ECONOMIC & DEMOGRAPHIC CHARACTERICTICS, SOCIAL CAPITAL, AND DEMAND FOR LIFE INSURANCE: EVIDENCE FROM CENTRAL REGION OF SRI LANKA

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1 ECONOMIC & DEMOGRAPHIC CHARACTERICTICS, SOCIAL CAPITAL, AND DEMAND FOR LIFE INSURANCE: EVIDENCE FROM CENTRAL REGION OF SRI LANKA Ssra Kumara NARADDA GAMAGE College of Economcs and Trade, Hunan Unversty, Changsha, Hunan, Chna L LIN lln963@vp.sna.com College of Economcs and Trade, Hunan Unversty, Changsha, Hunan, Chna Ihtsham ul HAQ htshamamn_99@yahoo.com College of Economcs and Trade, Hunan Unversty, Changsha, Hunan, Chna Abstract Ths paper presents fndngs of the determnants of demand for lfe nsurance n the central regon of Sr Lanka. It s a novel study n the sense that t ncorporated socal captal as a determnant of demand for lfe nsurance. Prmary data has been collected through random samplng and logstc model was used to examne the determnants of the demand for lfe nsurance. Results confrmed that gender, ncome, trust and socal captal has sgnfcant effect on demand for lfe nsurance n the study area. Income and trust came out postve contrbutors of lfe nsurance demand. However, t s worthy to note that although ncome has postve effect on lfe nsurance demand but ts odds rato makes t less mportant factor to nfluence demand for lfe nsurance. Gender has deterorated effect on demand for lfe nsurance ndcatng that male household head less lkely to purchase lfe nsurance. Smlarly, socal captal also has negatve mpact on demand for lfe nsurance. Other determnants lke age, relgous status, workng status, and educaton, has not sgnfcant effect on lfe nsurance demand. Polces are recommended on research fndngs. Keywords: Demographc characterstcs, Demand for lfe nsurance, Logstc model, Socal captal, Sr Lanka JEL Classfcaton: A13, D14, D22, D71 I. INTRODUCTION In the new global economy nsurance has become a key part of the fnancal sector n developed countres. Lfe nsurance s a major source of nvestment n the captal market as well as an mportant source of long term fnance (Catalan, Impavdo et al., 2000). Characterstcally, people decde to purchase a lfe nsurance polcy for managng the lfe rsk. They are ntended to protect the dependents aganst the loss of ncome that would result after the nsured person s death. There are some functons attached to nsurance lke to replace the ncome, to protect key employees and the busness tself and also to publc as t help publc to get potental estate taxes that would cease at the death of the nsured person. Economcally, purchasng a lfe nsurance can be recognzed as a form of household savngs methods and postponement of the current consumpton for the future household welfares. Accordngly, most of the people of the modern socety make the purchasng decson of the lfe nsurance polcy when they are marred, buyng a new home or have chldren, or when crtcal lfe movng events take place. The frst lfe nsurance was occuped n the early 18th century and the sale of lfe nsurance n Unted States nstgated n the late 1760s. At present, demand for lfe nsurance s growng up rapdly n developed economes. However, the nsurance culture n most of developng socetes s sgnfcantly under developed due to many socoeconomc concerns. Several studes have been documented negatve experence of people, economc nstablty and lower standard lvng along wth other socoeconomc as man reasons. The concept of nsurance was frst offered to Sr Lanka n the 1930 s. Lke other developng countres, Sr Lanka fnancal market s also not developed yet. Sr Lanka s a small and lower-mddle ncome developng country and well known for ts sgnfcant mprovement n the human development sde. In 1978, Sr Lanka moved the economy away from a close economy to open economy and opened up ts economy to world and encouraged overseas nvestments. It was the frst south Asan country to lberalze the economy. In the past three decades, the country was affected by natural dsasters and the cvl war. However, at present Sr Lanka has strong growth rates n recent years wth an economy worth USD 76 bllon. At present nsurance densty of the country s LKR (the 74

2 currency of Sr Lanka) 4831 and total premum ncome of all types of nsurance polces s LKR mllon whch s 1.02 percent of GDP (Central Bank of Sr Lanka, 2014). Yaar (1965) ntroduced the frst theoretcal framework for demand of lfe nsurance. He dscussed the factors of demand for lfe nsurance and proposed that lfe nsurance to be nsured aganst ambguty occasonng from the humanty rsk of ndvduals. Hammond et al. (1967) found that ncome, educaton, occupaton, net worth holdng and state n the lfe cycle affect lfe nsurance demand and consumpton. Some emprcal evdence consdered ncome as the most mportant factor for lfe nsurance demand. Such as, Fortune (1973), Lews (1989), and Kjosevsk (2012) dentfed that ncome s postvely assocated wth demand for lfe nsurance. However, Berekson (1972) argued that ncome does not have sgnfcant effect on demand for lfe nsurance and consdered that age and number of chldren have a sgnfcant effect on demand for lfe nsurance. Smlarly, Word and Zurbruegg (2002) and L and Moshran et al. (2007) emphaszed that although ncome of ndvdual s the most nfluental determnant of lfe nsurance demand along wth age and number of Chldren as they also have a sgnfcant effect on lfe nsurance demand. Besdes these factors, nflaton, prce of nsurance, and the level of fnancal development of socety are also dentfed as the other economc determnants (Word and Zurbruegg, 2002; Browne and Km, 1993). Beck and Webb (2003) explaned that nsttutonal factors also play vtal role n demand for lfe nsurance and they observed that ncome, bankng sector development, and relgous beleves affect demand for lfe nsurance. Esho and Krevsky et al., (2004) and Ofogh and Farsang (2013) documented that there exsts postve relaton between level of educaton and demand for lfe nsurance. Some studes evdent that the decson of purchasng an nsurance polcy manly depend on the socoeconomc, demographc, and nsttutonal factors. In the past three decades, a number of researchers have sought out a number of determnants of the demand for lfe nsurance at country level. However, to the best of our knowledge, no one has studed communty level factors that may affect demand for lfe nsurance. Smlarly, we also dd not trace any emprcal study that how socal captal of household wth soco-demographc determnants leads to the purchase of a lfe nsurance polcy. Besdes ths, the demand for lfe nsurance have not been closely studed and there has been no relable evdence n the Sr Lankan context. So, we fnd that a gap exsts, thus; ths paper wll fulfl ths research gap n the lterature. The man objectve of the study s to examne the soco-economc determnants of the demand for lfe nsurance n central regon of Sr Lanka. The rest of the paper s organzed as follows. The next secton provdes theoretcal and emprcal lterature on relevant determnants of the demand for lfe nsurance. The methodology s dscussed n the secton 3, whch encompassed the overvew of the study area, samplng method, data analyss tools and varables. The results and dscussons are gven n secton 4. In the last secton of ths paper, we added the concluson and suggestons for the polcy makers. II. LITERATURE REVIEW There s a growng body of lterature that dentfes the determnants of lfe nsurance demand and t became a major area of nterest wthn the feld of fnancal sector of the economy among the soco-economc researchers. The lfe cycle hypothess explans that how ndvduals desgn the savng behavor for the lfe. The frst theoretcal framework, the Lfe Cycle Utlty Model of a consumer together wth deductng the optmal consumpton and savng plan (Yaar: 1965, Hakasson: 1969). Accordng to ths model, demand for lfe nsurance s a functon of wealth, expected ncome, nterest rate, admnstratve cost of lfe nsurance and personal dscount rate for the consumer. Later Lews (1989) added the preferences of other members of household to ths framework. Accordng to the new model, the probablty of the prmary wage earner s death, the value of household consumpton, and degree of rsk effects postvely on demand for nsurance polcy whle admnstratve cost of nsurance polcy and the household wealth have negatve effect on demand for lfe nsurance. Factors found to be manpulatng the lfe nsurance demand have been explored n several studes. Some emprcal studes talked about many demand and supply sde factors of lfe nsurance. These factors can be dvded nto three sectons bascally, as demographc, soco-economc, and nsttutonal factors. Among these factors ncome s consdered as a vtal factor of demand for lfe nsurance. Theoretcally, t s expected that ncrease n ncome wll ncrease the demand for all knds of consumpton and human captal. Hgh ncome class as well as mddle class of the socety consder the lfe nsurance polces as a luxury good. Thus, most researchers lke Campbell (1980), Truett and Truett (1990), L and Moshran et al (2007), Feyen and Lester et al (2011), Park and Lemare(2011), and Kjosevsk (2012) had fnd out that ncome has postve mpact on lfe nsurance demand. Brown and Km (1993) had studed the determnants of lfe nsurance demand. Ther results confrmed that ncome, socal securty and dependency rato has postve and sgnfcant effect on lfe nsurances. Furthermore, they showed that lfe nsurance has a negatve relatonshp wth nsurance prce and nflaton n the economy. However, Hwang and Greenford (2005) who conducted study on factors of nsurance demand n Chna, Hong Kong and Tawan concluded that there s no correlaton between prce of nsurance and lfe nsurance consumpton. 75

3 Formal bankng sector development of the economy s another man economc varable whch develops the people s confdence on fnancal market ncludng nsurance nsttutons. Outrevlle (1996) showed that there exsts a postve assocaton between bankng sector development and lfe nsurance demand. Kjosevsk(2012) dentfed that GDP per capta, health expendture, nflaton, level of educaton are the most robust factors of lfe nsurance consumpton and stated that nterest rate, quas money rato, dependency rato, and government effectveness does not look to be a robustly correlated wth lfe nsurance consumpton. Alhassan and Bekpe(2016) observed that demographc factors better descrbe lfe nsurance consumpton compared to fnancal factors and found ncome, nflaton, lfe expectancy, and dependency rato lead to decay n lfe nsurance consumpton, nsttutonal qualty, fnancal development, and health expendture have a postve mpact on lfe nsurance markets n Afrca. Theoretcally, the level of educaton has a sgnfcant relatonshp wth lfe nsurance as hgh educated peoples have a more ablty to understand the lfe rsk and to manage the savngs for long term. Browne and Km (1993) and Gandolf and Mners (1996) among other researchers documented that long term savngs and lfe nsurance nspre people for hgher educaton and n return educaton propelled demand for lfe nsurance. Researchers lke Truett and Truett (1990), Hawang and Gao (2003) found that level of educaton has a postve nfluence on lfe nsurance demand. Therefore, we hope a postve relaton between level of educaton and lfe nsurance demand. However, Beck and Webb (2002) llustrated that educaton s not a sgnfcant factor of lfe nsurance demand. Dependency rato s also dscussed as an mportant factor of the lfe nsurance demand. Truett and Truett (1990) ponted out that the young dependency rato s postvely correlated wth lfe nsurance demand as well as there s postve relatonshp between age and the demand for lfe nsurance. Showers and Shotck (1994) also found the same result from ther emprcal study. Generally, socal captal conssts of notons of nterpersonal trust, belongng to a socal assocaton and mutual benefts. Coleman (1988) ponted out that socal captal s the creatve structure of relatonshp at ndvdual and group level. It s wdely understood to be the socal assocaton, network, norms and values that assst collaboraton between ndvduals and groups and t helps to expand ther socoeconomc welfare (Grootaert, 1999). Norms and values are embedded n communty networks (Putnam, 2000) and socal connecton can substtute for mssng legal structure n facltatng many fnancal transactons (Arrow, 1972). Naradda Gamage, Huq et al. (2015) added socal asset/captal as an exogenous varable to household assets based subjectve wellbeng framework. They explaned that socal captal can effect ncome dversfcaton through communty attachment whch n return can nfluence subjectve wellbeng. The emprcal studes acknowledged two approaches to quantfy socal captal. The frst approach s that socal captal stresses evdence on the organzaton membershp. The second approach s based on survey queston about trust. However, as mentoned n secton one that authors could not trace any emprcal paper about socal captal and lfe nsurance but we expect that socal captal can play a role n lfe nsurance demand. So ths study, ncluded Socal captal nto the emprcal model of ths study. It s a dummy whch replcates the structure of relatonshp of communty networks of the ndvduals. People n rural area do not care about nsurance as they have confdence to tackle any rsk through socal captal. On the other hand, people who lve n urban areas wth hgh ncome have a sgnfcant opportunty for collectng nformaton about hgh benefcal lfe nsurance polces through socal networks. Therefore, we can expect a relaton between socal captal and lfe nsurance demand whch may be postve or negatve. Trust s very closely related to concept of socal captal. Putnam (1993) concerns trust as a source of socal captal that sustans economc vtalty and government enactment economcally. Trust can be dentfed as assurance n the capablty and objectve of a buyer to pay at a future tme for goods delvered wthout presentday payment. As well as, trust s based on an ndvduals confdence concernng how another person wll mplement and behave on some future event. Sapenza and Zngales (2008) dentfed the concept of trust as a powerful motvator of economc behavour whch can levy real nfluence upon economc actons and t s one of the engnes of fnance growth. One can deduce from past lterature that people do not have trust on nsurance ndustry n developng countres and reason may be lack of awareness about nsurance. III. MATERIALS AND METHODS Study Area The central provnce of Sr Lanka has area of 5674 Km 2 and s dvded nto three admnstratve dstrcts namely Kandy, Nuwaraelya and Matale. The unverse of our study s Matale dstrct as ths dstrct s consdered as the central regon of Sr Lanka. The man cty of the regon s Matale and the cty s located some 25 klometers from Kandy the provncal captal and about 144 klometers from Colombo, the captal cty of the country. The total land area of the regon s 1993 Km 2. It delegates Northern part of the Central Provnce Spreads from to Eastern alttudes from 7.24 to 8.01 Nothern lattudes. Matale Dstrct has been dvded nto 11 Dvsonal Secretary s Dvsons (see fgure 1) and 1373 vllages and populaton s Snhala Buddhsts are n majorty whereas Muslms, Sr Lankan Taml and Indan Taml are n mnorty. More than 80% 76

4 of the people of the central regon lve n the rural area and 70% of the people are engaged n agrcultural related actvtes Fgure 1: Locaton of central regon of Sr Lanka and survey area Samplng Method Ths study s based on prmary data and three stage samplng procedures were adopted. In the frst stage, representatve two Dvsonal Secretary s Dvsons (DSD) were selected and second stage, representatve two vllages were selected. Fnally, 147 households were selected randomly wthn the selected vllages. The data were collected usng a structured questonnare durng December 2015 to January The questonnare obtaned nformaton on factors nfluencng the decson on lfe nsurance, economc, standard demographc characterstcs, and communty level nformaton on socal captal. Data Analyss Methods One way ANOVA, ch- square test and logstc regresson model were used to examne the data and to dentfy the characterstcs of the samplng data. In order to examne whether there s a dfference exsted between those who have or plannng to purchase a lfe nsurance and those who do not have a lfe nsurance. In addton, to study contnuance varables (age and household ncome) data we used one way ANOVA. The technque of ANOVA was used to determne whether lfe nsurance holders and non-holders group s mean value (ncome/age) are equal or not. The equaton for the one way ANOVA F- statstc s gven by F Y / 1 Y Y / N k n Y k j 2 2 (1) Where, n s the sample sze n the th group, Y s the sample mean n the th group, Y s the mean of the data and k s the number of group of the study, Yj s the j th observaton n the th out of k group, and N s the sample sze of study. We used ch-square test, In order to dentfy the sample characterstc and to determne whether there s sgnfcant change between expected ncdences and the observed ncdences. It s the most approprate and propeller method whch use smple random samplng method. The test statstcs s 2 OI EI 2 EI (2) r, c r, c r, c Σ / 2 where, s the ch square value, OI r, cs the observed ncdence count at level, r of varable, X1 and level c of varable X 2 and EI r, cs the expected ncdence count at level r of varable X 1 and level c of varable X 2 and r s the number of levels for 1 X varable, and c s the number of levels for 2 X varable. The Logstc regresson model s a nonlnear model that s used whenever the dependent varable of the research study s bnary and t s consdered the most approprate. The Standard bnary logstc regresson was used to study the determnants of demand for lfe nsurance n central Sr Lankan socety because of ts smplcty. The concept of logstc model s based on Bernoull dstrbuton whch estmate the probablty of the dependent varable to be one. Ths s the probablty that some event happens. Ronald and Yates (1938) have suggested the logt lnk for regresson model wth a bnary varable and the early acton of ths model was Berkson (1944). One approach s to consder the multple lnear regresson model (assumng Y has normal dstrbuton) of the form) 77

5 Y X X X... X n n (3) E Y p X X X... X (4) n n In Eq. 4 the expresson s known as lnear probablty model. As logstc model s assocated wth lnear probablty structural problem, hence, t s good to study models ncorporatng a curvlnear relatonshp between X and p. Mostly the transformaton of ths stuaton s the logstc defne as: p E p og Logt p l 1 p 1 1 Y n X 0 X 1 e prob event p X e 0 1 e 0 n X n X (5) Thus, the log of odds (logt) s presented n Eq. 5. p 0 log X X X... X (6) n n 1 p where, 0 s the ntercept and 1 n are the slope coeffcents, X X 1 n are the exogenous varables. Effects n the Logstc model refer to odds, and the estmated odds at one value of X dvded by the estmated odds at another value of X s an odds rato. The study focuses the determnants whch effect on demand of lfe nsurance. The possble outcome are ether havng a lfe nsurance polcy or are plannng to purchase (Y=1) or otherwse (Y=0). Based on past emprcal and theoretcal studes we ncluded eght ndependent varables n the emprcal logstc regresson model whch may explan the determnants of demand for lfe nsurance and these varables are presented n Table 1. Gender and age were ncluded n the model as two demographc factors and these two ndependent varables hypothetcally may have postve or negatve effect on lfe nsurance demand dependng on the socoeconomc stuaton of the socety. Especally relgous beleves can affect demand of lfe nsurance. Educaton level and ncome of households are other varables n our model whch were hypothetcally expected to have a postve relatonshp wth lfe nsurance demand. We added the trust of nsurance as a categorcal varable because of a large proporton of people have not a clear dea about the benefts of nsurance polces due to lack of awareness. It s beleved that less educated and people n rural area may not have a trust on lfe nsurance ndustry. Hypothetcally we expected that trust has a postve effect on the demand. Smlarly, communty level factors also can affect to demand for lfe nsurance. Table 1: Research varable descrptons and expected sgn Varable Defnton Expected sgn Gender (GE) dummy varable: 1 = male; 0 = female +/- Age (AG) scale varable; years +/- Relgon (RE) dummy varable: 1 = Buddhst ; 0 = otherwse +/- Workng Status (WO) dummy varable; 1 = workng; 0 = otherwse + Educaton level -(ED) categorcal varable 0 = prmary or no schoolng (grade 1 to 5) 1 = secondary (grade 6 to 11) 2 = collegate ( grade 12 to 13) 3 = Tertary (dploma and unversty level) 4 = vocaton educaton + Income (IN) scale varable; LKR + Trust on nsurance ndustry + (TR) dummy varable; 1 = have a trust on nsurance ndustry 0 = otherwse 78

6 Socal captal (SO) ECOFORUM dummy varable; 1 = partcpate for communty actvtes 0 = otherwse Socal captal was added to our emprcal model to capture effect of communty level factor on demand for lfe nsurance. Socal captal varable was proxy through partcpaton n communty meetngs and actvtes. The respondents were asked whether they partcpate n communty meetngs and actvtes or not. Hypothetcally we can expect postve or negatve relatonshp wth the demand for lfe nsurance. IV. RESULTS AND DISCUSSION Descrptve Statstcs & Results of One Way ANOVA Frst, quanttatve varables whch are monthly household ncome and age are analyzed through one-way ANOVA. These results are provded n Table 2. The mean monthly household ncome and standard devaton (SD) of the lfe nsurance holders are LKR , and LKR respectvely. Whle the mean ncome of household and SD are LKR and LKR respectvely for non-holders of lfe nsurance (see table 2). It can observed that there s a sgnfcant dfference between the mean ncome of holders and nonholders of the lfe nsurance. Furthermore, the results of ANOVA test, showed that there s no sgnfcant dfference between lfe nsurance holders and non-holders regardng the age of the household head. Table 2: The results of one way ANOVA Mean and Std. Devaton Varables LI holder Non- holder Overall P value Mean SD Mean SD Mean SD Income * Age N.B *sgnfcant at α = 0.05 Descrptve Statstcs & the Results of Ch-Square Test We used ch- square (χ 2 ) test, n order to dentfy whether there s sgnfcant dfference between expected frequences and the observed frequences of the categorcal varables. The ch-square (χ 2 ) test s the most approprate method f data satsfes certan requrements such as smple random samplng method n selectng the respondents of the study. Table 3 shows soco-economc characterstcs of the study sample. As llustrated n the table majorty of the household (87.76%) were male heads and only just 12.24% of respondents were female heads. The varable of gender was not possble to perform the ch-square test as there was not mnmum requred expected frequences. However, accordng to the exact sgnfcance statstcs (Fsher s exact), we detected a sgnfcance dfference between lfe nsurance holders and non-holders regardng the gender. Most of households (84.35%) were Buddhsts and above 88% of lfe nsurance holders (ether have a lfe nsurance polcy or plannng to purchase an nsurance polcy) were Buddhsts. However, we dd not observe a sgnfcant dfference between lfe nsurance holders and non-holders regardng the relgon. Table 3: Descrptve statstcs and the results of ch-square test % Varable holders Non Overall holders Gender(GE) male female Relgons(RE) Buddhst Non Buddhst Workng status(wo) workng Not workng Educaton Level (ED) Prmary or no schoolng Secondary Collegate Tertary Asymptotc/ Exact sg +/- Remarks f * Not sg f Not sg. - *** 79

7 Vocaton educaton Trust(TR) have a trust on nsurance have not Socal Captal(SO) partcpate to communty actvtes No partcpaton * * Note; * **Not possble to perform the ch-square test. f = fsher s exact test *sgnfcant at α = 0.05 Accordng to the Table 3, 29.93% of total respondents have collegate or tertary educaton. However 61.77% of lfe nsurance holders are ether have collegate or tertary educaton. It s not possble to check the sgnfcance dfference between lfe nsurance holders and non-holders on bass of educaton because of nsuffcent expected frequences as ch-square s less precse f there are not at least fve ndvduals expected n each cell. Regardng workng status, 87.07% of respondents belong to workng class however, the varable of workng status was not possble to perform the ch-square test as there was not mnmum requred expected frequences. Accordng to Fsher s exact test, the dfference to have work or not have work s not sgnfcant. Majorty of household head have not a trust on lfe nsurance ndustry and % of lfe nsurance holders have trust on lfe nsurance. But 66.37% of non- holders do not have trust on lfe nsurance ndustry. It s sgnfcant at 10% level. Among communty level factors, socal captal varable represent by partcpatng to communty actvtes. More than 76% of respondents partcpate to communty actvtes. However 47.06% of lfe nsurance holders were not partcpatng to communty actvtes. The results of Ch squared test show t s sgnfcant at 10% level. More than 76% of respondents who partcpate n communty actvtes, and 52.94% of them are lfe nsurance holders. It was sgnfcant at 5% level. Estmaton Results We employed the logstc regresson model, n order to fnd the determnants of demand for lfe nsurance n central regon of Sr Lanka. Pror to the Logstc regresson analyss, multcollnearty between the ndependent varables was tested to crcumvent ambguty about the results. Leech et al. (2005) suggested that a lnear regresson between categorcal ndependent and dependent varables should be tested for multcollnearty problem before proceedng to logstc regresson as ths technque have not a provson to overcome multcollnearty problem. The Collnearty statstcs of our ndependent varables are presented n Table 4. Table 4: Collnearty statstcs of ndependent varables n the model Varables AG RE WO ED IN TR SO Mean Tolerance value VIF Source: Authors calculaton from the survey data, 2016 The results showed the value of varance nflated factor (VIF) s less than n case of all ndependent varables. The values of tolerance of the ndependent varables are less than and the mean value of VIF s These results obvously clarfed that there s no multcollnearty problem among the ndependent varables of the model. Accordng to the results of the logstc regresson, the overall percentage the baselne model s accurate as ts predcton s accurate about 76.9% and s statstcally sgnfcant (p<0.00). The emprcal model wth explanatory varables s accurate 88.4% and the results of Omnbus test confrmed that the model wth explanatory varables s sgnfcantly better (P <0.000). Furthermore, the -2LL value for the model s that showed the model s sgnfcant and model explans 56.1% of the varaton n the outcome. The P value for Hosmer and Lemeshow goodness of ft test statstcs s (>0.05) thus, we cannot reject null hypothess. It confrmed that the model s a good ft to our survey data. The table 5 provdes the logstc regresson coeffcents, the Wald statstcs, odds rato, P value, and 95% confdence nterval. Among ndependent varables of the model gender, ncome, trust on nsurance ndustry and socal captal were statstcally sgnfcant at the 5% level whle the rest of varables were not statstcally sgnfcant. 80

8 Table 5: The results of logstc regresson model Varable Wald EXP(β) 95% C.I. for EXP(B) Β SE Statstcs (OR) P Lower Upper Constant Gender * Age Relgous status Workng status Educaton ED(1) ED(2) ED(3) ED(4) Income 0.000* Trust n nsurance 1.822* Socal captal * LL χ 2 = Df = 11 P<0.00 Nagelkerke pseudo R Hosmer & Lemeshow Classfcaton accuracy N.B: *sgnfcant at α = 0.05 Source: Authors calculaton from the survey data 2016 Accordng the results above, the effect of gender s sgnfcant and negatve, ndcatng that male household head s less lkely than female household head to purchase a lfe nsurance polcy (OR=0.085). Lookng at the results for monthly household ncome s hghly sgnfcant and ts coeffcent and odds rato s 0.00 and 1.00 respectvely. It explaned that event occurrng between two stuatons have the same probablty. For an addtonal unt (LKR) n ncome the odds of purchasng lfe nsurance s zero percent n the central regon of Sr Lanka. Ths result explaned that other factors are more mportant than ncome for lfe nsurance. Another sgnfcant varable s the trust on nsurance that the effect s postve, ndcatng that household head who has a trust on nsurance s more lkely than household head who has not a trust on nsurance to purchase a lfe nsurance polcy. The odds rato for trust ndcates that household head who has a trust on nsurance s tmes (518.6%) more lkely than household head who has not a trust on nsurance to purchase a lfe nsurance even after controllng for the other ndependent varables effects. The effect of socal captal on demand for lfe nsurance s also sgnfcant and negatve whch ndcatng that household head who partcpates n the communty level actvtes s less lkely to purchase the lfe nsurance polces than household head who does not partcpate n the communty level actvtes. Socal captal odds rato documented that household head who partcpates n the communty level actvtes s tme less lkely to purchase a lfe nsurance polcy after controllng for other factors of lfe nsurance demand. However, age, relgous status, educaton and workng status of the respondents do not have statstcally sgnfcant. V. CONCLUSION In ths paper we examned the determnants of demand for lfe nsurance n central regon of Sr Lanka. Ths emprcal research s based on the survey data and the sample conssts of 147 respondents and employed the mult-stage samplng method n selectng them. One-way ANOVA, ch-square test and logstc regresson model was used to analyze the data. Ths study presented some sgnfcant understandng nto lfe nsurance demand. Accordng to the results of the study gender of household head, household ncome, trust on nsurance ndustry and socal captal are statstcally sgnfcant determnants of demand for lfe nsurance n the study area. In addton, the effect of trust on nsurance on lfe nsurance demand s postve and sgnfcant where gender and socal captal were negatvely assocated wth t. We also found that household ncome has sgnfcant mpact on lfe nsurance demand but the odds rato of purchasng a lfe nsurance was one. Thus, ths result hghlghted that ncome s not as much mportant factor of lfe nsurance as gender, trust, and socal captal n the study area. Furthermore, age of the respondent, relgous and workng status, level of educaton has not statstcal sgnfcant mpact on demand for lfe nsurance. However, readers should bear n mnd that ths research study s not 81

9 wthout lmtaton and s based on just two vllages and 147 respondents data and nformaton from the central regon of the Sr Lanka. The overall results of ths study mples good nformaton for polcy and decson makers to mplement new programs regardng lfe nsurance polcy. It s also recommended that awareness has to be ncreased about the lfe nsurance and the nsurance ndustry to get frutful results from nsurance ndustry. VI. REFERENCES 1. Alhassan, A.L., Bekpe, N. (2016) Determnants of lfe Insurance consumpton n Afrca, Research n Internatonal Busness and Fnance, no. 37, pp Arrow, K. (1972) Gfts and exchanges, Phlosophy and Publc Affars, no 1, pp Beck, T., Webb, I. (2003) Economc, demographc, and nsttutonal determnants of lfe nsurance consumpton across countres, the World Bank Economc Revew, 17(1), pp Berekson, L.L. (1972) Brth order, anxety, afflaton and the purchase of lfe nsurance, the Journal of Rsk and Insurance, 39(1), pp Berkson, J. 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