Savings, Subsidies, and Technology Adoption: Field Experimental Evidence

from Mozambique

Michael Carter, UC DavisRachid Laajaj, Universidad de los Andes

Dean Yang, University of Michigan

• 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

Source: Morris et al (2007)

Africa lags in food production…

3

… 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

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

Burkina Faso Ethiopia Mali Kenya Nigeria Senegal Ghana Zambia Tanzania Malawi0%

10%

20%

30%

40%

50%

60%

70%

8.4%10.4%

18.1%

25.7% 26.0% 26.1%

29.9%

39.9%

46.0%

58.3%

Expe

nditu

res a

s % o

f pub

lic a

gricu

ltura

l spe

ndin

gISP expenditures in 10 SSA countries, 2011

6Source: Jayne and Rashid (2013)

• 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

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

• 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

• 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

• 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

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

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. 1/3prob. 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”

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

• 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

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

BOM’s “Bancomovil”

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

17

Savings game

18

Educational material on savings and fertilizer

19

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

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

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

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

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• 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

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*

2011 2012 2013-0.02

-3.46944695195361E-18

0.02

0.04

0.06

0.08

0.1

0.12

0.14 0.138

0.036

0.011Trea

tmen

t effe

ct o

n ex

tens

ive

mar

gin

of fe

rtiliz

er u

se

• 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

2011 2012 2013-0.02

-3.46944695195361E-18

0.02

0.04

0.06

0.08

0.1

0.12

0.14 0.139

0.054

0.062

0.138

0.027

-0.013

Trea

tmen

t effe

ct o

n ex

tens

ive

mar

gin

of fe

rtiliz

er u

se

• 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

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

Regression results: impact of subsidy

28

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

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

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

Alternate specifications

32

• 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

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

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

Impact of treatments on formal savings

36

• 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

Impacts on consumption per capita

38

• 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

• 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

• 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 consumptiona < 0 : subsidy makes consumption more sensitive to bad yearsb > 0 : savings makes consumption less sensitive to bad years

Coping with risk: regression equation

41

Coping with risk: regression results

42

0.2

.4.6

.8de

nsity

2 3 4 5 6 7log(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

T1: Subsidy T2: Basic savings T3: Basic savings + subsidy

T4: Matched savings T5: Matched savings + subsidy

-0.02

-3.46944695195361E-18

0.02

0.04

0.06

0.08

0.1

0.105

0.018

0.051

0.037

0.009

Trea

tmen

t effe

ct o

n st

d. d

ev. o

f log

cons

umpti

on p

er ca

pita

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

T1: Subsidy T2: Basic savings T3: Basic savings + subsidy T4: Matched savings T5: Matched savings + subsidy-0.02

-3.46944695195361E-18

0.02

0.04

0.06

0.08

0.1

0.105

0.018

0.051

0.037

0.009

Trea

tmen

t effe

ct o

n st

d. d

ev. o

f log

cons

umpti

on p

er ca

pita

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

• 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

• 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

Millennium Villages Project

48

• 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

Extra slides

50

• 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

• 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