Everyday Policy Studies No. en31

Index Based Livestock Insurance (IBLI) 09

 In my last 8 posts, we have been reviewing academic papers on IBLI. In this post, I would like to introduce Russell (2021), which is a much shorter document than academic papers but I have sentimental value with. I would like you to read his post before this post.
 The reason why I have sentimental value with his post is that not only it mentions me several times but it summarizes my work over 16 years from 2005 to 2021.
 My 3rd post https://apsf.jp/en/2020/11/04/everyday-policy-studies-no-en22/ mentions that I was so excited to go to Marsabit for household survey after a year of office work in Nairobi. The first sentence of his post mentions that I was very surprised by how dry Marsabit is. This video https://www.standardmedia.co.ke/ktnnews/ktn-prime/video/2000210938/pangs-of-neglect-how-marsabit-residents-battle-drought-water-scarcity-as-country-battles-covid-19 uploaded a week ago by one of major TV news companies in Kenya might give you an idea. Both Bubisa and Lontolio in the video are study sites for the IBLI Marsabit Survey. Here, I would like to add another surprise I got before I reached at Marsabit. At that time in 2009, the main road from Isiolo to Marsabit has not been paved with tarmac yet. I saw Marsabit was dry and scarcely populated but the most surprising for me might be the way our land cruiser went over corrugated dirt road with high speed.
 The seventh paragraph of his post mentions Ikegami et al. (2020) as social protection paradox but does not mention that I started working on it in 2005 when I started working as an research assistant for Michael Carter. It was also the paper I have submitted to a peer reviewed journal for the first time. I remember I was so excited when we submitted it.
 The fourth last paragraph of his post introduces Janzen et al. (forthcoming). We have started working on this paper in 2011. This paper follows up the previous paper by focusing index insurance as a tool to prevent households from falling into poverty traps. I am planing to summarize these two papers in my next post.

Reference
Ikegami, M., M. R. Carter, C. B. Barrett, and S. Janzen (2019) “Poverty Traps and the Social Protection Paradox” in C. Barrett, M.R. Carter and J.-P. Chavas eds. The Economics of Poverty Traps. University of Chicago Press. Chapter 6. pp.223-256 https://press.uchicago.edu/ucp/books/book/chicago/E/bo28559644.html
Janzen, S., M. R. Carter, M. Ikegami (forthcoming) “Can Insurance Alter Poverty Dynamics and Reduce the Cost of Social Protection in Developing Countries?” Journal of Risk and Insurance https://onlinelibrary.wiley.com/doi/full/10.1111/jori.12322
Russell, Alex (2021) “Insurance for Vulnerable Families Cuts Rural Poverty and the Cost of Aid by Half” https://basis.ucdavis.edu/news/insurance-rural-families-cuts-poverty-and-cost-aid-half Accessed on May 7th, 2021

(Author: Munenobu Ikegami)

Everyday Policy Studies No. en30

Index Based Livestock Insurance (IBLI) 08

 The previous essay reviews Takahashi et al. (2016) which study the determinants of IBLI uptake in the first two sales periods in Borena, Ethiopia. In this essay, we will review Takahashi et al. (2020) analyzing IBLI uptake not only in the first two but also the following four sales periods.
 Let us take a look at descriptive statistics of the uptake first. The study households are the 458 households who were interviewed in the all of the four rounds of our annual survey from 2011 to 2015. As mentioned in the previous essay, the insurance coverage period of IBLI contract is a year but potential buyers can purchase IBLI twice in a year. Because of this feature, it might be better to study uptake as ratio of households having valid policies (households who purchase IBLI not only in the current sales period but also in the previous sales period) rather than ratio of households purchasing in the current sales period. The uptake ratio changed from 28, 42, 38, 36, 29, to 28 percents from the first to sixth sales period. The average amount of livestock insured among households purchasing IBLI did not change so much (2.5, 2.5, 2.6, 2.4, 2.0, 2.3 Tropical Livestock Unit, TLU) over the six sales periods. IBLI uptake did not grow as I wished.
 Let us move to determinants of the uptake. As the potential determinants of the uptake, Takahashi et al. (2020) pick up price (discount rate of insurance premium), previous own uptake, other households’ uptake, and vegetation condition (Normalized Difference Vegetation Index, NDVI). We find that the current discount rate affects uptake but the previous discount rate does not, implying there is no price anchoring effects and that price subsidy in the initial stage of insurance market development would not hurt uptake in the future when subsidy is removed. Vegetation condition also matters. Pastoralists purchase IBLI more likely when vegetation condition is bad and payout is more likely. This damages sustainability of insurance market and an idea for a solution is to change insurance premium based on current vegetation condition. The results on the remaining two potential determinants, other households’ uptake and previous own uptake, are not so clear in the sense that the results depends on whether we use discount rate as instrument variable for endogeneity of IBLI uptake.
 Payout or no-payout may affect uptake in the following sales periods and there was no payout until November 2014. Timu et al. (2018) study this and thus Takahashi et al. (2020) do not study this to avoid an overlap of research questions.

Reference
Takahashi, K., Ikegami, M., Sheahan, M., and Barrett, C. B. (2016). “Experimental Evidence on the Drivers of Index-Based Livestock Insurance Demand in Southern Ethiopia.” World Development, 78, 324-340.
Takahashi, K., Noritomo, Y., Ikegami, M., & Jensen, N. D. (2020). “Understanding pastoralists’ dynamic insurance uptake decisions: Evidence from four-year panel data in Ethiopia.” Food Policy. 95.
Timu, A. G., Gustafson, C. R., Ikegami, M., & Jensen, N. D. (2018). “Indemnity Payouts and Index Insurance Demand in Ethiopia.” Working paper.

(Author: Munenobu Ikegami)

Everyday Policy Studies No. en29

Index Based Livestock Insurance (IBLI) 07

 The previous essay https://apsf.jp/en/2021/02/02/everyday-policy-studies-no-en28/ reviews Jensen, Barrett, and Mude (2017) as the second paper among the nine papers on impacts of IBLI. Instead of continuing to review the remaining papers on the impacts, we will start reviewing papers on uptake of IBLI.
 Takahashi et al. (2016) study the determinants of IBLI uptake in the first two sales periods in Borena, Ethiopia. The number of sample households is 474. 30% of the households purchased IBLI in the first sales period in August-September 2012. 18% of the household purchased IBLI in the second sales period in January-February 2013. Insurance coverage period is a year but 5% of the households purchased IBLI in the both sales periods. This complication is due to the fact that one does not have to insure all of her livestock (and even she does not have to prove that she has livestock). Due to this contract feature, she can purchase insurance for some of her livestock in August-September 2012 and she can purchase another insurance in January-February 2013 although her first insurance is still valid and will expire in September 2013.
 Uptake of index insurances has been low despite of enthusiasm in development industry and the numbers of IBLI uptake above are comparable to or even larger than other index insurances. What disappointed us more was that policy holders insured only 2.7 tropical livestock unit (TLU) on average although the 474 sample households have 14.7 TLU on average. That is, the policy holders insure only 18% (= 2.7 / 14.7) of their livestock. 1 TLU is equivalent to 1 cow, 0.7 camel, 10 goats or 10 sheep.
 In order to study not only correlation between household characteristics and IBLI uptake but also some determinants/causes of IBLI uptake, we generated external variation in price and knowledge of IBLI by some experimental tools. The first tool is insurance premium discount coupon, which we have already reviewed in one of the previous essays. The second one is comic, with which insurance extension agents can introduce IBLI to our sample households and the households can review by themselves. The third one is skit tape, with which insurance extension agents can play audio skit and introduce IBLI to our sample households though the audio skit. By randomizing provision of these 3 tools, we can generate external variation in potential determinants of uptake. Discount coupons allow us to study causality from insurance premium price to uptake and the all three tools allow us to study causality from knowledge to uptake.
 Takahashi et al. (2016) find the following. First, comic and skit tape improve knowledge of IBLI but the knowledge does not increase uptake. Second, lower insurance premium increases uptake as expected and insurance demand is sensitive to price. Third, lower insurance premium in the first sale period and increase in insurance premium from the first to second sale period do not decrease uptake in the second sale period. This implies that a policy encouraging uptake by subsidizing insurance premium and let potential policy holders learn the benefit of the insurance can be effective since it will not decrease uptake in the future when the subsidy is decreased or removed.
 In the next essay, we will review a paper or two which study IBLI uptake in a longer time span.

Reference
Jensen, N. D., Barrett, C. B., and Mude, A. (2017). “Cash Transfers and Index Insurance: A Comparative Impact Analysis from Northern Kenya.” Journal of Development Economics, 129, 14–28.
Takahashi, K., Ikegami, M., Sheahan, M., and Barrett, C. B. (2016). “Experimental Evidence on the Drivers of Index-Based Livestock Insurance Demand in Southern Ethiopia.” World Development, 78, 324-340.

(Author: Munenobu Ikegami)

Everyday Policy Studies No. en28

Index Based Livestock Insurance (IBLI) 06

 My previous essay https://apsf.jp/en/2021/01/08/everyday-policy-studies-no-en25/ reviews Janzen and Carter (2019). In this essay, I will review Jensen, Barrett, and Mude (2017), which is the second most cited paper among the nine papers on impacts of IBLI, following Janzen and Carter (2019).
 One of the major differences between Jensen et al. (2017) and the other eight papers is that Jensen et al. (2017) study not only the impacts of IBLI but also a conditional cash transfer program called Hunger Safety Net Programme (HSNP). HSNP provides 2,150 Kenya Shillings (KSH, about 29 United States dollar, USD) to beneficiary households once two months since April 2009. Marsabit County is one of the poorest counties in Kenya and it happened to be the pilot county not only for IBLI but also for HSNP and our households data have beneficiaries for either or both programs.
 Following our notation, equation of interest is Y=a+bX+e where Y is outcome variable, X is explanatory variable, a is constant term, and e is error term. Jensen et al. (2017) study many equations which have different X and Y. Explanatory variable X is having IBLI or having HSNP. Outcome variable Y is livestock sale, livestock herd size, veterinary expenditure, herding livestock with satellite camps, income from livestock milk, loss of livestock, household total income, school absenteeism, and health (measured by mid-upper arm circumference (MUAC) of children aged from zero to five). Jensen et al. (2017)’s central question is whether IBLI and HSNP have impacts on these outcome variables of household behavior and welfare. In order to control endogeneity of having IBLI or HSNP, they use instrument variable method. Instrument variable for having IBLI is insurance premium discount coupon as reviewed in my previous essay. Instrument variable for having HSNP is household eligibility status for HSNP.
 Jensen et al. (2017) find that either IBLI or HSNP has impacts on all outcome variables except school absenteeism. Based on estimated impacts (magnitude of b), they calculate and compare impact to cost ratios for IBLI and HSNP. Both total program cost per beneficiary and impact are similar between IBLI and HSNP and thus impact to cost ratios are also similar. On the other hand, marginal cost (how much it will cost for the next additional one beneficiary) for IBLI is 10 times smaller. It is because both IBLI and HSNP needs large initial cost for developing insurance product and setting up system for insurance transaction or cash transfer. As for marginal cost, HSNP incurs cost of cash to transfer but IBLI does not. Impact to marginal cost ratio is thus larger for IBLI.
 Jensen et al. (2017) do not find synergy between IBLI and HSNP as additional impacts when households have both IBLI and HSNP. This is studied further by Jensen, Ikegami, and Mude (2017).
 In the next essay, I will review another paper on the impacts of IBLI or proceed to studies on IBLI uptake.

Reference
Janzen, Sarah and Carter, Michael R. (2019). “After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection,” American Journal of Agricultural Economics, 101(3), 651-671.
Jensen, N. D., Barrett, C. B., and Mude, A. (2017). “Cash Transfers and Index Insurance: A Comparative Impact Analysis from Northern Kenya.” Journal of Development Economics, 129, 14–28.
Jensen, N., Ikegami, M., and Mude, A. (2017). “Integrating Social Protection Strategies for Improved Impact: A Comparative Evaluation of Cash Transfers and Index Insurance in Kenya.” The Geneva Papers on Risk and Insurance – Issues and Practice, 42(4), 675–707.

(Author: Munenobu Ikegami)

Everyday Policy Studies No. en27

Special Fixed-Sum Cash Benefits (continued)

 The projected cost of the special fixed-sum cash benefits (uniformly 100,000 yen per person) implemented as part of the “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)”, is 12,734,414 million yen (i.e. about 12.73 trillion yen). (See Note 1).
 Assuming that the tax revenue is 1% of the consumption tax rate (national / local) under the reduced tax rate system is about 2.1 trillion yen (See Note 2), the budget total of the special fixed-sum cash benefits is equivalent to the consumption tax rate of 6 (12.73 ÷ 2.1) %. It will be a budget amount that can lead to a reduction of the consumption tax. The special fixed-sum cash benefits of 12.73 trillion yen and the consumption tax reduction of 12.73 trillion yen (with a tax rate of 6%) are the same in terms of universalist policies that do not limit specific individuals or households. However, the special fixed-sum cash benefits of 12.73 trillion yen without the consumption tax reduction, and the consumption tax reduction of 12.73 trillion yen (with a tax rate of 6%), involve different legislative measures, different speeds of policy implementation and different administrative costs, and different beneficiaries, and this implies different types of a consumption tax system.
 If we assume that the consumption tax rate is a uniform 10% and the basic consumption is 1 million yen per person per year, providing a fixed-sum cash benefit of 100,000 yen means that the consumption tax amount related to the basic consumption will be refunded uniformly. The refund amount is 100,000 yen (10%×1 million yen). 
 In other words, tax payment = tax rate × (annual consumption−basic consumption) = (tax rate × annual consumption) − (tax rate × basic consumption) = consumption tax paid−fixed-sum benefit; so if you consider the proportional consumption tax and the fixed-sum benefit as a set, the set will be a “progressive” consumption (value-added) tax system. “Progressive” means that the higher the annual consumption, the higher the average consumption tax rate (consumption tax payment amount ÷ annual consumption). (See Note 3).
 In this regard, since the fixed-sum cash benefit is a negative poll tax, it is possible to present the “progressive value-added tax”, which is a combination of VAT (value added tax) and a negative poll tax, as a new expenditure tax. (See Note 4).
 On the other hand, the consumption tax reduction of 6% is a proportional consumption tax (VAT) of 4%. Therefore, it is possible that there is a difference in the consumption tax system between providing a fixed-sum cash benefit of 100,000 yen and having a consumption tax reduction of 6%.
 Since this special fixed-sum benefit is a temporary measure, it cannot be considered as a progressive consumption tax system unless it is continued from next year onward. Therefore, we may examine whether we should continue the fixed-sum cash benefit in some way from next year onward, and after the coronavirus crisis has settled down, whether we should consider the redemption of financial resources regarding public bonds for the emergency economic measures like as the reconstruction tax for the Great East Japan Earthquake.

(Note 1) Ministry of Internal Affairs and Communications, “The Special Fixed-Sum Cash Benefits (in Japanese)”.
https://www.soumu.go.jp/menu_seisaku/gyoumukanri_sonota/covid-19/kyufukin.html 
<Accessed on May 18, 2020>

(Note 2) “Under the reduced tax rate system, the consumption tax rate increase of 1% is expected to be about 2.04 (or 2.23) trillion yen.” (Y. Baba et al., Consideration on Public Finance in Japan (in Japanese), Tokyo: Yuhikaku, 2017, p.51)
Therefore, we assume that the tax revenue with a consumption tax rate of 1% is about 2.1 trillion yen. However, this tax revenue forecast will depend on the final consumption expenditure of the national accounts, so it will be significantly reduced due to the coronavirus pandemic.

(Note 3) “Progressive” in the term “progressive income tax” means that the higher the annual income is, the higher is the average income tax rate. The difference between a progressive income tax and a progressive consumption tax is whether an individual’s ability to pay (economic power) is considered in terms of income or consumption. Generally, it is said that the consumption tax is regressive because the higher the income is, the lower the ratio of the consumption tax payment to the income, and the regressive effect on income distribution. See H. Kato and A. Yokoyama, Tax System and Tax Politics: How Tax Reform Should Be Done (in Japanese), Tokyo: The Yomiuri Shimbun, 1995, pp. 217-218.

(Note 4) See A. Yokoyama, “Examining a New Expenditure Tax System (in Japanese),” Sozei-Kenkyu (Studies on Taxation), No. 535, pp4-12; A. Yokoyama et al., Modern Public Finance (in Japanese), Tokyo: Yuhikaku, 2009, p.271.

This essay is the English version of No. 147, May 19, 2020 on the Japanese website.

(Author: Akira Yokoyama)

Everyday Policy Studies No. en26

Special Fixed-Sum Cash Benefits

 The “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)”, the supplementary budget for fiscal year 2020, which includes Special Fixed-Sum Cash Benefits, was established on April 30, 2020. The special fixed-sum cash benefit was decided by the Cabinet on April 20, as follows. “With respect and thankfulness to the people who are engaged in various fields throughout the country including the healthcare field, we must consolidate, unite, and overcome the national crisis of fighting this invisible enemy. For this reason, while paying attention to the prevention of the spread of the disease, the Government will promptly and appropriately support households with a simple mechanism, and uniformly pay out 100,000 yen per person.” (See Note 1), and the recipients of the benefit are those listed in the Basic Resident Register on the recording date (April 27, 2020) (including foreign residents not having Japanese nationality). The beneficiary is the head of the household to which the person belongs.
 The special fixed-sum cash benefit is a modification of the 300,000 yen benefit for households with reduced revenues included under the “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)”, decided by the Cabinet on April 7. While the 300,000 yen benefit to households with reduced revenue is a support measure for households that are limited to households in need, the special fixed-sum cash benefit is a support measure for households that do not have such a limitation. The difference between the two benefits is the difference between the principles of selectivism and universalism.
Under the current child allowance system, there is in principle an income cap according to the number of dependent relatives, etc., but even if the income cap is exceeded, a monthly amount of 5,000 yen will be paid per child as Special Interim Allowances (See Note 2). This is a policy that is based on selectivism but also has universalism.
Support measures for households in the Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19) are based on the universalist policy of special fixed-sum cash benefits, but may require selective policies with additional benefits targeting households who are truly in need as a result of the outbreak of COVID-19. Therefore, just as the 300,000-yen benefit for households with reduced revenues caused a major issue with how to identify and demarcate households with reduced revenues, it is important to establish a clear and rational demarcation standard in selective policies.

(Note 1) Cabinet Office, Japan, “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)~Thoroughly secure people’s lives and moves toward economic revitalization~” p.30, April 20, 2020 https://www5.cao.go.jp/keizai1/keizaitaisaku/2020/20200420_economic_measures_all.pdf

(Note 2) Cabinet Office, Japan, “The Outline of the Act for Amending Part of the Child Allowance Act”, (Date of Enforcement, April 1, 2012),
https://www8.cao.go.jp/shoushi/jidouteate/pdf/gaiyou_kaisei-en.pdf
Furthermore, according to the “Emergency Economic Measures to Cope with the Novel Coronavirus (COVID-19)”, support to households with children, a temporary special benefit of an additional 10,000 yen per applicable child, will be paid to households receiving (regular) Child Allowance.

This essay is the English version of No. 143, May 5, 2020 on the Japanese website, but the notes and URLs have been changed. The final date of access to the URLs is January 2, 2021.

(Author: Akira Yokoyama)

Everyday Policy Studies No. en25

Index Based Livestock Insurance (IBLI) 05

 My previous essay https://apsf.jp/en/2020/12/09/everyday-policy-studies-no-en23/ reviews how to measure the impacts of the insurance using the household data and the insurance premium discount coupon as a instrument variable. In this essay, I will review previous studies on the impacts using this method.
 Following the notation of my previous essay, our equation of interest is Y = a + bX + e where Y is dependent variable, X is explanatory variable, a is constant term, and e is error term. In our case, since we are studying the impacts of the insurance, X is purchasing and having the insurance and for simplicity, let us say X takes one if a household has insurance and zero otherwise (instead of that X is how much livestock the household covers with the insurance). Y is outcome variable which insurance can improve, for example, Y can be how much livestock a household owns after a drought. We hope that the insurance will help households save their livestock from a drought and so we hope that b in the equation above is positive and large. As we discussed in my previous essay, a more able household buys the insurance more likely and she can save her livestock more regardless the insurance and thus we would overestimate the impact b. We will alleviate this overestimation using the insurance premium discount coupon as an instrument variable.
 There are seven papers in academic journals and two working papers studying the insurance impacts on using the household data and insurance premium discount coupon as instrument variable. Five out of the nine papers use IBLI Borena Ethiopia data and the remaining four use IBLI Marsabit Kenya data. Each of the papers studies different outcome variables Y and impact channel b. Based on Google Scholar https://scholar.google.com, the most cited paper among the nine papers is Janzen and Carter (2019).
 There was a drought in 2011 and an insurance payout in November 2011. In October 2011, Janzen and Carter (2019) ask households whether they would sell livestock and reduce meal in the following three months (October-December 2011) in order to cope with the drought in 2011. Note that Janzen and Carter (2019) ask households their expectation on their drought coping strategies in the future instead of what households did in the past. One of two outcome variables Y in Janzen and Carter (2019) is to sell livestock to cope with the drought and they expect and find b is negative. Our next question is how large this impact is. 27 percents of the households say that they would sell livestock and the insurance decreases this by 61 percentage points (from 27 percents to 11 percents). The other outcome variable Y in Janzen and Carter (2019) is to reduce mead to cope with the drought. They expect b is negative and find b is negative for the poor half of the households but b is zero (no impact) for the remaining rich half. 62 percents of the households say that they would reduce meal and the insurance decreases this by 49 percentage points (from 62 percents to 32 percents) for the poor half of the households.
 In my next essay, some of the remaining eight papers will be reviewed.

Reference
Janzen, Sarah and Carter, Michael R, (2019), “After the Drought: The Impact of Microinsurance on Consumption Smoothing and Asset Protection,” American Journal of Agricultural Economics, 101(3), 651-671.

(Author:Munenobu Ikegami)

Everyday Policy Studies No. en24

New Year’s Day 2021

 This year, although we had New Year’s Day, it is hard to say “Happy” New Year.
At the beginning of the year, I would like to express our sincere gratitude to all the medical personnel and many others who are still engaged in hard work fighting the menace of the novel coronavirus infection (COVID-19). Furthermore, I pray that the day will come as soon as possible when those who are experiencing trouble in their daily lives will feel at ease.
 Now is the time for the next generation of young people to think about COVID-19 and others. We have all lived for almost a year in a completely different environment. How did you spend most of your time in the anxious and uncertain environment of COVID-19? If you’ve written your schedule in a notebook, diary, or calendar, please try to measure how many hours you spent in a month or week in 2020. For example, what was the daily hourly average of how you passed your time in April, August, and December 2020? Compare it with the daily average of the way you spent your time in the same month a year previously.
 Please calculate the approximate average time spent on the following three kinds of activities: 1. Average time for physiologically necessary activities (primary activities) such as sleeping, eating, and bathing; 2. Average time for absolutely necessary activities (secondary activities) such as commuting to school or work, schoolwork, housework, childcare, and long-term care; and 3. Average time for activities (tertiary activities) in your free time, which are generally referred to as leisure activities, such as watching television, resting, learning other than at school, self-development, hobbies, entertainment, sports, and dating. (See Note).
 Especially, ask what was the activity that you spent the most time on in the breakdown of the tertiary activities? The spent time on those activities is the key to opening the door to your future. Do not ignore the activity that you devoted your maximum time to, rather cherish it, and think of it as a clue to connect the activity with your individuality, charm, and strength. I would like you to think about how you make use of that activity so that it will help those who are in trouble in their daily lives as a result of COVID-19. It may take 3 to 5 years or more to reach a conclusion about that.
 However, I would like young people to realize for themselves by adopting such a medium- to long-term perspective and by making the most of their individuality, charm, and strengths. I wish you all the best of luck this year as well.

(Note) For the definition of the average time of the primary activity, secondary activity, and tertiary activity here, see the living times outlined in the document entitled “Social Life: Basic Survey” (in Japanese), which is available at https://www.e-stat.go.jp/koumoku/koumoku_teigi/M (Accessed on 2020.12.30)

This essay is the English version of No. 198, January 1, 2021 on the Japanese website.

(Author: Akira Yokoyama)

Everyday Policy Studies No. en23

Index Based Livestock Insurance (IBLI) 04

 My previous essay reviews the household data we collected to study insurance uptake and impacts. In this essay, I will review how to measure the impact using the data and insurance premium discount coupons.
 The major challenge in measuring the impact is endogeneity of insurance uptake. Our central question is how much insurance improves households’ welfare. The cause is insurance and the result is welfare. Studying this causality is difficult due to unobserved household characteristics and endogeneity of insurance uptake. To simplify this reasoning, let us say unobserved household characteristics is household ability and more able households buy insurance more likely (and endogenously). Let us call the latter endogeneity of insurance uptake. If so, we cannot tell whether insurance improves household welfare or more able households improve their welfare and buy insurance at the same time. In this case, if we do not control this endogeneity of insurance uptake, our estimate of the impacts of insurance on household welfare will end up larger than the true value.
 To control this endogeneity, we designed a randomized controlled trial (RCT). Since it is difficult to ask insurance companies to sell insurance to particular households or in particular villages and not to sell to the others, we provided insurance premium discount coupons to particular households among our 924 sample households in Marsabit, Kenya and 515 sample households in Borena, Ethiopia. We call households who received the coupons treatment group and the other households who did not receive the coupons control group. We randomly split the households into treatment group and control group. The simplest RCT compares average welfare in the treatment group with average welfare in the control group and the impacts is the difference in welfare. In our case, if we did so, the difference in average welfare would be the impacts of discount coupon rather than the impacts of insurance.
 In order to obtain the impacts of insurance, we use an Econometircs method called instrument variable (IV) method. Our dependent variable Y is household welfare such as income and explanatory variable X is insurance uptake. Our equation of interest is Y = a + bX + e where a is average welfare (called constant term) and e is other factors influencing welfare (called error term). Coefficient b is the estimate we would like to have and the impacts of insurance (X) on household welfare (Y). IV method allows us the following. Even if insurance uptake (X) is influenced by ability captured in error term (e), we can estimate coefficient b using an instrument variable (Z) which is strongly related to insurance uptake (X) but unrelated to error term (e). Let us consider whether discount coupon can be an instrument variable (Z). First, discount coupon encourages insurance uptake and thus our instrument variable (Z) can strongly related to insurance uptake (X). Second, we randomly split households into treatment group who receives discount coupons and control group who does not and thus our instrument variable (Z) should be unrelated to other factors affecting household welfare (error term, e).
In this essay, I review that insurance premium discount coupon allows us to measure the impact of insurance on household welfare. In the next essay, I will review studies on insurance uptake and impacts using the household data and the discount coupons.

(Author: Munenobu Ikegami)

Everyday Policy Studies No. en22

Index Based Livestock Insurance (IBLI) 03

 My previous essay reviews the process from insurance design to pilot implementation. In this essay I will start reviewing how we study insurance uptake and impacts.
 Mude et al. (2009) proposes to study insurance uptake and impacts and we designed and implemented a baseline household survey in September 2009 before an insurance company started selling the insurance in January 2010. The number of sample households is 924 and they are located in 16 sublocations in Marsabit County in northern Kenya. Initially we had a research grant only for the baseline survey but we obtained several grants subsequently and could have annual follow up survey five times.
 The baseline survey in 2009 was new experience for me. Before that, I have been studying the data which were designed and collected by someone else. This was my first time to design and implement a survey. This was my first fieldwork as well. I have travelled in rural areas in developing countries but the length of each trip is less than two weeks and the purpose is to satisfy my curiosity rather than research or work. I remember I was very excited to go to Marsabit for the survey after a year of office work in Nairobi.
 We piloted the insurance in Borena Zone in southern Ethiopia as well. Borena Zone and Marsabit County are sharing the border and my colleagues had known the area from their previous research project. In Borena Zone, the baseline household survey was in March 2012 and the initial insurance sale was in August 2012. 515 sample households are located in 17 study areas. We could have annual follow up survey three times. Ikegami and Sheahan (2017, 2018) explains more details of each survey and the data are publicly available at http://data.ilri.org/portal/dataset/ibli-marsabit-r1 and http://data.ilri.org/portal/dataset/ibli-borena-r1.
 In my next essay, I will review how to measure the impacts of insurance using the data. Our main tool was insurance premium discount coupon.

References
Ikegami, M. and M. Sheahan (2017) “Index Based Livestock Insurance (IBLI) Marsabit Household Survey Codebook” http://data.ilri.org/portal/dataset/ibli-marsabit-r1
Ikegami, M. and M. Sheahan (2018) “Index Based Livestock Insurance (IBLI) Borena Household Survey Codebook” http://data.ilri.org/portal/dataset/ibli-borena-r1
Mude, A., C. B. Barrett, M. R. Carter, S. Chantarat, M. Ikegami, and J. McPeak (2009) “Index Based Livestock Insurance for Northern Kenya’s Arid and Semi-arid Lands: The Marsabit Pilot” http://ssrn.com/abstract=1844758

(Author: Munenobu Ikegami)