metadata: authors: 0: author: Barrett,Christopher B.
1: author: Ayana,Birhanu T.
2: author: Hirfrfot,Kibrom Tafere
3: author: Lentz,Erin
count: Ethiopia
docna: 0: docna: Insuring well-being ? buyer's remorse
            and peace of mind effects from insurance
docty: Policy Research Working Paper
subtopic: Livestock and Animal Husbandry,Social Development & Poverty,Gender and Development,Inequality,Natural Disasters
teratopic: Agriculture
abstracts: cdata!: This paper estimates the causal effects
            of index insurance coverage on subjective well-being among
            livestock herders in southern Ethiopia. The randomization of
            incentives to purchase index-based livestock insurance and
            three rounds of panel data are exploited to separately
            identify ex ante welfare gains from insurance that reduces
            risk exposure and ex post buyer's remorse effects that
            may arise after the resolution of uncertainty. Insurance
            coverage currently in force generates subjective well-being
            gains that are significantly higher than the buyer's
            remorse effect of an insurance that lapsed without paying
            out. Given the temporal correlation in insurance purchase
            propensity, failure to control for potential buyer's
            remorse effects can bias downward estimates of welfare gains
            from current insurance coverage.
geo_reg_key: 517191
repnme: repnme: Insuring well-being ? buyer's remorse
            and peace of mind effects from insurance
display_title: Insuring well-being buyer's
            remorse and peace of mind effects from insurance
listing_relative_url: /research/2017/11/28647611/insuring-well-being-buyers-remorse-peace-mind-effects-insurance
topicv3: Poverty m1326687 695,Social Insurance m1326950 751,Gender m1327621 615,Drought m1503313 3162,Livestock m1504490 4024
publishtoextweb_dt: 2017-11-27T00:00:00Z
historic_topic: Agriculture
no_of_pages: 49
author_keys: 0: author_key: 458106
1: author_key: 1579492
model_extractions: WPS8256 Policy Research Working Paper 8256

# Insuring Well-being?

Buyer’s Remorse and Peace of Mind Effects from Insurance _Kibrom Tafere Christopher B. Barrett Erin Lentz Birhanu T. Ayana_ Development Research Group Agriculture and Rural Development Team November 2017
Policy Research Working Paper 8256

# **Abstract**

This paper estimates the causal effects of index insurance coverage on subjective well-being among livestock herders in southern Ethiopia. The randomization of incentives to purchase index-based livestock insurance and three rounds of panel data are exploited to separately identify ex ante welfare gains from insurance that reduces risk exposure and ex post buyer’s remorse effects that may arise after the resolution of uncertainty. Insurance coverage currently in force generates subjective well-being gains that are significantly higher than the buyer’s remorse effect of an insurance that lapsed without paying out. Given the temporal correlation in insurance purchase propensity, failure to control for potential buyer’s remorse effects can bias downward estimates of welfare gains from current insurance coverage.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ktafere@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Insuring Well-being? Buyer’s Remorse and Peace of Mind from Insurance**

Kibrom Tafere<sup>*</sup> Christopher B. Barrett Erin Lentz Birhanu T. Ayana _Keywords_ : Index insurance, subjective well-being, vignettes, pastoralists, Ethiopia _JEL codes_ : D60, I31, I38, G22

> * Corresponding author: Kibrom Tafere. Email: ktafere@worldbank.org

Kibrom Tafere: Development Research Group, World Bank; Christopher B. Barrett: Charles H. Dyson School of Applied Economics and Management, and Department of Economics, Cornell University; Erin Lentz: Lyndon B. Johnson School of Public Affairs, University of Texas at Austin; Birhanu T. Ayana: International Livestock Research Institute, Addis Ababa, Ethiopia.

This work was made possible, in part, by support provided by the US Agency for International Development (USAID) Agreement No. LAG-A-00-96-90016-00 through Broadening Access and Strengthening Input Market Systems Collaborative Research Support Program (BASIS AMA CRSP), the Department of Foreign Affairs and Trade through the Australia Development Research Awards Scheme under an award titled “The human and environmental impacts of migratory pastoralism in arid and semi-arid East Africa”, and CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS) and Dryland Systems. All views, interpretations, recommendations, and conclusions expressed in this paper are those of the authors and not necessarily those of the supporting or cooperating institutions. We thank Liz Bageant, Munenobu Ikegami, Andrew Mude, Megan Sheahan, Kazushi Takahashi, IBLI enumerators, and seminar participants at the University of Connecticut, Cornell University, University of Texas at Austin, the 2014 AAEA annual meetings, and NEUDC 2014 for helpful comments on earlier versions.
# **1 Introduction**

Uninsured risk exposure in low-income rural communities is widely believed to cause serious welfare losses and to distort behaviors, potentially even resulting in poverty traps (Rosenzweig & Binswanger, 1993; Morduch, 1994; Carter & Barrett, 2006; Dercon & Christiaensen, 2011; Barrett & Carter, 2013; Santos & Barrett, 2016). However, standard insurance products are routinely unavailable due to moral hazard and adverse selection problems and high transaction costs in infrastructure-poor areas (Besley, 1995). In response to the lack of affordable standard insurance products, there has been a significant push to expand index insurance offerings in the developing world over the past decade.<sup>1</sup> Index insurance attempts to mitigate adverse selection, moral hazard and high transaction cost concerns by writing contracts not on policyholders’ realized losses but, instead, on a lowcost, observable indicator – the ‘index’ – believed to be strongly correlated with actual losses. There is, however, little empirical evidence demonstrating that index insurance generates welfare gains for poor, rural households.<sup>2</sup> Indeed, the low uptake of index insurance products in a range of countries suggests that perhaps many prospective buyers believe index insurance does not deliver welfare gains (Gin ́e, Townsend, & Vickery, 2008; Binswanger-Mkhize, 2012; Cole et al., 2013).<sup>3</sup> Index insurance uptake may even cause welfare losses for buyers for at least two reasons. First, high commercial loadings by insurers can drive premium rates above actuarially fair levels. Second, when the index does not closely track policyholders’ actual losses, the imperfect correlation creates “basis risk” that can result in uninsured losses despite the purchase of insurance. This can lead to uninsured catastrophic loss despite a premium payment; as a result, index insurance will not stochastically dominate remaining uninsured (Jensen et al., 2016).

1See Chantarat, Mude, Barrett, and Carter (2013) for an extensive discussion of these issues as they apply to a setting very similar to the one we study, and Miranda and Farrin (2012); Smith (2016) and Jensen and Barrett (2017) for broader reviews.

> 2Janzen and Carter (2013); Karlan, Osei, Osei-Akoto, and Udry (2014) and Jensen, Barrett, and Mude (2016, 2017) are notable recent exceptions.

> 3Gin ́e et al. (2008) report that take-up rate of a rainfall insurance product in Andhra Pradesh, India was very low, at just 4.6 percent. They argue this might reflect the short history of the product. Similarly, Cole et al. (2013) find that the take-up rate of livestock insurance among the untreated general population in Andhra Pradesh and Gujarat, India, is close to zero. Binswanger-Mkhize (2012) argues that there is low demand for index insurance because better-off farmers have already self-insured through diversification of their portfolios and informal social networks, while the poor face liquidity constraints that limit their participation. Karlan et al. (2014), on the contrary, find that at an actuarially fair price, almost half of the farmers in their sample from northern Ghana demand index insurance and purchase coverage for more than 60 percent of their acreage.

2
Estimating the welfare effects of insurance coverage is complicated because insurance produces two potentially opposite effects on the welfare of buyers. Holding insurance before the resolution of uncertainty generates _ex ante_ well-being effects. Insurance may increase _ex ante_ welfare for risk averse agents prior to the realization of stochastic events that may otherwise impose substantial losses. These _ex ante_ well-being effects of insurance may differ from, and be partly offset by, the _ex post_ well-being effects of lapsed insurance that did not pay any indemnity. _Ex post_ effects arise after the resolution of uncertainty. The same insurance that is _ex ante_ welfare improving may prove _ex post_ welfare reducing, in a later period, once the risk has passed and a purchaser realizes with perfect hindsight that she could have foregone the premium payment without consequence. In this case, the buyer has “lost” her premium and would have been unambiguously better off financially had she not bought insurance coverage after all. If insurance purchase is positively correlated over time, this then raises the possibility that buyer’s remorse can confound valuation of insurance coverage, biasing downwards estimates of the value of current insurance coverage following periods without indemnity payments, when insurance purchase lost the insuree money.

In this paper we take a novel approach to estimating the welfare impact of insurance on a poor, rural population, exploring whether index insurance coverage improves subjective well-being (SWB) and disentangling the potentially distinct effects of current and lapsed insurance coverage. The analysis of gains from insurance coverage has typically relied on either relatively weak tests of stochastic dominance or strong assumptions about utility functions (Williams, 1988; Feldman & Dowd, 1991; Halek & Eisenhauer, 2001; de Janvry, Dequiedt, & Sadoulet, 2014). Recent innovations in SWB measurement, however, permit relaxation of many of the strong assumptions on which such analyses rely. Further, measures of SWB often yield deeper insights beyond the traditional income and expenditure based wellbeing measures (Ravallion, 2012; Krueger & Stone, 2014). Indeed, conventional measures of well-being may underestimate the true value of a program. A program can have significant effects on SWB even if it does not generate observable material or physical impacts (Devoto, Duflo, Dupas, Parient ́e, & Pons, 2012; Finkelstein et al., 2012; Ludwig et al., 2013). As a result, SWB measures have become increasingly popular in welfare assessment (Frey & Stutzer, 2010; Clark, 2003; Fafchamps & Shilpi, 2008; Graham, 2012; Ravallion, Himelein, & Beegle, 2013; Kaminski, 2014; Krueger & Stone, 2014).

Several features of our data enable us to estimate the _ex ante_ and _ex post_ SWB effects of index insurance. First, the project’s experimental design enables us to use an instrumental 3
variables method to overcome potential selection issues in index-based livestock insurance (IBLI) uptake. We exploit the randomization of incentives to purchase IBLI, newly introduced in southern Ethiopia by a commercial underwriter in August 2012. The novelty of the product obviates the potential confounding of past, unobserved experience with IBLI on buyers’ reported SWB. Second, three-round panel data enables us to control for time-invariant household unobservable characteristics that might affect both SWB and IBLI uptake. Third, no indemnity payouts occurred during this period.<sup>4</sup> Without indemnity payments, we exploit the considerable intertemporal variation in households’ IBLI uptake to isolate the causal effect of IBLI on SWB. We use coverage active during a survey round to capture _ex ante_ welfare effects and coverage that had lapsed by the time of the survey to capture _ex post_ impacts. These data offer an unprecedented opportunity to estimate the SWB effects of insurance that arise purely from _ex ante_ risk reduction and to disentangle them from _ex post_ buyer’s remorse effects.

We find that current IBLI coverage improves SWB. Lapsed IBLI contracts that did not pay indemnities have a negative effect on SWB, consistent with the buyer’s remorse hypothesis. Although both effects are statistically significant, the welfare gains of current coverage significantly exceed the adverse buyer’s remorse effects. Our results are robust to a range of alternative estimators, corrections to address concerns on the measurement of SWB, variable definitions, model specifications and variations in the relevant panel sub-samples analyzed. Further, we show that the estimated SWB gains from insurance are downwardly biased if one omits control for lapsed insurance coverage that generates buyer’s remorse.

The implication is that, despite premiums set above actuarially fair rates, IBLI improves buyers’ SWB even over a period when pastoralists in southern Ethiopia lose money on the policy. The _ex ante_ peace of mind effect dominates any _ex post_ buyer’s remorse. In other words, even an insurance policy that does not pay out still improves people’s perceptions of their well-being.

The remainder of the paper is organized as follows. The next section presents the study setting and discusses IBLI and its contract design. Section 3 discusses the sampling and experimental design. Section 4 reports summary statistics of the data. Section 5 introduces our estimation strategy. Section 6 details our vignette correction strategy, following best current practice in the SWB literature. Section 7 reports our main results. Section 8 presents

> 4The first IBLI indemnity payments – on 509 contracts yielding total payments of ETB 526,000 (approximately $ 26,225) – occurred in October-November 2014, after the period covered by our data.

4
a range of robustness checks. Section 9 concludes.

# **2 Study Setting and Research Design**

The study area is Borana zone of Oromia region in southern Ethiopia. It is a vast pastoralist land mass consisting mainly of arid and semi-arid agro-ecological zones with a bimodal rainfall pattern and four distinct seasons: long rainy (March-May), long dry (June-September), short rainy (October-November), and short dry (December-February) seasons. Mobile pastoralism is the primary source of income and sustenance, with limited cereals cultivation for own consumption. Cyclical movement of livestock in search of forage and water characterizes the livestock production system in the zone (Coppock, 1994; Berhanu, 2011).

There are widespread concerns that more frequent droughts, perhaps associated with climate change, are making pastoralism more tenuous (Barrett and Santos 2014). Catastrophic droughts in the 1980s and 1990s resulted in herd losses of over 35% (Desta & Coppock, 2002; Lybbert, Barrett, Desta, & Coppock, 2004). These catastrophic droughts, which are covariate within a community, also put pressure on informal social insurance mechanisms, such as _iqub_ (rotating savings and credit associations (ROSCAs)) membership. Informal community networks facing high and widespread herd losses can no longer sufficiently mitigate the effects of shocks and are in decline (Lybbert et al., 2004; Santos & Barrett, 2011). Formal insurance might effectively transfer drought risk out of the pastoral system to underwriters, thereby cushioning pastoralists against catastrophic herd loss shocks. However, conventional indemnity insurance can be prohibitively costly to establish and sustain in this environment. Droughts that trigger payouts could bankrupt under-diversified insurers. Moral hazard and adverse selection problems and associated high monitoring costs, as well as high transaction costs in infrastructure-poor areas compound the challenges of delivering standard insurance products (Besley, 1995).

IBLI was developed for precisely such an environment. Originally designed for and successfully piloted in the neighboring region of northern Kenya beginning in January 2010, IBLI makes indemnity payouts based on an observable, exogenous index of rangeland conditions, as reflected in Normalized Difference Vegetation Index (NDVI) measures generated by remote sensors on satellite platforms. An IBLI policy provides indemnity payouts when pasture vegetation falls below a contractually stipulated threshold level that reflects the on5
set of drought conditions that typically lead to excess livestock mortality (Chantarat et al., 2013).

IBLI was piloted in 2012 in eight _woredas_<sup>5</sup> of Borana zone located directly across the border from the Kenyan region where IBLI first piloted. The index for IBLI Borana is calculated at the _woreda_ level as a cumulative deviation of periodic NDVI readings for each IBLI sales period.<sup>6</sup> Accordingly, the IBLI premium rate differs across _woredas_ and by livestock species but is the same for all buyers insuring the same livestock species within a _woreda_ , irrespective of individual loss experience. The _woreda_ specific premium rates are applied to the value of herd that an IBLI buyer chooses to insure to establish the total amount that must paid for IBLI coverage.

IBLI contracts are sold in two sales periods prior to the start of the short and long rainy seasons. The first IBLI contracts were sold in August-September 2012 (sales period 1). Contract sales were repeated in January-February 2013 (sales period 2), August-September 2013 (sales period 3) and January-February 2014 (sales period 4). The duration of contract coverage is 12 months. A contract sold in January 2014 covers March 2014-February 2015, while one sold in August 2013 covers October 2013-September 2014. Households can augment their coverage by acquiring new contracts in subsequent sales periods. Index readings for each sales period are announced and indemnity payments made to policyholders, if the contractually stipulated strike rate is triggered, at the end the season (See Figure 1).

# Figure 1 here

As with all index insurance products, the substantial basis risk associated with IBLI could leave livestock loss uninsured due to imperfect correlation between the drought predicted by the index and losses experienced at the household level (Jensen et al., 2016). Animal losses due to covariate shocks that are not covered by IBLI, such as animal disease unrelated to rangeland conditions, as well as idiosyncratic shocks such as wildlife predation or injury, are common.

Nonetheless, recent impact evaluations of the original IBLI pilot in northern Kenya find income and productivity gains, on average, for IBLI policyholders (Jensen et al., 2016,

> 5 _Woreda_ is a third-level administrative division in Ethiopia, below region and zone. The eight woredas of Borana zone covered in our sample are Arero, Dhas, Dillo, Dire, Miyo, Moyale, Teltele, and Yabello.

> 6For a more detailed discussion of the construction of the IBLI Borana index, see ILRI-IBLI (2013).

6
2017). But in that setting, significant indemnity payouts had occurred in the second year in which contracts were sold following the catastrophic 2011 regional drought, so average indemnity payouts substantially exceeded average premium expenses. Those results could, therefore, be purely the result of stochastic ordering of loss events and associated indemnity payments. Those indemnity payouts had sizable behavioral and welfare effects (Janzen & Carter, 2013). Because there were no indemnity payments in southern Ethiopia, our study isolates the welfare effects of insurance that arise purely from reduced _ex ante_ risk exposure, that is, just the peace of mind effects that arise from buyers’ risk aversion, abstracted from the complication of indemnity payments. The Ethiopia IBLI pilot and associated data enable us to get at these important issues in a novel way that sheds considerable light more generally on the value of insurance coverage.

# **3 Data and Descriptive Statistics**

## **3.1 Data and Study Design**

A baseline survey (R1) was designed and fielded in February-March 2012 before IBLI was developed or announced. Data on a broad range of household characteristics, livestock and other assets, livelihood activities, consumption, social networks, expectations and subjective well-being were collected. A year later, following sales period 2, a follow-up survey round (R2) of the original sample households was fielded in March-April 2013. Following sales period 4, a third round (R3) of survey data was then conducted in March 2014 from the same respondents as the first two survey rounds. We therefore have pre-experiment baseline data (R1), followed by two survey rounds (R2 and R3) with the same respondents. In R2, IBLI contracts purchased in sales periods 1 and 2 were in force. In R3 contracts from sales period 1 and 2 had lapsed but contracts purchased in sales periods 3 and 4 were in force (Figure 2).

### Figure 2 here

The sampling was clustered at the _reera_ level.<sup>7</sup> _Reeras_ were purposively selected based on geographic distribution, variation in market access, and agro-ecological variation across the

> 7 _Reera_ is the fourth level administrative division in Oromia region below zone, _woreda_ , and _kebele_ .

7
eight _woredas_ of Borana zone in our sample. Inaccessible _reeras_ were excluded for logistical reasons. In each _reera_ , households were grouped into three livestock holding classes (high, medium and low), measured in tropical livestock units (TLU).<sup>8</sup> Fifteen percent of households were randomly selected in each _reera_ such that a minimum of 25 households were selected with a balanced representation of the three TLU classes (terciles). In the event 15 percent of households in a _reera_ yields less than 25 households, neighboring _reeras_ were combined to form a bigger study site, resulting in a total of 17 study sites (ILRI, 2014).

The baseline sample included 515 households. In R2, 476 of the original (baseline) households were re-interviewed. Households that had dropped out were replaced by households from the same study site and TLU class. If replacements could not be found in the same TLU class, households in the adjacent TLU class were picked. Thus, 32 new replacement households were surveyed from the original population lists for a total of 508 households in R2. In R3, 500 R2 households and 14 replacement households were surveyed. In selecting replacements in R3 priority was given to original households (those sampled in R1 but missed in R2). Of the 14 R3 replacements, 10 were original households and 4 were new households.

Seven households had missing SWB measures or key independent variables and were dropped from the sample. The final estimation sample includes 550 unique households and 1,530 observations (515 in R1, 504 in R2 and 511 in R3), of which 465 households were surveyed in all three rounds, 50 households were surveyed in two rounds (8 in R1 and R2, 12 in R1 and R3, and 30 in R2 and R3), and 35 households were surveyed only once. A detailed treatment of potential attrition bias in the data and relevant corrections is presented in the Appendix.

To encourage IBLI uptake, various combinations of premium discount coupons and information interventions through audio tapes of a poem or comic books were randomly implemented in each of IBLI sales period (Table 1). Information was delivered via caricature representation of IBLI in comic books or audio tapes of a poem about IBLI recited in the local language, Oromifa, to sub-samples of respondents in sales period 1 and 2.<sup>9</sup> The encouragement design in sales periods 3 and 4 did not include information intervention. All four sales

> 8TLU is a measure used to aggregate livestock across species in relation to a common average metabolic weight such that 1 TLU = 1 cattle = 0.7 camels = 10 goats or sheep, collectively called ‘shoats’.

> 9In the comic book information treatment, a randomly selected sub-sample of respondents was provided with a caricature representation of the IBLI product prepared by the underwriter, Oromia Insurance Company (OIC). The contents of the material were first read to the sample households, then they were encouraged to look/read through it as many times as they wished. In the audio tape information treatment, development agents (DAs) were asked to play a tape that explains IBLI in Oromifa to a randomly selected sub-sample of respondents (for more details on the information interventions see ILRI (2014).

8
periods included randomized distribution of premium discount coupons.

Prior to each sales period, all communities received a basic briefing that described the IBLI product. In each study site, 80 percent of respondents were randomly selected to receive discount coupons that would allow them to purchase IBLI at a discounted price for up to 15 TLUs. Discount coupon recipients were evenly distributed across discount levels of 10, 20, 30, 40, 50, 60, 70, and 80 percent. The remaining 20 percent of respondents did not receive discount coupons.<sup>10</sup> The two information treatments – comic book and audio tape – were randomized in six sites each (in 12 of the 17 study sites, overall), with no overlap in assignment. Within the sites selected for information treatment, about 50 percent of respondents were randomly selected for treatment. In total, 20 percent of respondents received information treatment. The randomized assignment of respondents into information treatments and discount coupons with varying discount levels was implemented independently for each sales period. By creating exogenous variation in IBLI uptake and in the effective premium faced by prospective buyers, IBLI’s randomized encouragement design allows a rigorous analysis of the causal impacts of IBLI on SWB.

All sample households in our study sites had opportunities to insure against drought-related livestock loss. Yet, only 22 percent and 21 percent of households surveyed in R2 and R3, respectively, reported buying IBLI coverage. In both R2 and R3, IBLI purchases were lower in the January-February sales period than in the August-September sales period. Of the 504 households surveyed in R2, 130 purchased IBLI in sales period 1 and 94 in sales period 2. Similarly, of the 514 households surveyed in R3, 150 purchased IBLI in sales period 3, but only 62 in sales period 4. This difference might arise due to seasonality in household liquidity.<sup>11</sup> Or this may simply reflect the seasonality arising due to the initial launch of IBLI in August-September 2012, combined with the contracts’ 12 month duration.

Because IBLI contracts cover a full year but policies are sold in two sales periods each year, households can augment their coverage or allow contracts to lapse. Of the 130 IBLI buyers in sales period 1, 23 buyers augmented coverage further by buying additional policies in sales period 2, 53 allowed their policy to lapse after a year, and 77 extended their coverage in sales

> 10As part of a separate project, however, 10 respondents received IBLI coverage for up to 15 TLUs free of charge (100 percent discount) in each sales period.

> 11Extended dry conditions often lead to stress sales and collapse of livestock markets, which in turn limits ability to raise the necessary liquidity to insure against shocks (Barrett, Chabari, Bailey, Little, & Coppock, 2003; Lybbert et al., 2004).

9
period 3. The remaining 71 buyers in sales period 2 were first time buyers. Likewise, 73 of the 94 IBLI buyers in sales period 2 allowed their contracts to lapse and 21 renewed their contract in sales period 4. Among the 150 households who bought IBLI policies in sales period 3, 33 households bought additional coverage in sales period 4. The considerable intertemporal variation in households’ IBLI coverage, combined with the experimental design behind the IBLI pilot, enable us to disentangle the causal effects of current and lapsed insurance policies on respondents’ SWB.

# **4 Summary statistics**

Table 1 reports baseline treatment-control covariate balance tests on assignment to premium discount coupon in sales periods 1 and 2. There is very little pre-treatment difference in subjective well-being, wealth, expected livestock loss, various household characteristics, and group membership between those who purchased insurance and those who did not, confirming that the randomization was successful.<sup>12</sup> Detailed variable definitions are provided in Appendix Table A2. To complement these results, we also conducted formal joint orthogonality tests and found that selection into treatment is uncorrelated with observable household characteristics (Appendix Table A3). Joint significance tests from pooled OLS (linear probability model) regression of treatment dummies (discount coupon, audio tape and comic book) for the August-September and January-February sales periods on household income, livestock and non-livestock assets, expectations of future rangeland conditions, and various individual and household characteristics suggest that treatments are randomly assigned. We cannot reject the joint null of zero partial correlation of all covariates in these regressions. Apart from the discount coupon regression in the August-September sales period, pre-treatment differences in covariates between treatment and control households are statistically insignificant in almost all cases.

## Table 1 here

Table 2 reports summary statistics on key dependent and independent variables by insurance status.<sup>13</sup> The top four rows show that households who had IBLI coverage in R2 and/or R3

> 12Covariate balance tests on comic book and poet audio tape information treatments and discount coupon receipts in sales periods 3 and 4 also show that treatment assignment was indeed random. Findings are available upon request.

> 13Table 2 presents the averages of the variables in R2 and R3, during which IBLI was available for purchase.

10
– – report higher SWB by any of the four different measures discussed in the next section compared to their counterparts who have had no IBLI coverage in any of the survey rounds. Rows 5-9 show that IBLI purchase is strongly positively correlated with the discount coupon and information treatments. In each sales period, about 93 percent of IBLI contract holders had received discount coupons.<sup>14</sup> Similarly, households who received information treatments (comic book or audio tape) were more likely to buy IBLI. As expected, higher discount rates are strongly correlated with IBLI uptake. These simple descriptive statistics suggest that the random, exogenous assignment of discount coupons and information treatments are suitable predictors of IBLI adoption.

## Table 2 here

Insured and uninsured households are not distinguishable by observable characteristics, apart from number of TLU owned, which is weakly statistically significant. The value of nonlivestock assets, annual income, expected livestock loss, gender and age of household head, household size and composition, and membership in _iqub_ groups vary insignificantly between those that purchased insurance and those who did not. These findings on observable characteristics do not rule out potential differences based on unobservable characteristics. However, so long as such unobservable differences are time invariant, we can control for them using a fixed effects estimator. Concerns that time varying characteristics may determine IBLI adoption nonetheless remain. We exploit the random assignment of discount coupon and information treatments, each strongly correlated with IBLI uptake, to address these concerns.

# **5 Estimation strategy**

A key challenge in evaluating policy interventions where respondents can voluntarily “optin” is that selection into the program may not be random. Rather, participation could be systematically correlated with respondents observable and unobservable characteristics. Peoples’ SWB is likely correlated with their subjective assessment of risk, their planning horizons, and other unobserved factors that influence insurance uptake. The experimental

> 14Since survey rounds 2 and 3 were preceded by two sales periods each, a household who purchased IBLI in sales period 2 but had received discount coupon in sales period 1 is reported to have received discount coupon for the survey round, hence the slightly higher figures in Table 2.

11
design features of IBLI’s impact evaluation, including randomized exposure to various information treatments and randomized distribution of premium discount coupons, allow us to address the selection bias associated with insurance uptake choices. We first estimate selection into IBLI using randomized encouragement treatments as instruments. We then estimate the effect of instrumented IBLI on SWB. This approach allows us to derive unbiased and consistent causal estimates of IBLI’s impact on SWB.

IBLI uptake by household _i_ in village _v_ , sales period _s_ , and survey round _t_ is estimated using the linear probability model (LPM)<sup>15</sup> as: The randomly assigned treatments include dummy variables for receiving a randomly assigned premium discount coupon ( _D_ ) in the first sales period (August-September 2012), the second sales period (January-February 2013), or both; dummy variables for receiving randomly assigned extension treatments in either audio tape ( _A_ ) or comic book ( _C_ ) form in the first, the second or both sales periods, and a _woreda_ specific continuous measure of the randomly discounted IBLI premium rate ( _P_ ) in the first and second sales periods. These are all randomly assigned to households and should have no direct effect on SWB, only an indirect effect through their impact on inducing IBLI uptake. The lone possible exception is _P_ , since price variation has a (very modest) real income effect conditional on someone purchasing IBLI and thus could plausibly have some direct effect on SWB. A series of covariates, _X_ , that may influence the uptake of IBLI are included as controls, including household herd size and income, expectation of livestock death, gender, age and educational attainment of household head and household composition. Household fixed effects (FE), _τ_ , which control for, among other things, time invariant optimism or pessimism of individual respondents and survey round fixed effects, _κ_ , are also included.

We use the randomized coupon distribution and information treatments to instrument for the purchase of IBLI coverage in the first stage estimation. When applied to R2 data, equation (1) predicts current uptake, _IBLI_<sup>�</sup> _iv_ 2, based on purchases in sales periods 1 and 2. There were no lapsed contracts in R2. When applied to R3 data, it predicts current uptake, � _IBLI iv_ 3, based on purchases in sales periods 3 and 4. We use the _IBLI_<sup>�</sup> _iv_ 2 predicted value

> 15To avoid the “forbidden regression” problem associated with non-linear models such as logit or probit, we use an LPM to predict an endogenous dichotomous variable in the first stage of an instrumental variables (IV) regression (Angrist & Pischke, 2008; Wooldridge, 2010).

12
to capture lapsed contracts in R3.

In the second stage of our estimation, the predicted IBLI coverage is used to estimate the causal effect of IBLI on SWB in the second stage of our estimation. SWB includes ordinal responses to the question “on which step do you place your current economic condition,” ranging from 1 (very bad) to 5 (very good). The construction of our SWB measure and related robustness checks are discussed in more detail below. The second stage ordered logit regression includes predicted IBLI uptake, number of TLUs owned (TLUO), predicted lapsed IBLI uptake the probability of having acquired an IBLI contract that has lapsed (IBLIL), a series of controls _X_ , household fixed effects _χ_ , and survey round fixed effect _λ_ .

The coefficient estimate on predicted IBLI uptake, _β_<sup>�</sup> measures the effect of IBLI coverage – on the extensive margin the ordered log-odds estimate of possessing IBLI contract(s) on SWB. We expect that effect to be positive, reflecting the welfare gains from insurance in a � risky setting. The coefficient estimate on _IBLILivt_ , _σ_ measures the effect on SWB of an IBLI contract that was in force in R2 but had lapsed in R3. Since contracts in force are controlled for, this coefficient estimate isolates the _ex post_ SWB effect of insurance that did not pay, i.e., buyer’s remorse, and it is expected to be negative (ˆ _σ <_ 0).

A finding that _β_<sup>ˆ</sup> _> |σ_ ˆ _|_ indicates that even if insurance does not pay out, in expectation, the positive peace of mind effect exceeds the negative buyer’s remorse effect, and hence IBLI – improves expected welfare. If policy purchases and therefore current and lapsed policies – are correlated over time, failure to include lapsed contracts in equation (2) would lead to omitted relevant variable bias of the _β_ estimate, presumably downwards due to negative buyer’s remorse effects.

To capture the intensive margin of IBLI coverage, i.e., the marginal effect of increasing the volume of IBLI uptake by a unit, we re-estimate equation (1) replacing the IBLI uptake dummy variable with volume of TLUs insured (TLUI). The first stage equation for the negative censored continuous variable TLUI is estimated using Tobit as: We construct predicted values for current and lapsed IBLI coverage using the same approach 13
as we did for the discrete uptake variable earlier. The second stage ordered logit regression then includes predicted TLU insured and predicted lapsed TLU insured instead of predicted IBLI uptake to identify the causal effect of buying an additional TLU of IBLI coverage on SWB.

The second stage regression equations of both IBLI uptake (equation 2) and quantity of TLU insured (equation 4) include generated regressors. Conventional standard errors of the estimated coefficients would be biased downwards. To account for the lower variation in the predicted uptake and volume of TLUs insured, we estimate the standard errors using a bootstrapping method where both the first and second stage are included for every bootstrap sample. Further, to account for spatial correlation of observations estimated standard errors are clustered at the village ( _reera_ ) level in all regressions.

There are at least two possible mechanisms through which IBLI coverage could influence SWB. The first effect is the gross non-monetary benefits or costs associated with coverage, represented by the coefficient estimate on the instrumented IBLI, _β_<sup>ˆ</sup> , net of instrumented lapsed IBLI, ˆ _σ_ , ( _β_<sup>ˆ</sup> +ˆ _σ_ ), or the coefficient estimate on instrumented TLU insured, _β_<sup>ˆ ̃</sup> , multiplied by the number of TLUs insured net of the coefficient on instrumented lapsed TLU insured, _σ_ ˆ ̃, multiplied by the number of lapsed TLUs insured, ( _β_ ̃<sup>ˆ</sup> _×_ � _TLUI t_ + _σ_ ̃<sup>ˆ</sup> _× TLUL_<sup>�</sup> _t_ ). Purchasing insurance may reduce stress about possible adverse outcomes, which could lead to higher levels of SWB ( _β_<sup>ˆ</sup> _>_ 0), while greater coverage may lead to higher SWB ( _β_<sup>ˆ ̃</sup> _>_ 0). Conversely, if the basis risk on the product is high such that IBLI uptake is more like a lottery ticket than a conventional indemnity insurance policy, IBLI uptake could increase stress and reduce SWB ( _β_<sup>ˆ</sup> _<_ 0). For the same reason, greater IBLI coverage may cause higher stress and lower SWB ( _β_<sup>ˆ ̃</sup> _<_ 0).

The second influence on SWB arises from the net monetary benefit or cost of IBLI coverage on SWB. If net income or wealth influences SWB, as many studies suggest (Frey & Stutzer, 2010; Graham, 2012), then IBLI will also affect SWB through the premium amount paid for IBLI, which reduces net income or wealth, and any indemnity payment received in the event that the IBLI policy pays out, which increases net income or wealth, _ceteris paribus_ . This effect is captured by the coefficient estimate on the number of TLUs owned, _θ_<sup>ˆ ̃</sup> , multiplied by the net flow of funds associated with the period-specific net indemnity payments (indemnity receipts 14
minus premium payments) associated with the predicted IBLI uptake volume, converted into TLU units at prevailing livestock prices, NI.<sup>16</sup> We therefore estimate the aggregate effect of IBLI on SWB as: The point estimate _β_<sup>ˆ ̃</sup> in equation (5) reflects the SWB benefit of a unit of free IBLI with ̃ no indemnity payment. Likewise, the coefficient estimate _σ_<sup>ˆ</sup> measures the SWB loss due to a unit of free IBLI that has expired without payout. Note, however, that _β_<sup>ˆ ̃</sup> , _σ_ ̃<sup>ˆ</sup> and _θ_<sup>ˆ ̃</sup> measure effects on SWB in log-odds scales while SWB is measured in ordinal Likert scale. It is necessary to harmonize the units in which these coefficients and SWB are measured before one can calculate the overall effect of IBLI on SWB. We use the fact that the logistic and Normal distributions are similar, except at the tails of the distribution, to convert the coefficients from log-odds units to Normal equivalent deviates. The effects measured in logodds and their corresponding standard errors can be converted to approximate effects in Normal equivalent deviates by dividing by the standard deviation of the logistic distribution _s_ = _π/√_ 3 (Hasselblad & Hedges, 1995; Chinn, 2000).

Given that during R2 and R3 there were no indemnity payments but respondents paid for IBLI, our estimates provide a lower bound, reflecting the SWB associated with insurance coverage in the absence of any payout, i.e., a period in which insurance represents an unambiguous financial loss. A finding that ∆ _SWB_<sup>�</sup> _ivt >_ 0 _|NIivt <_ 0 would therefore represent a strong finding with respect to the welfare effects of index insurance in this setting.<sup>17</sup>

# **6 SWB and vignette correction**

Subjective measures of welfare are becoming increasingly popular but pose methodological challenges (Krueger & Schkade, 2008; Ravallion, 2012). Respondents may have different reference points when answering a subjective question, making interpersonal comparisons

> 16 _NI_ =<sup>Indemnity</sup><sup><u>per</u></sup> Price<sup>TLU</sup> per<sup>_−_Premium</sup> TLU<sup><u>per</u>TLU</sup> _× TLUI_<sup>�</sup> is the TLU equivalent wealth gained or lost due to IBLI purchase.

> 17Estimates for ∆ _SWB_ � are obtained by evaluating equation (5) at the average TLUs insured and NI. The price per TLU is obtained by weighting livestock prices from Haro Bake livestock market (the largest livestock market in Borana zone) with the TLU conversion units of each species (Table A1).

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problematic. To address any latent heterogeneity problems that might hinder interpersonal comparisons of subjective welfare, we adjust the subjective measures of well-being using hypothetical vignettes that provide an explicitly standardized reference point for all respondents’ comparisons in order to bring objective and subjective assessments into alignment (van Soest, Delaney, Harmon, Kapteyn, & Smith, 2011; Krueger & Stone, 2014).<sup>18</sup> Interpersonal comparisons using SWB data can be challenging due to potential unobserved heterogeneity in respondents’ reference points, which may depend on socio-economic conditions, and other observable and unobservable characteristics. Such latent heterogeneity in subjective well-being measures may render interpersonal comparisons meaningless and invalidate inference from subjective welfare regressions (King, Murray, Salomon, & Tandon, 2004; van Soest et al., 2011; Beegle, Himelein, & Ravallion, 2012; Ravallion et al., 2013).

King et al. (2004), King and Wand (2007), van Soest et al. (2011) and Beegle et al. (2012) suggest an approach for correcting latent heterogeneity problems that involves measuring the interpersonal incomparability of responses itself. Respondents are asked to assess their own circumstances relative to a set of hypothetical individuals described by short vignettes on the same scale. Responses to the hypothetical vignettes are then used to construct an interpersonally comparable welfare measure as respondents’ reference points have been exogenously standardized. The validity of this approach relies on two key assumptions: response consistency, and vignette equivalence. Response consistency requires that each respondent use response categories for a particular concept in the same way when selfassessing as when assessing hypothetical individuals. Vignette equivalence is the assumption that each respondent perceives the level of the variable represented by a particular vignette on the same unidimensional scale. That is, the variable being measured by vignettes should have a consistent meaning among respondents (King et al., 2004).

Following King et al. (2004), the reported SWB measures are corrected using a simple nonparametric approach. For notational ease, we momentarily suppress the village and time dimensions of the data. Suppose _SWBi_ is the categorical self-assessment for respondent _i_ ( _i_ = 1 _, ..., n_ ), and _Vij_ is the categorical survey response for respondent _i_ on vignette _j_ ( _j_ = 1 _, ..., J_ ). For respondents with identical vignette ordering (i.e. _Vi,j−_ 1 _< Vij_ ) the vignette adjusted measure of subjective well-being is given as:<sup>19</sup>

> 18As discussed further below, we test the robustness of our core results by re-estimating our model for direct (unadjusted) SWB responses and for responses to a similar SWB question that asks people about their well-being relative to other Borana pastoralists. The core findings prove stable.

> 19In our data, rescaling of self-assessments relative to vignettes does not generate vector responses, which

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The hypothetical vignettes used in this study involve households that fall in one of three well-being rungs: low, middle and high, which were constructed in consultation with local field researchers knowledgeable about the local socio-economic conditions in the study area. The lowest (poor), middle, and highest (rich) rungs were represented by a family that “ _has_ “ _no livestock and does not eat meat except on special occasions_ ,” a family that _has a dozen of shoats_ [goat and sheep], _but no camel or cattle and can eat meat only once a month_ ,” and a family that“ _has a lot of shoats and several camels and cattle and can eat meat whenever they choose_ ,” respectively.

The cross tabulation of SWB measures and vignette corrected SWB measures in Appendix Table A4 shows that vignette corrected SWB measures largely mirror SWB, particularly at the lower end of the scale. For example, in panel (a), out of the 120 observations with SWB score of one (very bad), 27 are rescaled to one and 93 to two on the vignette adjusted SWB. Similarly, of the 88 observations with SWB scores of five (very good), none is rescaled one, and only five to two on the vignette adjusted SWB. We observe similar correspondence between SWB relative to Borana pastoralists and its vignette corrected equivalence in panel (b).

To test the robustness of our results to potentially unstable responses, we re-estimate the model using alternative SWB measures vignette corrected SWB relative to Borana pastoralists and SWB relative to Borana pastoralists. The SWB relative to Borana pastoralists variable is similar to the SWB measure, but respondents are asked to gauge their life relative to other Borana pastoralists. The anchoring of subjective well-being questions reduces the likelihood that respondents may have different reference groups in mind when responding (Ravallion, 2012).

are associated with inconsistent vignette ordering or correspondence of self-assessment with more than one vignette responses. As a result, the standard class of econometric methods for ordered dependent variables is suitable for our analysis.

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# **7 Results**

We first discuss the estimated vignette-corrected SWB effects of IBLI on the extensive margin, followed by discussion of results on the intensive margin. Table 3 presents the first stage panel fixed effects LPM estimates of equation (1) (columns 1-2) and panel random-effects (RE) Tobit model of equation (3) (columns 3-4). Column 1 shows results from a basic model with just randomized discount coupon and audio tape and comic book information extension treatments in sales periods 1 and 2. In column 2, in addition to the randomized discount coupon and information treatments in column 1, we include a broad range of household characteristics, wealth measures, IBLI knowledge, expectations of livestock loss, membership in _iqub_ , and survey round fixed effects. The parameter estimates of both models show that randomized treatments had positive effects on IBLI uptake and, thus, can serve as suitable instruments. Receiving a discount coupon and the amount of the discount were especially strong predictors of IBLI uptake. Receiving a discount coupon in sales period 1 increases the probability of buying IBLI policy by over 20 percent. This effect is even greater for – the discount coupon in sales period 2 it increases the odds of buying IBLI by about 24 percent. Moreover, having received discount coupons in sales period 1 increases the probability of buying coverage for recipients of discount coupons in sales period 2. Besides the price effect of discount coupons, which is captured by the coefficient estimates on discount values, the discount coupon had informational value, offering holders a physical reminder of the insurance product. Conditional on the amount of discount received and other covariates, receiving a discount coupon had an independent positive effect on IBLI uptake.

## Table 3 here

Randomized provision of audio tape and comic book information treatments also had a positive, albeit weaker, effect on IBLI uptake. The audio tape treatment had a positive and statistically significant effect in sales period 2. The comic book treatment, however, had an effect on IBLI uptake only when offered in both sales periods, suggesting the effectiveness of repeated exposure to this informational approach. Both Sargan ( _χ_<sup>2</sup> (24) = 75 _._ 36, _prob > χ_<sup>2</sup> = 0 _._ 000) and Basmann ( _χ_<sup>2</sup> (24) = 79 _._ 17, _prob > χ_<sup>2</sup> = 0 _._ 000) over-identification tests fail to reject the null hypothesis that our instruments are valid. The Wald test for joint significance of all instruments also strongly rejects the null of jointly insignificant instruments ( _χ_<sup>2</sup> (9) = 137 _._ 8, _prob > χ_<sup>2</sup> = 0 _._ 000). Thus, this first stage appears to successfully instrument for endogenous IBLI uptake.

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IBLI uptake relates to our control variables in the expected ways. Uptake is positively correlated with knowledge about IBLI and wealth (livestock and non-livestock assets), but only number of TLUs owned is statistically significant. Income and _iqub_ membership are negatively but statistically insignificantly correlated with IBLI uptake. The later suggests that _iqub_ may crowd out IBLI. We also find that male headed households are less likely to buy IBLI and that larger households are more likely to buy IBLI.<sup>20</sup> We find similar results when estimating a Tobit model for volume of TLUs insured to study IBLI uptake at the intensive margin (columns 3-4, Table 3). Receiving discount coupons and the size of the discount carried by the coupon are strong predictors of the volume of TLUs insured. The audio and comic book information treatments were also found to be positively, but relatively weakly, related to the volume of IBLI coverage. The number of TLUs owned is positively related to volume of coverage. In line with the IBLI uptake results in columns 1 and 2, we find that IBLI knowledge influences the volume of uptake. Respondents with more correct answers to questions about the particulars of the IBLI contract are more likely to buy IBLI, a result consistent with ambiguity aversion (Gharad, 2013). _Iqub_ membership reduces the volume of TLUs insured, as such traditional institutions lower the demand for other forms of insurance.

Table 4 reports second stage ordered logit regression results of the effects of IBLI on vignette corrected SWB. Panel (a) shows the effects of IBLI in log-odds units. While these results are concise and more convenient for presentation purposes, their interpretation may not be straight forward. In panel (b), we present the corresponding marginal effects of the main results in panel (a). Columns 1-3 show the extensive margin effects of IBLI uptake on SWB. Since randomized discount coupon and information treatments were used as instruments for the potentially endogenous IBLI uptake in stage one, the coefficient on _IBLI_<sup>�</sup> measures the causal effect of IBLI on SWB. We find that IBLI has a strong positive effect on SWB, presumably because insurance coverage reduces risk exposure for risk averse buyers. The full model in column 3 shows that IBLI uptake increases the log-odds of reporting higher SWB by 0.86. That is, IBLI buyers are 2.4 ( _≈ e_<sup>0</sup><sup>_._86</sup> ) times more likely to report higher SWB than lower SWB. The probability estimates in panel (b) make this point more clear. IBLI reduces the probability of reporting lower SWB ( _SWB ≤_ 3) by 11 percent and increases the probability of reporting higher SWB ( _SWB ≥_ 5) by 11 percent. Our results are robust to

> 20As a robustness check, we also estimate a probit selection model. The results are strongly consistent with the LPM (Table A5).

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the inclusion of income, wealth, a range of demographic and household characteristics, and household composition variables.

# Table 4 here

At the time of the R3 survey implementation, IBLI policies from sales period 1 and sales � period 2 had already lapsed without payout. Thus, the coefficient estimate on _IBLIL_ captures the negative _ex post_ SWB effect of having bought an insurance policy that did not � pay out. Indeed, the negative and statistically significant coefficient estimate on _IBLIL_ indicates buyer’s remorse. Having bought an IBLI contract that lapsed without pay out reduces the log-odds of reporting high SWB by 0.42, which indicates that buyers of lapsed IBLI contracts are 1.5 ( _≈_ 1 _/e_<sup>_−_0</sup><sup>_._44</sup> ) times more likely to report lower SWB than higher SWB. In probability units, having bought a lapsed IBLI contract increases the probability of reporting low SWB ( _SWB ≤_ 3) by 5 percent and decreases the probability of reporting � high SWB ( _SWB ≥_ 5) by 5 percent. More importantly, the magnitude of the _IBLIL_ coefficient is statistically significantly smaller than that of _IBLI_<sup>�</sup> . This suggests that people are comforted by insurance coverage, and the positive _ex ante_ effect trumps the negative _ex post_ regret they feel once they realize that they paid for insurance that, in retrospect, they did not ultimately need.

As expected, SWB is positively correlated with various wealth measures. Both livestock and non-livestock assets are positively related to SWB. Male headed households are more likely to report higher SWB than their female headed counterparts. Household size is negatively correlated with SWB.

We find similar results for the volume of TLUs insured (columns 4-6). Vignette corrected SWB is increasing in the predicted number of TLUs insured. In the full model in column 6, a unit increase in the volume of TLUs insured increases the log-odds of reporting higher SWB by 0.14, which translates to 1.15 times more likelihood of reporting higher SWB than lower SWB. The corresponding column in panel (b) shows that an additional unit of TLUs insured reduces the probability of reporting low SWB ( _SWB ≤_ 3) by 2 percent and increases the probability of reporting high SWB ( _SWB ≥_ 5) by 2 percent. Yet, as IBLI policies lapse without paying, the more TLUs one had insured, the greater the buyer’s remorse one experiences. A unit increase in lapsed TLUs insured reduces the log-odds of reporting higher SWB by 0.07. An IBLI buyer with a unit more lapsed TLUs insured is 1.08 times more likely 20
to report lower SWB than higher SWB. That is, an additional unit of lapsed TLUs insured increases the probability of reporting low SWB ( _SWB ≤_ 3) by 0.9 percent and reduces the probability of reporting high SWB ( _SWB ≥_ 5) by 0.8 percent. As is the case with IBLI uptake, the positive effect of greater volume of TLUs insured statistically significantly exceeds the negative remorse it causes when the contract fails to pay out. We also find that livestock and non-livestock wealth are positively correlated with SWB, while household size is negatively correlated with SWB.

Appendix Table A9 presents the regression results from estimating equations (2) and (4) � using only currently active IBLI policies. Omission of _IBLIL_ leads to a considerably smaller, yet still statistically significant, point estimate on _IBLI_<sup>�</sup> . This finding underscores the prospective omitted relevant variable bias on the _ex ante_ SWB impact estimate that arises due to autocorrelation in insurance demand if one does not separately control for lapsed policies. In other words, econometric estimates of the gains from insurance will likely underreport the welfare effects of insurance coverage if the research design does not permit the researcher to disentangle the _ex ante_ and _ex post_ effects of insurance.

The net aggregate SWB effect of IBLI is presented in Table 5. The estimated ∆ _SWB_<sup>�</sup> is positive and statistically significant in the number of TLU insured. The point estimate suggests that insuring an extra TLU increases vignette corrected SWB by 0.2 points, although these units have no specific informational content given the ordinal nature of the dependent variable. But this magnitude indicates that, assuming a constant marginal SWB effect of IBLI, insuring about five TLUs bumps a household up by one rung on the SWB Likert scale, from, for example, “very bad” to “bad” or “good” to “very good”, on average. So, even insurance policies that did not pay out generate SWB gains. Given the actual financial losses experienced by households that purchased insurance policies in these poor communities in southern Ethiopia, this finding is important and reassuring.

Table 5 here

# **8 Robustness checks**

We complete several robustness checks to test whether our findings are sensitive to various specifications and variable definitions. First, we re-estimate our model for vignette corrected 21
SWB relative to Borana pastoralists, a refinement of our dependent variable (Appendix Table – A6). The results are consistent with our main findings, suggesting response stability that the phrasing of questions had little impact. As in the model for vignette adjusted SWB in Table 4, buying IBLI leads to higher SWB scores. The estimated coefficients on predicted IBLI as well as lapsed IBLI in the two models are comparable. As expected, the coefficients on predicted lapsed IBLI are negative and statistically significant. But, the positive effect of possessing IBLI policies is significantly higher than the negative buyer’s remorse effect. The number of TLUs owned is positively related to SWB. As before, greater household size is associated with lower SWB. Non-livestock assets and gender are, however, statistically insignificant. The results of the regression of SWB relative to Borana pastoralists on the volume of TLUs insured are also consistent with our main results in Table 4. The positive effect of active contracts exceeds the negative buyer’s remorse effect of lapsed coverage. The difference is statistically significant.

We then estimate our model using raw SWB, which has not been vignette corrected, for IBLI uptake and volume of TLUs insured (Table A7). The results are consistent with our main findings – SWB increases with IBLI uptake/ volume of TLUs insured, and livestock and nonlivestock wealth. Lapsed IBLI contracts cause remorse, hence negatively impact well-being. Male household heads are more likely to report higher SWB than female household heads. However, the coefficients on predicted IBLI and predicted TLUs insured are not statistically different from the absolute value of the corresponding coefficients on lapsed predicted IBLI uptake and predicted TLUs insured.

We also estimate our model for the balanced panel subsample to verify that the differential weighting of households in the unbalanced panel sample does not influence our estimates. Results for the balanced panel household sub-sample are presented in Tables A8. Again, we find that all of the estimated coefficients are consistent with our main results in Table 4. Predicted IBLI coverage and TLU insured increase vignette adjusted SWB, while lapsed contracts reduce it. The magnitudes of the positive effects of IBLI remain significantly higher than the negative estimated buyer’s remorse effects, and comparable to what we find in Table 4. As before, SWB rises with wealth and decreases with household size. Male household heads report higher SWB.

The multiple robustness checks we conduct strongly suggest that the positive _ex ante_ SWB effects of IBLI coverage, and the negative _ex post_ SWB effects of buyer’s remorse in response to a lapsed policy that did not pay out, are robust to both definitions of subjective well-being 22
measures, various specifications, and variations in the relevant panel sub-sample. The effects of wealth, gender and household size are also consistent throughout. These results give us more in the robustness of our results.

# **9 Conclusions**

Interest in the study of subjective well-being (SWB) has increased in recent years, as has research on index insurance in rural areas of the developing world. To date, much of the SWB research in low-income countries has focused on the relationship between SWB and income or assets. There is limited understanding of how institutional factors, access to services, or policy-related issues influence SWB, if at all (Fafchamps & Shilpi, 2008). Furthermore, few studies link policy-related variables, such as uptake of index based livestock insurance (IBLI), with changes in SWB (Kaminski, 2014). This study addresses that important gap in the literature while simultaneously making an important contribution to disentangling the _ex ante_ and _ex post_ welfare effects of insurance by isolating the buyer’s remorse effect that arises from lapsed insurance policies. Empirically, we demonstrate that index insurance such as IBLI, which reduces the drought-related risk faced by pastoralists in southern Ethiopia – has the potential to impact not only material well-being e.g., by replacing lost assets – and reducing adverse coping behaviors but also to improve non-material well-being, by providing valuable peace of mind for risk averse buyers even if they can reasonably anticipate experiencing buyer’s remorse if a policy lapses without payout.

We use three rounds of annual household panel data collected between 2012 and 2014, bracketing the introduction of IBLI in southern Ethiopia, and randomized encouragements to buy the product to identify the causal effect of IBLI on SWB. We separate out the _ex ante_ SWB effects of current coverage from the _ex post_ buyer’s remorse effect, exploiting the fact that some households had purchased IBLI in the second survey round and those policies had lapsed by the third survey round. We also show that if buyer’s remorse effects exist and there is any persistence in insurance purchases, such that current and lapsed coverage are positively correlated, then ignoring lapsed policies results in downwardly biased estimates of the well-being effects of insurance.

We find that current IBLI coverage has a strongly positive and statistically significant effect on SWB. We also find statistically significant evidence of a buyer’s remorse effect. The 23
negative buyer’s remorse effect is considerably smaller in magnitude than the positive effect of IBLI coverage, however, suggesting that the comfort people derive from insurance coverage more than compensates for any regret they suffer once they realize they did not need coverage. Therefore, in our survey sample, insurance purchase is _ex ante_ optimal, on average.

This could reflect the nature of the sample we study. Pastoralists in southern Ethiopia’s Borana Zone face decline in informal social insurance institutions at a time when pastoral livelihoods are becoming more risky. As a result, Borana pastoralists may experience greater well-being as a result of having access to index insurance, even if it did not pay out in the short-term. These results suggest that for people with precarious livelihoods, even an imperfect, commercially priced insurance policy that does not pay out can leave them feeling better Our findings also show that estimations of the welfare effects of insurance ought not ignore potential _ex post_ impacts. Prior purchases of insurance may induce buyer’s remorse once a buyer realizes that, in retrospect, costly insurance proved unnecessary. Survey-based SWB measures can capture all of these prospective effects without resorting to strong assumptions about the arguments and functional form of utility functions.

SWB measures seem especially appropriate to establishing the impacts of commercial insurance. Commercial insurance policies, including IBLI, intrinsically involve a tradeoff between material and non-material well-being if policies are priced above actuarially fair premium rates so as to cover the costs of and ensure a profit margin for the underwriter. Theory suggests that actuarially fair insurance is welfare enhancing, regardless of whether it pays out, because most people are risk averse and insurance mitigates risk. But when insurance is not actuarially fair, and perhaps especially if it offers incomplete coverage, as is inevitably the case with index insurance products subject to basis risk, the _ex ante_ expected monetary loss (because premiums exceed expected indemnity payments over time) and the _ex post_ buyer’s remorse that might result if no insurable loss occurs, might negate the oft-assumed of insurance.

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