Implications of within county yield heterogeneity for modeling crop insurance premiums
Purpose - Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to...
Gespeichert in:
Veröffentlicht in: | Agricultural finance review 2012-01, Vol.72 (1), p.134-155 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 155 |
---|---|
container_issue | 1 |
container_start_page | 134 |
container_title | Agricultural finance review |
container_volume | 72 |
creator | Cooper, Joseph Zulauf, Carl Langemeier, Michael Schnitkey, Gary |
description | Purpose - Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.Design methodology approach - The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.Findings - The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.Originality value - The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches. |
doi_str_mv | 10.1108/00021461211222213 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1108_00021461211222213</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2438985448</sourcerecordid><originalsourceid>FETCH-LOGICAL-c443t-4d27f92aa1fefa7bee58104224f3748ce0d43d99bda7d957be73a0a842d2e3cb3</originalsourceid><addsrcrecordid>eNp90M9LwzAUB_AgCs7pH-At4NVqfrVpjzJ0DgZe1GvJmpcto01q0iL7782YeJmay4O8zzePPISuKbmjlJT3hBBGRUEZpSwdyk_QhBFBs4Kz4hRN9v0sgeIcXcS4JYQTydgEvS-6vrWNGqx3EXuDP-2wsQ43fnTDDu8stBpvYIDg1-DApjvjA-68hta6NW6C77F1cQzKNYD7AJ0du3iJzoxqI1x91yl6e3p8nT1ny5f5YvawzBoh-JAJzaSpmFLUgFFyBZCXlAjGhOFSlA0QLbiuqpVWUld5ApIrokrBNAPerPgU3Rze7YP_GCEO9daPwaWRNRO8rMpciPI_RQllsihILpOiB5X-FGMAU_fBdirsEqr3S66Plpwyt4cMdBBUq38iR7TutUmc_M7_nvAFtNuKkQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1012766057</pqid></control><display><type>article</type><title>Implications of within county yield heterogeneity for modeling crop insurance premiums</title><source>Emerald Insight</source><creator>Cooper, Joseph ; Zulauf, Carl ; Langemeier, Michael ; Schnitkey, Gary</creator><creatorcontrib>Cooper, Joseph ; Zulauf, Carl ; Langemeier, Michael ; Schnitkey, Gary</creatorcontrib><description>Purpose - Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.Design methodology approach - The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.Findings - The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.Originality value - The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.</description><identifier>ISSN: 0002-1466</identifier><identifier>EISSN: 2041-6326</identifier><identifier>DOI: 10.1108/00021461211222213</identifier><language>eng</language><publisher>Bingley: Emerald Group Publishing Limited</publisher><subject>Agricultural production ; Agriculture ; Cattle ; Corn ; Cost control ; Crop insurance ; Crops ; Empirical analysis ; Farm management ; Farms ; Heterogeneity ; Hypotheses ; Information processing ; Insurance ; Insurance premiums ; Risk management ; Soybeans ; Standard deviation ; Studies ; Time series</subject><ispartof>Agricultural finance review, 2012-01, Vol.72 (1), p.134-155</ispartof><rights>Emerald Group Publishing Limited</rights><rights>Copyright Emerald Group Publishing Limited 2012</rights><rights>Emerald Group Publishing Limited 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c443t-4d27f92aa1fefa7bee58104224f3748ce0d43d99bda7d957be73a0a842d2e3cb3</citedby><cites>FETCH-LOGICAL-c443t-4d27f92aa1fefa7bee58104224f3748ce0d43d99bda7d957be73a0a842d2e3cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/00021461211222213/full/pdf$$EPDF$$P50$$Gemerald$$H</linktopdf><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/00021461211222213/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,967,11635,21695,27924,27925,52686,52689,53244,53372</link.rule.ids></links><search><creatorcontrib>Cooper, Joseph</creatorcontrib><creatorcontrib>Zulauf, Carl</creatorcontrib><creatorcontrib>Langemeier, Michael</creatorcontrib><creatorcontrib>Schnitkey, Gary</creatorcontrib><title>Implications of within county yield heterogeneity for modeling crop insurance premiums</title><title>Agricultural finance review</title><description>Purpose - Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.Design methodology approach - The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.Findings - The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.Originality value - The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.</description><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Cattle</subject><subject>Corn</subject><subject>Cost control</subject><subject>Crop insurance</subject><subject>Crops</subject><subject>Empirical analysis</subject><subject>Farm management</subject><subject>Farms</subject><subject>Heterogeneity</subject><subject>Hypotheses</subject><subject>Information processing</subject><subject>Insurance</subject><subject>Insurance premiums</subject><subject>Risk management</subject><subject>Soybeans</subject><subject>Standard deviation</subject><subject>Studies</subject><subject>Time series</subject><issn>0002-1466</issn><issn>2041-6326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp90M9LwzAUB_AgCs7pH-At4NVqfrVpjzJ0DgZe1GvJmpcto01q0iL7782YeJmay4O8zzePPISuKbmjlJT3hBBGRUEZpSwdyk_QhBFBs4Kz4hRN9v0sgeIcXcS4JYQTydgEvS-6vrWNGqx3EXuDP-2wsQ43fnTDDu8stBpvYIDg1-DApjvjA-68hta6NW6C77F1cQzKNYD7AJ0du3iJzoxqI1x91yl6e3p8nT1ny5f5YvawzBoh-JAJzaSpmFLUgFFyBZCXlAjGhOFSlA0QLbiuqpVWUld5ApIrokrBNAPerPgU3Rze7YP_GCEO9daPwaWRNRO8rMpciPI_RQllsihILpOiB5X-FGMAU_fBdirsEqr3S66Plpwyt4cMdBBUq38iR7TutUmc_M7_nvAFtNuKkQ</recordid><startdate>20120101</startdate><enddate>20120101</enddate><creator>Cooper, Joseph</creator><creator>Zulauf, Carl</creator><creator>Langemeier, Michael</creator><creator>Schnitkey, Gary</creator><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7RQ</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X2</scope><scope>7XB</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>AFKRA</scope><scope>ANIOZ</scope><scope>ATCPS</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F~G</scope><scope>HCIFZ</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M0K</scope><scope>M1F</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20120101</creationdate><title>Implications of within county yield heterogeneity for modeling crop insurance premiums</title><author>Cooper, Joseph ; Zulauf, Carl ; Langemeier, Michael ; Schnitkey, Gary</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c443t-4d27f92aa1fefa7bee58104224f3748ce0d43d99bda7d957be73a0a842d2e3cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Agricultural production</topic><topic>Agriculture</topic><topic>Cattle</topic><topic>Corn</topic><topic>Cost control</topic><topic>Crop insurance</topic><topic>Crops</topic><topic>Empirical analysis</topic><topic>Farm management</topic><topic>Farms</topic><topic>Heterogeneity</topic><topic>Hypotheses</topic><topic>Information processing</topic><topic>Insurance</topic><topic>Insurance premiums</topic><topic>Risk management</topic><topic>Soybeans</topic><topic>Standard deviation</topic><topic>Studies</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cooper, Joseph</creatorcontrib><creatorcontrib>Zulauf, Carl</creatorcontrib><creatorcontrib>Langemeier, Michael</creatorcontrib><creatorcontrib>Schnitkey, Gary</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Career and Technical Education</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Accounting, Tax & Banking Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Agriculture Science Database</collection><collection>Banking Information Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Agricultural finance review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cooper, Joseph</au><au>Zulauf, Carl</au><au>Langemeier, Michael</au><au>Schnitkey, Gary</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Implications of within county yield heterogeneity for modeling crop insurance premiums</atitle><jtitle>Agricultural finance review</jtitle><date>2012-01-01</date><risdate>2012</risdate><volume>72</volume><issue>1</issue><spage>134</spage><epage>155</epage><pages>134-155</pages><issn>0002-1466</issn><eissn>2041-6326</eissn><abstract>Purpose - Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.Design methodology approach - The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.Findings - The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.Originality value - The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.</abstract><cop>Bingley</cop><pub>Emerald Group Publishing Limited</pub><doi>10.1108/00021461211222213</doi><tpages>22</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0002-1466 |
ispartof | Agricultural finance review, 2012-01, Vol.72 (1), p.134-155 |
issn | 0002-1466 2041-6326 |
language | eng |
recordid | cdi_crossref_primary_10_1108_00021461211222213 |
source | Emerald Insight |
subjects | Agricultural production Agriculture Cattle Corn Cost control Crop insurance Crops Empirical analysis Farm management Farms Heterogeneity Hypotheses Information processing Insurance Insurance premiums Risk management Soybeans Standard deviation Studies Time series |
title | Implications of within county yield heterogeneity for modeling crop insurance premiums |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T10%3A59%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Implications%20of%20within%20county%20yield%20heterogeneity%20for%20modeling%20crop%20insurance%20premiums&rft.jtitle=Agricultural%20finance%20review&rft.au=Cooper,%20Joseph&rft.date=2012-01-01&rft.volume=72&rft.issue=1&rft.spage=134&rft.epage=155&rft.pages=134-155&rft.issn=0002-1466&rft.eissn=2041-6326&rft_id=info:doi/10.1108/00021461211222213&rft_dat=%3Cproquest_cross%3E2438985448%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1012766057&rft_id=info:pmid/&rfr_iscdi=true |