Download BSE and the US Economy: Input

BSE and the US Economy:
Input-Output Model Perspective
Osei-Agyeman Yeboah
North Carolina Agricultural & Technical State University
Greensboro, NC 27411
Ellene Kebede
Tuskegee University
Tuskegee, AL 36088
Victor Ofori-Boadu
North Carolina Agricultural & Technical State University
Greensboro, NC 27411
Albert Allen
Mississippi State University
Mississippi State, MS 39762
August 2006
BSE and the US Economy:
Input-Output Model Perspective
Abstract
The potential impact of a BSE outbreak on the US economy is modeled in input-output setting
using 2002 US IMPLAN data. An outbreak of BSE would hurt the US beef industry, other
agriculturally-related industry, and the rest of the economy as a whole. The worse effects occur
in the beef cattle and farming industries. Generally, the economy of every county would be hurt
given the fact that cattle are produced in all the 50 states. But it is apparent that the damage
would be substantial in those regions and households which already suffer the severest economic
damage.
1
BSE and the US Economy:
Input-Output Model Perspective
INTRODUCTION
For nearly two decades demand for US beef has been affected by bovine spongiform
encephalopathy (BSE) after its potential danger to human health was exposed by (Holt and
Phillips 1988); (Dealler 1993); (Sawcer 1993); and (Lacey 1993). The BSE outbreak in the UK
during the mid 1990s and its possible link to cases of human Creutzfeldt-Jakob disease led
various agencies of the US government to implement control measures. The spread of BSE to
Japan and Canada during 2003 led to increased surveillance and research funding by the USDA.
When BSE was reported in Washington the same year, stronger regulatory efforts increased
production cost by $0.45 per head and proposed BSE measures would cost 5 times that for fed
cattle (15 times that for non-fed cattle) according to Coffey, Mintert, Fox, Schroeder, and
Valentin (2005).
It has become commonplace to say that the consequences of the BSE crisis reach far
beyond the farm gate. However, quantifying the extent and significance of these secondary
impacts is more problematic. Aggregate estimates of employment and value-added generated by
beef farming and beef processing provide only a partial understanding that exist between beef
production, other farm sectors and ancillary industries and the sector’s role in the national
economy. These estimates do not capture the extent and economic importance of interindustry
linkage. The beef industry is linked to other sectors in the economy through a network of input
purchases and output sales. Changes in the level of demand for beef, both in U.S. and other
markets, will have spin-of effects on the quantity of inter-industry sales and purchases.
In this paper, U.S. input-output (I-O) model is developed using IMPLAN 2002 economic
data to capture not only the direct effects of reduction in demand for cattle industry output due to
2
BSE on beef production but also wider impacts on suppliers of farm inputs, food processors, and
other nonagricultural sectors. Output, income, and employment impacts arising from reduced
demand for beef are estimated. The long-run effects of BSE on the US economy would be
serious in the absence of effective stabilization measures. The worse effects would take place in
the beef cattle and farming industries.
A Review of BSE Industrial Impacts
In the week following the Washington BSE case in December 2003 cattle prices fell by
16% and cattle future prices 20% but they rose back during the first quarter of 2004. Had the
low prices held, the cattle industry would have lost $2 billion during the first quarter of 2004
(Coffey, Mintert, Fox, Schroeder, and Valentin 2005).
The consumer survey of Henderson (2003) suggests US beef demand could fall as much
as 15% with a BSE outbreak. Coffey, Mintert, Fox, Schroeder, and Valentin (2005) use a
regionally targeted consumer survey and find over three quarters of consumers did not change
habits because of the BSE case but that subsequent cases, particularly multiple ones, could have
a significant impact. Jin, Skripnitchenko, and Koo (2004) estimate there would be a 20%
domestic decline of beef consumption with additional BSE outbreaks in the United States.
Within days after the 2003 Washington case 53 countries including major importers such
as Japan, Mexico, South Korea, and Canada banned imports of US beef. Exports of $4 billion
accounted for 10% of US beef production during 2003 and the bans reduced exports by 82%. If
all exported beef were consumed domestically, the increased quantity would have lowered
domestic beef prices 16% as developed by Henderson (2003).
3
During the second half of 2004 cattle future prices remained perhaps 10% lower than had
been expected. Francl (2003) points out that based on the expected sale of over 25 billion
pounds in 2004 every $10 per hundredweight drop in price results in a $2.5 billion drop in
revenue. Lost exports would then account for a loss of $1 billion revenue with total lost revenue
of about $3.5 billion.
From 1998 to 2002, the US exported a yearly average of $1.4 billion of beef products and
Feuz (2005) estimates that exports during the period increased the price of cattle by about $40
per head. Gross receipts from sales of cattle and calves in 2000 totaled $41 million accounting
for 21% of all agricultural receipts. BSE would impact other agricultural sectors and the present
input-output model offers perspective on its impact on the aggregate economy as well.
A Brief Review of Literature on BSE and Consumer’s Reponses
BSE has mainly occurred in European countries and, therefore, studies of BSE outbreak
and consumers’ responses have focused on European cases. Studies have approached the BSE
case from three different directions. The first group investigated consumers’ responses using
contingent valuation method (CVM). The second group analyzes structural changes in
consumers’ preferences or producers’ profits while the third group investigates economic
impacts of the outbreaks.
Among these studies is an examination by Latouche, Rainelli, and Vermersch (1998).
They conducted a survey using CVM to analyze consumer behavior in the area of Rennes,
France after the BSE crisis. Their survey revealed that consumers were waiting for greater
transparency and they would accept paying for it.
4
In regard to the issue of consumers’ preferences after the BSE outbreak, Mangen and
Burell (2001) investigated a structural change in Ductch consumers’ preferences for meat and
fish, following U.K. government’s announcement. They used switching Almost Ideal Demand
System (AIDS) model and a sample period that covers January 1994 through May 1998. The
hypothesis of constancy of the parameters of the AIDS model for meat and fish was rejected
against a more general time-varying parameter model. The combined effects of the underlying
trends and irreversible components of the BSE effects were against beef, minced beef, and meat
products, in favor of pork, prepared meat, poultry and fish.
Under the third group, Ashworth and Mainland (1995) reviewed economic consequences
of the BSE outbreak for the British meat industry. Adda (2002) investigated the effects of past
consumption of risky goods on current consumption patterns, using the “mad cow” crisis as a
natural experiment. Consumers with intermediate levels of past consumption decreased their
demand for beef and sought higher quality products, while low-and high-stock consumers did not
alter their behavior after crisis. Verbeke and Ward (2001) investigated fresh meat consumption
in Belgium during the period from 1995 through 1998 using an AIDS model. In specifying the
demand system, they incorporated a media index mainly pertaining to BSE; their results showed
that television publicity has a negative impact on beef expenditure, in favor of pork. Burton and
Young (1996) investigated the impact of BSE on the demand for beef and other meats in the
United Kingdom. Indices of media coverage of BSE are incorporated in a dynamic AIDS model
of meat demand.
In the U.S, Henderson (2003) conducted consumer surveys during the BSE outbreak in
Washington, the results suggested U.S. beef demand could fall by as much as 15 percent.
However, research indicated that the impact on domestic demand could be small because
5
consumers respond differently to food safety concerns than to long-run health concerns. To
investigate the potential impact of additional U.S. BSE discoveries, Coffey, Mintert, Fox,
Schroeder, and Valentin (2005) used a regionally targeted consumer survey. The results
suggested that most consumers (77 percent) did not change consumption habits because of the
first U.S. BSE case, but that subsequent discoveries, particularly of multiple cases, could have
significant impact on demand. Jin, Skripnitchenko, and Koo (2004) estimated 20 percent
domestic decline in beef consumption if there are any additional BSE outbreaks in the United
States.
MODEL CONSTRUCTION AND STRUCTURE
Data from the American Meat Institute (AMI) puts the final demand uses of processed
cattle products as an estimated 2.52 billion pounds going into foreign markets and 24.25 billion
pounds into domestic markets in 2002 (Otto and Lawrence 2001). These estimates for the
various dimensions of the US beef industry at the producer and processor level represent the
direct component of the industry with production inputs purchased by these sectors representing
the indirect effects. In addition to these obvious producer and input supplying impacts, income
earned in these agriculturally-related components of the beef industry is spent in the rest of the
economy stimulating a wide range of sectors, including consumer-related businesses in urban
areas. This paper used Input-Output (I-O) analysis to show the economic consequences of BSE
outbreak in the U.S.
The input-output method is based on the interrelationship between sectors in the economy
and how each is affected by a change in the final demand for a sector’s output. The model can be
expressed in the following equation:
6
X   I  A Y
1
(1)
where X is sector output, A is intermediate input for sector X usually referred to as the
technical coefficient, Y is final demand for sector X, and (I - A)-1 is the Leontief inverse or the
multiplier matrix. The multiplier matrix shows the effect of a dollar change in final demand on
the sector’s output, a measure of the impact on the economy. Three frequently used types of
multipliers are outputs of the sectors of the economy, income earned by household, and
employment that is expected to be generated because of the new output. Multipliers can be type
I, direct and indirect effects; or type II, direct, indirect and induced effects. To identify and
estimate these multiplier effects, an Input-Output (I-O) model is developed for the U.S using
IMPLAN 2002 U.S. economic data. The industries were aggregated into 14 major sectors and
the output, income and employment multipliers were developed.
Output multiplier for sector j is defined as the total value of production in all sectors of
the economy that is necessary in order to satisfy a dollar’s worth of final demand for sector j’s
output. The output multiplier is the ratio of the direct and the indirect effect to the initial effect
expressed by the following equation:
n
O j    ij
(2)
i 1
where Oj is output multiplier and ij is the Leontief inverse matrix, (I - A)-1 .
The total output is the direct and indirect output effects without including the household.
Initial output effect on the economy is simply the initial dollar’s worth of sector j’s output
needed to satisfy the additional final demand. Output multiplier is the sum of the column of the
Leontief inverse and would show where this spending would have the greatest impact in terms of
total dollar value of output generated in the economy.
7
Income multiplier measures the impact of final demand into change in income received
by households. Income multiplier shows the direct and indirect effect of a dollar worth of output
in terms of dollar’s worth of new household income. It is expressed by the following equation:
n
I j   an1, i ij
(3)
i 1
where, Ij is income multiplier of sector j, an+1 is the income coefficient for each industry (row
matrix) and ij is the Leontief inverse matrix.
Employment multiplier measures the relationship between the value of the output of a
sector and employment in that sector. The job created per dollar of new final demand for the
sector expressed by the following equation:
n
E j   wn 1, i ij
(4)
i 1
where, Ej is employment multiplier, wn+1 is the labor input coefficient for each industry (row
matrix) and ij is the Leontief inverse matrix.
Table 1 provides the Type I output multiplier for cattle farming in U.S. Output multiplier
indicates the additional output of each sector required for a dollar of new final demand for output
in cattle farming. The table presents only the major industries supplying intermediate goods and
services. These industries accounted for 83% of any change, an increase or a decrease in final
demand for cattle ranching industry. The most affected would be the cattle production and
decline in output by a dollar would reduce the industries output by $1.2083, about 54% of the
total change. The next industries to be impacted were farming, real estate, animal feed
producing industries, and financial services for about 15, 8, 6, and 4% of the total change,
respectively. The industries which would be affected in terms of their importance to the cattle
8
industry are: financial agencies, power, agricultural and forest support, petroleum, veterinary
services, transportation, and wholesale trade. A decline in the demand for cattle products would
affect these industries and the economy in general.
*** Insert Table 1***.
BEEF DEMAND EFFECTS
Final demand which includes household, government, and export of beef products in
2002 at 2002 prices was $663.93 million (IMPLAN U.S. Economic data 2002). The import
sector was not included in the analysis. To accommodate all the possible scenarios based on the
literature, the analysis used 20%, 30%, and 50% BSE-induced reduction in the final demand for
the US cattle ranching industry. Under these scenarios it was assumed that no support
mechanism (including intervention) was paid to farmers as a compensation for the fall in
demand.
The results of the I-O analysis showing the estimates of total output, household income,
and employment for the economy are presented in Tables 2 through 4. These tables of baseline
scenarios for the nation’s cattle industry includes related economic activity of input suppliers to
the through processing level.
Total industry output measures total dollars of goods and services produced by an
industry, including government and non-government activity. Table 2 provides the results of the
direct and indirect impact of a decline in final demand for cattle industry on the industry’s as
well as output of the rest of the economy. The total value of output generated by the beef
industry and the associated intermediate industries in the U.S. economy would decline with an
increase in the reduction of the final demand for cattle industry. The total value of output
9
generated by the cattle industry in the U.S. economy would be $1,192.4, $834.7, and $417.4
million for an increase in the reduction in final demand of 20%, 30%, and 50%, respectively.
Gross output declines by 65% for reduction in final demand from 20% to 50%. The major
reduction would take place in the cattle industry where the total output would decrease by 54%
and farming would be the next highly affected industry, 15%, while real estate and processed
animal feed declined by 8% and 6%.
*** Insert Table 2 ***
Table 3 provides the reduction in total household income due to 20%, 30%, and 50%
reduction in the final demand for cattle and beef products. The direct and indirect effect of the
decline in final demand from 20% to 50% will result in a 65% reduction in total household
income. The effect on household income is a little different from the effect on gross output.
Even though cattle industry is affected directly by the reduction in final demand, the farming
sector providing the feed grains would experience the highest income reduction. The farming
industry would have the highest income decline, 26%, followed by the cattle industry, 19%, and
the real estate industry, 17%. The income decline in the farming industry was projected to be
$97.0, $67.89, and $33.94 million compared to the cattle industry; which would be $69.5,
$48.65, and $24.33 million, respectively for 20%, 30%, and 50% reduction in final demand.
Other industries to be affected would be energy providers and financial agencies giving loan and
other financial services.
***Insert Table 3***
10
Employment is the other macroeconomic variable that is affected by the change in the
final demand. Employment is based on a per unit job consistent with the definitions used by the
U.S. Department of Commerce. Table 4 shows the employment impact of a 20%, 30%, and 50%
reduction in the final demand for cattle products. Employment generated by the cattle industry
will decline from about 13.4 million to 4.7 million for a reduction in final demand from 20% to
50%. The distribution of impacts is similar to the pattern for the output indicator as it affects
cattle ranching farming most. Cattle production and farming are the two industries that would
account for 79% of the reduction in employment; cattle 59% and faming 20%. Ag/forestry
support industries and real estate, 9% and 4%, respectively would be the next industries to be
affected.
***Insert Table 4 ***
All the macroeconomic variables, output, income, and employment are indicating that the
rural economies mainly based on farming and ranching would be affected by any reduction in the
demand for cattle related products. The news about BSE has potential to cause concern among
the consumers and the cattle industry would be affected via the domestic and export demand.
The beef industry is primarily grain-fed beef, and is a major user of feed grains; and factors that
affect beef production and price also affects the production and price of feed grain, the grain
producers.
CONCLUSIONS
An outbreak of BSE would hurt the US beef industry, other agriculturally-related industry, and
the rest of the economy as a whole. The worse effects occur in the beef cattle and farming
11
industries. The present model provides perspective on the potential of BSE to affect the
economy. Since the cattle industry is a good source of income for real estate agencies the
reduction in business by the industry would have impact on land and other property distribution
and income. Generally, the economy of every county would be hurt given the fact that cattle are
produced in all the 50 states. But it is apparent that the damage would be substantial in those
regions and households which already suffer the severest economic damage. These would have
serious policy implications for rural economies.
In regards to the economic and social dislocation which could occur as a result of these
effects, there may be a case for the maintenance of income stabilization measures until the
prospects for beef demand become clearer. The government should embark on programs aimed
at restoring consumer confidence in the safety of beef, for example, through stricter production
control standards and marketing campaigns.
12
REFERENCES
Adda, J. 2002. “Behavior Towards Health Risks: An Empirical Study Using the “Mad
Cow” Crisis as an Experiment.” Working Paper, Department of Economics, University
College, London.
Ashworth, S.W. and D.D. Mainland. 1995. “The Economic Impact of BSE on the UK
Beef Industry.” Outlook on Agriculture 24(3): 151-54.
Burton, M.P. and T. Young. 1996. “The Impact of BSE on the Demand for Beef and
Other Meats in Great Britain.” Applied Economics 28: 678-93.
Coffey, B., J. Mintert, S. Fox, T. Schroeder, and L. Valentin. 2005. “The Economic Impact
of BSE.” A Research Summary by Kansas State University Agricultural Experiment
Station and Cooperative Extension Service, MF-2679.
Dealler, S.F. 1993. “Bovine Spongiform Encephalopathy (BSE): The Potential Effects
of the Epidemic on Human Population.” British Food Journal, 95(8), 22-34.
Feuz, Dillon. 2005. “Economics Impacts of BSE Still Playing Out, Says Economist.
Center for Global Food Issues.” University of Nebraska, Institute of Agriculture and
Natural Resources.
Francl, T. 2003. “The Economic Impact of BSE.” American Farm Bureau Federation.
Henderson, J. 2003. “FAQs About Mad cow Disease and Its Impacts.” The Main
Street Economist, Commentary on the Rural Economy. A quarterly report by the Center
for the Study of Rural America. Available online at www.kc.frb.org. [Accessed June 12,
2006]
Holt, T.A. and J. Phillips.1988. “Bovine Spongiform Encephalopathy.” British Medical
Journal 1296: 1581-1583.
Jin, J. H., A. Skripnitchenko, and W. W. Koo. 2004. “The Effects of the BSE
Outbreak in the United States on the Beef and Cattle Industry.” Center for Agricultural
Policy Studies Special Report No. 03-4, Department of Agribusiness Business and
Applied Economics, North Dakota State University.
Lacey, R.W. 1993. “The Gathering Crisis.” British Food Journal 95(4):17-21.
Latouche, K., P. Rainelli, and D. Vermerrsch. 1998. “Food Safety Issues and the BSE
Scare: Some Lessons from the French Case.” Food Policy 23(5): 347-56.
Mangen, M.J. and A.M. Burrell. 2001. “Decomposing Preference Shifts for Meat and Fish
in the Netherlands.” Journal of Agricultural Economics 85(4): 902-13.
13
Otto, D and J. D. Lawrence. 2001. “Economic Impact of the United States Beef
Industry.” Ames: Iowa State University, Department of Economics.
Sawcer, S.J., G.M. Yuill, T.F. Esmonde, P. Estibeiro, J.W. Ironside, J.E. Bell, R.G. Will. 1993.
“Creutzfeldt-Jacob Disease in an Individual Occupationally exposed to BSE.” The Lancet
341 (8845): pp642.
USDA/ERS. 2006. Background Statistics: U.S. Beef and Cattle Industry,
www.ers.usda.gov/news, updated March 6, 2006
Verbeke, W. and R.W. Ward. 2001. “A Fresh Meat Almost Ideal System Incorporating
Negative TV Press and Advertising Impact.” Agricultural Economics 23: 359-74.
14
Table 1. Output multiplier for cattle industry in the U.S, 2002
Industry
(1-A)-1
Percent of total
Farming
0.3301
15
Cattle
1.2083
54
Ag. /forestry support
0.0723
3
Mining and Power
0.0576
3
Construction
0.0114
1
Animal feed and soybean processing
0.1235
6
Petroleum
0.0404
2
Pesticide/ag.chemical
0.0159
1
Pharmaceutical
0.0341
2
Truck and rail transportation
0.0461
2
Trade and storage
0.0165
1
Financial agencies
0.0834
4
Real estate
0.1691
8
Waste Management Other service
0.0362
2
Total
2.2450
100
15
Table 2: Output of major industries affected by the a 20%, 30%, and 50% decline in the final
demand of cattle industry (in million dollars)
Industry
20%
30%
50%
Percent of total
Farming
175.33
122.73
61.36
15
Cattle
641.80
449.26
224.63
54
Ag. /forestry support
38.41
26.89
13.44
3
Mining and Power
30.61
21.43
10.71
3
Construction
6.04
4.23
2.11
1
Animal feed and soybean processing
65.62
45.93
22.97
6
Petroleum
21.44
15.01
7.50
2
Pesticide/ag.chemical
8.47
5.93
2.96
1
Pharmaceutical
18.10
12.67
6.34
2
Truck and rail transportation
24.50
17.15
8.57
2
Wholesale trade
8.77
6.14
3.07
1
Financial agencies
44.29
31.00
15.50
4
Real estate
89.80
62.86
31.43
8
Waste Management Other service
19.25
13.48
6.74
2
1192.43
834.70
417.35
100
Total
16
Table 3: The effect of the decline in the final demand of cattle industry on the income of major
industries (in million dollars)
Industry
20%
30%
50%
Percent of total
Farming
96.98
67.89
33.94
26
Cattle
69.50
48.65
24.33
19
Ag. /forestry support
28.96
20.27
10.14
8
Energy
37.06
25.94
12.97
10
Construction
5.48
3.83
1.92
1
Animal feed and soybean processing
4.95
3.46
1.73
1
Petroleum
2.18
1.53
0.76
1
Pesticide/ag.chemical
3.11
2.17
1.09
1
Pharmaceutical
7.78
5.45
2.72
2
Truck and rail transportation
10.98
7.69
3.84
3
Wholesale trade
6.35
4.44
2.22
2
Financial agencies
24.69
17.29
8.64
7
Real estate
63.36
44.35
22.18
17
Waste Management Other service
12.51
8.76
4.38
3
Total
373.89
261.72
130.86
100
17
Table 4: The effect of the decline in the final demand of cattle industry on the employment of
major industries
Industry
20%
30%
50%
Percent of total
Farming
2,649,136
1,854,395
927,198
20
Cattle
7,840,670
5,488,469
2,744,234
59
Ag. /forestry support
1,266,273
886,391
443,196
9
Mining and Power
172,708
120,896
60,448
1
Construction
115,840
81,088
40,544
1
Animal feed and soybean processing
61,513
43,059
21,530
0
Petroleum
7,926
5,548
2,774
0
Pesticide/ag.chemical
34,870
24,409
12,205
0
Pharmaceutical
30,166
21,116
10,558
0
Truck and rail transportation
189,049
132,334
66,167
1
Wholesale trade
60,418
42,292
21,146
0
Financial agencies
227,169
159,019
79,509
2
Real estate
533,547
373,483
186,742
4
Waste Management Other service
200,893
140,625
70,312
2
13,390,178
9,373,125
4,686,562
100
Total
18