1) The study examines the interaction between temporary input subsidies and formal savings programs on technology adoption in rural Mozambique. 2) It finds that the impact of subsidies on fertilizer use persists for two seasons in areas without savings programs, but disappears after one season in areas with basic or matched savings programs. 3) Savings programs led to substantial increases in formal savings balances, suggesting resources may have been diverted from fertilizer to savings in post-subsidy periods in savings areas. However, all treatment areas experienced similar consumption gains of around 8%.

1. Savings, Subsidies, and Technology Adoption:
Field Experimental Evidence
from Mozambique
Michael Carter, UC Davis
Rachid Laajaj, Universidad de los Andes
Dean Yang, University of Michigan

2. • Technology adoption has a central role in theoretical and empirical
work on economic growth and development
• For the majority of the world’s poor, key technologies are those
related to agricultural production
– In particular, adoption of fertilizer and improved seeds have
dramatically reduced rural poverty in developing countries
• But sub-Saharan Africa is an outlier in the context of the Green
Revolution
Technology adoption in developing countries
2

3. Source: Morris et al (2007)
Africa lags in food production…
3

4. … and in fertilizer use
• Low fertilizer utilization in sub-Saharan Africa has motivated decades of
policies seeking to stimulate adoption
– E.g., direct subsides, but also price controls, subsidized credit, and
direct provision in form of aid
4

5. Input subsidy programs (ISPs)
• Perhaps the most significant
recent development in
agricultural policy in Sub-
Saharan Africa
• Large-scale subsidization of
modern inputs (fertilizer,
improved seeds)
5
• Across 10 countries implementing ISPs, 2011 expenditures totaled
$1.05 billion, or 28.6% of public agricultural spending
• Substantial budgetary support by World Bank, other donors
– Represents an about-face for many development agencies,
which for decades opposed subsidies

6. 8.4%
10.4%
18.1%
25.7% 26.0% 26.1%
29.9%
39.9%
46.0%
58.3%
0%
10%
20%
30%
40%
50%
60%
70%
Burkina Faso Ethiopia Mali Kenya Nigeria Senegal Ghana Zambia Tanzania Malawi
Expendituresas%ofpublicagriculturalspendingISP expenditures in 10 SSA countries, 2011
6Source: Jayne and Rashid (2013)

7. • Subsidies for technology adoption may be justified on the basis of
a variety of market failures
– Credit constraints (Lloyd-Ellis et al 2000, Banerjee 2000)
– Imperfect information, need for learning-by-doing (Conley and
Udry 2010, Foster and Rosenzweig 1995, Munshi 2006)
• Of great policy interest: “graduation” from subsidies
– Continuation of large-scale ISPs depends on donor funding
– Under what circumstances can subsidies be phased out,
leaving households to self-finance future investments?
– Answer may depend on existence of financial services
• Improved financial intermediation facilitates accumulation of
investment capital, and ability to cope with risk, leading to higher
investment (Gine et al 2004, Greenwood et al 1990, Townsend et
al 2006)
• We are interested in particular in constraints on formal savings,
and how these interact with provision of subsidies
Subsidies and financial services
7

8. Savings
• Several recent RCTs on savings in developing countries
– Provide formal savings facilities to the poor, to complement
informal savings (e.g., Dupas and Robinson 2013, Brune et al
2016)
– Savings match programs have been attempted, mostly in
developed countries (e.g., Sherraden et al 2010, Schaner
2015)
• Experimental studies of savings interventions have not examined
their interaction with subsidies, or other development programs
8

9. • Can combining temporary subsidies with a formal savings-
facilitation program enhance impacts on technology adoption over
time?
• Two possibilities…
• Dynamic enhancement of subsidies
– Buffer stocks/precautionary savings may increase household
willingness to take on risky fertilizer use
– Savings may also facilitate carry-over of (fertilizer-boosted)
harvest income to next planting, for sustained fertilizer
investment
• Dynamic substitution of subsidies
– Savings can serve many purposes
• Buffer stocks / precautionary savings
• Asset accumulation for other investments
– These uses may compete with continued fertilizer investment
Savings and subsidies
9

10. • In our study, we ask:
– Over time, does facilitation of formal savings lead to
enhancement or substitution of subsidies for technology
adoption?
• In other words, how does the impact of technology
adoption subsidies change in the presence of
savings facilitation programs?
– Are households better or worse off with the combination
of programs?
– If there is an interaction, what mechanisms are
operative?
Key questions
10

11. • U.S. Agency for International Development
• Provincial Government of Manica
• Banco Oportunidade de Mocambique (BOM)
• Food and Agriculture Organization (FAO)
• European Commission (EC)
• International Fertilizer Development Corporation
Key collaborators
11

12. The study
• ~1,500 rural maize farmers in 94 localities
in Manica province, Mozambique
– A locality is a grouping of nearby
villages
• Study participants are “progressive”
farmers willing to use modern agricultural
inputs
– Lists generated by government
agricultural extension workers in each
village
12
• Maputo
• Beira

13. Randomization of treatments
• Each locality randomly assigned to one of three savings
treatment groups (control, basic savings, matched savings)
– After stratification into groups of 3 nearby localities
• Subsidy vouchers assigned by random lottery at participant level
within localities
13
No savings
program
(32 localities)
Basic savings
program
(30 localities)
Matched savings
program
(32 localities)
SubsidyNo
subsidy
prob. 1/2
prob. 1/3
prob. 1/3
prob. 1/3
prob. 1/2 prob. 1/2
prob. 1/2 prob. 1/2
prob. 1/2
94 study
localities
N=247N=267 N=278 N=303 N=246N=248
SubsidyNo
subsidy
SubsidyNo
subsidy
C: “Pure
control group”
T1: “Subsidy
only”
T2: “Basic
savings only”
T4: “Matched
savings only”
T5: “Matched
savings +
subsidy”
T3: “Basic
savings +
subsidy”

14. Subsidy vouchers
• 50% of study participants
within each locality
randomly assigned to
voucher receipt in late
2010 (at start of season)
• Provided 73% discount on
MZN 3,160 (~US$113)
package of fertilizer,
improved seeds
• Redeemed at local input
suppliers
• Deadline: Jan. 31, 2011
14

15. • Subsidies preceded savings treatments
• Study participants had no knowledge that savings
treatments would come later
Timing of treatments
Randomized
distribution of
subsidy
vouchers
within
localities (Sep-
Dec 2010)
Basic and
matched
savings info
sessions in
randomly-
chosen
localities
(Apr-Jul 2011)
1st matched
savings period
(Aug-Oct 2011)
2nd matched
savings period
(Aug-Oct
2012)
2010-11
agricultural
season
2011-12
agricultural
season
2012-13
agricultural
season
Dec. Jun. Dec. Jun. Dec. Jun.
15

16. Savings treatments
• Both savings treatments began with
village-level information sessions on
formal savings
– Emphasized use of savings for
both investment and self-
insurance
• Over next two months, one
representative per group of 5 study
participants receives follow-up
training, and asked to convey
information to group-mates
• Participants also encouraged to open
accounts at BOM, either at
Bancomovil or fixed branch locations
16

17. BOM’s “Bancomovil”
• Savings accounts at Banco Oportunidade de Mocambique (BOM)
• Access via 2 branches and scheduled visits by “Bancomovil” units
17

18. Savings game
18

19. Educational material on savings and fertilizer
19

20. Basic vs. matched savings
• Accounts offered in “basic savings” treatment were standard
savings accounts
• In “matched savings” treatment:
– Match is 50% of minimum balance over match period
– Matching funds capped at MZN 1500 (~$54)
– Match period: August 1 – October 31
– Designed with agricultural cycle in mind
• Match period ends just before next planting season
• If save full amount (MZN 3000), savings + match can
purchase input package sufficient for 3/4 hectare plot
– Two years of match promised: 2011 and 2012
20

21. Sussundenga
- Bancomovil (BOM)
- Barclays Bank
Manica
- Bancomovil (BOM)
- Barclays Bank
- BOM
- BIM
- BCI
Catandica
- Bancomovil (BOM)
- Caixa Financeira
- BIM
Chimoio
- Tchuma
- Standard Bank
- Barclays Bank
- BOM
- BIM
- BCI
- Socremo
- Banco Terra
Study localities, by savings treatment
Bancomovil
21

22. Timing of surveys
Randomized
distribution of
subsidy vouchers
within localities
(Sep-Dec 2010)
Basic and
matched savings
info sessions in
randomly-chosen
localities
(Apr-Jul 2011)
1st matched
savings period
(Aug-Oct 2011)
2nd matched
savings period
(Aug-Oct 2012)
Interim survey
(Apr 2011)
1st follow-up
survey
(Sep 2011)
3rd follow-up
survey
(Jul-Aug 2013)
2nd follow-up
survey
(Sep 2011)
2010-11
agricultural
season
2011-12
agricultural
season
2012-13
agricultural
season
Dec. Jun. Dec. Jun. Dec. Jun.
22

23. Take up of subsidies and savings
Note: Means presented in top row for each variable, with standard deviations in parentheses. Voucher use data are from April 2011 interim survey, prior to savings treatments but after subsidy treatment.
Savings account ownership are from 2011, 2012, and 2013 follow-up surveys. Savings match data are from BOM administrative records. In brackets: p-values of test of equality of mean in a given treatment
group with mean in pure control group, after partialling-out fixed effects for 32 stratification cells (groups of three nearby localities, within which savings treatments were randomly assigned). Standard errors
clustered at level of 94 localities. MZN = Mozambican meticais (27 MZN/US$).
 26-33 pp increase due to subsidy
 17-23 pp increase due savings
treatments
23

24. • What is the impact of subsidies on fertilizer adoption?
– Compare subsidy voucher winners and losers within
localities
– Should not find differences between localities with
differing savings treatment status in first (subsidized
year)
• Does the dynamic impact of subsidies vary in localities
getting the savings treatments?
– Is fertilizer use more or less persistent after the end of
subsidies in savings localities?
First questions
24

25. *
0.138
0.036
0.011
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Treatmenteffectonextensivemarginoffertilizeruse
• Across all study localities, impact of
subsidy declines dramatically in
subsequent years
Impact of subsidies
(subsidized
season)
(post subsidy) (post subsidy)
***
Significance levels: 1%***, 5%**, and 10%*. Fertilizer use in pure control group is 21.7% in 2011, 16.5% in 2012, and 15.7% in 2013. 25

26. 0.139
0.054
0.062
0.138
0.027
-0.013-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Treatmenteffectonextensivemarginoffertilizeruse
• Dynamic impact of subsidy is
heterogeneous
• Persistence of a good fraction of subsidy’s
effect in no-savings localities
• In savings localities, effect disappears by
two seasons later
Heterogeneity by savings treatment status
(subsidized
season)
(post subsidy) (post subsidy)
***
*
Significance levels: 1%***, 5%**, and 10%*. Fertilizer use in pure control group is 21.7% in 2011, 16.5% in 2012, and 15.7% in 2013.
***
**
No
savings
Savings No
savings
Savings
No
savings
Savings
26

27. Regression equation
27
For respondent i in locality j, stratification cell k, period t:
yijkt = z + aVijk + gb(Bjk*Vijk) + gm(Mjk*Vijk) + bbBjk + bmMjk + qk + εijkt
– yijkt = outcome variable
– Vijk = indicator for subsidy treatment (individual level)
– Locality-level indicators for savings treatments
• Bjk – Basic savings
• Mjk – Matched savings
– qk = fixed effect for stratification cell (group of 3 localities)
• OLS, standard errors clustered at level of 94 localities
• Dynamic enhancement: gb > 0, gm > 0
– Alternately, gb < 0, gm < 0 indicates dynamic substitutability

28. Regression results: impact of subsidy
28

29. Notes: Conditional distribution functions for log(1 + MZN value of fertilizer used in maize production), for no-savings, basic savings, and matched
savings localities. Fertilizer use data refers to use during subsidized 2010-11 season, reported in April 2011 interim survey.
• Subsidy raises fraction using
fertilizer on maize by 13.9pp
(over base of 21.7%)
• No difference in impacts
across localities by savings
treatment status
First (subsidized) season, 2010-11
29

30. Notes: Conditional distribution functions for log(1 + MZN value of fertilizer used in maize production), for no-savings, basic savings, and matched
savings localities. Fertilizer use data refers to use during post-subsidy 2011-12 season, reported in September 2012 follow-up survey.
• A positive impact remains in
no-savings and basic savings
localities, ~5-7pp
• Impact of subsidy disappears
in matched savings localities
Second season (post-subsidy), 2011-12
30

31. Notes: Conditional distribution functions for log(1 + MZN value of fertilizer used in maize production), for no-savings, basic savings, and matched
savings localities. Fertilizer use data refers to use during post-subsidy 2012-13 season, reported in September 2013 follow-up survey.
• A positive impact remains
only in no-savings localities
(6.2 pp, on top of base
15.7%)
• Zero impact of subsidy in both
types of savings localities
Third season (post-subsidy), 2012-13
31

32. Alternate specifications
32

33. • Savings can play multiple roles in household intertemporal
optimization
– Risk-management: holding buffer stocks / precautionary
savings to cope with shocks (self-insurance)
– Investment: funds accumulated and then used productively
• Savings opens up possibilities for alternate uses of household
resources, potentially competing with fertilizer
Savings as a general, multi-purpose technology
33

34. Notes: Conditional distribution functions for log(1 + MZN of formal savings). Formal savings balances reported in follow-up surveys of September 2011,
September 2012, and July-August 2013.
• Savings treatments lead to
substantial increases in formal
savings balances in all years
• Even those not receiving
subsidy have resources to
save
Impacts on formal savings
34

35. Regression equation highlighting each treatment
35
For respondent i in locality j, stratification cell k:
yijkt = z + aVijk + bbBjk + bbvBVijk + bmMjk + bmMVijk + qk + εijkt
– Replace interaction terms with dummies for each sub-treatment
separately

36. Impact of treatments on formal savings
36

37. • In savings localities, resources appear to be diverted from
fertilizer to savings in the post-subsidy years
• Magnitudes saved are more than large enough to “compete” with
fertilizer as a destination for household resources
• Are households in savings localities any better or worse off as a
result?
Household well-being
37

38. Impacts on consumption per capita
38

39. • All treatments lead to similar
consumption gains, ~8%
• Cannot reject that
consumption impacts are
similar across treatments T1-
T5
• But what about coping with
risk?
Impacts on consumption, post-subsidy years
Notes: Conditional distribution functions of average of log(daily consumption per capita in household) across September 2012 and July-August
2013 follow-up surveys.
39

40. • Do savings treatments help households insulate consumption from
negative agricultural shocks?
• We use panel data from 4 surveys on consumption and
agricultural shocks
– Households report whether past year was a “bad year” for
agriculture
– Bad years do not appear to be influenced by treatments
Coping with risk
40

41. • Regression equation for hh i, locality j, period t:
Consijt = z + g Badyearijt
+ a [Vij * Badyearijt] + b [Savingsjt * Badyearijt]
+ b Savingsjt
+ qi + wt + εijt
– Vij = indicator for subsidy treatment
– Savingsjt = indicator for any savings treatment active in period t
– HH and time fixed effects
– Four periods (t= 1, 2, 3, or 4)
– Subsidy treatment time-invariant (so main effect absorbed by qi)
– Savings treatment active in periods 2, 3, and 4
• Hypotheses:
g < 0 : bad years reduce consumption
a < 0 : subsidy makes consumption more sensitive to bad years
b > 0 : savings makes consumption less sensitive to bad years
Coping with risk: regression equation
41

42. Coping with risk: regression results
42

43. 0
.2.4.6.8
density
2 3 4 5 6 7
log(daily consumption per capita)
C: Pure control T1: Subsidy only
T2-T5: Any savings treatment
Impacts on consumption variance, 2013
• Subsidy leads to increase
in variance of
consumption
• Savings treatments have
lower consumption
variance
Notes: Probability density functions of log(daily consumption per capita in household) in July-August 2013 follow-up survey. 43

44. 0.105
0.018
0.051
0.037
0.009
-0.02
0
0.02
0.04
0.06
0.08
0.1
Treatmenteffectonstd.dev.oflogconsumptionpercapita
Significance levels: 1%***, 5%**, and 10%*. Data are from 2013 survey. Standard deviation of log consumption in control group is 0.493.
Impact of treatments on consumption variance
***
*
44

45. 0.105
0.018
0.051
0.037
0.009
-0.02
0
0.02
0.04
0.06
0.08
0.1
Treatmenteffectonstd.dev.oflogconsumptionpercapita
Impact of treatments on consumption variance
**
*
***
Only T1 raises consumption variance Increase in variance is statistically significantly
lower in 3 out of 4 savings treatments
Significance levels: 1%***, 5%**, and 10%*. Data are from 2013 survey. Standard deviation of log consumption in control group is 0.493.
***
45

46. • Estimates imprecise, but possible increase in total investment in
response to savings treatments
• Fertilizer on other crops responds positively as well
• Increase in non-agricultural investments in savings localities
What else are savings households doing?
46

47. • Subsidies have positive, persistent effects on fertilizer adoption
– But dynamic effects disappear when combined with programs
facilitating formal savings
• But households receiving savings treatments are just as well off
as subsidy recipients in no-savings localities
– In terms of mean consumption
– And better risk-coping
• Savings treatments help households pursue objectives beyond
fertilizer
– In particular, self-insurance via buffer stocks
• Households place high value on the risk-coping gains from formal
financial access
• Also: rare evidence of interactions between development
programs
In sum
47

48. Millennium Villages Project
48

49. • Many anti-poverty programs are “bundled”, in that they consist of
multiple components
• Millennium Villages implements interventions in food, education,
environment, health, etc.
• Programs to help the “ultrapoor” (Banerjee et al 2015)
– Resource transfers, skills training, savings, health, etc.
• But how do the components interact with one another? Are all
necessary? Do components complement one another?
• We contribute to knowledge on this front, but more such research
is needed
Fighting poverty with multiple interventions
49

50. Extra slides
50

51. • Risk averse farmers face the following market failures:
– Imperfect (downward-biased) info on fertilizer returns
– Credit constraints (no borrowing)
– High-cost savings (negative interest rate)
• Modern technology (fertilizer) riskier than traditional technology
• Precautionary savings motive
• Periods:
0: post-harvest. Consume, save for planting time.
1: planting. Consume, invest in planting, save buffer stocks.
2: next year’s post-harvest…
• Subsidies reduce cost of fertilizer investment in period 1
• Savings programs raise interest rate on savings in both periods 0 and 1
– Savings from period 0 to 1 can go to either investment or continued
buffer stock savings in period 1
Theory
51

52. • One-time fertilizer subsidy can raise fertilizer use in subsequent
(unsubsidized) seasons by raising beliefs about returns to fertilizer
• Savings program raises interest rate on savings, but has ambiguous
effects on fertilizer investment
– Dynamic enhancement
• Stimulates saving from period 0 to 1, raising fertilizer
investment in period 1
• Buffer stocks held in period 1 also promote risk-taking
(fertilizer investment) in period 1
– Dynamic substitution
• Buffer stocks compete with fertilizer in period 1; for poorly-
insured, risk-averse hhs, this can dominate
• Also: savings held from periods 0 to 1 could be put in other
(non-fertilizer) investments
• Risk-aversion makes substitution more likely, by raising
attractiveness of buffer stock savings in period 1
Predictions from theory
52