1. Aspirations and well-being outcomes in
Ethiopia
Evidence from a randomized field experiment
Tanguy Bernard1, Stefan Dercon2, Kate Orkin 2, and Alemayehu Seyoum Taffesse1
1International
Food Policy Research Institute
2 University
of Oxford
April 20, 2012
Department of Economics, Addis Ababa University

2. "Fatalism" in Ethiopia
"We live only for today"
"We have neither a dream nor an imagination"
"Waiting to die while seated"
"It is a life of no thought for tomorrow"
(Rahmato and Kidane,1999)

3. Under-investments by the poor
• Fatalistic outcome: not making the necessary investment to
improve one’s well-being, despite existing opportunities
• Explanations:
– Individual’s environment affect private returns
– Attributes of decision maker affect internal logic
• Mixed approach:
– Decision making depend on individuals’ beliefs and perception vis-
a-vis their environment.
– Individual condition affects perception of environment and related
investment to explore pathways into better wellbeing.

4. • Aspirations :
– A desire or an ambition to achieve something
– An aim and implied effort to reach it
– Combination of preferences and beliefs
• Related concepts
– Economics : Satisficing
– Psychology : self-efficacy, locus of control
– Anthropology : Aspiration failures
• Common elements
– Goals and aspirations are important to determine success
– Evolution through time in response to circumstances
– Role of social comparisons and learning from relevant others, beyond
social learning
• An individual-level yet culturally determined concept  towards
exploration of individual-group symbiosis

5. “Aspirations” project
Step 1 – correlates of aspiration-related concepts
Step 2 – test and validate a measurement strategy
Step 3 – assess validity of « aspiration window " theory
• A “mobile movie” experiment
– Exogenous shock to aspirations: Mini-documentaries of
local success stories screened to randomly selected
individuals. Placebo: local TV show.
– 3 rounds of data
• Baseline pre-treatment (Sept-Dec 2010)
• Aspirations retest immediately after treatment
• Follow-up (Mar-May 2011)

6. Aspiration measures
200,000 ETB ~ value of
one harvest of chat
from one hectare • 4 dimensions
– Annual income in cash
100,000 ETB ~ value of
one harvest of chat
from half a hectare
– Assets – house, furniture,
consumer goods, vehicles
– Social status – whether people in
0 ETB
the village ask advice on decisions
– Level of education of oldest child
• “What is the level of <> you would like to
achieve?”
• Individual specific weights
• Standardised
max
M d ,i z d ,i
ad ,i max min
Md Md

7. Aspirations - Determinants
asp_r1 a_income_r1 a_wealth_r1 a_educ_r1 a_status_r1
age 0.012 0.003 -0.008 0.035 -0.004
(2.99)** (0.38) (0.80) (2.92)** (0.33)
age2 -0.000 -0.000 0.000 -0.000 0.000
(2.80)** (0.73) (0.73) (2.57)* (0.85)
gender 0.178 0.203 0.074 0.262 0.167
(7.46)** (4.19)** (1.93) (5.90)** (3.20)**
read 0.102 -0.016 0.193 0.263 0.081
(3.04)** (0.28) (2.90)** (4.13)** (1.35)
R2 0.10 0.06 0.04 0.08 0.03
N 1,638 1,748 1,759 1,754 1,778
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

8. Aspirations - Determinants
asp_r1 a_income_r1 a_wealth_r1 a_educ_r1 a_status_r1
age 0.009 0.003 -0.008 0.034 -0.008
(2.93)** (0.46) (0.86) (2.88)** (0.77)
age2 -0.000 -0.000 0.000 -0.000 0.000
(2.70)** (0.89) (0.75) (2.52)* (1.18)
gender 0.179 0.196 0.073 0.270 0.160
(7.37)** (3.84)** (1.86) (6.18)** (3.29)**
read 0.117 0.040 0.201 0.244 0.100
(3.80)** (0.75) (3.06)** (4.06)** (1.85)
others_asp 0.033
(27.81)**
others_a_income 0.031
(41.01)**
others_a_wealth 0.019
(7.15)**
others_a_educ 0.021
(9.73)**
others_a_status 0.030
(18.14)**
R2 0.28 0.26 0.06 0.11 0.18
N 1,638 1,748 1,759 1,754 1,778
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

9. Aspirations – Impact
Hypothetical demand for credit
loan_1year_R1 loan_5years_R1 loan_10years_R1
asp_r1 5,382.324 21,487.324 61,547.013
(4.09)** (2.53)* (3.43)**
N 1,702 1,702 1,702
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses
Other effects
• Increase in withdrawal and deposit into savings among treatment group – small
net increase in savings;
• Decrease in proportion of treatment group who agree that poverty has
“fatalistic” (destiny, bad luck) causes;

10. Experimental design
16 Screening sites, 4 villages/screening sites (2 Treatment and 2 Control)
Treatment village Placebo village
Surveyed : Treatment, 6 households (12 individuals)/village
Placebo, 6 households (12 individuals)/village
Control, 6 households (12 individuals)/village
Non-Surveyed : Treatment, 18 households (36 individuals)/ treatment village
Placebo, 18 households (36 individuals)/ placebo village

11. Distribution of treatment
All villages Treatment villages Placebo villages
Treatment individuals 0.32 0.33 0.31
(0.46) (0.47) (0.46)
Placebo individuals 0.33 0.32 0.34
(0.47) (0.46) (0.47)
Control individuals 0.33 0.33 0.33
(0.47) (0.47) (0.47)
# peers invited to treatment 0.85 1.26 0.40
(0.93) (0.97) (0.63)
# peers invited to placebo 0.79 0.38 1.24
(0.89) (0.31) (0.93)
Sample balanced on gender, literacy, age and most outcomes

12. On going experiment

13. Compliance and power of treatment
• High and ‘clean’ compliance rate:
– Average of 30mn for people to come see the screening.
– 95% invited and interviewed showed up. No difference across treatment or placebo. No difference
across gender.
– 92% of invited only showed up. No difference across treatment or placebo. No difference across gender.
– No-one that was not invited saw the screening.
• Overwhelming majority of people appreciated the screening.
– 96% of treatment group ‘liked it a lot’, 73% in placebo group.
– 95% treatment group discussed content with neighbour, 71% in placebo group.
– 92% : documentaries generated ‘a lot’ of interest in village, 72% for placebo.
– 6 months later: 33% still discuss treatment, 21% still discuss placebo.
• But compliance does not mean ‘take-up’ here…
Think about the story you found the most relevant to your own life…
How was his/her present condition as compared to yours now
Worse The same Better
How was his/her Worse 60 9 258
initial as compared to The Same 31 16 78
your five years ago Better 43 11 136

14. Estimation strategy
16
ys2,v ,i T T
ns ,v ,i y1,v ,i
s s v i
s 1
• s=screening site, v=village, i=individual.
• T=treatment, nT=number of treated peers of ind i
• y1 = asp at round 1
• π=screening site fixed effects.
All standard errors clustered at village level, since part
of the treatment is done at the village level.

15. Impact on aspirations – final round
asp_r2 asp_r2 asp_r2 asp_r2
treat_cont 0.040 0.040
(1.15) (1.13)
plac_cont 0.005 0.004
(0.13) (0.12)
nb_doc 0.020 0.012
(0.96) (0.61)
nb_plac -0.020 -0.009
(0.93) (0.40)
asp_r1 0.446 0.447 0.418 0.419
(10.91)** (10.93)** (11.27)** (11.30)**
R2 0.19 0.19 0.17 0.17
N 1,061 1,061 1,076 1,076
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

16. Impact on aspirations – post screening
asp_fu asp_fu asp_fu asp_fu
treat_cont 0.014 0.013
(0.34) (0.32)
plac_cont -0.049 -0.046
(1.35) (1.26)
nb_doc 0.015 0.051
(0.74) (2.44)*
nb_plac -0.001 -0.001
(0.07) (0.05)
asp_r1 0.573 0.574 0.500 0.505
(10.20)** (10.32)** (10.40)** (10.27)**
R2 0.30 0.30 0.29 0.28
N 1,004 1,004 1,022 1,022
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

17. Above median initial aspiration – final round
asp_r2 asp_r2 asp_r2 asp_r2
treat_cont 0.025 0.024
(0.47) (0.45)
plac_cont -0.024 -0.023
(0.44) (0.42)
nb_doc 0.053 0.015
(2.34)* (0.70)
nb_plac -0.045 -0.021
(1.56) (0.71)
asp_r1 0.315 0.318 0.280 0.280
(4.23)** (4.25)** (4.25)** (4.25)**
2
R 0.09 0.09 0.09 0.09
N 539 539 523 523
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

18. Educational aspiration only – final round
a_educ_r2 a_educ_r2 a_educ_r2 a_educ_r2
treat_cont 0.107 0.107
(1.70) (1.72)
plac_cont 0.040 0.041
(0.67) (0.69)
nb_doc 0.058 0.055
(1.74) (1.58)
nb_plac -0.078 -0.007
(2.21)* (0.23)
a_educ_r1 0.240 0.241 0.242 0.244
(7.11)** (7.08)** (8.64)** (8.61)**
2
R 0.09 0.09 0.07 0.07
N 1,151 1,151 1,174 1,174
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

19. Educational aspiration only – post-screening
a_educ_fu a_educ_fu a_educ_fu a_educ_fu
treat_cont 0.100 0.101
(1.59) (1.61)
plac_cont 0.070 0.075
(1.07) (1.12)
nb_doc 0.017 0.076
(0.69) (2.76)**
nb_plac -0.034 0.002
(0.89) (0.06)
a_educ_r1 0.429 0.429 0.401 0.402
(7.43)** (7.42)** (6.85)** (6.76)**
R2 0.22 0.22 0.20 0.20
N 1,134 1,134 1,160 1,160
* p<0.05; ** p<0.01
Screening site fixed effects not reported
Robust standard errors clustered at village-level
t-stats in parentheses

20. Impact on demand for loans
loan_10years_R2 loan_10years_R2 loan_10years_R2 loan_10years_R2
treat_cont 5,670.973 4,897.515
(1.01) (0.89)
plac_cont 516.208 896.126
(0.12) (0.22)
nb_doc 5,278.431 5,778.825
(1.63) (2.12)*
nb_plac 3,802.248 4,224.977
(1.15) (1.38)
loan_10years_R1 0.277 0.283 0.591 0.595
(2.34)* (2.40)* (4.28)** (4.30)**
N 1,230 1,230 1,245 1,245
* p<0.05; ** p<0.01
observations left-censored at demand = 0
Robust standard errors clustered at village-level
t-stats in parentheses

21. Conclusion
• "Weak " treatment and very preliminary analysis,
but some indications that:
– Documentaries affect perception more than placebo
– Not so much seeing the documentary, but discussing it
with friends who’ve seen it – more of an aspiration
window story rather than a role model one.
– Impact more important on education-related aspiration
– Indication of positive effects onto demand for credit
– Although some decay, effects still visible 6 months after
treatment