The Price Effects of Cash Versus in-Kind Transfers

1. The Price Effects of Cash Versus In-Kind Transfers
Jesse Cunha (Naval Postgraduate School)
Giacomo De Giorgi (Stanford University)
Seema Jayachandran (Northwestern University)
September 2012

2. In-kind versus cash transfers
• Government transfers are often made in-kind
• One rationale is paternalism—boost consumption of certain goods
• Other potential reasons are self-targeting, political economy
• Weighed against constraining consumer choice and administrative
costs
• This paper: Price effects are another factor in this policy choice

3. Price effects of transfers
• Cash and in-kind transfers have an income (demand) effect
• Demand and prices for normal goods ↑
• In-kind transfers also inject supply into the local economy
– Setting where goods are provided (public housing) rather than
vouchers (Food Stamps)
• Influx of supply reduces prices
⇒ This paper: Empirically assess the size of price effects for cash
and in-kind transfers

4. Overview of paper
• Examine price effects of food transfer program in Mexico
– Randomized experiment across villages: in-kind transfers, cash
transfers, control group
• Find that prices decline for in-kind transfers relative to cash
transfers
• Larger effects in more remote villages, consistent with both...
– More closed economy
– Less competition
• Magnitudes small in non-remote villages; large in remote villages
• Differential effects for households that produce food

5. Effect of cash and in-kind transfers on prices
P
MC
Supply provided by govt
ΔPin‐kind < ΔPcash
ΔPcash > 0
ΔPcash
ΔPin‐kind
Income effect
MRin‐kind MR0 MRcash
Q

6. Hypotheses
• Cash transfers have positive income effect on prices
⇒ ∆pcash > 0
• In-kind transfers have positive income effect + negative supply
effect on prices
⇒ ∆pinkind < ∆pcash
• Sign of ∆pinkind is theoretically ambiguous without restrictions
on preferences
– But for, e.g., homothetic preferences, ∆pinkind < 0

7. Imperfect competition
• Can generate same predictions with imperfect competition
– Cash transfers cause price increase
– In-kind transfers cause prices to decrease, relatively
• Effects probably more likely to persist in the long run under
imperfect competition
– Long-run supply curve flatter than short-run curve
– If inherent barriers to entry lead to imperfect competition, may
persist in long run

8. Normative implications of price effects
• Lower price of transferred good furthers paternalistic goal of
encouraging consumption of transferred goods
• In-kind transfers redistribute from producers to consumers
(relative to cash transfers)
• If govt wants to tax producers to make transfers to consumers,
in-kind transfers could be a second-best tax instrument (Coate,
Johnson, and Zeckhauser 1994)
• With imperfect competition, no longer just a pecuniary externality
– Goods are undersupplied by the market
– Govt influx of supply could increase efficiency

9. Outline of rest of talk
• Background on PAL program + data
• Results
– Overall price effects for cash versus in-kind program
– Heterogeneity based on remoteness of village
– Quantifying the total effect
– Producer versus consumer households
• Conclusion

10. Transfer program we study
• Mexico’s food assistance program, Programa de Apoyo
Alimentario (PAL)
• PAL nationwide (in 2009): 200,000 households in 5,000 villages
• Targets poor households in villages too poor to be receiving
Oportunidades

11. PAL experiment
• Experiment in 2003-05: 208 villages
• Village-level randomization among eligible villages (small, rural,
poor) in 6 southern states
• Household-level targeting: 89% of households eligible
• 3 treatment arms – eligible HHs receive the following each month:
– Food box with 10 goods
– 150 pesos cash
– No transfer (control group)

12. Items in food box
Value per box Calories, as Village change
Amount per (pre-program, % of total in supply
Item Type box (kg) in pesos) box (∆Supply)
(1) (2) (3) (4) (5)
Corn flour basic 3 15.7 20% 1.00
Rice basic 2 12.7 12% 0.61
Beans basic 2 21.0 13% 0.29
Fortified powdered milk basic 1.92 76.2 17% 8.62
Packaged pasta soup basic 1.2 16.2 8% 0.93
Vegetable oil basic 1 (lt) 10.4 16% 0.25
Biscuits basic 1 18.7 8% 0.81
Lentils supplementary 1 10.3 2% 3.73
Canned tuna/sardines supplementary 0.6 14.8 2% 1.55
Breakfast cereal supplementary 0.2 9.3 1% 0.90
Notes:
(1) Value is calculated using the average of pre-treatment village-level median unit values. 10 pesos ≈ 1 USD.
(2) ∆Supply measures the PAL supply influx into villages, relative to what would have been consumed absent the
program. It is constructed as the average across all in-kind villages of the total amount of the good transferred to the
village divided by the average consumption of the good in control villages in the post-period.
(3) We do not know whether a household received canned tuna fish (0.35kg) or canned sardines (0.8kg); the analysis
assumes the mean weight and calories throughout.
(4) Biscuits are excluded from our analysis as post-program prices are missing.

13. Box of in-kind goods

14. PAL transfers being trucked into villages

15. Influx of cash and food was large
• Transfers are large: 19% of baseline monthly food expenditures
for recipients, 12% of total expenditures
• Given 89% eligibility, influx of 17% of baseline monthly food
expenditures for village
• Cash transfer was 8% of baseline total expenditures for village

16. Income effect of transfers
• Is the income effect from cash and in-kind transfers the same?
• Could be smaller effect for in-kind transfers – recipients value the
bundle less than its market value
• Could be larger for in-kind transfers, e.g., transfer signals quality
• Roughly, income effect is similar for both transfers in our setting

17. Equivalence of income effect for PAL program
• Cost for the in-kind box at prices in village was 206 pesos
• Government procurement cost was 150 pesos so set cash transfer
at this amount
• In-kind goods can’t be costlessly resold, so value is <206 pesos

18. What is value of PAL in-kind transfer?
• 116 pesos of the bundle was inframarginal, based on examining
control group’s consumption (Cunha 2012)
• In-kind HHs consume 34 pesos more of these goods than they
would have with cash transfer
• Another 56 pesos of transfer is extramarginal but not consumed;
transaction costs from resale
• Assume deadweight loss erodes two thirds of value in both cases;
90 pesos nominal value but valued at 30 pesos
• In-kind box valued at ∼146 pesos
• Even if consumers only value the inframarginal portion, different
income effect cannot explain magnitude of our results

19. Supply side of the market
• Food is sourced from manufacturers outside these villages
– We focus on only the local GE effects, ignoring possible Mexico-
wide effects
• Supply side within the village are grocery stores/shopkeepers
• Agricultural producers in the village supply substitute goods

20. Village stores

21. Village stores

22. Data
• Matched panel surveys of households and stores
– Pre-intervention (2003) / Follow-up (2005)
– Program underway for ≈ 1 year at follow-up
– For HH survey, interviewed random sample of 33 HHs per
village
• 14 of 208 villages not included because of missing data or program
began before baseline
• Final sample: 194 villages, 360 stores
• Randomization seems to have worked (Table 2)

23. Data on food prices
• 9 PAL food items
– 6 basic goods: corn flour, rice, beans, pasta, oil, fortified milk
– 3 supplementary goods: canned fish, packaged breakfast cereal,
and lentils
– Data for 10th PAL good (biscuits) not collected
• 51 non-PAL food items
• No price data for non-food items

24. Price data
• Our outcome variable is the good-store-village price (12,940
observations)
• Price surveys of local stores in each village
• Up to 3 stores per village but typically 1 or 2
• Looked for, or asked for, lowest priced product
• Incomplete baseline store data

25. Baseline price data: Unit values
• Household survey has food consumption, expenditure,
consumption out of own production by item, 7-day recall
• We calculate unit values (expenditures per unit purchased)
• Use median price for village-good
• Interpolate from other villages in municipality if missing
• Also use store prices, imputing missing values

26. Basic regression
pgsv = α + β 1InKindv + β 2Cashv + φpgsv,t−1 + σXgv + gsv
• g is good, s is store, v is village
• Control for lagged prices
• Control for indicator if lagged prices is imputed
• Cluster on village
• Two predictions are β 1 < β 2 and β 2 > 0

27. Effect of transfer program on price of PAL goods
All PAL Basic PAL All PAL Basic PAL All PAL Basic PA
goods goods goods goods goods goods
Outcome = price price price price price price
(1) (2) (3) (4) (5) (6)
In‐kind ‐0.037* ‐0.033 ‐0.036* ‐0.033 ‐0.032* ‐0.025
(0.020) (0.020) (0.020) (0.020) (0.017) (0.017)
Cash 0.002 0.014 0.003 0.012 0.001 0.011
(0.023) (0.027) (0.023) (0.026) (0.020) (0.022)
Lagged normalized unit value 0.027 0.127***
(0.021) (0.042)
Lagged normalized store price 0.325*** 0.335**
(0.052) (0.064)
Lagged ln(unit value)
Observations 2,335 1,617 2,335 1,617 2,335 1,617
Effect size: In‐kind ‐ Cash ‐0.039** ‐0.047** ‐0.038** ‐0.045** ‐0.034** ‐0.036**
H 0 : In‐kind = Cash (p‐value) 0.02 0.04 0.03 0.04 0.03 0.04
Notes: *** p<0.01, ** p<0.05, * p<0.1

28. Robustness checks
• Results similar across specifications
– Control for store prices
– Use log prices
• No evidence that results are driven by changes in quality

29. Heterogeneity based on remoteness of village
• Two reasons to expect larger price effects in more remote areas
– Less open economy (steeper supply curve)
– Imperfect competition
• Measured as travel time to market with fresh meat, vegetables,
fruit
– Use village median of self-reports in household survey

30. Results on remoteness of the village
All PAL goods Basic PAL goods only
Above‐ Below‐ Above‐ Below‐
median median All villages median median All villages
remotenes remotenes remotenes remotenes
Outcome = price price price price price price
(1) (2) (3) (4) (5) (6)
In‐kind ‐0.030 ‐0.044* ‐0.050 ‐0.014 ‐0.045* ‐0.033
(0.033) (0.024) (0.030) (0.027) (0.027) (0.031)
Cash 0.050 ‐0.029 0.013 0.062** ‐0.015 0.032
(0.034) (0.031) (0.031) (0.031) (0.038) (0.036)
ln(Remoteness) x In‐kind ‐0.028 ‐0.007
(0.033) (0.036)
ln(Remoteness) x Cash 0.023 0.033
(0.033) (0.037)
Observations 865 1,470 2,130 603 1,014 1,471
Effect size: In‐kind ‐ Cash ‐0.081*** ‐0.015 ‐0.076*** ‐0.030
H 0 : In‐kind = Cash (p‐value) 0.00 0.56 0.00 0.35
Effect size: ln(Remoteness) x In‐kind ‐
ln(Remoteness) x Cash ‐0.050** ‐0.040*
H 0 : ln(Remoteness) x In‐kind =
0.02 0.08
ln(Remoteness) x Cash (p‐value)
Notes: *** p<0.01, ** p<0.05, * p<0.1
(1) The outcome variable is the post‐treatment price; it varies at the village‐store‐good level. It is normalized by good; the
price is divided by the average price of the good across all observations in the control group. Standard errors are

31. Testing between competition and closed economy
explanations
• Don’t have census of stores per village
• Poor quality data when tried to collect it retrospectively in 2011
• Suggestive evidence using number of stores in the data collection
• Price effects persist for a year, even though long-run MC curve
seems like it should be flat ⇒ Also suggests imperfect competition

32. Results on number of stores
All PAL goods Basic PAL goods only
Outcome = price price price price
(1) (2) (3) (4)
In‐kind ‐0.030 ‐0.039 ‐0.018 ‐0.020
(0.058) (0.062) (0.064) (0.069)
Cash 0.065 0.056 0.109 0.104
(0.067) (0.071) (0.071) (0.076)
# stores x In‐kind ‐0.004 ‐0.006 ‐0.006 ‐0.007
(0.026) (0.025) (0.029) (0.029)
# stores x Cash ‐0.032 ‐0.022 ‐0.047 ‐0.037
(0.028) (0.030) (0.030) (0.031)
ln(Remoteness) x In‐kind ‐0.025 ‐0.006
(0.034) (0.037)
ln(Remoteness) x Cash 0.022 0.026
(0.035) (0.038)
Observations 2,130 2,130 1,471 1,471
Effect size: In‐kind ‐ Cash ‐0.095* ‐0.096* ‐0.127** ‐0.124**
H 0 : In‐kind = Cash (p‐value) 0.06 0.06 0.02 0.02
Effect size: # stores x In‐kind ‐ # stores x Cash 0.028 0.016 0.040* 0.030
H 0 : # stores x In‐kind = # stores x Cash (p‐
value) 0.15 0.47 0.05 0.18
Effect size: ln(Remoteness) x In‐kind ‐
ln(Remoteness) x Cash ‐0.047** ‐0.033
H 0 : ln(Remoteness) x In‐kind =
ln(Remoteness) x Cash (p‐value) 0.03 0.16
Notes: *** p<0.01, ** p<0.05, * p<0.1

33. Effects on non-PAL goods
• Other food items are substitutes for PAL goods
• Identify subset of goods that are close substitutes for PAL goods
• Examine price effects for all other food items
• No data for non-food prices

34. Substitutes
Set of PAL substitutes All non‐PAL goods
Above‐ Below‐ Above‐ Below‐
All villages median median All villages All villages median median All villages
remoteness remoteness remoteness remoteness
Outcome = price price price price price price price price
(1) (2) (3) (4) (5) (6) (7) (8)
In‐kind ‐0.013 0.010 ‐0.024 ‐0.014 0.010 0.000 0.014 ‐0.005
(0.025) (0.032) (0.036) (0.029) (0.019) (0.029) (0.024) (0.023)
Cash 0.027 0.035 0.024 0.024 0.009 0.039 ‐0.012 0.013
(0.031) (0.034) (0.045) (0.033) (0.022) (0.042) (0.023) (0.034)
ln(Remoteness) x In‐kind ‐0.006 ‐0.022
(0.034) (0.028)
ln(Remoteness) x Cash 0.002 0.014
(0.036) (0.032)
Observations 1,442 498 944 1,307 10,648 3,765 6,883 9,698
Effect size: In‐kind ‐ Cash ‐0.039 ‐0.025 ‐0.048 0.001 ‐0.039 0.026
H 0 : In‐kind = Cash (p‐value) 0.15 0.41 0.22 0.95 0.34 0.24
Effect size: ln(Remoteness) x
In‐kind ‐ ln(Remoteness) x
Cash ‐0.008 ‐0.036
H 0 : ln(Remoteness) x In‐kind
= ln(Remoteness) x Cash (p‐ 0.77 0.27
value)
Notes: *** p<0.01, ** p<0.05, * p<0.1
(1) The outcome variable is the post‐treatment price; it varies at the village‐store‐good level. It is normalized by good; the price is divided by the average price
of the good across all observations in the control group. Standard errors are clustered at the village level.
(2) Regressions control for the main effects of the interaction terms reported, as well as for the pre‐period normalized unit value and an indicator for imputed
pre‐program prices (see text).

35. Magnitude of the effects
• Multiply estimated change in prices by expenditure amount to
quantify price effect in pesos
• Expenditures per HH on PAL goods is 200 pesos and on non-PAL
goods, 1050 pesos per month (in control villages)
• Applies to non-recipients too
• Price effects small for non-remote villages

36. Magnitude of the effects
• Price effects have negligible effect on household purchasing power
for non-remote villages
• Price effects large in remote villages
⇒ Difference between in-kind and cash transfers equivalent to 60
extra pesos for a consumer (>30% of direct transfer)

37. Effects on food-producing households
• HHs are mainly consumers of the PAL goods, but some HHs are
agric. producers
• Welfare effects differ for producers
– e.g., Price increase from cash transfer is an extra benefit
• Production decisions might respond to the program
– e.g., Produce/sell more when prices rise in cash villages
• Income effect also could affect production, e.g., investment
affected if credit-constrained

38. Effects for food-producing HHs
Farm Farm ln(Expenditur ln(Expenditur Asset Asset
Outcome =
profits costs e per capita) e per capita) index index
(1) (2) (3) (4) (5) (6)
In‐kind 143.87 134.01 0.115** 0.084
(89.839) (119.511) (0.046) (0.075)
Cash 186.16* 345.32** 0.064 ‐0.040
(106.082) (140.378) (0.052) (0.106)
Producer x In‐Kind 0.001 ‐0.018 0.077 0.055
(0.060) (0.046) (0.115) (0.088)
Producer x Cash 0.087 0.015 0.266* 0.229**
(0.068) (0.051) (0.142) (0.109)
Producer ‐0.161*** ‐0.003 ‐0.308*** ‐0.007
(0.050) (0.036) (0.092) (0.071)
Control for pre‐period
outcome? yes yes yes yes yes yes
Village FE yes yes
Observations 4,924 5,038 5,534 5,534 5,571 5,571
Effect size: In‐kind ‐ Cash ‐42.29 ‐211.31* 0.050 0.124
H 0 : In‐kind = Cash (p‐value) 0.67 0.08 0.25 0.20
Effect size: Producer x In‐
Kind ‐ Producer x Cash ‐0.086 ‐0.033 ‐0.189 ‐0.174*
H 0 : Producer x In‐Kind =
Producer x Cash (p‐value) 0.13 0.47 0.13 0.07
Notes: *** p<0.01, ** p<0.05, * p<0.1

39. Summary of findings
• In rural Mexico, in-kind transfers cause prices of transferred goods
to fall relative to cash villages
• Results driven by more remote villages, perhaps because of less
supply-side competition
• In remote villages, in-kind transfers deliver 30% more to
consumers than cash transfers
• Welfare consequences of price changes are the opposite for
producers
– Price increase from cash transfers increases farm profits
– Lower prices from in-kind transfers hurt farm profits

40. Concluding thoughts
• Long-run effects might differ as supply adjusts
• Many other considerations when choosing in-kind vs. cash
transfers
– Paternalistic goals versus constraining household choices
– Govt may not be as efficient a supplier as the private sector
• But price effects are too large to ignore in remote villages
– High eligibility for social programs (typically ultra-poor)
– Fewer stores
– Less integrated with the outside economy
• In-kind transfers are one tool to reduce oligopolistic inefficiencies