A Monthly International Dataset for the Interwar Period:Taking the Debate to the Next Level I

Thilo Albers

School of Business and Economics, Humboldt University Berlin

Martin UebeleFaculty of Arts, University of Groningen

AbstractRecent research on the Great Depression has increased the demand for detailed data on business activity. This paperpresents a novel dataset that expands the data in three dimensions: (i) a larger cross-section with 28 countries, (ii)a higher (monthly) frequency of real economy data, and (iii) disaggregated data ensuring representativeness. Weaggregate in total 415 single series using Principal Component Analysis to construct a business activity indicator forindividual countries for the years 1925–1936. We date business cycle peaks and troughs during the Great Depression,and find that while pre-Depression peaks occurred in almost all countries in 1929, some countries were severely hit bythe crisis as late as 1930 and 1931. Furthermore, we analyse business cycle comovement during the interwar periodand find that exchange controls made countries more independent from the global economy, while the gold standardinduced comovement.

Keywords: Great Depression, time series analysis, gold standard, business cycle comovement,exchange controlsJEL codes: C38, E32, E58, N14

1. Introduction

This paper introduces a novel dataset on business activity during the Great Depression. Thedataset expands the so far available data in three dimensions: number of countries, time-frequency,and representativeness. Based on aggregated and disaggregated data, it covers 28 countries for aperiod of up to twelve years. The need and the rationale for composing such a dataset stems from

IWe thank Ulrich Pfister, Marvin Suesse and Nikolaus Wolf for their comments. Furthermore, we thank ChrisMeissner for answering our questions concerning the methodology in Mathy and Meissner (2011). Jan Tore Klovlandprovided his industrial production estimates. Moreover, we are deeply indebted to Daniel Gallardo-Albarrán, whotranscribed about the half of the seemingly endless pages of data that are a building bloc of this research project. Allerrors are ours.

Email addresses: thilo.albers@uni-muenster.de (Thilo Albers), m.uebele@rug.nl (MartinUebele)

the historiography of the Great Depression, its development throughout the last 80 years, and theprogress in econometrics and computing.

The Gold Standard Literature, which emerged in the 1980s, has shaped today’s understandingof the Great Depression and international macroeconomics. It suggested that the gold standardwas not only important for the transmission of the crisis, but that it was in fact causal to the GreatDepression (Eichengreen, 1992). The innovation that this literature brought to the scholarship ofthe Depression was simple and path-breaking at the same time: instead of analysing only onecountry (mainly the United States) it compared many countries. Related to the cross-sectionaldata dimension, Ben Bernanke (1995, p. 1) argued: “By effectively expanding the dataset fromone observation to twenty, thirty, or more, the shift to a comparative perspective substantiallyimproves our ability to identify – in the strict econometric sense – the forces responsible for theworld depression.”

The Gold Standard Literature challenged the previously dominant Friedman-Schwartz (1963)view. They had argued that monetary policy mistakes were causal to the Great Depression, butnot the monetary system (of the gold standard) itself. However, during the course of the last30 years, the comparative approach of the Gold Standard Literature became no less mainstreamthan the Friedman-Schwartz view once had been. This development might have obscured anotherdimension of “Depression data” - time or rather the frequency of observations. If one reads BenBernanke’s statement in this context, a complementary question comes immediately into one’smind: Would expanding the dataset from 10 to 120 points in time – as the shift from annual tomonthly data for a 10 years period would imply – not improve our ability to identify (in the stricteconometric sense) the forces responsible for the world depression?

More recently, a new stream of literature, which we coin the Post-Gold Standard Literature, sug-gests that an expansion of datasets in the time-frequency dimension is important (Wolf, 2008;Mathy and Meissner, 2011; Accominotti, 2012). They stress, among others, the relevance ofwithin-year timing during that relatively short period, which from the mid-1920s to the mid-1930sdoes not span more than one typical business cycle. Using monthly instead of annual data comesat a cost, though: either the reduction of the cross-sectional dimension of the dataset (Wolf, 2008;Mathy and Meissner, 2011) or the focus on a particular sector (see Accominotti, 2012, for thefinancial sector). Wolf (2008, p. 389) notes that the selection of countries is mainly determinedby the availability of key variables necessary for analysis. The most important key variable forthis type of research, though, is an economic activity or business cycle indicator, which is rarelyavailable. Our novel dataset provides a remedy for this problem.

The third dimension in which we expand the dataset is the representativeness of the businesscycle indicator. Clearly, pig iron, steel, and coal production contain information about the currentstate of the economy. Bank clearings, employment numbers and many other indicators do so as

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well. For agricultural countries, however, bacon exports might be more important than machineryproduction, whereas the opposite is likely to be true in industrialised countries. We therefore usea large number of disaggregated indicator series, and employ Principal Component Analysis as asimple but powerful statistical aggregation procedure to estimate business activity in the respectivecountries.

We collected the time series from two publications by the German official statistical office,the Statistische Reichsamt (“Statistisches Handbuch der Weltwirtschaft,” 1936, 1937), which wascompiled to give an empirical overview on the world economy.1 It bears much resemblance torelated NBER publications (Burns and Mitchell, 1946), but differs in its global coverage. Wecan estimate business activity indices for 17 countries from a total of 415 series of monthly datacovering ten to twelve years. More than 55,000 data points now provide us with a much betterestimate of the state of the economy in the countries of our sample, and thus with a better insightinto global crisis dynamics than ever before. After combining our business activity estimateswith eleven official counterparts contained in either the Handbuch or other sources (see AppendixD.18), the dataset finally covers 28 countries for a period of up to twelve years. After describingthe dataset and methodological aspects of its compilation, the main aims of this paper are thefollowing:

Firstly, we track the course of the Depression on a monthly basis. While in most countriesthe pre-Depression peak took place before autumn 1929, the crisis became more pronounced insome agricultural economies only in 1930 and 1931. A possible explanation would be that theywere hit indirectly by the depression of world trade. Moreover, some countries showed signs of ashort-lived stabilisation in 1930. Such findings illustrate the value of this new dataset. By usingannual data, such developments typically stay behind the veil of temporal aggregation.

Secondly, we show the advantages of the dataset by analysing the determinants of businesscycle comovement. We find that the gold standard had indeed a positive impact on comovement,and that exchange controls had a negative one. If it had been the policy-makers’ goal to make theirrespective countries more independent from the world economy, they succeeded. As a comparisonwith earlier evidence based on a smaller empirical base (Mathy and Meissner, 2011) shows, thisnew result is most likely due to the improved quality and quantity of our new data.

In sum, this paper shows the merits of taking all three data dimensions into account: (i) cross-section, (ii) time-frequency, and (iii) representativeness. The next section discusses the data, whilethe treatment of data is explained in Section 3. Thereafter, Section 4 discusses the course of themonthly business activity indices, and then replicates earlier work on business cycle comovementwith the novel data. Section 5 concludes.

1We are deeply indebted to Nikolaus Wolf, who pointed us towards this source.

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2. A new monthly dataset for the interwar period

This section presents the key features of the disaggregated data that we employ for the businessactivity estimates. The disaggregated dataset covers 17 countries and 415 indicator time series.Each of them contains up to 144 monthly observations. This yields a total of more than 55,000observations. All series have been manually transcribed from Statistische Reichsamt (1936, 1937).These collections of interwar economic data provide an exceptional source for research on theGreat Depression, covering the period 1925–1936. The Statistische Reichsamt gathered the datafrom the national statistical offices and from publications such as The Economist, Lloyds Registerof Shipping, and publications by private banks.

TABLE 1: SUMMARY TABLE - DISAGGREGATED DATA

Country Time Number Real Nominal Real Trade Prices Money &Period of Series Indicators Indicators Economy Trade Prices Banking

Gold BlocBelgium 1925–1936 24 12 12 10 7 2 5Lithuania 1925–1936 22 8 14 2 10 3 7Netherlands 1925–1936 32 11 21 12 2 2 16Switzerland 1925–1936 30 6 24 5 7 3 15

Sterling BlocAustralia 1926–1936 18 5 13 1 12 2 3Finland 1925–1936 30 11 19 5 9 2 14India 1925–1936 21 12 9 4 11 3 3New Zealand 1926–1936 11 6 5 3 5 2 1South Africa 1925–1936 24 9 15 8 2 2 12

Exchange Controls BlocBulgaria 1926–1936 17 2 15 2 2 5 8Hungary 1925–1936 35 13 22 5 11 8 11Italy 1925–1935 33 18 15 7 14 1 11Latvia 1926–1936 26 11 15 6 9 2 9Romania 1925–1936 20 8 12 4 6 1 9

Other Countries with Depreciated CurrenciesChile 1927–1936 14 8 6 6 4 0 4Estonia 1925–1936 23 9 14 4 7 3 9Japan 1925–1936 35 11 24 9 9 2 15

Total 415 160 255 93 127 43 152

Sources for bloc composition: See Appendix B. For Italy the data is only available until June 1935.

Table 1 provides an overview of the included countries. The series availability varies by country.This may induce a selection bias. One could, however, also interpret it as a “qualitative filter.”Those covered are likely to have been more relevant for the economy than those that have notbeen documented. As the number of series per country usually lies between 20 and 30, the filterinterpretation seems to be more convincing.

Besides the time period and the total number of series per country, the table summarises thevariables by their type in two ways. The first distinction is between “real” and “nominal” variables.This indicates whether or not the variable could be affected by price changes. Real indicatorsinclude those that are based on simple counting (such as number of individuals unemployed) or

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given in some kind of metric measure (such as tons of goods transported on railways). The columnfor nominal indicators counts all variables that are either given in the respective currency, that areprice indices or that resemble any kind of interest rate. Whenever a variable was given as an indexnumber, it was checked whether this index was based either on a real variable such as employmentor on a nominal variable such as stock turnover in the respective currency. Out of the 415 variables,about 40% are real indicator variables and 60% are nominal indicator variables.

It is quite important to have such a balanced setup. While economists generally believe inthe importance of the price mechanism, some might argue that prices in the interwar period areparticularly biased because of (bad) central banking. Others might argue that prices are indeeda good indicator and especially in the interwar period, in which central banks did not imposemeasures such as quantitative easing. Hence, this paper takes nominal indicators into account, butconsiders it important that there is a substantial fraction of real indicators in the sample.

Another possible characterisation of the variables is a sectoral one.2 Each variable was assignedto one of the four sectors: (i) real economy [22.4 %], (ii) trade [30.6 %], (iii) prices [10.4 %] or(iv) money and banking [36.6 %]. The real economy group includes series such as transporton rails and ships, unemployment numbers, agricultural production, and mining indices. Thetrade group includes only variables that are either exports or imports. The price group subsumesconsumer and wholesale price indices. Finally, the money and banking group includes all variablesrelated to either (central, public, or private) banks or the stock market. It is notable that thedistribution between the groups is quite balanced.

3. Data treatment and aggregation

3.1. Seasonal adjustment

Clearly, seasonality becomes a concern with such heterogeneous monthly data. The first step inpreparing the data for further estimation is thus to remove seasonal variation from the series. Thissection elaborates on the motivation for and the procedure of seasonal adjustment.

Not excluding seasonality could bias the aggregate, because some seasonality patterns mightwork in different directions. For example, retail sales usually increase just before Christmas, whileemployment is usually highest in summer. Since the included indicator series vary by country, thisbias would become even more relevant. A further potential bias emerges, because the degree ofseasonal variation may differ between series. Figure 1 illustrates this potential bias. The left panel

2At this point, we follow more or less the grouping suggested by Statistische Reichsamt (1936, 1937), althoughminor regroupings were considered necessary. Instead of putting the number of bankruptcies into “Zahlungss-chwierigkeiten,” or payment problems, it was added to the real economy group. The same is true for unemploymentnumbers.

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(a) Total Unemployment (Belgium) (b) Private Market Rate (Belgium)

Figure 1: Seasonal Adjustment of Indicators

shows Belgian unemployment numbers. For example, the seasonally adjusted series suggeststhat unemployment was increasing towards the end of 1935, when it was in fact decreasing. Incontrast, the Belgian private market rate exhibits almost no seasonal pattern (Figure 1(b)). Ifone standardises and aggregates the raw data, seasonal unemployment patterns might obscurevariations in the private market rate.

We apply a S3×5 seasonal filter to each of our series. This procedure is based on a movingaverage and allows the seasonal component to vary over time (see Mathworks, 2013). This isespecially important for longer time spans than ours. For example, one can think of the seasonalityof wheat yields before and after the emergence of irrigation systems. However, it might even berelevant for our case, considering e.g. the endogeneity of agricultural production and prices ofagricultural goods.3

In sum, seasonal adjustment eliminates a potential bias that could arise because of the heteroge-neous nature of the dataset. Furthermore, if the data exhibit no seasonal patterns, it would remainunchanged by applying the procedure described above. Thus there is no good reason for keepingthe seasonal components.

3.2. Detrending

After having carried out the seasonal adjustment procedure, one could estimate the principalcomponents analysis (PCA) with either trended or stationary data. Since PCA ultimately restson an estimated covariance matrix, one might argue that applying it to non-stationary data mightyield spurious results. On the other hand, we want to filter out common trends and do not make

3A detailed documentation of this procedure can be found in (Mathworks, 2013).

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any causality statements. Nevertheless, de-trending the data before estimating the principal com-ponents is certainly the more robust way in a strict econometric sense. Like many authors, we usethe Hodrick-Prescott Filter (HP-filter henceforth) to separate the cycle from the trend (Hodrickand Prescott, 1997).

However, there are two particular issues connected with this: (i) Our monthly dataset spansabout one business cycle which is by its very nature extremely volatile. It is actually the mainaim of this paper and the literature it belongs to to document the crisis from peak to trough andback and not filter out whatever a statistical procedure may typically label as “trend.” Therefore, amechanical application of typical filters needs to be reconsidered.

(ii) The differing degrees to which the individual series were hit by the crisis render making theindividual series stationary particularly cumbersome. Instead of “adjusting the Hodrick-PrescottFilter for the frequency of observations” (Ravn and Uhlig, 2002), adjusting the HP-filter for thenature of the indicator was therefore necessary in some cases. This is because the Depression dataprovokes a critical problem that might not be relevant in other applications of the HP-filter: Itsinability to induce mean stationarity to all series with the most common filter parameters.4

To distinguish between the cyclical and the trend component, first differencing would be thestandard way to ensure stationarity, unless the underlying trend has a polynomial far higher thanone. However, one reason for the emergence of other filter techniques is the loss of informationassociated with such a way of filtering. Canova (1998) provides an extensive overview aboutthe discussion, while A’Hearn and Woitek (2001) provide a focused discussion in the case ofhistorical business cycles, albeit for annual data. It is beyond the scope of this article to discussthe filter’s ability to transform a signal to a stationary series, while leaving its information contentunchanged at the desired frequencies. Instead, we only focus on the ability to transform the signalto a stationary one at all, since this is the primary aim of filtering, and has to be ensured beforediscussing how well the filter performs beyond that task. Thus, when choosing the smoothingparameter λ for the HP-filter, we adjust it to the outcome of a unit root test ex post.

Figure 2 illustrates the rationale for our concerns. It shows a stock market index and totalunemployment in the Netherlands. Each of the panels contains the seasonally adjusted originaldata, the trend and the cyclical component separated by the HP-filter with the smoothing parameterλ = 129, 600. In Figure 2(a) the filter with λ = 129, 600 is unable to induce stationarity to thecyclical component as the mean of the cyclical component changes over time, and local trends inthe series become apparent (1925–1930, 1930–1932, 1932–1936). The Dickey-Fuller (Dickey and

4Hodrick and Prescott (1997) suggest λ = 14, 400 for monthly data, while Ravn and Uhlig (2002) argue thatλ = 129, 600 for monthly data would be more appropriate, because it performs better in filtering out the same trendfrom the same data at different frequencies.

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(a) Stock Market (Netherlands) (b) Total Unemployment (Netherlands)

Figure 2: Application of the HP-filter using the Ravn-Uhlig Criterion (λ = 129, 600)

Fuller, 1979) test confirms the visual analysis, and is unable to reject the null hypothesis of a unitroot. In contrast, eyeballing and the Dickey-Fuller test find the unemployment series in Figure 2(b)to be stationary.5

In a heterogeneous dataset, a mix of stationary and non-stationary series creates a serious prob-lem for PCA. Because of their higher degree of correlations, the non-stationary series tend todominate the first component. This imposes obviously a problem for our empirical strategy. Thus,we propose an algorithm that initially runs the HP-filter with λ = 129, 600. Thereafter, the algo-rithm runs a Dickey-Fuller test to check for the existence of a unit root. If it is not able to rejectnon-stationarity, the algorithm reduces λ by 10x, where x is an arbitrary value chosen as a com-promise between precision and computing time, and tests again for a unit root, until stationarityis ensured. For this paper, we run the algorithm manually for the (very few) cases, in which theHP-filter with λ = 14, 400 is unable to induce stationarity. The advantage of this approach is thatit is rule-based, and that one could even argue that a “one size fits all” parameter is overly ad-hoc.

In sum, we choose individual smoothing parameters for the trend-cycle decomposition, sincethe “standard” values do not always induce stationarity. Despite the econometric importance ofde-trending, there is also a more substantial question attached to this: In times of a major crisis,what is trend and what is cycle? The trend-cycle decomposition literature usually operates withdecades of annual time series within a growth regime. In our case, between the mid-1920s to themid-1930s, this long run trend was presumably constant for most economies. Thus, the Great

5This problem would be smaller if we took logarithms before de-trending. Unfortunately, there are zero observa-tions in the dataset, e.g. in bankruptcies. An alternative is to replace the zeros by infinitesimal small values and thentake natural logarithms.

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Depression and the recovery afterwards are actually the cycle, not the trend. Yet, all standardsmoothing values advise filtering out large parts of this “trend” – which creates a major paradox.As the next subsection will show, we therefore chose an agnostic aggregation procedure, applicableto stationary and non-stationary data, which leaves the decision of this question ultimately to thereader.

3.3. Aggregation

There are several techniques to aggregate single time series to a business cycle index. Histori-cally, they include averaging, principal component analysis, and various versions of factor analysisincluding classical and Bayesian dynamic factor analysis (Stock and Watson, 2002; Kose et al.,2003).

Any form of averaging is certainly the most basic method of aggregation. To choose appro-priate weights, however, is quite a challenging task, especially in hindsight. A discussion in TheEconomist (1933) about its own business activity index illustrates such concerns. A previous ver-sion of the index, an arithmetic average, was not considered satisfactory: “On reflection, however,it was thought that although the general trend of the curve might be accurate enough, some of itsdetailed fluctuations were undoubtedly distorted by the sudden movement of one or more serieswhose importance in the economic life of the country was considerably exaggerated by the lack ofweighting.” After taking into account several measures of the importance and usefulness of everyparticular series, experts assigned weights to the 18 series and calculated a weighted arithmeticaverage (The Economist, 1933, p. 8).

While this kind of aggregation is certainly a good approximation for business activity, it re-quires much expert knowledge of the particular time period and country. Such an approach to gen-erate business indices is still not unusual, as Klovland’s (1998) estimates for the Nordic industrialproduction show.6 It does, however, not seem applicable for the scope of this paper: Estimatingbusiness indices for 17 countries. In general, it is questionable how well a historian can performsuch a weighting compared to contemporary experts.

As opposed to this ad-hoc procedure, there are a variety of agnostic procedures to estimatebusiness cycles such as PCA (Rhodes, 1937).7 Today, PCA is a usual tool in econometrics and partof many textbooks (see e.g. Jolliffe, 2002). In a nutshell, it aims to explain the variance in a dataset

6For example, Mathy and Meissner (2011) employ Klovland’s estimates.7As Mitchell et al. (2012, p. 545) note, Stock and Watson (2002), while not aware of Rhodes’ suggestion, brought

this methodology back on the agenda. For the British interwar business cycle, Rhodes (1937, p. 37) shows thegoodness of the fit of his approach compared to the index constructed by The Economist. Appendix A provides a casestudy on Belgium, illustrating that PCA is likely to reflect the state of the economy similarly but better than industrialproduction indices.

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of n indicator variables (our data for one country) by using n eigenvectors - the so-called principalcomponents. These are derived from an eigenvector-eigenvalue analysis of the variance-covariancematrix8 of the observables, which is standard linear algebra. The first component explains the mostof the variance, the second the second most and so on. Following this idea, if one employs thetransformed variance-covariance matrix to predict the first component over time, it would resemblean index of business activity.9 This methodology is arguably applicable to non-stationary data aswell under certain circumstances (see e.g. Hall et al., 1999). We do not aim at settling this debatehere but agnostically offer aggregates obtained from stationary and non-stationary data instead.Our regression analysis, however, will be strictly applied to stationary series.

4. Comovement during the depression

In this section we present results from the analysis of the new dataset. The first part is a shortqualitative discussion of evidence on the timing and nature of the Great Depression. The secondpart is a structural analysis of the relationship between business cycle comovement and monetarypolicy during the Great Depression. It replicates the empirical setup of Mathy and Meissner (2011)and makes use of the new dataset, which yields new results.

4.1. Timing

This section’s purpose is to date the Great Depression in a more accurate way than it has beendone before. We accomplish that by interacting two data dimensions. Firstly, we employ monthlyinstead of quarterly or annual data. Secondly, the cross-sectional dimension covers 28 countries,including 17 estimated business indices and 11 official indices of either industrial productionor business activity (see Appendix D.18). The focus of this exercise lies on the estimates withnon-stationary data for reasons stated above. Three preliminary findings emerge. First, the pre-Depression peak occurred in almost all countries in 1929. Second, the Great Depression hit someagricultural economies more severely as late as 1930 and 1931. Finally, some countries showed ashort period of stabilisation in 1930 before continuing their economic free fall.

Table 2 provides a simple overview of the peaks and troughs for the period 1929–1934. Werestricted the search routine to find peaks between 1929 and 1931 and troughs between 1929 and1934.10 In France, Lithuania, India, Denmark, Sweden, Bulgaria, and Estonia the pre-Depressionpeak took place in 1930 (Table 2, column 1). For almost all other countries, it occurred in the

8Naturally, one standardises the data before estimating the covariances.9In some cases, the second rather than the first component reflects business activity (see Appendix C).

10We excluded 1935 and 1936 because idiosyncratic events such as the strikes in the heavy industries in Belgiumwould bias the overall picture otherwise (see The Economist (1936) and Appendix A).

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first two quarters of 1929. Notably, no country experienced its pre-Depression peak in the directaftermath of “Black Thursday,” (October 24).11 In most countries, the economy was already ina downturn when the New York stock market crash hit the world economy. In sum, the pre-Depression peaks indicate that the Great Depression started in 1929 in most countries. However, itbecame more pronounced, especially for agricultural economies, in 1930 and 1931 (see AppendixD for the particular indices). Most likely, this is due to the collapse of world market prices foragricultural goods and the onset of protectionist measures. Leaving an econometric assessmentaside at this stage, it provides some evidence for Madsen’s (2001) hypothesis that agriculturemarkets constituted an important transmission channel for the Great Depression.

TABLE 2: PEAKS AND TROUGHS IN THE GREAT DEPRESSION, 1929–1934

Country Pre-Depression Peak Depression Trough Start of 1930(1929–1931) (1929–1934) Recovery Stabilisation

non-stationary stationary non-stationary stationaryGold BlocBelgium 1929/6 1929/6 1932/7 1933/5 1935 yesFrance 1930/2 1931/10 1932/7 1933/7 1935 -Lithuania 1930/12 1930/12 1934/12 1934/5 1935 yesNetherlands 1929/5 1929/8 1932/7 1934/3 - -Poland 1929/1 1929/4 1933/3 1934/3 1933 yesSwitzerland 1929/4 1931/4 1934/11 1934/4 - -

Sterling BlocAustralia 1929/1 1929/11 1933/3 1932/8 1933 -India 1930/3 1929/11 1931/9 1932/9 1931 -Denmark 1930/7 1930/6 1932/7 1934/3 1932 -Finland 1929/1 1930/7 1932/3 1934/6 1932 yesGreat Britain 1929/7 1929/7 1932/9 1934/1 1932 yesNew Zealand 1929/5 1930/6 1932/8 1934/2 1933 -Norway 1929/8 1929/8 1931/9 1932/6 1932 yesSouth Africa 1929/7 1931/9 1932/7 1933/10 1932 -Sweden 1930/1 1930/1 1932/7 1934/6 1932 -

Foreign Exchange Controls BlocAustria 1929/8 1929/8 1933/4 1934/3 - -Bulgaria 1930/1 1929/4 1934/7 1932/6 - -Czechoslovakia 1929/5 1929/5 1933/3 1934/4 1933 yesGermany 1929/4 1929/11 1932/8 1934/3 1932 yesHungary 1929/4 1929/4 1933/8 1932/2 1934 yesItaly 1929/9 1929/7 1932/5 1933/7 1932 -Latvia 1929/5 1931/1 1932/7 1933/9 1932 -Romania 1929/1 1930/8 1933/1 1932/12 - -

Other Countries with Depreciated CurrenciesCanada 1929/1 1929/1 1933/2 1934/5 1933 -Chile 1929/3 1929/4 1932/5 1933/6 1932 -Estonia 1930/3 1931/6 1933/4 1934/6 1933 -Japan 1929/7 1929/7 1931/11 1932/12 1932 -UnitedStates 1929/7 1929/7 1932/7 1933/7 1933 yes

The timing of the Depression troughs varies considerably throughout the sample. For example,Great Britain’s business activity index touched bottom only in September 1932, which is some-what at odds with the belief that the economy recovered immediately after the suspension of the

11See Temin (1991, p. 46) for a discussion on the irrelevance of the stock market crash for the start of the GreatDepression.

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gold standard in September 1931. It does not necessarily contradict the idea that countries leavingthe gold standard early recovered faster (Eichengreen and Sachs, 1985), since reaching the troughdoes not mean entering the recovery. Some countries, although they hit bottom at some pointbetween 1931 and 1934, remained depressed throughout the entire period under consideration.Furthermore, on average members of the Sterling bloc and other countries with depreciated cur-rencies hit bottom earlier than their counterparts from the two other blocs,12 which is consistentwith the Eichengreen-Sachs hypothesis. The longest-lasting recessions occurred in Switzerland,Lithuania, and Bulgaria, which reached the trough only in 1934.

Eyeballing of the individual indices (see Appendix D) suggests that a small period of stabilisa-tion in 1930 occurred in ten countries (Table 2, column 6). This does not necessarily imply that theeconomic downturn did not continue, but at least it lost pace for up to six months. In most casesthe recessionary slump picked up its old pace in 1931 or even before. There is no clear pattern interms of bloc membership, though. A possible explanation for this short-lived stabilisation is thatthe rise of protectionist policies hindered this stabilisation to continue (Eichengreen and Irwin,1995, 2010).

It turns out that, if recovery occurred at all up to 1936, it started the latest in the gold bloc(with Poland being the outlier). In contrast, the Sterling bloc, consisting of the British Empireand the Nordic countries, entered the recovery quite early. The results do not suggest that foreignexchange controls were an impediment to the start of the recovery: Four of eight foreign exchangecontrols bloc members started to recover in 1932 or 1933. The fact that most members of thegroup “Other Countries with Depreciated Currencies” imposed exchange controls as well andstarted their recoveries in 1932 or 1933 supports this finding. This questions Eichengreen andIrwin’s (2010, p. 894) assessment that exchange controls were an “ultimately futile effort to stemthe decline in output and rise in unemployment.”

In sum, while we did not look at the intensity of the recovery, its dating yields three interestingresults. It supports the idea that adhering to gold standard postponed the recovery (see e.g. Eichen-green and Sachs, 1985). Furthermore, exchange controls do not seem to have been an impedimentto recovery per se. Finally, the Great Depression hit some agricultural economies more severelyonly in 1930 and 1931.

4.2. The role of exchange controls revisited

This section illustrates another application of our dataset. It replicates recent work by Mathyand Meissner (2011), while making use of the greater number of observations. Mathy and Meiss-

12The column showing the depression troughs based on the analysis of stationary data deviates slightly, especiallyin the British case. Because it omits the downward trend for two years, a small interruption of the recovery in January1934 shows up as the low point of the business cycle.

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ner (2011) analyse business cycle comovement during the Great Depression and find a positivecorrelation between business cycle comovement and fixed exchange rate regimes. They also find apositive effect of exchange controls on business cycle comovement (Mathy and Meissner, 2011, p.371), which is rather counter-intuitive. A potential explanation offered by them is sample selectionbias. With the larger sample of 26 countries for a ten-year period,13 we are able to show that theirfinding on exchange controls is indeed a result of their small cross-section. When expanding thenumber of countries, which is now possible, exchange controls lead to international disintegrationof business cycles.

The remainder of this section is organised as follows. Firstly, we present Mathy’s and Meiss-ner’s empirical approach. Secondly, we discuss differences in our empirical setup. Thereafter, weelaborate on the results. Finally, we conclude and discuss what else may be done to assess businesscycle comovement during the interwar period based on the new dataset.

4.2.1. The Method - Mathy and Meissner’s (2011) Empirical Setup

Since this section starts with a replication exercise, the following is in part a summary of themethods section in Mathy and Meissner (2011). Mathy and Meissner (2011, p. 366) define thedependent variable as correlation between two countries’ industrial production indices. Theirbaseline sample includes ten countries and covers the period 1920–1938.14 They split up theirsample period in nine two-year periods and estimate the respective correlations for those periods.This yields a panel structure to employ the following regression equation:

ρijt = Xijtβ + γjt + µjt + δt + εijt, (4.1)

where ρijt is the bilateral correlation of countries i and j during the two-year-period t, and X is aset of exogenous variables. γjt and µjt represent time-varying fixed effects to take out the effect ofidiosyncratic time-variant policy changes. δt controls for period-specific shocks and ε representsthe pair-specific error term. In some instances, they also include pairwise country fixed effects.

As independent variables, they employ a measure of bilateral trade activity, a “peg-measure,”and binary indicators for being on gold and exercising exchange controls. The measure of bilateraltrade activity equals bilateral trade flows divided by the sum of two countries’ GDP in the firstyear of the two-year period (Mathy and Meissner, 2011, p. 367). To measure the peg, theymodify Shambaugh’s (2004) approach of determining de facto pegs. They first count the number

13We restrict our sample to this period and do not take into account the Chilean and Italian business cycle in orderto have a balanced sample.

14The countries included are: Austria, Belgium Canada, Denmark, France, Japan, Norway, Sweden, the UnitedStates and the United Kingdom. Their broader sample includes six more countries.

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of months in which the exchange rate stayed within a 2%-band and none of the two countriesexercised exchange controls, and then divide the result by 24. They argue that this procedureproxies the time on a de facto peg. In some regressions, they also include a binary variable thatindicates whether both countries were on the gold standard and another that indicates whether atleast one country imposed exchange controls.

4.2.2. Variations and additions

Although we aim to replicate the paper by Mathy and Meissner as closely as possible, wechange the data treatment and empirical setup to enhance the robustness of the results. The fol-lowing section discusses six changes, which include (i) seasonal adjustment, (ii) an adaptationof the HP-filter to crisis data, (iii) an alternative peg measure, (iv) a modification of the foreignexchange controls measure, (v) the choice of different fixed effects, and (vi) the treatment of in-significant correlations. Furthermore, we reduced the length of the respective periods from 24 to18 months, which increases the number periods.

Before going through the list, a general disclaimer may be necessary for the representativenessof our activity indicators: While we estimate indices that describe general business activity, wealso use official industrial production indices for some countries such as the France.15 In conse-quence, we have a somewhat heterogeneous dataset. Since Mathy and Meissner (2011) employonly industrial production indices, their analysis suffers from a similar problem: For three coun-tries out of ten they build indices only based on pig iron and steel production (Mathy and Meissner,2011, p. 366). At the same time, they employ some indices that provide a wider picture of theindustrial sector such as those estimated by Klovland (1998). However, those could also be mis-leading because they measure industrial production in largely agricultural economies, which mightnot accurately reflect general business activity.(i) A major difference in our data treatment is seasonal adjustment (Mathy and Meissner (2011)do not carry out seasonal adjustment). If not correcting for seasonality, countries with similarseasonal patterns such as agrarian countries might exhibit higher correlation than others. Sincewe want to exclude this potential bias, we run a seasonal adjustment procedure on all indices asdiscussed in Section 3.(ii) Mathy and Meissner’s (2011, p. 366f) application of the HP-filter is not reproduced one-by-one. They follow the smoothing parameter setting (λ = 129, 600) suggested by Ravn andUhlig (2002) but as discussed in Section 3.2, stationarity is a major concern for business activityestimates and official indices because if they are not stationary, the pairwise correlations will bespurious. Since the period of interest in Mathy and Meissner (2011) is longer than in our case, this

15See Appendix D.18 for the classification of the official indices.

14

must not necessarily be a problem for their results. Furthermore, they log-linearise the data beforede-trending it. To ensure stationarity of all business activity and industrial production indices, weemploy the value for the smoothing parameter for monthly data λ = 14, 400 suggested by Hodrickand Prescott (1997).16 This procedure circumvents the non-stationarity problem that would haveoccurred if we had applied the smoothing parameter suggested by Ravn and Uhlig (2002).(iii) Mathy and Meissner’s (2011, p. 367) measure of de facto pegs might in our view be harmedby endogeneity. The exchange rate reflects the relative value of two currencies, which depends onrelative price levels that in turn depend on economic activity, amongst others. Hence, if economicactivity moves in the same direction in both countries, relative price levels are likely to moveaccordingly, ceteris paribus, and thus a change in the exchange rate is rather unlikely.

Consequently, we employ a different criterion. We calculate the percentage of time in our 18-months periods in which each country was officially on the gold standard and did not devalue orimpose foreign exchange controls (Bernanke, 1995, p. 8). For each pair, we multiply the tworesulting percentages to calculate our “On Gold” variable. Since these criteria were due to policydecisions, the endogeneity potential described above should not be present.(iv) Mathy and Meissner (2011) employ a binary variable of foreign exchange controls assigning“1” if any country of a pair imposed foreign exchange controls and “0” otherwise. In contrast,we construct the measure as follows: For each country, we calculate the percentage of months ineach 18-months-period in which it imposed foreign exchange controls. We then sum up the twofractions and call the variable “FX Controls.” This procedure accounts for differing degrees ofexchange controls exercised by each country pair.(v) We are still in the process of collecting bilateral trade series, and therefore do not include avariable for bilateral trade integration such as Mathy and Meissner (2011, p. 367). However,since bilateral trade flows turn out to be very persistent over time, the application of country-pairfixed effects compensates to a large extent for this (Mathy and Meissner, 2011, p. 368).17 Fur-thermore, country-pair fixed effects control for slow-changing bilateral policies or other persistentphenomena (Mathy and Meissner, 2011, p. 368).(vi) Finally, our large sample enables us to acknowledge the fact that the dependent variable isitself an estimate and set correlations to zero, when their significance level is lower than 0.85. This

16In three cases, it was necessary to decrease the smoothing parameter λ even more to induce stationarity. Thosecases are Czechoslovakia (λ = 7000), France (λ = 400), and South Africa (λ = 1500). See Section 3.2 for adiscussion of this strategy.

17As a robustness check, Mathy and Meissner (2011, p. 368) perform a regression with country-pair fixed effects.They comment on the insignificant coefficient of the trade variable as follows: “given the strong persistence of bilateraltrade relationships, the coefficient on trade is no longer significant.” On the persistence of trade flows, see alsoEichengreen and Irwin (1998) and Wolf and Ritschl (2011).

15

provides an important robustness check, because including insignificant correlations may lead tounderstated standard errors. As it turns out, however, the results do not change significantly.18

In sum, we change the setup by Mathy and Meissner (2011) in six ways. We thereby account forthe lack of bilateral trade data and emphasise some important aspects of the empirical setup. Themost important change, however, is the number of countries included in our estimations, resultingin 325 country pairs for each of the 7 periods. Such a large number of observations facilitatethe application of country-pair and individual time-varying country fixed effects at the same time,which further improves the robustness of the results.19

4.2.3. Results

This section presents the regression results. The focus lies on two sub-periods, July 1927 –December 1931 and January 1932 – June 1936, since foreign exchange controls were only intro-duced in the 1930s. Furthermore, pairs that effectively stayed on gold after 1932 constitute lessthan 5% of the whole sample. Table 3 reports the results of the baseline specification, includingcountry-pair fixed effects and individual time-varying country fixed effects. Apart from “On Gold”and “FX Controls” as explained above we also report “Av. CP FE,” the average value of countrypair fixed effects that gives an impression of the level of bilateral comovement during the periodin question, which rests on pair-specific factors such as trade. The three columns marked with astar show results for the same specification, but treating insignificant correlations as zeros.

There are two important findings from this exercise: First, we find a positive and significanteffect for being on gold during the heyday of the interwar gold standard (1927–1931), and second,exchange controls effectively worked against business cycle comovement.

On the first result, the magnitude of the coefficient “On Gold” is with 0.21 about the same as inMathy and Meissner (2011, p. 370) with 0.18. However, the estimate is insignificant in their setup.The result is robust against interpreting insignificant pairwise correlation as zeros (column 1*). Itdoes neither harm the magnitude of the coefficient nor its preciseness. Mathy and Meissner (2011,p. 370) argue that the common shock of the Depression might explain most of the variance, andthus being on gold might therefore be found to be insignificant. However, if the gold standard wasan important transmission channel for the global crisis, the coefficient on gold should be significantfor this sub-period, too. Thus we think the small sample is more likely to be the reason for theimprecise estimation.20 For the full period 1926–1936, we find gold to be significant as well. It

18Of course, setting the correlation to zero is neither the most appropriate way. A proper approach would buildon a two-stage framework, correcting the standard errors in the comovement regression for the impreciseness of thepairwise correlation estimate.

19See Baldwin and Taglioni (2006) on this particular choice of fixed effects.20Moreover, Mathy and Meissner (2011, p. 370) find their peg measure constructed from exchange rates to be

16

Table 3: Regression Results by Period

Insignificant correlations Insignificant correlationsnot treated as zeros treated as zeros

(1) (2) (3) (1∗) (2∗) (3∗)1927-1931 1932–1936 1925–1936 1927-1931 1932–1936 1925–1936

On Gold 0.211∗ 0.178 0.142∗∗ 0.212∗ 0.0408 0.068(0.117) (0.117) (0.063) (0.110) (0.112) (0.063)

FX Controls -0.187∗∗ -0.197∗∗ -0.191∗∗ -0.145∗

(0.095) (0.095) (0.085) (0.086)

Av. CP FE 0.0304 0.210∗∗∗ 0.148∗∗ 0.001 0.218∗∗∗ 0.141∗∗

(0.075) (0.076) (0.060) (0.071) (0.069) (0.058)

Fixed Effects

Country-Pair Yes Yes Yes Yes Yes Yes

TV Country Yes Yes Yes Yes Yes Yes

N 975 975 2275 975 975 2275adj. R2 0.177 0.172 0.125 0.161 0.138 0.109

Dependent variable is the pairwise correlation of the cyclical components of the business activity indices.

Standard errors in parentheses; ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

does not survive our robustness check in column 3*, though. The reason is fairly simple. Sincethere are very few country pairs after 1935 (namely the gold bloc, consisting of five countries), thesample becomes too small for a reasonable estimate – outliers would dominate the picture. Thus,being on the gold standard increased comovement, especially during the crisis, which is in linewith the idea that the gold standard transmitted shocks throughout the world.

The second important finding is that the coefficient of “FX Controls” indicates that exchangecontrols effectively reduced business cycle comovement. If policymakers aimed to make theircountry more independent from global influences after the start of the crisis, this policy instrumentsuited its purpose. The finding is robust for treating insignificant correlations as zeros. In contrastto the gold standard variable, it survives even in the full period sample, although there were noexchange controls before 1930 in our sample.

There might be a multicollinearity problem between the foreign exchange controls variable andthe time-varying country fixed effects, though. However, in a robustness check, which includes

significant for the entire period but not for the heyday of the gold standard. This may suggest that unofficial andsemi-official pegs against pound and dollar after 1931 (see League of Nations, 1940, p. 219) drive the coefficient.

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time and country fixed effects, multicollinearity seems to be no problem (not shown). The foreignexchange control coefficient for the sub-period 1932–1936 remains stable, whereas the gold coef-ficient becomes significant and larger. This result is at odds with Mathy and Meissner (2011, p.370), who find a positive effect of exchange controls on business cycle comovement. They arguethat their result might be due to the inclusion of many Reichsmark bloc members. We can onlyspeculate that it might also be due to the binary nature of their exchange controls variable, or thesmaller sample compared to ours.

In sum, our analysis employing the new dataset yields two results that more easily reconciletheory and empirics of the international effect of the Great Depression than some previous work.For the heyday of the gold standard, we find a positive and significant influence of being on goldon economic comovement. Furthermore, the introduction of foreign exchange controls indeed ledto a decrease of comovement in the 1930s. If it had been the policymakers’ goal to make theireconomies more independent from recessionary dynamics in the aftermath of the Great Depres-sion, they in fact succeeded.

5. Conclusion

This paper contributes to the literature on the Great Depression in three ways. Firstly, it showsthe course of the Depression in different countries. Secondly, it analyses business cycle comove-ment during the Great Depression. Thirdly, and most importantly, this paper provides a noveldataset for research on the Great Depression. It allows researchers for using more frequent andmore representative business cycle indicators for more countries than ever and thus enables themto empirically analyse hypotheses that were nearly impossible to test up to now.

The need for this new dataset can be deduced from the historiography of the Great Depression.The Friedman-Schwartz view and the Gold Standard Literature differ in their regional focus andmethodological approach. The Gold Standard Literature took a larger number of countries intoaccount than the previous research and thus extended the cross-sectional dimension of the analy-sis. The merits of this literature are well known, but at the same time, the cross-sectional focusobscured another dimension of datasets – the time-frequency dimension. The few articles of whatwe call the Post-Gold Standard Literature show the merits of a larger time-frequency dimension,but either suffer from a smaller cross-sectional one or focus on a particular sector of the economy.The data underlying this paper expand all three dimensions, cross-sectional, time-frequency aswell as representativeness.

This paper demonstrates two fruitful applications of the new dataset. It describes the course ofthe Depression more accurately than before. While pre-Depression peaks took place in the firsthalf of 1929 in most of the 28 country cases, the Depression hit some countries as late as 1930

18

or the beginning of 1931, especially agricultural economies. This is consistent with the literatureon intensified protectionist measures from 1930 and 1931 onwards (e.g. Eichengreen and Irwin,2010). Moreover it supports the idea that the agricultural sector was an important transmissionchannel of crisis dynamics (Madsen, 2001).

The second application is a structural analysis concerning crisis spillovers, an important eco-nomic policy question. For the heyday of the gold standard, we find a positive effect for goldadherence on business cycle comovement. Moreover, we find that by restricting foreign capitalexchange in the 1930s policy-makers succeeded in making their countries more independent fromthe world economy – assuming this was indeed their goal. These results profit substantially fromthe broader and more frequent dataset we employ, as a comparison with Mathy and Meissner(2011) indicates.

The future agenda for this line of research is the following. Firstly, one should analyse theDepression in agricultural economies more deeply. Secondly, one should employ a more formalapproach for “dating the Depression.” Finally, the dataset facilitates a reasonable application oftime series econometrics to a large number of countries in order to intensify research on the prop-agation of the Great Depression.

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6. References

Accominotti, O., 2012. Asymmetric propagation of financial crises during the Great Depression. Modern and com-parative economic history seminar, January 26, 2012, London School of Economics and Political Science. Onlineat http://eprints.lse.ac.uk/41704/ (accessed on 16.7.2013).

A’Hearn, B., Woitek, U., 2001. More international evidence on the historical properties of business cycles. Journal ofMonetary Economics 47, 321–346.

Baldwin, R., Taglioni, D., September 2006. Gravity for dummies and dummies for gravity equations. Working Paper12516, National Bureau of Economic Research.

Bernanke, B., 1995. The macroeconomics of the Great Depression: A comparative approach. Journal of Money, Creditand Banking 27 (1), 1–28.

Bernanke, B., James, H., 1991. The gold standard, deflation, and financial crisis in the Great Depression: An interna-tional comparison. In: Hubbard, G. N. (Ed.), Financial Markets and Financial Crises. University of Chicago Press.Chicago, pp. 33–68.

Burns, A. F., Mitchell, W. C., 1946. Measuring Business Cycles. NBER Book Series Studies in Business Cycles.National Bureau of Economic Research. New York.

Canova, F., 1998. Detrending and business cycle facts. Journal of Monetary Economics 41 (3), 475 – 512.Dickey, D. A., Fuller, W. A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal

of the American Statistical Association 74 (366a), 427–431.Eichengreen, B., 1992. Golden fetters. The gold standard and the Great Depression, 1919–1939. Oxford University

Press. New York.Eichengreen, B., Irwin, D., 1995. Trade blocs, currency blocs and the reorientation of world trade in the 1930s. Journal

of International Economics 38, 1–24.Eichengreen, B., Irwin, D., 1998. The role of history in bilateral trade flows. In: The regionalization of the world

economy. National Bureau of Economic Research, pp. 33–62.Eichengreen, B., Irwin, D., 2010. The slide to protectionism in the Great Depression: Who succumbed and why?

Journal of Economic History 70 (4), 871–897.Eichengreen, B., Sachs, J., 1985. Exchange rates and economic recovery in the 1930s. Journal of Economic History

45 (4), 925–946.Federal Reserve Board, 1928. Annual report of the Bank of Italy. Federal Reserve Bulletin July, 488–497.Friedman, M., Schwartz, A. J., 1963. A monetary history of the United States, 1867–1960. Princeton University Press.

Princeton.Hall, S., Lazarova, S., Urga, G., 1999. A principal components analysis of common stochastic trends in heterogeneous

panel data: Some monte carlo evidence. Oxford Bulletin of Economics and Statistics 61 (S1), 749–767.Hodrick, R. J., Prescott, E. C., 1997. Postwar us business cycles: an empirical investigation. Journal of Money, Credit,

and Banking, 1–16.Jolliffe, I., 2002. Principal component analysis. Vol. 2. John Wiley & Sons.Karnups, V. P., 2012. The 1936 devaluation of the Lat and its effect on Latvian foreign trade. Humanaties and Social

Science Latvia. (1), 49–62.Klovland, J., 1998. Monetary policy and business cycles in the interwar years: the Scandinavian experience. European

Review of Economic History 2 (3), 309–344.Kose, A., Otrok, C., Whiteman, C. H., 2003. International business cycles: world, region and country-specific factors.

American Economic Review 93 (4), 1216–1239.

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League of Nations, 1940. Statistical Yearbook 1938–39. Economic Intelligence Service. Geneva.Madsen, J. B., 2001. Agricultural crises and the international transmission of the Great Depression. The Journal of

Economic History 61 (2), pp. 327–365.Mathworks, 2013. Seasonal adjustment using sn×m seasonal filters. Online at

http://www.mathworks.de/de/help/econ/seasonal-adjustment-using-snxd7m-seasonal-filters.html.Mathy, G. P., Meissner, C. M., 2011. Business cycle co-movement: Evidence from the Great Depression. Journal of

Monetary Economics 58 (4), 362 – 372.Mattesini, F., Quintieri, B., 1997. Italy and the Great Depression: An analysis of the Italian economy,1929–1936.

Explorations in Economic History 34 (3), 265–294.Mitchell, J., Solomou, S., Weale, M., 2012. Monthly GDP estimates for inter-war Britain. Explorations in Economic

History 49 (4), 543–556.Ravn, M. O., Uhlig, H., 2002. On adjusting the Hodrick-Prescott filter for the frequency of observations. Review of

Economics and Statistics 84 (2), 371–376.Rhodes, E. C., 1937. The construction of an index of business activity. Journal of the Royal Statistical Society 100 (1),

18–66.Shambaugh, J. C., 2004. The effect of fixed exchange rates on monetary policy. The Quarterly Journal of Economics

119 (1), 301–352.Statistische Reichsamt, 1936. Statistisches Handbuch der Weltwirtschaft. Verlag für Sozialpolitik, Wirtschaft und

Statistik. Berlin.Statistische Reichsamt, 1937. Statistisches Handbuch der Weltwirtschaft. Verlag für Sozialpolitik, Wirtschaft und

Statistik. Berlin.Stock, J. H., Watson, M. W., 2002. Macroeconomic forecasting using diffusion indexes. Journal of Business & Eco-

nomic Statistics 20 (2), 147–162.Temin, P., 1991. Lessons from the Great Depression. Vol. 1. The MIT Press. Cambridge.The Economist, 1926. Belgium - government finance - debt valorisation - taxation receipts - trade balance - coal -

bourse. The Economist Historical Archive 1843–2008 27 November 1926, 918.The Economist, 1931. Holland - Dutch East Indies finance - capital issues - money - saving banks - foreign trade. The

Economist Historical Archive 1843–2008 2 May 1931, 945–946.The Economist, 1932. Holland - foreign trade - industry - commercial policy. The Economist Historical Archive

1843–2008 2 Jan 1932, 20–21.The Economist, 1933. An index of business activity. The Economist Historical Archive 1843–2008 Supplement to the

Economist, 21 Oct. 1933, 1–8.The Economist, 1934. A review of Lithuania’s economic situation for the year 1934. The Economist Historical Archive

1843–2008 16 Feb. 1935, 370–371.The Economist, 1936. Strikes in Belgium. The Economist Historical Archive 1843–2008 20 Jun. 1936, 667.Wolf, N., 2008. Scylla and Charybdis. explaining Europe’s exit from gold, January 1928–December 1936. Explo-

rations in Economic History 45 (4), 383–401.Wolf, N., Ritschl, A. O., 2011. Endogeneity of currency areas and trade blocs: Evidence from a natural experiment.

Kyklos 64 (2), 291–312.

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AppendicesA. A Robustness Check - The Belgian Business Cycle, 1925–1936

The following section presents a case study on Belgium. The high correlation with the officialindustrial production index from the Institut des sciences économiques (Statistische Reichsamt,1936, 1937) verifies our use of Principal Component Analysis (PCA), especially for the the repli-cation of Mathy and Meissner (2011). Additionally, we make use of the historical archive of TheEconomist to underpin the validity of our estimates. Hence, this section provides a qualitative andquantitative robustness check for the method. Besides this robustness check, two features of theBelgian interwar experience attracted our attention. Belgium experienced sovereign debt problemsin the mid-1920s, which also led to a destabilisation of the business cycle (The Economist, 1926,p. 918). Furthermore, Belgium stayed on the gold standard until March 1935 (see Appendix B).If the gold standard hampered the economy, releasing the “golden fetters” should have fosteredthe recovery (Eichengreen and Sachs, 1985). We should be able to identify these features of theBelgian interwar experience in the data.

(a) Non-Stationary Data (b) Stationary Data (λ = 14, 400)

Figure 3: Fit of the Indicators - Line

Figure 3 shows the plots of the official index and the business activity estimates based on non-stationary data (Figure 3(a)) and non-stationary data (Figure 3(b)).21 At first glance the correlationseems quite high, while some differences become apparent. Apparently, the official industrial pro-duction index emphasises the downturn in mid-1936 more than the business activity estimate. Whywould this be the case, if both proxy business activity? Contemporary issues of The Economist,covering the period 1925 to 1936, and the gold standard literature shed light on this question.

21For the stationary case, the official index is hp-filtered with λ = 14, 400. We standardised the series such they allhave a zero mean and a standard deviation of 1 to improve the graphic comparability .

22

Hence, before going into detail about the correlation of the official and the estimated indices, it isworthwhile to review some historical background. The period 1926–1927 exhibits strong growth.The political re-orientation towards a balanced budget might have been the reason for this de-velopment (The Economist, 1926). Some institutional “bottlenecks” might have been removedand facilitated rapid growth. This, however, remains speculation. After the economic free fallfrom mid-1929 until mid-1932, the Belgian economy remains stagnant until early 1935. In March1935, Belgium left the gold standard (see Appendix B). A quite significant recovery period untilmid-1936 follows, when strikes in the heavy industries occurred (The Economist, 1936). Sincethe business activity index provides a broader picture of the economy, it emphasises the downturnrelated to the strikes in the heavy industries less than the industrial production index does. In con-clusion, narrative evidence and eyeballing underpin the quality of the business activity estimate.

(a) Non-Stationary Data (b) Stationary Data (λ = 14400)

Figure 4: Fit of the Indicators - Scatter Plot

Another way of assessing the fit is to produce a scatter plot and draw a regression line. Aperfect correlation (all dots on one line) would be a surprising result, since we present a widerdefined index than “just” industrial production. If there was no correlation at all, our approachwould have been proven wrong. Figure 4 makes this exercise for PCA applied to stationary dataand to non-stationary data. The scatter plots illustrate that the estimates are good approximationsof the official index, which confirms the impression from the eyeballing exercise earlier on. Thecoefficient for the regression line in Figure 4(a) is 0.95 with a t-statistic of 38.7 and an R2 of theregression of about 0.91. For the stationary case (Figure 4(b)), the corresponding regression yieldsa very significant correlation of 0.83 (t = 18.13) and an R2 of about 0.7.

The outliers in Figure 4(b) are compatible with the previous discussion. The industrial produc-tion index emphasises the strikes in the heavy industries more than the business activity estimate.Another outlier seems quite interesting. In June 1929, the value for the business activity index issignificantly larger than the value of the industrial production index. This is in line with the idea

23

that industrial production indices are leading indicators. While there might have been optimismthroughout the economy in June 1929 (the pre-Depression peak), industrial production did not in-crease in such dimension.22 In sum, the overall fit is quite good. We now turn to the interpretationof the coefficients that we estimated via PCA.

Table 4 shows the coefficients (weightings) for the business index that we estimated via PCA.23

Those resemble the first two columns of the matrix of eigenvectors. The appendix reports such atable for each country. In the following we focus on the coefficients derived by PCA applied tostationary data (last two columns).

TABLE 4: DATA BELGIUM

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2Production, Transport and EmploymentGlas Index 14400 0.24 -0.15 0.12 0.22Coal Tons 14400 0.10 0.25 0.20 -0.20Raw Iron Tons 14400 0.22 0.18 0.30 0.21Raw Steel Tons 14400 0.21 0.19 0.29 0.25Steel Manufacturing Tons 14400 0.24 0.12 0.29 0.24Textiles Index 14400 0.24 0.01 0.14 -0.20Coal Storage Tons 1000 -0.22 0.13 -0.22 -0.23Wool Conditioning Tons 14400 0.19 0.06 0.08 -0.15Total Unemployment Individuals 14400 -0.23 0.16 -0.17 0.31Insured Unemployed in Percent % 14400 -0.23 0.17 -0.17 0.27

TradeTotal Imports Franc 14400 0.25 0.11 0.28 -0.10Total Exports Franc 14400 0.25 0.16 0.32 -0.09Coal Exports Tons 14400 -0.01 0.32 0.08 -0.17Iron Exports Tons 14400 0.18 0.15 0.21 0.15Machines Exports Franc 14400 0.23 0.14 0.19 -0.17Textile Exports Franc 14400 0.26 0.04 0.27 -0.14Glas Exports Franc 14400 0.25 0.02 0.23 0.12

PricesWholesale Prices Index 10000 0.26 0.03 0.29 0.09Consumer Prices Index 10000 0.15 0.33 0.22 -0.14

Money and BankingCurrency in Circulation Franc 5000 -0.17 0.33 0.05 0.05Public Deposits Franc 14400 -0.12 0.11 0.09 -0.11Private Deposits Franc 14400 -0.12 0.24 0.11 -0.18Bank Rate % 14400 0.13 -0.38 0.04 0.34Market Interest Rate % 5000 0.14 -0.37 -0.02 0.37

Explained Variance in % 57.48 19.09 26.72 14.32

The coefficients in Table 4 indicate that heavy industries and prices were quite important forthe business cycle, whereas money and banking played a minor role. This looks quite different forother countries such as Belgium’s neighbour the Netherlands (see Appendix D.13).24 Moreover,

22This remains speculation at this stage of this research project.23It also reports the λ that we used for the HP filter and the units of the original series.24Typically series such as clearings or stock issues have heavy weights, because they are immediately related to the

real economy.

24

coal exports and the wool conditioning industry were not that decisive for the course of the busi-ness cycle. Naturally, both unemployment series have relatively high weights and a negative sign.Hence, if the economy was growing, unemployment decreased - a quite straightforward interpre-tation. The high positive loadings for prices are strongly related to the monetary regime, i.e. thegold standard. It forced Belgium into a severe deflation. In consequence, increasing prices (or atleast the stop of deflation) facilitated the Belgian economy to prosper.

Figure 5: Fit excluding 6 Production Series

Data Source: see Section D.2

Finally, Figure 5 resembles another robustness check for using PCA in this context. Let usassume, there is only one production series available, which is typically coal. We drop the pro-duction of glass, wool conditioning, textiles, raw iron, raw steel and steel manufacturing fromthe sample. How much would the “fit” of the method change? The goodness of the fit decreases(Figure 5). However, considering that we excluded almost all series that represent some form ofindustrial production themselves, the fit is surprisingly good. The underlying reason might be thattrade variables proxy production quite well.

In sum, the Belgian business activity estimate does not only match its official counterpart,but is also consistent with contemporary reports of The Economist. Anecdotal and quantitativeevidence supports the validity of PCA for the aggregation of individual indicator series. It isa good approximation of the industrial production index, even if industrial production indicatorvariables themselves are excluded. Naturally, one could also argue that the estimate captures thestate of the economy better than an industrial production index, which is usually thought of as aproxy itself.

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B. Gold Adherence and Introduction of Foreign Exchange Controls - Overview

TABLE 5: ADHERENCE TO GOLD AND INTRODUCTION OF FOREIGN EXCHANGE CONTROLS

Country Return to Gold Suspension of Gold Stan-dard/Devaluation

Introduction of Foreign Ex-change Controls

Gold BlocBelgium Oct-26 Mar 35- Apr 35 -France Jun-28 Oct-36 -Lithuania Jan-25 - Oct-35Netherlands Apr-25 Oct-36 -Poland Oct-27 Oct-36 Apr-36Switzerland Jan-25 Sep-36 -

Sterling BlocAustralia Apr-25 Dec-29 -India Jan-25 Sep-31 -Denmark Jan-27 Sep-31 Nov-31Finland Jan-26 Oct-31 Oct-1931–Dec-1931Great Britain May-25 Sep-31 -New Zealand Apr-25 Apr-30 -Norway May-28 Sep-31 -South Africa Jan-25 Dec-32 -Sweden Apr-24 Sep-31 -

Foreign Exchange Controls BlocAustria Apr-25 Apr-33 Oct-31Bulgaria Jan-29 - Oct-31Czechoslovakia Apr-26 Feb-34 Sep-31Germany Sep-24 - Jul-31Hungary Apr-25 - Jul-31Italy Dec-27 Oct-36 May-34Latvia Aug-22 - Oct-31Romania Feb-29 - May-32

Other Countries with Depreciated CurrenciesCanada Jul-26 Sep-31 -Chile Jan-26 Apr-32 Jul-31Estonia Jan-28 Jun-33 Nov-31Japan Dec-30 Dec-31 Jul-32UnitedStates Jun-19 Mar-33 Apr-33

Sources include mainly Bernanke and James (1991) and their cited references. In some cases, Statistisches Reichsamt (1936,1937) and League of Nations (1940) couldadd information. Lithuania, Switzerland, India and South Africa entered the gold standard before January 1925. It was not possible to find out the exact date, though.Sources for bloc composition: Eichengreen and Irwin (2010); Bernanke and James (1991); Wolf and Ritschl (2011).

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C. Problems with the Estimation of the Remaining Business Activity Indices

In general both estimates, employing non-stationary data and stationary data, yield plausibleand compatible results. In some cases of the estimation with non-stationary data (Japan, Lithuania,Bulgaria, Italy, Estonia, India), the coefficients and the associated scores for the first componentare implausible. In these instances, the second component turns out to reflect the business cyclemore accurately. This section discusses Japan as the typical case for this problem. It shows thattrends in the banking sector are too dominant in order to interpret the first component as a generalbusiness activity indicator. This finding does only relate to the estimates with non-stationary data.

(a) Non-stationary Data (b) Stationary Data

Figure 6: Principal Components for the Japan

Figure 6 shows the first and second component for Japan. From the comparison with an officialindex that was compiled from Statistische Reichsamt (1936, p. 379) from 1929 on, it is obviousthat the first principal component could hardly reflect the business cycle. The index by the Re-ichsamt has the following yearly averages: 1929: 110; 1930: 102.5: 1931: 102.1; 1932: 117.1.It seems that the Japanese economy was by no means in the downward spiral from 1925 on sug-gested by the first principal component in Figure 6(a). The Depression hit the country mainly in1930 and 1931, with growth recurring from 1932 onwards. The second component suggests thiscourse of the Depression. A glance at the first component in the right panel (stationary estimates)also supports this development of Japanese business activity.

If so, why would the first component in the left panel not reflect business activity as it usuallydoes? The analysis of the coefficients of the loadings in Table 6 sheds light on this question.High loadings for almost all Money and Banking series indicate that the first component ratherreflects some credit tightening than a general business indicator. Moreover, more than half of the

27

Production, Transport and Employment indicators do not have the expected sign and most of theTrade indicator series have the wrong sign, a coefficient near zero or both.

TABLE 6: DATA JAPAN

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2Production, Transport and Employment

Employment (Industry) Index (1926) 500 0.06 0.31 0.15 -0.11Coal Production 1000 t 14400 -0.11 0.27 0.19 0.06Textiles Production Index (1930) 14400 -0.23 0.11 0.16 -0.04Silk Production t 14400 -0.12 -0.09 0.03 0.01Turnover of Warehouses Mill. Yen 14400 0.08 0.28 0.14 0.23Stocks of Warehouses Mill. Yen 7000 -0.13 0.19 0.11 0.11Transported Goods (Railway) 1000 t 14400 -0.12 0.26 0.29 -0.01Wages Index (1926) 10000 0.24 -0.04 0.12 -0.09Real Wages Index (1926) 14400 0.18 0.10 0.27 0.01

Trade

Imports Mill. Yen 14400 0.01 0.31 0.26 0.04Exports Mill. Yen 14400 -0.03 0.31 0.28 0.07Imports - Raw Cotton 1000 t 14400 -0.08 0.10 0.06 0.03Imports - Wool t 14400 -0.17 -0.03 -0.06 0.00Imports - Coal 1000 t 14400 -0.23 0.12 0.13 0.09Imports - Pig Iron 1000 t 14400 -0.18 0.18 0.07 0.03Imports - Machines 1000 Yen 14400 -0.01 0.22 0.23 -0.07Exports - Raw Silk t 14400 -0.08 -0.04 0.00 0.06Exports - Cotton Threads t 14400 0.18 0.12 -0.03 -0.06

Prices

Wholesale Prices Index (Juli 1914) 3000 0.21 0.19 0.27 0.04Consumer Prices (Tokyo) Index (Juli 1914) 6000 0.23 0.17 0.29 0.06

Money and Banking

Gold Stock (Central Bank) Mill. Yen 1500 0.23 0.04 0.14 -0.01Bills of Exchange and Advances (Central Bank) Mill. Yen 14400 -0.18 -0.03 -0.05 0.41Currency in Circulation (Central Bank) Mill. Yen 14400 0.06 0.25 0.13 0.16Deposits by the Government (Central Bank) Mill. Yen 14400 0.23 0.06 -0.06 0.30Deposits by Banks (Central Bank) Mill. Yen 14400 0.04 -0.04 0.18 0.23Clearings Mill. Yen 14400 0.13 0.21 0.14 -0.34Bank Rate (Central Bank) % 14400 0.26 -0.06 -0.14 -0.14Market Rate % 4000 0.23 0.01 -0.17 0.01Market Rate - Demand Deposits % 14400 0.21 -0.02 -0.16 -0.15Clearing Banks - Cash Position Mill. Yen 14400 -0.12 0.08 0.19 0.14Clearing Banks - Stocks Mill. Yen 14400 -0.25 0.08 0.24 -0.10Clearing Banks - Bills of Exchange and Advances Mill. Yen 14400 0.12 0.15 -0.03 -0.40Clearing Banks - Deposits Mill. Yen 9000 -0.22 0.16 0.20 -0.32Giro Banks - Savings Mill. Yen 14400 -0.26 -0.03 -0.15 0.29Stock Emissions Mill. Yen 14400 0.02 0.23 0.04 -0.10

Explained Variance in % % 39.00 26.47 20.87 12.95

Hence, it is important to check the coefficients and then decide whether or not, the first com-ponent could accurately reflect business activity. We could verify our suggestion that credit tight-ening drives the first component by simply excluding Money and Banking series from the estima-tion. Figure 7 shows that our analysis was right. The first principal component scores now exhibitturning points similar to those suggested by the estimate of the first principal component with sta-tionary data (Figure 6(b)) and those suggested by the estimate of the second principal componentwith non-stationary data (Figure 6(a)).

In general, production and employment indicators should have relatively high coefficients andyield the expected signs in order to interpret the particular component as economic activity. Thedevelopment of a proper econometric rule-based framework lies beyond the scope of this paper.

28

Figure 7: Japanese Business Activity Index, 1925–1936

Comment: first principal component excluding Money and Banking indicators.

Eyeballing suggests that the problem occurs only with the business cycle estimates (with non-stationary data) in the cases of Japan, Lithuania, Bulgaria, Italy, Estonia, and India.25 To enablethe reader to judge the validity of this ad-hoc procedure, coefficient tables for each country canbe found in Appendix D. In section 4.1, the second principal component provides a proxy forbusiness activity for the countries mentioned above. For all other countries, we employ the firstone. The model in Section 4.2 relies on the estimates with stationary data. Hence, the problemsdescribed in this section do not apply.

25The Italian case is ambiguous to some extent. A comparison with the index of the Ministry of Corporations (seeMattesini and Quintieri, 1997), however, suggests that the second rather than the first component reflects businessactivity.

29

D. Data - Countries

D.1. Australia

TABLE A 1: DATA AUSTRALIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

AgricultureWheat Stocks 1000 t 14400 -0.19 -0.12 -0.02 -0.06

TradeImports 1000 Pounds 14400 0.30 0.09 0.42 0.03Exports 1000 Pounds 14400 0.20 0.42 -0.10 0.48Imports - Food and Beverages 1000 Pounds 14400 0.31 0.02 0.38 0.08Imports - Raw and Semi-processed Goods 1000 Pounds 14400 0.29 0.07 0.39 0.00Imports - Processed Goods 1000 Pounds 14400 0.30 0.10 0.40 0.04Exports - Food and Beverages 1000 Pounds 14400 0.00 0.09 -0.13 0.07Exports- Raw and Semi-processed Goods 1000 Pounds 14400 0.22 0.43 -0.04 0.53Exports - Wheat 1000 t 14400 -0.16 -0.16 -0.24 0.02Exports - Budder 1000 t 14400 -0.27 0.01 -0.12 -0.23Exports - Wool (non-washed) 1000 t 14400 -0.05 0.31 -0.07 0.45Exports - Wool (washed) 1000 t 14400 -0.15 0.46 -0.02 0.38Exports - Gold and Silver 1000 Pounds 14400 -0.06 -0.17 0.17 -0.10Prices

Wholesale Prices Index (1911) 14400 0.31 0.00 0.30 0.13Food Prices (Consumer) Index (1923/27) 14400 0.31 -0.13 0.36 0.04

Money and BankingGold Stock (Central Bank) Mill. Pounds 14400 0.26 -0.28 0.11 -0.12Currency in Circulation (Central Bank) Mill. Pounds 14400 0.28 -0.15 0.04 0.11Bank Rate (Central Bank) % 14400 0.24 -0.33 -0.06 -0.10

Explained Variance in % 54.18 13.28 25.47 11.64

Comments→ Number of total series: 18→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 6

30

(a) Non-stationary Data

(b) Stationary Data

Figure A 1: Principal Components for Australia

31

D.2. Belgium

TABLE A 2: DATA BELGIUM

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentGlas Index 14400 0.24 -0.15 0.12 0.22Coal Tons 14400 0.10 0.25 0.20 -0.20Raw Iron Tons 14400 0.22 0.18 0.30 0.21Raw Steel Tons 14400 0.21 0.19 0.29 0.25Steel Manufacturing Tons 14400 0.24 0.12 0.29 0.24Textiles Index 14400 0.24 0.01 0.14 -0.20Coal Storage Tons 1000 -0.22 0.13 -0.22 -0.23Wool Conditioning Tons 14400 0.19 0.06 0.08 -0.15Total Unemployment Individuals 14400 -0.23 0.16 -0.17 0.31Insured Unemployed in Percent % 14400 -0.23 0.17 -0.17 0.27

TradeTotal Imports Franc 14400 0.25 0.11 0.28 -0.10Total Exports Franc 14400 0.25 0.16 0.32 -0.09Coal Exports Tons 14400 -0.01 0.32 0.08 -0.17Iron Exports Tons 14400 0.18 0.15 0.21 0.15Machines Exports Franc 14400 0.23 0.14 0.19 -0.17Textile Exports Franc 14400 0.26 0.04 0.27 -0.14Glas Exports Franc 14400 0.25 0.02 0.23 0.12

PricesWholesale Prices Index 10000 0.26 0.03 0.29 0.09Consumer Prices Index 10000 0.15 0.33 0.22 -0.14

Money and BankingCurrency in Circulation Franc 5000 -0.17 0.33 0.05 0.05Public Deposits Franc 14400 -0.12 0.11 0.09 -0.11Private Deposits Franc 14400 -0.12 0.24 0.11 -0.18Bank Rate % 14400 0.13 -0.38 0.04 0.34Market Interest Rate % 5000 0.14 -0.37 -0.02 0.37

Explained Variance in % 57.48 19.09 26.72 14.32

Comments→ Number of total series: 24→ Broken Stick Criterion (non-stationary | stationary data) : 3 | 7

32

(a) Non-stationary Data

(b) Stationary Data

Figure A 2: Principal Components for Belgium

33

D.3. Bulgaria

TABLE A 3: DATA BULGARIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentCoal Production 1000 t 14400 -0.13 0.42 0.21 0.07Transported Goods (Shipping - Incoming) 1000 NRT 14400 -0.16 0.37 -0.06 0.12

TradeImports Mill. Leva 14400 0.27 0.07 0.30 -0.16Exports Mill. Leva 14400 0.24 0.15 -0.05 -0.06

PricesWholesale Prices Index(1932/34) 5000 0.29 0.06 0.42 0.08Wholesale Prices - Foodstuff (no meat) Index(1932/34) 5000 0.28 -0.07 0.40 -0.10Wholesale Prices - Foodstuff (meat only) Index(1932/34) 14400 0.29 0.02 0.38 0.08Consumer Prices Index(1933/34) 500 0.28 0.03 0.33 0.32Consumer Prices - Foodstuff Index(1933/34) 500 0.29 0.02 0.34 0.26

Money and BankingGold Stock (Central Bank) Mill. Leva 14400 -0.26 0.25 0.12 0.06Foreign Exchange (Central Bank) Mill. Leva 4.25 0.17 0.37 0.02 -0.08Bills of Exchange and Advances (Central Bank) Mill. Leva 4.25 0.26 0.22 -0.02 -0.03Advances to the Government (Central Bank) Mill. Leva 10000 0.20 -0.47 -0.10 -0.25Currency in Circulation (Central Bank) Mill. Leva 5000 0.28 0.11 0.13 -0.42Deposits (Central Bank) Mill. Leva 10000 0.18 -0.23 0.30 -0.27Bank Rate (Central Bank) % 10000 0.24 0.12 -0.13 0.41Protested Bills of Exchange (Central Bank) Mill. Leva 10000 0.19 0.32 -0.05 0.52

Explained Variance in % 67.83 12.00 26.72 14.32

Comments→ Total series: 17→ Broken Stick Criterion (non-stationary | stationary data) : 3 | 6→ Price indices were matched by re-basing them.→ We converted the pre-1928 gold stock values by employing the factor 3.83/100 using the information of the series“Goldwert der Valuta.”

34

(a) Non-stationary Data

(b) Stationary Data

Figure A 3: Principal Components for Bulgaria

35

D.4. Chile

TABLE A 4: DATA CHILE

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentMining - Production Index Index (1927/29) 14400 0.38 -0.03 0.29 0.36Mining - Employed in Salpetre Industry 1000 1000 0.36 -0.15 0.07 -0.14Mining - Employed in Cooper Industry 1000 1000 0.36 -0.10 0.16 0.25Cooper Production 1000 t 14400 0.33 0.10 0.20 0.47Coal Production 1000 t 14400 0.11 0.42 0.33 -0.11Retail Sailes (Santiago) Index

(1932/1934)14400 0.31 0.13 0.30 -0.29

TradeImports Mill. Pesos 14400 0.32 -0.24 0.39 -0.01Exports Mill. Pesos 14400 0.34 -0.22 0.29 0.30Exports - Wool (washed) t 14400 0.03 -0.01 0.03 0.07Exports - Cooper 1000 t 14400 0.30 0.08 0.16 0.43Money and BankingClearings Mill. Pesos 14400 0.20 0.39 0.42 -0.19Bank Rate (Central Bank) % 14400 0.13 -0.40 -0.24 0.24Savings Mill. Pesos 1000 0.08 0.42 0.20 -0.19Stock Market Index Index (1927) 5000 0.11 0.41 0.32 -0.25

Explained Variance in % 44.48 30.92 27.51 21.8

Comments→ Total series: 14→ Broken Stick Criterion (non-stationary | stationary data) : 3 | 5→ Workers in Saltpetre Industry: Value for February 1934 was missing and linearly interpolated.→ Retail Sales (Santiago): Two indices were linked via indexing. One is based on 20 companies and their branches,the other one on 18.→ Exports - Wool (washed): For January–March 1929, only the quarterly value was available. Hence monthly valueshave been assumed to be 1/3 of the quarterly value for each month.

36

(a) Non-stationary Data

(b) Stationary Data

Figure A 4: Principal Components for Chile

37

D.5. Estonia

TABLE A 5: DATA ESTONIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentEmployment (Mining and Industry) number 14400 0.00 0.25 0.25 0.16Transported Goods (Railway) 1000 t 14400 -0.04 0.37 0.23 0.08Transported Goods (Shipping - Incoming) 1000 NRT 14400 -0.04 0.23 0.12 0.30

TradeImports 1000 Ekr 14400 0.30 0.13 0.31 0.01Exports 1000 Ekr 14400 0.30 0.13 0.30 0.18Exports - Flax t 14400 0.16 -0.10 0.01 0.08Exports - Butter t 14400 -0.02 0.26 0.09 0.26Exports - Eggs 1000s 14400 -0.08 0.20 0.06 0.16Exports - Sawn Wood 1000 cbm 14400 0.14 -0.03 0.05 0.03Exports - Paper t 14400 0.30 0.01 0.03 0.35

PricesWholesale Prices Index (1913) 10000 0.33 -0.06 0.26 -0.29Consumer Prices (in Reval) Index (1913) 14400 0.33 0.05 0.20 -0.35Consumer Prices - Foodstuff (in Reval) Index (1913) 10000 0.34 -0.03 0.18 -0.28Unemployed Anzahl 14400 -0.16 -0.16 -0.24 -0.28

Money and BankingGold Stock (Central Bank) Mill. Ekr. 10000 -0.24 0.16 0.09 -0.09Foreign Exchange (Central Bank) Mill. Ekr. 10000 0.20 0.21 0.29 -0.19Bills of Exchange and Advances (Central Bank) Mill. Ekr. 144000 0.25 -0.23 -0.26 0.25Currency in Circulation (Central Bank) Mill. Ekr. 144000 0.04 0.20 0.13 -0.16Clearings Mill. Ekr. 144000 0.17 0.32 0.27 0.10Bank Rate (Central Bank) % 144000 0.29 -0.18 0.07 0.29Private Banking - Bills of Exchange and Advances Mill. Ekr. 144000 0.04 0.33 0.32 0.08Private Banking - Deposits Mill. Ekr. 144000 -0.02 0.39 0.34 -0.02Protested Bills of Exchange 1000 Ekr. 144000 0.19 0.06 0.03 0.17

Explained Variance in % % 35.93 25.98 24.23 11.70

Comments→ Total series: 23→ Broken Stick Criterion (non-stationary | stationary data) : 3 | 7→ “Flax Exports” have been linearly interpolated for two month (July 1928 and October 1930).→ The “Employment (Mining and Industry)” series has been linearly interpolated from July–December 1926.

38

(a) Non-stationary Data

(b) Stationary Data

Figure A 5: Principal Components for Estonia

39

D.6. Finland

TABLE A 6: DATA FINLAND

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentWholesale Turnover Mill Fmk 14400 0.26 -0.06 0.23 0.18Transported Goods (Railway) 1000t 14400 0.27 -0.11 0.25 0.00Transported Goods (Shipping) 1000 NRT 14400 0.27 0.05 0.18 0.04Bankruptcies - Total real number 14400 -0.15 0.28 -0.20 0.01Bankruptcies - Agriculture real number 14400 -0.14 0.30 -0.22 -0.03

TradeImports Mill Fmk 14400 0.10 -0.26 0.27 0.15Exports Mill Fmk 14400 0.20 -0.17 0.14 0.22Imports Stone Coal and Coke 1000t 14400 0.19 0.09 0.06 0.00Imports Cotton t 14400 0.19 -0.06 0.12 -0.05Exports Butter t 14400 -0.09 0.08 -0.08 0.01Exports Wood 1000 cbm 14400 0.06 -0.18 0.10 0.13Exports Cellulose 1000t 14400 0.23 0.18 0.03 0.26Exports Wood Pulp 1000t 14400 0.23 0.16 0.04 0.12Exports Paper 1000t 14400 0.26 0.12 0.11 0.09

PricesWholesale Prices Index (1926) 14400 -0.08 -0.34 0.16 0.27Consumer Prices Index (=1914) 14400 -0.14 -0.24 0.15 0.28

Money and BankingGold Stock (Central Bank) Mill Fmk 3000 0.22 -0.05 0.06 0.00Foreign Exchange (Central Bank) Mill Fmk 14400 0.16 -0.10 0.10 -0.38Bills of Exchange and Advances (Central Bank) Mill Fmk 3000 0.04 0.14 -0.05 0.41Currency in Circulation (Central Bank) Mill Fmk 14400 0.11 -0.21 0.27 0.02Deposits (Central Bank) Mill Fmk 14400 0.13 0.27 0.31 -0.16Clearings - Foreign and Domestic Stocks Mill Fmk 14400 0.25 -0.06 0.33 -0.05Bank Rate (Central Bank) % 14400 -0.27 -0.11 -0.12 0.32Private Banking - Bills of Exchange and Advances Mill Fmk 14400 -0.03 0.19 0.14 0.31Private Banking - Deposits Mill Fmk 14400 0.13 0.27 0.31 -0.16Saving Institutions - Savings Mill Fmk 7000 0.23 0.23 0.14 0.04New Life Insurances Mill Fmk 14400 0.08 -0.12 0.05 -0.05Stocks Index 8000 0.25 -0.09 0.28 -0.19Stock Turnover Mill Fmk 14400 0.07 -0.16 0.06 -0.14Protested Bills of Exchange Mill Fmk 14400 -0.15 0.23 -0.19 0.06

Explained Variance in % 35.13 24.11 17.92 15.47

Comments→ Total series: 30→ Broken Stick Criterion (non-stationary | stationary data) : 5 | 9→ Values of the Gold Stock for 1925 have been converted into the parity, which was in law from 1926 on.

40

(a) Non-stationary Data

(b) Stationary Data

Figure A 6: Principal Components for Finland

41

D.7. Hungary

TABLE A 7: DATA HUNGARY

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentBrown Coal Mining 1000 t 14400 0.00 0.33 0.17 0.13Iron Mining 1000 t 14400 0.07 0.30 0.06 0.16Transported Goods (Railway) 1000 t 14400 0.19 0.20 0.07 0.32Number of Applications per 100 Job Advertisements Individuals 14400 -0.17 -0.08 -0.08 -0.23Unemployed Union Members 1000s 14400 -0.06 -0.35 -0.11 -0.27

TradeImports Mill. Pengö 14400 0.21 0.09 0.16 0.31Exports Mill. Pengö 14400 0.19 0.08 -0.10 0.25Imports - Raw Cotton t 14400 -0.18 0.13 0.02 0.16Imports - Wood 1000 t 14400 0.21 0.07 0.12 0.21Imports Coal and Coke 1000 t 14400 0.20 0.11 0.07 0.27Imports Machines 1000 Pengö 14400 0.21 0.11 0.11 0.19Imports - Cotton Materials t 14400 0.21 -0.04 0.08 0.16Exports - Wheat 1000 t 14400 -0.03 0.16 -0.04 0.15Exports - Flower 1000 t 14400 0.17 0.06 0.06 0.24Exports - Cattle 1000s 14400 0.05 -0.03 -0.20 0.16Exports - Pigs 1000s 14400 -0.01 0.17 -0.07 0.06

PricesWholesale Prices - Price-elastic Goods Index (1925/27) 14400 0.22 0.02 0.04 0.07Wholesale Prices - All Goods Index (1913) 14400 0.21 -0.06 0.31 -0.15Wholesale Prices - Agricultural Goods Index (1913) 14400 0.21 -0.04 0.30 -0.15Wholesale Prices - Industry Index (1913) 14400 0.22 -0.03 0.28 0.06Wholesale Prices - Wheat Pengö per 100 kg 14400 0.20 -0.09 0.13 -0.04Consumer Price Index Index (1913) 14400 0.20 0.10 0.31 -0.04Consumer Price Index - Foodstuff Index (1913) 14400 0.22 0.00 0.31 -0.05Consumer Price Index - Textiles Index (1913) 14400 0.20 -0.08 0.21 0.02

Money and BankingGold Stock (Central Bank) Mill. Pengö 14400 0.13 0.26 0.02 0.06Foreign Exchange (Central Bank) Mill. Pengö 6000 0.16 -0.22 -0.06 -0.03Bills of Exchange and Advances (Central Bank) Mill. Pengö 14400 -0.18 0.14 0.26 -0.05Advances to the Government (Central Bank) Mill. Pengö 10000 0.19 -0.14 -0.13 -0.08Currency in Circulation (Central Bank) Mill. Pengö 14400 0.17 0.21 0.10 -0.10Deposits by the Government (Central Bank) Mill. Pengö 10000 0.18 0.03 0.22 -0.04Deposits Others (Central Bank) Mill. Pengö 10000 -0.10 -0.06 0.12 -0.21Bank Rate (Central Bank) % 14400 0.15 -0.17 0.23 -0.04Market Rate % 14400 0.16 -0.23 0.23 -0.06Current Account(Private Banks) Mill. Pengö 14400 -0.05 0.35 0.11 0.22Savings (Private Banks) Mill. Pengö 5000 -0.14 0.28 -0.12 0.27

Explained Variance in % 56.99 17.01 22.12 12.61

Comments→ Total series: 35→ Broken Stick Criterion (non-stationary | stationary data) : 5 | 10→ Wholesale prices of “All Goods”, “Agriculture Goods”, and “Industry” are based on a new series from 1929 on.However, the Reichsamt already matched the indices. They exhibit no structural breaks.→ The “Gold Stock” series shows little variation from 1932 on, which might be due to new regulation.

42

(a) Non-stationary Data

(b) Stationary Data

Figure A 7: Principal Components for Hungary

43

D.8. India

TABLE A 8: DATA INDIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentCotton Thread Production 1000t 144000 -0.24 0.10 0.05 0.49Processed Cotton Production 1000t 144000 -0.25 0.14 0.03 0.46Transported Goods (Shipping - Incoming) 1000 NRT 144000 0.00 0.42 0.13 -0.14Sea Freight Index Index 144000 0.24 -0.02 -0.02 0.31

TradeImports Mill Rupies 144000 0.29 0.02 0.35 0.09Exports Mill Rupies 144000 0.29 -0.01 0.34 -0.23Imports Gasoline 1000 hl 144000 0.05 0.23 0.09 -0.03Imports Iron and Iron Goods 1000t 144000 0.28 0.07 0.24 -0.10Imports Machines Mill Rupies 144000 0.22 0.28 0.28 -0.19Imports Processed Cotton Mill Meter 144000 0.26 0.02 0.31 0.21Exports Raw Cotton 1000t 144000 0.10 0.23 0.06 -0.32Exports - Raw Jute 1000t 144000 0.09 0.26 0.08 -0.06Exports - Processed Cotton in Pieces Mill Meter 144000 0.27 -0.11 0.00 0.30Exports - Jute Goods 1000t 144000 0.21 0.28 0.25 0.04Exports - Saks 1000t 144000 0.10 0.37 0.25 0.05

PricesWholesale Prices - Calcutta Index (1914) 144000 0.30 -0.07 0.37 -0.06Wholesale Prices - Bombay Index (1914) 144000 0.29 -0.12 0.38 0.07Consumer Prices (Bombay) Index (1914) 4000 0.29 -0.08 0.26 0.23

Money and BankingBank Rate (Imperial Bank of India) % 144000 0.18 -0.17 -0.06 -0.05Stock Issues Mill Rupies 144000 -0.08 0.23 0.01 0.04Stock Value of 5 Indian Railway Companies in London Mill Pound 5000 -0.05 0.44 0.12 -0.11

Explained Variance in % % 51.55 15.88 22.45 15.01

Comments→ Total series: 21→ Broken Stick Criterion (non-stationary | stationary data) : 5 | 7

44

(a) Non-stationary Data

(b) Stationary Data

Figure A 8: Principal Components for India

45

D.9. Italy

TABLE A 9: DATA ITALY

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentRaw Steel Production 1000t 14400 0.04 0.41 0.26 0.15Transported Goods (Railways) 1000t 14400 0.23 0.05 0.28 0.05Transported Goods (Shipping - Incoming) 1000t 14400 -0.06 0.41 0.05 0.17Transported Goods (Shipping - Outgoing) 1000t 14400 -0.11 0.35 0.18 0.06Unemployed 1000 6000 -0.24 -0.07 -0.30 -0.19Unemployed - Agriculture 1000 14400 -0.22 -0.09 -0.21 -0.15Unemployed - Industry 1000 2000 -0.23 -0.09 -0.25 -0.17

TradeImports Mill Lira 14400 0.24 0.02 0.28 0.15Exports Mill Lira 14400 0.24 -0.04 0.28 -0.04Imports - Wheat 1000t 14400 0.17 -0.01 -0.09 0.36Imports - Raw Cotton 1000t 14400 0.12 -0.01 0.13 -0.03Imports - Wool 1000t 14400 -0.11 -0.11 0.10 -0.08Imports - Wood 1000t 14400 0.19 0.10 0.19 0.09Imports - Coal 1000t 14400 0.07 0.35 0.12 -0.04Imports - Scrap Metal 1000t 14400 0.10 0.32 0.16 0.01Imports Machines Mill Lira 14400 0.22 0.06 0.29 -0.03Exports - Mandarin, Orange and Citrus t 14400 0.03 -0.15 0.02 0.09Exports - Olive Oil t 14400 0.06 0.06 0.08 0.01Exports - Cheese t 14400 0.13 -0.10 -0.02 0.09Exports - Raw Silk t 14400 0.18 -0.09 0.01 0.13Exports - Cars Number 14400 0.22 0.06 0.09 0.04

PricesWholesale Prices Index (1934 1000 0.23 -0.04 0.25 -0.01

Money and BankingGold Stock (Central Bank) Mill. Lira 1000 -0.22 0.04 0.06 0.15Foreign Exchange (Central Bank) Mill. Lira 14400 0.16 0.03 -0.18 0.28Bills of Exchange and Advances (Central Bank) Mill. Lira 14400 0.13 -0.14 0.21 -0.27Currency in Circulation (Central Bank) Mill. Lira 14400 0.24 -0.03 0.11 0.12Deposits (Central Bank) Mill. Lira 14400 -0.05 -0.38 -0.08 -0.19Clearings Index

(1932/1934)14400 0.20 -0.07 0.17 0.01

Clearings - Giro Cheques Mill. Lira 14400 -0.21 0.09 -0.08 0.37Bank Rate (Central Bank) % 8000 0.20 -0.12 0.09 -0.41Market Rate % 2500 0.20 -0.15 0.18 -0.34Capital Increases or Start-Ups for Listed Stock Corpora-tions

Mill. Lira 14400 0.12 0.08 0.11 -0.04

Capital Decreases or Liquidations of Listed Stock Corpora-tions

Mill. Lira 14400 -0.15 0.02 -0.02 0.05

Explained Variance in % % 52.46 12.60 22.27 9.65

Comments→ Total series: 33→ Broken Stick Criterion (non-stationary | stationary data) : 6 | 10→ Values for the first three years of the gold and foreign exchange series have been converted to the 1928 parity. Wefollow the explanations for Article 3 of the decree law No. 253 given in the Federal Reserve Bulletin (Federal ReserveBoard, 1928, p. 493).→ Two wholesale price series were linked via re-basing to create the series “Wholesale Prices.”→ The “Clearings” index was created by linking two series on clearings.

46

(a) Non-stationary Data

(b) Stationary Data

Figure A 9: Principal Components for Italy

47

D.10. Japan

TABLE A 10: DATA JAPAN

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentEmployment (Industry) Index (1926) 500 0.06 0.31 0.15 -0.11Coal Production 1000 t 14400 -0.11 0.27 0.19 0.06Textiles Production Index (1930) 14400 -0.23 0.11 0.16 -0.04Silk Production t 14400 -0.12 -0.09 0.03 0.01Turnover of Warehouses Mill. Yen 14400 0.08 0.28 0.14 0.23Stocks of Warehouses Mill. Yen 7000 -0.13 0.19 0.11 0.11Transported Goods (Railway) 1000 t 14400 -0.12 0.26 0.29 -0.01Wages Index (1926) 10000 0.24 -0.04 0.12 -0.09Real Wages Index (1926) 14400 0.18 0.10 0.27 0.01

TradeImports Mill. Yen 14400 0.01 0.31 0.26 0.04Exports Mill. Yen 14400 -0.03 0.31 0.28 0.07Imports - Raw Cotton 1000 t 14400 -0.08 0.10 0.06 0.03Imports - Wool t 14400 -0.17 -0.03 -0.06 0.00Imports - Coal 1000 t 14400 -0.23 0.12 0.13 0.09Imports - Pig Iron 1000 t 14400 -0.18 0.18 0.07 0.03Imports - Machines 1000 Yen 14400 -0.01 0.22 0.23 -0.07Exports - Raw Silk t 14400 -0.08 -0.04 0.00 0.06Exports - Cotton Threads t 14400 0.18 0.12 -0.03 -0.06

PricesWholesale Prices Index (July 1914) 3000 0.21 0.19 0.27 0.04Consumer Prices (Tokyo) Index (July 1914) 6000 0.23 0.17 0.29 0.06

Money and BankingGold Stock (Central Bank) Mill. Yen 1500 0.23 0.04 0.14 -0.01Bills of Exchange and Advances (Central Bank) Mill. Yen 14400 -0.18 -0.03 -0.05 0.41Currency in Circulation (Central Bank) Mill. Yen 14400 0.06 0.25 0.13 0.16Deposits by the Government (Central Bank) Mill. Yen 14400 0.23 0.06 -0.06 0.30Deposits by Banks (Central Bank) Mill. Yen 14400 0.04 -0.04 0.18 0.23Clearings Mill. Yen 14400 0.13 0.21 0.14 -0.34Bank Rate (Central Bank) % 14400 0.26 -0.06 -0.14 -0.14Market Rate % 4000 0.23 0.01 -0.17 0.01Market Rate - Demand Deposits % 14400 0.21 -0.02 -0.16 -0.15Clearing Banks - Cash Position Mill. Yen 14400 -0.12 0.08 0.19 0.14Clearing Banks - Stocks Mill. Yen 14400 -0.25 0.08 0.24 -0.10Clearing Banks - Bills of Exchange and Advances Mill. Yen 14400 0.12 0.15 -0.03 -0.40Clearing Banks - Deposits Mill. Yen 9000 -0.22 0.16 0.20 -0.32Giro Banks - Savings Mill. Yen 14400 -0.26 -0.03 -0.15 0.29Stock Emissions Mill. Yen 14400 0.02 0.23 0.04 -0.10

Explained Variance in % % 39.00 26.47 20.87 12.95

Comments→ Total series: 35→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 10→ The series on Bills of Exchange and Advances (Central Bank) were linked via indexing.→ Statistische Reichsamt (1937, p. 134) reports slightly different values for the years 1933 and 1934 compared toStatistische Reichsamt (1936, p. 385).→ The value for December 1936 of the “Transported Goods (Railway)” series was missing and assumed to take thesame value as in November.→ There is little variation in the gold stock series until 1930.

48

(a) Non-stationary Data

(b) Stationary Data

Figure A 10: Principal Components for Japan

49

D.11. Latvia

TABLE A 11: DATA LATVIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentTransported Goods (Railway) 1000t 14400 0.21 0.23 0.26 0.06Transported Goods (Shipping - Incoming) 1000 NRT 14400 0.23 0.04 0.18 0.16Healthinsurance Members 1000 1000 0.08 0.37 0.26 0.19Unemployed real number 10000 -0.14 -0.01 -0.30 -0.18Bankcruptcies (Number) real number 14400 -0.08 -0.05 -0.28 0.01Bankcruptices (Volume) 1000 Lat 14400 -0.03 -0.01 -0.10 0.10

TradeImports Mill lat 14400 0.30 -0.05 0.30 -0.11Exports Mill Lat 14400 0.30 -0.04 0.18 0.17Imports - Textiles 1000 Lat 14400 0.29 -0.10 0.33 -0.09Exports - Flax t 14400 0.06 -0.21 -0.04 0.28Exports - Lineseed t 14400 0.14 -0.22 0.04 0.24Exports - Butter t 14400 -0.12 0.20 0.03 0.02Exports - Bacon t 14400 0.00 -0.24 0.08 0.30Exports - Plancks t 14400 0.14 0.14 0.16 0.17Exports - Paper t 14400 0.17 0.16 0.19 -0.18

PricesConsumer Prices Index

(1930/1933)10000 0.29 -0.12 0.11 -0.31

Consumer Prices - Foodstuff Index(1930/1933)

14400 0.28 -0.12 0.06 -0.33

Money and BankingGold Stock (Central Bank) Mill Lat. 1000 -0.26 0.19 -0.06 -0.11Foreign Exchange (Central Bank) Mill Lat. 300 0.28 -0.04 0.05 -0.19Bills of Exchange and Advances (Central Bank) Mill Lat. 3000 0.18 0.11 0.09 0.27Currency in Circulation (Central Bank) Mill Lat. 8000 0.17 0.28 0.19 -0.18Deposits (Central Bank) Mill Lat. 7000 0.11 0.35 0.20 -0.26Bank Rate (Central Bank) % 14400 0.12 -0.38 -0.12 0.19Private Banks - Bills of Exchange and Advances Mill Lat. 1000 0.27 0.11 0.32 0.19Private Banks - Deposits Mill Lat. 1000 0.14 0.31 0.30 0.00Protested Bills of Exchange 1000 Lat 14400 0.15 -0.18 -0.17 0.23

Explained Variance in % % 52.46 12.60 22.27 9.65

Comments→ Total series: 26→ Broken Stick Criterion (non-stationary | stationary data) : 5 | 8→ For the consumer price series, new and old indices were linked.→ The gold stock series exhibits little variance for most of the time.→ The values for the gold stock and foreign exchange for September–December 1936 have been converted to the oldparity. The Lat was devalued by 40 % (Karnups, 2012, p. 56). See also “Goldwert der Valuta” series (StatistischeReichsamt, 1937, p. 70).

50

(a) Non-stationary Data

(b) Stationary Data

Figure A 11: Principal Components for Latvia

51

D.12. Lithuania

TABLE A 12: DATA LITHUANIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2Production, Transport and EmploymentTransported Goods (Railway) 1000 t 14400 -0.12 0.31 0.17 0.18Transported Goods (Ships - Incoming) 1000 NRT 14400 -0.26 0.07 0.21 0.19

TradeImports 1000 Lit 14400 0.28 0.21 0.40 0.22Exports 1000 Lit 14400 0.24 0.24 0.24 0.20Imported Agricultural Machines 1000 Lit 14400 0.23 0.17 0.13 0.14Imports Textiles 1000 Lit 14400 0.25 0.23 0.43 -0.03Exports Flax t 14400 0.09 -0.21 0.00 0.05Exports Linseed t 14400 0.07 -0.08 0.06 -0.15Exports Butter t 14400 -0.29 0.13 0.19 -0.11Export Eggs t 14400 0.18 0.05 0.14 -0.09Export Planks t 14400 -0.14 0.04 0.06 0.34Exports Cellulose t 14400 -0.08 0.09 0.15 0.26

PricesWholesale Prices Index (1913) 14400 0.33 -0.06 -0.09 0.50Wholesale Prices (Flax) Lit per kg 14400 0.26 -0.17 0.03 0.19Consumer Prices Index (1913) 14400 0.33 -0.05 -0.10 0.47

Money and BankingGold Stock (Central Bank) Mill. Lit 500 -0.29 0.03 -0.07 0.06Foreign Exchange (Central Bank) Mill. Lit 5000 0.23 0.30 0.38 -0.15Bills of Exchange and Advances (Central Bank) Mill. Lit 5000 -0.18 0.36 -0.09 0.17Currency in Circulation (Central Bank) Mill. Lit 5000 -0.10 0.35 0.25 0.02Deposits (Central Bank) Mill. Lit 1000 -0.03 0.44 0.15 -0.05Bank Rate (Central Bank) % 14400 0.21 0.12 -0.20 0.05Protested Bills of Exchange 1000 Lit 14400 0.04 0.23 -0.34 0.17

Explained Variance in % % 40.42 20.31 19.02 11.98

Comments→ Total series: 22→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 7→ The Economist (1934) pictured Lithuania as an agrarian country, which was hindered in its development mainlybecause of the global depression. The exports of agrarian goods fell, but even more did those of manufacturedgoods (The Economist, 1934, p. 370). Interestingly, the correspondent states that "while the condition of the before-mentioned economic branches was due to the results of the world crisis, more or less unsteady, it further remainsto mention briefly the stable financial and monetary situation in the country" (The Economist, 1934, p. 371). Thisstatement might be reflected by the high scores of the second component for the Money and Banking variablesregarding the central bank.

52

(a) Non-stationary Data

(b) Stationary Data

Figure A 12: Principal Components for Lithuania

53

D.13. Netherlands

TABLE A 13: DATA NETHERLANDS

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentCoal Production 1000t 14400 -0.13 -0.08 0.03 -0.14Completed Constructions Mill hlf 14400 0.16 -0.14 0.17 -0.15Transported Goods (Railways) 1000t 14400 0.16 -0.23 0.07 -0.07Traported Goods (Ships - Incoming) 1000 NRT 14400 0.17 0.08 0.15 -0.03Bankcrupcies Real Number 14400 -0.17 0.05 -0.29 0.13Insured Unemployed % 14400 -0.21 0.10 -0.12 -0.01Lost Days due to Unemployment (total) % 4.25 -0.21 0.11 -0.08 0.09Lost Days due to Unemployment (Coal Industry) % 4.25 -0.18 0.25 -0.01 0.04Lost Days due to Unemployment (Metal Industry) % 4.25 -0.21 0.06 -0.05 0.11Lost Days due to Unemployment (Foodprocessing Indus-try)

% 14400 -0.19 0.22 -0.24 0.06

Lost Days due to Unemployment (Textile Industry) % 14400 -0.21 0.08 -0.28 0.06Lost Days due to Unemployment (Construction) % 14400 -0.20 0.13 -0.28 0.14

TradeTotal Imports Mill hlf 14400 0.21 -0.07 0.27 0.00Total Exports Mill hlf 14400 0.21 -0.04 0.24 0.04

PricesWholesale Prices Index 7000 0.21 0.02 0.22 0.22Food Prices Index 10000 0.21 0.03 0.14 0.25

Money and BankingGold Stock (Central Bank) Mill Hfl 5000 -0.19 -0.17 -0.24 0.14Foreign Currency (Central Bank) Mill Hfl 14400 0.18 -0.22 0.06 -0.34Bills of Exchange (Central Bank) Mill Hfl 14400 0.08 0.27 0.07 0.26Currency in Circulation (Central Bank) Mill Hfl 4000 -0.09 -0.38 -0.12 0.24Deposits Total (Central Bank) Mill Hfl 14400 -0.17 -0.24 -0.25 0.05Private Deposits (Central Bank) Mill Hfl 14400 -0.16 -0.28 -0.25 0.07Clearings Mill Hfl 14400 0.21 0.02 0.22 0.01Giro Cheque Turnover Mill Hfl 14400 -0.14 0.14 0.09 -0.13Bank Rate (Central Bank) % 14400 0.13 0.28 0.08 0.36Market Rate % 14400 0.15 0.30 0.11 0.33Carryover Rate % 14400 0.16 0.28 0.09 0.38Deposits Saving Banks Mill Hfl 14400 -0.20 0.08 -0.17 0.15Total Issues of Obligation and Stocks Mill Hfl 14400 0.11 -0.14 -0.03 -0.13Stock Market Index Index (21/25) 9000 0.21 0.03 0.23 0.15Stock Market Index - Dutch Stocks only Index (21/25) 9000 0.21 0.02 0.23 0.18Revenues from Stock Exchange Tax 100 Hlf 14400 0.14 0.12 0.10 0.14

Explained Variance in % % 68.26 10.00 23.88 12.09

Comments→ Total series: 32→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 8→ The negative score for coal production seems surprising. However, The Economist (1932, p. 21) reports: “TheNetherlands coal industry, however, continues to distinguish itself by performing a satisfactory exception to the generaldepression."→ The series "Gold Stock" exhibits a structural break in 1931. Before that date, the gold stock had shown littlevariation, but grew rapidly afterwards. The foreign exchange series shows the reverse. Portfolio changes due to thetensions on foreign exchange markets are the most likely explanation (see The Economist, 1931, p. 945).

54

(a) Non-stationary Data

(b) Stationary Data

Figure A 13: Principal Components for the Netherlands

55

D.14. New Zealand

TABLE A 14: DATA NEW ZEALAND

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentConstruction Permits 1000 Pounds 14400 0.40 0.16 0.28 -0.10Incoming Ships 1000 NRT 14400 -0.18 0.37 0.12 0.00Unemployed Index 5000 -0.41 0.01 -0.38 0.16

TradeExports 1000 Pounds 14400 0.15 0.58 0.29 0.53Exports - Butter t 14400 -0.30 0.33 0.14 0.47Exports - Cheese t 14400 -0.14 0.23 0.19 0.35Exports - Meat t 14400 -0.27 0.17 0.04 0.29Exports - Wool t 14400 -0.13 0.37 0.13 0.38

PricesWholesale Prices Index (1909/13) 8000 0.42 0.06 0.49 -0.20Food Prices (for consumers) Index (1926/30) 8000 0.41 0.04 0.46 -0.22

Money and BankingStocks Index (1926) 5000 0.27 0.40 0.40 -0.14

Explained Variance in % % 49.28 19.99 27.65 46.82

Comments→ Total series: 11→ Broken Stick Criterion (non-stationary | stationary data) : 2 | 4→ The unemployment series was extrapolated with male unemployed from 1935 onwards.→ Stock market values for December 1926, 1927, 1928, 1930 were missing and therefore linearly interpolated.

56

(a) Non-stationary Data

(b) Stationary Data

Figure A 14: Principal Components for New Zealand

57

D.15. South Africa

TABLE A 15: DATA SOUTH AFRICA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentCoal Production 1000 t 14400 0.18 0.29 0.16 0.16Gold Production t 14400 0.10 -0.31 -0.28 -0.01Employed Europeans in Mining 1000s 14400 0.22 0.17 0.16 0.07Employed Non-Europeans in Mining 1000s 14400 0.25 0.10 0.18 0.08Employed Europeans in Gold Mining 1000s 2000 0.27 -0.08 0.09 0.42Employed Non-Europeans in Gold Mining 1000s 14400 0.27 -0.11 0.08 0.22Transported Goods (Shipping - Incoming) 1000 NRT 14400 0.23 0.08 0.02 0.01Unemployed Europeans (Job Wanted Advertisements) Individuals 14400 0.08 -0.38 -0.18 -0.25

TradeImports 1000 Pounds 14400 0.12 0.35 0.27 0.01Exports - Wool t 14400 -0.06 -0.19 -0.18 0.03

PricesConsumer Prices Index (1910) 14400 -0.19 0.30 0.22 0.31Foodstuff Prices (Consumer) Index (1910) 4000 -0.13 0.36 0.29 0.11

Money and BankingGold Stock (Central Bank) 1000 Pounds 5000 0.27 -0.01 0.17 0.22Foreign Exchange (Central Bank) 1000 Pounds 4000 0.11 -0.02 0.25 -0.26Bills of Exchange and Advances (Central Bank) 1000 Pounds 14400 -0.16 -0.17 -0.20 0.23Currency in Circulation (Central Bank) 1000 Pounds 14400 0.26 0.05 0.25 0.03Clearings Mill. Pounds 14400 0.26 0.01 0.16 -0.28Bank Rate (Central Bank) % 14400 -0.25 0.09 -0.25 0.29Private Banking - Bills of Exchange and Advances Mill. Pounds 4000 0.08 0.18 -0.01 0.33Private Banking - Demand Deposits Mill. Pounds 4000 0.28 0.03 0.30 -0.18Private Banking - Long-term Deposits Mill. Pounds 14400 -0.13 -0.20 0.13 -0.05Land and Agricultural Bank - Advances 1000 Pounds 14400 0.15 -0.33 -0.28 0.07Saving Banks - Deposits 1000 Pounds 500 0.28 -0.02 0.12 0.27Stock Market (6 Gold Mining Stocks in London) Index (1 Jan.

1923)5000 0.25 0.09 0.26 -0.08

Explained Variance in % % 52.02 23.98 31.14 13.38

Comments→ Total series: 24→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 6→ Unemployed Europeans: the value for November 1931 was missing and linearly interpolated. From July 1934 on,women are included. There is however no structural break in the series at that point.→ Stock Market Index: the value for December 1936 was missing and assumed to take the same value as the one forNovember 1936.

58

(a) Non-stationary Data

(b) Stationary Data

Figure A 15: Principal Components for South Africa

59

D.16. Switzerland

TABLE A 16: DATA SWITZERLAND

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentProduction of Watches 1000s 144000 0.21 -0.07 0.12 -0.13Transported Persons (Railway) 1000s 144000 0.02 0.44 0.12 -0.06Transported Goods (Railway) 1000 t 144000 0.17 0.26 0.20 0.10Unemployment (job requests) Individuals 5000 -0.20 -0.11 -0.23 -0.18Bankcrupcies Number 14400 -0.16 -0.08 -0.08 -0.10

TradeImports Mill. Fr. 144000 0.20 0.13 0.25 0.07Exports Mill. Fr. 144000 0.21 0.03 0.25 -0.09Imports - Raw and Semi-processed Goods Mill. Fr. 144000 0.21 0.06 0.20 -0.04Imports- Processed Goods Mill. Fr. 144000 0.18 0.24 0.23 0.06Exports - Raw Material Mill. Fr. 144000 0.20 -0.04 0.11 0.05Exports - Processed Goods Mill. Fr. 144000 0.21 0.03 0.24 -0.11Imports - Coal 1000 t 144000 0.00 0.31 0.06 -0.03

PricesConsumer Prices Index (1914) 10000 0.21 0.02 0.24 -0.08Food Prices Index (1914) 10000 0.21 -0.02 0.24 -0.07Textiles Consumer Prices Index (1914) 10000 0.21 -0.01 0.16 -0.10

Money and BankingGold Stock (Central Bank) Mill. Fr. 1000 -0.19 0.01 -0.18 -0.18Currency in Circulation (Central Bank) Mill. Fr. 2000 -0.20 0.03 -0.18 -0.11Deposits (Central Bank) Mill. Fr. 2000 -0.17 0.04 -0.17 -0.07Giro Transfers (Central Bank) Mill. Fr. 14400 0.17 0.22 0.20 0.24Clearings Mill. Fr. 14400 0.21 0.06 0.24 0.16Clearings - Giro Checks Mill. Fr. 14400 -0.15 0.31 0.22 0.05Bank Rate (Central Bank) % 5000 0.19 -0.13 -0.04 -0.37Market Rate % 1000 0.15 -0.03 -0.07 -0.17Kantonalbanken - Bills of Exchanges and Advances Mill. Fr. 14400 -0.05 0.43 0.21 -0.13Kantonalbanken - Stocks on Balance Sheet Mill. Fr. 6000 -0.21 0.08 -0.14 0.32Kantonalbanken - Mortgages Mill. Fr. 14400 -0.21 0.06 -0.15 0.36Kantonalbanken - Savings Mill. Fr. 8000 -0.21 0.07 -0.20 0.31Kantonalbanken - Advances on Current Account Mill. Fr. 14400 -0.15 0.33 0.09 0.25Kantonalbanken - Obligations Mill. Fr. 14400 -0.20 0.17 -0.05 0.39Stock Yields % 7000 0.18 0.19 0.23 0.11

Explained Variance in % % 73.05 14.50 27.53 12.75

Comments→ Total series: 30→ Broken Stick Criterion (non-stationary | stationary data) : 3 | 9→ From 1936 on, there is a new parity. The old one is 1 Frank = 0.290323 gram fine gold, thereafter it is: 0.215 finegold. Hence the last three values of the series have been adjusted to the old parity.→ The series stock yields has a new basis from 1934 on. However, the series shows no inconsistencies.→ The public banks “Kantonalbanken” change their balance sheets in 1932, which leads to minor changes in thecomposition of the series. However, the series exhibit no structural breaks.

60

(a) Non-stationary Data

(b) Stationary Data

Figure A 16: Principal Components for Switzerland

61

D.17. Romania

TABLE A 17: DATA ROMANIA

Variable Name Unit λ Principal Component CoefficientsNon-Stationary Stationary

PC1 PC2 PC1 PC2

Production, Transport and EmploymentOil Production 1000 t 14400 -0.28 0.02 0.24 -0.09Transported Goods (Railway) 1000 t 14400 -0.04 -0.32 0.33 -0.08Bankcruptcies Anzahl 14400 0.20 0.25 0.01 -0.18Moratoria Anzahl 14400 -0.12 0.32 0.03 0.23

TradeImports Mill. Lei 14400 0.29 -0.10 0.13 -0.06Exports Mill. Lei 14400 0.26 -0.04 0.38 0.17Exports - Cattle 1000 Stück 14400 0.20 0.02 0.27 -0.29Exports - Corn 1000 t 14400 -0.04 0.32 0.19 0.34Exports - Wood 1000 t 14400 0.29 -0.10 0.08 -0.28Exports - Gasoline 1000 t 14400 -0.29 0.03 0.16 -0.01

PricesRetail Prices Index

(1932/1934)14400 0.30 0.02 0.09 0.16

Money and BankingGold Stock (Central Bank) Mill. Lei 5000 -0.27 -0.10 0.11 0.14Currency (Central Bank) Mill. Lei 4000 -0.19 -0.24 -0.25 0.19Clearings (in Bukarest) Mill. Lei 14400 0.27 0.08 0.42 -0.15Bank Rate (Central Bank) % 14400 0.12 0.48 0.33 0.01Real Interest Rate Fixed-yield Investments % 14400 -0.18 0.32 0.02 0.50Stock Issues Mill. Lei 14400 0.22 -0.08 0.11 -0.04Stock Market - Interest Rate of Fixed-yield Bonds Index (1926) 14400 0.27 -0.09 -0.15 -0.45Stocks - Turnover Mill. Lei 14400 0.17 -0.30 -0.26 -0.16Protested Bills of Exchange Mill. Lei 14400 0.17 0.31 -0.19 0.06

Explained Variance in % % 49.79 12.89 15.53 14.79

Comments→ Total series: 20→ Broken Stick Criterion (non-stationary | stationary data) : 4 | 7→ For the “Retail Prices,” two series were linked.→ From May 1931 on, clearings declined rapidly. However, there are no comments on the series that indicate that itis not valid anymore.→ From 1934 on, the series “Real Interest Rate Fixed-yield Investments” does not take foreign treasury bills intoaccount, which lowers its value by around 2 %.→ Values for the Gold Stock for November and December 1936 have been converted to the old parity: 0.009 vs thenew one: 0.00625174 (Statistische Reichsamt, 1937, p. 98).

62

(a) Non-stationary Data

(b) Stationary Data

Figure A 17: Principal Components for Romania

63

D.18. Data - Countries with Official Indices on Business Activity

For some countries, there were official industrial production indices available. For the UnitedStates and the United Kingdom, there were business activity indices available. Since those arefrom various sources, we present them in this separate data section. To make the graphs compara-ble, all indices have been based on 1925. Moreover, all indices have been seasonally adjusted inthe same fashion as the disaggregated data.

D.18.1. France

Figure A 18: French Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 92), Statistische Reichsamt (1937, p. 38)citep

64

D.18.2. Belgium

Figure A 19: Belgian Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 44), Statistische Reichsamt (1937, p. 20)

D.18.3. Poland

Figure A 20: Polish Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 224), Statistische Reichsamt (1937, p. 88)

65

D.18.4. Denmark

Figure A 21: Danish Industrial Production

Source & CommentsType: industrial production indexSource: Klovland (1998)

D.18.5. United Kingdom

Figure A 22: British Business Activity

Source & CommentsType: business activity indexSource: The State of Trade Supplement of The Economist, various issues

66

D.18.6. Norway

Figure A 23: Norwegian Industrial Production

Source & CommentsType: industrial production indexSource: Klovland (1998)

D.18.7. Sweden

Figure A 24: Swedish Industrial Production

Source & CommentsType: industrial production indexSource: Klovland (1998)

67

D.18.8. Austria

Figure A 25: Austrian Business Activity

Source & CommentsType: business activity indexSource: Statistische Reichsamt (1936, p. 210; Table 2), Statistische Reichsamt (1937, p. 83, Table 2)Comment: Indices have been linked, via re-basing them to 1934

D.18.9. Czechoslovakia

Figure A 26: Czech Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 295), Statistische Reichsamt (1937, p. 112)

68

D.18.10. Germany

Figure A 27: German Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 20, Table 10), Statistische Reichsamt (1937, p. 12, Table 10)

D.18.11. Canada

Figure A 28: Canadian Industrial Production

Source & CommentsType: industrial production indexSource: Statistische Reichsamt (1936, p. 456), Statistische Reichsamt (1937, p. 147)

69

D.18.12. United States

Figure A 29: American Business Activity

Source & CommentsType: business activity indexSource: Statistische Reichsamt (1936, p. 502), Statistische Reichsamt (1937, p. 165)

70