Interaction between Crop Insurance and Technology Adoption Decisions

Transcription

1 Dscusson Paper Seres August 2017 EfD DP Interacton between Crop Insurance and Technology Adopton Decsons The Case of Wheat Farmers n Chle César Salazar Mónca M. Jame Crstán Pnto and Andrés Acuña

2 Centers Central Amerca Research Program n Economcs and Envronment for Development n Central Amerca Tropcal Agrcultural Research and Hgher Educaton Center CATIE Chle Research Nucleus on Envronmental and Natural Resource Economcs NENRE nversdad de Concepcón Chna Envronmental Economcs Program n Chna EEPC Pekng nversty Colomba The Research Group on Envronmental Natural Resource and Appled Economcs Studes REES-CEDE nversdad de los Andes Colomba Ethopa Envronment and Clmate Research Center ECRC Ethopan Development Research Insttute EDRI Inda Centre for Research on the Economcs of Clmate Food Energy and Envronment CECFEE at Indan Statstcal Insttute New Delh Inda Kenya School of Economcs nversty of Narob South Afrca Envronmental Economcs Polcy Research nt EPR nversty of Cape Town Sweden Envronmental Economcs nt nversty of Gothenburg Tanzana Envronment for Development Tanzana nversty of Dar es Salaam SA Washngton DC Resources for the Future RFF Vetnam nversty of Economcs Ho Ch Mnh Cty Vetnam The Envronment for Development EfD ntatve s an envronmental economcs program focused on nternatonal research collaboraton polcy advce and academc tranng. Fnancal support s provded by the Swedsh Internatonal Development Cooperaton Agency Sda. Learn more at or contact nfo@efdntatve.org.

3 Interacton between Crop Insurance and Technology Adopton Decsons: The Case of Wheat Farmers n Chle César Salazar Mónca M. Jame Crstán F. Pnto and Andrés A. Acuña Abstract Ths paper examnes relatonshps between crop nsurance partcpaton and nput technology decsons among Chlean wheat farmers. sng naton-wde farm-level data from the Natonal Agrculture and Forestry Census INE 2007 a bvarate probt model s estmated. In ths regard we nvestgate the extent to whch the adopton of certan producton nput technologes s assocated wth the partcpaton of farmers n the state-promoted agrculture clmate rsk nsurance program. We fnd that relatonshps between nsurance and technology decsons are sgnfcant among famly farmers but ths s not the case for large-scale farmers. Evdence also suggests that modern rrgaton reduces the lkelhood of adoptng crop nsurance suggestng that Chlean farmers perceve these two optons as substtutes. The latter mples that those usng tradtonal rrgaton methods preferentally partcpate n the nsurance program whch can be taken as evdence of adverse selecton n the Chlean nsurance market. Key Words: nsurance adopton farmng technologes adverse selecton bvarate probt model JEL Codes: D81 D82 G22 Q12 Q14 Dscusson papers are research materals crculated by ther authors for purposes of nformaton and dscusson. They have not necessarly undergone formal peer revew.

4 Contents 1. Introducton The Agrcultural Sector and Crop Insurance n Chle The Agrcultural Sector Crop Insurance n Chle Lterature Revew Crop Insurance Partcpaton Crop Insurance and Producton Input Technology Theoretcal Framework Data Emprcal Strategy Adopton of Agrcultural Insurance When Technology Decsons Are Exogenous Adopton of Agrcultural Insurance and Technology When Farmers Decsons Are Jontly Made Results Adopton of Agrcultural Insurance Technology Adopton and Agrcultural Insurance Robustness Checks Adopton Decsons n Homogeneous Areas Assessng Technologcal Change among Irrgators Controllng for Multple Relatonshps Instrumental Varable Approach Conclusons References Tables Appendx A. Addtonal Tables and Fgures... 34

5 Interacton between Crop Insurance and Technology Adopton Decsons: The Case of Wheat Farmers n Chle César Salazar Mónca M. Jame Crstán F. Pnto and Andrés A. Acuña 1. Introducton Agrcultural producton s frequently exposed to a varety of perls. Zorrlla 2002 dentfes fve categores of agrcultural rsks: clmatc e.g. hal frost drought flood wnd fre snow etc. santary e.g. plagues and dseases geologcal e.g. earthquakes and volcanc eruptons market e.g. domestc and nternatonal prce volatlty and changes n qualty standards preferences etc. and man-made rsks e.g. nsttutonal reforms wars economc and socal crses etc.. In theory there s a wde array of nformal rsk management strateges that farmers can use n order to mtgate agrcultural rsks. These strateges nclude crop dversfcaton savngs accumulaton off-farm actvtes and adopton of rsk-reducng producton technology Dercon In contrast there are more sophstcated strateges that deal wth rsks n a systemc way by developng a rsk market approach. Among these formal strateges are forward contracts futures and optons commodty markets revenue nsurance and crop nsurance Dercon The latter strateges are not always avalable for small-scale farmers n developng countres and n some cases markets are ncomplete or do not work properly. In Latn Amerca clmate events are the most mportant rsk for farmng producton. In that regard governments have desgned and promoted dfferent polces n order to reduce farmers vulnerablty. Mult-perl crop nsurance and state-funded dsaster relef programs are the man publc polcy responses regardng rsk management n agrculture Vla et al The experence wth crop nsurance nstruments n Latn César Salazar Correspondng Author emal: csalazar@ubobo.cl Department of Economcs and Fnance and Appled Sectoral Economcs Research Group GI /EF nversdad del Bo-Bo. Mónca M. Jame School of Management and Busness Research Nucleus on Envronmental and Natural Resource Economcs NENRE nversty of Concepcón. Crstán Pnto and Andres Acuña Department of Economcs and Fnance and Appled Sectoral Economcs Research Group GI /EF nversdad del Bo-Bo. 1 These rsk management polces are mplemented n dfferent modaltes across the regon. Accordng to Vla et al n Brazl Colomba Chle Ecuador Mexco Perú and ruguay the publc sector supports the prvate nsurance ndustry by provdng subsdes for crop nsurance and other nstruments of rsk management as well as management assstance and state guarantees whle n Bolva the state runs a unversal coverage agrcultural nsurance program. In contrast Argentna has chosen to develop a prvate crop nsurance ndustry and to ease access to dervatve markets for the most mportant agrcultural products. 1

6 Amercan countres s generally smlar. A low level of partcpaton especally among small farmers s the most sgnfcant feature Wenner In the case of Chle the crop nsurance partcpaton rate has been rather low reachng only 10% of the total arable farmng area n the coverage area n 2012 COMSA There are several potental explanatons for ths phenomenon. From a general perspectve many studes have attempted to explan t by focusng on asymmetrc nformaton problems that relate to adverse selecton and moral hazard ssues see e.g. Gardner and Kramer 1986; Quggn et al. 1993; Coble et al. 1996; Makk and Somwaru In the case of developng countres nformaton asymmetres are exacerbated by budgetary restrctons among market partcpants and hgh admnstratve costs of provdng sound actuaral studes promoton and supervson for ndvdual nsured farmers as well as nadequate procedures for assessng ndemntes Hazell In ths paper our am s to address the problem of farmer partcpaton n crop nsurance by explorng the relatonshp between crop nsurance adopton and producton technology choces. Gven that agrcultural rsks are dverse and the copng strategy can be mult-layered some nput decsons may have an nfluence on the degree to whch a farm s exposed to rsks Horowtz and Lchtenberg In ths case the decson whether to adopt nsurance can be nfluenced by nput technology choces. We wll focus our attenton on the Chlean crop nsurance market specfcally for the wheat farmng sector makng a dstncton between medum-large farms and small-scale farms. Avalable data come from the 2007 Natonal Agrculture and Forestry Census. Gven the nature of ths nformaton we examne the farmer s probablty of makng a certan decson. In partcular we want to fnd out whether there s any relatonshp between the farmer s decson whether or not to buy nsurance and her decson whether or not to adopt a specfc technology. A problem of endogenety may arse because these decsons mght be affected jontly by an unknown underlyng process smultanety or they could affect each other n both drectons reverse causalty. For nstance a farmer mght decde to adopt or not adopt a certan technology because he s nsured; smlarly he mght buy nsurance or not buy nsurance because he uses a certan technology. Because t s dffcult to fnd an approprate and observable nstrumental varable we deal wth estmaton bas by settng up and estmatng a bvarate probt model as a jont model for these two bnary outcomes and then examnng the correlaton between them. If a sgnfcant correlaton s found the decsons are nterrelated and the bvarate model wll be more approprate. 2

7 Followng Foud and Erdlenbruch 2012 the relatonshp between nsurance adopton and rrgaton technology s explored; nevertheless ths paper dffers from that study n a number of aspects. Frst we focus on the farmer s decson to adopt nsurance rather than on farm producton functons so multple explanatons for nsurance adopton are examned. Consequently a dfferent theoretcal framework data structure and estmaton methods are used. Second the role of technology adopton s further analyzed by ncorporatng a broader set of nput technologes such as crop seed selecton and bologcal control and results between small and large-scale farmers are compared. Each technology s expected to nteract wth agrcultural nsurance n a dfferent way dependng on ts characterstcs as well as on the scale of the farmng operaton. For example modern rrgaton s expected to reduce vulnerablty to water shortages n a manner smlar to nsurance n that both functon as rsk-reducng strateges. The adopton of certfed seed s expected to nteract wth clmate rsk although somewhat weakly whle bologcal control s not expected to be drectly related to clmate rsk. Fnally to our knowledge ths s the frst study amed at explanng farmng nsurance behavor and the nteracton between agrcultural technologes n the context of the Chlean agrcultural sector. The rest of the artcle s organzed as follows. Secton 2 descrbes the characterstcs of the agrcultural sector and crop nsurance program n Chle. Secton 3 dscusses the related lterature. Secton 4 revews a conceptual framework that lnks nsurance and technology adopton decsons. Secton 5 presents the data used and Secton 6 the econometrc model. Secton 7 dscusses the man results. Secton 8 consders a number of robustness tests and Secton 9 concludes. 2. The Agrcultural Sector and Crop Insurance n Chle 2.1. The Agrcultural Sector The agrcultural sector s at the core of the Chlean economy and t s the bass of an mportant value chan that ncludes food manufacturng and toursm. Ths sector accounts for around 15% of the country s GDP and employs 12% of the labor force; more than 50% of ts producton s sold n nternatonal markets makng the sector hghly dependent on world market prces. Agrculture also contrbutes to the food securty of the country especally through small-scale agrculture whch plays an mportant role n the agrcultural sector n Chle FAO Famly or small-scale agrcultural producers provde around 60% of the food consumed n the domestc market and control 85% of 3

8 total farms n the country comprsng 1.2 mllon people and generatng drect and ndrect jobs. These producers are manly nvolved n tradtonal farmng actvtes hre famly members and operate at low levels of workng captal INDAP Agrculture benefts from the vast clmate dversty n Chle whch allows a wde range of croppng actvtes. Some major agrcultural products nclude frut.e. grapes apples pears peaches and berres hortculture.e. garlc onons and asparagus and cereal and tubers.e. wheat maze and potatoes. Wheat s the most relevant annual crop n the world n general and n Chle n partcular. Ths crop occupes around 50% of the surface devoted to annual crops n Chle confrmng ts economc and socal mportance for agrcultural ncome-dependent farmers. Moreover ths crop extends across most of the terrtory and ts dstrbuton can be classfed nto dstnct agro-clmatc zones Crop Insurance n Chle The Farm Insurance Program herenafter FIA was created n 2000 by the Chlean Mnstry of Agrculture. Its man goal s to protect farmers aganst economc loss resultng from the most frequent clmate events such as droughts heavy or untmely rans freezes blzzards and hal. The program subsdzes farmers who buy crop nsurance. The nsurance covers not only annual crops such as cereals vegetables ndustral crops legumes grans and potatoes but also frut plantatons. The nsurance polcy assures the farmer of up to two-thrds of the potental value of the crop. The FIA s supervsed by the Agrcultural Insurance Commttee herenafter COMSA a government agency n whch the Agrculture Fnance and Economy mnstres are represented. Ths agency s operated by prvate nsurance companes supported by an extensve network of government nsttutons prvate agents and brokers. Subsdy payments are channeled through CORFO a governmental busness promoton agency drectly to the nsurance companes accordng to the number of polces ssued. Accordng to COMSA 2012 the premum cost subsdy conssts of a fxed contrbuton of 1.5 Foment nts F by ts Spansh acronym SD 50 for each polcy plus 50% of the net premum cost wth an 80 F SD 3000 cap payment per farmer all values for one season. For small farmers the subsdy may account for up to 80% of the premum cost. There are no major restrctons as to what type of farmers can be benefcares of the subsdy beyond beng regstered at the Internal Tax Servce SII or 4

9 beng a bankng customer or havng been approved for credt by a government promoton agency. At the begnnng of each season the nsurable areas and crops are establshed under the concept of Homogeneous Insurable Area HIA. The coverage areas go from Coqumbo Regon Regon IV to Los Lagos Regon Regon X plus Copapó Regon III Vallenar Regon III Lluta Azapa Regon I and Chaca valleys. These regons account for approxmately 70% of the Chlean terrtory. The coverage starts from the sowng perod or the date of the acceptance of rsk by the nsurer and ends at the end of harvest or the date establshed by the contract. The crop nsurance polcy covers two-thrds of the expected crop yeld. nder some condtons for specfc crops coverage mght reach three-fourths. For example wth a two-thrd coverage f the expected yeld s 30 unts per hectare and the farmer obtans 15 unts per hectare due to some of the clmate events specfed n the polcy the nsurance company wll compensate the farmer for 5 unts per hectare at market prce per unt. Ths prce must be below a maxmum prce that s determned beforehand. Coverage percentage standard yeld range maxmum compensaton prces maxmum and mnmum premum rates and other techncal parameters of the nsurance contract are specfed for every crop crop type geographc area known as nsurance homogeneous zone and sowng-harvest calendar n a document known as Subscrpton Norms whch s ssued annually by COMSA before each farmng season. Insurance coverage begns at the start of the sowng perod and ceases at the end of harvest of the entre crop. Premums are determned by applyng a premum rate on the amount nsured plus 0.6 F. The amount nsured s the coverage percentage ether two-thrd or three-fourth multpled by the standard yeld per hectare multpled by the total surface of the crop multpled by the estmated market unt prce of the crop product. Maxmum premum rates are specfed n the Subscrpton Norms. When a farmer decdes to nsure an annual crop she has to apply for an nsurance polcy by presentng a Crop Insurance Proposal to the nsurance company. Ths s an applcaton form where the farmer must declare specfc nformaton regardng the locaton and dmenson of the crop type of sol rrgaton type of seed sowng densty use of agrochemcals and estmated dates of sowng and harvest. The farmer must also declare the expected yeld of the crop. The expected yeld must fall wthn the range specfed n the Subscrpton Norms. If an adverse clmate event that s covered by the nsurance polcy occurs the farmer must mmedately notfy the nsurance company. The 5

10 nsurance company wll desgnate a clam adjuster who n turn wll name an nspector wth the necessary expertse to verfy and evaluate the damage to the crop. The farmer must notfy the nsurance company that an adverse weather event occurred before the start of harvest n order to verfy the real yeld obtaned from the crop. The nsurance company based on the ncdent reports and the real crop yeld wll ssue an nsurance adjustment report establshng the amounts and date of any compensaton payment. By the year 2012 the program was fnancng over polces annually 75% of whch were taken out by small farmers as part of a loan contract wth INDAP Natonal Insttute for Agrcultural Development the man agrcultural promoton agency. The total farmng area covered by the nsurance was approxmately hectares. At the same tme COMSA had dentfed potental farmers who were elgble to partcpate n the program whch ncludes almost all farmers actvely operatng. Of that number farms made up the target group for nsurance. The total arable farmng area n the potental coverage area s estmated at hectares COMSA Lterature Revew 3.1. Crop Insurance Partcpaton One of the frst experences of an nsurance program was the Federal Crop Insurance Corporaton whch was created n 1938 n the nted States to help farmers recoverng from the great drought known as the dust bowl. However crop nsurance was not a relevant feld for academc dscusson untl the enactment of the Federal Insurance Act of 1980 whch expanded the crop nsurance program to more crops and regons n the.s. by establshng a premum subsdy for the beneft of farmers. The frst challenge the enhanced program faced was the lower than expected level of partcpaton and the consequent economc losses of the program due to nsuffcent premum ncome. As hghlghted by Hazell 1992 ths publc polcy was demonstrated to be neffectve n practce manly because small growers could not afford an expensve nsurance premum although t was subsdzed. Moreover the ncentves to moral hazard behavor and farmer-nsurer colluson were not eradcated after ts mplementaton. These ssues motvated a seres of studes amed at explanng the elements that were drvng crop nsurance adopton decsons as well as detectng asymmetrc nformaton mperfectons n the nsurance market n partcular the eventual presence of adverse selecton. 6

11 One of the frst emprcal studes on crop nsurance partcpaton decsons was conducted by Gardner and Kramer sng county-level data the authors found that the expected rate of return on nsurance has a postve and statstcally sgnfcant effect on crop nsurance partcpaton. Ths result was also supported by subsequent smlar studes Hojjat and Bockstael 1988; Barnett et al that suggest the presence of adverse selecton as one of the reasons for the low partcpaton rate observed n crop nsurance markets. Quggn et al stressed the problem of dstngushng between adverse selecton and moral hazard when explanng nsurng behavor. They used crosssectonal data for the 1988 crop year to estmate a corn farm producton functon. They found a negatve and sgnfcant effect of nsurance adopton and expected output and questoned the vablty of a mult-perl crop nsurance program n lght of the contradctng ncentves for farmers. Smlarly Coble et al usng cross-sectonal data at the farm level developed a formal crop nsurance partcpaton model fndng statstcally sgnfcant effects on partcpaton of both market return and return to nsurance as well as both market return and return to nsurance varance. They also found partcpaton to be nelastc to premums. Just et al examned adverse selecton n.s. crop nsurance usng natonwde data. They suggest that farmers who partcpate n the crop nsurance program tend to have postve expected benefts from nsurance partcpaton whle unnsured farmers have negatve expected benefts reflectng defcences n prcng nsurance for lower-rsk farmers. In the same drecton Makk and Somwaru 2001 concluded that hgh-rsk farmers are more lkely to purchase revenue nsurance and are undercharged wth respect to lower-rsk farmers for a comparable nsurance contract. Sherrck et al concluded that mdwestern farmers who engage more extensvely n nsurance are the ones who operate larger acreages are more hghly leveraged are less wealthy and have hgher yeld rsks Crop Insurance and Producton Input Technology An nterestng aspect n the crop nsurance lterature s that of nput technology as a determnant of nsurance adopton. On the one hand rsk-reducng nputs such as rrgaton technques could act as a substtute for nsurance polces. Foud and Erdlenbruch 2012 explored the role of rrgaton technology n the farmng producton process fndng that rrgatng farmers have larger average profts wth less varance than non-rrgatng farmers and also that rrgatng farmers are less lkely to purchase yeld nsurance. On the other hand the farmer s choce of the specfc usage amount of some producton nputs such as fertlzers and pestcdes may ncrease the rsk-return profle of 7

12 the farm operaton makng room for the appearance of moral hazard ssues. Horowtz and Lchtenberg 1993 found evdence that nsured farmers sgnfcantly use more fertlzers herbcdes and pestcdes suggestng the presence of moral hazard f these nputs are consdered rsk-ncreasng producton technologes. However Babcock and Hennessy 1996 found no support for the hypothess that nsurance ncreases optmal fertlzer applcaton rates. They concluded that ntrogen fertlzer and nsurance are substtutes suggestng that those who purchase nsurance are lkely to decrease ntrogen fertlzer applcatons. Smth and Goodwn 1996 notced that the nsurance partcpaton decson s more or less contemporaneous wth nput choces. Ther conclusons ndcated that crop nsurance partcpaton has a sgnfcant negatve effect on total chemcal nput expendtures. Along the same lnes Goodwn et al ncluded the use of fertlzer n a model of partcpaton n crop nsurance for corn and soybean farmers fndng a statstcally sgnfcant negatve relatonshp between these two varables. 4. Theoretcal Framework Our theoretcal approach adapts the nsghts and theory proposed by Gné and ang 2009 and Mobarak and Rosenzweg Thus the model that gudes ths emprcal study s based on the followng assumptons and relatonshps. Suppose that a farmer has to make a decson about the mplementaton of a techncal change TA whch has three potental forms: hybrd seeds S enhanced rrgaton I and bologcal control B. All of them nvolve a hgher but rsker average yeld. That s f the farmer does not mplement one of the above mprovement alternatves then the yeld wll be equal to T. If the farmer ntroduces the mprovement TA then he could produce a hgher yeld H wth probablty p or a lower yeld L wth probablty 1-p. As ponted out by Mobarak and Rosenzweg 2012 assume that probablty p further captures whether the techncal change s a rsk-decreasng or rskncreasng technology. That s to say f p TA > 0 and p TA < 0 then the farmer s decson leans toward a rsk-decreasng technology. Conversely t holds that p TA < 0 and p TA > 0 for a rsk-ncreasng technology. Moreover the adopton of the techncal change guarantees that: 1 T < p TA H 1 p TA L 8

13 where the varable TA denotes the techncal change adopted by the farmer. Therefore pta s the probablty for a hgher yeld H after adoptng the techncal change TA. The nequalty stated n 1 holds for all the mprovement alternatves and mplctly suggests that the farmer dscrmnates among them. Gven that the compettve prce of the crop s normalzed to one and that techncal change s costly C t s assumed that C<L. Therefore f a farmer draws a dstncton among two technologes and j then she strctly prefers techncal change over the alternatve j f the followng condton s true: 2 p 1 p C > p j 1 p j C ; for j H L where p and pj are the probabltes for a hgher yeld H related to techncal mprovement and j respectvely whle C and C j represent the cost of mplementng the above technologes C C j. Thus the farmer wll select a techncal change that provdes a larger expected proft after deductng the cost of ts mplementaton. In order to address the nsurance adopton decson assume that farmers hold a set of llqud assets W where C<W. Hence under the unnsured scenaro the expected utlty of a farmer who chooses the productve enhancement u wll be equal to: H L j 3 = p C W 1 p C W u H L where s a contnuous and ncreasng utlty functon. On the other hand the fnancal system offers ranfall nsurance that allows dstrbutng rsk costs over tme and among producers. The nsurance contract ncludes an ndemnty equal to R whch covers the technology nvestment C and the potental loss from the casualty M. The contract further assumes that q s the probablty of hgh ranfall h and 1-q the probablty of poor ranfall l. Addtonally suppose that the nsurer faces problems of adverse selecton whch could be avoded by settng a premum that consders the characterstcs and rsk profle of the farmer. Formally the nsurance premum D depends on technologcal rsk γ crop rsk φ and farmer s wealth W. As remarked by Gné and ang 2009 f the nsurance premum D s farly prced then t wll be equal to 1-qR. Thus the expected utlty of a farmer who decdes to adopt a productve enhancement and take the ranfall nsurance I wll be equal to: 9

14 10 4 ] [ ] [ ] [ ] [ W R W D TA l f W W D C TA h f W R W D TA l f W W D C TA h f L L L L H H H H I = φ γ φ γ φ γ φ γ where f s the jont probablty densty functon for yeld technology and ranfall. Therefore when a farmer decdes to buy weather nsurance technologes and j are substtutes for nsurance f the followng condton holds: 5 R W D l j f W D C h j f R W D l j f W D C h j f R W D l f W D C h f R W D l f W D C h f j L L j j L L j H H j j H H L L L L H H H H = In addton a rsk-decreasng technology can be thought of as a substtute for weather nsurance or vce-versa. For nstance drought nsurance and rrgaton technology are expected to be substtutes whch can be formalzed as follows: 6 1 W R W D q W W D q M W C l f W C h f M W C l f W C h f T T L L L L H H H H = φ γ φ γ where the left-hand sde of Equaton 6 redefnes the expected utlty for an unnsured farmer who adopted the techncal change u whle the rght-hand sde s the expected utlty when she decded not to adopt the techncal change but dd buy ranfall nsurance T I. Thus the nsurance-adopton decson for farmer k can be smplfed through the followng ndcator functon dk: 7 > > = T k I k u k I k u k u T k I k u k I k R W M W D C d or If 0 or If 1 φ γ ; k = 1 2 m; = S I B In addton t s straghtforward to show that the nsurance adopton rate can be computed from the aggregaton of ndvdual decsons depcted by Equaton 7. In ths regard many governments have commtted to promotng farmng nsurance markets even f ths entals creatng subsdes for farmers at taxpayers expense. Despte such subsdes the adopton of nsurance among farmers s very lmted and nsurance companes often fal to meet partcpaton goals. In ths regard suppose that the government subsdzes crop nsurance n order to promote hedgng behavor among farmers. The subsdy reduces the amount of the nsurance premum by α percent.

15 Therefore the nsurance would be strctly preferred by the farmer f the followng nequalty holds: 8 u < f f L H h [ h [ L H C 1 α D γ φ W W ] f C 1 α D γ φ W W ] f L H l [ l [ L H 1 α D γ φ W R W ] 1 α D γ φ W R W ] nder ths scenaro the subsdy wll encourage partcpaton n the nsurance program partcularly amongst farmers who were ndfferent before ths publc polcy was n force.e. dk = 0 for some farmer k. The relatonshp between nsurance and technologcal decsons s determned by the technologcal rsk γ and the farmer s knowledge of ths rsk. If the nsurer knows the technologcal rsk then lower rsk wll reduce the amount of the premum. Hence the probablty of nsurance adopton wll be larger among farmers who mplement a rskdecreasng technology e.g. enhanced rrgaton. Ths s vald provded there are no nformaton asymmetres. Conversely f γ s not observable then the nsurer s unable to properly assess the dfferences n the rsk profle of ndvdual farmers and therefore charges a relatvely hgh average rsk nsurance premum to all farmers. Ths flat-rate polcy ntroduced by nsurance companes wll exacerbate the adverse selecton ssue. Specfcally there are economc ncentves for crop nsurance adopton among farmers who use rsk-ncreasng technologes. In addton farmers who adopt rsk-decreasng technologes are less lkely to clam large ndemntes. Nonetheless the premum that nsurers charge to these farmers wll be the average premum whch s too hgh for ther rsk profle makng t less attractve for them to adopt nsurance. 5. Data Ths study uses data from the 7th Natonal Agrculture and Forestry Census INE Ths survey consttutes the man data source on the current state of agrcultural and forestry resources and rural populaton n Chle. Data collecton s done n rural areas of the 15 regons of the country coverng agrcultural producers spread across 2 The Agrcultural and Forestry Census s conducted each 10 years. The prevous census was carred out n To our knowledge the 2007 Census was not desgned to be able to track a sub-sample of rural households from the prevous round. Therefore we cannot control for unobserved tme-nvarant effects as s requred when usng dfference n dfference technques. However t s hghly lkely that structural changes dfferently affectng rrgators and non-rrgators whch would make dfference n dfference methods problematc have taken place over ths perod. 11

16 the whole natonal terrtory. In partcular the survey collects nformaton on households demographc characterstcs assets savng credt extenson servces producton farm nputs technology use and publc support nstruments. Data s collected at the household and plot level and can be combned wth physcal varables defned by locaton such as sol and clmate characterstcs. The census data allows us to explore dfferences between famly farmers and nonfamly farmers. To dstngush each group we employed a crteron based on sze whch s used by the Mnstry of Agrculture to focus publc polcy. Ths crteron defnes famly farmers as those who hold 12 or fewer hectares of basc rrgaton HBI. Ths requres transformng nformaton about rrgated and dry land nto HBI by usng coeffcents of converson that capture dfferences n sol qualty across zones. For these purposes and followng FAO 2009 we utlze the coeffcents defned n Law enacted n 1967 under the agraran reform. A sample of farmers who report growng wheat was selected resultng n a sample of observatons of whch are classfed as famly farmers and 2642 observatons as non-famly farmers. To measure crop nsurance decsons we use self-reports on nsurance adopton. To explan these decsons we use a seres of control varables ncludng a dummy denotng whether the farmer s male; the farmer s age; the farmer s level of educaton; a categorcal varable that captures the degree of dependence on agrcultural actvty; a dummy varable ndcatng whether the farmer lves on the plot; a captal measure based on avalablty of agrcultural machnery and tools; 3 the farm s sze measured n total hectares; yeld; securty of tenure measured by the rato between the sum of famly-owned and rental land hectares over total hectares; a dummy varable ndcatng whether the farmer had access to at least one of the followng credt alternatves durng the last two years: loans from INDAP 4 loans from the state bank loans from prvate banks or a credt lne from ether nput provders or agro-ndustry; a dummy varable ndcatng partcpaton n any agrcultural organzaton; a dummy 3 The captal varable was bult usng nformaton wth respect to ownershp of draft mechancal captal. Ths was weghted by applyng the prncpal component method. For the constructon of the captal varable the followng tools and machnery were consdered: ploughs trucks vans carts choppers harvesters cultvators zero tllage spray machnes harrows rakes reapers seeders hoppers and tractors. 4 The Natonal Insttute for Agrcultural Development INDAP s a publc and decentralzed agency created to combat poverty and ncrease compettveness among small-scale farmers n Chle through actons amed at strengthenng human physcal and fnancal captal. 12

17 varable denotng whether the farmer receved extenson servces durng the last two years; percentage of non-eroded and slghtly eroded sol; 5 ranfall measured n mllmeters; 6 number of nsurance adopters per localty regardless of crops; and a set of dummy varables to control for unobserved spatal dfferences across zones. 7 As prevously mentoned to study the relatonshp between crop nsurance and technology decsons we focused on three key technologes: mproved seed S modern rrgaton I and bologcal control B. The purchase of mproved seeds provdes a determned crop varety wth unform germnaton and resstance to dsease whch may ncrease yeld and qualty. Bologcal control promotes the use and combnaton of natural factors and agrcultural practces to prevent damage caused by pests whle mnmzng human health rsk and collateral effects on non-targeted organsms and the envronment. Fnally the adopton of modern rrgaton s an alternatve to ncrease effectveness n water applcaton and mprove crop productvty. We expect a strong nteracton of modern rrgaton wth nsurance decsons because both are clmate rskdecreasng strateges. In contrast whle the adopton of mproved seed may somewhat relate to clmate rsk and thus to nsurance decsons the take-up of bologcal control s less lkely to be affected by clmate consderatons. Table 1 presents the descrptve statstcs of the nsurance varable technologes and control varables that are used n the estmatons. The average wheat yeld reaches around 2800 klos per hectare. Large farmers almost double the yeld observed among famly farmers suggestng a sgnfcant gap between small and large-scale producers n the agrculture sector n Chle. The fgures suggest a low level of nsurance and technology adopton especally among famly farmers. Only 2% of wheat farmers report usng crop nsurance a proporton that s slghtly reduced to 1.5% n the famly farmer group. Insurance partcpaton s greater among non-famly farmers amountng to 12% of the total. The use of new technology s 5 Sol characterstcs were proxed usng nformaton on sol eroson that was obtaned from the Center for Informaton on Natural Resources CIREN The methodology for determnng the level of eroson ntegrates a set of sol topographc clmatc and bologcal characterstcs. Thus eroson wll be more severe where sols are more porous and sander and where felds are more sloped and hold less vegetaton as well as n locatons where a large amount of ran falls n a short tme. 6 Cumulatve precptaton was calculated usng nformaton provded by the agro-clmatc system FDF- INIA-DMC. Clmate measures per localty were obtaned by matchng localtes wth the nearest meteorologcal staton. 7 Farm locaton was controlled by dstngushng the northern zone comprsng Regons IV and V; central zone ncludng Regons XIII VI and VII; and southern zone consstng of Regons VIII IX X and XIV. 13

18 also low and less wdespread among famly farmers relatve to larger farmers. Modern rrgaton and bologcal control adopton do not surpass 5% of total wheat farmers. Improved seed s more broadly used among wheat producers. Whle 18% of the famly farmers report purchasng certfed seeds ths fgure surpasses 60% amongst larger farmers. The use of certfed seeds depends on a purchase decson whose benefts are more evdent n the short term. In contrast bologcal control and modern rrgaton adopton nvolve nvestment decsons wth unknown potental benefts n the long run. Regardng farmers characterstcs and the nsttutonal settng n whch they operate t was observed that schoolng rates are rather low; n fact most famly farmers do not manage to surpass the prmary educatonal level. In addton only 8% of farmers report havng used credt nstruments. In relaton to extenson servces 22.6% of famly farmers have receved ths knd of government support n the last two years whch compares to the 14% observed among larger wheat producers. Around 20% of wheat farmers partcpate n organzatons. Ths fgure s larger among non-famly wheat farmers amountng to 45%. Regardng land property status 85% of total land corresponds ether to own land wth a regstered ttle or to rented land compared wth farmers wth less secure land tenancy. Ths percentage s also larger among large-scale farmers. 6. Emprcal Strategy 6.1. Adopton of Agrcultural Insurance When Technology Decsons Are Exogenous As a frst emprcal approach we begn by nvestgatng the determnants of the adopton of agrcultural nsurance assumng that farmers choces are exogenous to technology decsons. In lne wth the theoretcal framework the probablty that farmer has her producton nsured s gven by: 9 P y = 1 z x = Φ zγ xβ where y s a dummy varable ndcatng the adopton status of farmer n the year under study z s a vector of observed covarates at the farmer level e.g. socoeconomc characterstcs dependence on on-farm work farm level characterstcs ncludng nsttutonal aspects and the regonal factors e.g. envronmental characterstcs locaton. Smlarly x s a vector of nput producton technologes adopted by farmer 14

19 e.g. mproved seed bologcal control and modern rrgaton whch here are assumed to be exogenous to adopton decsons; γ and β are vectors of the parameters to be estmated; and Φ s the normal cumulatve densty functon. We are partcularly nterested n the drecton and magntude of β whch wll shed lght on the extent to whch nput producton technologes on the farm relate to the adopton of agrcultural nsurance when ether decson s assumed to be ndependent of the other. Therefore the purpose of ths baselne specfcaton s to llustrate the extent to whch the effects captured by β are based when jont decsons whenever present are not addressed. Ths equaton s estmated by means of unvarate probt models Adopton of Agrcultural Insurance and Technology When Farmers Decsons Are Jontly Made We next nvestgate the extent to whch the adopton of agrcultural nsurance s assocated wth the adopton of nput producton technologes that could be perceved by farmers as ether rsk-ncreasng or decreasng; that s to say these decsons now are allowed to be nterrelated. Specfcally we model the adopton of agrcultural nsurance assumng that farmers adopton decsons are related through some unobservable channel. Thus farmers decsons can be represented as follows: y 10 x * 1 * 2 = x β z γ ε * 2 = z γ ε where y1 * and x2 * are unobservable and related to the bnary dependent varables y accordng to the rule: * 1 f y > 0 11 y = ; y * = y1 * x2 * * 0 f y 0 Specfcally y are bnary varables denotng the adopton of agrcultural nsurance and agrcultural technologes respectvely. Smlarly z1 and z2 denote the vectors of explanatory varables explanng farmers decsons regardng the adopton of agrcultural nsurance and agrcultural technologes respectvely. There are no constrants regardng the covarates embedded n z1 and z2. The most mportant feature of ths model however s the relatonshp between the error terms. In partcular f the error terms n the equatons above are ndependent of one another.e. Cov[ε1 ε2]=0 then the model can be estmated by means of two separate probt regressons. Ths would gve us an ndcaton that farmers decsons are ndependent. By contrast f the error 15

20 terms are nterrelated t wll be the case that Cov[ε1 ε2] 0 and farmers decsons wll be drven by the same underlyng process. To take account of the relatonshp between these processes the error terms above can be represented as follows: 12 ε = η u ε = η u 2 It can be seen from Equaton 12 that whle there s a component of the error term that s unque to each equaton there s another component that s common to both. Ths term allows us to capture the relatonshp between the equatons Greene If t s assumed that the error terms are normally dstrbuted then ε1 and ε2 wll not only be normal but also dependent. Let us further assume that ρ denotes the extent to whch these errors are correlated. Because of our nterest n the jont probablty of y.e. y1 and x2 and because of the assumpton that the error terms are normally dstrbuted the equatons above can be consstently estmated by means of bvarate probt models. The sgnfcance of the coeffcent ρ wll provde nformaton on whether or not nsurance and technology decsons are nterrelated. If ρ s not statstcally sgnfcantly dfferent from zero the underlyng process of the nsurance decson s more lkely to be characterzed by Equaton Results We frst dscuss the results obtaned from probt model estmatons neglectng the potental nteractons between technologes and nsurance decsons. Then we analyze bvarate probt models that account for ths relatonshp Adopton of Agrcultural Insurance Estmaton of the nsurance decson process usng the probt model s depcted n Table 2. Results report the estmated coeffcents for Equaton 9. The frst column depcts the estmates usng the sub-sample of famly farmers. Column 2 shows the estmated parameters for the non-famly segment. Regardless of the sample chosen dependence yeld captal extenson credt access partcpaton number of adopters and ranfall are statstcally sgnfcant to explan adopton of crop nsurance. We fnd that farmers who are more agrcultural 16

21 ncome-dependent are more lkely to adopt nsurance.8 Gven that nsurance covers losses from unpredctable weather events farmers obtanng a larger proporton of ther ncome from agrculture would be more nclned to buy crop nsurance. A related explanaton consders the extent of dversfcaton nto non-agrcultural actvtes. Mohammed and Ortmann 2005 fnd that alternatve rsk management strateges such as off-farm nvestments reduce the probablty of agrcultural nsurance adopton. Smlarly Rchards 2000 fnd a negatve relatonshp between the extent of dversfcaton nto lvestock and the tendency to nsure crops. Results also confrm that farmers wth hgher yelds have hgher probabltes of partcpatng n the nsurance program. Market return and return to nsurance varables also have been found mportant n prevous lterature Coble et al. 1996; Cabas et al. 2008; Garrdo and Zlberman Extenson servces were also found mportant n explanng nsurance decsons. Captal sgnfcantly and postvely affects nsurance adopton. Ths s somewhat unexpected n lght of evdence that farmers rsk averson decreases as ther wealth ncreases; therefore wealther farms should be less lkely to nsure Serra et al However larger wealth sze may promote the adopton of nstruments to cover aganst hgher potental losses Santeramo et al We fnd that support from government agences s crucal whch s an expected fndng gven that t s dffcult for famly farmers to afford nsurance premums. We also fnd that farmers who reported havng access to credt are more lkely to adopt nsurance. In addton results show that farmers partcpatng n agraran organzatons are more lkely to use nsurance. Partcpaton n organzatons may serve as a vehcle for transmsson and dffuson of knowledge about fnancal nstruments to protect aganst rsk or face new market condtons. The number of adopters of nsurance at the local level s also sgnfcant. The latter may be assocated wth potental learnng effects among farmers who belong to related networks or adjacent geographcal areas. We also fnd evdence of dfferences n adopton due to varatons n precptaton across locatons. In partcular nsurance s more broadly adopted n drer zones as 8 Multcollnearty s always a concern. For example educaton s most lkely hghly correlated wth ncome varables; captal and land can ease access to credt; etc. We evaluate a potental correlaton among suspcous varables n two ways: Frst we report correlaton coeffcents of the suspcous varables. We obtan a coeffcent of 0.12 for land and credt 0.35 for land and captal and 0.12 for dependence on agrculture and captal. These correlatons suggest that multcollnearty s not an mportant ssue. Second we smply drop the suspcous varables and explore the senstvty of the results to the excluson of these varables. Results do not change fundamentally. The results of these tests are avalable upon request. 17

22 expected. Ths result s n lne wth the usual fndngs that hgher-rsk farms are more lkely to be nsured Enjolras and Sents For the famly farmers segment we addtonally fnd that the varables age resdence on the farm and farm sze matter. Age negatvely nfluences the probablty of adoptng nsurance. It s lkely that older farmers are more reluctant to try non-tradtonal nstruments n partcular fnancal nstruments whose benefts are lttle known or dffcult to perceve by more conservatve famly farmers. Sherrck et al also fnd that those preferrng revenue nsurance are younger. Regardng resdence on the farm farmers who lve on the farm may be more exposed to dffuson because nteractons and networks are strengthened wth reduced moblty. We fnd that the larger the farm the more probable the adopton of nsurance. ndoubtedly the cost-beneft ratos of adoptng nsurance are better when there s a larger crop area to protect gven the hgh admnstratve costs of nsurance. In addton growers wth operatons spread over large areas may beneft from geographcal dversfcaton Rchards 2000; Sherrck et al. 2004; Santeramo et al Educaton and secure tenure varables seem to be more mportant among non-famly farmers. Educaton s postvely assocated wth nsurance. Thus educaton may turn out to be a complement to nsurance when t s knowledge ntensve and when benefts are lnked to farmers management capacty. Farmers who hold a larger rato of both owned land and land under property rental contracts whch provde relatvely secure tenure are more lkely to buy nsurance. Ths fact confrms the mportance of desgnng tenancy arrangements that guarantee rghts to enjoy long-term benefts derved from farm economc actvtes. There were some dscrepances n the results concernng the sol qualty varable. Whle good sol qualty was mportant n promotng nsurance adopton among famly farmers nsurance was more lkely to be observed among non-famly farmers n locatons wth poorer sol condtons. Concernng technology adopton the fndngs reveal a postve assocaton between the three technologes under study and the probablty of adoptng nsurance rrespectve of the sample used only bologcal control s nsgnfcant n the famly farmer sample although the coeffcent s postve. In other words the greater the adopton of technologes the hgher the probablty that farmers wll partcpate n the crop nsurance program. The latter may be n lne wth arguments based on the rskncreasng effects of adoptng new technologes. Nevertheless these estmatons do not 18

23 take nto consderaton nteractons between the nsurance and technology adopton processes Technology Adopton and Agrcultural Insurance Tables 3 and 4 present the man results of the bvarate probt regressons for the sub-samples of famly farmers and non-famly farmers respectvely. Columns 1-2 dsplay farmers decsons regardng the adopton of agrcultural nsurance and certfed seed. Smlarly farmers decsons regardng the adopton of agrcultural nsurance and bologcal control are depcted n Columns 3-4. Columns 5-6 present estmated parameters assocated wth the nterplay between adopton of agrcultural nsurance and modern rrgaton. Estmated coeffcents suggest a number of fndngs that are worth mentonng. Frst there s evdence that famly farmers decsons are nterrelated regardless of the producton technology under analyss. Ths fndng s supported by the fact that the athrho coeffcent ρ s postve and statstcally sgnfcant at the 1% level for all the technologes ndcatng that both decsons are ndeed lnked. Consequently farmers decsons should be analyzed by means of a bvarate model. In contrast the estmated coeffcent of athrho ρ was statstcally nsgnfcant among non-famly farmers ndcatng that the decsons are ndependent. Ths suggests that the determnants of producton decsons may dffer between the two groups probably because of the dfferences n rsk copng strateges that are avalable to farmers. Second whle adopton of certfed seed by farmers n ether sub-sample has no effect on the probablty of beng nsured both bologcal control and modern rrgaton negatvely affect the probablty of beng nsured n the sub-sample of famly farmers. Ths dffers from the results n the unvarate model. The result for bologcal control was somewhat unexpected because agrcultural nsurance protects farmers aganst clmatc rsks exclusvely and not from losses due to pest nfecton and other producton shocks. In contrast the fndng that a farmer adoptng modern rrgaton s less lkely to adopt agrcultural nsurance s n lne wth Foud and Erdlenbruch Ths mples that modern rrgaton can be understood as a substtute for agrcultural nsurance.e. both nsurance and modern rrgaton protect farmers aganst clmatc rsks such as droughts suggestng a hgh nsurance partcpaton rate among hgher rsk profle farms whch can be taken as evdence of adverse selecton. 19

24 8. Robustness Checks 8.1. Adopton Decsons n Homogeneous Areas It was ponted out n the sectons above that agro-clmatc condtons n Chle exhbt a great deal of heterogenety. Because avalablty of water sources sol nutrents and other geographcal characterstcs affectng agrcultural actvty sgnfcantly change when movng from north to south t mght be thought that farmers choces regardng partcpaton n the clmate rsk nsurance program are manly drven by geography. In order to evaluate the senstvty of the results to exposure to gven agro-clmatc characterstcs we estmate the bvarate probt model n Equatons on a subsample of farmers located n Central Chle. By constructon farmers n ths sub-sample are located n more homogeneous zones. Estmated coeffcents are dsplayed n Table A1 Appendx A. As can be seen results are robust for modern rrgaton technology and less clear for bologcal control n that ts relatonshp wth nsurance becomes nsgnfcant Assessng Technologcal Change among Irrgators So far the uptake of agrcultural nsurance by farmers adoptng modern rrgaton has been analyzed usng the totalty of farmers. Because an mportant number of farmers do not rrgate we mght be comparng two groups of farmers that are systematcally dfferent.e. rrgators and non-rrgators. In order to account for the technologcal change due to the shft from tradtonal to modern rrgaton the analyss was constraned to the sample of rrgators. Results are shown n Table A2 Appendx A. As can be seen the sgn and statstcal sgnfcance of the estmated coeffcent of modern rrgaton technology on nsurance partcpaton remaned robust to the excluson of non-rrgators. Nonetheless results dd not hold for farmers adoptng bologcal pest control Controllng for Multple Relatonshps In the results above relatonshps between crop nsurance and technology decsons were consdered ndvdually assumng no correlaton among dfferent technologes. However relatonshps among technologes may be mportant. To account for ths we estmate a multvarate probt model on the sub-sample of famly famers and rrgators for the central zone. Results are shown n Table A3 Appendx A and confrm the sgnfcant relatonshp between nsurance adopton and modern rrgaton decsons. However the relatonshp between nsurance adopton and bologcal control becomes 20