Willingness to Pay for Health Insurance: An Analysis of the Potential Market for New Low Cost Health Insurance Products in Namibia

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1 Wllngness to Pay for Health Insurance: An Analyss of the Potental Market for New Low Cost Health Insurance Products n Namba Abay Asfaw Center for Dsease Control and Preventon\Natonal Insttute for Occupatonal Health & Safety (USA) Emly Gustafsson Wrght Jacques van der Gaag AIID RS 8 1/2 Abstract: Ths study analyzes the wllngness to pay for health nsurance and hence the potental market for new low cost health nsurance product n Namba, usng double bounded contngent valuaton (DBCV) method. The fndngs suggest that 87 percent of the unnsured respondents are wllng to jon the proposed health nsurance scheme and on average are wllng to nsure 3.2 ndvduals (around 9 percent of the average famly sze). On average respondents are wllng to pay NAD 48 per capta per month and respondents n the poorest ncome quntle are wllng to pay up to 11.4 percent of ther ncome. Ths mples that prvate voluntary health nsurance schemes, n addton to the potental for protectng the poor aganst the negatve fnancal shock of llness, may be able to serve as a relable ncome flow for health care provders n ths settng.

2 Introducton and Background In a world of plenty, t becomes ncreasngly unacceptable that people de or suffer because they have no access to even the most basc of medcal care. Equally as dstressng s when poverty s the result of large or catastrophc health expendtures. Evdence from surveys whch cover 89 percent of the world s populaton suggests that 15 mllon people globally suffer fnancal catastrophe every year due to out of pocket health expendtures. 1 Another potental scenaro s perpetual poverty due to the nablty to work because of poor health despte such expendtures. Ths downward spral of mpovershment and ll health could be slowed through mproved health fnancng mechansms. Constraned government budgets for health, however, are a serous problem n many developng countres. Offerng low cost health nsurance to low ncome households s one nnovatve method through whch to fnance health care provson and to avod catastrophc out of pocket health expendtures. Currently, prvate health expendtures are a major source of revenues whch are beng crowded out by government expendtures. 2 The success of ths approach depends on the effectve and sustaned demand for these voluntary (prvate) pre pad nsurance schemes. Determnng the demand or wllngness to pay for health nsurance s crucal n ascertanng the feasblty of such schemes, establshng prces, and settng potental subsdy levels. In the absence of real world experence, economsts gauge the wllngness to pay (WTP) for health nsurance n low ncome countres by means of contngent valuaton (CV) methods whch elct drectly what ndvduals would be wllng to pay for a hypothetcal health nsurance package. Ths study analyzes the wllngness to pay for health nsurance and hence the potental market for new lowcost health nsurance products n Namba usng CV method. Long term experence wth such schemes s stll lmted, but a growng lterature on the wllngness to pay for health nsurance suggests that the market for such schemes s large, even among the poor. Health Expendtures and Insurance n Namba In the Greater Wndhoek Area of Namba, about 44 percent of households are enrolled n health nsurance (coverng about 28 percent of ndvduals) whch s hgh for a Sub Saharan Afrcan (SSA) country. Whle nsurance enrollment rates are hgh, there are great dspartes between the rch and poor. Only 4 percent of ndvduals n the poorest consumpton quntle are enrolled n health nsurance whle 67 percent of ndvduals n the rchest quntle have nsurance benefts. More educated ndvduals are more lkely to have medcal nsurance. Thrty one percent of ndvduals who lve n male headed households are nsured compared to 21 percent of ndvduals lvng n female headed households. Nonetheless, overall only 1 Ke et al van der Gaag 28 and references theren. 2

3 about 14 percent of total health care costs are covered for ndvduals who reported an acute llness or njury. About 65% of those who report a chronc llness receve no rembursement from health nsurance. Almost 7 percent of hosptalzaton costs are not covered at all by any type of nsurance. For nsured ndvduals, 55 percent of hosptalzaton costs are fully covered, 36 percent are partally covered and 9 percent of costs are not covered. 3 The mplcatons of ths are that despte relatvely hgh nsurance coverage, out of pocket expendtures stll represent a large fnancal burden partcularly for low ncome households. In recent years, new low health nsurance products have come on the market n Wndhoek. We examne cost the wllngness to pay for these types of products. Revew of the Related Lterature Establshng a prce for a product s not always as straghtforward as fndng the ntersecton pont of the supply and demand curves as taught n Mcroeconomcs 11. One may run nto partcular dffcultes when attemptng to prce a product whch s a publc or non market good. Numerous methods have attempted to solve ths problem e.g. hedonc prcng, cost beneft analyss, travel cost and costeffectveness to name a few. 4 Much of the current WTP lterature uses CV method whch elcts drectly what ndvduals would be wllng to pay for a partcular product or good. In ths paper, the CV method s used to study the demand for new low cost health nsurance products n Namba. Despte ts apparent benefts, some researchers and polcy makers are skeptcal to CV methods due to varous reasons. The frst concern s related to the tangblty of the CV results snce both the markets and the preferences are hypothetcal. As quoted by Krström (199), the CV method s consdered to be based on hypothetcal answers to hypothetcal questons. Ths creates a psychologcal barrer to relyng on such results. The second reason revolves around the methodologcal ssues rangng from desgnng and admnstratng the questonnare to the estmaton technques. It s usually assumed that the CV results are hghly affected by measurement errors and ths mnmzes the relablty and valdty of the results. 5 Most researchers, however, agree that f prevaled preferences or market stuatons cannot be drectly observed or f the tem under consderaton s not traded n a real market, CV methods are the best alternatve to assess the value of publc or non traded goods and servces. It s argued that n spte of the fact that the CV methods have ther own lmtatons, there s no suffcent evdence to reject the results of carefully desgned and executed CV experments and the results can be proven to be consstent wth economc theory and other requrements (Krström 199, Hanemann, et al. 1991, Cameron 1991, Neumann and Johannesson 1994, Randall 1997, Carson 1997). 3 Republc of Namba Okamblmbl Survey (28). 4 Asfaw and von Braun (25). 5 For a crtcal revew of the CV method, see Hausman (1993) and McFadden (1994). 3

4 Contngent valuaton s ncreasngly beng used to evaluate the wllngness to pay (WTP) for health nsurance n developng countres. In Ethopa, a country very dfferent from Namba, a CV based study (Asfaw and von Braun, 25) fnds evdence supportng the feasblty of ntroducng communty based heath nsurance schemes (CBHIS). 6 In Asfaw and von Braun (24), the authors nvestgate the potental of such schemes to mtgate the mpacts of health shocks due to economc reforms on poor rural households. Ther fndngs suggest that such schemes ndeed would be helpful n protectng the poor aganst shocks. Asenso Okyere et al. (1997) found, n Ghana, that almost 64% of respondents were wllng to pay about Ced 5 or US$3. per month for a household of fve for a Natonal Health Insurance scheme amed at the nformal sector. Barnghausen et al. 27 examne WTP among nformal sector workers n Wuhan, Chna. Informal workers have a WTP that s hgher than the estmated cost of CBHIS based on past health expendtures. Dror et al. (27) use undrectonal bddng n a CV survey to obtan estmates of WTP for health nsurance n Inda. They fnd that the poor are wllng to pay a hgher percentage of ther ncome on health nsurance premums compared to hgher ncome groups. Asgary et al. 24 examne wllngness to pay for health nsurance n rural Iran fndng that households are wllng to pay on average US$2.77 per month for health nsurance. 7 Whle levels are not necessarly comparable across countres and dfferng products, ths evdence demonstrates that ndvduals n a varety of low ncome countres would be wllng to pay for low cost health nsurance schemes. Data Source and Measurement of Varables The data used for ths study come from a large scale household survey the Republc of Namba Okamblmbl Survey 28 (RNOS 28). 8 Ths survey, based on the World Bank Lvng Standards Measurements Surveys (LSMS) methodology, provdes socoeconomc data for the Greater Wndhoek Area of Namba. 9 The survey ncludes sectons on health, employment ncome, health nsurance (access to and WTP for), housng, household assets and other sources of ncome, consumpton expendtures on food and non food tems, deceases, and credts and loans. The target populaton of the study conssts of prvate households lvng n the Greater Wndhoek Area of Namba. The sample was desgned as a stratfed two stage probablty sample, consttutng a random sample of the Greater Wndhoek populaton. It used the natonal samplng frame from the Central Bureau of Statstcs of Namba. Stratfcaton was done over three soco economc groups: low, mddle and hgh ncome groups. In practce, the equal dstrbuton of Wndhoek households 6 In Ethopa 78% of prvate health expendtures are out-of-pocket compared to 18% n Namba. 7 The study doesn t consder per member WTP. 8 The survey was conducted by staff of the Multdscplnary Research and Consultancy Centre (MRCC) at the Unversty of Namba, and the Natonal Insttute of Pathology (NIP), n cooperaton wth the Amsterdam Insttute for Internatonal Development (AIID), and PharmAccess. 9 Grosh and Glewwe (2). 4

5 over the three soco economc groups makes t unnecessary to correct for the stratfcaton n terms of weghts and error terms. In the frst stage, 1 Prmary Samplng Unts (PSUs) were selected from the three soco economc groups proportonal to populaton sze. PSU selecton was done n proporton to the number of households per cluster from the Census 21. To the extent that relatve populaton szes have remaned approxmately the same n the past fve years, ths makes the sample a self weghted, equal probablty sample of Greater Wndhoek. In the second stage, 2 households per PSU were selected, based on the lsts from a household dentfcaton exercse. The fnal sample contaned 1,172 households ncludng 447 ndvduals. The secton on health nsurance access and WTP for health nsurance provdes a rch set of nformaton allowng us to analyze the potental market for health nsurance. Ths secton of the survey s based on the tradtonal contngent valuaton surveys. A CV survey should contan some basc common components. To begn wth, a CV survey must present a descrpton of the stuaton for whch the ndvdual would hypothetcally pay. In ths survey, the ntervewees are presented wth an nsurance card descrbng a potental nsurance products. Ths nsurance scheme s smlar to ones currently beng offered through a Dutch NGO led ntatve to provde nsurance to the unnsured. 1 The nsurance packages are low cost so they offer relatvely lmted servces. For example, one package ncludes: unlmted access to prvate nurse, sx annual vsts to prvate doctor, basc medcnes, HIV treatment, lmted prvate hosptalzaton, (the doctor only refers to the hosptal for urgent medcal treatment), and maternty benefts. Second, there must be a mechansm to elct the value from the respondent. There are several ways to structure ths mechansm ncludng open ended questons (contnuous format), bddng games or referendum formats (dchotomous format). In ths survey, a double bounded dchotomous choce elctaton method s used. Compared to most other elctaton methods, ths procedure has sgnfcant statstcal effcency gans (Yoo and Yang, 21). Under ths method, each respondent s asked f s/he s wllng to pay the frst bd. If s/he says yes to the frst bd, a second hgher bd wll be gven and her/hs wllngness to pay s asked. If s/he says no to the ntal bd, a second lower bd wll be provded. If s/he says no to both the frst and the second bds then s/he wll be asked to menton the maxmum that s/he s wllng to pay. Under ths elctaton procedure, we have two dscrete responses from every ndvdual (see Fgure 1 below). One potental lmtaton of contngent valuaton method s related to the bas whch may come from the startng pont of the bd. Evdence from one study showed that ndvduals n the hghest startng bd group were wllng to pay double that of those n the lowest (Kartman 1 The Dutch NGO PharmAccess develops low-ncome health nsurance products for a varety of lowncome workers n numerous Afrcan countres. The NGO started wth workplace programs n large nternatonal companes, provdng comprehensve health nsurance for the workers, ncludng HIV/AIDS counselng and treatment. Plot projects of ths knd are beng developed and mplemented n Namba, Ngera and Tanzana. 5

6 et al. 1996). In ths study, ths bas s reduced by usng the four dfferent startng bds (see Appendx 1). Indvduals who are currently enrolled n any health nsurance polcy are not ncluded n the analyss 11. Fnally, a CV survey should nclude questons regardng (soco economc) characterstcs of the respondents as well as questons whch relate to the product n queston. 12 The basc characterstcs of the sample and the defnton and mean values of dfferent varables used n the analyss are presented n Appendx 2.. Methodology Suppose that the ndrect utlty of an ndvdual depends on buyng health 1 nsurance polcy and on ncome y. Let q and q represent the level of utlty assocated wth and wthout health nsurance respectvely, WTP s the amount of money an ndvdual s wllng to pay as a premum, X represents the vector of other factors (such as age, sex, educaton, health status, etc.) that may affect the preferences of ndvduals, π shows the perceved probablty of fallng sck and ε captures other factors that are unobservable to the researcher. Then, the WTP that equates the two ndrect utlty functons wth and wthout health nsurance can be wrtten as: 1 v [( q, y WTP, X, π ) + ε ] = v[( q, y, X, π ) + ε ]. (1) whereε 1 and ε are assumed to be..d wth zero mean. 1 1 Therefore, WTP = ϕ( q, q, y, X, π, ε ) s the maxmum value ndvduals are wllng to forgo to avod medcal expenses assocated wth llness. Therefore, ndvduals wll 1 buy the health nsurance polcy f v[( q, y WTP, X, π ); ε1] v[( q, y, X, π ); ε ] and wll not buy otherwse. Ths shows that the premum level affects the decson of ndvduals to jon the proposed health nsurance. Ths s based on the assumpton that the ndvduals compare ther utlty from the proposed health nsurance scheme wth the current stuaton and decde whether to accept or reject the offered bd levels. Ths mples that the probablty that ndvduals to buy the proposed health nsurance polcy can be expressed as the dfference of ther utlty functons wth and wthout the proposed health nsurance. Then, assume that the true wllngness to pay of ndvdual for the health nsurance p roduct s gven by: WTP * = X ' β + ε (2) 11 Ths s manly because nsured and non-nsured ndvduals have dfferent preferences for potental health nsurance schemes. 12 Portney (1994). 6

7 Where X s a vector of explanatory varables, β s a vector of coeffcents to be estmated, ε s a random error term assumed to be randomly and ndependently dstrbuted wth mean zero and constant varance, σ 2. In dchotomous choce specfcaton, the WTP* value s not drectly observed. However, we observe a range of WTP values from the survey response. As we have shown above, we use double bounded dchotomous choce elctaton method 13. Under ths method, each respondent s gven two bds, the frst bd (P f ) and the second hgher (P h ) or the second lower (P l ) bds, dependng whether the ndvdual responds yes or no to the frst bd. Ths means that we have the followng four possble outcomes for each respondent (see also Fgure 1). D 11 st n = 1 f respondent says yes and yes to the 1 and 2 d hgher bds, respectvely D 1 st n = 1 f respondent says yes and no to the 1 and 2 d hgher bds, respectvely D 1 = 1 f respondent says no and yes to the 1 st and 2 nd lower bds, respectvely D nd = 1 f respondent says no and no to the 1 st and 2 lower bds, respectvely Then, the mean WTP s estmated by maxmzng the followng log lkelhood functon (Cameron and Quggn(1994) and Haab (1998) 14. N h 11 P ' 1 ln { ln 1 X β P ln L = D Φ + D Φ = 1 λ l 1 P ' ' X β P β + ln Φ Φ X D + D λ λ h X ' ' β P X β Φ λ λ l P ' β ln Φ X } λ (3) Where Φ(.) s the standard normal cumulatve dstrbuton functon and β and λ are parameters to be estmated. If the response of ndvduals to the second bd s ndependent of ther response to the frst bd, each response can be estmated ndependently. However, varous studes have shown that the second response s more lkely to be dependent on the frst response (Cameron and Quggn, 1994; An and Ayala, 1996; Asfaw and von Braun, 25). Therefore, n a double bounded dchotomous choce approach, the bvarate normal probablty densty functon s the approprate specfcaton to 13 We use multple-bounded elctaton method nstead of trple or quadruple methods because the addtonal effcency gan from addng thrd or fourth follow up queston s relatvely small and t can ncrease the chance of nducng response effects (Hanemann and Kannnen, 1999; Cooper and Hanemann, 1995 Yoo and Yang, 21). 14 Ths model can be estmated usng standard econometrcs packaged bvarate probt algorthms such as those offered n the LIMDEP software. 7

8 estmate consstent mean values 15. The mean WTP can then be computed based on the method suggested by Hanemann and Kannnen (1999), Krström (199), Nyqu st (1992) and Johannesso n et al. (1993). From equaton (1) we can derve that ε ε < v[( q, y WTP, X, π )] v[( q, y, X, π )]. Defne ε = ε ε and assume p represents the WTP varable, X collect all the varables n equaton (4), and 1 Δ v( z, p) v[( q, y WTP, X, π )] v[( q, y, X, π )]. Then, assume that y=1 f the respondent s wllng to pay p and otherwse. The probablty of agreement s therefore gven by Pr( y = 1) = Ω[ Δv( z, p)] where Ω s the dstrbuton of ε. As shown by Hanemann and Kannnen (1999) and Rchard, et al. (1997), mean WTP condtonal on W s gven by p* MWTP / z = Ω[ Δ v(z, p )]dp whch s equvalent to Ω[ Δv ( z, p)] dp where p* s some value that makes Ω[ Δv ( z, p*)] =. The uncondtonal mean WTP can also be calculated as MWTP = ( Ω[ Δv ( z, p)] dp) fz( s) ds where fz(.) s the jon densty functon of z. z To correct the potental sample selecton bas that arses from estmatng the mean WTP from those ndvduals who are wllng to buy the health nsurance polcy, we use a two stage probt (Heckman) selecton model, and the loglkelhood functon of the sample selecton model can be derved followng the procedure shown by van de Ven and van Praag (1981), Meng and Schmdt (1985), and Yoo and Yang (21). Results Before presentng the economc results, a descrptve analyss on the wllngness to jon and pay s useful. Fgure 1 provdes the summary statstcs of responses to double bounded dchotomous choce questons. Out of 1,75 respondents (wth complete answers) who are unnsured, 1,518 (86.74) are wllng to jon, ndcatng a very hgh wllngness to jon the new low cost health nsurance. An average respondent s wllng to nsure 3.2 ndvduals, whch s around 9 % of the average famly sze of the unnsured. 15 In specal cases where the correlaton coeffcent between the error terms of the frst and the second response equatons s zero, the two responses are ndependent and f the correlaton s 1, the two responses are essentally the same. In both cases the bvarate probt specfcaton s not approprate. 8

9 Fgure 1. Summary Statstcs to double bounded dchotomous choce questons WTJ 175 obs NO (13.26 %) YES (86.74 %) WTP frst bd WTP frst bd NAD 61 NO (57.75%) WTP 2WTP nd lower 2 nd bd NAD lower 42 bd YES (42.25%) WTP 2WTP nd hgher 2 nd bd NAD hgher 82 bd NO (D ) (54.11%) YES (D 1 ) (45.89%) NO (D 1 ) (54.11%) YES (D 11 ) (48.59%) Max WTP NAD 21 Max WTP NAD 66 However, ths hgh rate of wllngness to jon s not translated nto hgh wllngness to pay. As Fgure 1 shows, only 42% of the respondents who are wllng to jon the nsurance scheme are wllng to pay the frst bd, whch on the average s NAD 61 per person per month. In the follow up questons, nearly 49 percent of the respondents who are wllng to pay the frst bd are also wllng to pay the second hgher bd (average NAD 82) and 46 percent of the respondents who are not wllng to pay the frst bd are wllng to pay the second lower bd (NAD 42). Fgure 2 represents the estmated aggregate demand curve based on the response to the frst bd queston. The fgure gves a well behaved downward slopng demand curve. Ths demonstrates that the response of respondents to the CV queston s consstent wth standard economc theory. It also shows that the nsurance scheme s a normal good and the premum (bd value) s a key varable n affectng demand. As the graph clearly shows, as the bd level ncreases, the predcted probablty of sayng yes to the bd level declnes. 9

10 Fgure 2. The aggregate demand curve for health nsurance Percentage of respondents WTP the 1st bd Frst bd/person/month n NAD A probt model s estmated to examne the mpact of varous factors that affect the wllngness of respondent to jon the proposed nsurance scheme. The results are presented n Table 1. The second column shows the coeffcent of varous varables that affect the decson of households to jon the health nsurance plan. The young are more lkely to jon the scheme than the elderly. The ncome varable s statstcally nsgnfcant n explanng the decson of respondents to jon the scheme. However, educaton plays a statstcally sgnfcant role n determnng the decson of respondents to jon the scheme. The margnal coeffcent of the educaton varable shows that a one grade ncrease n the hghest grade completed wll ncrease the probablty of respondents to jon the proposed scheme by.6 percent, ceters parbus. Most of the health status ndcator varables show the absence of adverse selecton problem n jonng the scheme. Most of the coeffcents of the occupaton varables show no statstcally sgnfcant dfferences across dfferent occupatons. 1

11 Table 1. Determnants of wllngness to jon Varables Coeffcents Robust standard error Sex (1 male female) Age.38**.19 Age square.1***. Hghest grade completed.32*.19 Household sze.5.14 Ln ncome (Measured by per capta aggregate.35.6 consumpton) Job category Unemployed (Ref.) Offce workers Laborers Self employed Other groups.262*.146 HH got a loan HH bought n credt General health status Excellent/very good (Ref) Average.325**.134 Poor/bad.518*.284 Number of members sck Weght loss.467***.163 Constant 1.274*.729 Observatons 14 Pseudo R 2.56 Wald ch2(16) (prob > ch 2 ) (.) *** p<.1, ** p<.5, * p<.1 We also examned the mpact of varous covarates on the wllngness of households to pay and for the proposed health nsurance usng the double bounded model. Before the double bounded model s estmated, we examne f there s any selecton bas problem. Estmatng WTP values from respondents who are only wllng to jon the scheme can lead to based and nconsstent results due to two dfferent reasons (Eklöf and Karlsson, 1997). Frst, the response can be based f respondents who are wllng to jon the scheme have dfferent observable characterstcs compared to those who are wllng to jon (Whtehead, 1994; Mattsson and L, 1994). Second, 11

12 even f there s no sgnfcant dfference n observed characterstcs, the WTP can dffer due to unobservable characterstcs of respondents (Heckman, 1979). To address ths problem we estmate a sample selecton model 16. In the frst stage, a unvarate (selecton) model s estmated n the full sample and n the second stage a bvarate model s estmated n the selected sample n the way suggested by Heckman (1979). The Inverse Mlls Rato (IMR) varables n the bvarate probt models are not statstcally sgnfcant (not reported) n both the frst and second equatons ndcatng the absence of sample selecton problem. Therefore, the model s estmated wthout the IMR varable and the results are presented n Table 2. Table 2. WTP for new low cost health nsurance estmaton results for bvarate probt model Varables Wllng to pay the 1 st Wllng to pay the 2 nd bd bd C oeffcent S td. err. C oeffcent Std. err. Frst bd.13***.2 Second bd.13***.1 Sex (male= 1, female =).158**.79.31***.78 Age.41**.19.66***.19 Age square ***.24 Hghest grade completed.12***.17.57***.16 Household sze Ln per capta consumpton.136*** ***.52 Job category Unemployed (Ref.) Offce workers Self employed Laborers Other groups.481*** ***.18 HH got a loan HH bought n credt.174* General health status Excellent/very good (Ref) Average Poor/bad Number of members sck.149* Weght loss Constant athrho.266*** (.64) Rho The log-lkelhood functon of the sample selecton model n the case of double bounded model can be found n Yoo and Yang (21). 12

13 (.59) Lkelhood rato test of rho=: ch2(1) = Prob > ch2 =. Observatons 1236 Wald Ch Prob > ch2. Standard errors n parentheses Sgnfcant at 1%; ** sgnfcant at 5%; *** sgnfcant at 1% Coeffcents and standard errors multpled by 1 for the sake of presentaton. Out of 1,518 respondent who are wllng to jon, we have complete soco economc nformaton only for 1,236 respondents. Source: Computed from the Republc of Namba Okamblmbl Survey (26). Before nterpretng the results of Table 2, let us see the approprateness of our specfcaton. The value of ρ (the correlaton coeffcent between the error terms of the frst and second response equatons) s large and statstcally sgnfcant. As shown n the table, the lkelhood rato test of ρ= s rejected at less than 1 percent level. Ths ndcates that the second decson s endogenous n the system and estmatng ndvdual probt models wll gve neffcent results. Ths supports our bvarate probt specfcaton. The Wald statstcs also reveal that the varables ncluded n the model are jontly statstcally sgnfcant n explanng the WTP decson of respondents. In addton to the bd levels, all varables that affect the WTJ decson of respondents are ncluded n the WTP equatons. The results show that male respondents are more wllng to pay both the frst and second bds compared to female respondents. Young respondents were more wllng to pay both the frst and the second bds as shown by the negatve and postve coeffcents of the age and the age square varables. Educaton postvely affects the probablty of respondents to accept both the frst and the second bds. Famly sze and job category varables do not have sgnfcant mpact on WTP decsons. Most of the health status and health expendture ndcator varables do not affect the decson of respondents to pay the frst and the second bds. Income has a postve, consstent, and statstcally sgnfcant mpact on the wllngness of households to pay both the frst and the second hgher bds. A one percent ncrease n per capta ncome s lkely to ncrease the probablty respondents to pay the frst and the second hgher bd by 5.5 percent. Fnaly, l as expected, the coeffcents of the bd values take the expected negatve sgns and are hghly sgnfcant. One of the basc objectves of CV studes s to provde a summary measure of the WTP of respondents. As we have seen above, n the dscrete choce models, ths task may not be straghtforward snce the amount respondents are wllng to pay s not drectly observed. We estmate the mean and standard devaton of WTP of 13

14 respondents usng the method suggested by Hanemann (1984) and the results are presented n Table Table 3: Mean WTP for Low Cost Health Insurance by ncome quntle WTP/ person / month (n NAD) WTP as percentage of Mean Standard mean per capta Income quntle devaton consumpton Quntle Quntle Quntle Quntle Quntle Total Source: Calculatons and Estmatons based on Republc of Namba Okamblmbl Survey (28). Note: At tme of study exchange rate equaled NAD 7.2 to US$1.. On average, an unnsured ndvdual n the Greater Wndhoek Area of Namba s WTP 47.5 NAD or US$6.6 per capta per month. Ths shows that respondents are WTP on average 2.25% percent of ther ncome for the proposed health nsurance. As can be seen n Table 3, sgnfcant varaton s observed n the mean WTP values across dfferent ncome quntles. The rchest quntle s wllng to pay more than double that of the poorest quntle. As ncome ncrease, the mean WTP value also ncreases. However, despte the mean WTP value of respondents n the poorest quntle s small (NAD 33/person/month), they are wllng to pay more than 11 percent of ther ncome. Respondents n the rchest quntle are wllng to pay only 1.22 percent of ther ncome. Dscusson In developng countres, resources for health care are scarce and a large proporton of those resources are prvate. Donor ad should be desgned n such a way that the prvate resources stay n the health system, rather than beng crowded out. Prvate voluntary health nsurance may provde a mechansm to acheve ths. Potentally, the demand for sutably desgned low cost prvate health nsurance s large, even among the poor. In ths study a double bounded dchotomous choce format s used to examne the wllngness of households to pay for a new low cost health nsurance product. Ths elctaton method gves results whch are consstent wth economc 17 The mean WTP s computed by restrctng the coeffcents of the bprobt model to be equal across the two equatons. 14

15 theory as shown n Fgure 2. The downward slopng graph clearly shows an nverse relatonshp between prce and demand. It also shows that the nsurance scheme s a normal good and the premum (bd value) s a key varable n affectng demand. The fndngs of the study demonstrate that 87 percent of the unnsured respondents are wllng to jon the proposed health nsurance scheme and more than half of them are wllng to pay ether the frst (NAD 61/person/month) or the second (NAD 59/person/month) proposed bds. Those respondents who are not wllng to pay the frst and the second bds are wllng to pay on the average NAD 21 per person per month (half of the second lower bd). Ths mples that an nsurance premum of around NAD 2 per person per month would guarantee coverage of around two are wllng to jon. thrd of the respondents who We also examne factors that affect the wllngness of respondents to jon the proposed nsurance product. More educated and young respondents show more nterest n jonng the scheme. Interestngly, however, ncome and health status ndcator varables do not affect the decson of respondents to jon the scheme. Ths mples that f both the poor and the rch are more lkely to jon the proposed health plan there would be no serous adverse selecton problem. A bvarate probt model s also estmated to examne factors that affect the wllngness of respondents to pay for the proposed health nsurance scheme, and ther mean wllngness to pay. The results ndcate that male and young respondents are more lkely to say yes for both the frst and the second bds compared to female and old respondents. Educaton does not only affect the decson of respondent to jon the proposed health nsurance scheme but also affects the WTP decsons. The margnal coeffcent of the educaton varable shows that, ceters parbus, a one grade ncrease n the hghest level of educaton acheved ncreases the probablty of acceptng the frst bd by 1.5 percent and the second bd by 5.6 percent. Most of the health status ndcator varables such as health expendture, general health status, weght loss, etc., do not affect the decson of respondents to pay nether the frst nor the second bds. These results agan ndcate that the problem of adverse selecton may not be a serous concern n the sampled area. Despte the fact that ncome does not have a statstcally sgnfcant mpact on the WTJ decson of respondents, t has a statstcally sgnfcant mpact on the WTP decsons. To examne the mpact of ncome on the wllngness of respondents to pay the frst and second bds, we plot ts mpact on the probablty of respondents to say yes, yes, yes, no, no, yes, and no, no to the frst, second hgher, and second lower bds. The results are shown n Fgure 3. As the fgures clearly show, relatvely rch respondents are more lkely to say yes to both the frst and the second hgher bds compared to poor respondents. Income also postvely affects the probablty respondents to say yes to frst and no to the second bds. Generally, the probablty of rch respondents to say no to ether the frst or the second bds s relatvely low as shown by the negatve slope of the ftted values n the last two fgures. 15

16 Fgure 3. Predcted mpact of ncome on the WTP decson of respondents Pr(wtp 1st bd=1,wtp 2nd hgher bd=1) Pr(wtp 1st bd=1,wtp 2nd hgher bd=) Pr(D11=1) Pr(D1=1) Ln Income Ftted values Ln Income Ftted values Pr(wtp 1st bd=,wtp 2nd lower bd=1) Pr(wtp 1st bd=,wtp 2nd lower bd=) Pr(D1=1) Pr(D=1) Ln Income Ftted values Ln Income Ftted values Source: Calculatons and Estmatons based on Republc of Namba Okamblmbl Survey (28). Consstent wth our hypothess and descrptve results (see Fgure 2), the coeffcent of the bd values are negatve and statstcally sgnfcant. The margnal coeffcents of the bd values shows that a one NAD per person per month ncrease n the frst bd value s lkely to decrease the probablty of respondents to accept the frst offer by 1.3 percent, ceters parbus. All other thngs remanng constant, a one NAD ncrease n the second bd value also decreases the probablty of respondents to accept the second bd by 1.27 percent. 16

17 We also computed the mean WTP of respondents for the proposed low cost nsurance product. On the average respondents are wllng to pay nearly NAD 48 per person per month (NAD 576 or US$ 8 per person per month) or slghtly more than 2 percent of ther ncome. Respondents n the poorest quntle are wllng to pay NAD 33 per member per month, whch s equvalent to more than 11 percent of ther ncome. Respondents n the rchest quntle are wllng to pay NAD 85 per member per month (1.22 percent of ther ncome). To conclude, these types of low cost health nsurance schemes can be well accepted n developng countres lke Namba and have the potental to protect the poor aganst the negatve fnancal shock of havng to face large health care expendtures. As shown by the relatvely hgh mean WTP values, such schemes may also provde a relable and sustanable ncome flow for health care provders and guarantee relable and easy access to hgh qualty care. In addton to ths, n the absence of prevaled preferences and nsurance market data n developng countres, polcy makers, health care provders, and nsurance companes can beneft greatly from nput based on the type of wllngness to pay evaluatons demonstrated n ths study. 17

18 References An, Y., and Ayala, R A Mxture Model of Wllngness to Pay Dstrbutons. Duke Unversty and Central Bank, Ecuador. Asenso Okyere W.K., I. Ose Akoto, A. Anum and E.N. Appah Wllngness to pay for health nsurance n a developng economy. A plot study of the nformal sector of Ghana usng contngent valuaton. Health Polcy 42(3): Asfaw. A. and J. von Braun. 24.Can Communty health nsurance schemes sheld the poor aganst the downsde health effects of economc reforms? The case of rural Ethopa. Health Polcy 7(1): Asfaw, A. and J. von Braun. 25. Innovatons n Health Care Fnancng: New Evdence on the Prospect of Communty Health Insurance Schemes n the Rural Areas of Ethopa. Internatonal Journal of Health Care Fnance and Economcs 5: Asgary, A., K. Wlls, A. Akbar Taghvae, and M. Rafean. 24. Estmatng rural households wllngness to pay for health nsurance. European Journal of Health Economcs 5: Barnghausen, T., Y. Lu, X. Zhang, R. Sauerborn. 27. Wllngness to pay for socal health nsurance among nformal sector workers n Wuhan, Chna: a contngent valuaton study. BMC Health Servces Research 7:114. Cameron, T.A Combnng Contngent Valuaton and Travel Cost Data for the Valuaton of Nonmarket Goods. Land Economcs. 68(3): Cameron, T.A., and Quggn, J Estmaton Usng Contngent Valuaton Data from a Dchotomous Choce wth Follow Up Questonnare. Journal of Envronmental Economcs and Management. 27: Carson, R.T Contngent Valuaton: Theoretcal Advances and Emprcal Tests snce the NOAA Panel. Amercan Journal of AgrculturalEconomcs. 79(5): Cooper Joseph C. and Hanemann W. M Referendum contngent valuaton: how many bounds are enough? Manuscrpt, Unversty of Calforna, Berkeley. Dror, D.M., R. Radermacher, and R. Koren. 27. Wllngness to pay for health nsurance among rural and poor persons: Feld evdence from seven mcro health nsurance unts n Inda. Health Polcy 82: Eklöf, J. and Karlsson, S Testng and Correctng for Sample Selecton Bas n Dscrete Choce Contngent Valuaton Studes. Workng Paper no Stockholm School of Economcs, Sweden. Gaag, van der. J. 28. Towards a New Paradgm for Health Sector Reform. Amsterdam Insttute for Internatonal Development, mmeo. Amsterdam, The Netherlands. Gertler, P. and Glewwe, P.199. The wllngness to Pay for Educaton n Developng Countres: Evdence from Rural Peru. Journal of Publc Economcs 42(3). Gertler, P. and van der Gaag, J The Wllngness to Pay for Medcal Care: Evdence from Two Developng Countres. The World Bank. The Johns Hopkns Unversty Press. Baltmore and London. 18

19 Greene, W. H Econometrc Analyss (3rd ed.): Prentce Hall Internatonal, Inc. New York. Grffn, C User charges for health care n prncple and practce. Economc Development Insttute Semnar Paper 37. World Bank, Washngton D.C. Grosh, M. and P. Glewwe, eds. 2. Desgnng Household Survey Questonnares for Developng Countres: Lessons from Ffteen Years of the Lvng Standards Measurement Study. IBRD/The World Bank, Washngton, D.C. Haab, T.C Estmaton Usng Contngent Valuaton Data from a Dchotomous Choce wth Follow Up Questonnare: A Comment. Journal of Envronmental Economcs and Management 35: Hanemann, M.W Some Issues n Contnuous and Dscrete Response Contngent Valuaton Studes. Northern Journal of Agrcultural and Resource Economcs. 14 (4): Hanemann, M.W Welfare evaluatons n contngent valuaton experments wth dscrete responses. Amercan Journal of Agrcultural Economcs. 66: Hanemann, M. W., J. Looms and B. Kannnen Statstcal Effcency of Double Bounded Dchotomous Choce Contngent Valuaton. Amercan Journal of Agrcultural Economcs. 73: Hanemann, W.M. and Kannnen, B. (1999). The Statstcal analyss of dscrete response CV data. In Valung Envronmental Preferences, Bateman I.J. and Wlls K.G. (eds.). OUP: Oxford, 1999: Heckman, J Sample Selecton Bas as a Specfcaton Error. Econometrca. 47: Jalan, J. and Ravallon, M Are the poor less well nsured? Evdence on vulnerablty to ncome rsk n rural Chna. Journal of Development Economcs. 58: Johannesson, M., Johansson, P.O., Krstrom, B., and Gerdtham, U.G. (1993). Wllngness to pay for anthypertensve therapy further results. Journal of Health Economcs, 12: Johannesson, M., Jönsson, B and Borgqust L Wllngness to Pay for Anthypertensve Therapy Results of a Swedsh plot Study. Journal of Health Economcs. 1: Kartmann B, F. Andersson F., and M. Johannesson Wllngness to Pay for the Reductons n Angna Pectors Attacks. Medcal Decson Makng 16: Ke X., D.B. Evans, G. Carrn, A.M. Agular Rvera, P. Musgrove, and T. Evans. 27. Protectng Households from Catastrophc Health Spendng. Health Affars 26(4): Krström, B Valung Envronmental Benefts Usng the Contngent Valuaton method: An Econometrc Analyss. Umea Economc Studes No. 219, Unversty of Umea. Mattsson, L. and L, C.Z Sample Nonresponse n a Mal CV Survey: An Emprcal Test of the Effect on Value Inference. Journal of Lesure Scence 26: Meng, C L. and P. Schmdt On the cost of observablty n the bvarate probt model. Internatonal Economc Revew 26(1):71 85 Neumann, P. J. and Johannesson, M The Wllngness to Pay for In Vtro Fertlzaton: A Plot Study Usng Contngent Valuaton. Medcal Care. 32 (7):

20 Nyqust, H Optmal Desgns of Dscrete Response Experments n Contngent Valuaton Studes. The Revew of Economcs and Statstcs. 74(3): Portney P.R., The contngent valuaton debate: why economsts should care. The Journal of Economc Perspectves. 8(4):3 17. Randall, A The NOAA Panel Report: A New Begnnng or the End of an Era? Amercan Journal Agrcultural. Economcs. 79 (5): Rchard M. O'Conor and Blomqust, C. Glenn Measurement of consumer patent preferences usng a hybrd method. Journal of Health Economcs, 16(6): Shaw, R. P. and Grffn C.C Fnancng Health Care n Sub Saharan Afrca through User Fees and Insurance. Washngton, D.C. The World Bank. Ven, van den, W. and van Praag B The demand for deductbles n prvate health nsurance: A probt model wth sample selecton. Journal of Econometrcs 17(2): Whtehead, J. C., Blomqust, G.C., Hoban, T. J. and Clfford, W. B Assessng the Valdty and Relablty of Contngent Values: A Comparson of On Ste Users, Off Ste Users, and Non users. Journal of Envronmental Economcs and Management, 29: Yoo, S H and Yang, H J. (21). Applcaton of Sample Selecton Model to Double Bounded Dchotomous Choce Contngent Valuaton Studes. Envronmental and Resources Economcs. 2:

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