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 Institute2 University of Oxford

April 20, 2012Department of Economics, Addis Ababa University

"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)

• 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.

Under-investments by the poor

• 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

“Aspirations” project

Step 1 – correlates of aspiration-related conceptsStep 2 – test and validate a measurement strategyStep 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)

Aspiration measures200,000 ETB ~ value of one harvest of chat from one hectare

100,000 ETB ~ value of one harvest of chat from half a hectare

0 ETB

• 4 dimensions– Annual income in cash– Assets – house, furniture,

consumer goods, vehicles– Social status – whether people in

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, ,

, max mind i d i

d id d

M za

M M

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

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

Aspirations - Determinants

Aspirations – Impact

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;

Hypothetical demand for credit

Surveyed : Treatment, 6 households (12 individuals)/villagePlacebo, 6 households (12 individuals)/villageControl, 6 households (12 individuals)/village

Non-Surveyed : Treatment, 18 households (36 individuals)/ treatment villagePlacebo, 18 households (36 individuals)/ placebo village

Treatment village Placebo village

16 Screening sites, 4 villages/screening sites (2 Treatment and 2 Control)

Experimental design

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)

Distribution of treatment

Sample balanced on gender, literacy, age and most outcomes

On going experiment

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 initial as compared to your five years ago

Worse 60 9 258 The Same 31 16 78

Better 43 11 136

Estimation strategy

• s=screening site, v=village, i=individual. • T=treatment, nT=number of treated peers of ind i• y1 = asp at round 1 • =screening site fixed effects.π

All standard errors clustered at village level, since part of the treatment is done at the village level.

162 1, , , , , ,

1

Ts v i s v i s v i s v i

s

y T n y

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

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

Impact on aspirations – post screening

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)** R2 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

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)** R2 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

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

Educational aspiration only – post-screening

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

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