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

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Background and Motivation

Financial planning is difficult, especially when facing cash constraints,unpredictable incomes and expenditures (Collins et al., 2009)

Rural women affected most (Demirguc-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 provideextra services (Reardon et al., 2009; Minot and Sawyer, 2014)Local traders are trusted less to save one’s money(Casaburi and Macchiavello, 2015)

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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-handCorner solutions create treshold effects and nonlinearities

We estimate effects of cash at hand on milk marketing decisionsHigh-frequency panel data for dairy farmers in Kenya, measuring netinflows of cash from dairy vs. non-dairy activitiesSemiparametric techniques provide parameter-free estimates of howthese 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 priceThey do so especially when they are more cash-constrainedIn those weeks, the local market may pay them a higher price

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Conceptual Framework: Basic set-up

Every period, a household produces mt and decides how much to selloutside the cooperative, st , such that it optimizes

max0≤st≤mt

∞∑t=0

βtu(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 marketThe cooperative defers payments for mt − st by one periodNon-dairy net income, yt , is assumed to be predeterminedNo savings and borrowing outside the cooperative

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Conceptual Framework: Predictions

Relatively low market price (p < β): farmers sell all milk to thecooperative

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 themarket

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

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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 dayMilk 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 accountafter deducting service and input costsAt baseline, 50% of suppliers have health insurance, monthly premiumdeducted from milk paymentStudy farmers never deliver to other coops but Tanykina doescompete with traders, vendors and neighbors (local market)

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Saving and Credit Cooperative (SACCO)

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Agro-Vet Store

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Agro-Vet Store

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Data sources

Weekly interviews with 120 Tanykina members from Oct ‘12-Oct ‘13Individual 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 coverageProduction and consumption of agricultural output

Only two households dropped out. Sample construction:Omit last month, Christmas and electionsWe 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

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Table 1: Household characteristics

Variation in share of No variation in share ofincome 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 0.509Age of the household head 52.38 14.15 51.03 19.13Number 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 17.38Is household head 0.477 0.502 0.733 0.450Is spouse of household head 0.500 0.503 0.200 0.407Can 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 0.412Number of households 88 30

Notes: The sample excludes two households with high attrition and strong outliers. Further, in assessing whether there is variationover 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 ofdairy income received throughout the year.

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Table 1: Household characteristics

Variation in share of No variation in share ofincome 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 0.509Age of the household head 52.38 14.15 51.03 19.13Number 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 17.38Is household head 0.477 0.502 0.733 0.450Is spouse of household head 0.500 0.503 0.200 0.407Can 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 0.412Number of households 88 30

Notes: The sample excludes two households with high attrition and strong outliers. Further, in assessing whether there is variationover 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 ofdairy income received throughout the year.

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Table 1: Household characteristics

Variation in share of No variation in share ofincome 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 0.509Age of the household head 52.38 14.15 51.03 19.13Number 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 17.38Is household head 0.477 0.502 0.733 0.450Is spouse of household head 0.500 0.503 0.200 0.407Can 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 0.412Number of households 88 30

Notes: The sample excludes two households with high attrition and strong outliers. Further, in assessing whether there is variationover 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 ofdairy income received throughout the year.

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Table 1: Household characteristics

Variation in share of No variation in share ofincome 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 0.509Age of the household head 52.38 14.15 51.03 19.13Number 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 17.38Is household head 0.477 0.502 0.733 0.450Is spouse of household head 0.500 0.503 0.200 0.407Can 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 0.412Number of households 88 30

Notes: The sample excludes two households with high attrition and strong outliers. Further, in assessing whether there is variationover 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 ofdairy income received throughout the year.

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Table 2: Summary statistics of time-varying characteristics

Variation in share of No variation in share ofincome 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 34.44Non-dairy cash income in 1,000 Sh 2.009 4.065 2.924 2.031 4.907Non-food expenditures in 1,000 Sh 2.493 4.457 3.754 1.907 6.030Food expenditures in 1,000 Sh 0.537 0.912 0.844 0.549 1.372Health problem 0.263 0.440 0.391 0.271 0.445Has insurance coverage 0.344 0.475 0.245 0.390 0.488Sells 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 1.025Share 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 0.419Share sold in local market∗ 0.329 0.290 0.169 0.228 0.312Share consumed by the household 0.274 0.116 0.074 0.292 0.127Number 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 salesvalue by the Tanykina and other buyers’ milk prices, respectively.

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Table 2: Summary statistics of time-varying characteristics

Variation in share of No variation in share ofincome 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 34.44Non-dairy cash income in 1,000 Sh 2.009 4.065 2.924 2.031 4.907Non-food expenditures in 1,000 Sh 2.493 4.457 3.754 1.907 6.030Food expenditures in 1,000 Sh 0.537 0.912 0.844 0.549 1.372Health problem 0.263 0.440 0.391 0.271 0.445Has insurance coverage 0.344 0.475 0.245 0.390 0.488Sells 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 1.025Share 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 0.419Share sold in local market∗ 0.329 0.290 0.169 0.228 0.312Share consumed by the household 0.274 0.116 0.074 0.292 0.127Number 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 salesvalue by the Tanykina and other buyers’ milk prices, respectively.

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Figure 1: Price difference between Tanykina and other outlets across time

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Figure 2: Distribution of the income share received from Tanykina

0 0.2 0.4 0.6 0.8 1Log Milk Income Share from Tanykina

0

5

10

15

20

25

30

35

Per

cent

age

[%]

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Econometric strategy: Equation of interest

Sit = αi + f (mit−1, yit) + xitβ + εit

Sit is the milk selling decision for household i in week t:Share of milk sold to Tanykina and average milk priceShare 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 interactionProduction, median milk price (current/lag), food/milk consumption

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Econometric strategy: Semi-parametric estimation

Su and Ullah (2006) propose consistent estimators for semi-linear model,

Sit = αi + f (mit−1, yit) + xitβ + εit ,

using profile least squares, which goes as follows:1. Express estimator of f (·) assuming that Sit − αi − xitβ is observed as

dependent variable2. Substitute f (·) for the expression of this explicit but unfeasible

non-parametric estimator3. Rearrange again such that we obtain the parametric estimators using

traditional ordinary least squares4. Now, f (·) can be estimated given the parametric estimator

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Results: Outline

1. Semi-parametric estimates of the model forShare 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

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Figure 3: Fitted share of milk production sold to Tanykina

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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

L2M

ilkP

rod

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

L2M

ilkP

rod

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

L2M

ilkP

rod

75% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

75% log income-expense ratio95% confidence interval

3.81 4.22 4.67

Figure 5: Fitted average price at which farmer sells milk

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

Fitt

ed S

lope

of

PriceA

ve

w.r

.t.

L2M

ilkP

rod

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

PriceA

ve

w.r

.t.

L2M

ilkP

rod

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

PriceA

ve

w.r

.t.

L2M

ilkP

rod

75% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

MilkT

an

w.r

.t.

NetInc

75% log income-expense ratio95% confidence interval

3.81 4.22 4.67

Figure 7: Fitted share of dairy income received from Tanykina

Figure 8: Fitted slope of share received from Tanykina w.r.t. past production and netincome

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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

L2M

ilkP

rod

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

L2M

ilkP

rod

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

L2M

ilkP

rod

75% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

NetInc

25% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

NetInc

50% log income-expense ratio95% 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

Fitt

ed S

lope

of

Share

Tan

w.r

.t.

NetInc

75% log income-expense ratio95% confidence interval

3.81 4.22 4.67

Results: Overview

Findings thus far:1. Share of milk production sold to Tanykina is increasing in cash at

hand, but not across the entire distribution2. At median levels of cash at hand, local market prices appear to

decrease in cash at hand3. 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 billsInsured households may not need as much cash

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Table 3: Estimates of the linear part

Log average Share of Share ofprice 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 0.147Mean dependent variable 3.309 0.410 0.502Number of observations 3231 3231 3231Number 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.

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Table 3: Estimates of the linear part

Log average Share of Share ofprice 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 0.147Mean dependent variable 3.309 0.410 0.502Number of observations 3231 3231 3231Number 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.

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Table 3: Estimates of the linear part

Log average Share of Share ofprice 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 0.147Mean dependent variable 3.309 0.410 0.502Number of observations 3231 3231 3231Number 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.

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Results: Overview

Findings thus far:1. Share of milk production sold to Tanykina is increasing in cash at

hand, but not across the entire distribution2. At median levels of cash at hand, local market prices are decreasing in

cash at hand3. Combined, this implies that the share of dairy income received from

Tanykina increases in cash at hand4. Health shocks - as alternative measure - reduce share of milk sold to

Tanykina

Estimated using a semi-parametric model: Contribution of this approach?

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

Results: Overview

Findings thus far:Cash constraints appear to influence the decision where to sell, and atwhat price.Semi-parametric estimates provide richer description in context ofthreshold effects and nonlinearitiesLinear 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?

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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% quantileLog 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% quantileLog 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% quantileLog 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 0.335Number 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

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% quantileLog 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% quantileLog 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% quantileLog 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 0.335Number 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

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% quantileLog 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% quantileLog 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% quantileLog 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 0.335Number 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

Table 5: Home consumption versus commercialization

Sold any milk Share of milk sold (conditional)(1) (2)

Panel A. Centered at 25% quantileLog 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% quantileLog 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% quantileLog 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 0.732Number 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

Table 5: Home consumption versus commercialization

Sold any milk Share of milk sold (conditional)(1) (2)

Panel A. Centered at 25% quantileLog 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% quantileLog 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% quantileLog 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 0.732Number 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

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 TanykinaLog 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 soldLog 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 587Number 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

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 TanykinaLog 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 soldLog 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 587Number 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

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 TanykinaLog 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 soldLog 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 587Number 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

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 TanykinaLog 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 soldLog 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 689Number 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

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 TanykinaLog 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 soldLog 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 689Number 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

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 TanykinaLog 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 soldLog 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 689Number 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

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 allmilk

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 headIn the last week of the month

Geng, Kramer and Janssens (2016) Liquid Milk 33 / 37

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 paymentsLocal traders raise prices when in need, providing informal insurance

Policy implications for cooperatives:Farmers can benefit from collective marketingHowever, cash constraints hinder farmers’ loyalty to cooperativesPotential 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

Milk is liquid...

Thank you!

Geng, Kramer and Janssens (2016) Liquid Milk 35 / 37

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Evidence from changes in financial resources at payday. The American EconomicReview 106 (2), 260–284.

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Collins, D., Morduch, J., Rutherford, S., Ruthven, O., 2009. Portfolios of the poor: howthe world’s poor live on $2 a day. Princeton University Press.

Dean, M., Sautmann, A., 2016. Credit constraints and the measurement of timepreferences. Working paper.

Demirguc-Kunt, A., Klapper, L. F., 2012. Measuring financial inclusion: The globalfindex database. World Bank Policy Research Working Paper (6025).

Janssens, W., Kramer, B., Swart, L., 2016. Be patient when measuring hyperbolicdiscounting: Stationarity, time consistency and time invariance in a field experiment.Working paper.

Minot, N., Sawyer, B., 2014. Contract Farming in Developing Countries: Review of theEvidence. Prepared for the Investment Climate Unit of the International FinanceCorporation as a longer version of the IFC Viewpoints policy note on the same topic.

Reardon, T., Barrett, C. B., Berdegue, J. A., Swinnen, J. F. M., 2009. Agrifood IndustryTransformation and Small Farmers in Developing Countries. World Development37 (11), 1717–1727.

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