Working Paper Series∗ Department of Economics Alfred Lerner College of Business & Economics University of Delaware

Working Paper No. 2003-10

Agricultural Investment

and the Interwar Business Cycle†

James L. Butkiewicz and Matthew A. Martin

October 2003

∗http://www.be.udel.edu/economics/workingpaper.htm †°c 2003 by author(s). All rights reserved.

Agricultural Investment and the Interwar Business Cycle

James L. Butkiewicz Department of Economics University of Delaware Newark, DE 19716 [email protected]

Matthew A. Martin Economy.Com, Inc. 600 Willowbrook Lane West Chester, PA 19382 [email protected]

October 2003

This paper is based on sections of Matthew Martin’s Ph.D. dissertation at the University of Delaware. The authors acknowledge helpful comments and suggestions from Farley Grubb and Toni Whited. Responsibility for errors is ours.

Abstract

During the interwar period, the agricultural sector was a much larger component of the United States economy than at present. Thus, changes in agricultural fortunes had a larger impact on macroeconomic events than is the case today. The Great Depression and concomitant collapse of commodity prices adversely affected the farming sector, as did the drought that distressed many farming regions during this period. Farmers’ income plummeted, sharply curtailing investment in farm equipment. One key goal of the New Deal agricultural policies was to reverse the fortunes of the agricultural sector. Price supports and production control programs attempted to increase farmers’ incomes, enabling them to reverse the dramatic drop in equipment investment that occurred during the contraction period. This paper investigates the macroeconomic impact of investment in agricultural equipment on the aggregate economy. Results obtained support the hypothesis that increased expenditures for agricultural equipment contributed to the strength of the recovery, especially during the crucial early years of the recovery.

JEL Classification: N, E3

“Purchasing power placed in the hands of the people on the farm is certain to be used. Like a blood transfusion for a dying man, it circulates through the arteries of our economic body and brings a new vitality.” (George Peek in Nourse, Davis, and Black, 1937, p.422.)

“The tractors came over the roads and into the fields… Snub-nosed monsters, raising the dust and sticking their snouts into it, straight down the country, across the country, through fences, through dooryards, in and out of gullies in straight lines.” (John Steinbeck, 1984, p.36.)

1. Introduction During the interwar period (1919-1939), the agricultural sector experienced three independent and distinguishable events. The first was the broad adoption of new technologies, including the tractor and related machinery. The second was repeated and severe drought throughout the Midwest growing regions. The third was unprecedented government aid programs, including inaugural measures to limit agricultural production. In this paper, we examine the macroeconomic impact of these developments for the U.S. economy and provide evidence that government aid to farmers in the 1930s led to increased equipment expenditures, and furthermore, the increase in farm expenditures in the 1930s aided the general recovery, despite several years of repeated and severe drought. Table 1 lists the annual purchases of tractors and related equipment. As a testimony to the surge in agricultural equipment investment in the 1930s, consider the fact that more tractors were purchased in the 1930s (1,107,000) than in the 1920s (1,077,000), even though the 1930s includes nearly five years of economic contraction and several years of severe drought. Table 1 also shows that the annual purchases of other farm machinery increased substantially in the 1930s. The simultaneous increase in farm equipment expenditures and occurrence of drought seems counterintuitive at first. However, several important New Deal farm policies were introduced in

1 1933, particularly the Agricultural Adjustment Act, which provided farmers with additional forms of income, much of which was spent on new machinery. To understand the relationship of these events to the aggregate economy, the remainder of the paper is arranged in the following way: the next section summarizes the important developments for agriculture in the interwar period; the third section examines the transmission of agricultural shocks to the broader economy. The fourth section discusses the empirical methods, while the fifth presents the empirical results. The final section offers some conclusions.

2. Historical Summary1 Agriculture in the 1920s can be summarized as a period of overproduction and low farm prices in the aftermath of World War I. Figure 1 shows how grain prices fell in the early 1920s. Nevertheless, as technological improvements in the early 1920s made the tractor and other farm machinery more efficient and cost effective, purchases of these items increased. Table 1 shows that tractor purchases reached a pre-depression peak of 140,000 in 1920, but purchases averaged only 104,000 annually for the remainder of the decade. Part of the reason for the lower average stems from the number of farm failures due to low agricultural prices and related debt repayment problems. The farm foreclosure rate increased to 10.7 per thousand in the 1920s, up from 3.2 per thousand the previous decade (Alston, 1983). During the Great Contraction, farmers received little government assistance even though agricultural prices fell more quickly than the price level.2 The Roosevelt administration, however, placed priority on agricultural assistance and on May 12, 1933, the Agricultural Adjustment Act became law.3 By the fall of 1933, the AAA announced the first programs for future crop reduction. These programs varied by crop, but all programs presented farmers with some sort of “adjustment payment” in return for agreeing to limit acreage under cultivation. Farmers could then decide if they wished to participate. Once an individual farmer signed a contract, part of the adjustment payment

2 was often reserved until compliance was assured. In the end, a majority of farmers growing corn, wheat, and cotton agreed to sign production control contracts.4 Production control programs also existed for tobacco, rice, sugar, and peanuts, and a variety of smaller crops. In addition to the production control programs of the AAA, the New Deal assisted farmers through the Commodity Credit Corporation (CCC) and the Farm Credit Administration (FCA). The first of these programs put cash into the hands of farmers by loaning them money based on a fixed price per bushel times the number of bushels placed into storage. If the market price of the crop climbed above the loan price, farmers could sell the crop, pay off the loan and pocket the difference. CCC loans were no recourse loans, so repayment was not required if market prices remained below the loan price. The FCA enabled farmers to borrow new funds and refinance existing debt. Thus, farmers were able to reduce the burden of the debt accumulated in the previous decade at the same time that they received some security regarding their cash receipts in the near future. The sign up for the 1934 production control programs had been largely completed when it became clear that the weather was going to affect certain crops. The winter wheat crop, which had been planted the previous fall, gave the first indication that the rest of the wheat crop would be partially destroyed by drought. Since the drought seemed likely to accomplish crop reduction without a government program, Secretary Wallace removed many of the planting restrictions that contract signers had agreed to earlier. However, farmers still received the benefit payments promised by the government. A similar production control program was in force through 1935. In 1936, the Supreme Court declared the AAA to be unconstitutional, but the program continued under the auspices of soil conservation. That same year was also one of severe drought. From 1933 to 1936, the AAA distributed $1.8 billion as benefit payments to farmers under the production control programs. The wheat and corn-hog programs alone received over

3 $800 million (U.S. Department of Agriculture, 1942, p. 749). Additional payments were made in the following years under the soil conservation program. The FCA was credited with helping half a million farmers keep their homes (Saloutos, 1982). Additionally, CCC commodity loans totaled nearly a billion and a half dollars for the years 1933 through 1939. Rucker and Alston (1987) estimate that farm credit programs prevented 77 thousand farm failures, while all forms of government aid are estimated to have prevented 186 thousand failures. Perhaps more important than the actual dollar amounts received by farmers was the fact that, beginning in the latter part of 1933, most farmers had a portion of their income secured by government assistance that allowed many to withstand the ravages of drought of the 1930s. Farm foreclosures peaked in 1933, then fell quickly so that by 1935 the rate was similar to that of the late 1920s (Alston, 1983). As farmers began to receive cash income and other assistance through government programs, the number of tractors purchased annually increased substantially. Table 1 shows that tractors purchases were at an interwar period low of 25,000 in 1933, but increased to a record 221,000 by 1937. Similarly, the purchase of other farm implements, especially those pulled by tractors, increased dramatically. Only 15,000 tractor drawn cultivators were purchased in 1931, a figure that increased to 127,000 by 1937. 1937 was also a record year for the purchase of tractor plows and cultivators, while 1938 was a record year for corn picker and combine purchases (U.S. Department of Agriculture, 1941, p. 565).

3. Transmission of Agricultural Shocks The transmission of sectoral shocks to the macro economy has long been a part of economic theory. In the early part of the twentieth century, business cycle research included several mechanisms through which agricultural shocks could affect the aggregate economy. Pigou (1927) and Timoshenko (1930) are examples of early business cycle research identifying both demand and

4 supply side effects due to fluctuations in agricultural production. Most of these transmission mechanisms relate to changes in commodity prices due to variations in crop yields. Generally, a good crop meant lower prices, which meant lower input costs for producers, more discretionary income for consumers, and more exports. However, these early studies did not consider the effects of technological change or government assistance programs. Schumpeter (1939), reflecting on the previous decade, noted that the farm sector contributed to the depression in the United States. He reasoned that the process of mechanization that had been occurring within agriculture had caused significant structural unemployment and a migration of labor toward the cities. For Schumpeter, this preceded the “world crisis” and contributed to the depth of the downturn. Commenting on the AAA, he noted that the program had increased farm revenue considerably and had helped to promote general recovery. Although he thought the changes occurring within the farm sector were important for aggregate economic activity, Schumpeter cautioned against overstating either the problems of farmers or the results of the AAA. Madsen (2001) develops econometric models to quantify the impact of agricultural prices on economic activity. He presents evidence that agricultural price deflation transmitted the depression globally, and that reflation of farm prices contributed to overall recovery from depression, although he finds the deflationary impact was greater. In addition, he also finds that agricultural price deflation contributed to the decline of aggregate investment during 1929 – 1933. Recent economic research has evaluated the importance of equipment investment for economic growth. DeLong and Summers (1992) report that a three or four percent increase in the share of product devoted to equipment investment will lead to an increase in output per worker growth of one percent a year. Furthermore, they find that this result is very robust across specifications that account for broader time periods and competing hypotheses. Delong (1992) further demonstrates the robustness of this result by including eight different time periods extending

5 back to the nineteenth century and covering six countries. These empirical results reinforce the historical narratives of Landes (1969) and Rosenberg (1976), among others, describing the role of mechanization in increasing productivity over time. Clarke (1991) makes this point convincingly with regard to the adoption of the tractor by evaluating the number of farms that could have experienced cost savings by purchasing a tractor. In Iowa, for example, approximately 72 percent of all farms could have experienced cost savings by purchasing a tractor in 1929, but only 29 percent actually owned one. By 1939, these figures were 72 percent and 55 percent, respectively. Clarke points out that the government assistance programs provided farmers with security, encouraging them to make equipment investments that they would have continued to postpone. Essentially, the mechanization process that had stalled in the depression was permitted to continue despite the droughts. A comparison of farm expenditures in 1932 and 1935 supports this notion. Farmers spent $186 million on farm implements in 1932. This figure increases rapidly to $593 million in 1935. Other farm expenses increase more slowly, such as wages and fertilizer costs, or decline, such as interest payments on farm mortgages (U.S. Department of Agriculture, 1938, p. 434). Temin and Wigmore (1990) provide further support for the notion that farm spending on equipment expenditures was related to government policy. They claim that the rapid increase in farm prices was a direct result of New Deal policy. Furthermore, they argue that the price increase was expected to be permanent, increasing farm income so that farmers were more likely to purchase durable goods. In a regression of auto sales on farm income and other income, they find that a dollar increase in farm income generated three times the number of auto sales as a dollar increase in other income in 1933. The extension of this argument to include the purchase of farm implements is straightforward and supported by the increase in the number of tractors and related farm machinery purchased in the following years.

6 If the government enabled farmers to adopt tractors and related farm implements more quickly, then the 1930s should have been a period of substantial gains in productivity. This is indeed the case. Clarke notes that productivity increases for agriculture averaged only 0.8 percent in the 1920s. For the years 1934 to 1939, the years in which the government programs enabled farmers to purchase more equipment, the average gain in productivity is 3 percent. Her conclusion is that government assistance provided farmers with the security and liquidity necessary to promote the adoption of new technology on farms. The remaining task of this study is to demonstrate that this sequence of events is useful for understanding aggregate economic activity in the interwar period.

4. Methods The structural vector autoregression (VAR) is particularly well suited to investigating the relative importance of agricultural shocks. Through impulse response functions (IRFs) and variance decompositions (VDs), a VAR can track the effect of an unobserved shock to a variable over time. A VAR has the advantage of allowing the data to determine the lagged interactions among the variables of importance. A well-documented criticism of Sims (1980) initial VAR estimates is that identification of the unrestricted VAR requires a somewhat arbitrary recursive structure for identification. However, beginning with the work of Sims (1986) and Bernanke (1986), structural relationships have been applied to the contemporaneous interaction of the variables. In other words, the structural VAR is still a reduced form model that allows the data to determine lag structure, but with structural relationships determining the contemporaneous relationships.5 In this study, I use the Bernanke method for estimating structural VARs. This is a method of moments estimator and is, therefore, consistent under any distributional assumptions. An important and generally unresolved issue with VARs concerns the inclusion of deterministic components. Specifically, many studies have found that the inclusion of a trend significantly alters the results of the IRFs and VDs. Bernanke (1986) finds that including a trend term

7 prevents the inclusion of an interest rate from diluting the strength of the effect of the money stock disturbance. Runkle (1987) finds that inclusion of a trend term significantly alters variance decomposition results. Both the Bernanke and Runkle studies involve the use of post-World War II data, where there is a general consensus that at least some univariate series contain unit roots. The alternative is generally a trend stationary process, possibly with a trend break. Perron (1989) began a continuing investigation into the trend break alternative. He reasoned that the most likely trend break point would be during the Great Depression. Using a trend break alternative, he rejects the hypothesis of a unit root for eleven of fourteen macroeconomic series. Zivot and Andrews (1992) develop a test for the presence of a unit root against the alternative of trend stationarity with a trend break, where the trend is not known a priori. For most of the series they examine, the trend break alternative is a break during the depression years. They reject the unit root hypothesis for seven of the fourteen series. For the structural VAR models presented in this study, a trend break is included among the deterministic components. This is based on the results of previously mentioned unit root studies as well as the intuition of Temin and Wigmore (1990). The authors claim that the beginning of the recovery in 1933 was prompted by the change in policy regime signaled by Roosevelt’s decision to abandon the gold standard and further reinforced by other new deal programs such as the AAA. Thus, in the second quarter of 1933 there is a clear structural change that is modeled as a trend break within a structural VAR analysis.

5. Results The data used in this study come from various sources. All price series are from the BLS Wholesale Price Index; the industrial production index is from Miron and Romer (1991); monetary measures are from Friedman and Schwartz (1963); and the gold stock and treasury bill rate are from Banking and Monetary Statistics (1943). Those that are originally monthly series have

8 been converted into quarterly series by averaging. The sample range is from the first quarter of 1921 to the third quarter of 1939. This sample range avoids changes in the business cycle directly attributable to either war. For all models, a grain price index is used to represent shocks to the farm sector. We use this variable for three reasons. First, since the demand for agricultural commodities is relatively inelastic and the shocks to agricultural production in the interwar period are primarily supply related, an agricultural price series is a good representation of general agricultural welfare. Second, economic theory describes several transmission mechanisms through which changes in farm commodity prices can affect the economy. For example, many farm products were important inputs to production, so that an abrupt increase in farm commodity prices would lead to a decline in production. Alternatively, an increase in grain prices in the presence of substantial government assistance represents an increase in farm income, both from the assistance itself and from the increase in income from commodity sales. Similarly, a decrease in grain prices represents deterioration in the terms of trade for farmers relative to the rest of the economy. Third, grain crops represent the most important agricultural commodities produced, in terms of value. The first model contains the following series: the gold stock (g), the monetary base (b), M1 money (m), grain price index (c), industrial production (y), and the wholesale price index (p). All variables are in natural logarithms and the lag length is set at six. As a departure point, we estimate an unrestricted VAR using the same variables. Since this is the first step in the method of moments approach, it is worth examining the impulse response functions (IRFs) and variance decompositions (VDs) in order to note the degree of difference in the performance of the unrestricted VAR versus the structural VAR. IRFs track the dynamic response of a variable due to a shock to another variable within the system. VDs indicate how important these responses are by breaking down the portion of the forecast variance due to each series in percentage terms. Because

9 of their similarity to the structural model IRFs, the IRFs for the unrestricted model are not presented here, but are available upon request. The VDs for typical forecast horizons are presented in Table 2. The structural relationships for the first model are shown in equations (1) through (6). The first equation states that gold flows are independent of current shocks to any series. The second equation is the Fed reaction function and the third is a money demand equation. The fourth equation models grain prices as contemporaneously dependent upon money and wholesale prices. The last two equations permit industrial production and wholesale prices to be interdependent in the current time period. Variance decompositions for wholesale prices and industrial production are presented in Table 3. The VD results indicate that grain prices can account for a substantial portion of the forecast variance of industrial production and wholesale prices, although generally not as much as demonstrated by the results from the Cholesky decomposition. Impulse responses for the first structural model are in Figure 2 and contain 95% confidence intervals computed through bootstrap methods.6 Notice that the monetary variables demonstrate the expected responses. Positive shocks to gold increase the monetary base, while positive shocks to the monetary base, in turn, increase the money stock. The monetary variables, in particular innovations to the monetary base and the money stock, have the expected effects on prices. Equations 5 and 6 show that the contemporaneous impact of a positive shock to grain prices is negative for both production and prices. It appears that the very short-term effect conforms to intuition that an increase in grain prices increases the cost of production. An examination of the IRFs for industrial production, however, reveals a positive and significant response to grain price increases at horizons of three, seven, and eight quarters. Wholesale prices respond in a similar fashion over time, as expected. In general, the first structural model behaves similarly to the unrestricted VAR, although the grain price series is generally less important for explaining the variance of prices and industrial production in the structural model.

10

g = u1 (1)

b = 0.108g - 0.457m + u2 (2) (11.42) (0.546)

m = 1.374b - 0.034y - 0.157p + u3 (3) (1.384) (21.08) (1.375)

c = -0.493m + 6.154w + u4 (4) (0.073) (0.734)

y = 2.088m - 0.159c + 0.173p + u5 (5) (0.568) (1.942) (0.074)

p = -0.314g + 0.513b + 1.043m - 0.183c + 0.168y + u6 (6) (10.45) (1.767) (1.756) (7.368) (11.45)

The second model includes the following series: the monetary base (b), the t-bill rate (t), M1 (m), grain prices ( c), industrial production (y), and wholesale prices (p). The second model is similar to the first except that the gold stock has been replaced by the t-bill rate and the base is now considered to be unaffected by other current shocks. We include the t-bill rate to investigate whether aggregate economic variables during the interwar period might respond to the inclusion of an interest rate in a manner similar to postwar studies. The unrestricted VDs are presented in Table 4. As with the first model, the IRFs from the Cholesky decomposition are very similar to the structural model and are not presented. The structural coefficients for the second model are shown below in equations (7) through (12). These are not measured as precisely as in the first model, which may be due in part to the lack of variation in the t-bill rate from 1934 through the end of the sample. 7 During this time period, the nominal short-term interest rate remained very close to zero, except for one episode in

11 1937. However, the contemporaneous relationship between the short-term interest rate and the money stock is negative and significant, as expected.

b = u1 (7)

t = 6.073b + u2 (8) (0.261)

m = -0.009t + 0.057y + 0.349p + u3 (9) (5.059) (1.494) (0.036)

c = 2.991m + 2.243w + u4 (10) (0.782) (1.608)

y = -0.269t - 3.404m + 0.426c + 17.80p + u5 (11) (0.167) (0.001) (0.267) (0.004)

p = -33.05b + 0.193t + 18.91m - 0.385c + 6.293y + u6 (12) (0.0005) (0.099) (0.0009) (0.030) (0.003)

The IRFs for this model are quite similar to the first. In Figure 3, a positive shock to grain prices has a positive and significant impact on production at horizons of three and four quarters. The effect on the price series is also positive and significant for the first four quarters. The variance decomposition results in Table 5 show that money gains predictive power when the t-bill rate is included. Grain prices, however, are much less important for predicting industrial production. Nonetheless, the impulse response function in Figure 3 shows a significant and positive response of industrial production to a grain price shock. The response of wholesale prices to grain price shocks is not appreciably different from the first model. The empirical results are generally similar across all structural and unrestricted VAR models. Positive shocks to grain prices tend to have a significant and positive effect on industrial production. The relative importance of this effect does change with the particular specification, with

12 the variance decompositions at the four-quarter horizon between 6.5 and 16.5 percent. Positive grain price shocks also have a positive and significant effect on the price index, with variance decompositions fluctuating between 10.1 and 29.0 percent at the one-year horizon. These results are consistent with the explanation that increases in farm income in the 1930s led to substantial increases in equipment expenditures with a positive impact on overall production in the economy. Table 6 presents the dollar amounts of expenditures on farm implements and producer’s durable equipment. As reported by the U.S. Department of Agriculture, farm implement expenditures include spending on tractors and other farm machinery, including a fraction of expenditures on trucks and automobiles. The values from 1929 to 1935 are as reported by the Department of Agriculture. The values from 1936 to 1939, however, are not reported and are calculated from known expenditures on tractors and related farm machinery plus the 1934-35 average percentage of total farm expenditures spent on trucks and autos (approximately 33 percent). Table 6 also presents the ratio of farm implement to producer’s equipment expenditures. From 1932 to 1935, farm equipment expenditures share of producer’s durable equipment increases to 20.5 percent and remains near that level for the rest of the decade. The magnitude of this effect on the economy can be found using the results from the first structural model, where the largest significant response of industrial production to a one standard deviation shock to grain prices occurs at the three quarter horizon and has an approximate value of 0.025. Since the series are in natural logarithm form, this can be interpreted as a percentage response in decimal form. The mean value for the industrial production index for this sample period is 184.9. This value would increase to 189.6 based on the response due to a grain price shock three quarters previously. Industrial production would have increased by approximately three percent to 190.4 over the four quarters after the price shock, based on the point values from the IRFs.

13 The preceding discussion is predicated on the assumption that there were grain price shocks of a large enough magnitude to alter industrial production as described. In levels, a one standard deviation in the grain price index is a change of 22.59, or a 27.5% change for the mean value of 82.12. During the interwar period, there were seven quarters where grain prices changed by approximately one standard deviation from the previous quarter (greater than 20% change). These are listed in Table 7. Five of these shocks are positive; and four of them occur during the recovery years. The repeated, clustered shocks in the years 1933 to 1936 are concomitant with the large increases in farm implement expenditures. If each increase in grain prices led to a three percent increase in industrial production over the next year, then these shocks account for approximately 12.5% of the increase in industrial production that occurred from 1933 to 1937. The total increase in industrial production over this time period was 79.8%, so nearly one sixth was due to agricultural developments. It could be argued that the grain price shocks were an expected response to monetary developments. However, the first two price shocks occur in 1933 before the advent of large, positive increases in monetary aggregates. The remaining two shocks may be partially explained by monetary developments, setting the minimum contribution of agricultural developments at approximately 6% of industrial production during the early recovery.

6. Summary and Conclusions The results of this study suggest three conclusions. First, this study has focused on the recovery in an effort to understand the confluence of events in the 1930s. However, the relationship between farm income, farm prices and equipment expenditure apply equally to the 1920s. 8 The results of this study suggest that depressed agricultural prices reduced farm income, causing the mechanization process to stall during the depression. Further expenditures were possible only after farmers received government assistance that increased their general purchasing power, especially in

14 the presence of severe drought. The increase in agricultural income increased farmer’s ability to purchase goods of all types. However, farmers were likely to use additional increases in income to purchase cost saving machinery in the 1930s. The resulting changes in farm equipment expenditures produced an increase in industrial production.9 Second, the majority of the largest agricultural price shocks occur during the early years of the recovery. This result may provide a partial explanation for the course of the recovery. Specifically, gold inflows provide a plausible explanation for the strength of the recovery after 1934. However, there were some gains in industrial production before monetary developments could have had an influence. Increases in agricultural equipment expenditures provide an explanation for some of this earlier growth and fill in a gap in our previous understanding of the early recovery. Third, our understanding of the impact of agricultural mechanization would be advanced by a better understanding of the changes to the farm sector labor force. The increased spending by farmers to purchase of new equipment would tend to displace labor. Farm labor statistics show that there was a net migration away from farms in the 1930s, just as Shumpeter observed. However, the potential impact of this development on the aggregate economy has not been considered here and is left for future research.

15 References

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Bernanke, Ben S. "Alternative Explanations of the Money-Income Correlation," Carnegie- Rochester Conference Series on Public Policy 25 (1986): 49-100.

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Bureau of the Census. Historical Statistics of the United States: Colonial Times to 1957. Washington, DC: Government Printing Office, 1961.

Campbell, John Y. and Pierre Perron. “Pitfalls and Opportunities: What Every Macroeconomist Should Know About Unit Roots,” NBER Macroeconomics Annual (1991).

Clark, Sally. "New Deal Regulation and the Revolution in American Farm Productivity: A Case Study of the Diffusion of the Tractor in the Corn Belt, 1920-1940," The Journal of Economic History 51 (March 1991): 101-123.

DeLong, J. Bradford. “Productivity Growth and Machinery Investment: A Long Run Look: 1870- 1980,” The Journal of Economic History 52 (June 1992): 307-324.

DeLong, J. Bradford and Lawrence H. Summers. “Equipment Investment and Economic Growth: How Strong Is the Nexus?,” Brookings Papers on Economic Activity (1992:2).

Federal Reserve Board. Banking and Monetary Statistics, 1913-1941. Washington, DC: National Capital Press, 1943.

Freidman, Milton and Anna J. Schwartz. A Monetary History of the United States, 1867-1960. Princeton: Princeton Univ. Press, 1963.

Gardner, Bruce L. American Agriculture in the Twentieth Century, Cambridge: Harvard University Press, 2002.

Landes, David. The Unbound Prometheus. Cambridge: Cambridge Univ. Press, 1969.

Leeper, Eric M., Christopher A. Sims and Tao Zha. “What Does Monetary Policy Do?” Brookings Papers on Economic Activity, Volume 2 (1996):1-63.

Madsen, Jakob B. “Agricultural Crises and the International Transmission of the Great Depression,” Journal of Economic History, Vol. 61 (June 2001): 327-365.

16 Miron, Jeffery A. and Christina D. Romer. "A New Monthly Index of Industrial Production, 1884- 1940," Journal of Economic History 50 (June 1990): 321-337.

Nourse, Edwin G., Joseph S. Davis, and John D. Black. Three Years of the Agricultural Adjustment Administration, Washington: Brookings Institute, 1937.

Perkins, Van L. Crisis in Agriculture: The Agricultural Adjustment Administration and the New Deal, 1933, Berkeley, 1969.

Perron, Pierre. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica 57 (1989): 1361-1401.

Pigou, A. C. Industrial Fluctuations. London: MacMillan, 1927.

Romer, Christina D. "What Ended the Great Depression?," The Journal of Economic History 52 (December 1992):19-39.

Rosenberg, Nathan. Perspectives on Technology. Cambridge: Cambridge Univ. Press, 1976.

Rucker, Randal R. and Lee J. Alston. “Farm Failures and Government Intervention: A Case Study of the 1930s,” American Economic Review 77 (1987): 724-730.

Runkle, David E. "Vector Autoregressions and Reality," Journal of Business and Economic Statistics 5 (1987): 437-54.

Saloutos, Theodore. The American Farmer and the New Deal, New York: Ames, 1982.

Schumpeter, Joseph A. Business Cycles. New York: McGraw-Hill, 1939.

Sims, Christopher. "Macroeconomics and Reality," Econometrica 48 (1980): 1-48.

Sims, Christopher. "Are Forecasting Models Usable for Policy Analysis?" Federal Reserve Bank of Minneapolis Quarterly Review 10 (1986): 2-16.

Steinbeck, John. The Grapes of Wrath, New York: Octopus Books, 1984 (first printing, 1939).

Temin, Peter and Barry Wigmore, "The End of One Big Deflation," Explorations in Economic History 27 (Oct. 1990): 483-502.

Timoshenko, Vladimir P. “The Role of Agricultural Fluctuations in the Business Cycle,” Michigan Business Studies 2 (June 1930), no. 9.

17 U.S. Department of Agriculture. Agricultural Statistics. Washington, DC: Government Printing Office, various years.

Zivot, Eric and Donald W.K. Andrews. “Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit Root Hypothesis,” Journal of Business and Economic Statistics 10 (1992): 251-270.

18 250

200

150

100

50

0 20 22 24 26 28 30 32 34 36 38

Grain Prices WPI

Figure 1 Grain Prices and the Wholesale Price Index

19 GOLD BASE MONEY GRAIN IIP WPI 0.03 0.015 0.06 0.30 0.14 0.08

0.05 0.25 0.12 0.02 0.010 0.10 0.06 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10

-0.005 0.04 GOLD 0.02 0.01 0.05 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.03 0.015 0.06 0.30 0.14 0.08

0.12 0.010 0.05 0.25 0.02 0.06 0.10 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10

-0.005 0.04 BASE 0.02 0.01 0.05 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.03 0.015 0.06 0.30 0.14 0.08

0.05 0.25 0.12 0.02 0.010 0.10 0.06 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10

-0.005 0.04 MONEY 0.02 0.01 0.05 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.03 0.015 0.06 0.30 0.14 0.08

0.12 0.010 0.05 0.25 0.02 0.06 0.10 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10 -0.005 0.04 GRAIN 0.02 0.01 0.05 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.03 0.015 0.06 0.30 0.14 0.08

0.12 0.010 0.05 0.25 0.02 0.10 0.06 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10

-0.005 0.04 IIP 0.02 0.01 0.05 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.03 0.015 0.06 0.30 0.14 0.08

0.05 0.25 0.12 0.02 0.010 0.06 0.10 0.04 0.20 0.005 0.01 0.08 0.03 0.15 0.04 0.000 0.06 0.00 0.02 0.10 0.04 WPI -0.005 0.01 0.05 0.02 -0.01 0.02 -0.010 0.00 0.00 0.00 0.00 -0.02 -0.015 -0.01 -0.05 -0.02

-0.03 -0.020 -0.02 -0.10 -0.04 -0.02 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

Figure 2 First Structural Model IRFs: Each Row Represents the Impulse Responses of All Series to a One Standard Deviation Shock of the Series Listed on the Left

20 BASE TBILL MONEY GRAIN IIP WPI

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.48 0.20 0.048 0.024 0.072 0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 0.006 0.018 BASE 0.05 0.012

-0.025 0.00 -0.000 0.000 0.00 0.000 -0.006 -0.018 -0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.48 0.20 0.048 0.024 0.072

0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 TBILL 0.006 0.05 0.018 0.012 -0.025 0.00 -0.000 0.000 0.00 0.000

-0.006 -0.018 -0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.20 0.048 0.48 0.024 0.072 0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 MONEY 0.006 0.05 0.018 0.012

-0.025 0.00 -0.000 0.000 0.00 0.000 -0.006 -0.018

-0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.20 0.048 0.48 0.024 0.072 0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 GRAIN 0.006 0.05 0.018 0.012

-0.025 0.00 -0.000 0.000 0.00 0.000 -0.006 -0.018 -0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.48 0.20 0.048 0.024 0.072 0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 0.006 0.018 IIP 0.05 0.012

-0.025 0.00 -0.000 0.000 0.00 0.000 -0.006 -0.018 -0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

0.025 0.64 0.036 0.25 0.108 0.060

0.030 0.090 0.48 0.20 0.048 0.024 0.072

0.15 0.036 0.000 0.32 0.018 0.054

0.012 0.10 0.036 0.024 0.16 WPI 0.006 0.05 0.018 0.012 -0.025 0.00 -0.000 0.000 0.00 0.000

-0.006 -0.018 -0.16 -0.05 -0.012 -0.012 -0.036

-0.050 -0.32 -0.018 -0.10 -0.054 -0.024 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12 0 2 4 6 8 10 12

Figure 3 Second Structural Model IRFs: Each Row Represents the Impulse Responses of All Series to a One Standard Deviation Shock of the Series Listed on the Left

21 Table 1 Farm Income and Purchases of Farm Equipment

Year Cash Receipts a,c Tractors b,d Tractor Tractor Mold- Cultivators b,e Board Plows b,e 1920 12.6 140 1.1 143.5 1921 8.1 65 1.6 23.9 1922 8.6 95 1.6 44.3 1923 9.5 108 2.0 65.1 1924 10.2 91 nrf 38.4 1925 11.0 113 1.7 58.1 1926 10.6 113 10.4 91.1 1927 10.7 133 10.2 81.6 1928 11.0 82 12.9 95.5 1929 11.3 137 34.6 122.9 1930 9.1 116 56.3 107.7 1931 6.4 58 15.6 26.8 1932 4.7 25 nrf nrf 1933 5.5 25 nrf nrf 1934 6.8 65 nrf nrf 1935 7.7 122 54.5 57.9 1936 8.7 165 116.0 116.2 1937 9.2 221 127.2 149.0 1938 8.2 151 90.8 118.0 1939 8.6 159 65.5 98.7 Notes: a billions of current dollars. b thousands of units purchased. c Historical Statistics of the U.S., p. 283. d Agricultural Statistics, 1942, Table 749. e Agricultural Statistics, 1941, Table 695, p. 565. f indicates years for which that figure was not reported.

22 Table 2 Variance Decomposition Results for the First Cholesky Decomposition

Panel A: Variance Decomposition of IIP from shocks to: Forecast SE Gold Base Money Grain IIP WPI Horizon 2 0.063 5.095 2.690 14.391 6.143 62.581 9.101 4 0.794 3.668 5.740 15.397 12.588 56.828 5.780 8 0.115 8.015 3.085 33.087 21.489 31.080 3.245 12 0.155 6.862 5.883 30.665 34.716 19.564 2.310

Panel B: Variance Decomposition of WPI from Shocks to: Forecast SE Gold Base Money Grain IIP WPI Horizon 2 0.020 0.692 16.942 36.804 17.113 2.832 25.617 4 0.036 0.492 14.986 46.170 28.996 1.549 7.807 8 0.059 0.395 10.153 50.226 34.677 1.131 3.418 12 0.072 0.413 7.389 46.987 41.431 1.128 2.653

23 Table 3 Variance Decomposition Results for the First Structural Identification

Panel A: Variance Decomposition of IIP from shocks to: Forecast SE Gold Base Money Grain IIP WPI Horizon 2 0.062 2.062 6.889 15.952 11.762 62.266 1.069 4 0.079 1.953 9.920 16.534 16.456 43.308 11.830 8 0.114 4.315 8.755 28.796 18.805 22.627 16.702 12 0.169 2.750 16.338 32.345 19.613 13.194 15.760

Panel B: Variance Decomposition of WPI from Shocks to: Forecast SE Gold Base Money Grain IIP WPI Horizon 2 0.020 3.257 31.061 29.390 8.617 5.838 21.837 4 0.039 1.914 33.881 32.660 10.100 2.355 19.094 8 0.064 0.882 25.415 36.136 16.405 3.896 17.266 12 0.090 0.862 17.332 38.207 26.027 4.375 13.197

24 Table 4 Variance Decomposition Results for the Second Cholesky Decomposition

Panel A: Variance Decomposition of IIP from shocks to: Forecast SE Base T-bill Money Grain IIP WPI Horizon 2 0.062 26.671 3.565 6.794 1.491 58.712 2.768 4 0.080 16.634 6.910 20.322 6.533 47.505 2.096 8 0.122 15.344 12.108 34.108 7.009 30.270 1.160 12 0.145 11.448 11.780 36.548 15.833 22.352 2.040

Panel B: Variance Decomposition of WPI from Shocks to: Forecast SE Base T-bill Money Grain IIP WPI Horizon 2 0.016 2.239 13.743 41.631 16.143 1.106 25.138 4 0.033 1.036 10.050 58.165 19.608 4.085 7.055 8 0.054 0.980 9.607 62.730 16.294 7.601 2.788 12 0.064 1.885 7.517 61.095 19.026 8.135 2.343

25 Table 5 Variance Decomposition Results for the Second Structural Identification

Panel A: Variance Decomposition of IIP from shocks to: Forecast SE Base T-bill Money Grain IIP WPI Horizon 2 0.063 26.958 3.703 13.822 2.165 3.705 49.648 4 0.081 16.745 7.001 31.379 6.742 5.391 32.737 8 0.124 16.202 12.704 40.800 6.081 7.764 16.451 12 0.147 12.142 12.124 41.315 11.746 10.346 12.326

Panel B: Variance Decomposition of WPI from Shocks to: Forecast SE Base T-bill Money Grain IIP WPI Horizon 2 0.016 2.857 15.105 20.901 6.658 46.621 7.858 4 0.033 1.229 10.207 52.965 13.589 19.061 2.949 8 0.055 1.017 10.638 58.548 10.929 17.356 1.512 12 0.064 1.792 8.531 57.984 13.080 17.299 1.313

26 Table 6 Expenditures on Farm Implements and Producers Durable Equipment

Year Farm Implements a,b Producer Equipment a,c FI/PE 1929 916 5570 .165 1930 677 4247 .160 1931 366 2700 .136 1932 186 1495 .124 1933 218 1476 .148 1934 375 2149 .175 1935 593 2893 .205 1936 795 3953 .201 1937 923 4853 .190 1938 706 3471 .203 1939 751 3960 .190 Notes: a in millions of dollars. b Agricultural Statistics, 1938, Table 557, p. 434 and in the text. c National Income and Product Account, Table 5.6.

27 Table 7 The Quarter and Size of the Largest Grain Price Increases

Quarter Change a 24:3 21.0% 31:3 -23.8% 33:2 42.2% 33:3 26.4% 34:3 24.4% 36:3 29.9% 37:4 -24.7% Notes: a BLS bulletin #572.

28

1 See Perkins (1969) and Saloutos (1982) for detailed historical accounts of the events surrounding the AAA and the droughts.

2 In each year from 1924 through 1928, Congress introduced the McNary-Haugen bill designed to assist farmers through export promotion. Each year the bill was defeated or vetoed. See Gardner (2002, pp. 214-215).

3 Note that the National Industrial Recovery Act was passed over a month later on June 16, 1933, a significant amount of time during the rush of legislative activity that occurred during the early months of the New Deal.

4 Nourse, Davis, and Black (1937) report that up to 75% of all acreage in the major crops was under contract, while over 90% of acreage was under contract in the Corn Belt proper.

5 Leeper, Sims, and Zha (1996) provide a recent and detailed account of the issues and advantages of VARs.

6 For more details on this procedure, see Runkle (1987).

7 Unfortunately, insignificant coefficients are quite common for exactly identified models. For example, Bernanke (1986) estimates two structural relationships. In both cases, the majority of the structural coefficients are insignificant.

8 These results are also consistent with Madsen’s findings that the deflation of agricultural output prices contributed to the contraction.

9 The New Deal economic policies are criticized for confusing aggregate supply and aggregate demand, and restricting supply when increasing demand was the correct policy. While the New Deal farm programs did restrict supply, the programs did, also, increase equipment investment by farmers, thereby stimulating aggregate demand.

29