061806656
2017-10	108.2874954162	103.0110935024
2017-11	108.7275394206	103.4231378764
2017-12	109.9743307664	103.8985736926
2018-01	109.2775944261	104.7860538827
2018-02	109.0942427576	103.7083993661
2018-03	107.2973964063	103.3597464342
2018-04	106.9306930693	103.0110935024
2018-05	106.6740007334	103.9619651347
2018-06	106.0139347268	104.2155309033
2018-07	105.500550055	103.9302694136
2018-08	104.8771543821	103.4548335975
2018-09	105.3538687202	102.9160063391
2018-10	105.9772643931	103.0110935024
2018-11	105.7205720572	102.06022187
2018-12	106.0506050605	101.7115689382
2019-01	104.9871653832	101.3312202853
2019-02	103.1169783645	101.4896988906
2019-03	101.0634396773	101.648177496
2019-04	100.2200220022	101.3946117274
2019-05	99.9633296663	101.4580031696
2019-06	99.8533186652	100.7923930269
2019-07	99.633296663	100.4754358162
2019-08	99.8166483315	100.0633914422
2019-09	100.0366703337	99.7147385103
2019-10	100.2566923359	99.2709984152
2019-11	100.1833516685	98.795562599
2019-12	99.0465713238	97.8446909667

02 / THE HISTORICAL BENCHMARK

Where does the recovery disappear?

The Industrial Conversion Gap asks a deliberately conditional question: given recent orders, what production level would the historical relationship imply? The main model uses monthly data from 2010–2019. It relates the log of manufacturing production to the average log order level one, three and six months earlier. The frozen relationship is then applied to subsequent orders.

Industrial Conversion Gap = conditional production benchmark − observed production

The retransformed benchmark is a conditional median in index units. It is not an estimate of output that would have existed without the energy crisis, nor a measure of cancelled orders. For July 2026, it gives 103.6 against an observed 90.3: a gap of 13.3 index points. A linear-trend alternative gives 14.5 points. Each index point refers to the 2021=100 production scale.

Witness 1 / Establish the order signal

A 13.3-point gap opens against the old order relationship. July 2026: conditional benchmark 103.6; actual 90.3. Shading is a model-conditional 95% observation interval, not a forecast guarantee or causal loss estimate. — July 2026: conditional benchmark 103.6; actual 90.3. Shading is a model-conditional 95% observation interval, not a forecast guarantee or causal loss estimate.

time	sector	specification	actual	expected	lower	upper	gap
2010-07	C	No trend	92.3	95.264142	92.848698	97.742422	2.9641415
2010-08	C	No trend	93.8	95.221076	92.804874	97.700184	1.421076
2010-09	C	No trend	95.2	97.108677	94.718799	99.558855	1.908677
2010-10	C	No trend	97.2	97.205789	94.816868	99.6549	0.0057892822
2010-11	C	No trend	96.7	97.931895	95.548838	100.37439	1.2318953
2010-12	C	No trend	98.3	99.126705	96.748189	101.5637	0.82670548
2011-01	C	No trend	98.1	98.394765	96.014234	100.83432	0.29476512
2011-02	C	No trend	99.2	100.88244	98.498524	103.32406	1.682441
2011-03	C	No trend	99.8	100.17132	97.791369	102.6092	0.37132496
2011-04	C	No trend	100.3	100.47218	98.090851	102.91133	0.17218448
2011-05	C	No trend	101.6	102.10579	99.709272	104.55991	0.50579254
2011-06	C	No trend	100.0	101.37275	98.984667	103.81845	1.3727489
2011-07	C	No trend	103.1	102.48317	100.08126	104.94272	-0.61682946
2011-08	C	No trend	102.3	102.93032	100.52111	105.39728	0.63032203
2011-09	C	No trend	100.6	102.02925	99.63374	104.48237	1.4292545
2011-10	C	No trend	101.9	101.10259	98.716951	103.54589	-0.7974073
2011-11	C	No trend	101.1	102.02375	99.628303	104.47679	0.92374627
2011-12	C	No trend	99.8	100.43193	98.050803	102.87088	0.63192716
2012-01	C	No trend	100.2	100.69992	98.317258	103.14033	0.49992471
2012-02	C	No trend	100.1	99.660338	97.28173	102.09711	-0.43966176
2012-03	C	No trend	101.2	99.438603	97.060195	101.87529	-1.7613967
2012-04	C	No trend	99.5	99.961226	97.581974	102.39849	0.46122582
2012-05	C	No trend	101.4	98.97997	96.601247	101.41727	-2.4200298
2012-06	C	No trend	100.1	100.13317	97.753354	102.57092	0.033167158
2012-07	C	No trend	101.5	98.883995	96.505083	101.32155	-2.6160047
2012-08	C	No trend	101.3	99.532959	97.154494	101.96965	-1.7670408
2012-09	C	No trend	100.1	99.408711	97.030312	101.84541	-0.69128916
2012-10	C	No trend	98.8	98.68683	96.307396	101.12505	-0.11317016
2012-11	C	No trend	98.1	99.653247	97.274649	102.09001	1.5532472
2012-12	C	No trend	98.8	98.01218	95.629629	100.45409	-0.78781966
2013-01	C	No trend	97.8	99.026811	96.648165	101.464	1.2268114
2013-02	C	No trend	98.4	98.374919	95.994299	100.81458	-0.025081246
2013-03	C	No trend	99.8	98.468884	96.088668	100.90806	-1.3311158
2013-04	C	No trend	100.0	99.478973	97.100546	101.91566	-0.52102656
2013-05	C	No trend	99.5	98.760079	96.38086	101.19803	-0.73992086
2013-06	C	No trend	101.1	99.359247	96.980855	101.79597	-1.7407526
2013-07	C	No trend	99.3	99.65852	97.279914	102.09529	0.35852037
2013-08	C	No trend	101.6	99.724853	97.346142	102.16169	-1.8751473
2013-09	C	No trend	101.1	101.13446	98.74855	103.57802	0.034461154
2013-10	C	No trend	100.4	100.64215	98.259848	103.08222	0.24215279
2013-11	C	No trend	102.7	100.65098	98.26862	103.0911	-2.0490206
2013-12	C	No trend	102.9	102.2974	99.898233	104.75419	-0.60259996
2014-01	C	No trend	102.3	101.34746	98.959622	103.79292	-0.95254006
2014-02	C	No trend	102.4	102.10338	99.706894	104.55747	-0.29661786
2014-03	C	No trend	102.8	102.43798	100.03676	104.89684	-0.3620185
2014-04	C	No trend	102.5	102.08248	99.686266	104.53629	-0.41752332
2014-05	C	No trend	101.5	102.76931	100.36284	105.23348	1.2693111
2014-06	C	No trend	101.6	101.34911	98.961261	103.79459	-0.25088522
2014-07	C	No trend	103.7	102.17079	99.77339	104.62579	-1.5292125
2014-08	C	No trend	99.7	102.77237	100.36585	105.23659	3.0723693
2014-09	C	No trend	102.1	101.18326	98.796926	103.62723	-0.91674175
2014-10	C	No trend	102.3	103.27843	100.86283	105.75187	0.97842549
2014-11	C	No trend	102.4	101.90742	99.513443	104.35898	-0.49258353
2014-12	C	No trend	104.1	101.65524	99.264224	104.10386	-2.444757
2015-01	C	No trend	101.4	104.19818	101.76277	106.69187	2.7981807
2015-02	C	No trend	102.2	102.24593	99.847491	104.70198	0.045928961
2015-03	C	No trend	102.1	102.95111	100.54154	105.41844	0.85111288
2015-04	C	No trend	103.1	103.26264	100.84734	105.73578	0.1626379
2015-05	C	No trend	103.5	102.78812	100.38134	105.25261	-0.71187952
2015-06	C	No trend	103.2	103.44829	101.02935	105.92514	0.2482883

OrdersProductionMarginsInvestment

Conceptual sequence. Capacity, energy, logistics and institutions condition the links; widths do not measure losses.

Orders recover in the headline.

Broad orders fell in July. Begin by separating a large-contract impulse from a common demand improvement.

Production responds unevenly.

The historical benchmark is materially above observed manufacturing output. The July automotive shutdown is one immediate alternative explanation.

Energy remains expensive and exposed.

Wholesale gas, industrial bills and storage buffers measure different channels. None should stand in for all three.

Capacity is underused.

Utilization is 77.9%. Insufficient demand is reported more frequently than equipment constraints. Spare capacity complicates a pure bottleneck account.

The Rhine is a local mechanism.

Low water reduces loading efficiency. A national chemicals series dilutes that exposure and mixes it with energy and demand shocks.

Political concentration can obstruct coalitions.

Saxony-Anhalt’s effective party count fell. Under an AfD exclusion assumption, the minimum majority nevertheless expands to five parties.

The gap survives. Its decomposition does not.

A persistent conditional discrepancy warrants explanation. These data cannot assign defensible percentages to energy, logistics, finance or politics.

A useful benchmark must earn its place. Trained only through 2016, the no-trend specification predicts the 2017–2019 holdout with a root mean squared error of 1.23 index points, compared with 1.39 for a previous-month production benchmark. The trend specification is slightly worse than that simple comparator. A pre-period residual stationarity test rejects a unit root, but this is a diagnostic rather than proof of a permanent economic law.

The benchmark also remains sensitive to the demand measure and estimation window. Over the same shorter 2015–2019 training period, total orders imply a July gap of 13.4 points and orders excluding major contracts imply 17.1. The latter history combines an archived pre-2025 vintage with the current extract. It is a sensitivity check, not a cleaner real-time forecast. The gap is economically intuitive precisely because its assumptions can be inspected.

The short-run response is imprecisely estimated. Sixty-month rolling growth regressions control for previous output growth. HAC 95% intervals cross zero at both ends of the displayed sample. This differs from the level benchmark. — Sixty-month rolling growth regressions control for previous output growth. HAC 95% intervals cross zero at both ends of the displayed sample. This differs from the level benchmark.

time	sector	beta	lower	upper	n
2015-07	C	0.14481668	-0.058462314	0.34809568	60
2015-08	C	0.17412068	-0.031280987	0.37952234	60
2015-09	C	0.1223694	-0.078674193	0.323413	60
2015-10	C	0.1424316	-0.050600968	0.33546416	60
2015-11	C	0.1470731	-0.042438873	0.33658508	60
2015-12	C	0.11081273	-0.081051621	0.30267707	60
2016-01	C	0.096146195	-0.10741991	0.2997123	60
2016-02	C	0.077705467	-0.1590505	0.31446143	60
2016-03	C	0.11535509	-0.12823747	0.35894766	60
2016-04	C	0.11214378	-0.12040918	0.34469673	60
2016-05	C	0.10322842	-0.12156361	0.32802045	60
2016-06	C	0.11140015	-0.11867653	0.34147683	60
2016-07	C	0.070362469	-0.14288507	0.28361001	60
2016-08	C	0.077819914	-0.13961378	0.29525361	60
2016-09	C	0.057952985	-0.14674179	0.26264775	60
2016-10	C	0.061660183	-0.14336494	0.2666853	60
2016-11	C	0.061705188	-0.14374422	0.2671546	60
2016-12	C	0.04946317	-0.16916733	0.26809367	60
2017-01	C	0.070061831	-0.11695317	0.25707684	60
2017-02	C	0.026534443	-0.1497571	0.20282599	60
2017-03	C	0.026451769	-0.14740962	0.20031316	60
2017-04	C	0.014849952	-0.16188035	0.19158025	60
2017-05	C	0.021079532	-0.15919905	0.20135812	60
2017-06	C	0.025391252	-0.15694191	0.20772442	60
2017-07	C	0.028818588	-0.1557949	0.21343208	60
2017-08	C	0.047370805	-0.142742	0.23748361	60
2017-09	C	0.045711527	-0.1445336	0.23595665	60
2017-10	C	0.010879038	-0.17883762	0.20059569	60
2017-11	C	0.05850231	-0.12974167	0.24674629	60
2017-12	C	0.053335898	-0.14625017	0.25292197	60
2018-01	C	0.057883922	-0.14760822	0.26337607	60
2018-02	C	0.078106255	-0.13451235	0.29072486	60
2018-03	C	0.081992286	-0.12821254	0.29219711	60
2018-04	C	0.058922925	-0.13381687	0.25166272	60
2018-05	C	0.035870986	-0.14921733	0.2209593	60
2018-06	C	0.030955901	-0.15358109	0.21549289	60
2018-07	C	0.098076084	-0.12616662	0.32231879	60
2018-08	C	0.091058948	-0.12997839	0.31209629	60
2018-09	C	0.09163036	-0.13000964	0.31327036	60
2018-10	C	0.089151783	-0.13125848	0.30956205	60
2018-11	C	0.072046711	-0.13856268	0.2826561	60
2018-12	C	0.058970268	-0.1420871	0.26002764	60
2019-01	C	0.05151873	-0.1534405	0.25647796	60
2019-02	C	0.06030029	-0.14365888	0.26425946	60
2019-03	C	0.055426195	-0.14636791	0.2572203	60
2019-04	C	0.052515798	-0.15401467	0.25904627	60
2019-05	C	0.060234198	-0.14683553	0.26730393	60
2019-06	C	0.052765223	-0.15744619	0.26297663	60
2019-07	C	0.039189251	-0.17550852	0.25388702	60
2019-08	C	0.058716003	-0.14946634	0.26689835	60
2019-09	C	0.066276294	-0.152707	0.28525959	60
2019-10	C	0.042901865	-0.18606336	0.27186709	60
2019-11	C	0.051292773	-0.17226123	0.27484677	60
2019-12	C	0.048443165	-0.16592249	0.26280882	60
2020-01	C	0.12955356	-0.073431866	0.33253898	60
2020-02	C	0.13999648	-0.082512754	0.36250571	60
2020-03	C	0.18776386	-0.094039921	0.46956764	60
2020-04	C	0.56396096	0.0001300859	1.1277918	60
2020-05	C	-0.10626065	-0.73820231	0.52568101	60
2020-06	C	-0.20789011	-0.57401132	0.1582311	60

The short-run elasticity test asks a different question. Monthly production growth is regressed on order growth at one, three and six months, with previous production growth as a control. In rolling five-year windows, the combined coefficient is small and uncertain at the latest endpoint. The first and last confidence intervals both cross zero. This does not establish that the order-to-output elasticity has permanently collapsed.

No single lag settles the conversion question. Full-sample ARX alternatives, each with one order lag and previous output growth. Intervals use HAC standard errors; these estimates are descriptive. — Full-sample ARX alternatives, each with one order lag and previous output growth. Intervals use HAC standard errors; these estimates are descriptive.

sector	model	term	coefficient	lower	upper	p	n	adj r2	label
C	Growth lag 1	order_lag1	0.10498848	-0.012029809	0.22200677	0.078666861	197	0.0083669707	Growth lag 1
C	Growth lag 3	order_lag3	-0.06318741	-0.18821842	0.0618436	0.32192263	195	0.0006558903	Growth lag 3
C	Growth lag 6	order_lag6	-0.026618752	-0.099734521	0.046497018	0.47550517	192	-0.008228379	Growth lag 6

The statistical appendix reports the full lag alternatives, sector models, known-date Chow tests, robust interaction tests and a globally optimized segmented least-squares search. A change in the joint regression parameters is not necessarily a decline in the order coefficient. Classical Chow and robust tests can disagree; the newest candidate break has a particularly short post-break sample. All candidate dates and multiplicity adjustments remain in the appendix.

The conditional gap is concentrated across sectors. Expected and observed production are compared within matched NACE sectors. Different historical models can make rankings unstable; the article selector exposes the trend alternative. — Expected and observed production are compared within matched NACE sectors. Different historical models can make rankings unstable; the article selector exposes the trend alternative.

time	sector	specification	actual	expected	lower	upper	gap	label
2026-07	C29	No trend	96.0	128.28857	116.98953	140.6789	32.288572	Automotive
2026-07	C20	No trend	76.0	93.564048	86.609278	101.07729	17.564048	Chemicals
2026-07	C28	No trend	84.4	101.77772	97.104439	106.67591	17.37772	Machinery
2026-07	C26	No trend	106.7	120.61152	111.91809	129.98023	13.91152	Electronics
2026-07	C21	No trend	97.9	108.01288	93.454712	124.83888	10.112876	Pharma
2026-07	C24	No trend	88.2	98.102608	92.683551	103.83851	9.9026084	Basic metals
2026-07	C27	No trend	91.4	100.29625	96.552008	104.18569	8.8962504	Electrical equipment
2026-07	C17	No trend	78.9	84.839122	78.04612	92.223375	5.9391217	Paper
2026-07	C25	No trend	82.8	87.43533	84.198872	90.796191	4.6353298	Fabricated metals
2026-07	C30	No trend	135.7	105.6122	79.043338	141.11167	-30.087796	Other transport

03 / THE FACTORY EVIDENCE

Spare capacity is not the same as usable capacity.

Germany can have empty production capacity and still face problems turning a particular order into output. The spare machine may make the wrong product, sit at a high-cost location, lack a technician or depend on a congested link. Yet the survey evidence puts a clear limit on that argument: insufficient demand remains the most frequently reported constraint.

Germany still has substantial spare capacity

Manufacturing capacity utilization, %

Hover, tap or focus the chart and use arrow keys for exact observations. Source: European Commission business survey / Eurostat ei_bsin_q_r2, ei_bsin_m_r2. Capacity utilization was 77.9% in Q3 2026. Spare capacity can coexist with weak demand and localized bottlenecks; it is not evidence that every plant can expand profitably.

Capacity utilization was 77.9% in Q3 2026. Spare capacity can coexist with weak demand and localized bottlenecks; it is not evidence that every plant can expand profitably.

Source: European Commission business survey / Eurostat ei_bsin_q_r2, ei_bsin_m_r2 · Retrieved September 8, 2026.

Exact values & download / 67 observations in default view

First 60 of 67 default-view observations. The CSV contains every observation.

Download default values as CSV

freq	indic	s adj	geo	time	value	status	source	release
Q	BS-ICU-PC	SA	DE	2010-01	74.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2010-04	79.4	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2010-07	82.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2010-10	83.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2011-01	84.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2011-04	85.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2011-07	85.9	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2011-10	85.6	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2012-01	84.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2012-04	84.4	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2012-07	83.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2012-10	82.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2013-01	82.6	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2013-04	82.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2013-07	83.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2013-10	83.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2014-01	84.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2014-04	84.4	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2014-07	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2014-10	84.2	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2015-01	84.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2015-04	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2015-07	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2015-10	84.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2016-01	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2016-04	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2016-07	84.6	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2016-10	85.2	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2017-01	85.4	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2017-04	86.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2017-07	87.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2017-10	87.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2018-01	87.9	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2018-04	87.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2018-07	87.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2018-10	87.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2019-01	86.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2019-04	85.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2019-07	84.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2019-10	83.2	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2020-01	83.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2020-04	69.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2020-07	74.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2020-10	81.6	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2021-01	82.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2021-04	85.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2021-07	86.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2021-10	85.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2022-01	86.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2022-04	85.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2022-07	85.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2022-10	85.0	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2023-01	84.3	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2023-04	84.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2023-07	83.2	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2023-10	82.1	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2024-01	80.9	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2024-04	79.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2024-07	77.9	Unavailable	capacity.json	2026-07-30T11:00:00+0200
Q	BS-ICU-PC	SA	DE	2024-10	76.8	Unavailable	capacity.json	2026-07-30T11:00:00+0200

Demand remains the most frequently reported constraint. Q3 2026: 37.5% reported insufficient demand, versus 15.7% equipment constraints. Multiple answers are possible; the shares must not be added. — Q3 2026: 37.5% reported insufficient demand, versus 15.7% equipment constraints. Multiple answers are possible; the shares must not be added.

freq	indic	s adj	geo	time	value	status	source	release	label
Q	BS-FLP2-PC	SA	DE	2026-Q3	37.5	Unavailable	capacity.json	2026-07-30T11:00:00+0200	Insufficient demand
Q	BS-FLP3-PC	SA	DE	2026-Q3	16.4	Unavailable	capacity.json	2026-07-30T11:00:00+0200	Labor
Q	BS-FLP4-PC	SA	DE	2026-Q3	15.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200	Equipment
Q	BS-FLP5-PC	SA	DE	2026-Q3	9.2	Unavailable	capacity.json	2026-07-30T11:00:00+0200	Other constraints
Q	BS-FLP6-PC	SA	DE	2026-Q3	3.7	Unavailable	capacity.json	2026-07-30T11:00:00+0200	Financing

Capacity utilization belongs beside the conversion mechanism, rather than after production as a separate volume stage. Production and utilization move together by construction. Including utilization in an explanatory risk index can therefore create a partly mechanical association with an output gap. The robustness tests below remove it.

Energy exposure explains less than the simple story suggests. Logarithmic horizontal scale; bubble area is 2019 gross value added. Fourteen matched sectors; three-month output means through July 2026. Feedstocks are included. The controlled cross-section coefficient is inconclusive. — Logarithmic horizontal scale; bubble area is 2019 gross value added. Fourteen matched sectors; three-month output means through July 2026. Feedstocks are included. The controlled cross-section coefficient is inconclusive.

sector	label	gva 2019 meur	energy mj eur	fuel mj eur	feedstock mj eur	output since 2021 pct	pretrend pct	log energy
C16	Wood	7887	12.927361	12.927361	0.0	-26.449883	15.856984	2.6338554
C17	Paper	11958	21.395116	21.395116	0.0	-19.979852	-4.9907579	3.1088429
C19	Refining	8585	42.366523	42.366523	0.0	11.764216	-4.9580895	3.7696878
C20	Chemicals	46378	31.496986	13.596776	17.900209	-21.657754	-6.1022495	3.4811473
C21	Pharma	24631	1.0170517	0.97843774	0.038613942	0.4691689	27.870564	0.70163688
C22	Rubber & plastics	31445	2.7858229	2.7858229	0.0	-14.98994	12.417592	1.3312633
C23	Mineral products	19167	14.81801	14.81801	0.0	-23.035594	10.301035	2.7611492
C24	Basic metals	21001	34.567644	34.567644	0.0	-10.739098	-1.3726567	3.5714364
C25	Fabricated metals	57971	1.7121112	1.7121112	0.0	-15.342673	22.735858	0.99772738
C26	Electronics	46080	0.68845486	0.68845486	0.0	5.6160939	38.148104	0.52381383
C27	Electrical equipment	44398	0.72016532	0.72016532	0.0	-8.938172	4.4312881	0.5424204
C28	Machinery	109298	0.77292174	0.77292174	0.0	-14.070099	20.427086	0.57262888
C29	Automotive	132606	1.0757085	1.0482399	0.027468591	3.1265844	14.315679	0.73030254
C30	Other transport	15970	0.87252348	0.87252348	0.0	33.646506	69.657379	0.62728698

Energy-intensive activities are prominent among the weak sectors, especially chemicals, paper, wood and mineral products. The cross section nevertheless contains important exceptions. Refining and other transport do not follow a uniform decline. Across fourteen matched sectors, pre-shock energy intensity does not have a precisely estimated negative association with subsequent output once the earlier sector trend is included. This small sample cannot isolate energy from industrial composition, trade exposure and investment history.

The exposure measure is 2019 net domestic energy use divided by gross value added, in megajoules per euro. Feedstock energy is separately recorded. This matters especially for chemicals, where gas and other hydrocarbons can enter both the furnace and the product. An energy/GVA ratio is not an estimate of a sector’s gas bill. The result follows the distinction developed in “Energy Prices Explain the Cliff, Not the Plateau”: a severe shock can deepen a decline whose causes began earlier.

04 / THE WINTER BUFFER

A thin buffer is a vulnerability, not a forecast.

On September 6, German gas storage was 54.52% full. The figure was retrieved on September 8: the gas-day and retrieval date are not interchangeable. The available Bundesnetzagentur comparison places this level below its 2018–2021 historical band for that point in the gas year. It does not establish a record over the full 2017–2026 period requested for this investigation.

The autumn buffer is thin in the available comparison

Storage fill, % of reported working-gas capacity

2025/262024/252018–21 historical range

Hover, tap or focus the chart and use arrow keys for exact observations. Source: Bundesnetzagentur, German gas-storage chart; AGSI underlying data. 54.52% on gas-day September 6. Band: 2018–2021 gas-year daily minimum–maximum; lines: 2024/25 and 2025/26. This is not a full 2017–2026 percentile history.

54.52% on gas-day September 6. Band: 2018–2021 gas-year daily minimum–maximum; lines: 2024/25 and 2025/26. This is not a full 2017–2026 percentile history.

Source: Bundesnetzagentur, German gas-storage chart; AGSI underlying data · Retrieved September 8, 2026.

Exact values & download / 365 observations in default view

First 60 of 365 default-view observations. The CSV contains every observation.

Download default values as CSV

calendar day	2024-25	2025-26	historic min 2018 21	historic max 2018 21	gas year day
01.10.	96.09	76.31	80.12	98.34	0
02.10.	96.01	76.21	80.49	98.41	1
03.10.	96.07	76.2	80.49	98.46	2
04.10.	96.09	76.34	80.64	98.45	3
05.10.	96.16	76.4	80.95	98.53	4
06.10.	96.26	76.19	81.4	98.62	5
07.10.	96.36	76.08	81.8	98.63	6
08.10.	96.55	76.01	81.99	98.64	7
09.10.	96.77	76.01	82.14	98.64	8
10.10.	96.93	76.03	82.35	98.65	9
11.10.	96.95	76.08	82.74	98.66	10
12.10.	97.08	76.1	83.3	98.77	11
13.10.	97.2	75.92	83.94	98.93	12
14.10.	97.08	75.82	84.6	99.01	13
15.10.	97.07	75.67	85.05	99.06	14
16.10.	97.16	75.48	85.37	99.08	15
17.10.	97.25	75.43	85.67	99.09	16
18.10.	97.32	75.44	86.02	99.1	17
19.10.	97.52	75.47	86.35	99.2	18
20.10.	97.73	75.44	86.7	99.31	19
21.10.	97.77	75.41	87.06	99.33	20
22.10.	97.78	75.37	87.23	99.34	21
23.10.	97.76	75.39	87.44	99.34	22
24.10.	97.77	75.34	87.66	99.35	23
25.10.	97.82	75.34	87.79	99.39	24
26.10.	97.92	75.33	87.84	99.53	25
27.10.	98.03	75.15	87.91	99.62	26
28.10.	98.04	75.15	87.9	99.59	27
29.10.	98.04	75.05	87.72	99.52	28
30.10.	98.04	74.97	87.71	99.41	29
31.10.	98.06	74.94	87.68	99.26	30
01.11.	98.19	75.13	87.75	99.21	31
02.11.	98.27	75.23	87.78	99.37	32
03.11.	98.31	75.24	87.81	99.53	33
04.11.	98.15	75.31	87.87	99.6	34
05.11.	97.93	75.32	87.84	99.57	35
06.11.	97.65	75.15	87.83	99.5	36
07.11.	97.28	74.96	87.86	99.56	37
08.11.	97.0	74.88	87.8	99.55	38
09.11.	96.86	74.83	87.78	99.61	39
10.11.	96.75	74.66	87.89	99.61	40
11.11.	96.52	74.67	88.04	99.54	41
12.11.	96.17	74.71	88.11	99.45	42
13.11.	95.72	74.72	88.1	99.32	43
14.11.	95.44	74.68	88.02	99.26	44
15.11.	95.22	74.66	87.92	99.21	45
16.11.	95.16	74.62	87.66	99.23	46
17.11.	95.11	74.3	87.47	99.25	47
18.11.	94.77	73.68	87.29	99.21	48
19.11.	94.42	73.16	86.9	99.11	49
20.11.	93.97	72.47	86.46	98.92	50
21.11.	93.43	71.69	85.81	98.69	51
22.11.	92.84	71.15	85.26	98.64	52
23.11.	92.49	70.64	84.83	98.72	53
24.11.	92.47	69.98	84.57	98.78	54
25.11.	92.4	69.26	84.35	98.71	55
26.11.	92.22	68.45	83.98	98.68	56
27.11.	91.97	67.82	83.62	98.76	57
28.11.	91.72	67.4	83.28	98.81	58
29.11.	91.24	67.26	83.01	98.8	59

The recent pace would leave storage near 67% on November 1. A 30-day mean net injection of 0.223 points/day is extended mechanically from September 6 to November 1. Capacity and flows can change; the result is not a forecast. — A 30-day mean net injection of 0.223 points/day is extended mechanically from September 6 to November 1. Capacity and flows can change; the result is not a forecast.

calendar day	2024-25	2025-26	historic min 2018 21	historic max 2018 21	gas year day	daily change
09.07.	53.54	43.56	44.04	89.88	281	0.11
10.07.	54.02	43.65	44.46	89.94	282	0.09
11.07.	54.37	43.94	44.87	90.07	283	0.29
12.07.	54.93	44.26	45.16	90.28	284	0.32
13.07.	55.37	44.48	45.2	90.41	285	0.22
14.07.	55.61	44.65	45.21	90.34	286	0.17
15.07.	55.84	44.86	45.4	90.19	287	0.21
16.07.	56.1	44.91	45.64	90.04	288	0.05
17.07.	56.29	45.02	45.86	89.91	289	0.11
18.07.	56.55	45.21	46.08	89.83	290	0.19
19.07.	57.0	45.43	46.23	89.78	291	0.22
20.07.	57.44	45.53	46.34	89.63	292	0.1
21.07.	57.7	45.53	46.39	89.4	293	0.0
22.07.	57.95	45.62	46.4	89.08	294	0.09
23.07.	58.2	45.7	46.75	88.87	295	0.08
24.07.	58.36	45.8	47.23	88.7	296	0.1
25.07.	58.6	45.98	47.62	88.58	297	0.18
26.07.	59.0	46.19	47.95	88.79	298	0.21
27.07.	59.45	46.29	48.33	88.85	299	0.1
28.07.	59.85	46.38	48.73	88.92	300	0.09
29.07.	60.18	46.54	49.1	89.02	301	0.16
30.07.	60.56	46.67	49.43	89.04	302	0.13
31.07.	60.8	46.67	49.79	89.11	303	0.0
01.08.	61.02	46.74	50.18	89.33	304	0.07
02.08.	61.6	46.95	50.54	89.55	305	0.21
03.08.	62.07	47.12	50.8	89.67	306	0.17
04.08.	62.46	47.32	51.1	89.76	307	0.2
05.08.	62.9	47.52	51.39	89.92	308	0.2
06.08.	63.26	47.68	51.85	89.88	309	0.16
07.08.	63.59	47.83	52.21	90.19	310	0.15
08.08.	63.93	48.08	52.56	90.44	311	0.25
09.08.	64.22	48.35	52.82	90.67	312	0.27
10.08.	64.68	48.53	53.07	90.8	313	0.18
11.08.	65.04	48.72	53.3	91.2	314	0.19
12.08.	65.18	48.9	53.48	91.48	315	0.18
13.08.	65.43	49.14	53.77	91.68	316	0.24
14.08.	65.73	49.43	54.15	91.83	317	0.29
15.08.	66.12	49.71	54.49	92.08	318	0.28
16.08.	66.47	49.97	54.79	92.36	319	0.26
17.08.	66.85	50.06	54.95	92.75	320	0.09
18.08.	67.11	50.14	55.15	93.14	321	0.08
19.08.	67.41	50.19	55.36	93.49	322	0.05
20.08.	67.66	50.25	55.65	93.72	323	0.06
21.08.	67.94	50.43	56.01	93.93	324	0.18
22.08.	68.19	50.71	56.38	94.13	325	0.28
23.08.	68.56	51.01	56.76	94.35	326	0.3
24.08.	68.91	51.25	57.15	94.6	327	0.24
25.08.	69.17	51.47	57.49	94.86	328	0.22
26.08.	69.42	51.66	57.86	94.98	329	0.19
27.08.	69.6	51.95	58.26	95.15	330	0.29
28.08.	69.92	52.25	58.68	95.27	331	0.3
29.08.	70.26	52.63	59.09	95.25	332	0.38
30.08.	70.65	53.03	59.46	95.41	333	0.4
31.08.	71.05	53.31	59.69	95.7	334	0.28
01.09.	72.54	53.55	59.9	95.97	335	0.24
02.09.	72.54	53.67	60.1	96.13	336	0.12
03.09.	72.74	53.8	60.33	96.31	337	0.13
04.09.	73.08	54.04	60.66	96.48	338	0.24
05.09.	73.33	54.16	61.0	96.59	339	0.12
06.09.	73.69	54.52	61.24	96.59	340	0.36

At the preceding 30-day net injection pace, storage would reach approximately 67% on November 1. This is arithmetic with a fixed capacity denominator, not a forecast. Net injections can accelerate; LNG supply, pipeline imports, domestic consumption and storage economics can all change. A percentage fill also does not reveal deliverability, regional network constraints or the TWh of gas available without a verified working-capacity series.

Winter assumption“Normal” is the central assumed case, not an estimated historical-normal winter.

Winter outcomes depend on two explicit flow assumptions. Assumed net injection before November 1: 0.28/0.24/0.20/0.10 points/day. Net withdrawal afterward: 0.20/0.30/0.40/0.50. Clipping at zero is accounting; it does not establish a rationing date. — Assumed net injection before November 1: 0.28/0.24/0.20/0.10 points/day. Net withdrawal afterward: 0.20/0.30/0.40/0.50. Clipping at zero is accounting; it does not establish a rationing date.

scenario	date	storage pct	unconstrained balance pct	injection pp day	withdrawal pp day
Mild	2026-09-06	54.52	54.52	0.28	0.2
Mild	2026-09-07	54.8	54.8	0.28	0.2
Mild	2026-09-08	55.08	55.08	0.28	0.2
Mild	2026-09-09	55.36	55.36	0.28	0.2
Mild	2026-09-10	55.64	55.64	0.28	0.2
Mild	2026-09-11	55.92	55.92	0.28	0.2
Mild	2026-09-12	56.2	56.2	0.28	0.2
Mild	2026-09-13	56.48	56.48	0.28	0.2
Mild	2026-09-14	56.76	56.76	0.28	0.2
Mild	2026-09-15	57.04	57.04	0.28	0.2
Mild	2026-09-16	57.32	57.32	0.28	0.2
Mild	2026-09-17	57.6	57.6	0.28	0.2
Mild	2026-09-18	57.88	57.88	0.28	0.2
Mild	2026-09-19	58.16	58.16	0.28	0.2
Mild	2026-09-20	58.44	58.44	0.28	0.2
Mild	2026-09-21	58.72	58.72	0.28	0.2
Mild	2026-09-22	59.0	59.0	0.28	0.2
Mild	2026-09-23	59.28	59.28	0.28	0.2
Mild	2026-09-24	59.56	59.56	0.28	0.2
Mild	2026-09-25	59.84	59.84	0.28	0.2
Mild	2026-09-26	60.12	60.12	0.28	0.2
Mild	2026-09-27	60.4	60.4	0.28	0.2
Mild	2026-09-28	60.68	60.68	0.28	0.2
Mild	2026-09-29	60.96	60.96	0.28	0.2
Mild	2026-09-30	61.24	61.24	0.28	0.2
Mild	2026-10-01	61.52	61.52	0.28	0.2
Mild	2026-10-02	61.8	61.8	0.28	0.2
Mild	2026-10-03	62.08	62.08	0.28	0.2
Mild	2026-10-04	62.36	62.36	0.28	0.2
Mild	2026-10-05	62.64	62.64	0.28	0.2
Mild	2026-10-06	62.92	62.92	0.28	0.2
Mild	2026-10-07	63.2	63.2	0.28	0.2
Mild	2026-10-08	63.48	63.48	0.28	0.2
Mild	2026-10-09	63.76	63.76	0.28	0.2
Mild	2026-10-10	64.04	64.04	0.28	0.2
Mild	2026-10-11	64.32	64.32	0.28	0.2
Mild	2026-10-12	64.6	64.6	0.28	0.2
Mild	2026-10-13	64.88	64.88	0.28	0.2
Mild	2026-10-14	65.16	65.16	0.28	0.2
Mild	2026-10-15	65.44	65.44	0.28	0.2
Mild	2026-10-16	65.72	65.72	0.28	0.2
Mild	2026-10-17	66.0	66.0	0.28	0.2
Mild	2026-10-18	66.28	66.28	0.28	0.2
Mild	2026-10-19	66.56	66.56	0.28	0.2
Mild	2026-10-20	66.84	66.84	0.28	0.2
Mild	2026-10-21	67.12	67.12	0.28	0.2
Mild	2026-10-22	67.4	67.4	0.28	0.2
Mild	2026-10-23	67.68	67.68	0.28	0.2
Mild	2026-10-24	67.96	67.96	0.28	0.2
Mild	2026-10-25	68.24	68.24	0.28	0.2
Mild	2026-10-26	68.52	68.52	0.28	0.2
Mild	2026-10-27	68.8	68.8	0.28	0.2
Mild	2026-10-28	69.08	69.08	0.28	0.2
Mild	2026-10-29	69.36	69.36	0.28	0.2
Mild	2026-10-30	69.64	69.64	0.28	0.2
Mild	2026-10-31	69.92	69.92	0.28	0.2
Mild	2026-11-01	70.2	70.2	0.28	0.2
Mild	2026-11-02	70.0	70.0	0.28	0.2
Mild	2026-11-03	69.8	69.8	0.28	0.2
Mild	2026-11-04	69.6	69.6	0.28	0.2

The four stress paths deliberately expose their assumptions. They use constant daily net injection rates until November 1 and constant net withdrawals afterward. The severe case combines slower injections with greater winter net withdrawals; it is a stylized representation of a cold winter with constrained external supply. None of the paths has an assigned probability. Where the assumed balance falls below zero, the chart records an unmet flow requirement rather than predicting industrial rationing.

The gas premium survives the inflation adjustment. US CPI deflation holds the dollar unit constant; this is not a German industrial purchasing-cost index. Monthly World Bank European gas is not a TTF front-month futures series. — US CPI deflation holds the dollar unit constant; this is not a German industrial purchasing-cost index. Monthly World Bank European gas is not a TTF front-month futures series.

time	series	value
2010-01	Brent, real USD	151.4384471606
2010-02	Brent, real USD	147.4161783352
2010-03	Brent, real USD	157.2843950105
2010-04	Brent, real USD	168.5510581728
2010-05	Brent, real USD	151.3780431732
2010-06	Brent, real USD	148.4642425738
2010-07	Brent, real USD	147.9891317655
2010-08	Brent, real USD	151.7296243619
2010-09	Brent, real USD	153.6574729049
2010-10	Brent, real USD	163.1620313037
2010-11	Brent, real USD	168.2466218064
2010-12	Brent, real USD	179.5011876458
2011-01	Brent, real USD	187.6915676813
2011-02	Brent, real USD	202.0496263496
2011-03	Brent, real USD	221.1106500047
2011-04	Brent, real USD	236.8142534016
2011-05	Brent, real USD	219.5713525012
2011-06	Brent, real USD	218.2289948287
2011-07	Brent, real USD	222.8228579483
2011-08	Brent, real USD	209.9197662676
2011-09	Brent, real USD	210.9868904506
2011-10	Brent, real USD	208.182837561
2011-11	Brent, real USD	209.6965543022
2011-12	Brent, real USD	204.7138534768
2012-01	Brent, real USD	210.4016187956
2012-02	Brent, real USD	226.0014152227
2012-03	Brent, real USD	235.3267076183
2012-04	Brent, real USD	226.6601409323
2012-05	Brent, real USD	208.2809280651
2012-06	Brent, real USD	180.3450155496
2012-07	Brent, real USD	194.4372669949
2012-08	Brent, real USD	212.4393786169
2012-09	Brent, real USD	211.6172024591
2012-10	Brent, real USD	208.4425067501
2012-11	Brent, real USD	204.5054301261
2012-12	Brent, real USD	204.5302014774
2013-01	Brent, real USD	210.2663965586
2013-02	Brent, real USD	215.6083241348
2013-03	Brent, real USD	202.6679995449
2013-04	Brent, real USD	191.3751974638
2013-05	Brent, real USD	191.4818710978
2013-06	Brent, real USD	191.2126238777
2013-07	Brent, real USD	199.3536972888
2013-08	Brent, real USD	204.9726963921
2013-09	Brent, real USD	206.0030092341
2013-10	Brent, real USD	202.0184808861
2013-11	Brent, real USD	199.0684136119
2013-12	Brent, real USD	203.3187660457
2014-01	Brent, real USD	196.7807481226
2014-02	Brent, real USD	199.1266574835
2014-03	Brent, real USD	196.1637913968
2014-04	Brent, real USD	196.5280095158
2014-05	Brent, real USD	199.6120005968
2014-06	Brent, real USD	203.3465221003
2014-07	Brent, real USD	194.2235656095
2014-08	Brent, real USD	184.9957735224
2014-09	Brent, real USD	176.6319940277
2014-10	Brent, real USD	158.5100271752
2014-11	Brent, real USD	142.6188596254
2014-12	Brent, real USD	113.6817207921

The factory bill is different from the wholesale benchmark. Gas band I3 (10,000–99,999 GJ/year); electricity band ID (2,000–19,999 MWh/year). Compare each band with its own 2015–2019 mean. These are representative consumption bands, not BASF tariffs. — Gas band I3 (10,000–99,999 GJ/year); electricity band ID (2,000–19,999 MWh/year). Compare each band with its own 2015–2019 mean. These are representative consumption bands, not BASF tariffs.

fuel	baseline	latest	period
Electricity	128.36	192.2	2025-S2
Gas	33.17	71.3	2025-S2

Prices create another conversion channel: a plant may be physically capable of producing but unable to do so at an attractive contribution margin. The relevant industrial tariff is not the same as the spot benchmark. The package therefore separates the World Bank monthly European gas series, German THE day-ahead and month-ahead prices, and Eurostat non-household consumption bands. It does not relabel THE as TTF or a monthly benchmark as a front-month futures contract.

The Brent scenarios of $80, $100 and $120 a barrel should now be compared with BASF’s updated $80 planning assumption. The earlier $65 assumption is stale. August’s World Bank monthly Brent observation was $90.9. A higher oil price can raise feedstock costs, product prices and working capital simultaneously; its net EBITDA effect depends on spreads and pass-through.

05 / THE PHYSICAL LINK

The Rhine is consequential where exposure is concentrated.

Low water can reduce cargo per vessel, raise freight costs and complicate feedstock scheduling. That is a plausible plant-level mechanism. Its national output signature is less straightforward. Kaub is an exposure indicator for a transport corridor, not a direct measurement of every German chemical site’s logistics constraint.

Low water arrives in episodes, not a continuous national penalty

Days below Kaub GlW 77 cm per month

Hover, tap or focus the chart and use arrow keys for exact observations. Source: WSV/PEGELONLINE Kaub; frozen Schym daily-to-monthly aggregation. A gauge threshold is a reference for loading conditions, not a universal navigation ban. Latest month must be complete; the frozen August Schym archive supplies the daily aggregation.

A gauge threshold is a reference for loading conditions, not a universal navigation ban. Latest month must be complete; the frozen August Schym archive supplies the daily aggregation.

Source: WSV/PEGELONLINE Kaub; frozen Schym daily-to-monthly aggregation · Retrieved September 8, 2026.

Exact values & download / 199 observations in default view

First 60 of 199 default-view observations. The CSV contains every observation.

Download default values as CSV

period	meanCm	minimumDailyMeanCm	daysBelowGlw	daysBelowMnw	lowWaterEquivalentDays	nonlinearStress	observedDays	complete	year	month
2010-01	235.4	154.2	0	0	0.0	0.0	31	True	2010	01
2010-02	208.3	141.1	0	0	0.0	0.0	28	True	2010	02
2010-03	244.6	166.3	0	0	0.0	0.0	31	True	2010	03
2010-04	175.2	121.7	0	0	0.0	0.0	30	True	2010	04
2010-05	215.4	131.0	0	0	0.0	0.0	31	True	2010	05
2010-06	291.3	216.5	0	0	0.0	0.0	30	True	2010	06
2010-07	197.2	163.1	0	0	0.0	0.0	31	True	2010	07
2010-08	302.4	257.4	0	0	0.0	0.0	31	True	2010	08
2010-09	243.4	155.3	0	0	0.0	0.0	30	True	2010	09
2010-10	158.4	123.7	0	0	0.0	0.0	31	True	2010	10
2010-11	208.0	121.8	0	0	0.0	0.0	30	True	2010	11
2010-12	335.4	164.1	0	0	0.0	0.0	31	True	2010	12
2011-01	407.2	217.5	0	0	0.0	0.0	31	True	2011	01
2011-02	193.4	157.6	0	0	0.0	0.0	28	True	2011	02
2011-03	141.4	123.4	0	0	0.0	0.0	31	True	2011	03
2011-04	116.0	89.5	0	0	0.0	0.0	30	True	2011	04
2011-05	88.8	70.6	5	0	0.262	0.0171	31	True	2011	05
2011-06	142.1	97.1	0	0	0.0	0.0	30	True	2011	06
2011-07	208.2	145.4	0	0	0.0	0.0	31	True	2011	07
2011-08	185.1	150.2	0	0	0.0	0.0	31	True	2011	08
2011-09	141.1	112.9	0	0	0.0	0.0	30	True	2011	09
2011-10	146.4	88.2	0	0	0.0	0.0	31	True	2011	10
2011-11	70.4	48.2	20	14	4.186	1.1265	30	True	2011	11
2011-12	239.6	49.2	4	4	1.324	0.4475	31	True	2011	12
2012-01	379.6	236.1	0	0	0.0	0.0	31	True	2012	01
2012-02	181.7	147.5	0	0	0.0	0.0	29	True	2012	02
2012-03	163.7	148.7	0	0	0.0	0.0	31	True	2012	03
2012-04	181.2	145.5	0	0	0.0	0.0	30	True	2012	04
2012-05	220.8	178.4	0	0	0.0	0.0	31	True	2012	05
2012-06	282.6	203.2	0	0	0.0	0.0	30	True	2012	06
2012-07	238.9	161.9	0	0	0.0	0.0	31	True	2012	07
2012-08	140.5	117.6	0	0	0.0	0.0	31	True	2012	08
2012-09	184.9	152.6	0	0	0.0	0.0	30	True	2012	09
2012-10	227.5	160.0	0	0	0.0	0.0	31	True	2012	10
2012-11	243.4	158.2	0	0	0.0	0.0	30	True	2012	11
2012-12	391.4	212.2	0	0	0.0	0.0	31	True	2012	12
2013-01	277.3	176.0	0	0	0.0	0.0	31	True	2013	01
2013-02	347.7	202.4	0	0	0.0	0.0	28	True	2013	02
2013-03	221.6	182.1	0	0	0.0	0.0	31	True	2013	03
2013-04	272.1	174.5	0	0	0.0	0.0	30	True	2013	04
2013-05	330.7	285.1	0	0	0.0	0.0	31	True	2013	05
2013-06	459.1	321.3	0	0	0.0	0.0	30	True	2013	06
2013-07	236.6	181.4	0	0	0.0	0.0	31	True	2013	07
2013-08	175.7	125.9	0	0	0.0	0.0	31	True	2013	08
2013-09	192.1	112.6	0	0	0.0	0.0	30	True	2013	09
2013-10	217.6	151.5	0	0	0.0	0.0	31	True	2013	10
2013-11	303.0	220.7	0	0	0.0	0.0	30	True	2013	11
2013-12	193.8	147.5	0	0	0.0	0.0	31	True	2013	12
2014-01	236.8	210.4	0	0	0.0	0.0	31	True	2014	01
2014-02	224.3	179.9	0	0	0.0	0.0	28	True	2014	02
2014-03	162.7	133.5	0	0	0.0	0.0	31	True	2014	03
2014-04	128.9	111.0	0	0	0.0	0.0	30	True	2014	04
2014-05	204.9	175.0	0	0	0.0	0.0	31	True	2014	05
2014-06	148.3	111.3	0	0	0.0	0.0	30	True	2014	06
2014-07	283.8	157.5	0	0	0.0	0.0	31	True	2014	07
2014-08	301.7	213.5	0	0	0.0	0.0	31	True	2014	08
2014-09	221.0	169.5	0	0	0.0	0.0	30	True	2014	09
2014-10	175.2	135.2	0	0	0.0	0.0	31	True	2014	10
2014-11	215.6	168.1	0	0	0.0	0.0	30	True	2014	11
2014-12	185.9	139.8	0	0	0.0	0.0	31	True	2014	12

Two drought episodes do not identify one production effect. Event zero is August, separately for 2018 and 2022. These uncontrolled event windows are descriptive; the 2022 war and energy shock are major confounders. — Event zero is August, separately for 2018 and 2022. These uncontrolled event windows are descriptive; the 2022 war and energy shock are major confounders.

event	month	time	output reference100	low water days
2018	-6	2018-02	97.414075	0
2018	-5	2018-03	96.03928	0
2018	-4	2018-04	96.13748	0
2018	-3	2018-05	100.75286	0
2018	-2	2018-06	99.279869	0
2018	-1	2018-07	99.967267	0
2018	0	2018-08	97.610475	28
2018	1	2018-09	94.959083	13
2018	2	2018-10	94.271686	30
2018	3	2018-11	93.289689	30
2018	4	2018-12	94.468085	3
2018	5	2019-01	96.235679	0
2018	6	2019-02	95.253682	0
2022	-6	2022-02	105.0	0
2022	-5	2022-03	105.21898	0
2022	-4	2022-04	103.79562	0
2022	-3	2022-05	101.49635	0
2022	-2	2022-06	100.29197	0
2022	-1	2022-07	98.211679	14
2022	0	2022-08	95.80292	21
2022	1	2022-09	94.59854	6
2022	2	2022-10	89.124088	0
2022	3	2022-11	90.109489	0
2022	4	2022-12	81.788321	0
2022	5	2023-01	87.919708	0
2022	6	2023-02	87.919708	0

The 2018 and 2022 event windows show the coincidence of water stress and chemical output movements. They are not treatment effects. The second episode overlaps the invasion of Ukraine, the gas-price shock and changing demand. The earlier Schym low-water investigation found no stable negative national output penalty in its primary specification. That counterevidence remains part of this story.

A stronger test would connect individual sites to river-dependent inputs and shipments, then distinguish water-driven interruptions from energy-price and demand changes. Without that exposure map, a weak national coefficient cannot prove that the river is irrelevant; a dramatic low-water photograph cannot prove that it explains Germany’s industrial recession.

06 / THE COMPARISON TEST

Domestic output can lag while parts of the economy grow.

A rebound in US exports coexists with weakness elsewhere. July 2026; adjusted values. EU and non-EU aggregates overlap the named countries and must not be summed with them. Destination levels in EUR billion appear in exact values. — July 2026; adjusted values. EU and non-EU aggregates overlap the named countries and must not be summed with them. Destination levels in EUR billion appear in exact values.

destination	eur bn	mom pct	time
United States	14.4	19.1	2026-07
China	5.6	-9.5	2026-07
United Kingdom	6.7	-7.2	2026-07
EU	78.0	-1.6	2026-07
Non-EU	60.2	0.2	2026-07

German exports to the United States rebounded in July, while exports to China and the United Kingdom declined. These are nominal adjusted trade values, not domestic value added. A stronger export value can reflect prices, re-exports, a product mix change or value created in global supply chains. It does not by itself establish a stronger domestic order-to-production relationship.

The European comparison tests a different demand signal. Order books are survey balances standardized over 2010–2019. Production uses a three-month mean at each country’s latest common observation. This is a descriptive peer comparison, not difference-in-differences identification. — Order books are survey balances standardized over 2010–2019. Production uses a three-month mean at each country’s latest common observation. This is a descriptive peer comparison, not difference-in-differences identification.

country	label	through	production 2019 100	orderbook z	production change 3m
DE	Germany	2026-06	87.828843	-1.8359684	0.50779833
FR	France	2026-06	97.186701	0.27388237	0.32
IT	Italy	2026-06	94.219701	-0.066832768	0.35536603
ES	Spain	2026-06	100.89329	0.50679143	1.7359974
NL	Netherlands	2026-06	113.33681	0.15647877	4.054917
PL	Poland	2026-07	130.81619	-0.10782013	1.0446895
EU27_2020	EU27	2026-06	103.12315	-0.69926429	1.2969737

There is no single German industrial trajectory

Output change from 2021 annual mean, %

Hover, tap or focus the chart and use arrow keys for exact observations. Source: Destatis 42153-0001/-0002; Eurostat sts_inpr_m. 2026 cells are January–July means, not full-year totals. The color field keeps refining, other transport and electronics visible as counterexamples to a uniform energy-collapse narrative.

2026 cells are January–July means, not full-year totals. The color field keeps refining, other transport and electronics visible as counterexamples to a uniform energy-collapse narrative.

Source: Destatis 42153-0001/-0002; Eurostat sts_inpr_m · Retrieved September 8, 2026.

Exact values & download / 180 observations in default view

First 60 of 180 default-view observations. The CSV contains every observation.

Download default values as CSV

nace r2	year	value	change	label
C10	2015	98.225	-1.2896742316	Food
C10	2016	99.3333333333	-0.175864668	Food
C10	2017	101.35	1.8507662675	Food
C10	2018	100.35	0.8458253078	Food
C10	2019	101.7333333333	2.2359936354	Food
C10	2020	99.4333333333	-0.075370572	Food
C10	2021	99.5083333333	0.0	Food
C10	2022	99.375	-0.133992128	Food
C10	2023	96.1583333333	-3.3665522151	Food
C10	2024	97.0416666667	-2.4788543673	Food
C10	2025	97.4916666667	-2.0266309354	Food
C10	2026	99.4857142857	-0.0227308074	Food
C16	2015	91.5666666667	-7.9114984915	Wood
C16	2016	91.4583333333	-8.0204492122	Wood
C16	2017	95.225	-4.2323164599	Wood
C16	2018	97.15	-2.2963459604	Wood
C16	2019	95.5916666667	-3.8635601743	Wood
C16	2020	100.4916666667	1.0643647335	Wood
C16	2021	99.4333333333	0.0	Wood
C16	2022	88.9666666667	-10.5263157895	Wood
C16	2023	83.325	-16.2001340932	Wood
C16	2024	80.7166666667	-18.8233322159	Wood
C16	2025	77.4416666667	-22.1169963124	Wood
C16	2026	73.0571428571	-26.5265073512	Wood
C17	2015	100.6333333333	1.3767629281	Paper
C17	2016	99.4666666667	0.2014775017	Paper
C17	2017	101.05	1.7965077233	Paper
C17	2018	100.5416666667	1.2844190732	Paper
C17	2019	98.5166666667	-0.7555406313	Paper
C17	2020	94.3333333333	-4.9697783747	Paper
C17	2021	99.2666666667	0.0	Paper
C17	2022	93.7166666667	-5.5910006716	Paper
C17	2023	82.4333333333	-16.9576897246	Paper
C17	2024	82.6166666667	-16.7730020148	Paper
C17	2025	80.6083333333	-18.7961719275	Paper
C17	2026	79.4571428571	-19.955866833	Paper
C19	2015	106.2666666667	6.1693447673	Refining
C19	2016	106.375	6.277578886	Refining
C19	2017	105.9083333333	5.8113396054	Refining
C19	2018	102.925	2.8307384897	Refining
C19	2019	100.1583333333	0.0666056115	Refining
C19	2020	98.8833333333	-1.2072267089	Refining
C19	2021	100.0916666667	0.0	Refining
C19	2022	107.7583333333	7.6596453251	Refining
C19	2023	91.2916666667	-8.791940721	Refining
C19	2024	96.6833333333	-3.4052118891	Refining
C19	2025	94.8166666667	-5.2701690117	Refining
C19	2026	111.0428571429	10.9411610785	Refining
C20	2015	98.8916666667	-0.8439171123	Chemicals
C20	2016	99.0583333333	-0.6768048128	Chemicals
C20	2017	100.8916666667	1.1614304813	Chemicals
C20	2018	98.7	-1.0360962567	Chemicals
C20	2019	95.6583333333	-4.0858957219	Chemicals
C20	2020	94.3583333333	-5.3893716578	Chemicals
C20	2021	99.7333333333	0.0	Chemicals
C20	2022	89.4	-10.3609625668	Chemicals
C20	2023	78.6166666667	-21.1731283422	Chemicals
C20	2024	80.9583333333	-18.8252005348	Chemicals
C20	2025	78.525	-21.265040107	Chemicals
C20	2026	77.6285714286	-22.1638655462	Chemicals

The European comparison uses a harmonized manufacturing production index and a common order-book survey balance. It includes France, Italy, Spain, the Netherlands, Poland and EU27. The demand coordinate is standardized against each country’s 2010–2019 distribution. It is not the German volume-order measure, and the chart must not be described as a like-for-like international conversion-gap estimate.

Germany can simultaneously report stronger orders, higher GDP and weaker industrial production. Orders may be concentrated in long-delivery products. GDP may grow because services or construction expand, while factories contract. Imports can satisfy domestic demand; foreign subsidiaries can produce goods associated with German corporate activity. Inventory movements and changing import content can further loosen the monthly relationship. Establishing that demand “leaks” abroad, however, requires input-output, shipment and firm-level evidence that this package does not contain.

07 / THE INSTITUTIONAL LINK

Political execution risk is not an ideological score.

Saxony-Anhalt’s September 6 election supplies a useful entry point because it separates three quantities often collapsed into one: electoral concentration, coalition arithmetic and governing capacity. AfD received 43.8% of valid second votes in the provisional result. Turnout rose to 77.8%. The effective number of electoral parties nevertheless fell from 4.77 in 2021 to 4.01 in 2026.

Fewer effective parties can still make governing harder. Vote ENP fell from 4.77 in 2021 to 4.01 in 2026. ENP measures concentration, not bargaining friction. Current values use provisional counts; older values use final official counts. — Vote ENP fell from 4.77 in 2021 to 4.01 in 2026. ENP measures concentration, not bargaining friction. Current values use provisional counts; older values use final official counts.

date	series	value
2011-03-20	Vote ENP	4.5899358
2016-03-13	Vote ENP	5.2156968
2021-06-06	Vote ENP	4.7722269
2026-09-06	Vote ENP	4.0125922
2011-03-20	Seat ENP	3.3623056
2016-03-13	Seat ENP	3.9278672
2021-06-06	Seat ENP	3.8577286
2026-09-06	Seat ENP	3.5094244

The seat arithmetic tells a different story. A majority requires 42 of 83 seats. AfD has 39. CDU has 15; Linke, SPD and Greens have eight each; BSW has five. If AfD is excluded, the remaining five parties together hold 44 seats. All five are required. If Linke is also excluded, there is no majority among the remaining parties. These are conditional mathematical statements, not forecasts of a coalition.

Coalition assumptionThese scenarios expose exclusions. They do not assert that every displayed combination is politically available.

One five-party majority remains when AfD is excluded. Dots denote actual party membership in mathematically minimal majorities. The exclusion control is an assumption, not a prediction about negotiations or a score of ideology. — Dots denote actual party membership in mathematically minimal majorities. The exclusion control is an assumption, not a prediction about negotiations or a score of ideology.

date	scenario	parties	party list	seats	majority
2026-09-06	Arithmetic only	CDU + AfD	CDU\|AfD	54	42
2026-09-06	Arithmetic only	AfD + Linke	AfD\|Linke	47	42
2026-09-06	Arithmetic only	AfD + SPD	AfD\|SPD	47	42
2026-09-06	Arithmetic only	AfD + Greens	AfD\|Greens	47	42
2026-09-06	Arithmetic only	AfD + BSW	AfD\|BSW	44	42
2026-09-06	Arithmetic only	CDU + Linke + SPD + Greens + BSW	CDU\|Linke\|SPD\|Greens\|BSW	44	42

Saxony-Anhalt’s electorate has changed markedly. 2011, 2016 and 2021 final; September 2026 provisional. An absent party is not assigned a fabricated zero before it contested the election. — 2011, 2016 and 2021 final; September 2026 provisional. An absent party is not assigned a fabricated zero before it contested the election.

state	date	party	votes	seats	valid	turnout	status	share
Saxony-Anhalt	2026-09-06	CDU	226622	15	1315315	77.816458	provisional 2026-09-07	17.229485
Saxony-Anhalt	2026-09-06	AfD	576037	39	1315315	77.816458	provisional 2026-09-07	43.794604
Saxony-Anhalt	2026-09-06	Linke	112541	8	1315315	77.816458	provisional 2026-09-07	8.5562014
Saxony-Anhalt	2026-09-06	SPD	122303	8	1315315	77.816458	provisional 2026-09-07	9.298381
Saxony-Anhalt	2026-09-06	Greens	117498	8	1315315	77.816458	provisional 2026-09-07	8.9330693
Saxony-Anhalt	2026-09-06	BSW	69291	5	1315315	77.816458	provisional 2026-09-07	5.2680156
Saxony-Anhalt	2021-06-06	CDU	394810	40	1063697	60.3179	final	37.116773
Saxony-Anhalt	2021-06-06	AfD	221487	23	1063697	60.3179	final	20.822377
Saxony-Anhalt	2021-06-06	Linke	116927	12	1063697	60.3179	final	10.99251
Saxony-Anhalt	2021-06-06	SPD	89475	9	1063697	60.3179	final	8.4116999
Saxony-Anhalt	2021-06-06	Greens	63145	6	1063697	60.3179	final	5.936371
Saxony-Anhalt	2016-03-13	CDU	334139	30	1122877	61.113552	final	29.7574
Saxony-Anhalt	2016-03-13	AfD	272496	25	1122877	61.113552	final	24.267662
Saxony-Anhalt	2016-03-13	Linke	183290	16	1122877	61.113552	final	16.323248
Saxony-Anhalt	2016-03-13	SPD	119368	11	1122877	61.113552	final	10.63055
Saxony-Anhalt	2016-03-13	Greens	58209	5	1122877	61.113552	final	5.183916
Saxony-Anhalt	2011-03-20	CDU	323019	41	993502	51.177765	final	32.513171
Saxony-Anhalt	2011-03-20	Linke	235011	29	993502	51.177765	final	23.654809
Saxony-Anhalt	2011-03-20	SPD	213611	26	993502	51.177765	final	21.500812
Saxony-Anhalt	2011-03-20	Greens	70922	9	993502	51.177765	final	7.1385865

The experimental Coalition Complexity Index combines two quantities: the minimum party count needed for a majority and the fraction of all eligible party combinations that command a majority. Each receives equal weight. Under the AfD-exclusion assumption, it rises from 39.0 in 2021 to 88.4 in 2026. Under arithmetic alone, it is unchanged at 34.6. The assumption does much of the work; the interface makes it impossible to hide.

No ideological-distance score or minority-government probability is fabricated. A coalition with more partners may encounter more bargaining constraints, but party count cannot tell us which government will repair a bridge, accelerate a permit or maintain a stable budget. Measured implementation outcomes are necessary for that claim.

Uncertainty and investment are associated, with feedback in both directions. Total-economy gross fixed capital formation, not manufacturing investment. The lagged association is not a causal effect of voting or coalition composition. — Total-economy gross fixed capital formation, not manufacturing investment. The lagged association is not a causal effect of voting or coalition composition.

time	Energy	Logistics	Politics	Financing	Capacity	GIERI	GIERI ex capacity	GIERI energy double	GIERI politics half	Energy contribution	Logistics contribution	Politics contribution	Financing contribution	Capacity contribution	gap	investment growth	lagged uncertainty
2010-10-01	0.28972618	-0.29254931	-0.39550451	1.3819919	0.37999458	0.27273178	0.24591608	0.27556418	0.34698025	0.057945235	-0.058509861	-0.079100902	0.27639839	0.075998917	0.68813002	-1.0918261	-0.29442034
2011-01-01	0.57150554	-0.29254931	-0.94523213	0.078225959	-0.13235766	-0.14408152	-0.14701248	-0.02481701	-0.055064785	0.11430111	-0.058509861	-0.18904643	0.015645192	-0.026471533	0.78284368	4.9674206	-0.39550451
2011-04-01	0.81460823	-0.011251896	-1.1711698	0.33897916	-0.60201389	-0.12616963	-0.0072085696	0.030626677	-0.010058508	0.16292165	-0.0022503793	-0.23423395	0.067795831	-0.12040278	0.68357532	0.97079701	-0.94523213
2011-07-01	0.96634484	-0.29254931	1.4688356	0.73010895	-0.64470991	0.44560604	0.71818503	0.53239584	0.33191387	0.19326897	-0.058509861	0.29376713	0.14602179	-0.12894198	0.48091569	1.3734744	-1.1711698
2011-10-01	1.1013474	1.0576783	1.1814411	0.59973235	-0.51662185	0.68471546	0.98504979	0.75415412	0.62952372	0.22026948	0.21153565	0.23628822	0.11994647	-0.10332437	0.25275538	-0.41777324	1.4688356
2012-01-01	1.1220242	-0.29254931	-0.42964132	0.46935575	-0.17505368	0.13882713	0.21729734	0.30269332	0.2019903	0.22440485	-0.058509861	-0.085928263	0.093871151	-0.035010737	-0.56704457	-0.13891831	1.1814411
2012-04-01	1.1240244	-0.29254931	0.10652326	-0.052150639	-0.0042696021	0.17631561	0.22146192	0.33426707	0.18407032	0.22480487	-0.058509861	0.021304652	-0.010430128	-0.0008539204	-0.64187896	0.27248847	-0.42964132
2012-07-01	1.02888	-0.29254931	0.68913109	0.59973235	0.37999458	0.48103774	0.50629853	0.57234478	0.45791626	0.205776	-0.058509861	0.13782622	0.11994647	0.075998917	-1.6914449	-0.2491177	0.10652326
2012-10-01	1.1759551	-0.29254931	0.38547552	0.46935575	0.80695479	0.50903837	0.43455926	0.62019115	0.52276757	0.23519101	-0.058509861	0.077095105	0.093871151	0.16139096	0.21741914	-0.70372261	0.68913109
2013-01-01	1.2005012	-0.29254931	0.33783022	-0.57365703	0.76425877	0.28727678	0.16803128	0.43948085	0.28165973	0.24010025	-0.058509861	0.067566045	-0.11473141	0.15285175	-0.043128558	-3.8180313	0.38547552
2013-04-01	1.3204762	-0.29254931	-1.3403487	0.46935575	0.80695479	0.19277774	0.039233482	0.38072749	0.36312513	0.26409524	-0.058509861	-0.26806975	0.093871151	0.16139096	-1.0005667	3.9339598	0.33783022
2013-07-01	1.1189408	-0.29254931	-0.43493608	0.73010895	0.55077867	0.33446861	0.28039109	0.46521397	0.41995802	0.22378816	-0.058509861	-0.086987216	0.14602179	0.11015573	-0.49405524	0.16005008	-1.3403487
2013-10-01	1.1070539	-0.29254931	-0.11483624	0.46935575	0.25190652	0.28418612	0.29225602	0.42133074	0.32852194	0.22141077	-0.058509861	-0.022967248	0.093871151	0.050381304	-0.80315593	1.918028	-0.43493608
2014-01-01	1.0595104	-0.29254931	-0.81057992	0.59973235	0.16651448	0.1445256	0.13902838	0.29702306	0.25064843	0.21190208	-0.058509861	-0.16211598	0.11994647	0.033302896	-0.53705881	1.7055542	-0.11483624
2014-04-01	0.79267466	-0.29254931	-1.5762922	0.46935575	-0.0042696021	-0.12221615	-0.15170278	0.030265654	0.039347863	0.15853493	-0.058509861	-0.31525845	0.093871151	-0.0008539204	0.20030086	-1.0243691	-0.81057992
2014-07-01	0.50294166	-0.29254931	-0.46690589	0.20860256	0.038426419	-0.0018969132	-0.011977746	0.082242848	0.049770751	0.10058833	-0.058509861	-0.093381178	0.041720511	0.0076852837	0.20880505	-0.12239059	-1.5762922
2014-10-01	0.58455725	-0.29254931	-0.52293011	-0.052150639	0.081122439	-0.040390072	-0.0707682	0.063767816	0.013225487	0.11691145	-0.058509861	-0.10458602	-0.010430128	0.016224488	-0.65297169	0.83563589	-0.46690589
2015-01-01	0.30490572	-0.29254931	-0.48416853	-0.96478683	0.16651448	-0.25401689	-0.35914974	-0.16086313	-0.22844449	0.060981143	-0.058509861	-0.096833707	-0.19295737	0.033302896	1.2317408	-0.30108208	-0.52293011
2015-04-01	-0.38341339	-0.29254931	-0.51027949	-0.96478683	0.038426419	-0.42252052	-0.53775725	-0.41600266	-0.41276952	-0.076682677	-0.058509861	-0.1020559	-0.19295737	0.0076852837	-0.10031777	0.37681198	-0.48416853
2015-07-01	-0.4975366	-0.067511378	0.07362751	-1.22554	0.038426419	-0.33570681	-0.42924012	-0.36267844	-0.38118841	-0.099507319	-0.013502276	0.014725502	-0.245108	0.0076852837	0.63280772	0.58516425	-0.51027949
2015-10-01	-0.9228637	1.1139377	0.067896122	-0.96478683	0.12381846	-0.11639964	-0.17645417	-0.25081032	-0.13687695	-0.18457274	0.22278755	0.013579224	-0.19295737	0.024763692	0.54468048	2.2612805	0.07362751
2016-01-01	-1.7637909	-0.29254931	0.48237972	-0.57365703	0.038426419	-0.42183823	-0.53690439	-0.64549701	-0.52230689	-0.35275819	-0.058509861	0.096475944	-0.11473141	0.0076852837	-1.1656572	1.6396415	0.067896122
2016-04-01	-1.595884	-0.29254931	0.52809051	-0.83441023	0.038426419	-0.43126532	-0.54868826	-0.62536844	-0.53786042	-0.3191768	-0.058509861	0.1056181	-0.16688205	0.0076852837	-0.23655	-1.0349454	0.48237972
2016-07-01	-1.6124136	-0.29254931	1.2888871	-1.0951634	-0.089661643	-0.36018017	-0.4278098	-0.56888574	-0.54340987	-0.32248272	-0.058509861	0.25777743	-0.21903269	-0.017932329	-0.12255899	1.0777623	0.52809051
2016-10-01	-0.94585671	0.72012137	0.97939635	-0.96478683	-0.34583777	-0.11139272	-0.052781452	-0.25047005	-0.2325915	-0.18917134	0.14402427	0.19587927	-0.19295737	-0.069167553	-0.058676494	0.47507668	1.2888871
2017-01-01	-0.83406712	1.2827162	1.0518793	-0.57365703	-0.43122981	0.099128307	0.23171784	-0.056404264	-0.0067329152	-0.16681342	0.25654324	0.21037586	-0.11473141	-0.086245962	0.46753293	0.41244962	0.97939635
2017-04-01	-1.2098237	-0.29254931	0.31647405	-0.70403363	-0.81549399	-0.54108531	-0.47248314	-0.65254171	-0.63636969	-0.24196474	-0.058509861	0.063294809	-0.14080673	-0.1630988	-0.41812579	2.3859283	1.0518793
2017-07-01	-0.9536801	-0.29254931	-0.5439999	-0.83441023	-1.1143661	-0.74780113	-0.65615988	-0.78211429	-0.77044572	-0.19073602	-0.058509861	-0.10879998	-0.16688205	-0.22287323	-1.6905781	0.69506278	0.31647405
2017-10-01	-0.40348157	-0.29254931	-0.35222128	-1.0951634	-1.3278462	-0.69425236	-0.53585389	-0.64579056	-0.73225582	-0.080696314	-0.058509861	-0.070444256	-0.21903269	-0.26556925	-0.94003193	0.45674032	-0.5439999
2018-01-01	-0.38891858	-0.29254931	-0.92748165	-0.44328043	-1.4986303	-0.71017206	-0.51305749	-0.65662981	-0.68602655	-0.077783717	-0.058509861	-0.18549633	-0.088656087	-0.29972606	0.92735491	0.55821835	-0.35222128
2018-04-01	-0.15256187	-0.29254931	0.09954101	-0.57365703	-1.4559343	-0.4750323	-0.2298068	-0.42128723	-0.53887378	-0.030512373	-0.058509861	0.019908202	-0.11473141	-0.29118686	-0.33699595	1.4368603	-0.92748165
2018-07-01	0.23152725	2.0140895	0.7012074	-1.3559166	-1.4132383	0.035533842	0.39772687	0.06819941	-0.038429887	0.046305451	0.40281789	0.14024148	-0.27118332	-0.28264766	-0.15601191	1.0797713	0.09954101
2018-10-01	0.22432734	3.2517981	0.39920863	-0.96478683	-1.1570622	0.35069701	0.7276368	0.3296354	0.34530683	0.044865469	0.65035961	0.079841726	-0.19295737	-0.23141243	0.88550945	0.7643721	0.7012074
2019-01-01	-0.65164816	-0.29254931	0.40221499	-0.31290384	-0.68740593	-0.30845845	-0.21372158	-0.36565674	-0.38742217	-0.13032963	-0.058509861	0.080442998	-0.062580767	-0.13748119	0.40594919	0.52272446	0.39920863
2019-04-01	-1.6565199	-0.29254931	0.13739912	-0.31290384	-0.30314175	-0.48554314	-0.53114349	-0.68070594	-0.55475895	-0.33130399	-0.058509861	0.027479824	-0.062580767	-0.060628349	-0.26093981	0.37734084	0.40221499
2019-07-01	-1.9469254	-0.29254931	1.2782713	0.20860256	0.16651448	-0.11721727	-0.1881502	-0.42216862	-0.27227155	-0.38938508	-0.058509861	0.25565426	0.041720511	0.033302896	-0.44487706	-0.12532489	0.13739912
2019-10-01	-1.2369946	-0.29254931	0.59098745	-0.052150639	0.50808265	-0.096524894	-0.24767678	-0.28660318	-0.17291515	-0.24739892	-0.058509861	0.11819749	-0.010430128	0.10161653	1.3830362	-0.44390492	1.2782713
2020-01-01	-2.5684976	-0.29254931	1.4889079	0.46935575	0.55077867	-0.070400906	-0.2256958	-0.48675035	-0.24365744	-0.51369952	-0.058509861	0.29778159	0.093871151	0.11015573	2.7834524	-2.0503093	0.59098745
2020-04-01	-4.0457402	-0.29254931	2.2043308	1.9034983	6.2720454	1.208317	-0.057615093	0.33264081	1.0976488	-0.80914804	-0.058509861	0.44086616	0.38069967	1.2544091	14.021954	-5.294424	1.4889079
2020-07-01	-2.8897646	-0.29254931	1.4898648	1.1212387	4.0945484	0.7046676	-0.14280259	0.10559556	0.61742347	-0.57795293	-0.058509861	0.29797296	0.22424775	0.81890968	0.91975631	3.8558286	2.2043308
2020-10-01	-1.1054942	-0.29254931	2.1467686	0.078225959	1.191219	0.403634	0.20673776	0.15211264	0.20995238	-0.22109884	-0.058509861	0.42935371	0.015645192	0.2382438	-3.0331092	2.8473654	1.4898648
2021-01-01	-0.47096755	-0.29254931	1.0272552	-0.57365703	0.97773887	0.13356404	-0.077479663	0.032808778	0.034265022	-0.09419351	-0.058509861	0.20545105	-0.11473141	0.19554777	4.6828927	-2.2671456	2.1467686
2021-04-01	0.33886187	-0.29254931	0.5617801	-0.83441023	-0.26044573	-0.097352657	-0.05657939	-0.024650236	-0.17058963	0.067772375	-0.058509861	0.11235602	-0.16688205	-0.052089145	9.0474041	2.3523585	1.0272552
2021-07-01	2.1091849	-0.29254931	2.2950981	-1.4862932	-0.73010195	0.3790677	0.65636011	0.66742056	0.16617543	0.42183698	-0.058509861	0.45901961	-0.29725864	-0.14602039	13.8857	-3.2273966	0.5617801
2021-10-01	3.9689421	0.27004551	2.7371979	-0.44328043	-0.60201389	1.1861782	1.6332262	1.6499722	1.0138427	0.79378841	0.054009103	0.54743957	-0.088656087	-0.12040278	10.849453	0.66702821	2.2950981
2022-01-01	3.9690953	-0.29254931	3.4377185	-0.18252724	-0.68740593	1.2488663	1.7329343	1.7022378	1.0056605	0.79381906	-0.058509861	0.6875437	-0.036505448	-0.13748119	11.805773	1.5911981	2.7371979
2022-04-01	3.9372774	-0.29254931	3.9740624	0.59973235	-0.26044573	1.5916154	2.0546307	1.9825591	1.3268991	0.78745549	-0.058509861	0.79481247	0.11994647	-0.052089145	10.612219	-0.63242494	3.4377185
2022-07-01	5.7105798	2.0140895	4.5815749	0.46935575	-0.30314175	2.4944916	3.1939	3.0305063	2.2625935	1.142116	0.40281789	0.91631499	0.093871151	-0.060628349	8.6048034	0.49725527	3.9740624
2022-10-01	4.3703243	-0.29254931	4.239992	1.5123685	-0.26044573	1.913938	2.4575339	2.3233357	1.6554875	0.87406485	-0.058509861	0.84799841	0.30247371	-0.052089145	5.9106709	-1.3359892	4.5815749
2023-01-01	2.150551	-0.29254931	3.3879987	1.7731217	0.038426419	1.4115097	1.7547805	1.5346833	1.1918998	0.43011021	-0.058509861	0.67759974	0.35462435	0.0076852837	3.952819	-0.15547101	4.239992
2023-04-01	1.0528284	-0.29254931	2.9020248	0.99086215	0.12381846	0.95539691	1.1632915	0.9716355	0.73910492	0.21056569	-0.058509861	0.58040497	0.19817243	0.024763692	2.9750455	0.25113784	3.3879987
2023-07-01	0.92603438	-0.29254931	3.9621073	0.99086215	0.50808265	1.2189074	1.3966136	1.1700953	0.91410745	0.18520688	-0.058509861	0.79242147	0.19817243	0.10161653	5.537678	-0.31059163	2.9020248
2023-10-01	1.5555624	-0.067511378	4.0643761	0.99086215	0.97773887	1.5042056	1.6358223	1.5127651	1.2197422	0.31111247	-0.013502276	0.81287523	0.19817243	0.19554777	6.2039069	-1.7187898	3.9621073
2024-01-01	0.3529009	-0.29254931	3.9055088	1.7731217	1.4900911	1.4458147	1.4347455	1.2636624	1.1725153	0.070580179	-0.058509861	0.78110177	0.35462435	0.29801822	6.209811	0.63401601	4.0643761
2024-04-01	0.73036011	-0.29254931	4.0308	0.86048555	2.0024434	1.4663079	1.3322741	1.34365	1.1813644	0.14607202	-0.058509861	0.80616	0.17209711	0.40048867	5.6928202	-1.565008	3.9055088
2024-07-01	1.1122897	-0.29254931	4.2157215	0.99086215	2.7709717	1.7594592	1.506581	1.6515976	1.4865411	0.22245794	-0.058509861	0.8431443	0.19817243	0.55419435	6.4798676	-0.34071073	4.0308
2024-10-01	1.5799005	-0.29254931	5.2342695	1.3819919	3.240628	2.2288481	1.9759032	2.1206902	1.8949124	0.31598011	-0.058509861	1.0468539	0.27639839	0.64812559	8.3352717	0.48687442	4.2157215
2025-01-01	1.7443945	-0.29254931	5.210344	0.99086215	3.240628	2.1787358	1.9132628	2.1063456	1.8418905	0.34887889	-0.058509861	1.0420688	0.19817243	0.64812559	7.2407027	0.25774004	5.2342695
2025-04-01	1.2079347	-0.29254931	5.7996297	1.2516153	3.1979319	2.2329125	1.9916576	2.0620829	1.8366106	0.24158695	-0.058509861	1.1599259	0.25032307	0.63958639	8.3288628	-1.1413502	5.210344
2025-07-01	1.0697712	-0.29254931	5.5601847	1.2516153	3.0698439	2.1317732	1.8972555	1.9547728	1.7508385	0.21395423	-0.058509861	1.1120369	0.25032307	0.61396878	8.7273438	0.19761874	5.7996297

Political uncertainty is associated with subsequent investment weakness in the aggregate quarterly data, although explanatory power is modest. Reverse causality and omitted variables remain central: economic disappointments can raise uncertainty and alter voting; difficult coalition formation can then affect policy choices and perceived investment risk. The earlier regional-retreat analysis provides demographic context, without identifying that feedback loop.

08 / THE EXPLANATION TEST

The index is less convincing than the gap.

GIERI, the German Industrial Execution Risk Index, is secondary by design. Its five-component monthly history combines standardized gas prices, low-water days, policy uncertainty, financing constraints and negative capacity utilization. Equal weights provide a transparent starting point. Alternative weights and an index excluding utilization test how much the result depends on the construction.

GIERI is an audit of five proxies, not a national risk probability. Baseline 2010–2019. Components: log European gas, Kaub low-water days, log EPU, survey financing constraints and negative utilization. Gas storage and coalitions are excluded from the monthly history. — Baseline 2010–2019. Components: log European gas, Kaub low-water days, log EPU, survey financing constraints and negative utilization. Gas storage and coalitions are excluded from the monthly history.

time	component	value
2010-01	Energy	0.074802771
2010-02	Energy	0.074802771
2010-03	Energy	0.082979852
2010-04	Energy	-0.012844546
2010-05	Energy	-0.031697053
2010-06	Energy	0.0032342731
2010-07	Energy	0.024438503
2010-08	Energy	0.052172255
2010-09	Energy	0.040839804
2010-10	Energy	0.041512832
2010-11	Energy	0.061334969
2010-12	Energy	0.070987905
2011-01	Energy	0.1239012
2011-02	Energy	0.10920335
2011-03	Energy	0.10979877
2011-04	Energy	0.16580404
2011-05	Energy	0.16256528
2011-06	Energy	0.16039562
2011-07	Energy	0.19872137
2011-08	Energy	0.18951302
2011-09	Energy	0.19157251
2011-10	Energy	0.22012246
2011-11	Energy	0.21521826
2011-12	Energy	0.22546773
2012-01	Energy	0.22158535
2012-02	Energy	0.20527853
2012-03	Energy	0.24635066
2012-04	Energy	0.22012246
2012-05	Energy	0.23076224
2012-06	Energy	0.22352992
2012-07	Energy	0.20577975
2012-08	Energy	0.2082791
2012-09	Energy	0.20326914
2012-10	Energy	0.22788055
2012-11	Energy	0.23979053
2012-12	Energy	0.23790195
2013-01	Energy	0.24167274
2013-02	Energy	0.23695525
2013-03	Energy	0.24167274
2013-04	Energy	0.28720753
2013-05	Energy	0.26106159
2013-06	Energy	0.24401661
2013-07	Energy	0.22884277
2013-08	Energy	0.23076224
2013-09	Energy	0.21175947
2013-10	Energy	0.21767575
2013-11	Energy	0.22012246
2013-12	Energy	0.22643411
2014-01	Energy	0.22836187
2014-02	Energy	0.21423222
2014-03	Energy	0.19311214
2014-04	Energy	0.1853711
2014-05	Energy	0.1571252
2014-06	Energy	0.1331085
2014-07	Energy	0.10381583
2014-08	Energy	0.095940795
2014-09	Energy	0.10200836
2014-10	Energy	0.1331085
2014-11	Energy	0.081103449
2014-12	Energy	0.13652241

The attractive level correlation weakens in changes. Regressions use complete quarters only. Differencing and removing utilization weaken the relationship. Level fit is not the percentage of output caused by execution risk. — Regressions use complete quarters only. Differencing and removing utilization weaken the relationship. Level fit is not the percentage of output caused by execution risk.

sector	model	term	coefficient	lower	upper	p	n	adj r2	label
Manufacturing	GIERI	GIERI	3.7133695	3.1496884	4.2770507	3.8677001e-38	64	0.55951221	GIERI
Manufacturing	GIERI / differences	GIERI	2.9681631	-0.80309718	6.7394233	0.12293129	63	0.13213833	GIERI / differences
Manufacturing	Without capacity	GIERI_ex_capacity	3.3468228	2.5302307	4.1634149	9.5148465e-16	64	0.49426821	Without capacity
Manufacturing	Without capacity / differences	GIERI_ex_capacity	0.66531914	-0.37809227	1.7087305	0.21139154	63	-0.0077427437	Without capacity / differences

The level association is strong enough to be visually seductive. It is also vulnerable to shared trends and the mechanical link between output and utilization. In complete-quarter change regressions, the headline coefficient is no longer precisely estimated. Removing utilization weakens the relationship further. This is the reason to keep GIERI in the explanatory appendix rather than make it the headline discovery.

The decomposition of the index is exact: each displayed component contributes its standardized value divided by five. A decomposition of Germany’s industrial problem into causal percentages would be a different exercise. Adjusted R² is not an attribution share. Regression coefficients on correlated, endogenous proxies cannot be turned into “X% energy” and “Y% politics.”

Poland is a useful contrast, with a different industrial structure. Harmonized Eurostat series; different sector mix, investment cycles and price competitiveness preclude a causal Germany-versus-Poland experiment. The country selector adds another comparator. — Harmonized Eurostat series; different sector mix, investment cycles and price competitiveness preclude a causal Germany-versus-Poland experiment. The country selector adds another comparator.

freq	indic bt	nace r2	s adj	unit	geo	time	value	status	source	release	country
M	PRD	C	SCA	I21	DE	2010-01	82.3454833597	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-02	81.7749603803	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-03	84.3423137876	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-04	85.8637083994	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-05	88.5261489699	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-06	88.4310618067	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-07	87.765451664	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-08	89.1917591125	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-09	90.5229793978	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-10	92.4247226624	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-11	91.9492868463	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2010-12	93.470681458	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-01	93.2805071315	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-02	94.3264659271	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-03	94.8969889065	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-04	95.3724247227	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-05	96.6085578447	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-06	95.087163233	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-07	98.0348652932	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-08	97.2741679873	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-09	95.6576862124	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-10	96.8938193344	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-11	96.1331220285	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2011-12	94.8969889065	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-01	95.2773375594	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-02	95.1822503962	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-03	96.2282091918	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-04	94.6117274168	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-05	96.4183835182	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-06	95.1822503962	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-07	96.5134706815	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-08	96.323296355	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-09	95.1822503962	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-10	93.9461172742	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-11	93.2805071315	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2012-12	93.9461172742	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-01	92.9952456418	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-02	93.5657686212	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-03	94.8969889065	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-04	95.087163233	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-05	94.6117274168	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-06	96.1331220285	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-07	94.4215530903	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-08	96.6085578447	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-09	96.1331220285	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-10	95.4675118859	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-11	97.6545166403	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2013-12	97.8446909667	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-01	97.2741679873	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-02	97.3692551506	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-03	97.7496038035	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-04	97.4643423138	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-05	96.5134706815	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-06	96.6085578447	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-07	98.6053882726	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-08	94.8019017433	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-09	97.0839936609	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-10	97.2741679873	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-11	97.3692551506	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany
M	PRD	C	SCA	I21	DE	2014-12	98.9857369255	Unavailable	eu-production.json	2026-09-05T11:00:00+0200	Germany

Poland provides a useful comparison, but not a counterfactual Germany. Industrial composition, investment, productivity catch-up and trade integration differ. Its trajectory helps reject the idea that every European manufacturing economy must share Germany’s path. It cannot isolate the effect of German energy or political institutions.

09 / THE OPERATING IMPLICATION

For BASF, conversion extends beyond the factory gate.

BASF’s second quarter supplies a valuable counterexample to a uniformly failing industrial sector. EBITDA before special items increased to €2.4 billion, and the company raised its full-year guidance. Operating cash flow was only €524 million; free cash flow was −€189 million. More cash was tied up in inventories and receivables. The group’s global earnings improvement therefore coexisted with weaker cash conversion.

BASF’s earnings recovery did not become operating cash. BASF Group worldwide, not Ludwigshafen alone. Earlier EBITDA and operating cash are rounded approximations from the release; exact current cash flow is EUR524m. FCF turned negative. — BASF Group worldwide, not Ludwigshafen alone. Earlier EBITDA and operating cash are rounded approximations from the release; exact current cash flow is EUR524m. FCF turned negative.

metric	q2 2025 eur bn	q2 2026 eur bn	precision note
EBITDA before special items	1.5	2.4	2025 sales/EBITDA/OCF are approximate from rounded release changes; 2025 FCF derived exactly from disclosed EUR721m decline.
Operating cash flow	1.6	0.524	2025 sales/EBITDA/OCF are approximate from rounded release changes; 2025 FCF derived exactly from disclosed EUR721m decline.
Free cash flow	0.532	-0.189	2025 sales/EBITDA/OCF are approximate from rounded release changes; 2025 FCF derived exactly from disclosed EUR721m decline.

These are worldwide group results. They cannot be assigned to Ludwigshafen or treated as a German chemical-industry margin series. Ludwigshafen’s integrated production network makes scheduling, energy and feedstock choices interdependent. Rhine access matters for selected flows, while China exposure changes the group’s production and market portfolio. European demand, plant utilization and the location of new capacity must be assessed together.

The useful digital question is operational: which decision becomes faster or better under a constraint? A demand-sensing model cannot repair an unprofitable spread. An energy optimizer cannot guarantee a shipment. The strongest applications connect the order book to feasible production plans, transport options and cash requirements.

Opportunity	EBITDA potential score	Feasibility score	Speed score	Indicative pilot/value window	Decision and value measure	Product score
Dynamic production scheduling	5	4	4	3–9 months	Contribution margin under equipment, energy and order constraints; compare realized margin and service against a frozen planner baseline.	80
Energy optimization	5	4	4	3–9 months	Reduce verified net energy cost per saleable tonne, with yield and product quality held constant.	80
Logistics rerouting	4	4	4	2–6 months	Reduce delivered cost and missed shipments using river, rail, road and terminal alternatives.	64
Inventory optimization	2	5	5	2–6 months	Primarily cash release; separately measure carrying cost, obsolescence and service effects on EBITDA.	50
Predictive maintenance	4	4	3	6–12 months	Reduce unplanned downtime on constrained assets; subtract maintenance and false-alarm costs.	48
Demand sensing	3	4	4	3–6 months	Improve allocation and forecast error where shorter-horizon signals affect feasible production decisions.	48
Geopolitical scenario intelligence	3	4	4	2–6 months	Shorten detection-to-decision time; value comes from changed purchases or operating plans, not alert counts.	48
Feedstock substitution	4	2	2	12–24 months	Identify technically qualified substitutions and evaluate full yield, quality, conversion and input-cost effects.	16

Author judgments, September 8, 2026. Scores 1–5; higher is more attractive. Time windows assume an accessible data baseline and a bounded pilot. No BASF project return is asserted.

The ranking is an explicit management judgment, not a quantified BASF EBITDA forecast. Impact potential, feasibility and speed are scored from one to five; the product provides a prioritization aid. Inventory optimization is separately marked for working-capital value so that cash release is not mislabeled EBITDA. Actual project sizing requires site-level baselines, ownership and counterfactual measurement.

A practical value model

For each €100 million of exposed annual energy or feedstock spend, a 1% verified net saving is €1 million before implementation costs and operational offsets. For €100 million of addressable inventory, a 5% reduction releases €5 million of cash; EBITDA improves only through reduced carrying cost, waste or service effects. At Brent $100 or $120, a mechanically oil-proportional input costs 25% or 50% more than at $80. Those percentages are sensitivity assumptions, not BASF earnings sensitivities.

10 / MAKE THE ASSUMPTION VISIBLE

How much winter buffer do the flows leave?

Net injection before November 1Net withdrawal from November 1

0.240 / 0.300 percentage points per day

November 1: 68.0% · January 1: 49.7% · March 31: 23.0%. Deterministic net-balance scenario.

The winter buffer is a balance sheet of flows. September 6 starting fill 54.52%; 56 injection days and 150 withdrawal days. Negative values are unmet assumed net withdrawals, not observed shortages. The simulator changes both rates explicitly. — September 6 starting fill 54.52%; 56 injection days and 150 withdrawal days. Negative values are unmet assumed net withdrawals, not observed shortages. The simulator changes both rates explicitly.

injection	withdrawal	march balance	inj	draw
0.1	0.15	37.62	0.1	0.15
0.1	0.175	33.87	0.1	0.175
0.1	0.2	30.12	0.1	0.2
0.1	0.225	26.37	0.1	0.225
0.1	0.25	22.62	0.1	0.25
0.1	0.275	18.87	0.1	0.275
0.1	0.3	15.12	0.1	0.3
0.1	0.325	11.37	0.1	0.325
0.1	0.35	7.62	0.1	0.35
0.1	0.375	3.87	0.1	0.375
0.1	0.4	0.12	0.1	0.4
0.1	0.425	-3.63	0.1	0.425
0.1	0.45	-7.38	0.1	0.45
0.1	0.475	-11.13	0.1	0.475
0.1	0.5	-14.88	0.1	0.5
0.1	0.525	-18.63	0.1	0.525
0.1	0.55	-22.38	0.1	0.55
0.125	0.15	39.02	0.125	0.15
0.125	0.175	35.27	0.125	0.175
0.125	0.2	31.52	0.125	0.2
0.125	0.225	27.77	0.125	0.225
0.125	0.25	24.02	0.125	0.25
0.125	0.275	20.27	0.125	0.275
0.125	0.3	16.52	0.125	0.3
0.125	0.325	12.77	0.125	0.325
0.125	0.35	9.02	0.125	0.35
0.125	0.375	5.27	0.125	0.375
0.125	0.4	1.52	0.125	0.4
0.125	0.425	-2.23	0.125	0.425
0.125	0.45	-5.98	0.125	0.45
0.125	0.475	-9.73	0.125	0.475
0.125	0.5	-13.48	0.125	0.5
0.125	0.525	-17.23	0.125	0.525
0.125	0.55	-20.98	0.125	0.55
0.15	0.15	40.42	0.15	0.15
0.15	0.175	36.67	0.15	0.175
0.15	0.2	32.92	0.15	0.2
0.15	0.225	29.17	0.15	0.225
0.15	0.25	25.42	0.15	0.25
0.15	0.275	21.67	0.15	0.275
0.15	0.3	17.92	0.15	0.3
0.15	0.325	14.17	0.15	0.325
0.15	0.35	10.42	0.15	0.35
0.15	0.375	6.67	0.15	0.375
0.15	0.4	2.92	0.15	0.4
0.15	0.425	-0.83	0.15	0.425
0.15	0.45	-4.58	0.15	0.45
0.15	0.475	-8.33	0.15	0.475
0.15	0.5	-12.08	0.15	0.5
0.15	0.525	-15.83	0.15	0.525
0.15	0.55	-19.58	0.15	0.55
0.175	0.15	41.82	0.175	0.15
0.175	0.175	38.07	0.175	0.175
0.175	0.2	34.32	0.175	0.2
0.175	0.225	30.57	0.175	0.225
0.175	0.25	26.82	0.175	0.25
0.175	0.275	23.07	0.175	0.275
0.175	0.3	19.32	0.175	0.3
0.175	0.325	15.57	0.175	0.325
0.175	0.35	11.82	0.175	0.35

Stronger orders and stronger production are separate tests. Three-month means through July 2026. Quadrants classify outcomes; they do not establish why a sector occupies a quadrant. New orders are volatile, especially in other transport. — Three-month means through July 2026. Quadrants classify outcomes; they do not establish why a sector occupies a quadrant. New orders are volatile, especially in other transport.

sector	label	orders change	production change
C17	Paper	-26.3034367142	-19.9798522498
C20	Chemicals	-26.3105007536	-21.6577540107
C21	Pharma	4.7435143985	0.4691689008
C24	Basic metals	-17.3883853642	-10.7390976814
C25	Fabricated metals	-18.2008894856	-15.3426725946
C26	Electronics	15.4103852596	5.616093881
C27	Electrical equipment	-5.8507512801	-8.938172043
C28	Machinery	-14.1898691714	-14.0700989435
C29	Automotive	-13.2024828049	3.1265844178
C30	Other transport	138.8538290977	33.6465061656

THE VERDICT / CONDITIONAL, NOT CAUSAL

A conversion problem is visible.
Its permanence is not established.

The strongest finding is a persistent discrepancy between production and a historical order relationship. It survives a simple trend alternative and the removal of major orders in a shorter training sample. The weakest version of the story is that demand has broadly returned and identifiable energy, logistics and political constraints account for a known share of lost production.

Supported

A conditional production-level gap; expensive energy; a thin storage buffer in the available historical comparison; restrictive coalition arithmetic under explicit exclusions.

Still plausible

A delayed cyclical recovery, sector-specific conversion constraints and structural relocation can coexist. Their relative importance remains unsettled.

Not identified

A permanent nationwide elasticity collapse; voting causing industrial decline; causal percentages by constraint; an inevitable winter shortage.

A delayed cyclical recovery remains plausible because broad demand is weak and utilization is low. A structural conversion problem deserves serious attention because production remains below its historical conditional benchmark. An industrial regime change, in which German companies create value through a different geography and mix of production, is a further hypothesis. This dataset does not demonstrate permanent offshoring or quantify the shift toward services, intellectual property and high-tech activity.

Germany may increasingly face a problem of turning demand into production, production into margins, and political intent into execution. The qualification is essential: the evidence does not yet justify replacing the demand problem with that diagnosis.

If demand returns but the conversion machinery remains impaired, what exactly does “economic recovery” mean?

The research package

Inspect the evidence.

Download cleaned datasets, methods, statistical appendix and reproducible notebook. The archive includes the frozen source receipts and analysis code. Source snapshots remain separate from transformed data. Download the statistical snapshots and election snapshots separately; the included README explains where to place them.

Methodology · Statistical appendix · Data dictionary · Source and release ledger

Open the source & methodology drawer

The evidence contract

Retrieved September 8, 2026 (UTC). Release dates, source byte hashes, revision flags, data dictionaries and exact calculations accompany the downloadable research package. Publication is an empirical ad-hoc analysis; model results are associations and conditional discrepancies.

What is observed and modeled

199 monthly observations where available, 2010–July 2026. Ten matched order-reporting sectors plus manufacturing. Eurostat production histories with a separately flagged Destatis July append. German quarterly surveys, six EU peers plus EU27, 2019 sector energy accounts, daily gas-chart snapshots, provisional Saxony-Anhalt 2026 counts and reconciled historical election counts.

Models: fixed pre-2020 log-level benchmark; growth ARX at 1/3/6 months; HAC inference; 60-month rolling coefficients; known-date breaks with Holm adjustment; segmented RSS/BIC search; energy cross section with HC3 errors and leave-one-out estimates; complete-quarter GIERI sensitivity regressions.

What remains unavailable or unidentified

Complete AGSI daily 2017–2026 history, verified TWh working capacity and injection/withdrawal flows; a consistent public chemical-sector utilization panel; plant margins; sector gas/electricity shares on a reconciled basis; permitting times, public infrastructure completion and local investment execution; causal offshoring, import leakage and inventory channels.

The historical state-election PDF extractor retains 47 elections in 15 Länder that reconcile exactly to valid vote counts. Saxony-Anhalt 2026 is added separately. It is not an all-election census; Bremen and unparsed election pages are outside the retained panel. Federal Land comparisons use 2021 and 2025 second votes.

Definitions that change the conclusion

Gap: exp(fitted log production given lagged orders) minus observed production, in 2021 index points. ENP: 1 / Σs², using exact party counts. CCI: 100 × [0.5 × (minimum majority party count − 1)/(all represented party count − 1) + 0.5 × (1 − fraction of eligible subsets that win)]. With no majority, the first component is one. Exclusion scenarios are assumptions, not estimated political probabilities.

GIERI: the equal-weight mean of five z-scores fixed to 2010–2019; log gas, Kaub days, log policy uncertainty, survey financial constraints and negative utilization. Quarterly models use complete quarters. Storage and elections are not interpolated into the monthly index. Gas scenarios: net flows in percentage points of capacity; no stochastic interpretation. No causal percentage allocation is reported.

Primary sources and explicitly identified archived transformations

The default figures, exact values and source notes are available below each exhibit.

Germany’s recovery,interrupted.

Reopen the conversion case.

First establish what has recovered.

The order headline overstates the breadth of recovery

Orders and output occupy different recoveries

The path back is not the path out

Where does the recovery disappear?

A 13.3-point gap opens against the old order relationship

Orders recover in the headline.

Production responds unevenly.

Energy remains expensive and exposed.

Capacity is underused.

The Rhine is a local mechanism.

Political concentration can obstruct coalitions.

The gap survives. Its decomposition does not.

The short-run response is imprecisely estimated

No single lag settles the conversion question

The conditional gap is concentrated across sectors

Spare capacity is not the same as usable capacity.

Germany still has substantial spare capacity

Demand remains the most frequently reported constraint

Energy exposure explains less than the simple story suggests

A thin buffer is a vulnerability, not a forecast.

The autumn buffer is thin in the available comparison

The recent pace would leave storage near 67% on November 1

Winter outcomes depend on two explicit flow assumptions

The gas premium survives the inflation adjustment

The factory bill is different from the wholesale benchmark

The Rhine is consequential where exposure is concentrated.

Low water arrives in episodes, not a continuous national penalty

Two drought episodes do not identify one production effect

Domestic output can lag while parts of the economy grow.

A rebound in US exports coexists with weakness elsewhere

The European comparison tests a different demand signal

There is no single German industrial trajectory

Political execution risk is not an ideological score.

Fewer effective parties can still make governing harder

One five-party majority remains when AfD is excluded

Saxony-Anhalt’s electorate has changed markedly

Uncertainty and investment are associated, with feedback in both directions

The index is less convincing than the gap.

GIERI is an audit of five proxies, not a national risk probability

The attractive level correlation weakens in changes

Poland is a useful contrast, with a different industrial structure

For BASF, conversion extends beyond the factory gate.

BASF’s earnings recovery did not become operating cash

A practical value model

How much winter buffer do the flows leave?

The winter buffer is a balance sheet of flows

Stronger orders and stronger production are separate tests

A conversion problem is visible.Its permanence is not established.

Inspect the evidence.

The evidence contract

What is observed and modeled

What remains unavailable or unidentified

Definitions that change the conclusion

Primary sources and explicitly identified archived transformations

Germany’s recovery,
interrupted.

A conversion problem is visible.
Its permanence is not established.