The Impact of Insurance Provision on Household Production and Financial Decisions

Transcription

1 The Impact of Insurance Provision on Household Production and Financial Decisions Jing Cai University of Michigan October 4, 204 Abstract This paper uses a natural experiment and a rich household-level panel dataset to study the impact of an agricultural insurance program on household production, borrowing, and saving behavior. The empirical strategy includes both difference-indifference and triple difference estimations. The results show that insurance provision increases the production area of insured crops by 6% and raises credit demand by 25%. Interestingly, it does not affect total household savings; however, it does affect the relative proportion of flexible-term savings. Furthermore, the effects on production and savings persist in the long-run, while the effect on borrowing is significant in only the medium-run. Keywords: Insurance; Production; Borrowing; Saving JEL Codes: D4, G2, G22, O6, Q2 I am grateful to Alain de Janvry, Elisabeth Sadoulet, Michael Anderson, David Levine, Ethan Ligon, Jeremy Magruder, Craig McIntosh, Edward Miguel, Jeremy Tobacman, Christopher Udry, and participants at the FERDI workshop, the Midwest Development Conference 203, NEUDC 202, Annual Bank Conference on Development Economics, and UC Berkeley for helpful suggestions and comments. I thank the Rural Credit Cooperative of Jiangxi province for sharing the data. Financial support from the Center for Chinese Studies of UC Berkeley and the Institute of Business and Economic Research is greatly appreciated. All errors are my own. Department of Economics, University of Michigan, 6 Tappan Street, 365A Lorch Hall, Ann Arbor, MI ( caijing@umich.edu)

2 Introduction Poor households in rural areas are exposed to substantial weather disasters, which can generate large fluctuations in income and consumption if insurance markets are incomplete. To protect themselves from such risks, rural households undertake risk management strategies such as using informal insurance, avoiding high risk-high return agricultural activities, holding precautionary savings, and reducing their investment in production (Morduch (995), Rosenzweig and Stark (989)). However, informal insurance mechanisms cannot effectively reduce the negative impact of regional weather shocks (Townsend (994)). In the absence of formal insurance markets, the negative shocks and forgone profitable opportunities can lead to highly variable household income and persistent poverty (Dercon and Christiaensen (20), Jensen (2000), Rosenzweig and Wolpin (993)). To shield farmers from weather-related risk, a number of developing countries have started to develop and market formal insurance products. However, the take-up rate for formal weather insurance is usually surprisingly low, even with heavy government subsidies. While there is a growing body of literature that explores ways to improve insurance demand (Cole et al. (203a), Cai et al. (203), Cai and Song (203), Bryan (203)), rigorous evaluations of the impact of insurance provision on subsequent household behaviors are quite rare. This study uses a householdlevel panel dataset for the period from the Rural Credit Cooperative (RCC) of China 2 to study the effect of insurance provision on individual household production, borrowing, and saving decisions. The insurance program in this study is a weather insurance policy for tobacco farmers offered by the People s Insurance Company of China (PICC). This program was started in 2003 in selected counties of Jiangxi province. It For example, Giné et al. (2008) find a low take-up rate (4.6%) for a rainfall insurance policy among farmers in rural India in 2004, while Cole et al. (203a) find an adoption rate of 5% - 0% for a similar insurance policy in two regions of India in RCC is the most important financial institution in rural China. It is the main provider of microcredit, and most farmers have saving accounts there. 2

3 was subsequently expanded to other areas at the end of The purchase of insurance was made compulsory for tobacco farmers in the treatment regions in the study. I take advantage of the variation in insurance provision across both regions and household types (tobacco households vs. other households) to estimate the effect of insurance provision on household behavior, focusing on the initial stage of the rollout of the policy in To test the impact of insurance adoption on household behavior, my empirical strategy includes both difference-in-difference (DD) and triple difference (DDD) estimations. Because the purchase of insurance in the treatment regions was compulsory, household insurance take-up decisions are not endogenous. In the study, I use tobacco households outside of the treatment region to control for industry-specific trends in outcomes, and use non-tobacco households both within and outside the treatment region to control for regionspecific trends in the absence of the provision of insurance. This design allows me to attribute any changes in household behavior for tobacco households in the treatment regions to the presence of the insurance policy. I find that first, insurance provision has a significantly positive effect on the production of the insured crop; specifically, it raises tobacco production by around 6%. Second, insured households tend to borrow more from the rural bank for investment in tobacco production than uninsured households (about 25% more), even though the credit supply remains the same across the groups. Third, I show that the presence of insurance encourages more flexiblevs. fixed-term saving, although it does not impact the overall level of savings. Fourth, my estimation of the dynamic effects of insurance provision shows that, while the effect of insurance on borrowing decreases by the end of my sample, the impact on both production and savings increases and persists in the longrun. Finally, I find that the impact of insurance on household decisions is greater for farmers with smaller total acreage as well as households with lower migration remittance. This paper contributes to the existing literature in a number of ways. First, it contributes to the literature on insurance take-up decisions and their impact on household behavior. Estimating the causal effect of insurance on household 3

4 behavior is challenging due to the endogenous nature of insurance purchase decisions. Several studies have addressed this issue through the use of different estimation strategies. For example, Cole et al. (203b) use a randomized experiment to study the effect of the provision of free rainfall insurance for selected farmers in India. They find that the provision of insurance induces farmers to shift production towards higher-return, higher-risk cash crops. In another study, Karlan et al. (203) use experimental methods to study the impact of insurance in Ghana. They find a strong effect of insurance availability on production investment. Finally, Gine and Yang (2009) use an experimental design that randomly bundles insurance with loans for selected farmers in Malawi, and find a negative effect of insurance on borrowing. Other studies have used a simulation method to study the effects of insurance provision on household decisions and behaviors. For example, Carter et al. (2007) use a simulation method to show that insurance provision significantly improves farmer welfare, access to credit supply, and loan repayment rates in Peru. In contrast, using a simulation method, Rosenzweig and Wolpin (993) find a minimal gain from weather insurance for Indian farmers. They attribute this lack of effect as being due to the existence of competing informal insurance mechanisms. Overall, this paper complements the existing literature on insurance demand and impact by using a rigorous estimation strategy to test both the short- and long-term effects of insurance provision on household production, borrowing, and saving behavior in China. It does so by taking advantage of administrative borrowing and savings data obtained from the rural bank in China. Because large and significant impacts of insurance policy are found in this paper, it supports the proposition that studying ways to improve voluntary insurance take-up is important. Second, the paper also contributes to the literature on technology adoption in developing countries. This stream of literature seeks to explain why individuals in developing countries exhibit a relatively low level of investment in new technologies made available to them. Credit constraints, as well as the lack of information or knowledge, are often proposed as explanations for low invest- 4

5 ment levels (Feder et al. (985)). In addition, Duflo et al. (20) argue that behavioral biases limit profitable agricultural investments. This study shows that low adoption rates of new technology can be explained by the perceived riskiness of production. Thus, this study provides insight for policymakers by suggesting that reducing production risks can persistently improve investment on agriculture production. The rest of the paper is organized as follows. Section 2 describes the background for the study and provides the details of the insurance contract. Section 3 explains the data and summary statistics. Section 4 presents estimation strategies and results, and Section 5 concludes. 2 Background Tobacco is an important cash crop in China. Indeed, more than two million rural households in China subsist on the income earned from tobacco production. These households have a financial incentive to grow tobacco, as the net profit from tobacco production is around 2000 RMB per mu 3, a return three to five times greater than that of food crops such as rice. In China, most tobacco producing counties are poor and located in mountainous areas. In Jiangxi province, there are 2 main tobacco production counties 4. In the late 990s, these counties developed poverty-reduction initiatives by encouraging farmers to cultivate tobacco and organizing tobacco associations to teach tobacco production techniques. Taxes on tobacco production are now the main source of government revenue in these counties. Despite the success of the move toward tobacco production in the area, tobacco production, as with other crops, can be greatly influenced by weather risks. For example, in 2002, a flood destroyed a majority of tobacco crops in some counties. This destruction caused huge losses in both household income and government revenue. To prevent such consequences from future weather 3 RMB = 0.6 USD; mu = hectare 4 The 2 tobacco production counties include Ganxian, Guangchang, Huichang, Lean, Ningdu, Shicheng, Xinfeng, Xinguo, Quannan, Ruijin, Yihuang, and Zixi. 5

6 disasters, the vice-head of Guangchang County, who was previously a manager of an insurance company, proposed a cooperative effort with insurance companies. This effort shields tobacco farmers from frequent weather disasters to improve their incentive to continue tobacco production. As a result of this effort, in 2003, the People s Insurance Company of China (PICC) designed and offered the first tobacco production insurance program to households in Guangchang. In 2008, the policy offer was extended to three other counties 5. The insurance contract offered to the tobacco farmers contains the following terms. The actuarially fair price estimated by the insurance company is 2 RMB per mu. The county and town government together provide a 67% subsidy on the premium, which leaves 4 RMB per mu for the farmers to pay. During the period of the study, all households with tobacco production as the main source of income were required to buy the insurance for all their tobacco areas. The insurance policy is designed to cover the following natural disasters: heavy rains, flood, windstorms, high or low temperature, and drought. According to the terms of the policy, if any of the listed events leads to a 30% or more loss in yield, farmers are eligible to receive payouts from the insurance company. The amount of payout increases linearly with the loss rate in yield, with a maximum payout of 420 RMB. To determine the amount of the payout, a group of insurance agents and agricultural experts investigates and then identifies the loss rate in yield 6. The average net income per household from cultivating tobacco is around 2000 RMB per mu, and the production cost is around 400 RMB to 600 RMB per mu (not including labor costs). Thus, the insurance program provides partial insurance that covers around 20% of the gross income, or most of the production cost for the tobacco production. 5 Since the policy was extended to these counties at the end of year 2008, I treat these counties as a control group for 2008 in the empirical analysis. 6 For example, consider a farmer who has 5 mu in tobacco production. If the normal yield per mu is 500kg and a windstorm decreases the farmer s yield to 250kg per mu, then the loss rate is 50% and he receives 420*50% = 20 RMB per mu from the insurance company. 6

7 3 Theoretical Model In this section, I outline a two-period, two-state model to show how the provision of insurance influences household investment and financial decisions 7. Intuitively, in the first period, the availability of insurance increases production investment as it raises the expected production income. As a result, insurance provision has a positive effect on household borrowing for investment on production. The relation between insurance provision and household savings is ambiguous. First, households may decrease savings to finance more production investment. Second, if insurance reduces income uncertainty, then people have a lower precautionary incentive to save. Third, assuming rational expectations, if insurance increases the expectation of becoming richer in the future, then farmers may increase current consumption and reduce savings. Finally, if the purchase of insurance is subsidized, then this would have a positive effect on farmers wealth, which would have a positive effect on savings. The detail of the model is as follows. Consider a farmer who lives for two periods with initial wealth W 0. the first period, the farmer consumes C and uses the remaining wealth for investment. There are two ways by which the farmer may choose to invest this money: one is to save it in the bank with an interest rate R f, while the other is to invest it in a risky project like crop production, which has a return function F ( ). For the risky project investment, the farmer can borrow from a local bank at an interest rate of R B. This means that the total investment I in the risky project includes the initial wealth less consumption and savings, plus a loan equal to B from the bank. By definition, the return on the risky project is uncertain. In this case, the success of the project depends on whether a disaster occurs in period one. In my simple model, I assume two states: a good state (no disaster) and a bad state (disaster). In the good state, the farmer receives F (I) from the investment, while in the bad state, he receives no return on the investment. 7 Throughout the model, I assume that those farmers with access to insurance buy it in every period, due to the compulsory purchase stipulation, while those without access to insurance do not buy it in any period. In 7

8 Assuming no strategic default and limited liability, in the good state, the farmer will repay fully in the second period. By contrast, in the bad state, the farmer will default on the loan if he does not have money to repay it. I next suppose that a farmer who invests I on the risky project (production) will pay a premium which equals δi to buy a crop insurance 89. In the bad state, the farmer will be reimbursed by the insurance company by an amount equal to part of the cost invested in the risky project, γi. As a result, even in the bad state, the farmer who purchases insurance will be able to repay all or part of the production investment loan. I solve the two-period model separately for the insured and uninsured groups, as their second period consumption levels differ in the bad state. I assume that the farmers in my model are price takers; that is, they do not believe they can influence either insurance premiums or interest rates. 3. Two-period model when insurance is not provided For farmers who do not have access to insurance, the optimization problem is as follows: max C,S,B U(C ) + EβU(C 2 ) max C,S,B U(C ) + βpu F (I) ( + R B )B + ( + R f )S + β( p)u ( + R f )S s.t. I = W 0 C S + B I next assume that the return function and the utility function are represented respectively as follows: F (I) = ψi α, α < 0 8 In my data, δ should be quite low because a farmer will pay only 4 RMB per mu to buy the insurance, even though the production cost (I) is around RMB per mu. 9 I also assume that, to reduce average risk and prevent adverse selection, the insurance company will require the farmer to buy insurance for his entire production area. 0 This return function form can exclude the case of infinite investment. 8

9 U(C) = log C This yields the following first-order conditions: U (C ) = βpu F (I) ( + R B )B + ( + R f )S F (I) (3.) βpu F (I) ( + R B )B + ( + R f )S ( + R f ) F (I) + β( p)u ( + R f )S ( + R f ) = 0 (3.2) βpu (F (I) ( + R B )B + ( + R f )S) F (I) ( + R B ) = 0 (3.3) F (I ) = + R B (3.4) Using the return function form, I rewrite equation (3.4) as: F (I ) = ψαi α = + R B ( I = ) +R α B ψα (3.5) Overall, the optimal level of investment decreases in the borrowing interest rate R B. In other words, people will invest more in a risky project when the cost of borrowing is lower. Part of Appendix A outlines the solution to the above optimization problem. 3.2 Two-period model when insurance is provided When a farmer has insurance, the optimization problem is as follows: max C,B,S U(C ) + βpu C g + β( p)u C b s.t.i = B + W 0 C S δi I = W 0 C S +δ + B +δ 9

10 where C g and C b represent the farmer s consumption in the second period under the good and bad state, respectively. The biggest difference in this model is that, under the bad state, the farmer receives a payout from the insurance company to cover part of the production cost. This payout equals γi = γ W 0 C S + γ B. Thus, the return on the production investment under +δ +δ the bad state is γi. Since I assume no strategic default, the farmer will repay the bank γ B, +δ which represents the return generated by a loan with size B. Given this repayment, the consumption under the good and bad states in the second period is represented by the following, respectively: C b = C g = F (I) ( + R B )B + ( + R f )S γ (W +δ 0 C S + B) γ B + ( + R +δ f)s The three first-order conditions are: U (C ) βpu (C g )F (I) +δ β( p)u (C b ) γ +δ = 0 (3.6) βpu (C g ) ( + R B ) + F (I) +δ = 0 (3.7) βpu (C g ) ( + R f ) F (I) +δ + β( p)u (C b ) γ + + R +δ f = 0 (3.8) Under the insurance condition, the utility and return function forms are the same as those in the previous section: U(C) = log C F (I) = ψi α, 0 < α < Part 2 of Appendix A provides the solution for the above optimization problem. 0

11 3.3 Combination of the two models In this subsection, I provide solutions to the optimization problem for the insured and uninsured groups. Specifically, the respective expressions for the optimal investment, consumption, savings, and borrowing levels for the insured and uninsured farmer groups are as follows: I (insured) = ( I (unisured) = ( (+RB )(+δ) ψα ) α ) +R α B ψα C(insured) = W +β 0 + (α )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) +R f C(uninsured) = +β W 0 + (α ) ( +R B α S (insured) = β W +β 0 + (α )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) S (unisured) = β +β W 0 + (α ) ( +R B α ) α ψ α +R f (+R f )(+δ) γ +β γ+β(+r f )(+δ) ) α ψ α B (insured) = ( + R B ) α α ψ α ( + δ) α α α +R α + f +R B )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) +R f β + γ+β(+r f )(+δ) (+R f )(+δ) γ B (uninsured) = α α α ψ α ( + RB ) α +β (α 3.4 Break-even condition for the bank In this subsection, I specify the break-even condition for the bank. If a farmer does not have insurance, the bank receives no repayment in the bad state. In this case, the break-even condition is: B( + R f ) = p( + R B )B For simplicity, I assume that the institution s objective is to break even.

12 R B = + R f p If the farmer has insurance, the break-even condition for the bank becomes: In summary: ( + R f )B = p( + R B )B + ( p) γ R B = + R f ( p)γ +δ. p +δ B R B = + R f, if not insured p + R f ( p)γ, if insured +δ p From the above, we can see that the bank sets a lower interest rate for people who have insurance because they are more likely to be able to repay. 3.5 Predictions of the model In this subsection, I present the implications of the model for the effects I examine in the study. Specifically, I plug the interest rate into the optimal decisions outlined in 3.3 to compare the respective magnitudes of investment, consumption, savings and borrowing levels exhibited by insured versus uninsured farmers. Investment: The model predicts that farmers with insurance will invest more compared to those without insurance. I (insured) = ( (+RB )(+δ) ψα ( I (unisured) = ) α ) +R α B ψα = = ( +Rf p ( +Rf p ψα ( p)γ (+δ)p ψα ) α ) α Because α < 0, so if ( p)γ > 0, the investment level increases as a result (+δ)p of the provision of insurance. Intuitively, when insurance is provided, borrowing becomes less expensive and the expected return from the risky project increases. Thus, investing in the risky project becomes more attractive. 2

13 Consumption: The model predicts that farmers with insurance will consume more in the first period than those without insurance. C(insured) = W +β 0 + (α )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) +R f C(uninsured) = +β W 0 + (α ) ( +R B α ) α ψ α I need to compare the following two terms: (+R B ) α (+δ) α p(+r B)(+δ) +R f when insurance is offered and ( + R B ) α when insurance is not offered. Let s consider two cases: first, when p( + R B )( + δ) > + R f, obviously C (insured) > C (unisured); second, when p( + R B )( + δ) < + R f : ( + R B ) α ( + δ) α p(+r B)(+δ) +R f = ( + R B ) α α ( + δ) α α p( + Rf ) > ( + R f ) α α p α α p( + Rf ) = ( +R f p ) α So C (insured) > C (unisured) A farmer with insurance expects that he will be richer in the second period. Consequently, he smooths his consumption across the two periods by increasing the consumption in period one. Savings: The model predicts that farmers with insurance will increase their savings rate in the first period compared to those without insurance. S (insured) = β W +β 0 + (α )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) S (unisured) = β +β W 0 + (α ) ( +R B α +R f (+R f )(+δ) γ +β γ+β(+r f )(+δ) ) α ψ α Since ( + R B ) α ( + δ) α p(+r B)(+δ) +R f > ( +R f p ) α, γ+β(+r f )(+δ) (+R f )(+δ) γ > β, so S (insured) > S (unisured). One potential explanation of this result is that because borrowing becomes cheaper when insurance is provided, compared with depending on savings, it s less costly to use borrowing to finance investment than before. 3

14 Borrowing: The model predicts that borrowing will increase once insurance is offered. B (insured) = ( + R B ) α α ψ α ( + δ) α α α +R α + f +R B )( +R B ) α α ψ α ( + δ) α p(+r B)(+δ) +R f β + γ+β(+r f )(+δ) (+R f )(+δ) γ B (uninsured) = α α α ψ α ( + RB ) α +β (α Because I (insured) > I (uninsured), so ( + R Binsured ) α α ( + δ) α > ( + R Buninsured ) γ+β(+r α. Moreover, β < f )(+δ). (+R f )(+δ) γ As a result, B(insured) > B(unisured). 4 Data and Summary Statistics The empirical analysis is based on data obtained from 2 tobacco production counties in the Jiangxi province of China. Across these 2 counties, only tobacco farmers in the county of Guangchang were eligible to buy the tobacco insurance policy during the period of the study. In this county, only households with their main source of income from tobacco production were offered insurance. The data in this study is a household-level panel dataset provided by the Rural Credit Cooperative (RCC), the main rural bank in China 2. The sample includes information on around 6000 households during the period The dataset is composed of two parts. The first part includes administrative data on household saving and borrowing information. Specifically, it includes information on variables such as loan certification numbers, total borrowing during the year, monthly interest rates, use of a loan, repayment on loans, total annual savings, savings in a deposit account, savings in a current account, and 2 RCC is the major rural bank in China. In my study regions, RCC is the main microfinance provider for farmers, accounting for more than 90% of the rural lending. Most farmers have saving accounts in RCC, because RCC has branches in each town, making deposit and withdrawal very convenient. 4

15 savings interest rate 3. The second part of the dataset includes RCC annual survey data 4 which contains two broad categories of information. The first is family background data: head of household information, including age, national ID, gender, occupation and education, as well as total household income, family address, and household size. The second category provides more detailed information regarding household income and production, including total annual income, household income from different sources, remittance income, total area of land for cultivation, and production area by crop. In sum, the data covers 5746 households, of which 3466 households are tobacco households, and 2280 households are other households whose main source of income is not tobacco production 5. For the tobacco households, 259 of them are in the treatment region where the insurance policy is available, while 2207 of them are in control regions. Table provides the summary statistics for the key variables for the period , before the implementation of the insurance policy. The data in Table show that household heads are almost exclusively male with an average age of around 40. The average household size is around five people, and household heads have an average education level between primary and secondary school. The data further show that the tobacco production scale is larger for households in treatment regions than for those in control regions (5.37 vs. 4.3). The average annual household income equals 4,000 RMB for tobacco households in the treatment regions, while that of tobacco households in the control regions is a bit higher, around 5,000 RMB. Annual income of non-tobacco households is significantly lower, with around 2,000 RMB. 3 While RCC is the main place for farmers to make deposits, households may have saving accounts with other institutions. As a result, the amount of savings recorded by the RCC may not represent a household s total savings level. To account for this potential inaccuracy, RCC reports the village-level ratio of RCC savings to total household savings based on their annual survey. In this study, I adjust the RCC savings data by this ratio in all of my subsequent empirical analyses. 4 RCC implements a household survey every year in order to adjust its lending interest rates and loan ceilings for each household. 5 These households receive their income from either agricultural activities such as rice production and or from non-agricultural activities. 5

16 Regarding the variables of interest in the study, the data in Table shows that non-tobacco households have the highest borrowing levels (5,40 RMB), followed by tobacco households in the control regions (4,20 RMB), and tobacco households in the treatment regions (3,430 RMB). The average borrowing interest rate across all regions in the study is around monthly during the sample period, with the lowest rate received by non-tobacco households. In terms of household savings rates, tobacco households in the control regions exhibit the highest savings rate (7.3%), followed by tobacco households in the treatment regions (6.9%), and then non-tobacco households (5.9%). In addition to the savings rate, I include the flexible-term savings for each household, measured as the ratio of net savings in a checking account compared to the total net savings of a household Estimation Strategies and Results 5. Empirical Strategies The introduction of the tobacco insurance policy introduced variations in insurance provision in three dimensions: years before and after the policy was introduced, regions with and without the policy, and eligible and ineligible households (tobacco households vs. non-tobacco households). These variations allow me to use both difference-in-difference (DD) and difference-in-differencein-difference (DDD) estimations in my empirical analysis. 5.. The Impact of Insurance Provision on Tobacco Production I first use a difference-in-difference (DD) estimation to test the impact of insurance on tobacco production. To understand how insurance provision affects 6 Households can withdraw savings from their checking account at any time, while they can withdraw money from a fixed-term savings account only after a certain period. Usually the interest rate of the fixed-term account is higher than that of the flexible-term savings account. 7 To obtain more accurate results, I delete outlier households (those in the lowest or highest % in income, loan size, or savings) from the sample for analysis. 6

17 production, I plot the evolution of tobacco production in the treatment vs. control regions, as shown in Figure. The plot in Figure shows that, while tobacco production was higher but similar in trend for tobacco households in the treatment and control regions before the introduction of the insurance policy ( ), production increased at a higher rate in the treatment regions after To check whether DD is an appropriate strategy to examine this difference, I test the common trend assumptions using the following regression with the pre-insurance household data ( ) 8 : P roduction irt = η 0 +η Y ear t +η 2 Insurance ir +η 3 Y ear t Insurance ir +ɛ irt () where i, r, t are household, region, and year indices, respectively. Furthermore, P roduction irt is the area of tobacco production (mu), Insurance ir is a treatment indicator equal to for treatment regions and 0 for control regions. Finally, Y ear t is a time trend variable. According to column () in Table 2, the common trend assumption is valid because the coefficient of the interaction term, η 3, is statistically insignificant. I then use the following regression to estimate the impact of insurance provision on tobacco production, based on the sample of tobacco households: P roduction irt = α 0 +α After it +α 2 Insurance ir +α 3 After it Insurance ir +ɛ irt (2) where After is a dummy variable equal to for the period and 0 for years This variable controls for the impact of time-varying aggregate economic environment and policy measures on tobacco production. Other variables are defined the same as in equation (). The coefficient of interest is the one before the interaction term between After and Insurance ir, α 3. 8 Because the implementation of the insurance policy was on the county level, in all estimations, I cluster the standard error to the county level. Since there are only 2 counties in my sample, I use bootstrap to generate standard errors in all estimations. 7

18 5..2 The Impact of Insurance Provision on Borrowing and Saving To estimate the impact of insurance provision on borrowing and saving, I use DDD. This is a more appropriate choice than DD as it controls for potential region-specific effects. An examination of the evolution of borrowing and saving suggests the possibility of such a region-specific effect. To see this, Figure 2. indicates that, while tobacco households in treatment regions borrow less than those in control regions before 2002, the pattern reverses after Figure 2.2 shows that the borrowing pattern is also different across the sample period between non-tobacco households in the treatment and control regions, which suggests a region-specific trend for which I should control when estimating the insurance effect. Similarly, the data show a potential region-specific effect on household savings rates. According to Figures 3. and 3.2, household saving rates increase significantly faster in the treatment regions, even among non-tobacco households. To see this more clearly, in Table 3, I report the average area of tobacco production, size of loans, and saving rate for households, by time period, region, and sector eligibility. As an example, consider loan size. For each region-sector category, the average loan size increases from the period to the period , reflecting an aggregate economic trend. According to columns ()-(3) in Table 3, for tobacco households only, the average loan size in the treatment regions increases by 3,2 RMB, which is greater than the loan size increase for households in control regions. This phenomenon could reflect the introduction of the insurance policy or other region-specific changes. For example, as shown in columns (4)-(6), for non-tobacco households, the average loan size also grows more quickly in the treatment regions than in the control regions. In summary, there may be some other contemporary changes in the economic environment or other policies specific to the treatment region that influence household production and financial decisions. To control for such changes, I take another level of DD analysis, which compares the behavior of non-tobacco households in the treatment regions before and after 2002 with that of non-tobacco households in the control regions. This DDD framework 8

19 + β 6 T obacco ij Insurance ir + β 7 After it Insurance ir T obacco ij + ɛ ijrt (4) can further control for any potential region-specific trends. Specifically, the DDD framework requires only the assumption that tobacco households and other households have similar trends in outcomes in absence of the insurance policy. This common trend assumption for the DDD analysis is estimated as follows: Y irt = η 0 + η Y eardummies t + η 2 Insurance ir + η 3 T obacco ir + η 4 Y eardummies t Insurance ir + η 5 Insurance ir T obacco ir + η 6 Y eardummies t T obacco ir + η 7 Y eardummies t Insurance ir T obacco ir + ɛ irt (3) where Y eardummies t includes a set of year dummies for the years 200 and The common trend assumption holds if the coefficients of the interaction term between Y eardummies t, Insurance ir, and T obacco ir are not statistically significant. Columns (2)-(5) in Table 2 report the results from this estimation. The results show that, before the introduction of the insurance policy, there s no significant difference in the trend of borrowing and savings between tobacco and non-tobacco households. As a result, we can use DDD as a valid estimation strategy to test the impact of insurance provision on borrowing and savings. I now specify the DDD regression as follows: Y ijrt = β 0 + β After it + β 2 Insurance ir + β 3 T obacco ij + β 4 After it Insurance ir + β 5 After it T obacco ij where j is a sector indicator, and T obacco ij is a dummy variable equal to for tobacco households and 0 otherwise. In equation (4), the coefficient for the time, region, and sector interaction (β 7 ) captures the average effect of insurance provision on household behavior. 9

20 5..3 Dynamic Effects and Heterogeneity Test The effect of insurance introduction on household production and investment decisions may take place shortly after the policy is introduced or several years later. In addition, the magnitude of this effect may change over time. Consequently, it is appropriate to test the dynamic effect of insurance availability on household behavior. The estimation equation is as follows. Equation (5) tests the dynamic effect of insurance provision on household production, and equation (6) tests the dynamic impact on borrowing and saving. Y ijrt = ρ 0 + ρ Y ear t + ρ 2 Insurance ir + ρ 3 Y ear t Insurance ir + ɛ ijrt (5) Y ijrt = ρ 0 +ρ Y ear t + ρ 2 Insurance ir + ρ 3 T obacco ij + ρ 4 Y ear t Insurance ir + ρ 5 Y ear t T obacco ij + ρ 6 T obacco ij Insurance ir + ρ 7 Y ear t Insurance ir T obacco ij + ɛ ijrt (6) where Y ear t includes a set of year dummies. The magnitude of the impact of insurance on household behavior may differ across households depending on total farm size or the importance of migration remittance in household income. To address these possibilities, I estimate the following regression: Y ijrt = γ 0 + γ After it + γ 2 Insurance ir + γ 3 T obacco ij + γ 4 After it Insurance ir + γ 5 After it T obacco ij + γ 6 T obacco ij Insurance ir + γ 7 After it Insurance ir T obacco ij + γ 8 Index it + γ 9 Index it After it + γ 0 Index it Insurance ir + γ Index it T obacco ij + γ 2 Index it After it Insurance ir + γ 3 Index it After it T obacco ij + γ 4 Index it Insurance ir T obacco ij + γ 5 Index it After it Insurance ir T obacco ij + ɛ ijrt (7) where Index it is an indicator equal to if, in the pre-insurance period (

21 2002), the household total production area or percentage of migration remittance to total income is higher than for the sample median, and 0 otherwise. The coefficient of interest is γ Estimation Results Tables 4-6 report the DD and DDD estimation results for the effect of insurance availability on household production, borrowing, and savings decisions, respectively 9. Table 4, column () presents the results for the effect of insurance on production. Specifically, the results show that the increase in tobacco production after 2002 is mu larger for households in treatment regions compared to households in the control regions. Because the pre-insurance mean for tobacco production in the treatment regions is about 5.37 mu (refer to Table ), this result means that the availability of insurance raises tobacco production by around 6%. This finding is consistent with the idea that, since insurance increases the expected return of tobacco production, it gives households a greater incentive to invest more in tobacco production. Column (2) provides the regression result when year dummies are included. Column (3) presents the results controlling for household characteristics, including household size and head of household age and education level. Adding these controls yields a similar effect of insurance on tobacco production. The results also indicate that larger households, as well as those with more welleducated and younger household heads, are likely to have a larger production scale overall. This finding may reflect the fact that successful tobacco production requires not only more labor than other crop production, but also thorough knowledge of the techniques necessary to have high yield and good quality tobacco. Table 5 reports the DDD estimation results for the effect of insurance provision on household borrowing. The results in column (3) suggest a significant insurance treatment effect on borrowing (around 876 RMB). Comparing this 9 Note that the DDD framework is not appropriate for estimating the effect of insurance provision on household tobacco production, because there is almost no tobacco production in the non-tobacco households. 2

22 result to the average loan size incurred by tobacco households in the treatment regions prior to 2003 (shown in Table ), tobacco households borrow 25% more once their production is insured. This finding can be explained by the impact of insurance availability on both the supply and demand side of borrowing. For the supply side, the introduction of tobacco insurance may encourage the rural bank to provide more favorable borrowing terms (such as higher loan ceilings and lower interest rates) to farmers whose production is insured. To look into this effect, I estimate the impact of insurance on rural bank lending interest rates. Results in columns (4)-(6) suggest that the lending interest rate is lower for insured tobacco farmers. However, the effect is not precisely estimated. As a result, the impact of insurance provision on household borrowing is mainly driven by the demand side effect. Table 6 presents the results of the effect of insurance provision on household savings. Columns () and (2) show an insignificant effect of insurance provision on household saving rates. Columns (3) and (4) report the results of the insurance effect on the amount of net savings. These results show a similar insignificant effect. Together, these results suggest that farmers do not change their savings levels after obtaining insurance. There are several potential explanations for this result. First, farmers may hold savings for different reasons such as child education, health services, etc. Consequently, even though their crop production is insured, they may still need sufficient savings to cope with other types of risks or activities. Second, this result may reflect cultural preferences for holding as much savings as possible. While the savings rates do not appear to be impacted by insurance, it is interesting to examine whether the type of savings is influenced. Columns (5) and (6) show that insurance does have a significantly positive effect on the proportion of flexible-term savings comprising a household s total savings. As a result, although farmers did not save less, they tend to make their savings more flexible after the introduction of insurance. I next examine the dynamic impact of insurance on household borrowing and saving behavior. The results in Table 7, column (), indicate that the effect of insurance on tobacco production becomes significant one year after 22

23 its introduction. Furthermore, the magnitude of this effect increases over time. At the end of the sample period (2008), the effect is still significant. Turning to the dynamic impact of insurance provision on borrowing, the results in column (2) show that the impact on loan size is significant one year after the introduction of insurance, but decreases in magnitude and significance after Lastly, columns (4) and (5) report the dynamic impact of insurance on savings behavior. The results in column (4) show an insignificant effect of insurance availability on total savings, but a significantly positive effect on the proportion of flexible- vs. fixed-term savings. This effect persists across the sample period. In Table 8, I report the findings on the heterogeneity of the insurance impact. The results in columns () - (3) show that insurance has a larger effect on the saving behavior of smaller farmers, while the effect on production and saving is not statistically different across farm size. The results in columns (4) - (6) show that insurance has a smaller impact on the savings composition of households who depend more on migration remittance. Finally, once the insurance policy was implemented for tobacco farmers, we may expect an endogenous switch of non-tobacco households to tobacco production. In Table 9, I report the percentage of households that stay in the same sector, switch from tobacco to the non-tobacco sector, or switch from the non-tobacco sector to the tobacco sector between the previous and current year. I report these results for both the treatment and control regions. The results in Table 9 show that only a very small fraction of households change sectors during the sample period. A robustness check that excludes all households that have ever switched sectors yields similar results. 6 Conclusions Household income in developing rural economies is subject to great uncertainty for a multitude of reasons, including the vulnerability of such income to weather shocks. As a result, many developing countries are making efforts to improve the quality and coverage of agricultural insurance products to mitigate 23

24 the impacts of weather disasters. Taking advantage of a natural experiment in the introduction of weather insurance to tobacco farmers in rural China, this paper uses both DD and DDD estimations to study the effect of insurance provision on household production and financial decisions. I find that households that obtain such insurance tend to increase subsequent tobacco production. Moreover, I find that insurance not only encourages households to borrow more from the bank, but also increases the proportion of flexible-term savings they carry as part of their total household savings. However, the insurance impact on credit supply is not significant. Finally, I find that the impact of insurance on both production and savings persists in the long-run. However, the impact of insurance on borrowing behavior diminishes over time. These results suggest a dynamic nature to the impact of insurance on household choices for some types of decisions. 24

25 References Bryan, Gharad, Ambiguity Aversion Decreases Demand for Partial Insurance: Evidence from African Farmers, Working Paper, 203. Cai, Jing, Alain de Janvry, and Elisabeth Sadoulet, Social Networks and the Decision to Insure: Evidence from Randomized Experiments in China, Working Paper, 203. and Changcheng Song, Insurance Take-up in Rural China: Learning from Hypothetical Experience, Working Paper, 203. Carter, Michael R., Francisco Galarza, and Stephen Boucher, Underwriting Area-Based Yield Insurance to Crowd-in Credit Supply and Demand, Savings and Development, 2007, 3, Cole, Shawn, Petia Topalova,, Xavier Gene, Jeremy Tobacman, Robert Townsend, and James Vickery, Barriers to Household Risk Management: Evidence from India, American Economic Journal: Applied Economics, 203, 5 (), , Xavier Giné, and James Vickery, How Does Risk Management Influence Production Decisions? Evidence from a Field Experiment, Working Paper, 203. Dercon, Stefan and Luc Christiaensen, Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia, Journal of Development Economics, November 20, 96 (2), Duflo, Esther, Michael Kremer, and Jonathan Robinson, Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya, American Economic Review, October 20, 0 (6), Feder, Gershon, Richard E Just, and David Zilberman, Adoption of Agricultural Innovations in Developing Countries: A Survey, Economic Development and Cultural Change, January 985, 33 (2),

26 Gine, Xavier and Dean Yang, Insurance, credit, and technology adoption: Field experimental evidencefrom Malawi, Journal of Development Economics, May 2009, 89 (),. Giné, Xavier, Robert Townsend, and James Vickery, Patterns of Rainfall Insurance Participation in Rural India, World Bank Economic Review, October 2008, 22 (3), Jensen, Robert, Agricultural Volatility and Investments in Children, American Economic Review, May 2000, 90 (2), Karlan, Dean, Robert Osei, Isaac Osei-Akoto, and Christopher Udry, Agricultural Decisions after Relaxing Credit and Risk Constraints, Working Paper, Yale University, 203. Morduch, Jonathan, Income Smoothing and Consumption Smoothing, Journal of Economic Perspectives, Summer 995, 9 (3), Rosenzweig, Mark R and Kenneth I Wolpin, Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investment in Bullocks in India, Journal of Political Economy, April 993, 0 (2), and Oded Stark, Consumption Smoothing, Migration, and Marriage: Evidence from Rural India, Journal of Political Economy, August 989, 97 (4), Townsend, Robert M, Risk and Insurance in Village India, Econometrica, May 994, 62 (3),

27 Figure. Evolution of Tobacco Production, by Treatment 27

28 Figure 2.. Evolution of Loan Size for Tobacco Households, by Treatment Figure 2.2. Evolution of Loan Size for Other Households, by Treatment 28

29 Figure 3.. Evolution of Saving for Tobacco Households, by Treatment Figure 3.2. Evolution of Saving for Other Households, by Treatment 29

30 Figure 4.. Evolution of Flexible-term Saving for Tobacco Households, by Treatment Figure 4.2. Evolution of Flexible-term Saving for Other Households, by Treatment 30

31 Table. Summary Statistics Other Households All Sample Tobacco Households Treated Control Difference () (2) (3) (4) (5) Number of Households Gender of Household Head *** ( = Male, 0 = Female) (0.069) (0.32) (0.000) (0.53) (0.3) Age *** (8.842) (8.082) (0.000) (8.754) (8.526) Household Size *** (.085) (.338) (0.000) (.324) (.284) Education (0=illiteracy, =primary, *** =secondary, 3=high school, 4=college) (0.543) (0.99) (0.000) (0.64) (0.746) Area of Tobacco Production (mu) *** (2.372) (3.687) (0.000) (.079) (3.406) Annual Household Income *** (0,000 RMB) (0.523) (.44) (0.000) (.224) (.086) Loan Size *** (0,000 RMB) (0.234) (0.75) (0.00) (0.092) (0.5) Loan Monthly Interest Rate ( ) *** (0.672) (0.464) (0.003) (0.562) (0.552) Saving Rate (Net Saving / Income) (0.087) (0.3) (0.237) (0.) (0.07) Flexible-term Saving *** (Net Checking / Net Saving) (0.32) (0.376) (0.000) (0.47) (0.397) Notes: This table reports the mean of key variables in pre-treatment periods ( ). Saving rate is defined as the annual growth in total saving (netsaving(t)=totalsaving(t)-totalsaving(t-)) divided by the current year household income. Flexible-term saving is calculated by the ratio between net saving in checking account and the total net saving. For columns (), (2), (4) and (5), standard deviations are in brackets. For column (3), P-value for F test of equal means of two groups are in brackets. *** p<0.0, ** p<0.05, * p<0. 3

32 Table 2. Test Common Trend in Key Outcome Variables Before Policy Intervention Production Borrowing Saving VARIABLES Area of Tobacco Production (mu) Loan Size (0,000 RMB) Interest Rate ( ) Saving Rate Flexibleterm Saving () (2) (3) (4) (5) Year 0.206*** (0.0586) Insurance ** *** *** (=0 if control region, = if treatment region) (0.4735) (0.0043) (0.4535) (0.008) (0.096) Year*Insurance (0.066) Tobacco Household ** (= 0 if No, = if Yes) (0.0997) (0.0905) (0.0527) (0.0863) Tobacco Household * Insurance * -0.02* (0.002) (0.453) (0.009) (0.4) 200 * Insurance * Tobacco Household (0.08) (0.466) 2002 * Insurance * Tobacco Household (0.274) (0.7579) (0.008) (0.066) Observations Household Characteristics Yes Yes Yes Yes Yes Year Dummies * Insurance No Yes Yes Yes Yes Year Dummies * Tobacco Household No Yes Yes Yes Yes R-squared Notes: Bootstrap clustered standard errors in parentheses. Column () tests the common trend assumption of DD estimation of the insurance impact on tobacco production. Columns (2)-(5) tests the common trend assumption of DDD estimations of the insurance impact on borrowing and saving. Saving rate (column (4)) is defined as the annual growth in total saving (netsaving(t)=totalsaving(t)-totalsaving(t-)) divided by the current year household income. Flexible-term saving (column (5)) is calculated by the ratio between net saving in checking account and the total net saving. Household characteristics including household size, education, gender, and age of household heads are controlled in all estimations. *** p<0.0, ** p<0.05, * p<0. 32