NMSU: Economic Impact of Southern New Mexico Vegetable Production and Processing
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Economic Impact of Southern New Mexico Vegetable Production and Processing1


New Mexico Chile Task Force Report 9
T. Y. Hall and R. K. Skaggs
Agricultural Experiment Station, College of Agricultural, Consumer and Environmental Sciences


Authors: Respectively, former graduate research assistant, Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces. Professor, Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces. E-mail: rskaggs@nmsu.edu (Print-friendly PDF)

Introduction

Concern about the long-term viability of commercial chile pepper production in New Mexico prompted creation of the  New Mexico Chile Task Force in December 1998. The objectives of this interdisciplinary team are to identify and implement ways to keep chile pepper production and processing profitable in New Mexico and to maintain and enhance the research and development partnership between the New Mexico chile industry and New Mexico State University.

Image 1: Illustration of a farmhouse with farmland fields depicted in money symbols.

Since the initiation of the Task Force, numerous questions regarding the size, nature and economic impact of the New Mexico chile industry have been raised. These questions often have come before the Task Force in the context of wondering what would be the economic impact of losing the local industry and also in situations where the challenges faced by the industry are being communicated to the public, government agencies and New Mexico state legislators.

This report discusses the southern New Mexico chile pepper industry and provides economic impact estimates of all vegetable production and processing in the region. The study area is confined to the three New Mexico counties where the majority of commercial chile pepper and other vegetable production and processing takes place. The information reported here is the result of graduate thesis research conducted by T. Y. Hall (Hall, 2001). The research was performed in coordination with and in support of other New Mexico Chile Task Force activities. Impact Analysis for Planning (IMPLAN) software and data were used in this research.

Description of the chile pepper industry

Chile peppers ranked fifth in commodity cash receipts in New Mexico in 2000, following beef cattle, milk, hay and greenhouse nursery crops (NMDA, 2000). In 2000, there were 19,000 acres of chile peppers harvested in New Mexico, yielding 80,500 tons of green chile and cayenne peppers (wet weight) and 28,500 tons of red chile peppers (dry weight). Although peppers are produced in several other counties, Doña Ana, Luna and Hidalgo accounted for 86 percent of the state’s chile pepper production and 74 percent of the state’s harvested chile acreage in 2000 (NMDA, 2000). In 1997, New Mexico accounted for 46 percent of harvested hot pepper acreage in the United States (NASS, 1999).

New Mexico also is home to numerous chile pepper processing facilities of different sizes that use a variety of raw materials and technologies. Recent research has led to an estimated 112,009 wet tons of green chile, cayenne and jalapeño peppers and 36,735 dry tons of red chile peppers processed in New Mexico in 2000 (Hall and Skaggs, 2003). Many green chile and jalapeño peppers are imported from Mexico (approximately 40,000 tons in 2000), while dried red chile peppers and powders are imported from several Asian, African and Latin American countries for further processing in New Mexico.

The state’s chile pepper processing sector is extremely diverse but has become more concentrated in recent years. The industry is comprised of numerous small firms and a few large firms. Firms are reluctant to disclose information due to privacy concerns and the industry’s competitive nature. Descriptive data for chile pepper processing in New Mexico are scarce, some existing government data are not publicly available due to disclosure issues and other publicly available data are confounded by the chile peppers moving between counties at various processing stages. A description of the pepper types produced, the nature of New Mexico chile processing and the sources of peppers that flow into the state’s processing industry can be found in Hall and Skaggs (2003).

Prior to this study, no research had been conducted to estimate the economic impact of the New Mexico chile pepper industry, specifically, or the state’s overall vegetable production and processing. Data limitations currently prevent estimating chile pepper industry economic impacts separately from those of other vegetable production and processing sectors (e.g., onions, lettuce). In this research, an input-output model, using IMPLAN software and limited data, was constructed to develop preliminary impact estimates of all vegetable production and processing in Doña Ana, Luna and Hidalgo counties. These counties were chosen for analysis because of their predominance in farm-level pepper production, both red and green chile processing and production of other vegetables.

Eighty-eight percent of New Mexico’s onion acreage (6,800 acres) is located and 87 percent of onion production occurs in Doña Ana and Hidalgo counties (NMDA, 2000). The 1997 Census of Agriculture reported approximately 1,600 acres of lettuce in Doña Ana and Luna counties, 850 acres of cabbage and spinach in Doña Ana County and 1,150 acres of watermelons in Luna County. Plantings of other vegetables in Doña Ana, Luna and Hidalgo counties are small (less than 200 acres total) (NASS, 1999). Overall, 74 percent of New Mexico’s total vegetable acreage is located in the three counties (NASS, 1999).

Input-output analysis and IMPLAN applications in the agricultural sector

Input-output modeling is a technique used to measure interactions among firms, industries and institutions within a local economy (Mulkey and Hodges, 2001). It is a methodology often applied when there are questions about the impact of a particular industry or effects of changes in an industry on a local economy. An input-output model is a system of linear equations describing the circular flow of income and product throughout an economy (Holland and Yeo, 2001). The model represents all consumption and production in an economy, which is divided into sectors that include firms or organizations with similar characteristics or activities.


This study used IMpact Analysis for PLANning (IMPLAN) software for the input-output modeling. The U.S. Department of Agriculture’s Forest Service developed IMPLAN in the 1970s, in cooperation with the Federal Emergency Management Agency and the U.S. Department of Interior’s Bureau of Land Management. The software program originally was used to evaluate the effects on local economies of closing roads in wilderness areas (MIG, 2000). In 1993, Minnesota IMPLAN Group Inc. (MIG) obtained exclusive rights to the IMPLAN software. Their most recent version, IMPLAN Professional 2.0, released in 1999, was used in this research, along with 1998 economic data for New Mexico (also sold by the Minnesota IMPLAN Group).

The IMPLAN databases that are used in developing input-output models come from a variety of governmental agencies. Sources of IMPLAN data include the Bureau of Labor Statistics, Bureau of Economic Analysis, County Business Patterns and the U.S. Census of Agriculture.

IMPLAN uses three categories of effects to describe the economic impacts of a selected industry. The categories are direct effects, indirect effects and induced effects. These effects are founded on the observation that when money is brought into an economy by a particular industry (through sales of their goods and/or services), it is spent within the local economy. The money continues to be spent in the local or regional economy until it leaks out of that economy into another (Mulkey and Hodges, 2001). Direct effects are those experienced by a particular  ndustry that produces and sells goods and/or services. As economic impacts spread from firm to firm throughout the local economy, indirect effects are changes in interindustry transactions that occur as other industries produce and sell more goods and services to the directly affected industries (Mulkey and Hodges, 2001). Induced effects are felt by businesses selling goods and services to households that receive income as a result of increases or changes in local economic activity. The changes in local spending are due to income changes in the directly and indirectly affected industries or economic sectors (Mulkey and Hodges, 2001).

There are three basic types of multipliers used to construct an input-output model in IMPLAN. Type SAM (Social Accounting Matrix), the recommended and default multiplier used in IMPLAN input-output models, was used in this research. Type SAM multipliers give direct, indirect and induced effects of industries and/or changes in the industries.

IMPLAN input-output model multipliers trace backward linkages only (MIG, 2000). Backward linkages refer to goods and services purchased from an industry to produce a product. Examples include labor, utilities and transportation. Forward linkages not captured in the model are those between an industry producing a good or service and the consumers of that good or service (MIG, 2000). Consumers could be other industries that add value or further services before the product reaches the final consumer. Examples of forward linkages include exporting costs, household consumption and value-added remanufacturing.


IMPLAN does not count the number of jobs in an industry or a local economy in terms of full-time equivalents (FTE). IMPLAN counts full and part-time employment as being the same (MIG, 2000). If an employee works one or seven days per week, he simply is considered an employee and one job exists. This feature of IMPLAN creates challenges when attempting to estimate the economic impacts of industries characterized by a high degree of less than fulltime employment (such as agriculture).

Numerous researchers have used IMPLAN to evaluate the regional economic impacts of agriculture and agribusiness. For example, Miller and Armbruster (1991) used IMPLAN to investigate the economic impact of grape juice production in Arkansas. Leones and Conklin (1993) used IMPLAN to analyze direct, indirect and induced agriculture effects in Arizona. Holland and Yeo (2001) used IMPLAN to study Washington’s potato industry and calculate agricultural multipliers that affect the state’s economy. Robison et al. (1991) used IMPLAN to investigate the impact of agriculture, food processing, timber, recreation and mining on Idaho’s economy.

IMPLAN modeling procedures

Using IMPLAN, an input-output model was constructed for Doña Ana, Luna and Hidalgo counties. The vegetable industries and particularly the chile pepper industry in the three counties are linked tightly at both farm and processing levels. Doña Ana, Luna and Hidalgo counties are major producers of green chile peppers and jalapeños, and both Doña Ana and Luna counties have large processing plants within their boundaries. Green and jalapeño chile peppers grown in the three counties are processed there, but may cross county lines one or more times during processing for value adding or container labeling. The largest green and jalapeño chile processor in New Mexico has processing plants located in both Doña Ana and Luna counties and regularly moves products at varying stages of processing between the two plants. Red chile peppers and powders at various processing stages also move between locations in the three counties.

The IMPLAN economic sectors most relevant for examining the economic impacts of green chile pepper and jalapeño production and processing are sectors 18, 67 and 70 (table 1). Sector 18, the vegetable sector, includes farms that produce Irish potatoes, vegetables and melons. In southern New Mexico, this would include farms that produce chile peppers, onions, lettuce, spinach, cabbage, watermelons and other vegetables. Sector 67, canned fruits and vegetables, includes establishments primarily involved in canning fruits and vegetables. Sector 70 is frozen fruits, juices and vegetables. Sector 70 could not be represented in the models, because IMPLAN shows no data for sector 70 in Doña Ana, Luna or Hidalgo counties, even though there is at least one green chile freezing plant operating in the region.

Table 1. Economic sectors relevant to vegetable production and processing in Doña Ana, Luna and Hidalgo counties.

Source: MIG, 2000; and OMB, 1987.
Sector # Sector Title Description
18 Vegetable production Irish potatoes, vegetables and melons
67 Canned fruits and vegetables Canned fruits, vegetables, preserves, jams and jellies
68 Dehydrated foods Dried and dehydrated fruits, vegetables and soup mixes
70 Frozen fruits, juices and vegetables Frozen fruits, fruit juices and vegetables, dried fruit pulp, quick frozen and coldpack fruits and vegetables
103 Food preperation—Not elsewhere classified Food preperations, not elsewhere classified (NEC) (chili pepper or powder)

The IMPLAN sectors most relevant to the red chile pepper industry were sectors 18, 68 and 103 (table 1). Sector 18 is defined above. Sector 68 (dehydrated foods) includes items, such as dried fruits, vegetables and soup mixes. In southern New Mexico, this includes dehydrated red chile powder, spice mixes and dehydrated onions. Sector 103 (food preparations-NEC) includes all foods that are not elsewhere classified (NEC) into other categories, including “chili pepper or powder” (OMB, 1987). Both sectors 68 and 103 were included here, because data for the region’s red chile processors likely are included in both sectors.

For the three counties, IMPLAN reports that sector 18 (vegetables) has 843 employees, sector 67 (canned fruits and vegetables) has 649 employees, sector 68 (dehydrated food products) has142 employees, and sector 103 (food preparations—NEC) has 278 jobs. In Doña Ana, Luna and Hidalgo counties, a total of 84,592 people were employed in all industries in 1998. Thus, sectors 18, 67, 68 and 103 employed about 2.3 percent of the working population in the three counties (1,912 jobs). Again, these data are for 1998.

IMPLAN agricultural employment data are based on standard, national output to employment ratios and underestimate the total number of sector 18 employees in Doña Ana, Luna and Hidalgo counties. Individuals knowledgeable about the southern New Mexico farm labor situation estimate there are at least 10,000 farmworkers employed part-time or full-time in the region from September through November, primarily engaged in chile pepper harvesting (Hall, 2001).

From the preceding discussion and the sector definitions presented in table 1, it is obvious that data or information for other vegetables are combined with farm-level chile pepper production data. Furthermore, data for the processing sectors (table 1) includes data for vegetables other than chile peppers. However, given the nature of the vegetable industry in Doña Ana, Luna and Hidalgo counties, the majority of vegetable production and processing activities for which data are reported and included in IMPLAN is directly related to chile peppers. The same cannot be said for sector 18, which includes data for onions and lettuce, both of which are produced in significant quantities in the study region.

Table 2. Aggregated input-output model results for employment impact of vegetable production and processing in Doña Ana, Luna and Hidalgo counties.

Economic Sector Title Direct Effects Indirect Effects Induced Effects Total Effects
Agriculture 843 1,720 10 2,573
Construction 0 91 16 107
Manufacturing 1,069 56 13 1,138
Transportation, communication and utilities 0 184 27 211
Retail and wholesale trade 0 239 281 520
Finance, insurance and real estate 0 97 56 153
Services 0 288 285 573
Government 0 14 10 24
Other 0 0 21 21
Total Employment 1,912 2,689 719 5,320

IMPLAN input-output model results for the aggregated direct, indirect and induced employment impacts of sectors 18, 67, 68 and 103 on Doña Ana, Luna and Hidalgo counties are shown in table 2. The 1,912 workers directly employed in sectors 18, 67, 68 and 103 are shown at the bottom of the column labeled “Direct Effects.”

Indirect employment in the region related to activities in sectors 18, 67, 68 and 103 equals 2,689 jobs. Unaggregated employment impacts are presented in appendix A, where it is shown that 59 percent of the indirect employment workers are employed in the agricultural services sector (#26). This sector includes activities, such as soil preparation, crop services, farm labor contracting and farm management services. Other sectors with relatively large indirect employment effects include motor freight transport and warehousing (#435) and wholesale trade (#447).

The total induced employment impact is 719 jobs (table 2). Induced employment effects are distributed widely throughout the three-county region, with some of the largest effects noted for general merchandise stores (#449), food stores (#450), automotive dealers and service stations (#451), eating and drinking establishments (#454), miscellaneous retail (#455), doctors and dentists (#490) and hospitals (#492) (see appendix A). Thus, approximately 5,320 jobs in Doña Ana, Luna and Hidalgo counties are directly or indirectly related to vegetable production and processing in the region.

Aggregated output impact results from the IMPLAN model are presented in table 3. Output is defined as the gross receipts of local industries. The direct output impact of sectors 18, 67, 68 and 103 (i.e., vegetable production and processing) was estimated at $281.3 million. The indirect output impact of the four sectors was $97.6 million, while the induced output impact was $39.5 million for a total output impact (in 1998 dollars) of $418.4 million. From the input-output model results, this is shown to be approximately 7.9 percent of total industry output value in Doña Ana, Luna and Hidalgo counties in 1998. Unaggregated output impacts are presented in appendix B.

The multipliers in table 4 are predictive tools and describe the local economy’s likely response to a change in sectors 18, 67, 68 and 103. The multipliers summarize the total impact that can be expected from a change in economic activity in the four sectors. For instance, hiring an additional 200 employees in sector 67 (canned fruits and vegetables) is predicted to lead to the creation of 268 additional jobs ((2.34*200)—200) in Doña Ana, Luna and Hidalgo counties. As noted previously, this includes both full- and part-time employment. Alternatively, reducing 200 employees in sector 67 is predicted to lead to a loss of 268 other jobs. The output multiplier estimates the total change in local output resulting from changes in output by the listed sectors, including the initial output from the sector experiencing thechange.

The output multiplier of 1.61 for sector 18 (vegetable production) indicates that for every $1 of vegetables produced, an additional $0.61 of output is produced in Doña Ana, Luna and Hidalgo counties.

Footnote

1This article was reviewed by Lowell Catlett, professor, James D. Libbin, professor, and Frank A. Ward, professor, Department of Agricultural Economics and Agricultural Business; and Richard Phillips, project manager, New Mexico Chile Task Force; all with New Mexico State University, Las Cruces; and David Layton, agriculture manager, Border Foods Inc., Deming, N.M. The New Mexico Agricultural Experiment Station and the New Mexico Chile Task Force supported this research.

Conclusion

Numerous factors have prompted chile growers, processors, researchers and others involved in the New Mexico chile pepper industry to take a closer look at all aspects of the industry. This report is a small step in documenting the size and nature of all vegetable production and processing in three southern New Mexico counties. IMPLAN data and IMPLAN modeling often are used to create information about the economic impacts of specific local economic sectors or for the effects of various changes in a local economy. IMPLAN data and IMPLAN modeling are not perfect tools. However, they do give researchers, industries and communities a relatively cost effective means of assessing economic impacts. There was no manipulation of or “corrections” to IMPLAN data in order to generate the information contained in this report. IMPLAN data are the result of data collection efforts conducted by numerous federal government agencies. Much of the data is obtained through voluntary reporting by firms. These data are not above criticism, but they are widely accepted as the best available data for the U.S. economy.

Table 3. Aggregated input-output model results for output impact of vegetable production and processing in Doña Ana, Luna and Hidalgo counties.

Economic Sector Title Direct Effects $ Indirect Effects $ Induced Effects $ Total Effects $
Agriculture 100,036,096 31,837,992 490,533 132,364,616
Construction 0 4,4409,639 869,293 5,278,932
Manufacturing 181,262,032 5,798,642 1,669,713 188,730,284
Transportation, communication and utilities 0 19,671,628 3,572,593 22,244,220
Retail and wholesale trade 0 13,740,763 9,429,785 23,170,548
Finance, insurance and real estate 0 9,118,776 9,594,333 18,713,108
Services 0 12,212,461 12,464,955 24,675,416
Government 0 1,765,597 1,236,002 3,001,600
Other 0 31,260 164,348 195,600
Total Output 281,298,128 97,586,758 39,489,556 418,374,431

Table 4. Employment and output multipliers for vegetable production and processing in Doña Ana, Luna and Hidalgo counties.

Sector # Sector Title Employment Multiplier Output Multiplier
18 Vegetable production 3.54 1.61
67 Canned fruits and vegetables 2.34 1.45
68 Dehydrated foods 1.90 1.40
103 Food preperations—NEC 1.97 1.35

Extensive data and information are not available for the New Mexico or U.S. chile pepper industries. This is due to the nature of the chile pepper industry, and its small size relative to the overall agricultural economy. Members of the industry have a strong desire and need to keep information private, which makes it even more difficult to assess the impacts, size and nature of chile pepper production and processing in New Mexico. However, even with its limitations, it is hoped that the information presented in this report contributes to a greater understanding of the New Mexico chile pepper industry.

References

Hall, T. Y. (2001). The New Mexico Chile Pepper Industry: Description, Labor Issues, and Economic Impacts. Unpublished master’s thesis, Department of Agricultural Economics and AgriculturalBusiness, College of Agriculture and Home Economics, New Mexico State University, Las Cruces, N.M.

Hall, T. Y. and R. K. Skaggs (2003). New Mexico’s Chile Pepper Industry: Chile Types and Product Sourcing, New Mexico Chile Task Force Report No. 8. New Mexico State University, College of Agriculture and Home Economics, Las Cruces, N.M.

Holland, D. and J. H. Yeo (2001). The Economic Impact of Potatoes in Washington State. XB1039, Agricultural Research Center, College of Agriculture and Home Economics, Washington State University, Pullman, Wash.

Leones, J. P. and N. C. Conklin (1993). Agriculture in the Arizona Economy. University of Arizona, College of Agriculture, Tucson, Ariz.

MIG, Inc. (2000). IMPLAN PRO User’s Guide, 2nd ed. Minnesota IMPLAN Group, Inc., Stillwater, Minn.

Miller, W. P. and T. Armbruster. (1991). Economic Impact of Table Grape Production in Arkansas. University of Arkansas, Cooperative Extension Service, Little Rock, Ark.

Mulkey, D. and A. W. Hodges. (2001). Using IMPLAN to Assess Economic Impacts. www.fred.ifas.ufl. edu/impact. Department of Food and Resource Economics, IFAS, University of Florida, Gainesville, Fla.

National Agricultural Statistics Service (NASS). (1999). 1997 Census of Agriculture—New Mexico State and County Data. AC97-A-31, Volume 1. United States Department of Agriculture, Washington D.C.

New Mexico Department of Agriculture (NMDA). (2000). New Mexico Agricultural Statistics 2000. New Mexico Agricultural Statistics Service, Las Cruces, N.M.

Office of Management and Budget (OMB). (1987). Standard Industrial Classification Manual. Executive Office of the President, Washington, D.C.

Robison, M. H., N. L. Meyer, R. Coupal, and C. C. Harris. (1991). The Role of Agriculture and Other Natural Resource-Based Industries in Idaho’s Economy. A.E. Extension Series No. 91-4. Department of Agricultural Economics and Rural Sociology, University of Idaho, Moscow, Idaho.


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