DETERMINANTS OF ORGANIC FARMERS DEMAND FOR NON-FAMILY FARM LABOR CARRIE ELAINE NEELY. (Under the Direction of Cesar Escalante) ABSTRACT

Transcription

1 DETERMINANTS OF ORGANIC FARMERS DEMAND FOR NON-FAMILY FARM LABOR by CARRIE ELAINE NEELY (Under the Drecton of Cesar Escalante) ABSTRACT Organc farms are recognzed as dsplayng trends of more labor ntensveness than conventonal farms, despte smaller average acreages and larger percentages of famly-based operatons. Results ndcate only half of all organc farm operatons hred some postve quantty of non-famly labor, although n total numbers, the quantty of non-famly labor was more than twce that of famly labor. Ths study utlzes natonal organc farm level survey data to determne the sgnfcance of organc farmng systems use of hred non-famly farm labor as a functon of ndvdual farm characterstcs, especally as the farm structure evolves through extensfcaton and alternate markets. Results ndcate ndvdual farm management characterstcs affect non-famly labor hred, both n magntude and n absolute terms. INDEX WORDS: Organc Agrculture, Non-Famly Farm Labor, Farm Labor Demand Estmaton, Survey Data, Heckman Two-Step Procedure

2 DETERMINANTS OF ORGANIC FARMERS DEMAND FOR NON-FAMILY FARM LABOR by CARRIE ELAINE NEELY B.S.E.S., The Unversty of Georga, 2003 A Thess Submtted to the Graduate Faculty of The Unversty of Georga n Partal Fulfllment of the Requrements for the Degree MASTER OF SCIENCE ATHENS, GEORGIA 2006

3 2006 Carre Elane Neely All Rghts Reserved

4 DETERMINANTS OF ORGANIC FARMERS DEMAND FOR NON-FAMILY FARM LABOR by CARRIE ELAINE NEELY Major Professor: Commttee: Cesar Escalante Jack E. Houston Jeffrey L. Jordan Mchael E. Wetzsten Electronc Verson Approved: Maureen Grasso Dean of the Graduate School The Unversty of Georga August 2006

5 ACKNOWLEDGEMENTS I would lke to express the hghest levels of grattude and apprecaton to my major professor, Dr. Cesar Escalante, for all hs encouragement, gudance, and drecton offered to me throughout my thess research. I feel blessed to have been able to work wth you. I would lke to offer many thanks to my commttee members, Dr. Mchael E. Wetzsten, Dr. Jack E. Houston and Dr. Jeffrey L. Jordan, for all of ther comments, recommendatons and assstance. I apprecate every sentence read, every reference consdered, and every bt of helpful advce. To all of my offce-mates and departmental collogues, Kata Romero-Leon, Jula Beckhusen-Holbert, Ozgur Kaya, Yongchong Mao, Farz Ahmadov, Joe Goodenbery, Tatana Gubanova, and Sarah Deng, thank you for every aspect of your presence. I would not have survved wthout the brthday cakes, tea breaks, language lessons, foregn confectons, and most of all your moral support. Also, to Dr. Barry Barnett, I apprecate all the moral and sprtual gudance you have offered; t has kept me grounded. To all my famly, frends, and my husband, who know nothng of envronmental economcs and econometrc regressons, your love and support has meant the world to me. You have enabled me to not become overwhelmed whle all the tme pushng me to contnue. You have been my strength. I would also lke to offer sncere apprecaton to Dr. Fred Whte and the department of Agrcultural and Appled Economcs for the assstantshp by whch my graduate studes were made possble. v

6 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS... v LIST OF TABLES... v LIST OF FIGURES... v CHAPTER 1 INTRODUCTION...1 Problem Statement...3 Objectves...4 Procedure LITERATURE REVIEW...6 Organc Management Practce Overvew...6 Klepper, Lockeretz, Commoner, Gertler, Fast, O Leary, and Blobaum (1977)...10 Lockeretz, Shearer, and Kohl (1981)...11 Drnkwater, Letourneau, Workneh and van Bruggen, and Shennan (1995)...12 Hred Non-Famly Labor EMPIRICAL MODEL...19 Theoretcal Background (Mcroeconomc)...19 Theoretcal Background (Econometrc)...20 Varable Descrpton...23 Econometrc Procedures...28 v

7 4 DATA SOURCES AND DESCRIPTIVE ANALYSIS ECONOMETRIC ANALYSIS...38 Heckman Two-Step Procedure Results...38 Tobt Results...42 Comparatve Results of All Models SUMMARY AND CONCLUSIONS...57 REFERENCES...60 APPENDIX...66 v

8 LIST OF TABLES Page Table 3.1 Descrpton of Explanatory Varables...30 Table 4.1 Dependent Varable Mean Values and (Standard Devatons)...35 Table 4.2 Descrptve Labor Statstcs for the Growng Season...36 Table 4.3: Expected Sgn of Explanatory Varables...37 Table 5.1: Heckman Two-Step Regresson Results...49 Table 5.2: Heckman Margnal Effects and Standard Errors of Sgnfcant Varables...50 Table 5.3: Tobt Regresson Results...51 Table 5.4: Tobt Margnal Effects and Standard Errors of Sgnfcant Varables...52 Table 5.5: Comparson of Margnal Effects of Sgnfcant Varables...53 Table 5.6: Comparson of Margnal Effects of Sgnfcant Varables by Category...54 Table 5.7: Elastctes of Sgnfcant Varables...55 Table 5.8: Elastctes of Contnuous Sgnfcant Varables...56 v

9 LIST OF FIGURES Page Fgure 3.1: Dstortons Resultng from Censored Data...29 Fgure 3.2: USDA Farm Producton Regons...29 Fgure 4.1: Dstrbuton of Famly and Non-Famly Labor...36 v

10 CHAPTER 1 INTRODUCTION Unted States Department of Agrculture (USDA) certfed organc food s, by defnton, produced by farmers who emphasze the use of renewable resources and conservaton of sol and water (USDA 2006). USDA organc standards specfy organc foods as beng produced wthout usng most conventonal pestcdes; fertlzers made wth synthetc ngredents or sewage sludge; or boengneerng (USDA 2006). Standards also dentfy organc meat, poultry, eggs, and dary products as those whch orgnate from anmals gven no antbotcs or growth hormones. In addton to prohbton of nputs, these natonal standards also requre ndvdualzed farm management plans that outlne the technques by whch the operaton wll further mprove managed sol and water qualty for future generatons. Prvate organzatons began certfyng organc foods as early as However, t was not untl the Organc Foods Producton Act of 1990 that a natonal set of certfcaton standards was adopted. Under ths act, the Natonal Organc Program (NOP) of the USDA was gven authorty over organc certfcaton n the U.S. Accordngly, certfyng accredtaton has been granted to 15 state government agences and 34 prvate organzatons. There s at least one certfyng body n each state as well as 43 foregn certfers. It s recommended by the USDA to obtan certfcaton through the agency n the state where the operaton s located, although under the law, any natonal certfyng agency s sutable (USDA 2006). In 2003, the USDA recognzed 8,035 certfed organc farm operatons n the U.S., comprsed of cropland, pasture, and lvestock productons. These operatons nclude over two 1

11 mllon acres and 8.9 mllon head of lvestock and poultry. As such, organc agrculture s one of the fastest growng agrcultural ndustres, wth certfed cropland and lvestock producton more than quadruplng over the past decade (USDA 2005). Even whle demand contnues to rse, organc farms have not adopted many of those management technques characterstc of conventonal agrculture productvty. Indeed, beyond even the dssmlartes derved from certfcaton standards, organc farms have many noteworthy dstnctons n regards to farmland management. For the 2003 growng season, conventonal average acreage was 441 acres compared to the organc average acreage of 273 acres (USDA 2005). Further data ndcate 40% of organc operatons farm 100 acres or less and 68% farm 300 acres or less, drectly mplyng a majorty of small organc farms and lmted large-scale operatons (OFRF 2004). Natonal Organc Farmers Survey (NOFS) data from the last decade also outlne the consstent prevalence of famly-based organc operatons, wth between 83% and 87% of organc farms lstng ther busness structure as ether sngle famly or famly partnershps (OFRF 1995, 1997, 1999, and 2004). Despte these mplcatons of smaller, famly-run farms, organc operatons dsplay trends of more labor ntensveness than ther conventonal counter-parts. Characterstc lmted use of synthetc nputs requres organc farms to mplement alternatve technques for pest removal, sol addtons and conservaton, commonly dependng on manual practces. Fertlzers are replaced wth ntrogen-fxng cover crops and composted anmal and green manures, whch can requre manual applcaton as well as processng. Cultvatons and hand weedng are used n leu of pestcdes to control nvasve and unwanted plant speces. Pest nsects are suppressed by multcrop rotatons and often a type of comprehensve ecosystem management whch creates a buldup of benefcal/predator nsect speces. Studes consstently ndcate hgher labor demands for 2

12 all spectrums of organc producton as compared to conventonal methods (Klepper, et al 1977, Lockeretz, Shearer and Kohl 1981, Lockeretz 1997, Jansen 2000, and Schneeberger, et al 2002). Natonal Organc Farmer s Survey (NOFS) data specfc to organc farms for the growng season ndcate larger percentages of yearly hred farm workers derved from famly labor pools rather than non-famly sources, and n regards to the prevalng busness structure of sngle famly organc operatons, ths s to be expected (OFRF 2004). There are however, theoretcal lmtatons of farm famly labor supply; as famly members have a fnte lmt, so do the number of laborers avalable n a famly, especally those seasonally avalable. Concurrent NOFS data ndcate that although nearly half (45.65%) of all organc operatons hred only famly labor or no labor at all, the actual number of non-famly workers hred s more than double that from famly sources (OFRF 2004). Labor hrng practces of natonal farm operatons (both organc and conventonal) specfy hred farm labor occurrng ten tmes as often as contracted labor, specfcally seasonally hred mgrant labor (NASS 2006). The frst Natonal Agrcultural Workers survey reported foregn-born persons comprsng 78% of all crop workers n the U.S., wth 75% of all U.S. crop laborers beng from Mexco (NAWS 2005). These data emphasze the mportance of foregn-born mgrant labor to the natonal agrcultural ndustry as well as the potental effects of a growng organc sector on the market for agrcultural laborers. Problem Statement Wthn the exstng lterature, there are no known studes whch fully examne the range of potental factors affectng non-famly labor demand of organc operatons. Although ndvdual characterstcs have been sngularly observed as sgnfcant factors, there has been no study of the complex management approach of organc agrculture and the nfluence of ths 3

13 approach on the organc labor market. Ths oversght, especally n vew of the specfc nonfamly labor requrements of organc operatons, leads to sgnfcance n the study of determnants of organc farmers demand for non-famly farm labor. Objectve Ths study wll determne the sgnfcance of organc (and transtonng to organc) farmng systems use of hred non-famly labor as a functon of ndvdual farm characterstcs, especally as the farm structure evolves through extensfcaton and alternate target markets. Non-famly labor was chosen rather than famly labor due to a desre to separate farm labor choces from nherently famly labor choces, as well as ts prevalent use by both conventonal and organc farmng systems. Ths s not a study of natonal market demand for hred labor, but rather an ndvdual farmer s relatve demand for hred labor and the magntude by whch ther ndvdual farm management characterstcs affect ths demand. Procedures Ths study wll examne the current characterstcs of organc farms and how varatons n these characterstcs affect a farm s non-famly labor choces utlzng farm level survey data outlnng farm operatons for the growng season obtaned through the Organc Farmng Research Foundaton s 4 th Natonal Organc Farmers Survey. Analyss wll utlze the Heckman two-step procedure, essentally dvdng the regresson nto two analyses. The frst step wll explore the farmer s yes/no decson to hred non-famly labor usng a Probt type model. Ths step consders only whether a farmer hred non-famly labor and not at the quantty of labor hred, f any. The second step wll utlze a corrected OLS model and examne only those 4

14 operatons whch chose to hred non-famly labor thereby determnng the magntude by whch each farm characterstc affects the quantty of laborers hred. A comparatve analyss wll utlze a Tobt procedure whch corrects for censored dependent varables characterstc of ths data set. The resultng analyss s comparatve to the Heckman OLS stage n whch only those farms whch chose to hred non-famly labor are ncluded. The Tobt analyss does not consder the choce mechansms of those farms whch do not employ non-famly labor and does not dfferentate between the varables affectng the yes/no decson to hre labor and those affectng the quantty of labor hred. Specfc farm characterstcs to be examned are dvded nto eght categores: structural, demographc, sze, ncome, marketng, experence, percepton, and locaton. The orgnal study data set wll be regressed on both the Tobt model and the Heckman two-step procedure. Two addtonal data subsets wll be regressed on the Tobt model only. The subsets are separated by category of whch one of two crops was prmarly grown: vegetables and grans. Ths s due to the relatvely more labor ntensveness of vegetable crops as well as the relatvely larger acreage requrements of gran crops. 5

15 CHAPTER 2 LITERATURE REVIEW To the knowledge of the author, there s no known study whch attempts to examne the specfc objectves formerly mentoned. Thus, the revewed lterature was comprsed of two sectons wth the purpose of both further examnng the background of the objectve and also to obtan a more complete understandng of the components necessary to the formulaton of the study s emprcal model. The frst secton s devoted to an overvew of the management practces n organc systems as determned by prevous research. These studes allow for nsght nto varatons n management technques between organc and conventonal systems whch have underlyng mplcatons for labor demand. They also outlne the evoluton of organc agrculture from a dervatve of conventonal systems to a separate ecosystem management technque. Secton two consders prevous analyss concernng conventonal farm labor demand estmaton and the areas n whch these studes devate from organc farms. Ths secton also assesses the lmted research nto determnants of organc farmers labor demand and the recommendatons of one scholar as to the drecton these studes should assume. Organc Management Practce Overvew Economc research regardng organc agrculture s largely devoted to drect comparsons to conventonal farmng operatons. Cacek and Langner (1986) contend there are three types of research concernng the economc feasblty of organc farms, 1) drect comparsons of 6

16 economc returns between organc and conventonal farms, 2) analyss of economc returns based on research plot yeld data, and 3) modelng comparsons of organc and conventonal farms. Generally, most of these studes agree organc farms ncur lower varable nput costs n comparson to conventonal farms, but no consensus s reached when yelds, prces, and costs are consdered jontly. Lampkn (1994), for nstance, contends that the combned effects of lower yelds, hgher prces (due to prce premums), and lower nput costs allow organc farms to realze smlar or lower gross margns than those of conventonal growers. He does qualfy, however, that ths stuaton depends on the crop analyzed. Conversely, n a revew of studes conducted by sx land-grant unverstes n the Mdwest on the producton optons of corn, soybean, wheat, and oat crops, Welsh (2000) found organc farms to be more proftable than conventonal farms, even when prce premums were removed from the analyses, n half of the studes. The remander of the studes, whch ndcated less proftable organc farms (sans consderaton for prce premums), found that when prce premums were ncluded organc farms showed more domnant proftablty condtons than conventonal farms. Some research has ndcated that organc crops endurance under drer condtons (leadng to reduced nput costs) combned wth hgh prce premums have enhanced ther relatve proftablty (Marra and Kaval, 2000, Jans and Fernandez-Cornejo, 2001, and Barkely, 2002). Yet other doubt the organc advantage due to hgher labor costs; a SARE funded study found the producton costs of organc apples n Calforna were 10% to 25% hgher than conventonal farms as a result of more substantal materal and labor costs (Fredman, SARE, 2003). Another SARE funded study of 18 conventonal and organc potato farmers n Idaho found lower average materal costs for organc producton but hgher organc labor costs. The overall effect of these 7

17 two offsettng trends can be seen as the nsgnfcance of dfferences n fxed and varable costs for the two types of farmng systems. Despte the foundatons and fndngs of these studes, ther crtcs reman. Holmes (1993) asserts that there are few accurate economc comparsons between conventonal and organc farm system. Organc systems yeld a varety of non-market goods such as reduced sol eroson and ncreased sol fertlty, whch are rarely assgned a value n cost-beneft analyss. Short-term studes tend to overlook these tme senstve benefts resultng from mproved sol, water and ar qualty, whle smultaneously gnorng the cost of envronmental damage due to conventonal farmng methods. Drnkwater (1995) further llustrates perceved lmtatons of face-value analyss of organc and conventonal farms, especally when comparng organc research plots to commercally operated conventonal agrculture. The controlled varables whch make research plots so mportant can also lead to nadequaces n accurately representng a proft-maxmzng organc farmng system, as a commercally vable organc farmer must be able to respond to envronmental and ecologcal stmul wthout fear of study desgn. Drnkwater (1995) refers to ths as the choce of researchers to contnue wth optmal management versus consstent management. A lack of experence wth commercally operated organc systems can also lead to msleadng research results or results whch do not consder the whole-farm system when conducted on test plots. Wth consderaton for these prevous fndngs as well as concern for the professed lmtatons, the followng lterature was selected to prmarly reflect on-farm studes rather than test stes when possble. Ths revew of farm management characterstcs does not attempt to further evaluate economc comparsons or to nclude fnancal matters, especally those related to 8

18 cost-beneft analyses relatng to farm management technques. Instead, the characterstcs revewed are those whch outlne the changng nature of organc management rangng from early management by excluson to more current approaches of overall agroecosystem supervson, wth a focus on the varatons n technque from conventonally managed systems. Ths overvew of organc technques wll lead to a greater understandng of the farm management regme characterstc of organc farmers and whch varatons from conventonal methods mght lead to an ncreased labor demand. At ts ncepton, organc farm certfcaton was overseen by prvate nterest organzatons rather than governmental enttes. Early certfcaton looked to lst prohbted substances (synthetc fertlzers, hormones, and pestcdes) and dd not regulate the physcal methods by whch farmers managed ther resources. Ths oversght was crtczed by many early organc enthusasts, ncludng J. I. Rodale, prevous edtor of Organc Gardenng (1942-Present). Rodale frst promnently advocated farmng based on understandng and symboss wth natural ecosystems rather than control (Klonsky and Tourte 1998). Klonsky and Tourte (1998) explan that, due to the nature of organc producton, organc farmers operate under a dfferent set of constrants than ther conventonal counterparts. Essentally, conventonal management n the absence of norganc fertlzers and pestcdes can not reman economcally vable; organc farmng must utlze a dfferent set of resources (Klonsky and Tourte 1998). The scentfc communty was somewhat skeptcal of the clams set forth by Rodale and other early organc enthusasts and remaned so untl organc farm studes of the 1970 s began to gve mert to these deas (W., 1975). Durng ths decade researchers became nterested n the economc vablty of organc farms, especally whle faced wth nternatonal petroleum ssues and a natonal ban of many popular pestcdes (DDT, chlordane, BHC, etc.). Low-nput farmng 9

19 held potental as a safer and more energy effcent alternatve but was stll skeptcally vewed by the majorty of farmers and farm organzatons (W., 1975). A number of comparson studes were ntroduced at ths tme to contrast the characterstcs and performance of organc and conventonal farms. Farmng practces, energy effcency, and relatve proftablty were all called nto queston by such researchers as Klepper, et al. (1977), Lockeretz, Shearer, and Kohl (1981), and Drnkwater, et al. The defnng characterstcs of both types of farmng systems, as observed by these studes, are presented n the followng subsectons. Klepper, Lockeretz, Commoner, Gertler, Fast, O Leary, and Blobaum (1977) Klepper et al. s (1977) early comparson contrasts the economc performance and energy ntensveness of both organc and conventonal farms n the Corn Belt for the 1974 and 1975 crop years. The organc farms n ths study were located prmarly by word of mouth as there were no avalable survey data or smlar lstngs of lkely canddates. All nformaton on farmng practces, plantng schedules, ncome and costs were derved drectly from the recollecton of the farm operators. These farms were qualfed by ther commercal scale producton pared wth management whch seemngly mmcs conventonal farmng n all but ther use of chemcal nputs. Wth the excepton of one, all organc farms refraned from employng norganc fertlzers, nsectcdes and herbcdes. Overall farm acreage means vared from 429 (organc) to 479 (conventonal) however, mean cropland acreage was more nconsstent: 250 acres (organc) and 348 acres (conventonal). Results showed dfferences n crop area beng attrbuted to organc farms dependence on hay and pasture rotaton for green manures and legume addtons (organc farms averaged 52% of acreage n cropland compared to 73% for conventonal farms). 10

20 All farms had a mx of lvestock and crop operatons. Lvestock manure was appled by both types of farms, wth very smlar rates of applcaton and over a smlar dstrbuton of land area. As demonstrated by the results, conventonal farms tended to have hgher concentratons of lvestock, whereas organc operatons offered more grazng area on pasture and less confned pasture access. Ths practce agan llustrates the land allocaton dfferences n the two farmng systems. Plowng machnery dffered lttle n ownershp but was segregated n usage. All organc farms used chsel plows compared to half of all conventonal operatons. Eghty-sx percent of conventonal farms utlze moldboard plowng, but only 29% of organc farms (moldboard plowng has the hghest fuel and labor costs; both have hgh sol eroson and mosture loss potental). Labor requrements were 19.8 hours per $1000 of crop output for organc farms and 17.8 hours per $1000 of crop output for conventonal farms. However, the authors mply that ths unt of comparson leads to a hgher dvergence n requrements than would a comparson by ndvdual crop types. For example, corn and small gran labor requrement (hours per acre) were the same for both types of farms but organc farmers requred slghtly more labor (3.8 versus 2.6) for soybeans. Lockeretz, Shearer and Kohl (1981) Publshed four years after Klepper et al. s study on organc farmng n the Corn Belt, Lockeretz, Shearer and Kohl (1981) make use of the same data but focus nstead on a general comparson of conventonal and organc farmng practces. Ther study conssts of 37 organc farms located n the Corn Belt studed over two tme perods, and agan n 1977 and Interestngly enough, the farm practces mentoned were obtaned not from the source of 11

21 the current data but from separate surveys of organc farms, all located n the Corn Belt and all havng a mnmum sze of 40 ha. The context of these early practces must be remembered as occurrng durng a tme when homogenous organc certfcaton was almost non-exstent and organc farmng was thought of as not much beyond conventonal farmng wthout chemcals. Weed control by organc farmers vared from conventonal farms by ncreased use of mechancal cultvaton. At ths early stage n certfcaton, occasonal spot use of herbcdes was allowed for organc operatons, but mostly avoded (Lockeretz, et al 1981). Crop rotaton was the prmary technque of nsect control on organc farms. Integrated pest management and bologcal control technques were not employed. Fertlty addtons were made wth anmal manure and some organc amendments, however conventonal farms appled manure at almost the same rate as organc operatons. Addtonal growth of legumes gave organc operatons a secondary Ntrogen source, but agan, conventonal farmers also grew legumes, but prmarly as a forage for lvestock. Nne-tenths of organc farms had mxed operatons of feld crops and lvestock, wth the most prevalent crops beng corn, soybeans, hay, oats and wheat. Medan organc farmsze was reported as 18% less than all other farms wthn the same regon. Labor requrements were deemed slghtly hgher for organc farms, but wthout much mplcaton as to the reasons why. Drnkwater, Letourneau, Workneh and van Bruggen, and Shennan (1995) In contrast to earler analyss that focused on economc consderatons, Fundamental Dfferences between Conventonal and Organc Tomato Agroecosystems n Calforna nstead regards the ecologcal benefts resultng from organc management (Drnkwater et al. 1995). The study utlzes a statstcal desgn to recognze ecosystem propertes resultng from organc farm 12

22 management and to dstngush these from varatons resultng from envronmental factors. It s mportant to recognze the begnnng of a notceable dvergence n technque between the two types of farm management. The data are derved from fresh market tomato growers on 20 farms n close proxmty of the Central Valley, Calforna. Farms that suppled plant nutrents through the use of green manures or other organc sol amendments and used no synthetc fertlzers and pestcdes were consdered organc. All other farms were classfed as conventonally managed. The authors state that 70% of all tomato producng organc farms wth cropland of greater than 0.5 ha n ths growng regon were represented and that the majorty of these organc farms were well establshed (4-10 yrs. of organc management). Over-wnter sol management technques ncluded cover-croppng and the use of wnter annual weeds on all organc farms. Comparatvely, the majorty of conventonal farmers followed the tradtonal practce of allowng fallow sol to reman bare. Ntrogen addtons by organc farms were n the form of legume resdue, earthworm castng, manure, compost and blood-meal. These practces were not mmcked by conventonal farmers. Irrgaton practces were smlar between both groups and conssted of furrow, drp and sprnkle. No pest control was requred n 35 % of organc felds. Where necessary, sulfur addtons, Bacllus thurngenss and soap applcaton were the most prevalent methods of control. Weed control was lmted to cultvaton n organc felds, whle all but two conventonal farmers appled herbcdes. Both farm types retaned feld borders consstng of varous combnatons of perennal crops, rparan zones and fallow felds. Whle ths mght not have necessarly been the ntenton of farm managers, ths borderng practce can allow for a buld-up of benefcal/predator nsect speces, thereby allowng the agroecosystem to effcently manage pest speces. 13

23 In the last decade the dvergence of organc systems management has seen a broad gap narrowed to a slghter margn. The last element mentoned n the above secton (feld borders as nset banks) hghlghts a mnor overlappng of management technques. Whereas tradtonally t has been conventonal technques whch are utlzed by both systems, ths nstance hghlghts a begnnng of organc technques beng mplemented n conventonal systems. Even as early as the 1990 s, Holmes (1993) notces the gradual recognton of lower-nput practces that prove to save money by the manstream farmers. Stll, as Klonsky and Tourte (1998) profle emergng trats smlar to both systems (land preparaton, plantng operatons, cultvatons and rrgaton), sol and pest management are consdered areas of lngerng sgnfcant dfferences. Hred Non-Famly Labor Tradtonal economc research regardng hred non-famly labor has focused on supply and demand estmaton of conventonally operated farms. Grlches (1959) was one of the frst researchers to apply methods of demand estmaton to hred famly labor. He worked wth tme seres data from to estmate demand of farm nputs ncludng fertlzer, tractors and hred labor. Hs estmaton technques were based on theory mplyng the desred level of any one nput s dependent on the expected prce of products, the prce of the nput, the prces of other nputs and the rate of nterest (when applcable) (Grlches 1959). Although Grlches does not utlze smultaneous equatons, he suggests that ths s the approprate approach for further research of hred labor supply and demand. In Schuh (1962) and Tyrchnewcz and Schuh (1966 and 1969) t s agan proposed that hred farm labor demand and supply should be estmated through the use of smultaneous equatons. These studes, as well as Hammond (1973) and Duffeld and Coltrane (1992) follow 14

24 ths recommended procedure. For these studes t s assumed that farm operators must accept as gven the prces they pay for nputs (other than labor) and the prces they receve for ther products. Farm operators wll adjust the number of laborers hred to reflect these prce constrants as well as the prce of hred labor and the technologcal optons avalable to them (Schuh 1962). Hred labor demand s gven as a functon of (1) real wage rates of hred farm labor, (2) an ndex of the prces of agrcultural products, (3) an ndex of the prces of other nputs, and (4) a measure of technology (Schuh 1962, Tyrchnewcz and Schuh 1966 and 1969, Hammond 1973, and Duffeld and Coltrane 1992). These studes address the theory underlyng the specfc objectves presented n ths study and addtonally mply the separate constrants appled to the labor market for organc farms. The above factors of estmaton can not be appled to organc farms wthout transformatons to adequately reflect the dfferences n technque, management, and technology constrants. An ndex of the prce of organc agrcultural products (2) must represent the prce premums obtaned for most organc goods as well as the alternate outlet of conventonal markets and the correspondng prces. An ndex of the prce of other nputs n organc systems (3) must recognze the heterogeneous nature of organc farm management as well as the presence of ecosystem-type management whch mght requre few nputs other than labor. A prce ndex of other nputs must also recognze the absence of certan substances vtal to conventonal agrculture (pestcdes, fertlzers, etc.). A measure of technology for organc management (4) must recognze that organc farms follow few of the same technologcal advances or producton technques used on conventonal operatons. A measure of the real wage rates of hred farm labor (1) should be relatvely comparable between organc and conventonal systems, although one study has mpled a preference of farm workers to organc systems due to the added costs assocated wth 15

25 health rsks resultng from conventonally used fertlzers and pestcdes, essentally lowerng the wage rate receved (Moore 2002). These contrasts outlne the constrant dfferences between the two farmng systems and the challenge n comparatve estmaton of farm labor supply and demand. Whle these consderatons mght lend to accurate labor demand estmaton, they nether capture the labor allocaton decsons of organc farmers, nor the ratonale behnd the more labor ntensve nature of organc farms. Indeed, few studes have attempted to explan the factors affectng ths labor ntensve characterstc. Some have attempted to qualfy the changes n labor demand as a result of converson to organc by outlnng the transtons n drect to consumer marketng (Rapp 1998) or the changes n labor requrements by ndvdual crops (Bouwman 1996). Lampkn (1994) has suggested that labor requrements may be more tellng of the ndvdual farmer n regards to ther experence or ther nadequate management whch results n lowered captal nvestments nto the farmng operaton and more manual labor. In an extensve revew of the exstng lterature on organc labor use and allocaton Jansen (2000) agan observes the lack of avalable detaled nformaton regardng the ntrcate changes that occur durng converson, specfcally the transformaton of producton processes and farm organzaton, and the resultant effects on labor demand. Jansen (2000) goes further to suggest a group of ten factors whch drectly nfluence the patterns of labor dstrbuton and labor demand by organc farms whch have not been collectvely studed. These are as follow: 1. Choce crop, more labor ntensve crops: Alternatves to fertlzers nclude labor-ntensve ntrogen-fxng crops and crops whch can be recycled nto green manures. Also, organc demand has tradtonally been for labor ntensve crops such as vegetables. 16

26 2. Rotaton systems and mxed croppng: Rotaton of crops leads to pest and dsease resstance, but as rotatons ncrease so does labor requrement. Mult-croppng s another technque by whch pests are controlled for, however multple crops on a sngle plot ncrease the lkelhood of addtonal labor needs. 3. Non-chemcal pest control, ncludng weedng: Hand weedng and mechancal methods requre more labor than herbcde sprayng. 4. Nutrent supply: As mentoned above crops are recycled nto green manures as a nutrent source. Another source s anmal manure, both of whch are commonly appled manually. 5. Degree of mechanzaton, especally n harvestng and transport: Specfc farm structures lead to less applcable mechanzaton and more dependence on manual labor. 6. Degree of specalzaton/ number of agrcultural actvtes: Mxed-farmng actvtes allow for benefts from crop and lvestock nteractons, however they also result n greater labor requrements. 7. Level of knowledge ntensty: Organc farmng s knowledge ntensve, especally durng converson. There s a learnng curve assocated wth the ecosystem and agroecosystem nteractons. 8. Tmng: Preventve measures rather than correctve measures, especally wth weed and pest control, can lead to many small tasks rather than a few large tasks. 9. On-farm processng: Value-addng and drect marketng take labor away from other onfarm actvtes, but are often necessary to obtan prce premums where markets are not well establshed. 10. Drect marketng: Same as for on-farm processng. 17

27 Ths study wll consder the characterstcs observed by Jansen as those whch are the most probably to prmarly affect the labor allocaton dscrepances among conventonal and organc agrcultural systems. Due to the lack of exstng lterature whch collectvely consders these factors, they wll be equally weghted n regards to ther nfluence on labor demand. Wth addtonal concern for those areas of management whch partcularly vary between the two type of farmng system (as outlned n the frst secton of the lterature revew), an estmaton of the aggregate nfluence of these factors on non-famly farm labor demand and dstrbuton for organc farms wll be analyzed. 18

28 CHAPTER 3 EMPIRICAL MODEL Theoretcal Background (Mcroeconomc) Mcroeconomc theory tells us that producton (or output), q, s a functon of nputs, namely, land, M, labor, L, and captal, K. Theory also tells us that each of these nputs also has an assocated cost: the wage rate for labor, w, the cost relatng to natural resources used, n, and the rate of rent or nterest for captal, v. Pure profts related to short-run producton are equal to total revenue, TR, or output prce tme output, mnus total costs, TC, or nput prce tmes nputs. Mathematcally: π = TR TC, where TR = pq, and TC = wl + vk + nm. For a conventonal farmer ths objectve functon takes the form: max π = max (TR TC) = max (pq wl vk nm) = max (p f(k, L, M) wl vk nm). Because of certfcaton standards mandated by law, organc farmers face an addtonal technologcal constrant, T, whch s a functon of all regulatons assocated wth organc farmng,.e. lmted fertlzers, pestcdes, hormones, etc.: max π = max (TR TC) s.t. T = max (p o q wl vk nm ) s.t. T = max(p o f(k, L, M) wl vk nm ) s.t T 19

29 Addtonally, p, the output prce of conventonally produced goods, and p o, the output prce of organcally produced goods, are not equal consderng the prce premum obtaned for most organc goods. The frst order condtons of ths objectve functon yeld the nput (Labor) demand functon as a functon of the output prce, p o, the nput (Labor) prce, w, the change n producton, q, and the change n technology, T. Ths reaffrms the theory presented n the second secton of the lterature revew. Consderng all organc frms face essentally the same output prce, p o (dependent on crop type), and a comparatve wage rate, w (wth slght dscrepances for locatonal varatons), the varable of nterest are those whch affect producton decsons and those ted to technologcal constrants of organc certfcaton standards. These resultng varables of nterest are categorzed nto 8 characterstc types as derved from the revewed lterature: structural, demographc, sze, ncome, marketng, experence, percepton, and locaton. Theoretcal Background (Econometrc) The dependent varable mplemented n ths analyss s quanttatve n nature but censored at the pont of zero. Farm characterstcs nfluencng the demand for non-famly labor led 46% of represented farms to hre some postve quantty of non-famly farm workers. For these observatons, the proxy value of ther demand s equal to the number of laborers hred. These same characterstcs affect the demand of the remanng percentage of represented farms, but as these farms hred no non-famly labor, ther demand s unobservable and ther correspondng dependent varable s assgned the value of zero. One vew of the dstorton n the data resultng from censorng s llustrated by fgure 3.1, below, wth the y-axs representng the dependent varable and the x-axs representng an 20

30 explanatory varable. In vew of the censored nature of the dependent varable, the coeffcent estmates regardng the organc farms labor hrng decsons cannot be estmated usng ordnary least squares (OLS) regresson because of nconsstent and based results stemmng from selectvty bas. Ths ssue s further llustrated below. Let y = β + ε, wth all basc assumptons satsfed, where * x * * y represents the number of non-famly laborers demanded gven x, an explanatory varable. β represents the change n * y resultng from a one unt change n x, and * ε represents the resdual or error term. * Graphcally, ε represents the dstance from the observaton x to the lne of estmaton as determned by the coeffcent estmate, β. For all farms hrng non-famly labor, postve value, but for all other farms, hre (Kmenta 1997, Greene 1993). For ths second set of farms * y takes on a * y represents the ndex or latent varable,.e. the desre to * y s unobserved and s assgned the value of zero. Therefore, nstead of observng * y, the tangble observaton s y where y = max(0, * y ): y y = 0 = y * f f y * y 0, * > 0 The resultng equaton, y = β + ε, s dfferentable from x y = β + ε also by the * x * mplcatons of non-equal means, µ, of both the dependent varables and resduals: µ y µ y * and µ ε µ ε *, whch results n msspecfcatons of an OLS estmate (Kmenta 1997). A common approach to ths resultng regresson, y β + ε = x, where y = max (0, * y ), s the Tobt model, a censored regresson technque that s conducted usng maxmum lkelhood estmaton. The maxmum lkelhood estmaton of β wthn a correctly specfed Tobt model 21

31 yelds consstent results. One drawback of the Tobt model s the assumpton regardng those characterstcs whch affect a farm s hrng decsons. Tobt models offer no dstncton between factors affectng the decson to hre labor and the factors affectng the quantty of laborers hred. Ths lack of dstncton may not be correct as t s not wholly descrptve of the decson makng process; certan characterstcs affectng the choce to hre non-famly labor mght not nfluence the total quantty of labor hred, and vce-versa. A second approach to ths censorng problem s the Heckman two-step procedure whch consders the varables affectng the decson to hre non-famly labor (Z ) as possbly dfferent from the varables affectng the number of non-famly laborers to hre (X ) (Gujarat 2003). The Heckman procedure frst utlzes the Probt model, a non-lnear functon based on the normal cumulatve densty functon and usng maxmum lkelhood estmaton, whch allows for coeffcent estmaton of Z. The Probt model also allows for estmaton of the nverse Mlls rato whch s a functon of the standard normal densty and the cumulatve dstrbuton functon of the coeffcents of Z (Johnson and DNardo 1997). The nverse Mlls rato (λ) provdes the means by whch to correct for selectvty bas n an OLS regresson utlzng censored data. In the fnal step of the Heckman estmaton, the varables affectng the number of laborers hred, X, and the added correctng varable, λ, are regressed n an OLS model yeldng consstent results. For both the Tobt model and the Heckman two-step procedure, the same orgnal form of the estmatng equaton s appled: NFL = β 0 + β 1 STRUCTURAL + β 2 DEMOGRAPHIC + β 3 SIZE + β 4 INCOME + β 5 MARKETING + β 6 EXPERIENCE + β 7 PERCEPTION + β 8 LOCATION + ε. 22

32 Varables lsted n the estmatng equaton (STRUCTURAL, DEMOGRAPHIC, SIZE, etc.) represent a category of ndvdual varables, wth multple coeffcents beng estmated wthn each set. Wthn the 8 varable categores, there are 33 ndvdual ndependent varables. Varable Descrpton The dependent varable, NFL, s the total number of non-famly farm workers hred durng the entre producton year. Ths can nclude seasonal, yearly, part-tme, or full-tme workers and s representatve of the mpled demand for non-famly labor. The eght categores of explanatory varables used n ths study (structural, demographc, sze, ncome, marketng, experence, percepton, and locaton) are dscussed below and summarzed n table 3.1 below. Structural varables outlne those factors of ndvdual farmland management arrangements whch defne the farm operaton and whch the ndvdual farm operator has control over. These factors are not predetermned, and although some reman sem-permanent n nature, all have the ablty to change from one season to the next. These varables are: 1. TENURERAT, the tenure rato calculated as the proporton of owned land to total acreage farmed organcally. Ths varable s used to dstngush owned acres from rented land or land used free of charge. 2. CROPSGRWN, representng the total number of commodtes grown. Commodtes are not segregated by use, whether for commercal value or for use on the farm operaton. 3. CONVDUM, a dummy varable used to test for the producton of conventonal commodtes as well as organc (1 = some conventonal acres, 0 otherwse). Ths varable also consders the farm s devoton to organc methods. 23

33 4. SOLEPROP, a dummy varable comparng famly farms to other busness structures (1 = sngle famly or sole propretorshp, 0 otherwse). The busness structure ndcates the mentalty of the management (famly vs. corporate) as well as the potental for a famly labor source. 5. FULLTIME, a dummy varable to consder the tme devoted to on-farm operatons (1 = full tme farm managers, 0 = part-tme farm managers). 6. OFFFARM, a dummy varable ndrectly assgnng a value to the farm operaton, essentally consderng the trade-off between farm actvtes and off-farm ncome generatng actvtes or nvestments (1 = no off-farm ncome actvtes, 0 otherwse) 7. VAPERDUM, a dummy varable allowng for whether any ncome s generated from value-added products processed by or for the farm ( 1 = some percentage of ncome derved from value-added products processed by or for the farm, 0 otherwse). These farmland controls are tellng of the mentalty of the prmary farm operator and how ther actons mght nfluence labor hrng decsons. For example, farms operators that own larger portons of the land they tll are expected to nvest more tme workng on the farm,.e. they are more strongly bound to the land and ts productvty. Ths also mples that these operators are more nclned to be full-tme farmers and would try to avod off-farm ncome generatng actvtes unless prudence deemed t necessary. Conversely, famly based busness structures, such as sole propretorshps, are expected to rely less heavly on non-famly labor than other, more complex busness structures. These famly operatons mght also be more nclned to seek ncome from non-farm sources n order to further nvest n the farm operaton. Jansen (2000) further attrbutes greater demand for hred labor to hgher degrees of commodty dversfcaton and ncreased on-farm processng actvtes whch effectvely result n hgher prce premums. 24

34 Demographc varables are those background characterstcs of the prmary farm operator whch nherently affect the farm management strategy. These varables are AGE (n years), GENDER (1 = male, 0 = female), and EDUDUM, an ordered dummy to capture the effects of dfferent levels of educatonal attanment (0 for educatonal attanment at hgh school level or below, 1 for junor college/trade school and some college educaton, 2 for completed bachelor s degree and some graduate study work, and 3 for a completed graduate degree). Jansen (2000) contends that the knowledge-ntensve nature of organc farmng operatons make both age and educaton crucal scalar varables. Addtonally, Rooj et al (1995) have descrbed women on smaller farms as havng a much greater management nfluence than on larger farms and that women tend to place more emphass on dversfcaton and broadenng farm actvtes rather than specalzaton. Sze varables, especally n regards to organc operatons, are dffcult to qualfy. The mxed operatons and product dversfcaton common to organc farms lead to complex ssues when dscussng farm sze sgnfcance. Duram s (1999) study of organc farms n Colorado show mean farm sze as 1,611 acres but medan farm sze as only 170 acres. Farms studed also showed varable ncomes on smlar acreages: A 2 acre farm had a gross operatonal ncome of $40,000 or less and an 8 acre farm had a gross operatonal ncome of $150,000 or more. As such, the varables chosen for ths analyss are the percentage of acreage allocated to two crops, vegetables and grans (PERACRSVEG and PERACRSGR, respectvely). These measures were chosen due to the generally accepted relatonshp of acreage to crops grown,.e. grans are generally spatally demandng but lax n labor requrements, whereas vegetables generally requre much less acreage but are relatvely labor ntensve, even by organc producton standards. 25

35 Income varables are relatvely straght-forward. INCDUM s an ordered dummy varable for ncome generated by the organc farmng operaton (0 f organc ncome s $4,999 or less, 1 f $5,000 to $29,999, 2 for $30,000 to $99,999, and 4 for ncome contrbutons of $100,000 and above). The dstrbuton of ths dummy varable s arranged so that each class of ncome represents roughly one quarter each of the represented farms. However, whereas hgher overall ncome mght ndcate the ablty to hre non-famly labor, the percent of ncome from partcular crops mght be more tellng of the need for non-famly labor. Therefore, addtonal ncome varables, PERINCVEG and PERINCGR, represent the percentage of ncome derved from vegetables and grans, respectvely, where hgh percentages of ncome derved from vegetable crops would ndcate a greater need for labor than would a smlar percentage of ncome from a gran crop. Marketng varables examne the role of marketng outlets on the amount of labor requred. Seventy-nne percent of organc farms surveyed n a study by Rapp (1998) were devoted to drect marketng technques. One reason for ths s the assurance of maxmum prce premum retreval, especally n the absence of well establshed markets. In a separate study publshed the same year, Rapp (1998) also found that a change to drect marketng leads to a hgher relatve demand for labor. PERVEGW and PERGRW represent the percentage of vegetable and gran commodtes, respectvely, sold on wholesale markets. Smlarly, PERVEGDTC and PERGRDTC represent the percentage of vegetables and grans sold drectly to the consumer. Experence varables depct both the farmer s overall experence and ther experence wth organc operatons. YRSEXP (total number of years farmng) gves an ndcaton of the farmer s overall knowledge of farmng technques (but not necessarly organc technques). 26

36 ORGYRSRAT (rato of total number of years farmng organc to total number of years farmng) ndcates the relatve maturty of knowledge and technque concernng organc farmng. Ths varable mght also ndcate the degree to whch the farmer s wllng or unwllng to adopt certan elements of organc farm management as determned by age snce transton. Lampkn (1994) found evdence of an ncrease n labor requrements durng the transtonng perod between conventonal and organc systems. CERTYRSRAT (rato of years of certfed organc farmng to total years organc farmng) s representatve of the farmer s devoton to certfcaton standards or ther quckness n meetng certfcaton standards. Ths varable can also ndcate the avalablty of prce premums resultng from certfed organc goods. Percepton varables take nto account the role that a farmer s percepton of market factors can have on ther hrng decsons. PRICEPERCEP and DEMPERCEP are ordered dummy varables correspondng to the farmer s percepton of product prces and market demand, respectvely, for the growng season (0 f poor, 1 f far, 2 f good and 3 f excellent). When mplementng these types of varables, the subjectve nature of ther orgn must be remembered. The base for the farmers expectatons should be consdered, especally whether they have recently experenced a loss or gan, as these factors can have a lagged effect and can affect the nterpretaton of the coeffcent estmates. When ths nformaton s not avalable, as n ths study, mplcatons based on regresson results should be regarded cautously. Locaton varables are ncluded to ndcate the nfluence a geographc regon mght have on labor requrements and labor avalablty thereby nfluencng a farm s hrng patterns. The chosen categorzaton s the USDA s classfcaton of producton regons, whch recognzes state boundares n defnng the regons. Ths classfcaton seems approprate as t accounts for both physcal characterstcs affectng plant dstrbuton and sutablty (weather, clmate, sol types, 27

37 etc.), as well as cvl characterstcs of state and government boundares whch can nfluence transtory patterns of mgrant farm workers. The locaton varables are NTHEAST (Northeast), LAKE (Lake States), CORNBELT (Corn Belt), NPLAINS (Northern Plans), APPAL (Appalachan), STHEAST (Southeast), DELTA (Delta States), SPLAINS (Southern Plans), and MTNS (Mountan States). The excluded geographcal category s the Pacfc Regon. These regons are shown n fgure 3.2 below. Econometrc Procedures As prevously mentoned, ths thess wll utlze both the Tobt model and the Heckman two-step procedures appled to the estmatng equaton dscussed above. All regressons are run usng the statstcal program Stata, verson 9. For each of these regressons, a data set of 332 observatons, representatve of natonal organc farmer survey responses, wll be used. Two addtonal regressons of the Tobt model wll utlze data subsets derved by ther represented farms predomnant producton of two target crops: vegetables and grans. The vegetable subset has 138 observatons and the gran subset has 169 observatons. Ths separaton attempts to solate the effects of the nversely related labor and acreage producton requrements of these commodtes. Addtonally, these two crops are the two most commonly produced commodtes and have tradtonally both been crops regarded wth great market demand (ORFR 1995, 1997, 1999, 2004). 28

38 Quantty of Non-Famly Labor Hred versus Years Farmng Laborers Hred Years Farmng Fgure 3.1 Dstortons Resultng from Censored Data Fgure 3.2 USDA Farm Producton Regons 29