[IFPRI Gender Methods Seminar] Liquid milk: Cash Constraints and the Timing of Income

1. Liquid Milk: Cash Constraints and the Timing of Income Xin Geng, Berber Kramer and Wendy Janssens IFPRI Gender Methods Brown Bag Seminar, December 13, 2016 Geng, Kramer and Janssens (2016) Liquid Milk 1 / 37

2. Background and Motivation Financial planning is difficult, especially when facing cash constraints, unpredictable incomes and expenditures (Collins et al., 2009) Rural women affected most (Demirgüç-Kunt and Klapper, 2012) Cash constraints affect intertemporal allocations of experimental gifts (Dean and Sautmann, 2016; Janssens et al., 2016; Carvalho et al., 2016) Do cash constraints affect preferences over timing of ‘real’ income? We address this question by studying where farmers sell agricultural output: Cooperatives defer payments at potentially higher prices, and provide extra services (Reardon et al., 2009; Minot and Sawyer, 2014) Local traders are trusted less to save one’s money (Casaburi and Macchiavello, 2015) Geng, Kramer and Janssens (2016) Liquid Milk 2 / 37

3. Preview of the Presentation Does cash at hand affect the choice where to sell milk? Market vs. cooperative: Sooner-smaller vs. later-larger trade-off The share of milk sold to the cooperative increases in cash-at-hand Corner solutions create treshold effects and nonlinearities We estimate effects of cash at hand on milk marketing decisions High-frequency panel data for dairy farmers in Kenya, measuring net inflows of cash from dairy vs. non-dairy activities Semiparametric techniques provide parameter-free estimates of how these two variables affect marketing decisions We find evidence that the market provides informal insurance: Farmers often sell milk in the market, despite a lower milk price They do so especially when they are more cash-constrained In those weeks, the local market may pay them a higher price Geng, Kramer and Janssens (2016) Liquid Milk 3 / 37

4. Conceptual Framework: Basic set-up Every period, a household produces mt and decides how much to sell outside the cooperative, st, such that it optimizes max 0≤st ≤mt ∞ t=0 βt u(ct) (1) subject to the following budget constraint: ct = yt + ptst + mt−1 − st−1 (2) where ct represents (food) consumption and pt the market milk price. Farmers are paid immediately for milk sold in the market The cooperative defers payments for mt − st by one period Non-dairy net income, yt, is assumed to be predetermined No savings and borrowing outside the cooperative Geng, Kramer and Janssens (2016) Liquid Milk 4 / 37

5. Conceptual Framework: Predictions Relatively low market price (p < β): farmers sell all milk to the cooperative Increase in cash at hand (yt + mt−1): No effects (Sufficiently large) decrease: Sell some milk in local market Relatively high market price (p > β): farmers sell all milk in the market Decrease in cash at hand (yt + mt−1): No effects (Sufficiently large) decrease: Sell some milk to the cooperative Threshold effects are absent only when p = β Geng, Kramer and Janssens (2016) Liquid Milk 5 / 37

6. Context: Dairy cooperative Tanykina Dairies Limited in western Kenya: Farmer-owned dairy company in the highlands near Eldoret, operational since 2005, processing approx. 30,000 liters per day Milk collectors pick up the milk, take it to a nearby center, weigh it, and farmers receive a ﬁxed price per kg of milk Seven collection centers in total (we focus on three) Milk payments deposited the next month in a village bank account after deducting service and input costs At baseline, 50% of suppliers have health insurance, monthly premium deducted from milk payment Study farmers never deliver to other coops but Tanykina does compete with traders, vendors and neighbors (local market) Geng, Kramer and Janssens (2016) Liquid Milk 6 / 37

7. Saving and Credit Cooperative (SACCO) Geng, Kramer and Janssens (2016) Liquid Milk 7 / 37

8. Agro-Vet Store Geng, Kramer and Janssens (2016) Liquid Milk 8 / 37

9. Agro-Vet Store Geng, Kramer and Janssens (2016) Liquid Milk 9 / 37

10. Data sources Weekly interviews with 120 Tanykina members from Oct ‘12-Oct ‘13 Individual level: Financial transactions (amount, with whom, how) Total value of milk sold to Tanykina vs. others (not Q or P) Non-dairy income, non-food and food expenditures Data collected weekly at the household level: Incidence of health problems and insurance coverage Production and consumption of agricultural output Only two households dropped out. Sample construction: Omit last month, Christmas and elections We focus on weeks in which households sell milk (85%) Sample with variation over time: 88 households, avg. 34 weeks Other data sources: Baseline survey and monthly market surveys Geng, Kramer and Janssens (2016) Liquid Milk 10 / 37

11. Table 1: Household characteristics Variation in share of No variation in share of income from Tanykina income from Tanykina Mean s.e. Mean s.e. (1) (2) (3) (4) Household head is male 0.705 0.459 0.500 0.509 Age of the household head 52.38 14.15 51.03 19.13 Number of HH members selling milk 1.489 0.547 1.300 0.466 Number of cows at baseline 4.227 2.509 3.200 1.669 Main dairy farmer: Is male 0.216 0.414 0.300 0.466 Age 47.57 14.34 46.40 17.38 Is household head 0.477 0.502 0.733 0.450 Is spouse of household head 0.500 0.503 0.200 0.407 Can keep part of cattle income 0.659 0.477 0.793 0.412 Decides how to spend cattle income 0.655 0.478 0.793 0.412 Number of households 88 30 Notes: The sample excludes two households with high attrition and strong outliers. Further, in assessing whether there is variation over time in the share of income received from Tanykina, we omit weeks with high attrition due to Christmas (2 weeks), elections (1 week) and the last ﬁeldwork month (4 weeks). The main dairy farmer is the household member reporting the highest value of dairy income received throughout the year. Geng, Kramer and Janssens (2016) Liquid Milk 11 / 37

15. Table 2: Summary statistics of time-varying characteristics Variation in share of No variation in share of income from Tanykina income from Tanykina Mean s.e. within Mean s.e. (1) (2) (3) (4) (5) Liters of milk produced 71.50 37.49 19.16 52.97 34.44 Non-dairy cash income in 1,000 Sh 2.009 4.065 2.924 2.031 4.907 Non-food expenditures in 1,000 Sh 2.493 4.457 3.754 1.907 6.030 Food expenditures in 1,000 Sh 0.537 0.912 0.844 0.549 1.372 Health problem 0.263 0.440 0.391 0.271 0.445 Has insurance coverage 0.344 0.475 0.245 0.390 0.488 Sells milk 0.847 0.360 0.276 0.697 0.460 Conditional on selling milk... Total dairy income in 1,000 Sh 1.572 0.936 0.523 1.325 1.025 Share received from Tanykina 0.503 0.413 0.232 0.629 0.483 Share sold to Tanykina∗ 0.300 0.309 0.227 0.395 0.419 Share sold in local market∗ 0.329 0.290 0.169 0.228 0.312 Share consumed by the household 0.274 0.116 0.074 0.292 0.127 Number of households (total N) 88 (3997) 30 (1381) Notes: Sample excludes two households who dropped out. In assessing variation in the share of income received from Tanykina, we omit Christmas (2 weeks), elections (1 week) and the last ﬁeldwork month (4 weeks). ∗ Estimated from dividing total sales value by the Tanykina and other buyers’ milk prices, respectively. Geng, Kramer and Janssens (2016) Liquid Milk 12 / 37

16. Table 2: Summary statistics of time-varying characteristics Variation in share of No variation in share of income from Tanykina income from Tanykina Mean s.e. within Mean s.e. (1) (2) (3) (4) (5) Liters of milk produced 71.50 37.49 19.16 52.97 34.44 Non-dairy cash income in 1,000 Sh 2.009 4.065 2.924 2.031 4.907 Non-food expenditures in 1,000 Sh 2.493 4.457 3.754 1.907 6.030 Food expenditures in 1,000 Sh 0.537 0.912 0.844 0.549 1.372 Health problem 0.263 0.440 0.391 0.271 0.445 Has insurance coverage 0.344 0.475 0.245 0.390 0.488 Sells milk 0.847 0.360 0.276 0.697 0.460 Conditional on selling milk... Total dairy income in 1,000 Sh 1.572 0.936 0.523 1.325 1.025 Share received from Tanykina 0.503 0.413 0.232 0.629 0.483 Share sold to Tanykina∗ 0.300 0.309 0.227 0.395 0.419 Share sold in local market∗ 0.329 0.290 0.169 0.228 0.312 Share consumed by the household 0.274 0.116 0.074 0.292 0.127 Number of households (total N) 88 (3997) 30 (1381) Notes: Sample excludes two households who dropped out. In assessing variation in the share of income received from Tanykina, we omit Christmas (2 weeks), elections (1 week) and the last ﬁeldwork month (4 weeks). ∗ Estimated from dividing total sales value by the Tanykina and other buyers’ milk prices, respectively. Geng, Kramer and Janssens (2016) Liquid Milk 12 / 37

17. Figure 1: Price diﬀerence between Tanykina and other outlets across time Geng, Kramer and Janssens (2016) Liquid Milk 13 / 37

18. Figure 2: Distribution of the income share received from Tanykina 0 0.2 0.4 0.6 0.8 1 Log Milk Income Share from Tanykina 0 5 10 15 20 25 30 35 Percentage[%] Geng, Kramer and Janssens (2016) Liquid Milk 14 / 37

19. Econometric strategy: Equation of interest Sit = αi + f (mit−1, yit) + xitβ + it Sit is the milk selling decision for household i in week t: Share of milk sold to Tanykina and average milk price Share of dairy income received from Tanykina f (·) is an unknown smooth function of two variables: Milk production in the last month (mit−1) Non-dairy income net of (non-food) expenditures (yit) Linear part: Household ﬁxed eﬀect (αi ) and others (xit) Health problems, insurance coverage, and interaction Production, median milk price (current/lag), food/milk consumption Geng, Kramer and Janssens (2016) Liquid Milk 15 / 37

20. Econometric strategy: Semi-parametric estimation Su and Ullah (2006) propose consistent estimators for semi-linear model, Sit = αi + f (mit−1, yit) + xitβ + it, using proﬁle least squares, which goes as follows: 1. Express estimator of f (·) assuming that Sit − αi − xitβ is observed as dependent variable 2. Substitute f (·) for the expression of this explicit but unfeasible non-parametric estimator 3. Rearrange again such that we obtain the parametric estimators using traditional ordinary least squares 4. Now, f (·) can be estimated given the parametric estimator Geng, Kramer and Janssens (2016) Liquid Milk 16 / 37

21. Results: Outline 1. Semi-parametric estimates of the model for Share of milk sold to Tanykina (estimated) Average milk price (estimated) Share of dairy income received from Tanykina (observed) 2. Comparison with a fully linear model 3. Additional analyses: Do we observe eﬀects on the extensive or intensive margin? Does cash at hand inﬂuence milk consumption? Heterogeneity by household type and time of the year Geng, Kramer and Janssens (2016) Liquid Milk 17 / 37

22. Figure 3: Fitted share of milk production sold to Tanykina

23. Figure 4: Fitted slope of milk sold to Tanykina w.r.t. past production and net income 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 FittedSlopeofShareMilkTanw.r.t.L2MilkProd 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 FittedSlopeofShareMilkTanw.r.t.L2MilkProd 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 FittedSlopeofShareMilkTanw.r.t.L2MilkProd 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareMilkTanw.r.t.NetInc 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareMilkTanw.r.t.NetInc 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareMilkTanw.r.t.NetInc 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67

24. Figure 5: Fitted average price at which farmer sells milk

25. Figure 6: Fitted slope of average price w.r.t. past production and net income 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofPriceAvew.r.t.L2MilkProd 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofPriceAvew.r.t.L2MilkProd 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofPriceAvew.r.t.L2MilkProd 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofShareMilkTanw.r.t.NetInc 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofShareMilkTanw.r.t.NetInc 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 FittedSlopeofShareMilkTanw.r.t.NetInc 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67

26. Figure 7: Fitted share of dairy income received from Tanykina

27. Figure 8: Fitted slope of share received from Tanykina w.r.t. past production and net income 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.L2MilkProd 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.L2MilkProd 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.L2MilkProd 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.NetInc 25% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.NetInc 50% log income-expense ratio 95% confidence interval 3.81 4.22 4.67 3.6 3.8 4 4.2 4.4 4.6 4.8 Log Milk Production (L2) -0.2 -0.1 0 0.1 0.2 0.3 0.4 FittedSlopeofShareTanw.r.t.NetInc 75% log income-expense ratio 95% confidence interval 3.81 4.22 4.67

28. Results: Overview Findings thus far: 1. Share of milk production sold to Tanykina is increasing in cash at hand, but not across the entire distribution 2. At median levels of cash at hand, local market prices appear to decrease in cash at hand 3. Combined, this implies that the share of dairy income received from Tanykina increases in cash at hand Next, explore health shocks as alternative measure of cash constraints. Uninsured households will need cash to pay medical bills Insured households may not need as much cash Geng, Kramer and Janssens (2016) Liquid Milk 24 / 37

29. Table 3: Estimates of the linear part Log average Share of Share of price of milk sold dairy income milk sold to Tanykina from Tanykina (1) (2) (3) Log food expenditures in 1,000 Sh 0.124∗∗ 0.037 0.015 (0.061) (0.037) (0.023) HH member has health symptoms -0.007 -0.069∗∗∗ -0.058∗∗ (0.024) (0.024) (0.025) HH has insurance coverage 0.080∗∗ -0.016 -0.009 (0.037) (0.031) (0.029) ... X HH member has health symptoms 0.009 0.067∗∗ 0.049 (0.042) (0.031) (0.031) R-squared within households 0.002 0.106 0.147 Mean dependent variable 3.309 0.410 0.502 Number of observations 3231 3231 3231 Number of households 88 88 88 Notes: Standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Controls: Milk production, milk consumption, and district-month eﬀects. Geng, Kramer and Janssens (2016) Liquid Milk 25 / 37

32. Results: Overview Findings thus far: 1. Share of milk production sold to Tanykina is increasing in cash at hand, but not across the entire distribution 2. At median levels of cash at hand, local market prices are decreasing in cash at hand 3. Combined, this implies that the share of dairy income received from Tanykina increases in cash at hand 4. Health shocks - as alternative measure - reduce share of milk sold to Tanykina Estimated using a semi-parametric model: Contribution of this approach? Geng, Kramer and Janssens (2016) Liquid Milk 26 / 37

33. Figure 9: Fitted share of dairy income from Tanykina: Semi-parametric vs. Linear

34. Results: Overview Findings thus far: Cash constraints appear to influence the decision where to sell, and at what price. Semi-parametric estimates provide richer description in context of threshold effects and nonlinearities Linear model provides an average approximation Next set of analyses, using the fully linear model: 1. Are our findings strongest at the extensive versus intensive margin? 2. Do cash constraints influence milk consumption decisions? 3. Is there heterogeneity by household type and time of the month? Geng, Kramer and Janssens (2016) Liquid Milk 28 / 37

35. Table 4: Extensive vs. intensive margin: selling no, some or all milk to Tanykina No milk Some milk All milk (1) (2) (3) Panel A. Centered at 25% quantile Log production last month -0.057∗∗ -0.038∗∗ 0.095∗∗∗ (0.025) (0.018) (0.025) Log income-expense ratio -0.014 -0.014 0.028∗ (0.016) (0.012) (0.016) Panel B. Centered at 50% quantile Log production last month -0.053∗∗ -0.033∗ 0.086∗∗∗ (0.025) (0.018) (0.024) Log income-expense ratio -0.009 -0.007 0.016 (0.011) (0.008) (0.011) Panel C. Centered at 75% quantile Log production last month -0.048∗ -0.027 0.075∗∗∗ (0.026) (0.019) (0.025) Log income-expense ratio -0.000 0.004 -0.004 (0.009) (0.006) (0.009) Interaction term 0.014 0.018 -0.032∗∗ (0.017) (0.012) (0.016) Mean dependent variable 0.319 0.347 0.335 Number of observations 2962 2962 2962 Notes: Standard errors in parentheses. Controls: MilkProd L1MilkProd PriceMed L1PriceMed c.HP1##c.Ins1 Commu- nity#i.Month. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

38. Table 5: Home consumption versus commercialization Sold any milk Share of milk sold (conditional) (1) (2) Panel A. Centered at 25% quantile Log production last month -0.010 0.002 (0.022) (0.006) Log income-expense ratio -0.026∗∗ -0.007∗ (0.012) (0.004) Panel B. Centered at 50% quantile Log production last month -0.006 0.004 (0.022) (0.006) Log income-expense ratio -0.021∗∗ -0.003 (0.009) (0.003) Panel C. Centered at 75% quantile Log production last month -0.001 0.008 (0.022) (0.006) Log income-expense ratio -0.011 0.003 (0.009) (0.002) Interaction term 0.015 0.010∗∗ (0.014) (0.004) Mean dependent variable 0.851 0.732 Number of observations 3480 2962 Standard errors in parentheses. Controls: MilkProd L1MilkProd PriceMed L1PriceMed c.HP1##c.Ins1 Community#i.Month. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

39. Table 5: Home consumption versus commercialization Sold any milk Share of milk sold (conditional) (1) (2) Panel A. Centered at 25% quantile Log production last month -0.010 0.002 (0.022) (0.006) Log income-expense ratio -0.026∗∗ -0.007∗ (0.012) (0.004) Panel B. Centered at 50% quantile Log production last month -0.006 0.004 (0.022) (0.006) Log income-expense ratio -0.021∗∗ -0.003 (0.009) (0.003) Panel C. Centered at 75% quantile Log production last month -0.001 0.008 (0.022) (0.006) Log income-expense ratio -0.011 0.003 (0.009) (0.002) Interaction term 0.015 0.010∗∗ (0.014) (0.004) Mean dependent variable 0.851 0.732 Number of observations 3480 2962 Standard errors in parentheses. Controls: MilkProd L1MilkProd PriceMed L1PriceMed c.HP1##c.Ins1 Community#i.Month. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

40. Table 6: Estimates by household type (variables centered at 50% quantile) Female head Female farmer Male farmer (1) (2) (3) Panel A. Share of milk sold to Tanykina Log production last month 0.137∗∗∗ 0.051 0.347∗∗ (0.032) (0.048) (0.143) Log income-expense ratio -0.008 0.051∗∗ 0.011 (0.014) (0.023) (0.070) ... X Log production last month 0.031 -0.069∗∗ 0.163 (0.021) (0.032) (0.179) Mean dependent variable 0.386 0.330 0.572 Panel B. Log price per liter of milk sold Log production last month 0.181∗∗∗ -0.029 0.111 (0.049) (0.047) (0.078) Log income-expense ratio -0.051∗∗ -0.068∗∗∗ -0.057 (0.021) (0.022) (0.038) ... X Log production last month 0.072∗∗ 0.045 0.131 (0.032) (0.031) (0.097) Mean dependent variable 3.323 3.268 3.333 Number of observations 909 1466 587 Number of household 26 44 18 Standard errors in parentheses. Controls: MilkProd L1MilkProd PriceMed L1PriceMed c.HP##c.Ins1 Community#i.Month. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

43. Table 7: Estimates by week (variables centered at 50% quantile) Week 1 Week 2 Week 3 Week 4 (1) (2) (3) (4) Panel A. Share of milk sold to Tanykina Log production last month 0.122 0.240∗∗∗ 0.060 -0.013 (0.079) (0.077) (0.075) (0.039) Log income-expense ratio 0.029 -0.090∗∗∗ 0.042 0.072∗∗∗ (0.030) (0.035) (0.036) (0.023) ... X Log production last month -0.055 0.136∗∗∗ -0.029 -0.085∗∗ (0.044) (0.049) (0.054) (0.036) Mean dependent variable 0.404 0.407 0.389 0.380 Panel B. Log price per liter of milk sold Log production last month -0.162∗∗ 0.153∗∗ -0.020 -0.017 (0.081) (0.063) (0.054) (0.048) Log income-expense ratio 0.038 -0.155∗∗∗ -0.070∗∗∗ -0.053∗ (0.031) (0.028) (0.026) (0.029) ... X Log production last month -0.063 0.181∗∗∗ 0.037 0.008 (0.045) (0.040) (0.039) (0.045) Mean dependent variable 3.303 3.298 3.286 3.306 Number of observations 627 914 732 689 Number of household 88 88 88 88 Standard errors in parentheses. Controls: MilkProd L1MilkProd PriceMed L1PriceMed c.HP##c.Ins1 Community#i.Month. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

46. Additional analyses: Summary 1. Are our findings strongest at the extensive versus intensive margin? Cash at hand increases ⇒ Switch from selling none/some to selling all milk 2. Do cash constraints influence milk consumption decisions? Only non-dairy income at below-median levels of milk production 3. Is there heterogeneity by household type and time of the month? Milk production last month affects marketing decisions mainly: When farmer is the household head (male or female) Around the time that the milk payment is due (second week) Non-dairy income increases share of milk sold to Tanykina mainly: Among female farmers who are not the household head In the last week of the month Geng, Kramer and Janssens (2016) Liquid Milk 33 / 37

47. Conclusion Do cash constraints affect preferences over the timing of income? Evidence so far focuses on experimental gifts (Dean and Sautmann, 2016; Janssens et al., 2016; Carvalho et al., 2016) Cash constraints influence choice when to receive milk payments Local traders raise prices when in need, providing informal insurance Policy implications for cooperatives: Farmers can benefit from collective marketing However, cash constraints hinder farmers’ loyalty to cooperatives Potential benefits from relaxing farmers’ cash constraints However, low demand for weekly payments (Kramer and Kunst, 2016) Increase access to savings devices and low-cost advance payments? Provide insurance through cooperative (potentially as incentive)? Geng, Kramer and Janssens (2016) Liquid Milk 34 / 37

48. Milk is liquid... Thank you! Geng, Kramer and Janssens (2016) Liquid Milk 35 / 37

49. References Carvalho, L. S., Meier, S., Wang, S. W., 2016. Poverty and economic decision-making: Evidence from changes in financial resources at payday. The American Economic Review 106 (2), 260–284. Casaburi, L., Macchiavello, R., 2015. Firm and Market Response to Saving Constraints: Evidence from the Kenyan Dairy Industry. CEPR Discussion Paper No. DP10952. Collins, D., Morduch, J., Rutherford, S., Ruthven, O., 2009. Portfolios of the poor: how the world’s poor live on $2 a day. Princeton University Press. Dean, M., Sautmann, A., 2016. Credit constraints and the measurement of time preferences. Working paper. Demirgüç-Kunt, A., Klapper, L. F., 2012. Measuring financial inclusion: The global findex database. World Bank Policy Research Working Paper (6025). Janssens, W., Kramer, B., Swart, L., 2016. Be patient when measuring hyperbolic discounting: Stationarity, time consistency and time invariance in a field experiment. Working paper. Minot, N., Sawyer, B., 2014. Contract Farming in Developing Countries: Review of the Evidence. Prepared for the Investment Climate Unit of the International Finance Corporation as a longer version of the IFC Viewpoints policy note on the same topic. Reardon, T., Barrett, C. B., Berdegué, J. A., Swinnen, J. F. M., 2009. Agrifood Industry Transformation and Small Farmers in Developing Countries. World Development 37 (11), 1717–1727. Geng, Kramer and Janssens (2016) Liquid Milk 36 / 37

50. Figure 10: Milk production and income-expenditure ratio (in logs)

[IFPRI Gender Methods Seminar] Liquid milk: Cash Constraints and the Timing of Income

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[IFPRI Gender Methods Seminar] Liquid milk: Cash Constraints and the Timing of Income