KEO Discussion Paper No. 179

Swinging Unstable Market after the Great Depression:
Daily Rice Pricing of Japan’s Futures and Spot Trades

Kiyotaka Maeda

Swinging Unstable Market after the Great Depression:
Daily Rice Pricing of Japan’s Futures and Spot Trades *Kiyotaka Maeda †Keio University

April 2024
Abstract
This study quantitatively investigates daily rice prices in Osaka’s futures and spot
markets from 1925 to 1939 to examine how the futures market served as the index price ofthe spot trade under the government’s frequent interventions. The functions of the futures
market fundamentally varied with seasons in response to the rice harvest. After the Great
Depression, the government extended its manipulations to control rice prices, and its
interventions greatly reduced the futures market’s role at times other than during the harvest
season. Consequently, the unstable futures market functionally swung in the 1930s.

Keywords: futures market, government intervention, agricultural product, JapanJEL classification codes: C22, G13, N25*

We acknowledge the Zengin Foundation for Studies on Economics and Finance for its financial
assistance as provided through the Grants-in-Aid for Scientific Research in 2024 (no. 2324) and theKansai University Library for its support to utilize the historical documents on the Osaka-Dojima Rice
Exchange.
†
Department of Japanese History, Faculty of Letters, Keio University, 2-15-45 Mita, Minato-ku, Tokyo
108-8345, Japan; E-mail: k-maeda@keio.jp

1. Introduction
Economists have been discussing the roles and failures of the government and the market, andeconomic historians have investigated how the government dynamically interacts with the market.
Particularly, they pay close attention to monetary policies and related financial markets (Barro andDavid 1983; Clarida, Gali, and Gertler 1999; Quennouëlle-Corre 2016, pp. 421–22). By contrast, there
is little literature on commodity markets and related policies, whereas Federico (2013, p. 164) assertsthat the government strengthened its intervention in commodity markets after the First World War(WWI). During the modern period, the futures market plays a crucial role in the pricing of commodity
trade.
The futures market collects disseminated information to provide index prices for spot trading(Goss 1981, p. 150; 1986, p. 2). Japan has the longest commodity futures market history and traded ricein futures markets. Rice is a significant commodity in Japanese society. Asian people have consumed
rice as a staple since ancient times, and the Japanese authorities collected rice as tax from farmers until
the pre-modern period (Hane and Perez 2015, pp. 198–201; Latham and Larry 1983, p. 260). Japanestablished the rice futures market in Osaka at the end of the seventeenth century, and rice dealers tradedin the futures market to hedge their risks until 1939 (Schaede 1989, pp. 499–502). Western countries
also established futures commodity markets in the mid-nineteenth century, such as the Chicago Boardof Trade in 1848 (Kaufmann 1984, p. 11). During the same century, the rice futures market in Osaka
was transformed into a modernized commodity exchange, and the Osaka-Dojima Rice Exchange(ODRE) began operating in 1893 (Bakken 1966, pp. 12–13). At the beginning of the next century,
governments tightened their regulations on commodity futures markets.
WWI altered the equilibrium of the commodity market. Specifically, agricultural production
expanded in the Americas during WWI, and farm product prices plummeted after the war ended (Nealand Cameron 2016, p. 334). To respond to catastrophic situations, the United States (US) government
enacted the Futures Trading Act of 1921 and the Grain Futures Act of 1922 to supervise and regulate
futures markets (Markham 2014, pp. 44–56). The US government reinforced its regulation of the futurestrade after the 1920s (Lurie 1980, pp. 233–56).
Other Western countries also strengthened their regulations and interventions in the market,
and governments enlarged their roles during the interwar period. Even after WWI, countries
participating in the war did not repeal their protectionist policies (Findlay and O’Rourke 2007, pp. 443–
48). Furthermore, the Great Depression boosted the government’s role. The US government launched
protectionist policies to bolster the American economy and intervened in agricultural markets throughthe Commodity Credit Corporation and the Federal Farm Board (Fausold 1977, pp. 362–77; Rucker
and Pasour 2007, pp. 459–62). European countries have also protected their domestic industries(Persson and Sharp 2015, p. 215); some imposed heavy tariffs and established quantitative restrictions
on agricultural commodities (Fernández 2016, pp. 104–06). These policies led to the disintegration of
the international commodity market (Hynes, Jacks, and O’Rourke 2012, pp. 119–43). Analogous toWestern countries, as the next section circumstantially mentions, the Japanese government strengthened1its control over the rice market after WWI and enacted new legislation to fortify its capacity in 1933. It
considered rice policies to be food policies aimed at stabilizing general prices.
Rice price fluctuations were a fundamental cause of general price-level changes in Japan. Riceaccounted for 13.1 percent of the weight of the Bank of Japan’s 1933-based wholesale price index,
whereas wheat accounted for only 1.6 percent (Bank of Japan, Research and Statistics Department 1987,
p. 40). This measurement of the price index was based on the fact that rice was one of the most important
goods in Japan’s commodity futures markets until the 1930s. It held 69.5 percent of the commodity
futures trade value from January 1923 to December 1932, while grain consisting of wheat and barleyaccounted for approximately 50 percent of the trade value in US commodity exchanges in 1921 (Figure
A1; Carlton 1984, pp. 245, 247). In short, rice had two remarkable aspects during the interwar period
in Japan: it was an object handled by economic policies and was a futures commodity. Hence, we focus
on the relationship between the markets and rice policies in Japan to explore how the government’s
interventions affected pricing in the commodity futures market after the 1920s because few studies
focus on the influence of the government’s policies on the commodity market’s performance.
Many scholars have studied the political history of government regulations and themanipulation of commodity futures markets during the interwar period. Economists, historians, andscholars of jurisprudence and public administration have analyzed futures market policies in the US
since the 1980s (Keaveny 2005; Lurie 1980; Markham 1987; 2014; Stassen 1982). The latest literatureinvestigates historical documents that were not utilized by previous studies, and Saleuddin (2018)
analyzes the Chicago Mercantile Exchange Group Archive’s documents. Antithetically, economic
historians have finally scrutinized pricing in commodity futures markets since the late 2000s, alongwith the diffusion of cliometric techniques.
Previous studies have shed light on cases in the US and the United Kingdom (UK). Jacks(2007) and Harrison (2023) focus on the US’s commodity futures markets. The former asserts that the
futures market suppressed price volatility from the late nineteenth century to the mid-twentieth century,
whereas the latter stresses that commodity market integration progressed after the 1910s. Cifarelli andPaesani (2016) analyze the Liverpool cotton futures market and maintain that speculative feedback
trades became a major cause of price fluctuations during the interwar period. By contrast, Chambers
and Saleuddin (2020) contend that copper and tin option prices approximated the Black-Scholes-Merton
theoretical values on the London Metal Exchange during the same period. Although these studies
employ advanced cliometric techniques, they do not extend their perspectives on the relationshipbetween pricing in commodity futures markets and related policies. The literature on commodity futures
markets in Japan shows the same tendency as that in the US and the UK.
Much literature exists on the history of rice policies and commodity exchanges in Japan. The
former includes Kawahigashi (1990), Mochida (1970), and Omameuda (1993), whereas the latterincludes Haneji (1989), Kotani (1953), and Teranishi (2011). However, there are only two quantitative
studies on pricing in rice futures markets and related policies. Ito, Maeda, and Noda (2017) find thatthe government’s policies to boost colonial rice distribution during WWI precluded appropriate rice2

pricing. Ito, Maeda, and Noda (2018) mention that the variation in the rice futures market’s role ingenerating the index prices of the spot trade relied on fluctuations in the quality difference between
domestic and colonial rice from the 1900s to the 1920s. However, these studies have two limitations.
The first limitation is the lack of an investigation into the period after 1933 when thegovernment introduced new legislation to tighten the control of rice prices. The second limitation is theuse of monthly data. Empirical studies on Japan’s stock market before the Second World War employ atime-series analysis based on daily data to grasp minute changes in pricing (Bassino and LagoardeSegot 2015; Daglish and Moore 2013). High-frequency data are requisites for an accurate understandingof the continuous changes in the relationship between market pricing and related policies. This study
tackles these limitations and quantitatively investigates the daily rice prices in the futures and spot
markets in Osaka from 1925 to 1939. We examine how the futures market served the index prices ofthe spot trade under the government’s frequent interventions.
The remainder of this paper is organized as follows. Section 2 provides a brief history of therice market and government rice policies in Japan during the interwar period. Section 3 presents ourtime-series econometric models and analysis strategies for scrutinizing rice pricing in the futures andspot markets. Section 4 illustrates the properties of our daily price data and preliminary test results.
Section 5 utilizes an impulse response function (IRF) analysis to indicate the time-varying structure ofrice markets. Section 6 employs a historical decomposition (HD) technique to examine the relationshipbetween pricing in rice futures and spot markets and the government’s rice policies. Finally, Section 7
presents our conclusions.

2. Historical Settings

2.1. Rice Market During the First Half of the Twentieth Century
Japan experienced modernization and industrialization after the 1880s, and its population andliving standards increased. These social changes promoted the expansion of rice demand, and thevolume of rice consumption per capita grew until the 1910s. After the following decade, although thetrend in rice consumption per capita was sustained, the total volume of rice consumption continued toincrease because of population growth (Figure A2).
The mechanism behind the growth in the rice supply changed drastically by the end of the
1910s. Before this turning point, growth in domestic rice production led to an increase in the rice supply(Figure A3). Local governments constructed irrigation facilities in agricultural regions, and theseutilities enabled farmers in Western Japan with a warm climate to utilize the double-cropping method(Ohkawa 1970, pp. 11–12). This change in farming methods boosted agricultural land productivity, andthe production volume per hectare of rice paddy soared from approximately 15 kokus in the 1890s to
approximately 19 kokus by the end of the 1910s (Figure A4). 1 However, the growth in domestic ricecultivation had not kept pace with the expansion in rice demand.
1

The “koku” is a standard unit of measurement in Japanese agriculture. One koku is equal to 180.39

liters and is roughly equivalent to 150 kilograms.
3

Japan increased its colonial rice imports from Taiwan and Korea after WWI (Figure A3). Japanestablished Taiwan and Korea as colonies in 1895 and 1910, respectively. These colonies initiallycultivated rice breeds that were different from the domestic ones, and colonial rice did not suit theJapanese palate. Accordingly, colonial governments modified the rice breeds in Taiwan and Korea toameliorate their taste and texture to make them similar to those of domestic rice. This countermeasure
diffused colonial rice in Japan after the late 1910s (Ito, Maeda, and Noda 2017, pp. 331–33).
Improvements in cropping methods and the growth in colonial imports raised the rice supply
in Japan. Nevertheless, these factors did not suppress supply fluctuations because climate change
affected rice cultivation (Figure A3). Land productivity and rice prices oscillated annually (Figures A2and A4). These properties forced rice dealers to face price volatility risk, and the futures trade functioned
to hedge this risk.
There were 17 rice exchanges in August 1939, when the rice futures trade ceased because ofwartime regulations. The two major rice exchanges, the ODRE and the Tokyo Rice and Merchandise
Exchange, held 36.2 percent and 26.8 percent of the futures trade volume in all rice exchanges fromJanuary 1930 to August 1939 (Tokyo Stock Exchange, Investigation Department 1930a; 1930b; 1931–
1939 (monthly series)). They handled more than 60 percent of the rice futures trade, and the ODRE
thrived as the largest rice exchange in Japan. The rice futures trade in every exchange consisted of threecontract types: deferred contracts (three months), second-nearest contracts (two months), and nearby
contracts (one month). There were differences in trade volume among these contracts, with the deferred
contract having the largest volume. From January 1930 to August 1939, in the ODRE, deferred contracts
accounted for 65.1 percent of the total trade volume, while second-nearest and nearby contracts
accounted for 21.3 percent and 13.6 percent, respectively (Osaka-Dojima Rice Exchange 1930–1935;
1936a; 1937–1939 (monthly series); Tokyo Stock Exchange, Investigation Department 1930a (monthly
series)).

2.2. Skyrocketing Rice Prices and the Rice Riots
The government labeled the futures trade as a cause of the speculative increase in rice prices.
Japan suffered from famine for two years starting in 1917, while enjoying consecutively abundant crops
for three years until 1916 (Figure A4). Although this change in harvest conditions induced a surge inrice prices, the government misconstrued active purchases in the futures market as the reason for the
sharp price movement in the spot market (Ministry of Agriculture and Commerce, Food Bureau 1924,
p. 52; Figure A2). The Ministry of Agriculture and Commerce decided to suppress the speculative tradeof essentials for life and enacted the Bōri Torishimari Rei (Ban on Anti-Excessive Profits) in September
1917. 2 Nevertheless, rice prices continued to skyrocket the following year because of famine and theoverseas deployment of troops.
2

Nakashoji, Ren (Minister of Agriculture and Commerce). Bōri wo Mokuteki to suru Baibai no

Torishimari ni kansuru Shōrei Happu no Ken wo kettei su (The Decision of Issuance of the Ban of AntiExcessive Profit). National Archives of Japan. Rui-01265100. Tokyo, Japan, August 29 1917.
4

According to the Anglo-Japanese Alliance, Japan deployed its military force in Siberia in
August 1918 as a part of WWI (Dunscomb 2011, pp. 55–56). This military action required large
quantities of rice. Rice demand suddenly expanded, and traders hoarded rice, expecting higher prices.
These circumstances raised rice prices considerably, and ordinary people fell into hardship (Figure A2).
Consequently, the Kome Sōdō (Rice Riots) occurred in the summer of 1918. A few million protesters
displayed disorderly behavior throughout the country and ousted the Terauchi Cabinet in September
1918 (Lewis 1990, pp. 11–15). The government utilized the Ban on Anti-Excessive Profits to crack
down on speculative transactions in rice exchanges from the end of 1917. Eleven cases were detectedfrom November 1917 to August 1918, and dealers in the ODRE engaged in five cases (Ministry ofAgriculture and Commerce, Food Bureau 1924, pp. 66–68). The ODRE also imposed strict controls on
speculative trade and punished 35 dealers from 1917 to 1919, while it had approximately 60 dealers(Osaka-Dojima Rice Exchange, 1917a, pp. 3–4; 1917b, p. 6; 1918b, p. 6; 1919a, p. 8; 1919b, pp. 7–8).
Japanese rice cultivation experienced abundant crops for two years beginning in 1919, andrice prices stopped increasing (Figures A2 and A4). However, the government maintained its hostile
view of futures transactions even after 1921 and ordered the ODRE to strictly supervise trade anddealers in August 1922. 3 Through the Rice Riots, the Japanese government realized the importance ofrice market control. Accordingly, the government provided the necessary legislation.

2.3. Market Control by the Rice Law
The government enforced the Beikoku Hō (Rice Law) to adjust the balance of the rice supply
and demand on 4 April 1921. This law allowed the government to sell or purchase rice when the rice
market faced a shortage or excess supply. 4 However, it did not stipulate that the government could trade
in response to price fluctuations. The law required the government to estimate the balance between therice supply and demand before intervening. Accordingly, the government could not quickly respond to
the sudden movement in rice prices and revised the Rice Law three times after 1925 (Ministry ofAgriculture and Forestry 1959, pp. 173–74).
The government enforced the first, second, and third revised laws on 1 November 1925, 1
July 1931, and 4 October 1932, respectively. The first revised law empowered it to intervene directly in3

Tsurumi, Sakio (Chief, Commercial Bureau, Ministry of Agriculture and Commerce). Osaka Dojima

Beikoku Torihikijo Rijichō ate Tsūchō, Kaisei Torihikijo Hō Shikō no Ken (Notice of the Revised
Exchange Law to the president of the Osaka-Dojima Rice Exchange). Kansai University Library.
Dōjima Kome Shijō Monjo 1-101. Suita City, Osaka Prefecture, Japan, 30 August 1922.
4

Yamamoto, Tatsuo (Minister of Agriculture and Commerce). Beikoku Hō An Riyūsho (The Statementof Reason of the Establishment of the Rice Law). National Archives of Japan. Rui-01405100. Tokyo,
Japan, 16 February 1921.
5

the market to adjust rice prices. 5 Nevertheless, the government faced difficulties in controlling prices
because the first revised law did not state the criteria for government intervention. The second revised
law clearly indicated these criteria. According to this law, the government fixed the maximum andminimum rice prices by deliberating on fluctuations in rice and general prices. When rice prices
deviated from the range between the maximum and minimum prices, the government intervened in therice spot market. 6 During the same period, the depression and decreased rice prices negatively affected
farmers’ lives.
Japanese farmers earned income from agriculture and sericulture. The latter was one of therepresentative side businesses in farming villages, and raw silk was Japan’s most significant export
commodity to the US from the end of the nineteenth century to the 1930s (Hemmi 1970, pp. 312–13).
However, the Great Depression of 1929 had a tremendous negative impact on Japan’s raw silk market.
From 1929 to the following year, raw silk prices and export values decreased by 34 percent and 47
percent, respectively (Bank of Japan, Research and Statistics Department 1987, p. 51; Kobara 1935, pp.
376–77). Furthermore, Japanese agriculture experienced bumper crops in 1930 and rice prices fellsignificantly the following year (Figures A2 and A4). The Great Depression and the plummeting pricesof agricultural products caused the Showa Depression. These depressions suppressed rice prices until
1934 (Figure A2). Farmers faced a reduction in their income and suffered poverty (Gordon 2009, p.
182). This situation required the government to strengthen its control over rice prices.
The government enforced its third revised Rice Law in October 1932. This law stipulated thatthe government should factor in rice production costs when deciding minimum prices. This was a
support measure for farmers. The Imperial Diet discussed further agricultural assistance measures.
Japanese politics introduced a two-party system in the 1920s: the Rikken Seiyūkai (Associationof Friends of Constitutional Government: AFCG) and Rikken Minseitō (Constitutional Democratic
Party). On 4 June 1928, the Kwantung Troop of the Japanese Army assassinated Zhang Zuolin, Generalof the Fengtian clique in China, without the Japanese government’s order. This incident reversed thestandings between the governing and opposition parties and resulted in the banishment of the AFCG
from politics in July 1929. The AFCG planned to strongly support the farmers in winning the next
election. It proposed the introduction of the Rice Monopoly System to rigidly control rice prices inAugust 1931 (Confederation of Japanese Exchanges, Executive Board of Rice Division 1932, pp. 1–4).
This proposal stimulated discussions on the stabilization of rice prices. The government established the
Beikoku Tōsei Chōsakai (Investigation Committee on Rice Control) to incorporate the plan for rice
5

Ministry of Agriculture and Forestry. Setsumei: Beikoku Hō chū Kaisei Hōritsu An (Explanations:

Draft of the Revised Rice Law). National Archives of Japan. Hei12-Nōsui00002100. Tokyo, Japan, 24
January 1925.
6

Machida, Chuji (Minister of Agriculture and Forestry), Inoue Junnosuke (Minister of Finance), andMatsuda, Genji (Minister of Colonial Affairs). Beikoku Hō chū Kaisei Hōritsu An Riyūsho (TheStatement of Reasons of the Revised Rice Law). National Archives of Japan. Rui-01759100. Tokyo,
Japan, 23 January 1931.
6

price control in November 1932. 7 Finally, it abolished the third revised Rice Law on 31 October andenforced the Beikoku Tōsei Hō (Rice Control Law) on 1 November. 8

2.4. The Rice Control Law and Wartime Regime
The Rice Control Law strengthened the promptness, autonomy, and scale of the government’s
authority to control rice prices (Ministry of Agriculture and Forestry 1959, pp. 206–07). First, thegovernment acquired the ability to respond quickly to fluctuations in rice prices. Under the Rice Law,
it received permission from the Beikoku Iinkai (Rice Committee) before intervening in the rice market.
By contrast, the Rice Control Law did not require the government to obtain clearance from thecommittee. This shortened procedure improved the promptness of government price controls. Second,the Rice Control Law offered autonomy over the government’s control of rice prices. The government
autonomously determined intervention prices to steer market prices, while the Rice Law mandated thatthe government purchased and sold rice at market prices. Third, the Rice Control Law increased thescale of government intervention. The government raised the budget for purchasing rice by 37 percent,from 350 million yen to 480 million yen (Omameuda 1993, pp. 296–97).
The Rice Control Law was characterized by the above features and increased the scale andfrequency of government intervention. Figure 1 shows the monthly volume of domestic rice suppliedby the government from April 1921 to December 1937. 9
During the Rice Law’s operation, the government occasionally intervened in the rice spot
market, and the intervention volume was less than 0.5 million kokus. By contrast, after enforcing the
Rice Control Law in November 1933, it intervened nearly every month and increased its intervention
volume. The Rice Control Law intensified the government’s activities to control rice prices.
Furthermore, in 1936, the government acquired the ability to adjust the inflow volume from production
areas to distribution and consumption regions.
Rice prices remained low until 1934, and farmers suffered from poverty even after the RiceControl Law was enforced in 1933 (Figure A2). In response, the government established the Beikoku
Taisaku Chōsakai (Committee on the Rice Problem) in September 1934 to investigate
countermeasures. 10 The committee recommended that the government should create the Beikoku Jichi

7

Investigation Committee on Rice Control. Beikoku Tōsei Chōsakai Kansei (Edict of the Investigation

Committee on Rice Control). National Archives of Japan. Hei15-Zaimu00309100. Tokyo, Japan, 8November 1932.
8

Ministry of Agriculture and Forestry. Beikoku Tōsei Hō Shikō Kijitsu no Ken (Implementation date of

the Rice Control Law). National Archives of Japan. Hei12-Nōsui00011100. Tokyo, Japan, 22 September
1923.
9There is no date on the intervention volume after January 1938.

10

The Cabinet. Beikoku Taisaku Chōsakai Kansei wo sadamu (Enactment of the Committee on Rice

Problem). National Archives of Japan. Rui-01849100. Tokyo, Japan, 31 August 1934.
7

Figure 1
Government’s supply of domestic rice in Japan (April 1921–December 1937)
Sources: Ministry of Agriculture and Commerce, Food Bureau 1922–1923, 1925a,
1925b; Ministry of Agriculture and Forestry, Agricultural Bureau 1926–1930;
Hasumi 1957; Ota 1938.

Kanri Hō (Rice Autonomous Management Law) in January 1935. 11 In compliance with thisrecommendation, the government promulgated and enforced the law on 28 May and 20 September 1936,
respectively. This law enabled the government to force production areas to store excess rice in exchange
for subsidies. 12 When rice farmers had abundant crops, the government could reduce the inflow of ricein the spot market to prevent a decrease in prices. Nevertheless, it did not invoke this law since rice
prices skyrocketed after 1935 (Figure A2; Ministry of Agriculture and Forestry 1959, pp. 250–51).
The Japanese government strengthened its ability to control rice prices in the spot market in
1925 and gained power to manipulate rice distribution in 1936. These policies were transformed in 1937
when Japan initiated a wartime regime.
Japan and China exchanged gunfire at the Marco Polo Bridge Incident in the suburbs ofBeijing on 7 July 1937. Then, they expanded their hostility and launched the Second Sino-Japanese War
11

Committee on Rice Problem. Beikoku Taisaku An (Plan of Rice Control). National Archives of Japan.

San-02124100. Tokyo, Japan, 19 January 1935.
12

The Cabinet. Beikoku Jichi Kanri Hō no Shikō Kijitsu wo sadamu (Implementation date of the Rice

Autonomous Management Law). National Archives of Japan. Rui-01994100. Tokyo, Japan, 15
September 1936.
8

(Tanaka 2023, pp. 140–42). The Japanese government constituted wartime legislation and enforced the
Kokka Sōdōin Hō (National Mobilization Law) on 5 May 1938. 13 Wartime legislation attempted tomaximize Japan’s industrial and agricultural production capacities. 14 This regime compelled ricepolicies to secure and control necessities (Ministry of Agriculture, Forestry and Fisheries 1980, pp. 321–
22). Finally, the government enacted the Beikoku Haikyū Tōsei Hō (Rice Distribution Control Law) on
11 April 1939. 15 This law forbade the rice futures trade because the government directly controlled theprices and distribution of staple food. Thus, all rice exchanges had abolished their futures markets by
the end of August 1939.
This section explores the government’s control of the rice market from the 1910s to the 1930s
and reveals two points. First, the government regarded the futures trade as a cause of disturbances inthe spot market and cracked down on dealers in exchanges. Second, it intervened in the spot market tocontrol rice prices after the first revision of the Rice Law and changed legislation frequently until theend of the 1930s. Accordingly, we focus on the relationship between futures and spot prices and theinfluence of government policies on rice pricing. The next section examines the methodology used toinvestigate these two points.

3. Methodology
We apply a four-dimensional vector error correction (VEC) model using spot prices and threeseries of futures prices: deferred, second-nearest, and nearby contracts. The VEC model originates fromthe following vector autoregressive model:𝑝𝑝

𝒚𝒚𝒕𝒕 = 𝒗𝒗 + ∑𝑖𝑖=1 𝑨𝑨𝒊𝒊𝟏𝟏 𝒚𝒚𝒕𝒕−𝒊𝒊 + 𝜺𝜺𝒕𝒕

(1)where 𝒚𝒚𝒕𝒕 = [𝑦𝑦1𝑡𝑡 , 𝑦𝑦2𝑡𝑡 , 𝑦𝑦3𝑡𝑡 , 𝑦𝑦4𝑡𝑡 ]′: 𝑦𝑦1𝑡𝑡 , 𝑦𝑦2𝑡𝑡 , 𝑦𝑦3𝑡𝑡 , and 𝑦𝑦4𝑡𝑡 are the futures prices of the deferred, second-

nearest, and nearby contracts, and the spot prices, respectively; 𝒗𝒗 is a four-dimensional constant vector;

𝑨𝑨𝒊𝒊𝟏𝟏 is a four-by-four parameter vector; and a four-dimensional white-noise vector 𝜺𝜺𝒕𝒕 =[𝜀𝜀1𝑡𝑡 , 𝜀𝜀2𝑡𝑡 , 𝜀𝜀3𝑡𝑡 , 𝜀𝜀4𝑡𝑡 ]′.

We subtract 𝒚𝒚𝒕𝒕−𝟏𝟏 from both sides of Equation 1 and present the results in Equation 2:𝒑𝒑(2)

Δ𝒚𝒚𝒕𝒕 = 𝒗𝒗 + 𝚷𝚷𝒚𝒚𝒕𝒕−𝟏𝟏 + ∑𝒊𝒊=𝟏𝟏 𝚪𝚪𝒊𝒊 Δ𝒚𝒚𝒕𝒕−𝒊𝒊 + 𝜺𝜺𝒕𝒕
13

The Cabinet. Kokka Sōdōin Hō wo sadamu (Enactment of the National Mobilization Law). National

Archives of Japan. Rui-02157100. Tokyo, Japan, 31 March 1938.
14

The Cabinet. Gunjyu Kōgyō Dōin Hō no Tekiyō ni kansuru Hōritsu wo sadamu (Enactment of the

Applicable Law of the Military Industry and Mobilization Act). National Archives of Japan. Rui02075100. Tokyo, Japan, September 9 1937.
15

The Cabinet. Beikoku Haikyū Tōsei Hō wo sadamu (Enactment of the Rice Distribution Control Law).

National Archives of Japan. Rui-02262100. Tokyo, Japan, 29 March 1939.
9𝑝𝑝

where a coefficient matrix 𝚷𝚷 = ∑𝑗𝑗=1 𝑨𝑨𝒋𝒋 − 𝑰𝑰𝒎𝒎 contains a loading matrix 𝜶𝜶 and a cointegration matrix𝑝𝑝

𝜷𝜷 , such that 𝚷𝚷 = 𝛂𝛂𝛂𝛂′ . If the cointegration order is 𝑟𝑟 , both matrices 𝚷𝚷 and 𝚪𝚪𝒊𝒊 = − ∑𝑗𝑗=𝑖𝑖+1 𝑨𝑨𝒋𝒋 have 𝑟𝑟
values less than four. Finally, the VEC model is as follows:𝑝𝑝

Δ𝒚𝒚𝒕𝒕 = 𝒗𝒗 + 𝜶𝜶𝜶𝜶′𝒚𝒚𝒕𝒕−𝟏𝟏 + ∑𝑖𝑖=1 𝚪𝚪𝒊𝒊 Δ𝒚𝒚𝒕𝒕−𝒊𝒊 + 𝜺𝜺𝒕𝒕

(3)Using the calculated parameters of the VEC model, we estimate a vector autoregressive model to apply
innovation accounting that stems from a vector moving average (VMA) representation. The VMA form
can be expressed as follows:𝒚𝒚𝒕𝒕 = 𝝁𝝁 + ∑∞𝑖𝑖=0 𝝓𝝓𝒊𝒊 𝜺𝜺𝒕𝒕−𝒊𝒊(4)where 𝝁𝝁 = (𝑰𝑰 − 𝑨𝑨𝟏𝟏 𝑳𝑳)−1 𝒗𝒗 and 𝝓𝝓𝒊𝒊 = 𝑨𝑨𝒊𝒊𝟏𝟏 . Equation 4 is a VMA representation, enabling us to computean IRF. We supply a standard deviation unit of impulse on the disturbance term in the zero period in
Equation 4 and multiply the parameters by the impulses in each period to compute the IRF. The IRF

indicates the time path of various shocks to variables in a VEC system (Sims 1980). We then apply an
IRF analysis to capture the government’s political influence on pricing in the futures and spot markets.
The IRF analysis procedure consists of four steps.
First, we divide the entire sample into multiple subsamples according to the alterations in thegovernment’s legislation for controlling rice prices. Second, we utilize subsamples and estimate
multiple VEC models. Third, we employ the parameters derived from the estimated VEC models tocompute the IRFs. Fourth, we compare the IRFs stemming from the different subsamples. The
distinction between the IRFs demonstrates that pricing in the futures and spot markets varied withchanges in the government’s control of rice prices. If we detect this distinction, we apply an additional
analysis to precisely identify the relationship between government policies and rice pricing.
Balcilar et al. (2018) assert that an IRF cannot grasp the influence of a subsequent shock on
the variables and illustrates only the average structure of the market. By contrast, Burbidge and Harrison(1985) propose an HD that captures the cumulative effects of subsequent shocks and the variability ofrelative shocks (Kilian and Lütkepohl 2017). This method is based on the VEC model in Equation 4
and is specified as follows:
𝒋𝒋−𝟏𝟏

𝒚𝒚𝒕𝒕+𝒋𝒋 = ∑𝒊𝒊=𝟎𝟎 𝝍𝝍𝒊𝒊 𝜺𝜺𝒕𝒕+𝒋𝒋−𝒊𝒊 + �𝑿𝑿𝒕𝒕+𝒋𝒋 𝜷𝜷 + ∑∞𝒊𝒊=𝒋𝒋 𝝍𝝍𝒊𝒊 𝜺𝜺𝒕𝒕+𝒋𝒋−𝒊𝒊 �(5)where 𝒚𝒚𝒕𝒕+𝒋𝒋 is a multivariate stochastic process; 𝜺𝜺 is its multivariate noise process; 𝑿𝑿 is the deterministic

part of 𝒚𝒚𝒕𝒕+𝒋𝒋 ; and 𝒊𝒊 is the number of periods. The first term on the right-hand side represents the part of𝒚𝒚𝒕𝒕+𝒋𝒋 caused by the shock and the second term illustrates the prediction of the price series stemmingfrom information at time 𝒕𝒕 indicating the event date. We run the HD analysis using the entire sample toinvestigate consecutive changes in rice pricing.

10This methodology requires a large number of data points on rice prices. For this, we use the
ODRE’s historical documents to create our own high-frequency datasets.

4. Data

4.1. Data Sources
The official statistics of the Japanese government report only monthly rice price data, andmany previous studies refer to the Ministry of Agriculture and Commerce, Commercial Bureau (1925–
1926), Ministry of Commerce and Industry, Commercial Bureau (1927–1929; 1933–1940), andMinistry of Commerce and Industry (1930–1932). However, we need to explore daily prices, because
the investigation of monthly prices cannot reveal the government’s political influence on rice pricing.
The government frequently changed the legislation for rice price control during the 1920s and thefollowing decade and the monthly price data do not provide a sufficient sample size to obtain accurate
results. For example, the government implemented the second and third revisions of the Rice Law inJuly 1931 and October 1932, respectively; hence, it revised the law again after only 15 months from theprevious revision. No econometric technique can accept only 15 pieces of price data; hence, an increasein sample size is required. To overcome this problem, we do not rely on published official statistics;
rather, we discover historical documents issued by the ODRE to construct daily price data.
There were only a few historical documents on the futures trade in Japan until the 1930s since
the US Armed Forces repeatedly carried out air raids on most Japanese cities from 1942 to 1945 (Totman
2014, p. 447). These assaults burned massive amounts of historical material stored in cities.
Nevertheless, the ODRE’s historical documents miraculously survived. The ODRE abandoned its
operations in 1939 and moved its documents to Kansai University in Osaka Prefecture (Kansai
University Library 1960, p. 179). This university was located in Chisato Village, which was mergedwith three adjacent towns and villages in Suita City in 1940 (Kansai University, Editorial Committeeof the Seventy Years’ History 1956, pp. 5, 232; Suita City Office 1989, pp. 283–85). This city isapproximately 10 km from the center of Osaka City, where the US launched 28 massive air strikes in1945 (Osaka City Office 1953, pp. 620–26). Suita suffered only minor war damage (Suita City Office
1989, p. 285). Thus, we collaborated with the Kansai University Library to employ the rare documents
and records of the ODRE.
These documents supply the daily futures prices of the three contract months in the ODRE
from 1 September 1914 to 19 August 1939. The ODRE recorded the daily prices of all contract months
in a ledger entitled Kome Seisan Torihiki Sōbahyō (Price Table of Rice Futures Trade), and this material
provides the daily prices from September 1914 to December 1920. 16 During this period, the ODRE also
recorded the daily trade volume of every contract month in another ledger entitled Genzai Torikumidaka
Baibaidekidaka oyobi Tesūryō Shūnyūdaka Hōkokuhyō (Daily Report of Open Interest, Trade Volume,
16

Osaka-Dojima Rice Exchange. Kome Seisan Torihiki Sōbahyō, Yori Taisho 3-nen Itaru Showa 9-nen(Price Table of Rice Futures Trade, 1914–1934). Kansai University Library. Dōjima Kome Shijō Monjo
1-87. Suita City, Osaka Prefecture, Japan, 1914–1934.
11

and Commission Revenue). 17 In January 1921, it began to issue the monthly report entitled OsakaDojima Beikoku Torihikijo Seisanbu Geppō (Monthly Report of the Osaka-Dojima Rice Exchange),
which includes the daily prices and trade volume of all contract months. We use these documents asdata sources for daily prices and trade volumes in futures markets (Osaka-Dojima Rice Exchange 1921–
1926; 1927a; 1928–1939). By contrast, acquiring daily spot prices is an arduous task.
Data sources indicating continuous daily spot prices in Osaka until October 1925 do not exist;
however, we can obtain data after November 1925 from two data sources. The first is the Ministry ofAgriculture and Forestry Rice Bureau (1937), which contains data from November 1925 to April 1937.
The second is Osaka Asahi Shimbun (1937–1939), a major daily newspaper in Osaka, which provides
the same kind of data for the remaining period until August 1939. 18 The futures and spot price data
include various types of data breaches.

4.2. Data Breaches
Primarily, the futures and spot markets were closed every Sunday; hence, our data include six
prices in each series per week. Furthermore, there are three other types of breaches. The first is regularholidays, which consist of national holidays and religious ceremonies at the Imamiya Ebisu Shrine andOsaka Tenmangu Shrine near the ODRE (Table A1). The second type is temporal national holidays,
which comprise four types of events: military reviews attended by the emperor, state funerals, imperial
ceremonies, and religious ceremonies at the Meiji Jingu Shrine, Ise Jingu Shrine, and Atsuta Jingu
Shrine (Table A2). These shrines were closely related to the Imperial Family. The third type is atemporary trade suspension, which can be caused by four things (Table A3).
The first is the government’s order. In April 1927, the government ordered all rice exchanges
to suspend futures trading. From March to May 1927, Japan faced a banking panic, and 11 private banks
went into liquidation (Flath 2014, p. 58). Under this upheaval in the domestic financial market, thegovernment issued a moratorium in April 1927 directing financial institutions to extend payments.
According to this order, the ODRE suspended its operations from 22 April to 13 May 1927 (OsakaDojima Rice Exchange 1927b, p. 2). The second reason is the ODRE’s decision to resolve market
disturbances. The ODRE occasionally stopped its futures trade to calm turmoil when futures prices
17

Osaka-Dojima Rice Exchange. Genzai Torikumidaka Baibaidekidaka oyobi Tesūryō Shūnyūdaka

Hōkokuhyō, Yori Meiji 45-nen Itaru Taisho 5-nen (Daily Report of Open Interest, Trade Volume, andCommission Revenue, 1912–1916). Kansai University Library. Dōjima Kome Shijō Monjo 3-3. Suita
City, Osaka Prefecture, Japan, 1912–1916; Osaka-Dojima Rice Exchange. Genzai Torikumidaka
Baibaidekidaka oyobi Tesūryō Shūnyūdaka Hōkokuhyō, Yori Taisho 6-nen Itaru Taisho 10-nen (Daily
Report of Open Interest, Trade Volume, and Commission Revenue, 1917–1921). Kansai University
Library. Dōjima Kome Shijō Monjo 3-4. Suita City, Osaka Prefecture, Japan, 1917–1921.
18

Osaka Asahi Shimbun, various dates (2 May 1937–26 August 1939). We browse this newspaper byutilizing the Asahi Shimbun Cross-Search (https://database.asahi.com/index.shtml), an online article
database provided by the Asahi Shimbun Company in Japan.
12

skyrocketed. The third reason is natural disasters such as typhoons halting Osaka’s regular functioning.
For example, the ODRE closed its market on 21 September 1934 after a heavy typhoon hit Osaka anddestroyed major warehouses. The ODRE could not deliver rice and suspended trade in nearby contracts
from 25 to 29 September 1934. The fourth reason is coup attempts. Japan experienced coup attempts
by military officers on 15 May 1932 and 26 February 1936, resulting in the ODRE stopping trading on
16 May 1932 and over 26–29 February 1936, respectively.
As our daily dataset contains these breaches, we analyze only the days when the ODRE
operated its futures market. We ignore these breaches and connect the data before and after suspension.
These connected data include a few breaches of nearby contracts every month because the expiration
date of nearby contracts was a few days earlier than that of the other contracts. A nearby contract was
required to ensure sufficient days to deliver physical rice. Therefore, we disregard these breaches andconnect them to the remaining data.
In contrast to futures prices, the spot market closed for only nine days after November 1925,
when the ODRE was in operation. We apply the Catmull-Rom spline interpolation technique tointerpolate the nine missing spot prices (Catmull and Rom 1974). The completed dataset includes 3,762
observations in each series from 2 November 1925 to 19 August 1939.

4.3. Preliminary Analyses
Figure 2 shows the daily futures and spot rice prices in Osaka from 2 November 1925 to 19
August 1939.
We take the first difference in the natural log of the four rice price series to satisfy thestationarity condition and obtain a sample of 3,761 observations. We divide the entire sample into fivesubsamples according to the enforcement of the legislation for controlling rice prices: the second revised
Rice Law on 1 July 1931, the third revised Rice Law on 4 October 1932, the Rice Control Law on 1
November 1933, and the Rice Autonomous Management Law on 20 September 1936. These fivesubsamples contain 1,517, 351, 298, 792, and 803 observations, respectively (Table A4). Before
estimating the VEC models, we conduct the unit root test, seasonal-trend decomposition procedure
based on local regression (STL), and cointegration test in the preliminary analysis.
As the first step in the preliminary analysis, we apply the augmented Dickey-Fuller andPhillips-Perron tests as unit root tests to confirm whether the data satisfy the stationarity condition. The
augmented Dickey-Fuller test uses the Akaike information criterion to select the optimal lag length. The
Phillips-Perron test adopts Newey and West’s (1987) method based on the Bartlett kernel to fix the
optimal bandwidth. These tests indicate that the first difference in the natural log of all price seriessatisfies the stationarity condition (Table A4).
The second step of the preliminary analysis is the STL analysis. The rice trade fluctuatedseasonally, and the trade volume increased during the harvest season. Japanese farmers harvest ricefrom September to November, and rice circulation expanded before and after the year-end during thefirst half of the twentieth century. The volume of rice stocked in Osaka tended to peak in December or

13

40
36
32
28
24
20
16
12

40
36
32
28
24
20
16
12

1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939

Price (yen per koku)

40
36
32
28
24
20
16
12

(D) Spot

1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939

Price (yen per koku)(C) Nearby

1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939

Price (yen per koku)

40
36
32
28
24
20
16
12

(B) Second-nearest

1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939

Price (yen per koku)(A) DeferredFigure 2
Futures and spot prices of rice in Osaka (2 November 1925–19 August 1939)
Sources: Ministry of Agriculture and Forestry, Rice Bureau 1937; Osaka Asahi Shimbun 2 May 1937–26
August 1939; Osaka-Dojima Rice Exchange 1921–1924, 1925a, 1926a, 1927a, 1928–1935, 1936a,
1937a, 1938a, 1939; Kome Seisan Torihiki Sōbahyō, Yori Taisho 3-nen Itaru Showa 9-nen (Price
Table of Rice Futures Trade, 1914–1934). Kansai University Library. Dōjima Kome Shijō Monjo 187. Suita City, Osaka Prefecture, Japan, 1914–1934.

January between 1926 and 1939 (Figure A5). The futures market thrived during the harvest season.
Figure 3 shows the daily volume of the rice futures trade in the ODRE from November 1925 to August
1939.
Deferred contracts held approximately 70 percent of the total futures trade volume in the
ODRE, and its volume tended to increase during the fall. Therefore, we should assume the possibility
that price data exhibit seasonality.
Econometric studies ordinarily utilize seasonal unit root tests such as the HEGY and Canova
and Hansen tests to confirm the seasonality of data (Canova and Hansen 1995; Hylleberg et al. 1990).
These tests require the periodicity to be set according to the data frequency. For example, the periodicity
of monthly data is 12. However, fixing the periodicity of our data is difficult because the number ofobservations per year fluctuates from 250 to 277. Alternatively, we apply the STL analysis to the entire
sample to confirm seasonality.
Cleveland et al. (1990) suggest the following basic idea of the STL analysis:(6)

𝒀𝒀𝒗𝒗 = 𝑻𝑻𝒗𝒗 + 𝑺𝑺𝒗𝒗 + 𝑹𝑹𝒗𝒗
14

Figure 3
Daily volume of the rice futures trade in the ODRE (2 November 1925–19 August 1939)
Sources: Osaka-Dojima Rice Exchange 1925a, 1926a, 1927a, 1928–1935, 1936a, 1937a, 1938a, 1939.

where 𝒀𝒀𝒗𝒗 , 𝑻𝑻𝒗𝒗 , 𝑺𝑺𝒗𝒗 , and 𝑹𝑹𝒗𝒗 represent the price, trend, seasonal, and remainder components, respectively.

We assign 272, 23, 437, and 273 as the cycle specification, seasonal smoothing parameter, trendsmoothing parameter, and smoothing parameter of the low-pass filter, respectively, following Cleveland15

et al. (1990). The cycle specification is the annual average number of observations. The results of the
STL analysis show that the seasonal components of the four price series border on zero (Figure A6),
suggesting that we can ignore the seasonality of the price data. The conversion of the original price data
into the first difference of the natural log of the data removes such seasonality.
The final step of the preliminary analysis is a cointegration test. A VEC model requires datawith cointegration relationships. We apply Johansen’s (1991) trace test to the natural log of the entire
sample and five subsamples. This test rejects at least one null hypothesis at the one percent significancelevel: 𝑟𝑟 = 0 (no cointegration). It also indicates the number of cointegration relationships among thevariables of each sample as requisite information for VEC estimations (Table A5).

The next section utilizes the five subsamples and employs IRF analysis to investigate whether

changes in the government’s rice policies affected rice pricing in the futures and spot markets.

5. IRF Analyses on the Subsamples

5.1. VEC Estimation and IRF Computation
We apply the lag exclusion Wald test to the subsamples to obtain the optimal lag length for
the VEC estimations (Panels A to E of Table A6). This study estimates five VEC models by utilizing
the optimal lag length and number of cointegration relationships derived from Johansen’s (1991) trace
test (Table A5 and Panels A to E of Table A7).
We use these five estimated VEC models to compute the IRFs. Before calculating the IRFs,
we determine the Cholesky order as follows: deferred, second-nearest, nearby futures prices, and spot
prices. This sequence of futures prices is followed by the trade volume in the ODRE, as Figure 3 shows.
Finally, we compute five IRFs for the subsamples. Each panel of Figure 4 shows the IRFs of thesubsamples in each period.
We observe these five IRFs to explore changes in the relationships among the four price series,
along with alterations in the government’s rice policies in each period.

5.2. Interpretations of the IRFs
Panel A in Figure 4 shows the IRF for the first period under the first revised Rice Law. This
indicates the following. First, the futures market formed deferred prices independently. The first row ofPanel A demonstrates that the other prices did not affect fluctuations in deferred prices. Second, thefluctuation in deferred prices influenced other futures and spot prices, according to the first column ofPanel A. Third, as the fourth row of Panel A indicates, the variation in deferred and spot prices
determined the movement of spot prices. In summary, deferred prices acted as index prices in the futuresand spot markets during the first period.
Panel B presents the IRF during the second period under the second revised Rice Law. The
shape of the IRF is the same as that of the first period. This implies that the pricing structure did notchange from the first to the second period. Nevertheless, the four functions in the first column andbottom-right corner are higher than those in the first period. Therefore, the deferred contract trade in

16(A) First period (2 November 1925–29 June 1931)(B) Second period (1 July 1931–3 October 1932)17

(C) Third period (4 October 1932–30 October 1933)(D) Fourth period (1 November 1933–19 September 1936)18

(E) Fifth period (21 September 1936–19 August 1939)

Figure 4
Impulse response functions for the rice futures and spot trades in Osaka
Note: Dashed lines represent the 99% confidence interval.

the futures market and spot trade enhanced their power to generate prices under the second revised Rice
Law.
Panel C illustrates the third period under the third revised Rice Law. This explains that therelationships among the four price series remained fixed. The interrelation between deferred and spot
prices did not change from the second to the third period, although the influence of deferred prices on
second-nearest and nearby prices declined slightly in the futures market.
Panel D indicates the IRF of the fourth period when the government frequently intervened in
the rice market according to the Rice Control Law. The pricing structure was altered from the second
and third periods to the fourth period. The first column shows that the three functions from deferred
prices to second-nearest, nearby, and spot prices decreased. The deferred contract’s role in providing
index prices in the futures and spot markets declined after the enforcement of the Rice Control Law.
Panel E shows the IRF during the fifth period when the government administered the RiceControl Law and the Rice Autonomous Management Law simultaneously. The pricing relationshipbetween the futures and spot markets disappeared during this period. According to the first column, thedeferred price’s role as an index price declined markedly. Specifically, the function in the bottom-left19corner, which describes the influence of deferred prices on spot prices, borders zero. On the other hand,the function in the upper-right corner indicates that the interrelation between spot and deferred prices
remains zero. Consequently, the Rice Control Law and the Rice Autonomous Management Law
separated the futures and spot markets.
The IRF analyses in this section demonstrate that the pricing structures in the futures and spot
rice markets varied accordance to alterations in rice price control legislation. Accordingly, the following
section applies the HD analyses to the entire sample and investigates consecutive changes in rice pricing.

6. HD Analyses on the Whole Sample

6.1. Computation of the 30-Day Moving Average Absolute HD Ratio
By employing the optimal lag length and the number of cointegration relationships that stemfrom the lag exclusion Wald test and Johansen’s (1991) trace test, respectively, we estimate the VEC
model to compute the HD (Table A5, Panel F of Table A6, and Panel F of Table A7).
Panel A of Figure 5 shows the HD value and total stochastic value. The HD value is the degreeof influence of the causal variable on the outcome variable. The total stochastic values are equal to thesum of the HD values in the same column. Hence, they have equal values in the same column.
Accordingly, we divide the HD value by its relevant total stochastic value to monitor the relative
contribution to fluctuations in the outcome variable. This is the HD ratio (HDR), whose deviation fromzero indicates the power of the causal variable to alter the outcome variable (Panel A of Figure A7). We
observe this deviation by converting the HDR to the absolute HDR (AHDR) (Panel B of Figure A7).
The AHDR varies widely because this ratio is based on daily data, and we transform the AHDR to the
30-day moving average AHDR (30DMA-AHDR) to focus on changes in the AHDR values. Panel B of
Figure 5 illustrates the 30DMA-AHDR.

6.2. Interpretations of the 30DMA-AHDR
Panel B of Figure 5 indicates the same results for rice pricing under the Rice Law until 1933
as for the IRF analyses in the previous section, which consists of three points. First, deferred prices
were index prices in the futures and spot markets. Second, deferred prices enhanced the role of index
prices after the enforcement of the second revised Rice Law in July 1931. Third, the spot market
strengthened its ability to generate prices under the second revised Rice Law. By contrast, the 30DMAAHDR and IRF analyses show different rice pricing structures after the enforcement of the Rice Control
Law in November 1933.
When the government implemented only the Rice Control Law, the IRF analyses show thatthe futures market did not provide index prices for spot trades. By contrast, the 30DMA-AHDR
demonstrates that the deferred trade strengthened its role in offering index prices, and the influence offluctuations in spot prices on deferred prices also increased. Under the Rice Control Law and the Rice
Autonomous Management Law, the IRF analyses indicate that the relationship between prices in the
futures and spot markets disappeared. Antithetically, the 30DMA-AHDR shows that the effect of the20(B) 30DMA-AHDR
Nearby ---> DeferredFigure 5
HDRs and 30DMA-AHDRs of the rice futures and spot trades in Osaka

1938

1936

1934

1930

1932

1938

1936

1932

1934

1938

1936

1938

1936

1934

1938

1936

1934

1932

1930

1928

1926

1938

1936

1934

1932

0%

1930

0%

1928

0%

1926

0%

1938

400%

1936

400%

1934

400%

1932

400%

1930

800%

1928

800%

1926

800%

1932

Spot ---> Spot

800%

21

1934

1938

1936

1934

1932

1930

Nearby ---> Spot

1930

Second-nearest ---> Spot

1928

1926

1938

1936

1934

0%

1932

0%

1930

0%

1928

0%

1926

400%

1938

400%

1936

400%

1934

400%

1932

800%

1930

800%

1928

800%

1926

800%

Deferred ---> Spot

1928

Spot ---> Nearby

1932

Nearby ---> Nearby

1930

1938

1936

1934

1932

1930

1928

1926

1938

1936

Second-nearest ---> Nearby

1926

Deferred ---> Nearby

1934

0%

1932

0%

1930

0%

1928

0%

1926

400%

1938

400%

1936

400%

1934

400%

1932

800%

1930

800%

1928

800%

1926

800%

1930

Spot ---> Second-nearest

1928

Nearby ---> Second-nearest

1926

1938

1936

1934

1932

1930

1928

1926

1938

1936

Second-nearest ---> Second-nearest

1928

Deferred ---> Second-nearest

1934

0%

1932

0%

1930

0%

1928

0%

1926

400%

1938

400%

1936

400%

1934

400%

1932

800%

1930

800%

1928

800%

1926

800%

1926

Spot ---> Deferred

1928

Second-nearest ---> Deferred

1926

Deferred ---> Deferredvariation in spot prices on deferred prices increased, while the deferred trade’s function to supply the
spot trade index prices diminished. Accordingly, the following subsection examines changes in therelationship between futures and spot prices.

6.3. Relationship Between Futures and Spot Prices
We calculate two ratios stemming from the 30DMA-AHDR to observe alterations in therelationship between deferred and spot prices. The first is the ratio of the 30DMA-AHDR from deferred
prices to spot prices to that of to and from spot prices. This indicates the deferred price’s power to alter
spot prices against spot prices’ ability to fluctuate. The second ratio is derived by dividing the 30DMAAHDR from spot prices to deferred prices by that of to and from deferred prices. This shows theinfluence of the opposite direction to the first ratio and denotes the spot price’s capability to change
deferred prices against the deferred price’s capacity to vary. Panels A and B of Figure 6 show these two
ratios, and Panel C illustrates the differences calculated by subtracting the second ratio from the first.
Figure 6 shows the seasonal trends of the relative powers of deferred and spot trades togenerate index prices. During the harvest season, the influence of deferred prices on variations in spot
prices strengthened, whereas the opposite effect declined. It fluctuated seasonally, and the futures
market did not stably display its ability to offer index prices throughout the year. Specifically, theinfluence of deferred prices on spot prices was less than the opposite effect, except during the harvest
season until 1929 (Panel C of Figure 6). That is, the deferred trade provided the index prices of spot
trades only during the harvest season even before the Great Depression. Fundamentally, Osaka’s rice
market had a fluid structure. The relationship between deferred and spot prices sustained this seasonal
trend until 1939. Harvest conditions in the fall determined the fluctuations and levels of rice prices fora year until the next harvest. The dealers strived to predict the rice price movement over a few months
and actively traded deferred contracts in the harvest season. However, the deferred trade’s function of
supplying index prices fluctuated further after the Great Depression.

6.4. Enhancement of Rice Price Policies after the Great Depression
The Great Depression of 1929 and the bumper crops of the following year depressed rice
prices during the harvest season of 1930 (Figure 2). The plummeting prices of agricultural products
triggered the Showa Depression. Japan’s gross domestic product per capita faltered until 1932, and therecession interrupted the growth in rice consumption per capita (Figure A2). Accordingly, after 1930,
the government implemented significant interventions in the spot market to control rice prices according
to the Rice Law. Figure 7 shows the volume of trade and government supply in Osaka’s rice spot market
from 1926 to 1933.
Figure 7 shows that, after 1930, the government sold and purchased large quantities of rice inthe spot market. In the previous year, Japanese farmers experienced a poor crop (Figure A2). The
government was wary of surges in rice prices and sold its stock rice in the spot market from July to
September 1930. However, the government encountered bumper crops in the fall. Thus, it thus

22

1939

1938

1937

1936

1935

1934

1933

1932

1931

1930

1929

1926
1927
1928

Ratio

200%
100%
0%
-100%
-200%

(C) Difference between ratios of (A) and (B)Figure 630 days moving average trade volumes and AHDRs for deferred contracts and spot prices
Note: Panel (C) shows the four vertical lines that indicate the enforcement of the second revised Rice Law on 1
July 1931, the third revised Rice Law on 4 October 1932, the Rice Control Law on 1 November 1933, andthe Rice Autonomous Management Law on 20 September 1936.

purchased rice after December 1930 to boost prices, and the ratio of its purchase volume to the estimatedtrade volume skyrocketed frequently in the 1930s. The government re-enacted the Rice Law twice andintervened in the spot market to control rice prices.

23

Figure7
Trade volumes and government supply in Osaka’s rice spot market
Note: The “estimated trade volume of domestic and Korean rice in the spot market” consists of the estimatedconsumption volume in Osaka and outflow volume from Osaka. The estimated consumption volume in
Osaka is derived using the following formula:
The estimated consumption volume = the stocked volume at the end of the previous month + the inflowing
volume – the outflowing volume – the stocked volume at the end of
the month
Sources: Ministry of Agriculture and Commerce, Food Bureau 1922–1923, 1925a, 1925b; Ministry of Agriculture
and Forestry, Agricultural Bureau 1926–1930; Hasumi 1957; Osaka Chamber of Commerce 1918, 1920a,
1920b, 1922, 1923a, 1923b, 1924–1926; Ota 1938; Tokyo Chamber of Commerce and Industry 1928–
1933

These interventions counteracted spot price trends because they attempted to curb pricefluctuation. This feature stimulated the deferred price function. The deferred trade strengthened its
opposing forces against the government’s inducements, and its impact on the spot market militated thespot price trend (Figure A7). Consequently, government intervention under the second and third revised
Rice Laws ironically amplified the influence of deferred prices on spot prices. Nevertheless, after24

enforcing the Rice Control Law, the government expanded the scale of its intervention because Japan
registered its largest rice harvest in 1933 (Figures 1, A3, and A4). Therefore, the deferred trade strongly
influenced the spot market until 1935 (Figure 6).
The relationship between deferred pricing and spot pricing gradually changed after 1936.
According to Figure 6, the effect of spot prices on the change in deferred prices increased, whereas the
opposite influence decreased. The February 26 incident in 1936 triggered a structural change in the rice
market. This significant historical event drastically transformed Japanese politics and the economy.

6.5. Structural Market Changes during Wartime
The Japanese Kwantung Army in the Liaodong Peninsula, which became Japanese-leased
territory due to the Portsmouth Treaty of 1905 after the Russo-Japanese War, caused the Manchurian
Incident in September 1931. It invaded Northeast China, and the military obtained political power after
the incident (Matsusaka 1996, pp. 103–04). Military action in Manchuria raised arms expenditure, andthe number of outstanding government bonds increased. However, the Minister of Finance, Korekiyo
Takahashi, planned to reduce arms expenditure to maintain fiscal discipline. Young army officers
regarded government leaders as hostile and executed a coup d’état on 26 February 1936 (Berger 1998,
pp. 118–20). They assassinated the Finance Minister, and the military greatly strengthened its politicalpower. In this situation, the dealers in the ODRE felt uneasy about the future of Japanese society. They
could not predict the future of the rice market, which was susceptible to government intervention, andreduced their trade (Osaka-Dojima Rice Exchange 1936b, p. 7). Furthermore, the Rice Autonomous
Management Law was enacted in the same year, and the rice futures market continued to struggle.
The government promulgated and enforced the Rice Autonomous Management Law in 1936
on 28 May and 20 September, respectively. This law boosted its authority to prevent price decreases
when rice cultivation experienced bumper crops. Although the government did not apply the law tocontrol rice distribution, rice traders feared that the government would reduce the rice supply because
Japan had continuously experienced rich rice harvests since 1936 (Figures A3 and A4). In contrast to
the Rice Control Law, which permitted the government to trade only in the spot market, the Rice
Autonomous Management Law allowed the government to adjust the rice supply from agricultural areas
to distribution hubs throughout the Japanese Empire. If the government invoked the law, its actions
would seriously impact rice pricing. Accordingly, dealers in the ODRE were wary of government
intervention under the Rice Autonomous Management Law and refrained from active dealings (OsakaDojima Rice Exchange 1936b, p. 7).
Under the circumstances of bumper rice crops, dealers who considered the possibility of thegovernment’s direct intervention in rice distribution could not predict futures prices, and the futures
market’s function to provide index prices declined after 1936. The government carefully monitored thespot market to determine whether it had invoked the Rice Autonomous Management Law. Hence,
futures market dealers also paid close attention to price movements in the spot market. In its business
report in the first half of 1937, the ODRE stated, “Futures prices are stabilized because the spot market25

is steady” (Osaka-Dojima Rice Exchange 1937b, p. 8). According to this statement, the ODRE
recognized that the futures market followed the spot market. Panel B of Figure 6 also shows that the
30DMA-AHDR from spot prices to deferred prices increased in 1936. Thereafter, it frequently exceeded
100 percent, except during the harvest season. That is, the fluctuation in spot prices determined pricemovement in the futures market at any time other than the harvest season after 1936. This pricing
tendency in the futures and spot markets became prominent the following year (Figure 6).
Japan began the Second Sino-Japanese War in July 1937 and reworked its economy to meetits wartime regime. The Japanese military intended to clinch a brief struggle but, instead, perpetuatedthe war. The government enforced the National Mobilization Law in May 1938 and assumed fullauthority to operate the wartime economy (Tipton 2016, pp. 144–59). Wartime legislation permitted thegovernment to intervene in the commodity market on a discretionary basis, and wartime controls of theeconomy threatened the rice futures trade. Consequently, both the number of dealers and the deferred
trade volume in the ODRE decreased further (Figure 3). In May 1938, the ODRE mentioned, “Thewartime controls of the economy are strengthened, and the dealers refrain from active trade. These
factors stabilize rice prices. The futures market is altered to a non-lucrative market, and trade volume
shrinks.” (Osaka-Dojima Rice Exchange 1938b, pp. 7–8). The volume ratio of the spot trade to thedeferred trade increased and remained above ten percent after the National Mobilization Law was
enforced in May 1938. The ratio exceeded 25 percent by the end of 1938 (Figure A8). These changes
indicated that the futures market had forfeited its significant position in the rice market.
The wartime controls on the economy during the Second Sino-Japanese War after 1938 caused
the rice futures market to shrink. The 30DMA-AHDR of spot prices tended to surpass that of deferred
prices meaning that the deferred trade failed to maintain its function of generating index prices for the
spot trade (Figure 6). The 30DMA-AHDR also indicated that spot prices led to futures prices during
the war, in contrast to the theoretical assumption. Nevertheless, the deferred trade provided index prices
for the spot trade only during the harvest season (Figures 3 and 6). Until the abolition of all riceexchanges at the end of August 1939, the government allowed dealers to trade in futures, and dealers
struggled to predict rice prices before and after the harvest season. The deferred trade initiallystrengthened its function of generating index prices during the harvest season, and the function partiallysurvived even during wartime.
These seasonal fluctuations in the functions of the deferred and spot markets intensified after
1936 under the Rice Control Law. Furthermore, they escalated during the war. The fluid structure of
Osaka’s rice market had swung. Under these circumstances, the IRF analyses misidentify that the twomarkets were separate. They illustrate only the average market structure, but the influences of thedeferred and spot prices offset each other (Panels D and E of Figure 4). Accordingly, the IRF analyses
capture only the results after offsetting. The difference in assumptions between the IRF and HD analyses
causes a distinction in their results.

26

7. Conclusions
Osaka has a history of more than 300 years in the commodity futures market, and its rice
futures trade prospered during the interwar period (Malliaris and Ziemba 2015, p. 13). However, thefunctions were unstable. Even before 1930, when the government began to severely intervene in themarket, the relationship between futures and spot markets fluctuated seasonally. The futures market
improved its capacity to supply the index prices of the spot trade during the harvest season when theequilibrium of rice varied heavily. Antithetically, the futures market’s function to generate index prices
declined at times other than during the harvest season. Furthermore, the government strengthened its
control over rice prices after the Great Depression, and the role of the ODRE fluctuated.
The government implemented two measures to adjust rice prices after the depression. The firstmeasure was the government’s constant intervention in the spot market, according to the Rice Control
Law of 1933. The second was the government’s control of the rice supply volume from farming regions
to cities, according to the Rice Autonomous Management Law of 1936. The government utilized thefirst measure to induce rice prices to move in the opposite direction from the market equilibrium.
Specifically, although Japan faced bumper crops in 1933, the government continuously bought a
tremendous volume of rice in the spot market to raise prices because farmers suffered from plummeting
prices after the depression. These political actions, which were contrary to the price trends in response
to harvest conditions, boosted the futures market’s function, leading spot prices to reflect crop situations.
Unlike the first measure, the second reduced the ODRE’s function. Rice dealers were afraid of thegovernment’s control of the rice supply and held off their trade in the futures market when Japan
experienced abundant crops, although the government did not invoke the Rice Autonomous
Management Law after 1936. Consequently, the suppression of trades was debased on the function of
the ODRE.
After the decline in the ODRE’s role, the spot market supplied index prices to the futures
market at times other than the harvest season because the reinforcement of the government’s capacity
to control the rice market distorted the relationship between the futures and spot markets. The wartime
economic controls after the outbreak of the Second Sino-Japanese War in 1937 restrained the futurestrade and further distorted the rice market. The ODRE lost its role, except during the harvest season,even before its abolition in 1939.
This study employs an HD analysis to investigate high-frequency price data and examines therelationship between the pricing of rice futures and spot markets during the interwar period in Japan.
The results show that the relationship between futures and spot pricing had distinct seasonality inresponse to the rice harvest. The futures market’s functions varied seasonally, and the rice market in
Osaka sustained this seasonality even after the Great Depression. These seasonal fluctuations
fundamentally characterized commodity futures markets. Furthermore, the government extended thescale of its manipulation to control rice prices, and its interventions heavily affected pricing in the
futures and spot markets after the depression. The government intervened only in spot markets. In otherwords, it did not participate in the futures market directly. However, government intervention greatly27reduced the futures market’s role at times other than during the harvest season. It amplified the seasonal
fluctuation of the futures market’s functions. Consequently, the futures market functionally swung after
the Great Depression.
Scholars in economics and economic history have considered commodity exchange as a
crucial part of the commodity market in modern times. Many commodity exchanges collectdisseminated information to generate index prices (Goss 1986, p. 2); they now list not only agricultural
products, but also metals, industrial materials, and financial commodities (Johnson 2009, p. 313).
However, their primary listed goods until the 1960s were agricultural products such as grain (Carlton
1984, pp. 245–46). These agricultural products, including rice, exhibit seasonal price fluctuations.
Accordingly, the pricing and functions of many commodity exchanges may vary seasonally, as with the
ODRE. In such a case, economic historians must re-evaluate the role of the commodity futures market.
Specifically, they must downgrade their assessment of the futures market’s functions when thegovernment frequently manipulated the commodity market after the Great Depression. This study
requires further research to reassess the functions of the futures market. However, it has three limitations.
The first concerns the causal relationship between futures market pricing and government
intervention. This study does not investigate the causal relationship directly because daily statistical
data on the Japanese government’s interventions are unavailable. This limitation requires further
investigation to discover the related historical documents. The second considers the causes of seasonal
fluctuations in futures market pricing. The Japanese government forbade rice exchanges fromconcluding futures contracts for over three months, whereas Western countries have permitted contracts
for more than half a year. Rice dealers in Japan could not hedge their long-term risk of price volatility,
even though they continued to desire that the government allow more extended contracts in the futures
market after the 1900s (Tanaka 1910, pp. 277–78; Nagamitsu 1924, pp. 120–21). Accordingly, this point
demands further study to investigate how the government regulation of contract terms affected theperformance of the futures market. The third covers the period and objects in the analysis. This study
examines Japan’s rice futures market between 1925 and 1939. This limitation necessitates future
research studies, using high-frequency data, focusing on other commodity futures markets globally andbefore 1925.

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34

Appendices
Table A1Date
1–3 Jan.
5 Jan.
10 Jan.
11 Feb.
Spring equinox
3 Apr.
29 Apr.
25 Jul.
30 Jul.
31 Aug.
Autumn equinox
17 Oct.
31 Oct.
3 Nov.

Regular holiday closures of the ODRE (November 1925–August 1939)
Reason (remark)
New Year holidays
New Year’s party
Imamiya Ebisu Shrine ritual(This holiday was newly set in 1938.)
Empire Day
Imperial ceremony of ancestor worship formerly held on the vernal equinox(Approximately 22 March)
Ceremony of the Imperial Household to honor the mythical First Emperor Jinmu
Emperor Showa’s birthday(This holiday was newly set in 1927 because Emperor Showa succeeded to the throne from
Emperor Taisho, who died on 25 December 1926.)
Osaka Tenmangu Shrine ritual(The exchange opened its market on 25 July from 1928 to 1932.)
Anniversary of the demise of Emperor Meiji(This holiday was abolished in 1927 because of the demise of Emperor Taisho, an heir to
the Imperial Throne from Emperor Meiji, in 1926.)
Emperor Taisho’s birthday(This holiday was abolished in 1927 because of the demise of Emperor Taisho.)Imperial ceremony of ancestor worship formerly held on the autumnal equinox(Approximately 24 September)
Annual ceremony of offering the year’s new rice harvest by the emperor
Holiday in lieu of Emperor Taisho’s birthday(This holiday was abolished in 1927 because of the demise of Emperor Taisho.)Emperor Meiji’s birthday(This holiday was newly set in 1927.)

35

Japanese nameShinnen kyūgyō
Shinnen enkai
Dotonbori ebisu
Kigen setsu
Shunki kōreisai
Jinmu Tennō sai
Tenchō setsu
Tenjin sai
Meiji Tennō sai
Tenchō setsu
Shūki kōreisai
Kanname sai
Tenchō setsu shukujitsu
Meiji setsu

Table A1 (Continued)
Date
23 Nov.

5 Dec.
27–31 Dec.

Regular holiday closures of the ODRE (November 1925–August 1939)
Reason (remark)Annual ceremony of offering to the Imperial Ancestor and the Deities(This holiday was not set in 1915 and 1928 because the government held the Daijō sai (thegreat ceremony of offering to the Imperial Ancestor and the Deities by the newly enthroned
His Majesty the Emperor) as a substitute for this holiday.)Anniversary of the demise of Emperor Taisho (This holiday was newly set in 1927.)
Year-end holidays(The exchange suspended its operation on 26 December as the year-end holiday in 1938 andoperated its market temporarily on 28 December 1926.)36

Japanese name
Niiname sai

Taisho Tennō sai
Nenmatsu kyūgyōTable A2
Date
10 Jun. 1926
22 Oct. 1926
25–27 Dec. 1926
7–8 Feb. 1927
10 Nov. 1928
14 Nov. 1928
16 Nov. 1928
4 Jun. 1929
2 Oct. 1929
27 Apr. 1932
14 Nov. 1932
27 Apr. 1933
27 Apr. 1934
5 Jun. 1934
5 Nov. 1935
29 Oct. 1936
27 Apr. 1937
26 Apr. 1938
19 Oct. 1938
25 Apr. 1939

Temporal national holiday closures of the ODRE (November 1925–August 1939)
Reason (remark)Japanese name
State funeral of Sunjong (the abdicated second Emperor of Imperial Korea)
Li daiō denka kokusō
Ceremony of the addition of the former 98th Emperor Chokei in an imperial line
Chōkei Tennō shinkoku sai
The period of national mourning for the demised Emperor Taisho
GotaisōThe Rites of the Imperial FuneralTaisō rei
Ceremonies of the Accession to the Throne
Sokui reiGreat ceremony of offering to the Imperial Ancestor and the Deities by the newly enthroned Oname sai
His Majesty the Emperor
Ceremony of the grand banquet
Daikyō kai
Emperor’s visit to Osaka
Tennō gyōkei
Ceremony of renewing the Ise Jingu Shrine
Jingū shikinen sai
The special ceremony of the Yasukuni Shrine with the attendance of the Emperor andYasukuni Jinja rinji taisai
Empress
Special military review
Dai-kanpei shiki
The special ceremony at the Yasukuni Shrine
Yasukuni Jinja rinji taisai
The special ceremony at the Yasukuni Shrine
Yasukuni Jinja rinji taisai
State Funeral of Heihachiro Togo (the Fleet Admiral)
Togo Heihachiro kokusō
Ceremony of renewing the Atsuta Jingu Shrine
Atsuta Jingū senza sai
Fleet review
Kankan shiki
The special ceremony at the Yasukuni Shrine
Yasukuni Jinja rinji taisai
The special ceremony at the Yasukuni Shrine
Yasukuni Jinja rinji taisai
The grand ceremony of the Yasukuni Shrine
Yasukuni Jinja taisai
The special ceremony at the Yasukuni Shrine
Yasukuni Jinja rinji taisai

37

Table A3
Period22 Apr.–13 May 1927
3–4 Oct. 1930
15–16 Dec. 1931
16 May 1932

Type ofcontract
All
All
All
All

21 Sep. 1934
25–29 Sep. 1934

All
Nearby26–29 Feb. 1936

All

Temporary trade suspensions of the ODRE (November 1925–August 1939)
ReasonThe government ordered the exchange to stop the trade as a part of the debt moratorium of the financial crisis.
Due to the stormy situation of the trade.
Due to the stormy situation of the trade.
Due to the May 15 Incident, a coup attempt. The culprits, including some naval officers, assassinated thesitting prime minister, Tsuyoshi Inukai.
Due to the typhoon hitting Osaka
The exchange could not deliver physical rice to settle the nearby contract due to the destruction of rice
warehouses by the typhoon, which hit Osaka on 24 September 1934.
Due to the February 26 Incident, a coup attempt. The culprits, who mainly consisted of young army officers,
assassinated four key government officials such as Korekiyo Takahashi, the sitting financial minister, and
Makoto Saito, the former prime minister.

38

Table A4
Descriptive statistics and unit root test results of the first difference in the natural log of the futures and spot prices
First period
Second period
2 November 1925–29 June 1931
1 July 1931–3 October 1932
SecondSecondDeferred
NearbySpotDeferred
NearbySpotnearestnearestDescriptive statistics-0.000-0.000-0.000-0.001
Mean
0.000
0.000
0.000
0.000
Median-0.000-0.000

0.000

0.000

-0.000-0.001-0.0010.000

Maximum

0.057

0.060

0.075

0.081

0.095

0.099

0.120

0.212

Minimum-0.126-0.152-0.187-0.104-0.058-0.061-0.065-0.047

Std. Dev.

0.011

0.011

0.013

0.007

0.015

0.015

0.016

0.014

N

1,517

1,517

1,517

1,517

351

351

351

351

Unit root test results-7.678***ADF-7.174***-13.05***-7.297***-16.90***0-17.53***0-18.66***
0

Lags
PP
Bandwidth

16

18

7

14

-16.42***
0

***

***

***

-36.04***-16.42***-16.86***-17.51***-18.81***

14

20

0

3

5

8

-34.588-34.504-34.5239

Table A4 (Continued)
Descriptive statistics and unit root test results of the first difference in the natural log of the futures and spot prices
Third period
Fourth period
4 October 1932–30 October 1933
1 November 1933–19 September 1936
SecondSecondDeferred
NearbySpotDeferred
NearbySpotnearestnearestDescriptive statistics
0.000
0.000
0.001
0.000
Mean
0.000
0.000
0.000
0.001
Median-0.001-0.001-0.001

0.000

0.000

0.000

0.000

0.000

Maximum

0.042

0.057

0.055

0.138

0.065

0.041

0.078

0.117

Minimum-0.030-0.028-0.028-0.037-0.056-0.042-0.047-0.027

Std. Dev.

0.009

0.009

0.010

0.010

0.006

0.006

0.006

0.006

298

298

298

298

792

792

792

792

Unit root test results-9.776***ADF-9.827***-15.85***-15.12***-19.66***1-25.67***0-24.91***0-14.16***
2

N

Lags
PP
Bandwidth

3

3

0

0

***

***

***

***

-24.39***-25.69***-24.98***-26.09***

3

4

7

5

6

-14.6310-14.3911-15.80-15.097

40

Table A4 (Continued)
Descriptive statistics and unit root test results of the first difference in the natural log of the futures and spot prices
Fifth period
All periods
21 September 1936–19 August 1939
2 November 1925–19 August 1939
SecondSecondDeferred
NearbySpotDeferred
NearbySpotnearestnearestDescriptive statistics
0.000
0.000
0.000
0.000
Mean
0.000
0.000
0.000
0.000
Median

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

Maximum

0.038

0.034

0.026

0.021

0.095

0.099

0.120

0.212

Minimum-0.042-0.033-0.088-0.018-0.126-0.152-0.187-0.104

Std. Dev.

0.005

0.005

0.007

0.003

0.009

0.009

0.011

0.007

803

803

803

803

3,761

3,761

3,761

3,761

Unit root test results-11.38***ADF-26.04***-9.686***-11.23***-12.85***16-9.952***35-10.34***30-11.91***
16

N

Lags

5

0

9

5

***

***

***

***

-54.16***-54.62***-55.52***-59.26***

6

12

7

1

25

-24.43

PP
Bandwidth1-26.01

11-27.06-25.64

13

Notes: “N,” “ADF,” “Lags,” “PP,” and “Bandwidth” denote the number of observations, augmented Dickey–Fuller test statistics
with a time trend and a constant, lag order selected by the Akaike information criterion, Phillips–Perron test statistics, andNewey–West bandwidth by using the Bartlett kernel, respectively. “***” means significant at the 1 percent level.

41

Table A5
Johansen’s trace test results
Second period
1 July 1931–3 October 1932
EigenTest
Critical value
value
statistics
0.101262.64854.6820.039025.94935.458At Most 1

First period
2 November 1925–29 June 1931
EigenTest
Critical Value
value
statistics
0.0597161.5254.6820.025968.72635.458At Most 2

0.018129.23519.9370.0257

12.248At Most 3

0.00111.6595

6.63490.00953.2893

None

Third period
4 October 1932–30 October 1933
EigenTest
Critical value
value
statistics
0.088864.78354.6820.065637.52935.45819.937

0.053117.65319.937

6.63490.00571.67396.6349Fourth period
1 November 1933–19 September 1936

Fifth period
21 September 1936–19 August 1939

All periods
2 November 1925–19 August 1939

Eigenvalue

Critical value

Eigenvalue

Test
statistics

Critical value

Test
statistics

Critical value

Eigenvalue

Test
statistics

None

0.050880.72354.6820.0797102.9154.6820.0587326.8654.682

At Most 1

0.030639.75835.4580.036436.98235.4580.0154102.2035.458At Most 2

0.013715.38319.9370.00807.518319.9370.010743.95919.937

At Most 3

0.00584.59396.63490.0014

1.1222

6.63490.00093.5044

6.6349Notes: This table presents the results of Johansen’s (1991) trace tests. The “Critical Value” is the critical value at the 1 percent level for each test.

42

Table A6
Chi-squared test statistics of the lag exclusion Wald test
Deferred(A) First period (2 November 1925–29 June 1931)
Second-nearest
Nearby
Spot

Joint

Lag 1

264.55 (0.000)

326.95 (0.000)

263.90 (0.000)

951.77 (0.000)

1047.7 (0.000)

Lag 2

92.286 (0.000)

130.41 (0.000)

117.04 (0.000)

412.89 (0.000)

490.86 (0.000)

Lag 3

85.549 (0.000)

121.59 (0.000)

104.42 (0.000)

288.72 (0.000)

349.38 (0.000)

Lag 451.267 (0.000)

76.935 (0.000)

65.007 (0.000)

177.26 (0.000)

230.71 (0.000)

Lag 5

49.554 (0.000)

58.842 (0.000)

52.984 (0.000)

134.25 (0.000)

165.93 (0.000)

Lag 648.339 (0.000)

52.567 (0.000)

56.900 (0.000)

107.45 (0.000)

132.07 (0.000)

Lag 7

29.207 (0.000)

34.511 (0.000)

31.578 (0.000)

65.822 (0.000)

87.716 (0.000)

Lag 8

15.827 (0.003)

21.137 (0.000)

28.890 (0.000)

46.378 (0.000)

68.202 (0.000)

Lag 99.6069 (0.048)

9.0184 (0.061)

14.503 (0.006)

24.953 (0.000)

44.526 (0.000)

Lag 1015.742 (0.003)

16.158 (0.003)

6.5176 (0.164)

13.223 (0.010)

43.322 (0.000)

Lag 1113.060 (0.011)

10.571 (0.032)

5.0547 (0.282)

11.536 (0.021)

25.761 (0.058)(B) Second period (1 July 1931–3 October 1932).
DeferredSecond-nearest

Nearby

Spot

Joint

Lag 1
Lag 2
Lag 3
Lag 4
Lag 5

166.29 (0.000)
96.417 (0.000)
51.317 (0.000)
30.174 (0.000)
17.258 (0.002)

159.27 (0.000)
92.860 (0.000)
51.673 (0.000)
30.922 (0.000)
19.859 (0.001)

194.24 (0.000)
105.17 (0.000)
55.129 (0.000)
30.055 (0.000)
23.303 (0.000)

262.12 (0.000)
127.88 (0.000)
84.468 (0.000)
30.612 (0.000)
24.116 (0.000)

836.30 (0.000)
361.61 (0.000)
200.50 (0.000)
102.76 (0.000)
78.032 (0.000)

Lag 6
Lag 7

9.0032 (0.061)
3.9669 (0.411)

8.0971 (0.088)
4.3786 (0.357)

10.368 (0.035)
3.6096 (0.461)

15.488 (0.004)
5.3451 (0.254)

64.421 (0.000)
26.932 (0.042)(C) Third period (4 October 1932–30 October 1933)
Lag 1
Lag 2
Lag 3
Lag 4
Lag 5

DeferredSecond-nearest

Nearby

Spot

Joint

81.798 (0.000)
52.434 (0.000)
20.588 (0.000)
23.643 (0.000)
10.332 (0.035)

21.813 (0.000)
14.951 (0.005)
6.5506 (0.162)
11.098 (0.026)
4.4966 (0.343)

76.135 (0.000)
56.069 (0.000)
25.713 (0.000)
17.673 (0.001)
5.3165 (0.256)

63.321 (0.000)
38.460 (0.000)
16.309 (0.003)
7.6389 (0.106)
2.9147 (0.572)

432.85 (0.000)
226.24 (0.000)
107.78 (0.000)
62.927 (0.000)
30.292 (0.017)

43

Table A6 (Continued)
Chi-squared test statistics of the lag exclusion Wald test(D) Fourth period (1 November 1933–19 September 1936)
DeferredSecond-nearest
Nearby
Spot

Joint

Lag 1
Lag 2
Lag 3
Lag 4
Lag 5

439.00 (0.000)
257.90 (0.000)
154.23 (0.000)
87.886 (0.000)
54.264 (0.000)

287.31 (0.000)
171.79 (0.000)
100.26 (0.000)
54.876 (0.000)
30.832 (0.000)

357.56 (0.000)
176.09 (0.000)
106.97 (0.000)
59.551 (0.000)
27.931 (0.000)

279.79 (0.000)
126.15 (0.000)
59.993 (0.000)
28.018 (0.000)
14.429 (0.006)

1057.4 (0.000)
511.01 (0.000)
285.26 (0.000)
178.39 (0.000)
108.24 (0.000)

Lag 6
Lag 7

34.255 (0.000)
15.903 (0.003)

19.443 (0.001)
10.448 (0.034)

31.922 (0.000)
12.090 (0.017)

15.486 (0.004)
1.7980 (0.773)

66.707 (0.000)
26.274 (0.050)(E) Fifth period (21 September 1936–19 August 1939)
DeferredSecond-nearest

Nearby

Spot

Joint

Lag 1

517.99 (0.000)

590.16 (0.000)

161.10 (0.000)

28.541 (0.000)

1420.8 (0.000)

Lag 2

285.18 (0.000)

276.71 (0.000)

63.423 (0.000)

28.358 (0.000)

665.10 (0.000)

Lag 3

155.96 (0.000)

162.65 (0.000)

34.236 (0.000)

36.875 (0.000)

403.50 (0.000)

Lag 4

129.11 (0.000)

130.31 (0.000)

24.126 (0.000)

31.912 (0.000)

316.57 (0.000)

Lag 5

62.651 (0.000)

88.550 (0.000)

18.336 (0.001)

55.935 (0.000)

258.29 (0.000)

Lag 6

67.057 (0.000)

73.227 (0.000)

19.247 (0.001)

26.652 (0.000)

170.93 (0.000)

Lag 7

44.426 (0.000)

32.091 (0.000)

10.759 (0.029)

16.946 (0.000)

109.28 (0.000)

Lag 8

38.696 (0.000)

21.950 (0.000)

6.8863 (0.142)

2.4896 (0.647)

59.519 (0.000)

Lag 9

19.661 (0.001)

12.116 (0.017)

8.5583 (0.073)

2.8675 (0.580)

29.760 (0.019)

44

Table A6 (Continued)
Chi-squared test statistics of the lag exclusion Wald test
Deferred(F) All periods (2 November 1925–19 August 1939)
Second-nearest
Nearby
Spot

Joint

Lag 1

1071.6 (0.000)

1251.5 (0.000)

1077.2 (0.000)

2696.2 (0.000)

2952.8 (0.000)

Lag 2

509.13 (0.000)

619.35 (0.000)

561.99 (0.000)

1347.1 (0.000)

1524.3 (0.000)

Lag 3

380.84 (0.000)

460.08 (0.000)

423.23 (0.000)

840.82 (0.000)

1003.1 (0.000)

Lag 4

253.91 (0.000)

321.05 (0.000)

293.58 (0.000)

502.72 (0.000)

657.64 (0.000)

Lag 5

198.53 (0.000)

235.60 (0.000)

229.44 (0.000)

386.48 (0.000)

513.10 (0.000)

Lag 6

165.01 (0.000)

192.13 (0.000)

211.22 (0.000)

305.91 (0.000)

426.98 (0.000)

Lag 7

115.67 (0.000)

142.95 (0.000)

154.17 (0.000)

227.99 (0.000)

340.46 (0.000)

Lag 8

95.423 (0.000)

121.97 (0.000)

139.28 (0.000)

199.22 (0.000)

311.87 (0.000)

Lag 968.785 (0.000)

82.749 (0.000)

100.51 (0.000)

167.14 (0.000)

269.00 (0.000)

Lag 1063.214 (0.000)

79.133 (0.000)

81.126 (0.000)

136.39 (0.000)

239.74 (0.000)

Lag 1140.242 (0.000)

54.122 (0.000)

64.405 (0.000)

107.72 (0.000)

194.31 (0.000)

Lag 1230.201 (0.000)

43.041 (0.000)

55.415 (0.000)

84.977 (0.000)

167.80 (0.000)

Lag 1330.921 (0.000)

45.175 (0.000)

60.615 (0.000)

89.527 (0.000)

178.52 (0.000)

Lag 1422.904 (0.000)

39.584 (0.000)

66.615 (0.000)

78.254 (0.000)

162.83 (0.000)

Lag 159.4795 (0.050)

17.908 (0.001)

30.965 (0.000)

38.301 (0.000)

106.98 (0.000)

Lag 1618.295 (0.001)

18.060 (0.001)

25.329 (0.000)

37.656 (0.000)

88.073 (0.000)

Lag 1710.659 (0.031)

10.253 (0.036)

13.007 (0.011)

9.6028 (0.048)

54.204 (0.000)

Lag 185.8747 (0.209)

6.1402 (0.189)

3.2900 (0.511)

5.8450 (0.211)

24.510 (0.079)

Note: Numbers in parentheses are p-values.

45

Table A7
VEC estimations

𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1
Cointegration
Equation 2
Cointegration
Equation 3∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦1,𝑡𝑡−5∆𝑦𝑦1,𝑡𝑡−6∆𝑦𝑦1,𝑡𝑡−7∆𝑦𝑦1,𝑡𝑡−8∆𝑦𝑦1,𝑡𝑡−9∆𝑦𝑦1,𝑡𝑡−10∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−5∆𝑦𝑦2,𝑡𝑡−6∆𝑦𝑦2,𝑡𝑡−7∆𝑦𝑦2,𝑡𝑡−8∆𝑦𝑦2,𝑡𝑡−9∆𝑦𝑦2,𝑡𝑡−10∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−5∆𝑦𝑦3,𝑡𝑡−6∆𝑦𝑦3,𝑡𝑡−7∆𝑦𝑦3,𝑡𝑡−8(A) First period (2 November 1925–29 June 1931)
Cointegration equation
Equation 1
Equation 2
Equation 3
1.00000.00000.00000.00001.00000.00000.00000.00001.0000-0.7692 [0.0805] -0.9668 [0.0773] -1.0289 [0.0694]-0.0000-0.0001-0.0001
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡-1.2924 [0.2013]-0.4074 [0.2128]-0.8037 [0.2507]-0.2149 [0.1232]

0.6491 [0.2681]-0.1515 [0.2834]

1.5978 [0.3339]

0.4018 [0.1641]

0.0722 [0.1588]-0.1342 [0.1678]-1.6048 [0.1977]

0.1576 [0.0972]

0.4747 [0.1928]
0.4084 [0.1835]
0.4169 [0.1735]
0.4088 [0.1623]
0.4946 [0.1502]
0.4666 [0.1364]
0.2831 [0.1205]
0.2468 [0.1038]
0.2191 [0.0863]
0.1820 [0.0612]-0.7004 [0.2564]-0.8262 [0.2439]-0.6782 [0.2303]-0.6219 [0.2152]-0.5130 [0.1995]-0.5333 [0.1792]-0.3520 [0.1565]-0.3650 [0.1326]-0.2553 [0.1075]-0.2392 [0.0737]-0.7004 [0.2564]-0.8262 [0.2439]-0.6782 [0.2303]-0.6219 [0.2152]-0.5130 [0.1995]
0.0135 [0.0997]-0.0062 [0.0873]
0.0781 [0.0730]

0.6045 [0.2038]
0.5030 [0.1940]
0.5487 [0.1834]
0.6373 [0.1716]
0.6744 [0.1589]
0.5821 [0.1442]
0.3625 [0.1273]
0.3625 [0.1273]
0.2369 [0.0912]
0.1320 [0.0647]-0.9776 [0.2710]-1.0331 [0.2579]-0.9516 [0.2435]-1.0092 [0.2274]-0.8444 [0.2109]-0.7663 [0.1894]-0.5360 [0.1654]-0.4984 [0.1402]-0.3305 [0.1136]-0.2207 [0.0779]-0.9776 [0.2710]-1.0331 [0.2579]-0.9516 [0.2435]-1.0092 [0.2274]-0.8444 [0.2109]
0.1498 [0.1054]
0.1173 [0.0923]
0.1770 [0.0771]

0.9965 [0.2402]
0.8412 [0.2286]
0.7741 [0.2161]
0.8785 [0.2121]
0.9048 [0.1871]
0.8121 [0.1699]
0.5234 [0.1500]
0.5234 [0.1500]
0.3963 [0.1074]
0.1532 [0.0762]-1.6344 [0.3193]-1.5631 [0.3038]-1.3581 [0.2868]-1.4067 [0.2679]-1.2028 [0.2485]-1.2146 [0.2232]-0.8280 [0.1949]-0.8323 [0.1652]-0.5804 [0.1339]-0.2308 [0.0917]-1.6344 [0.3193]-1.5631 [0.3038]-1.3581 [0.2868]-1.4067 [0.2679]-1.2028 [0.2485]
0.3483 [0.1241]
0.2592 [0.1088]
0.3396 [0.0909]

0.3189 [0.1180]
0.3136 [0.1123]
0.3478 [0.1062]
0.3310 [0.0993]
0.4058 [0.0919]
0.3904 [0.0835]
0.2845 [0.0737]
0.2845 [0.0737]
0.2059 [0.0528]
0.0862 [0.0374]-0.3785 [0.1569]-0.4823 [0.1493]-0.4392 [0.1410]-0.4043 [0.1317]-0.3996 [0.1221]-0.4341 [0.1097]-0.3548 [0.0958]-0.3989 [0.0812]-0.2689 [0.0658]-0.0898 [0.0451]-0.3785 [0.1569]-0.4823 [0.1493]-0.4392 [0.1410]-0.4043 [0.1317]-0.3996 [0.1221]-0.0040 [0.0610]
0.0327 [0.0534]
0.1412 [0.0447]

46∆𝑦𝑦4,𝑡𝑡

Table A7 (Continued)
VEC estimations∆𝑦𝑦3,𝑡𝑡−9∆𝑦𝑦3,𝑡𝑡−10∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−5∆𝑦𝑦4,𝑡𝑡−6∆𝑦𝑦4,𝑡𝑡−7∆𝑦𝑦4,𝑡𝑡−8∆𝑦𝑦4,𝑡𝑡−9∆𝑦𝑦4,𝑡𝑡−10
Constant
𝑅𝑅� 2
𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦1,𝑡𝑡−5∆𝑦𝑦1,𝑡𝑡−6∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−5∆𝑦𝑦2,𝑡𝑡−6∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3(A) First period (2 November 1925–29 June 1931) (continued)
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡
0.0205 [0.0567]
0.0965 [0.0599]
0.2126 [0.0706]
0.0647 [0.0347]
0.0415 [0.0392]
0.0835 [0.0415]
0.1032 [0.0489]
0.0074 [0.0240]-0.4083 [0.1008] -0.6824 [0.1065] -0.7960 [0.1255] -0.5697 [0.0617]-0.2047 [0.0994] -0.5022 [0.1051] -0.5790 [0.1238] -0.4794 [0.0609]-0.2875 [0.0976] -0.5644 [0.1031] -0.5837 [0.1215] -0.4478 [0.0597]-0.2432 [0.0958] -0.4688 [0.1013] -0.4798 [0.1194] -0.3786 [0.0587]-0.3003 [0.0934] -0.4605 [0.0988] -0.4863 [0.1163] -0.3353 [0.0572]-0.3160 [0.0901] -0.4037 [0.0952] -0.4284 [0.1122] -0.3023 [0.0551]-0.1883 [0.0850] -0.2914 [0.0898] -0.3030 [0.1058] -0.2236 [0.0520]-0.1039 [0.0774] -0.2914 [0.0898] -0.3030 [0.1058] -0.2236 [0.0520]-0.0521 [0.0661] -0.1015 [0.0698] -0.1322 [0.0823] -0.0959 [0.0404]-0.0816 [0.0504] -0.1263 [0.0533] -0.0733 [0.0628] -0.0468 [0.0309]
0.0000 [0.0003]
0.0000 [0.0003]
0.0000 [0.0003]
0.0000 [0.0002]
0.46660.45860.45800.4646(B) Second period (1 July 1931–3 October 1932)
Equation 1
1.0000-3.6427 [0.6714]
0.7764 [0.6502]
1.6324 [0.2113]-0.0002∆𝑦𝑦1,𝑡𝑡

Cointegration equation

Error correction∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡

0.2536 [0.1049]

0.3129 [0.1070]

0.2907 [0.1120]-0.4760 [0.0939]-0.7157 [0.2572]-0.6958 [0.3136]-0.0220 [0.3310]-0.0185 [0.3301]
0.4646 [0.2959]
0.0380 [0.2422]
0.7479 [0.4427]
0.6069 [0.4700]-0.0060 [0.4539]
0.0235 [0.4251]-0.3480 [0.3678]-0.0665 [0.2737]-0.2671 [0.1914]-0.1150 [0.2609]-0.0116 [0.2889]

0.0270 [0.2622]-0.0512 [0.3197]
0.4483 [0.3374]
0.3664 [0.3365]
0.7802 [0.3016]
0.2132 [0.2469]
0.1108 [0.4513]
0.0308 [0.4791]-0.4975 [0.4627]-0.4031 [0.4334]-0.5909 [0.3749]-0.1966 [0.2790]-0.2860 [0.1951]-0.1157 [0.2660]
0.0628 [0.2945]

0.1763 [0.2746]-0.0254 [0.3348]
0.6054 [0.3534]
0.3102 [0.3524]
0.7403 [0.3159]
0.3007 [0.2585]
1.0340 [0.4726]
0.8458 [0.5018]
0.0922 [0.4846]
0.2074 [0.4539]-0.1893 [0.3926]-0.0319 [0.2922]-1.3698 [0.2043]-0.9961 [0.2786]-0.6914 [0.3084]

0.8845 [0.2303]
0.5085 [0.2807]
1.3423 [0.2963]
0.8963 [0.2955]
0.9098 [0.2649]
0.5798 [0.2168]-1.6238 [0.3963]-1.4336 [0.4208]-1.7713 [0.4063]-0.9503 [0.3806]-0.8534 [0.3292]-0.4653 [0.2450]
0.1790 [0.1713]
0.4142 [0.2336]
0.0609 [0.2586]

47

Table A7 (Continued)
VEC estimations∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−5∆𝑦𝑦3,𝑡𝑡−6∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−5∆𝑦𝑦4,𝑡𝑡−6
Constant
𝑅𝑅� 2
𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1
Cointegration
Equation 2∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4(B) Second period (1 July 1931–3 October 1932) (continued)
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡-0.0146 [0.2861]
0.0607 [0.2917]-0.4806 [0.3055] -0.1226 [0.2562]-0.1125 [0.2574] -0.1370 [0.2624] -0.5087 [0.2748] -0.1969 [0.2305]-0.0122 [0.1780] -0.0026 [0.1814] -0.2353 [0.1900] -0.1502 [0.1593]-0.5294 [0.1535] -0.6110 [0.1565] -0.5291 [0.1639] -0.3499 [0.1374]-0.5367 [0.1415] -0.6094 [0.1443] -0.5267 [0.1511] -0.3765 [0.1267]-0.5851 [0.1297] -0.6288 [0.1323] -0.5658 [0.1385] -0.3245 [0.1161]-0.4796 [0.1206] -0.5102 [0.1229] -0.4522 [0.1287] -0.1820 [0.1079]-0.3117 [0.1035] -0.3311 [0.1056] -0.3115 [0.1105] -0.0825 [0.0927]-0.1354 [0.0740] -0.1449 [0.0754] -0.1484 [0.0790] -0.0234 [0.0662]
0.0000 [0.0008]
0.0000 [0.0008]
0.0001 [0.0009]-0.0001 [0.0007]
0.39090.40640.42440.5403(C) Third period (4 October 1932–30 October 1933)∆𝑦𝑦1,𝑡𝑡-0.5105 [0.3590]

Cointegration equation
Equation 20.00001.0000-0.1575 [0.1283]-0.0846 [0.1224]-0.0003
Error correction∆𝑦𝑦2,𝑡𝑡
0.1579 [0.3821]∆𝑦𝑦3,𝑡𝑡

1.1544 [0.4319]-0.7695 [0.4275]-0.7529 [0.1955]-1.1685 [0.2081]-1.4009 [0.2352]

0.0190 [0.2328]-0.0611 [0.3367]-0.1025 [0.3001]
0.1049 [0.2417]
0.0242 [0.1691]
0.5892 [0.2124]
0.6870 [0.2111]
0.4033 [0.1919]
0.1763 [0.1485]-0.4230 [0.1897]-0.4667 [0.1683]-0.3665 [0.1382]-0.2294 [0.0944]-0.1525 [0.0641]-0.1874 [0.0749]-0.0911 [0.0740]-0.0240 [0.0598]

0.1787 [0.3583]
0.0492 [0.3194]
0.1225 [0.2573]-0.0181 [0.1800]
0.2002 [0.2261]
0.4328 [0.2247]
0.2755 [0.2042]
0.1176 [0.1580]-0.1354 [0.2019]-0.2299 [0.1791]-0.1786 [0.1471]-0.1099 [0.1004]-0.2484 [0.0797]-0.2484 [0.0797]-0.0979 [0.0788]-0.0238 [0.0637]-0.4042 [0.4050]-0.2332 [0.3610]-0.0475 [0.2908]-0.0646 [0.2035]
1.1687 [0.2556]
1.1561 [0.2539]
0.7536 [0.2308]
0.3217 [0.1786]-0.6525 [0.2282]-0.7436 [0.2025]-0.5489 [0.1662]-0.3085 [0.1135]-0.2941 [0.0771]-0.3019 [0.0901]-0.1331 [0.0890]-0.0281 [0.0720]

1.1088 [0.4009]
0.8838 [0.3573]
0.6953 [0.2878]
0.2546 [0.2014]-0.0845 [0.2530]
0.1539 [0.2514]
0.0667 [0.2285]
0.0613 [0.1768]-0.4260 [0.2259]-0.4473 [0.2004]-0.2499 [0.1645]-0.1471 [0.1124]-0.8668 [0.0763]-0.7188 [0.0892]-0.4798 [0.0881]-0.2425 [0.0713]

Equation 1
1.00000.0000-0.6348 [0.0716]
0.0517 [0.0683]
0.000048∆𝑦𝑦4,𝑡𝑡

Table A7 (Continued)
VEC estimations(C) Third period (4 October 1932–30 October 1933) (continued).
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡
Constant
0.0000 [0.0005]
0.0000 [0.0005]
0.0000 [0.0006]
0.0000 [0.0006]
0.4076
0.41820.45300.3870𝑅𝑅� 2(D) Fourth period (1 November 1933–19 September 1936)

𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1
Cointegration
Equation 2∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦1,𝑡𝑡−5∆𝑦𝑦1,𝑡𝑡−6∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−5∆𝑦𝑦2,𝑡𝑡−6∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−5∆𝑦𝑦3,𝑡𝑡−6∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−5∆𝑦𝑦4,𝑡𝑡−6
Constant
𝑅𝑅� 2∆𝑦𝑦1,𝑡𝑡-0.5823 [0.1274]

Cointegration equation
Equation 20.00001.0000-21.802 [2.0731]
17.802 [2.0096]-0.0014
Error correction∆𝑦𝑦2,𝑡𝑡0.1853 [0.1142]∆𝑦𝑦3,𝑡𝑡-0.3117 [0.1232]-0.3285 [0.1289]

0.9172 [0.1974]-0.2675 [0.1770]

0.5109 [0.1909]

0.4854 [0.1997]-0.2565 [0.1227]-0.3042 [0.1173]-0.2923 [0.1090]-0.3053 [0.0978]-0.2287 [0.0834]-0.1239 [0.0616]-0.8093 [0.1851]-0.7027 [0.1714]-0.4775 [0.1554]-0.2782 [0.1349]-0.1693 [0.1096]
0.0160 [0.0751]
0.5842 [0.1097]
0.5160 [0.1066]
0.3768 [0.1013]
0.3472 [0.0918]
0.2368 [0.0786]
0.0339 [0.0561]-0.3181 [0.0766]-0.2218 [0.0732]-0.1987 [0.0687]-0.1369 [0.0628]-0.0725 [0.0532]-0.0764 [0.0392]-0.0000 [0.0002]
0.3901-0.0509 [0.1100]-0.1214 [0.1052]-0.0898 [0.0978]-0.1549 [0.0877]-0.1212 [0.0748]-0.0516 [0.0552]-0.6794 [0.1659]-0.5890 [0.1537]-0.3984 [0.1393]-0.2130 [0.1210]-0.1319 [0.0982]-0.0042 [0.0673]
0.3906 [0.0983]
0.3669 [0.0956]
0.2104 [0.0908]
0.2091 [0.0823]
0.1531 [0.0705]
0.0211 [0.0503]-0.3868 [0.0687]-0.3010 [0.0656]-0.2570 [0.0616]-0.1774 [0.0563]-0.0912 [0.0477]-0.0823 [0.0351]-0.0000 [0.0002]
0.41380.4097 [0.1187]
0.2667 [0.1134]
0.2205 [0.1054]
0.1013 [0.0945]
0.0631 [0.0806]
0.0027 [0.0596]-0.4217 [0.1790]-0.3815 [0.1657]-0.2510 [0.1503]-0.0964 [0.1305]-0.0275 [0.1061]
0.0728 [0.0726]-0.1706 [0.1061]-0.1108 [0.1031]-0.1613 [0.0980]-0.0937 [0.0888]-0.0878 [0.0760]-0.0867 [0.0542]-0.4987 [0.0741]-0.3828 [0.0708]-0.2902 [0.0664]-0.2313 [0.0607]-0.0962 [0.0515]-0.1103 [0.0379]
0.0000 [0.0002]
0.40700.4380 [0.1241]
0.3284 [0.1186]
0.2498 [0.1103]
0.1917 [0.0989]
0.1562 [0.0844]
0.0259 [0.0623]-0.4133 [0.1872]-0.3394 [0.1734]-0.3142 [0.1572]-0.1569 [0.1365]-0.1048 [0.1108]-0.0247 [0.0759]-0.3212 [0.1110]-0.2899 [0.1079]-0.2280 [0.1025]-0.1878 [0.0929]-0.1306 [0.0795]-0.0965 [0.0567]-0.4948 [0.0775]-0.3854 [0.0740]-0.2509 [0.0695]-0.1811 [0.0635]-0.1227 [0.0539]-0.0969 [0.0396]-0.0000 [0.0002]
0.4321Equation 1
1.00000.0000-33.358 [3.2077]
27.572 [3.1095]-0.002149∆𝑦𝑦4,𝑡𝑡

Table A7 (Continued)
VEC estimations

𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1
Cointegration
Equation 2∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦1,𝑡𝑡−5∆𝑦𝑦1,𝑡𝑡−6∆𝑦𝑦1,𝑡𝑡−7∆𝑦𝑦1,𝑡𝑡−8∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−5∆𝑦𝑦2,𝑡𝑡−6∆𝑦𝑦2,𝑡𝑡−7∆𝑦𝑦2,𝑡𝑡−8∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−5∆𝑦𝑦3,𝑡𝑡−6∆𝑦𝑦3,𝑡𝑡−7∆𝑦𝑦3,𝑡𝑡−8∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−5∆𝑦𝑦4,𝑡𝑡−6∆𝑦𝑦4,𝑡𝑡−7(E) Fifth period (21 September 1936–19 August 1939)
Cointegration equation
Equation 1
Equation 2
1.00000.00000.00001.0000-0.7258 [0.1407] -1.3416 [0.0853]-2.0722 [0.2990] -0.0183 [0.1811]
0.00030.0001
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡0.2173 [0.1050]

0.3148 [0.1144]

0.2824 [0.1415]

0.4701 [0.0512]-0.2419 [0.1426]

0.0161 [0.1553]

0.9846 [0.1921]-0.5059 [0.0696]-0.9241 [0.1212]-0.7303 [0.1312]-0.5770 [0.1352]-0.4539 [0.1342]-0.1645 [0.1299]-0.0990 [0.1200]-0.1064 [0.1027]-0.2250 [0.0718]
0.1959 [0.1549]
0.0175 [0.1667]
0.0268 [0.1698]-0.0785 [0.1673]-0.2278 [0.1613]-0.2838 [0.1488]-0.1768 [0.1257]
0.0512 [0.0822]-0.1650 [0.1377]-0.0913 [0.1290]-0.1305 [0.1182]-0.0862 [0.1074]-0.0105 [0.0950]
0.0357 [0.0800]
0.0506 [0.0620]
0.0346 [0.0399]
0.3815 [0.2032]
0.3452 [0.1885]
0.3248 [0.1742]
0.1011 [0.1591]
0.0558 [0.1445]-0.1356 [0.1256]-0.0671 [0.1023]-0.0328 [0.1319]
0.0556 [0.1428]
0.0814 [0.1472]
0.1393 [0.1461]
0.3555 [0.1414]
0.2851 [0.1307]
0.1485 [0.1118]-0.1140 [0.0782]-1.0933 [0.1687]-1.0476 [0.1815]-0.8913 [0.1849]-0.8776 [0.1821]-0.9390 [0.1757]-0.8086 [0.1620]-0.4755 [0.1369]-0.0957 [0.0895]
0.2678 [0.1499]
0.2301 [0.1405]
0.1462 [0.1287]
0.1384 [0.1170]
0.1743 [0.1035]
0.1765 [0.0871]
0.1445 [0.0675]
0.0886 [0.0434]
0.5672 [0.2212]
0.5292 [0.2052]
0.4749 [0.1897]
0.2140 [0.1732]
0.1538 [0.1573]-0.0725 [0.1367]
0.0283 [0.1114]-0.1802 [0.1632]-0.0673 [0.1767]-0.0722 [0.1822]-0.0752 [0.1807]
0.1340 [0.1750]
0.1174 [0.1617]
0.0622 [0.1383]-0.0764 [0.0967]-0.7262 [0.2087]-0.6051 [0.2246]-0.3933 [0.2287]-0.3226 [0.2254]-0.3864 [0.2173]-0.4134 [0.2005]-0.2253 [0.1693]-0.0414 [0.1107]
0.4043 [0.1855]
0.2823 [0.1738]
0.1357 [0.1593]
0.1023 [0.1447]
0.1294 [0.1280]
0.1096 [0.1077]
0.1028 [0.0835]
0.1013 [0.0537]
0.5865 [0.2737]
0.3724 [0.2539]
0.2917 [0.2347]
0.1586 [0.2143]
0.1810 [0.1946]
0.0442 [0.1692]
0.1359 [0.1378]-0.3533 [0.0591]-0.3204 [0.0640]-0.2563 [0.0660]-0.3202 [0.0654]-0.1513 [0.0634]-0.1298 [0.0586]-0.0212 [0.0501]-0.0314 [0.0350]
0.4891 [0.0756]
0.4576 [0.0813]
0.4811 [0.0828]
0.5030 [0.0816]
0.4253 [0.0787]
0.3024 [0.0726]
0.1400 [0.0613]
0.0419 [0.0401]-0.3024 [0.0671]-0.2460 [0.0629]-0.2711 [0.0577]-0.2386 [0.0524]-0.2261 [0.0464]-0.1543 [0.0390]-0.0658 [0.0302]-0.0033 [0.0195]-0.0450 [0.0991]-0.1448 [0.0919]-0.0244 [0.0850]-0.0317 [0.0776]
0.0206 [0.0705]-0.0786 [0.0613]-0.0547 [0.0499]

50∆𝑦𝑦4,𝑡𝑡

Table A7 (Continued)
VEC estimations(E) Fifth period (21 September 1936–19 August 1939) (continued)
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡-0.0024 [0.0738]
0.0507 [0.0803]
0.0916 [0.0994]-0.0266 [0.0360]∆𝑦𝑦4,𝑡𝑡−8
Constant
0.0000-0.0000-0.0000
0.00002
�
0.39410.44230.48750.5366𝑅𝑅
𝑦𝑦1,𝑡𝑡−1
𝑦𝑦2,𝑡𝑡−1
𝑦𝑦3,𝑡𝑡−1
𝑦𝑦4,𝑡𝑡−1
Constant
Cointegration
Equation 1
Cointegration
Equation 2
Cointegration
Equation 3∆𝑦𝑦1,𝑡𝑡−1∆𝑦𝑦1,𝑡𝑡−2∆𝑦𝑦1,𝑡𝑡−3∆𝑦𝑦1,𝑡𝑡−4∆𝑦𝑦1,𝑡𝑡−5∆𝑦𝑦1,𝑡𝑡−6∆𝑦𝑦1,𝑡𝑡−7∆𝑦𝑦1,𝑡𝑡−8∆𝑦𝑦1,𝑡𝑡−9∆𝑦𝑦1,𝑡𝑡−10∆𝑦𝑦1,𝑡𝑡−11∆𝑦𝑦1,𝑡𝑡−12∆𝑦𝑦1,𝑡𝑡−13∆𝑦𝑦1,𝑡𝑡−14∆𝑦𝑦1,𝑡𝑡−15∆𝑦𝑦1,𝑡𝑡−16∆𝑦𝑦1,𝑡𝑡−17∆𝑦𝑦2,𝑡𝑡−1∆𝑦𝑦2,𝑡𝑡−2∆𝑦𝑦2,𝑡𝑡−3∆𝑦𝑦2,𝑡𝑡−4∆𝑦𝑦2,𝑡𝑡−5∆𝑦𝑦2,𝑡𝑡−6(F) All periods (2 November 1925–19 August 1939)
Cointegration equation
Equation 1
Equation 2
Equation 3
1.00000.00000.00000.00001.00000.00000.00000.00001.0000-0.6172 [0.0600] -0.7209 [0.0529] -0.8429 [0.0416]-0.0000-0.0000-0.0000
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡-1.0301 [0.1671]

0.0785 [0.1727]-0.3857 [0.1985]-0.3843 [0.1328]

0.4268 [0.2219]-0.6117 [0.2293]

1.2203 [0.2635]

0.6345 [0.1763]

0.0773 [0.1441]-0.1364 [0.1489]-1.7698 [0.1711]

0.2761 [0.1145]

0.2079 [0.1619]
0.1602 [0.1563]
0.1945 [0.1510]
0.1808 [0.1458]
0.2839 [0.1405]
0.2388 [0.1352]
0.1563 [0.1296]
0.1265 [0.1242]
0.0798 [0.1185]
0.0001 [0.1124]-0.0902 [0.1055]-0.0306 [0.0976]-0.0182 [0.0886]
0.0027 [0.0783]-0.0193 [0.0674]-0.0134 [0.0553]-0.0002 [0.0391]-0.4584 [0.2150]-0.5525 [0.2077]-0.4721 [0.2005]-0.4605 [0.1931]-0.4380 [0.1855]-0.4265 [0.1782]

0.0965 [0.1673]
0.0374 [0.1615]
0.1012 [0.1560]
0.1505 [0.1507]
0.2439 [0.1452]
0.1842 [0.1398]
0.0652 [0.1340]-0.0025 [0.1284]-0.0478 [0.1224]-0.1420 [0.1162]-0.2094 [0.1090]-0.1393 [0.1009]-0.1184 [0.0916]-0.0725 [0.0809]-0.0761 [0.0697]-0.0138 [0.0571]-0.0271 [0.0404]-0.4685 [0.2222]-0.5277 [0.2147]-0.5012 [0.2073]-0.5666 [0.1996]-0.5185 [0.1917]-0.4740 [0.1841]

0.5475 [0.1922]
0.4798 [0.1856]
0.5073 [0.1793]
0.5604 [0.1731]
0.6428 [0.1669]
0.6042 [0.1606]
0.4543 [0.1539]
0.3961 [0.1475]
0.3547 [0.1407]
0.2126 [0.1335]
0.1299 [0.1253]
0.1752 [0.1159]
0.1981 [0.1053]
0.2255 [0.0930]
0.1961 [0.0801]
0.2234 [0.0656]
0.0900 [0.0465]-1.1595 [0.2553]-1.1347 [0.2467]-1.0462 [0.2381]-1.0919 [0.2294]-1.0064 [0.2203]-1.0435 [0.2116]

0.5254 [0.1286]
0.4919 [0.1242]
0.5658 [0.1200]
0.5312 [0.1158]
0.5920 [0.1117]
0.5701 [0.1075]
0.5007 [0.1030]
0.4630 [0.0987]
0.4370 [0.0941]
0.3747 [0.0893]
0.3289 [0.0838]
0.3111 [0.0776]
0.2702 [0.0704]
0.2648 [0.0622]
0.1941 [0.0536]
0.1703 [0.0439]
0.0745 [0.0311]-0.5897 [0.1708]-0.6238 [0.1651]-0.6144 [0.1593]-0.5376 [0.1535]-0.5259 [0.1474]-0.5320 [0.1416]

51∆𝑦𝑦4,𝑡𝑡

Table A7 (Continued)
VEC estimations∆𝑦𝑦2,𝑡𝑡−7∆𝑦𝑦2,𝑡𝑡−8∆𝑦𝑦2,𝑡𝑡−9∆𝑦𝑦2,𝑡𝑡−10∆𝑦𝑦2,𝑡𝑡−11∆𝑦𝑦2,𝑡𝑡−12∆𝑦𝑦2,𝑡𝑡−13∆𝑦𝑦2,𝑡𝑡−14∆𝑦𝑦2,𝑡𝑡−15∆𝑦𝑦2,𝑡𝑡−16∆𝑦𝑦2,𝑡𝑡−17∆𝑦𝑦3,𝑡𝑡−1∆𝑦𝑦3,𝑡𝑡−2∆𝑦𝑦3,𝑡𝑡−3∆𝑦𝑦3,𝑡𝑡−4∆𝑦𝑦3,𝑡𝑡−5∆𝑦𝑦3,𝑡𝑡−6∆𝑦𝑦3,𝑡𝑡−7∆𝑦𝑦3,𝑡𝑡−8∆𝑦𝑦3,𝑡𝑡−9∆𝑦𝑦3,𝑡𝑡−10∆𝑦𝑦3,𝑡𝑡−11∆𝑦𝑦3,𝑡𝑡−12∆𝑦𝑦3,𝑡𝑡−13∆𝑦𝑦3,𝑡𝑡−14∆𝑦𝑦3,𝑡𝑡−15∆𝑦𝑦3,𝑡𝑡−16∆𝑦𝑦3,𝑡𝑡−17∆𝑦𝑦4,𝑡𝑡−1∆𝑦𝑦4,𝑡𝑡−2∆𝑦𝑦4,𝑡𝑡−3∆𝑦𝑦4,𝑡𝑡−4∆𝑦𝑦4,𝑡𝑡−5∆𝑦𝑦4,𝑡𝑡−6∆𝑦𝑦4,𝑡𝑡−7∆𝑦𝑦4,𝑡𝑡−8∆𝑦𝑦4,𝑡𝑡−9∆𝑦𝑦4,𝑡𝑡−10∆𝑦𝑦4,𝑡𝑡−11∆𝑦𝑦4,𝑡𝑡−12∆𝑦𝑦4,𝑡𝑡−13(F) All periods (2 November 1925–19 August 1939) (continued)
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡-0.3360 [0.1707] -0.3460 [0.1764] -0.8621 [0.2027] -0.4814 [0.1357]-0.3711 [0.1629] -0.3512 [0.1384] -0.8761 [0.1935] -0.5434 [0.1295]-0.2972 [0.1552] -0.2405 [0.1604] -0.7563 [0.1842] -0.4996 [0.1233]-0.2367 [0.1466] -0.1506 [0.1515] -0.5515 [0.1740] -0.4125 [0.1165]-0.0968 [0.1370] -0.0215 [0.1416] -0.4094 [0.1627] -0.3633 [0.1088]-0.1606 [0.1264] -0.0851 [0.1307] -0.4272 [0.1501] -0.3585 [0.1005]-0.1574 [0.1138] -0.1129 [0.1176] -0.4122 [0.1352] -0.3322 [0.0904]-0.1384 [0.0996] -0.1049 [0.1029] -0.4037 [0.1182] -0.3138 [0.0791]-0.0940 [0.0845] -0.0455 [0.0873] -0.2721 [0.1003] -0.2053 [0.0671]-0.0918 [0.0679] -0.0754 [0.0701] -0.2758 [0.0806] -0.1855 [0.0539]
0.0176 [0.0462]
0.0506 [0.0478]-0.0799 [0.0549] -0.0329 [0.0367]-0.0086 [0.1392]
0.2466 [0.1439]
0.7824 [0.1653]-0.2559 [0.1106]
0.1012 [0.1341]
0.3314 [0.1386]
0.7619 [0.1592]-0.1644 [0.1066]
0.0184 [0.1284]
0.2542 [0.1327]
0.6291 [0.1525]-0.2049 [0.1021]
0.0495 [0.1226]
0.2790 [0.1267]
0.6494 [0.1456]-0.1818 [0.0975]-0.0360 [0.1167]
0.1760 [0.1206]
0.5137 [0.1386]-0.2166 [0.0927]-0.0067 [0.1109]
0.1697 [0.1146]
0.5275 [0.1317]-0.1596 [0.0881]-0.0028 [0.1051]
0.1636 [0.1087]
0.4946 [0.1248]-0.1112 [0.0835]
0.0848 [0.0993]
0.2327 [0.1026]
0.5609 [0.1179]-0.0103 [0.0789]
0.0561 [0.0935]
0.1888 [0.0967]
0.4984 [0.1111]-0.0121 [0.0743]
0.0793 [0.0875]
0.1939 [0.0905]
0.4388 [0.1039]-0.0215 [0.0695]
0.0784 [0.0810]
0.1638 [0.0837]
0.3766 [0.0962]
0.0094 [0.0643]
0.0980 [0.0741]
0.1721 [0.0765]
0.3444 [0.0879]
0.0133 [0.0588]
0.1099 [0.0663]
0.2090 [0.0686]
0.3222 [0.0788]
0.0326 [0.0527]
0.1410 [0.0582]
0.2123 [0.0601]
0.3099 [0.0691]
0.0475 [0.0462]
0.1227 [0.0488]
0.1682 [0.0504]
0.2182 [0.0579]
0.0416 [0.0388]
0.0641 [0.0379]
0.0788 [0.0392]
0.1311 [0.0451]
0.0359 [0.0301]-0.0195 [0.0257] -0.0036 [0.0266] 0.0547 [0.0305]
0.0008 [0.0204]-0.3705 [0.0703] -0.5982 [0.0727] -0.9281 [0.0835] -0.5792 [0.0559]-0.2930 [0.0691] -0.5218 [0.0714] -0.8300 [0.0820] -0.5495 [0.0549]-0.3250 [0.0679] -0.5317 [0.0701] -0.8142 [0.0806] -0.5089 [0.0539]-0.2913 [0.0667] -0.4777 [0.0690] -0.7569 [0.0792] -0.4542 [0.0530]-0.2790 [0.0654] -0.4406 [0.0676] -0.7225 [0.0776] -0.4240 [0.0519]-0.2565 [0.0638] -0.3919 [0.0660] -0.6719 [0.0758] -0.4075 [0.0507]-0.2044 [0.0619] -0.3371 [0.0640] -0.5947 [0.0735] -0.3793 [0.0492]-0.1951 [0.0599] -0.2990 [0.0619] -0.5586 [0.0711] -0.3635 [0.0476]-0.1341 [0.0577] -0.2353 [0.0596] -0.4829 [0.0685] -0.3388 [0.0458]-0.1286 [0.0553] -0.2238 [0.0572] -0.4394 [0.0657] -0.3113 [0.0440]-0.1042 [0.0526] -0.1743 [0.0544] -0.3871 [0.0625] -0.2952 [0.0418]-0.0720 [0.0499] -0.1466 [0.0515] -0.3437 [0.0592] -0.2350 [0.0396]-0.1006 [0.0468] -0.1679 [0.0484] -0.3614 [0.0556] -0.2408 [0.0372]
52

Table A7 (Continued)
VEC estimations(F) All periods (2 November 1925–19 August 1939) (continued)
Error correction∆𝑦𝑦1,𝑡𝑡∆𝑦𝑦2,𝑡𝑡∆𝑦𝑦3,𝑡𝑡∆𝑦𝑦4,𝑡𝑡-0.1006 [0.0468] -0.1679 [0.0484] -0.3614 [0.0556] -0.2408 [0.0372]∆𝑦𝑦4,𝑡𝑡−13-0.1312 [0.0433] -0.1897 [0.0447] -0.3752 [0.0514] -0.2289 [0.0344]∆𝑦𝑦4,𝑡𝑡−14-0.0573 [0.0389] -0.0925 [0.0402] -0.2312 [0.0461] -0.1634 [0.0309]∆𝑦𝑦4,𝑡𝑡−15-0.0538 [0.0326] -0.0694 [0.0337] -0.1550 [0.0387] -0.1346 [0.0259]∆𝑦𝑦4,𝑡𝑡−16-0.0399 [0.0239] -0.0589 [0.0247] -0.1031 [0.0283] -0.0644 [0.0190]∆𝑦𝑦4,𝑡𝑡−17
Constant
0.0000 [0.0002]
0.0000 [0.0002]
0.0000 [0.0002]
0.0000 [0.0001]
0.44920.45490.46250.4836
𝑅𝑅� 2

Note: 𝑅𝑅�2 denotes the adjusted R-squared value and standard errors are in parentheses.

53Figure A1
Real value of the futures trade in Japan’s commodity exchanges
Note: There are no dates for August 1923, October 1925, or June 1929 because of the lack of data.
Sources: Bank of Japan, Research and Statistics Department 1987, pp. 24–25; Tokyo Stock Exchange,
Investigation Department 1923–1929, 1930a, 1930b, 1931–1943 (monthly series).

54

Figure A2
Indices of rice supply and demand in Japan.
Note: These indices are based on the average data from 1890 to 1894.
Sources: Bank of Japan, Statistics Department 1966, pp. 12–13; Bank of Japan, Research and Statistics
Department 1987, pp. 24–25; Fukao et al. 2017a, p. 276; 2017b, p. 282; Ministry of Agriculture
and Commerce, Food Control Bureau 1944, pp. 50–51, 54; Ministry of Agriculture and Forestry,
Economy of Agriculture and Forestry Bureau, Statistical Investigation Unit 1955, pp. 160–61;
Sasaki 1937, pp. 347–48, 350, 352–60, and 365–74.

55

Figure A3
Rice supply volume
Note: The “koku” is a standard unit of measurement in Japanese agriculture. One koku is equal to 180.39
liters and is roughly equivalent to 150 kg.
Sources: Ministry of Agriculture and Commerce, Food Control Bureau 1944, pp. 50–51; Ministry ofAgriculture and Forestry, Economy of Agriculture and Forestry Bureau, Statistical Investigation
Unit 1955, pp. 160–61; Toyo Keizai Shimpo Sha 1935, pp 484–85, 592–93.

56

Figure A4
Production volume per hectare of rice paddies
Sources: Nippon Agricultural Research Institute 1981, p. 706; Ministry of Agriculture and Commerce, FoodControl Bureau 1944, pp. 1, 50–51.

57

Figure A5
Monthly percentage changes in stocked domestic and Korean rice volumes in Osaka
Note: This figure indicates the year and month in which the ratio peaked.
Sources: Osaka-Dojima Rice Exchange 1925a, 1926b, 1927b (semi-annual series); Osaka-Dojima Rice
Exchange 1928–1935; 1936a, 1937a, 1938a, 1939 (monthly series).

58

(A) DeferredFirst difference in the natural log of prices
.15
.10
.05
.00
-.05
-.10
-.15

1926 1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

1939

1934

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1937

1938

1939

Trend component
.15
.10
.05
.00
-.05
-.10
-.15

1926 1927

1928

1929

1930

1931

1932

1933

Seasonal component
.15
.10
.05
.00
-.05
-.10
-.15

1926 1927

1928

1929

1930

1931

1932

1933

1934

Remainder component
.15
.10
.05
.00
-.05
-.10
-.15

1926 1927

1928

1929

1930

1931

1932

1933

1934

Seasonal adjusted first difference in the natural log of prices
.15
.10
.05
.00
-.05
-.10
-.15

1926 1927

1928

1929

1930

1931

1932

59

1933

1934

1935

1936

(B) Second-nearest
First difference in the natural log of prices
.16
.08
.00
-.08
-.16

1926 1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

1939

1934

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1937

1938

1939

Trend component
.16
.08
.00
-.08
-.16

1926 1927

1928

1929

1930

1931

1932

1933

Seasonal component
.16
.08
.00
-.08
-.16

1926 1927

1928

1929

1930

1931

1932

1933

1934

Remainder component
.16
.08
.00
-.08
-.16

1926 1927

1928

1929

1930

1931

1932

1933

1934

Seasonal adjusted first difference in the natural log of prices
.16
.08
.00
-.08
-.16

1926 1927

1928

1929

1930

1931

1932

60

1933

1934

1935

1936

(C) Nearby
First difference in the natural log of prices
.2
.1
.0
-.1
-.2

1926 1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

1939

1934

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1937

1938

1939

Trend component
.2
.1
.0
-.1
-.2

1926 1927

1928

1929

1930

1931

1932

1933

Seasonal component
.2
.1
.0
-.1
-.2

1926 1927

1928

1929

1930

1931

1932

1933

1934

Remainder component
.2
.1
.0
-.1
-.2

1926 1927

1928

1929

1930

1931

1932

1933

1934

Seasonal adjusted first difference in the natural log of prices
.2
.1
.0
-.1
-.2

1926 1927

1928

1929

1930

1931

1932

61

1933

1934

1935

1936

(D) SpotFirst difference in the natural log of prices
.3
.2
.1
.0
-.1
-.2
-.3

1926 1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

1937

1938

1939

1934

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1935

1936

1937

1938

1939

1937

1938

1939

Trend component
.3
.2
.1
.0
-.1
-.2
-.3

1926 1927

1928

1929

1930

1931

1932

1933

Seasonal component
.3
.2
.1
.0
-.1
-.2
-.3

1926 1927

1928

1929

1930

1931

1932

1933

1934

Remainder component
.3
.2
.1
.0
-.1
-.2
-.3

1926 1927

1928

1929

1930

1931

1932

1933

1934

Seasonal adjusted first difference in the natural log of prices
.3
.2
.1
.0
-.1
-.2
-.3

1926 1927

1928

1929

1930

1931

1932

1933

1934

1935

1936

Figure A6
Results of the STL decomposition
Note: The numbers of observations in each cycle of the seasonal component, smoothing parameter for the
low-pass filter, smoothing parameter for the trend component, and smoothing parameter for theseasonal component are 272, 273, 437, and 23, respectively.

62

(A) HDR

1939

1937

1935

1933

1939

1937

1933

1935

1937

1939

1937

1939

1935
1935

1939

1937

1935

1933

1931

1929

1927

-1,000%1939-1,000%1937-1,000%1935-1,000%

1933

-500%

1931

-500%

1929

-500%

1927

0%

-500%

1939

500%

0%

1937

500%

0%

1935

500%

0%

1933

500%

1931

1,000%

1929

1,000%

1933

Spot ---> Spot

1,000%

1927

1933

1939

1937

1935

1933

1931

Nearby ---> Spot

1,000%

1931

Second-nearest ---> Spot

1929

1927

1939

-1,000%1937-1,000%1935-1,000%1933-1,000%

1931

-500%

1929

-500%

1927

-500%

1939

0%

-500%

1937

500%

0%

1935

500%

0%

1933

500%

0%

1931

500%

1929

1,000%

1927

1,000%

1931

Spot ---> Nearby

1,000%

Deferred ---> Spot

1931

1939

1937

1935

1933

1931

Nearby ---> Nearby

1,000%

1927

Second-nearest ---> Nearby

1929

1927

1939

1937

-1,000%1935-1,000%1933-1,000%1931-1,000%

1929

-500%

1927

-500%

1939

-500%

1937

0%

-500%

1935

500%

0%

1933

500%

0%

1931

500%

0%

1929

500%

1927

1,000%

1929

Spot ---> Second-nearest

1,000%

Deferred ---> Nearby

1931

1939

1937

1935

1933

1931

1929

1927

Nearby ---> Second-nearest

1,000%

1927

Second-nearest ---> Second-nearest

1,000%

1929

Deferred ---> Second-nearest

1939-1,000%1937-1,000%1935-1,000%1933-1,000%

1931

-500%

1929

-500%

1927

-500%

1939

0%

-500%

1937

500%

0%

1935

500%

0%

1933

500%

0%

1931

500%

1929

1,000%

1927

1,000%

1929

Spot ---> Deferred

1,000%

1927

Nearby ---> Deferred

1,000%

1929

Second-nearest ---> Deferred

1927

Deferred ---> Deferred(B) AHDR
Second-nearest ---> Deferred

Nearby ---> Deferred

Spot ---> Deferred

1,000%

1939

1937

1935

1933

1931

1929

1927

1939

1937

1935

0%

1933

0%

1931

0%

1929

0%

1927

400%
200%

1939

400%
200%

1937

400%
200%

1935

400%
200%

1933

1939

600%

1931

1939

800%

600%

Figure A7
HDRs and AHDRs of the rice futures and spot markets in Osaka

1935

800%

600%

1933

800%

600%

1929

1935

Spot ---> Spot
1,000%

800%

1927

1939

1939

1937

1935

1933

1931

Nearby ---> Spot
1,000%

63

1937

Second-nearest ---> Spot

1929

1927

1939

1937

1935

1933

1931

1929

1927

0%

1939

0%

1937

0%

1935

0%

1933

400%
200%

1931

400%
200%

1929

400%
200%

1927

400%
200%

1,000%

1937

800%
600%

1935

800%
600%

1933

800%
600%

1931

1,000%

600%

Deferred ---> Spot

1933

Spot ---> Nearby

1,000%

800%

1,000%

1931

1939

1937

1935

1933

1931

1929

Nearby ---> Nearby

1,000%

1937

Second-nearest ---> Nearby

1,000%

1931

Deferred ---> Nearby

1927

0%

1939

200%

0%

1937

200%

0%

1935

200%

0%

1933

200%

1931

400%

1929

400%

1927

400%

1939

600%

400%

1937

800%

600%

1935

800%

600%

1933

800%

600%

1931

800%

1929

1,000%

1927

1,000%

1929

Spot ---> Second-nearest

1,000%

1927

Nearby ---> Second-nearest

1939

1939

1937

1935

1933

1931

1929

1927

1939

1937

1935

1933

1931

1929

Second-nearest ---> Second-nearest

1,000%

1927

Deferred ---> Second-nearest

1927

0%

1939

0%

1937

0%

1935

0%

1933

400%
200%

1931

400%
200%

1929

400%
200%

1927

400%
200%

1937

600%

1935

800%

600%

1933

800%

600%

1931

800%

600%

1929

800%

1927

1,000%

1929

1,000%

1929

1,000%

1927

Deferred ---> DeferredFigure A8
Trade volumes of the futures (deferred contracts) and spot markets in Osaka
Notes: See Figure 7.
Sources: Osaka Chamber of Commerce 1926; Osaka-Dojima Rice Exchange 1925a, 1926a, 1927a, 1928–
35, 1936a, 1937a, 1938a, 1939; Tokyo Chamber of Commerce and Industry 1926–1940.

64